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Towards the sustainable discovery and development of new antibiotics

Abstract

An ever-increasing demand for novel antimicrobials to treat life-threatening infections caused by the global spread of multidrug-resistant bacterial pathogens stands in stark contrast to the current level of investment in their development, particularly in the fields of natural-product-derived and synthetic small molecules. New agents displaying innovative chemistry and modes of action are desperately needed worldwide to tackle the public health menace posed by antimicrobial resistance. Here, our consortium presents a strategic blueprint to substantially improve our ability to discover and develop new antibiotics. We propose both short-term and long-term solutions to overcome the most urgent limitations in the various sectors of research and funding, aiming to bridge the gap between academic, industrial and political stakeholders, and to unite interdisciplinary expertise in order to efficiently fuel the translational pipeline for the benefit of future generations.

Introduction

This article is conceived as a general roadmap with the central aim of promoting and accelerating translational science in the early stages of novel antibiotic discovery towards lead candidate development. The overuse and misuse of antibiotics in healthcare and agriculture, together with inappropriate waste management and environmental transmission, have led to substantially increased antimicrobial resistance (AMR)1,2,3,4,5 and associated bacterial persistence6,7. This is of major public concern, since most areas of modern medicine are inconceivable without access to effective antimicrobial treatment8. It is estimated that at least 700,000 people worldwide die each year as a result of drug-resistant infections, and this could rise to as much as 10 million by 2050 if the problem of AMR is not addressed9,10.

The anticipated death toll caused by drug-resistant infections over the next years and decades may be compared with the global fatality rate of the current SARS-CoV-2 (COVID-19) pandemic (https://coronavirus.jhu.edu/), which has already led to multibillion-dollar investments in vaccine development, repurposing existing drugs and antiviral discovery. A perhaps overlooked aspect of concern with the COVID-19 pandemic is the high numbers of secondary infections, often associated with multidrug-resistant bacteria, which are observed especially in hospitalized patients and those with already compromised immune systems11,12. Associated with this problem is the massive use of antibiotics as a COVID-19 (co)treatment worldwide13,14,15,16,17,18,19,20,21,22,23,24, which is predicted to add to the ongoing emergence of AMR25,26,27,28,29. This multiplying effect of COVID-19 on the spread of bacterial resistance will most likely have further negative clinical, economic and societal consequences in the near future30,31.

Unfortunately, the dramatic worldwide rise of bacterial pathogens resistant to antibacterial agents32 cannot be counteracted by the current low development pace of therapeutics with new mode(s) of action (MoA(s)). While there are nearly 4,000 immuno-oncology agents in development33, only about 30–40 new antibacterial compounds are currently in the clinical trial phases of development, and, notably, those candidates targeting World Health Organization (WHO) priority pathogens are derivatives of existing classes34,35. Indeed, less than 25% of current drugs in the clinical development pipeline represent a novel class or act through a novel mechanism, and none of these are potentially active against Gram-negative ESKAPE or WHO critical threat pathogens34,36. In fact, only a small fraction of the antibiotics approved over the past 40 years represents new compound classes, while the majority were derived from already known chemical structures, and the most recent new class of antibiotics was discovered during the 1980s37.

Thus, strategic investment in new therapeutic options to fight AMR is urgently required to address unmet patient need and, additionally, to counterbalance the exponentially increasing financial burden on global health systems38. Consequently, the research field should aim to leverage hit identification and hit-to-lead optimization programmes to ensure a sustainable flow of new antibacterial drug candidates into the development pipeline. For this purpose, the initial stages of drug discovery and development need to be strengthened, since they are essential to identify and validate novel therapeutic candidates effective to fight antibacterial resistance. However, for many years, such early-stage projects have been mainly conducted by academia and are generally underfunded, while increased allocation of funding into early-stage and mid-stage research and development (R&D) has been recommended to make the pipeline more robust39,40,41,42. Our network has identified major funding gaps especially within the academic sector, as well as for small and medium-sized enterprises (SMEs), where research is mainly associated with the early hit discovery and hit-to-lead phases, as well as with late lead optimization prior to preclinical candidate nomination (Fig. 1). Large pharmaceutical companies across the globe are extremely hesitant to fund early antibiotic R&D and, particularly, new classes of compounds, since the return on investment in this area is generally low or even negative. Further, the costs of developing entirely new scaffolds are much higher than for derivatives of established compound classes, while the attrition rate in antibacterial drug discovery has been particularly high in the recent decades, reflected by the fact that no new class of Gram-negative antibiotics has been launched for more than 50 years43,44. In the commercial sector, innovation has, thus, been left to SMEs, which must deal with high attrition associated with the early phases of discovery and optimization39,43,45,46,47,48, and the huge capital risks49,50.

Fig. 1: General scheme of antimicrobial drug development.
figure1

Large funding gaps can be seen in the early stages of hit discovery, as well as during hit and lead optimization, which are associated mainly with academic research and small and medium-sized enterprises (SMEs). Indicated figures are representative numbers of typical broad-spectrum antibiotic development programmes leading from several thousands of initial hits to the approval of at least one marketable candidate72,318,319,320,321. *Timings are dependent on a number of factors and can vary greatly. A minimum to maximum range for complete development (discovery to market) is 8–18 years (average 13–14 years). **The cost per molecule/candidate (in million euros, m€) does not include extended costs for attrition (failed programmes) and lost opportunities associated with increased cycle time until reaching the next development phase; such extensions can increase the required budget for the early stages up to 50–100 m€ (refs39,48,322). N (orange diamond), nomination of (pre)clinical candidate(s); PPPs, public–private partnerships; ROI, return on investment.

New economic models for development specifically designed for this area are sorely needed to ensure future advancements51,52,53,54. A recent initiative that supports SMEs in the late-stage development of new antibiotics is the AMR Action Fund, which was launched by more than 20 leading biopharmaceutical companies to push mainly phase II and III trials of advanced candidates55. Unfortunately, the fund does not cater for the early stages of research. In addition, several countries are implementing new pull incentive programmes with different priorities. While the Swedish model aims at securing sustained access to relevant antibiotics that have already been approved56, plans in the UK57,58 as well as in the USA (e.g. PASTEUR59 and DISARM60 acts) strive to stimulate the development of new antibacterial products by using subscription models or delinkage models51. Such initiatives are promising, as they introduce much-needed market entry rewards, but they might fall short on a global scale if they do not include the ‘critical mass’ of the world’s largest economies.

Innovation in the early stages of antibiotic drug discovery can also be driven by the academic sector. However, from the academic perspective, partnering with external funders such as the pharmaceutical industry is, in many cases, only realistic after the nomination of extensively validated preclinical candidates, and often even requires phase I clinical data. Typically, this cannot be achieved by research-driven funding and infrastructure alone. Several global health organizations and public–private partnerships (PPPs), including the Global Antibiotic Research and Development Partnership (GARDP), Combating Antibiotic-Resistant Bacteria Biopharmaceutical Accelerator (CARB-X), Innovative Medicines Initiative (IMI) and others, started to support, at least partially, the mid-to-late lead optimization through to clinical proof of concept61,62,63, possibly accompanied by stakeholders associated with the Biotech companies in Europe combating AntiMicrobial resistance (BEAM) Alliance or the Replenishing and Enabling the Pipeline for Anti-Infective Resistance (REPAIR) Impact Fund64,65. However, even the growing diversity of such push incentives are, in many cases, insufficient and primarily focused on companies. In addition to these approaches, a strategy is required that helps academic researchers to advance their project portfolio to a level that facilitates early interaction and possibly partnering with pharmaceutical companies in the interest of a successful, cross-sectoral development pipeline66. Hence, creating new incentive models in the field is an essential process that can only be moved forward if the public, academic and industrial sectors join forces39,67,68,69.

In this respect, our position paper provides an overview of the early phases of antibacterial drug discovery, including hit and lead identification, optimization and development to the (pre)clinical stages by summarizing current limitations, relevant approaches and future perspectives, as well as by presenting selected case studies. In terms of a principal guidance for researchers in the field, we suggest possible solutions for a number of obstacles to improve both quality and quantity of antibacterial hits and leads. To strengthen and emphasize these early stages as an absolute necessity for a sustained generation of novel antibiotics, we are recommending a new level of interaction between the various stakeholders and academic disciplines in the area of antibiotic drug research. The strengths and opportunities that small-molecule therapeutics offer can help address antibiotic resistance more successfully during the coming years, in the interests of both patients and investors, provided that the multiplicity of hurdles along the translational path will be overcome (Table 1). Altogether, our aims are in line with the ‘One Health Action Plan against Antimicrobial Resistance’ introduced by the European Commission70, as well as the WHO programme to fight the rising number of bacterial priority pathogens with steadily growing impact on global public health71.

Table 1 SWOT (strength, weaknesses, opportunities and threats) analysis summarizing a path forward in the development of novel antibiotic drugs

Synthetic hit compounds

Here, we address the development of profitable strategies to identify and prioritize novel antibacterial hit compounds, with a particular focus on synthetic small molecules. As a foundation, we introduce three main pillars that represent core elements of fruitful hit discovery programmes.

Hit definition, chemical libraries and medicinal chemistry

The concept of ‘hit compound’72 as it is widely accepted today needs to be expanded to address the needs imposed by the threat of antibacterial resistance. In this context, a hit compound is a molecule with reproducible activity, with a defined chemical structure (or set of structures), against one or more bacterial target(s). Although the selectivity and cytotoxicity of initial hits are seen as important characteristics, their improvement should remain tasks for the hit-to-lead optimization phase (see below). The activity of hits against (selected) pathogens must be proven in relevant assays, initially in vitro (for example, using exposed/isolated targets or a whole-cell approach), which can be complemented later in the process by the use of animal models of infection to evaluate pharmacokinetic (PK) and pharmacodynamic (PD) properties. In any event, the chemical identity and integrity of a hit must be demonstrated, whereas the actual target and the precise MoA may remain unknown until a later stage. Thus, the initial activity readout for a hit can be on either the molecular or the cellular level (Box 1).

When considering the definition of valuable hits, it is important to look beyond the simple model of a single molecule addressing one particular target. Compounds that hit multiple defined targets (known as polypharmacology73), or a combination therapy, in which the effects of several molecules are combined, can be equally valuable74. Depending on the target(s), hit combinations may act synergistically, preferably with different MoAs, or in an additive fashion. Such combinations can be useful in potentiating the activity of an existing antibiotic, slowing the onset of resistance and restoring the activity of antibiotics that have become inefficient because of resistance.

A major approach to identify novel hit compounds is by high-throughput screening of chemical libraries. It is important to select the correct set of compounds for each screen, for example, a (large) diverse set, a target-focused set or a fragment library. The make-up of a library should be based on specific characteristics or property space requirements, including chemical, structural and physicochemical aspects (Box 2); these may be tailored to a particular disease area75,76. We believe that carefully designed, and possibly even preselected (‘biased’), chemical libraries, which enable screening of a suitable chemical space against the bacterial target(s) of interest, represent an important first step to start a reliable hit identification campaign towards treatment of a specific bacterial infection. The design, assembly and curation of such libraries are costly processes that require the input of highly skilled practitioners. This frequently falls outside the funding range of most academic groups and, indeed, of many small companies. Models need to be found to grant access to the most useful libraries or compound collections for hit discovery, which should be facilitated at least for non-profit research entities.

Interactions and collaborations between academic researchers and pharmaceutical companies can accelerate hit discovery by, for example, using the high-throughput screening infrastructure of companies to interrogate novel targets. At the same time, pharmaceutical partners might search for close analogues of hits initially identified in academic labs, possibly together with existing biological and chemical property profiles. Such analogue series and accompanying data sets can be extremely valuable in enabling early improvement of antibacterial potency, as well as hit series validation. Pharmaceutical partners might also begin building profiles of absorption, distribution, metabolism, excretion and toxicity (ADMET) parameters, thus, accelerating the hit-to-lead transition. Sharing the relevant information will reinforce the efforts of medicinal chemistry and enhance its reliability and robustness. This, in turn, allows programmes to reach Go/No-Go decisions more quickly and can improve the chances of securing external funding or early partnering deals based on the impact of the medical need.

Notably, medicinal chemistry is the key discipline for the subsequent optimization of hits (see case studies in Boxes 14). A lack of sufficient funding and expertise to support medicinal chemistry at this early stage is highly detrimental for the entire translational process. Encouragingly, in 2016, a large number of pharmaceutical companies with interests in AMR signed the AMR Industry Declaration77, in which they jointly committed to support antibiotic R&D processes at virtually all stages. This has led to the formation of the AMR Industry Alliance (https://www.amrindustryalliance.org/). Additionally, the implementation of new AMR-specific capital resources, for example, through the REPAIR Impact Fund and the AMR Action Fund, and the direct involvement of PPPs like CARB-X in hit-to-lead campaigns during recent years should lead to intensified collaborations between industry and academia as a near-term goal to drive the chemical optimization of hits and leads forward towards new preclinical candidates.

Those academic groups that have already built the capacity to carry out such optimization efforts, including broad know-how in medicinal chemistry, biological assays and ADMET studies, would still benefit greatly from early partnering with biopharmaceutical companies, particularly as their projects will stand a greater chance of attracting external investment. Both not-for-profit initiatives, like the European Research Infrastructure Consortium for Chemical Biology and early Drug Discovery (EU-OPENSCREEN; https://www.eu-openscreen.eu/), and collaborative PPP models as implemented by the European Lead Factory (ELF)78,79, allowing for open drug discovery programmes based on Europe-wide screening resources (for example, the Joint European Compound Library, JECL), could pave the way for such early cross-sectoral interactions and exchanges for the benefit of all involved partners80.

Nature of the target

We recommend that hit identification against bacteria follows two convergent approaches: (i) identification of molecules active against molecular targets that are vital for all stages of the bacterial life cycle (‘essential targets’), thus, directly promoting clearance of the bacteria from the host/patient, and (ii) searching for molecules that inhibit so-called ‘non-essential targets’53,81,82. The latter can be defined as bacterial structures that are not vital under standard laboratory growth conditions but become critical during processes of host colonization and infection, for example, by regulating virulence development, by evading host immune response or by triggering bacterial defence mechanisms83. Molecules hitting such targets may have weak or even no activity towards bacterial cells under non-infectious (in vitro) screening conditions, but might display highly synergistic or additive effects when tested in relevant in vivo infection models, either alone or in combination with antibacterial agents addressing essential targets. The latter molecules may be found among the current antibacterial arsenal or may be new chemical entities, identified as described above.

Compounds interacting with non-essential targets are usually classified as antibiotic adjuvants, potentiators or resistance breakers84,85. Examples of non-essential target inhibitors are represented by:

  1. (i)

    Inhibitors of virulence-conferring factors or pathways (also known as anti-virulence compounds or pathoblockers86 that target, for example, quorum sensing mechanisms87, biofilm formation88, bacterial secretion systems89,90, enzymes for tissue penetration91 or intracellular survival92).

  2. (ii)

    Efflux pump inhibitors93.

  3. (iii)

    Suicide substrates such as β-lactamase inhibitors94,95.

  4. (iv)

    Inhibitors of pathways serving as a mechanism of defence, e.g. glutathione biosynthesis96,97.

  5. (v)

    Modulators and inhibitors of energy metabolism98,99.

  6. (vi)

    Host/pathogen epigenetic modulators100,101.

For some of the mentioned targets, such as efflux pumps, it has been demonstrated that their inhibition can reverse resistance to several antibacterials102. Therefore, an attractive therapeutic combination might be composed of a bactericidal agent and an adjuvant molecule, with the aim of potentiating the antibacterial effect(s) and significantly reducing resistance (either intrinsic or evolved)103. Since the pathoblocker approach is anticipated to be less susceptible towards resistance development and, in addition, to preserve the commensal bacteria of the microbiome86, it represents a non-traditional strategy for a focused disarming of resistant high-priority pathogens, most likely to be deployed as an adjunctive therapy in addition to antibiotic standard treatment81 (Box 3).

Advanced screening and profiling based on standardized assays

There is a fundamental need for assays to identify hit compounds (both synthetic and natural-product-based hits, the latter are addressed below) specifically for the clinically most relevant indications. In addition to using focused libraries that cover desirable chemical diversity and property space, innovative screens are essential to increase the chances for identifying potent hits against most prevalent common infections associated with Gram-positive or Gram-negative pathogens, such as hospital-acquired pneumonia, community-acquired pneumonia, complicated urinary tract infection or complicated intra-abdominal infection104. To establish a reliable foundation for future development, both academia and industry must use state-of-the-art library screening procedures based on generally accepted rules and basic concepts of standardization.

It is important that a range of relevant assays is used to thoroughly select and profile novel hit compounds. These assays should have a high physiological significance, which may be applicable to biomimetic assays105, for example, by using defined culture media such as artificial urine for activity screens with uropathogens106,107, iron-depleted media that simulate bacterial growth conditions during bloodstream or wound infections108,109 or assaying host–bacteria interactions110. Such schemes can further include the screening for new MoA(s), new drug sensitizing modes, non-killing mechanisms (e.g. anti-virulence factors like pathoblockers), compounds acting against biofilms and molecules acting synergistically with existing or new antimicrobials to overcome drug resistance111,112,113,114. Similarly, because hits generated by conventional biochemical assays or screens often fail to become whole-cell active leads, alternative phenotypic assays such as novel target-based whole-cell screening115 are also a promising foundation for the identification of useful hits. Even known chemical libraries (including proprietary compound archives of pharmaceutical companies), which have failed to deliver antibacterial hits by simple growth inhibition measurement, might bear fruit if reassayed following these approaches. The way in which these innovative screens are envisaged could make them a more appropriate strategy to provide novel hits with a potential therapeutic impact compared with the molecular-target-based drug design approach116.

A further aim of the consortium is to design and develop informative assays that can provide information about the desired antibacterial effect, together with further characteristics such as target engagement, bacterial penetration characteristics (for example, kinetics of compound permeation through Gram-negative cell envelope models117,118) and potential cytotoxicity.

In addition to devising standardized panels of assays according to contemporary technology, developing the respective standard operating procedures (SOPs) is mandatory to meet the requirements for good research practice, which facilitates the transfer of compounds with potential to become new drugs from academia to non-profit or private organizations for continued development. By using standardized proof-of-concept assays under predefined SOPs, more robust hit series will emerge, increasing their potential for late-stage development and minimizing reproducibility issues. For example, minimum inhibitory concentrations, and possibly also minimum bactericidal concentrations, should always be evaluated in a screening campaign, for example, by using the European Committee on Antimicrobial Susceptibility Testing (EUCAST) (https://eucast.org/) or the Clinical and Laboratory Standards Institute (CLSI) (https://clsi.org/meetings/ast/) guidelines. In addition, selected hits from standard screening panels should be consequently tested against contemporary clinical isolates to demonstrate that they overcome existing resistance mechanisms.

Owing to the high attrition rates from early hit discovery to advanced hits and leads, it is especially important in the field of antibacterials to diversify and generate multiple hit series, and to characterize them thoroughly regarding all features that appear relevant to the intended therapeutic use. This includes explorations to expand scaffold diversity in the context of understanding the target-based chemical and physicochemical requirements, as well as potential liabilities, like ADMET.

A summary of early target hit profiles is essential to nominate the most valuable hit series acting against the pathogen(s) or medical indication(s) of interest. The selection of hit series for lead generation follows the target candidate profile (TCP), which is predefined at the outset of the development programme according to the desired target product profile (TPP) (Fig. 2). Thus, the optimization of hits should generally be driven by TCPs and compound progression criteria that, in turn, are driven by chosen TPPs. If several TPPs have been selected or outlined for a campaign, for example, based on different indications, together with their corresponding TCPs, it has to be decided which TCP should be used as a base to aim at for a given chemical series or possibly natural-product-based hit that emerges from mining of biological sources (see below).

Fig. 2: Summary of major steps and processes in antibacterial drug discovery and development.
figure2

Approaches marked with * can be linked with emerging artificial intelligence (AI)-based technology, for example, for advanced data mining, screening or property predictions, to increase efficiency and outcome. ADMET, absorption, distribution, metabolism, excretion and toxicity; CTA, clinical trial application; DRF, dose range finding; EMA, European Medicines Agency; FDA, U.S. Food and Drug Administration; FoR, frequency of resistance; GLP, good laboratory practice; ICH, International Council for Harmonisation of Technical Requirements for Pharmaceuticals for Human Use; IND, investigational new drug; MedChem, medicinal chemistry; MICs, minimal inhibitory concentrations; MoR, mechanism of resistance; phys-chem, physicochemical properties; PK/PD, pharmacokinetics/pharmacodynamics; POC, proof of concept; SAR, structure–activity relationship; TPP, target product profile.

It is important to implement physicochemical and in vitro ADMET profiling at the start of hit optimization, to make sure that any PK issues are identified early and can be addressed through the entire chemistry programme. In this respect, a standardized list of essential compound properties is required for successful transfer of hits and early leads into the following discovery and development stages. Depending on the defined TPP, such a dossier on physicochemical and biological properties should comprise a set of minimal criteria for compound progression based on selected, standardized assays or attributes with clear benchmarks for transition to the next stages in the drug discovery pathway and for continued (pre)clinical development according to the International Council for Harmonisation of Technical Requirements for Pharmaceuticals for Human Use (ICH) guidelines (https://www.ich.org/page/ich-guidelines). This will help to ensure developable compounds of clinical relevance are produced, which are also attractive for potential industrial partners. Relevant parameters (depending on the particular stage of transition) may include:

  • Potency/cellular activity (e.g. based on minimum inhibitory concentrations and minimum bactericidal concentrations).

  • Chemical and metabolic stability, solubility, permeability (e.g. based on logP or, for ionizable compounds, logD, or complex membrane partitioning).

  • Distribution, efflux avoidance, selectivity/off-target avoidance (e.g. inhibition assays on receptor panels, hERG etc.).

  • Acid/base properties based on pKa.

  • Cytotoxicity (especially human cell lines).

  • Lack of reactive metabolites.

  • Phototoxicity.

  • Protein binding.

  • In vivo efficacy and human dose prediction.

  • (Oral) bioavailability.

  • Genotoxicity (e.g. based on Ames or mouse micronucleus tests).

  • Drug–drug interactions.

  • PK linearity.

  • Safety (in vivo toxicity).

  • Compound access (e.g. synthetic feasibility and scaling up to gram or kilogram).

  • Achievable degree of purity.

  • Formulation.

Once the hit discovery transitions into the hit-to-lead and lead optimization phases (see below), it is necessary to enlarge the scope of biological studies. These may include bacterial killing kinetics, MoA, frequency of resistance, mechanism of resistance and PK/PD analyses, which will deliver valuable parameters to assess a compound’s in vivo efficacy (assuming sufficient free drug exposure in a relevant animal model with acceptable tolerability). At this level, it is, once again, important to acquire information on a substantial number of structurally related analogues through extensive medicinal chemistry efforts (perhaps in collaboration with PPPs or the pharmaceutical industry, as suggested above) in order to establish clear and reliable dossiers of structure–activity relationship (SAR) and structure–property relationship. These data are essential to consistently improve all the required parameters as a basis for a continuous advancement of lead structures towards the selection of (pre)clinical candidates. Computational methods based on machine learning techniques like profile-quantitative structure–activity relationship (pQSAR) can help to build predictive models regarding activity, selectivity, toxicity, MoA and further parameters for specific compound classes, hence, providing valuable in silico input for more effective hit discovery and lead design119,120.

Natural-product-based hit compounds

Historically, microbial natural products have been the most important source of antibiotic lead compounds; over the last 40 years, about 60% of all new chemical entities in the field of antibacterials were based on or derived from natural products121. Here, to complement the key aspects described above for synthetic hits, we outline the major requirements specific to the identification and prioritization of antibacterial natural product hits. We focus on efficiency and, particularly for the academic sector, achievability in terms of technological and financial demands.

Identification of new chemotypes from natural sources

The known antibiotic activity of natural products has, in general, been identified by phenotypic screening campaigns that determine activity against panels of test organisms in standardized assays. These screens, which constitute the basis for bioactivity-guided isolation of natural products from complex mixtures, efficiently retrieve bioactive compounds when libraries of crude extracts are evaluated. However, their ability to reveal useful novelty is limited by both a high rediscovery rate of already known molecules associated with pre-existing resistance mechanisms, as well as a substantial proportion of hits that show significant cytotoxicity or poor ADMET properties.

We emphasize that there is a general lack of efficient tools and strategies to increase the number of new chemotypes and to reduce the rediscovery rates in antibacterial screening approaches. Even on a global scale, the number of newly discovered chemotypes, especially novel scaffolds acting against Gram-negative bacteria, is consistently low. Several approaches are relevant to improve this situation:

One possibility to enforce the identification of new antibacterial chemistry is to limit screening of already broadly characterized groups of secondary metabolite producers, for example, actinomycetes, and to expand efforts on identifying new types of producers by extensive biodiversity mining. This can be achieved by focusing on the ~99.999% of microbial taxa of the Earth’s microbiome that remain undiscovered122,123, including the as yet underexplored taxa of human and animal microbiomes124,125,126,127. Emerging innovative isolation and cultivation techniques such as diffusion bioreactors (also carried out on the microscale as with the iChip128,129,130), microfluidics131,132,133, elicitors134 and various co-cultivations135,136 will help to access and understand the rare and less-studied groups of microorganisms from diverse habitats137,138,139. Further, molecular (co-)evolution acting to generate novel metabolites for efficient microbial warfare could be exploited140,141, for example, by sampling from environments heavily contaminated with antibiotics (like sewage in Southeast Asia or South America), which are known to contain highly resistant microbes142,143. Complementarily, this can be achieved by laboratory exposure of potent producers to subinhibitory antibiotic concentrations144 or by co-culturing them together with drug-resistant (pathogenic) strains145. Beyond microbial producers, a great variety of plants146,147, macroscopic filamentous fungi (e.g. Basidiomycota)148 and animals149 bear the potential to deliver useful compounds as a base for novel antimicrobials. Altogether, the exploration of untapped biological resources, which represent a major reservoir for future therapeutics, should generally be extended within the academic and industrial sector.

After genome mining of novel microbial isolates or metagenome-driven discovery of novel natural products150,151,152,153, selected biosynthetic gene clusters (BGCs) that potentially produce unknown secondary metabolites should be systematically expressed in specialized heterologous host strains154,155,156. This helps to facilitate a straightforward detection and isolation of the new compounds, particularly if their BGCs are ‘silent’ (i.e. not expressed under known conditions) in the native host. Such heterologous hosts or chassis strains can be based on microbial species that commonly produce a large variety of natural products, but have been made devoid of their own secondary metabolite BGCs and/or have been further optimized to efficiently express BGCs originating from ‘non-common’ sources (for example, rare actinomycetes or fungi)154,157,158. However, only a limited set of such specialized host strains is available so far, and a much more diverse array of microbial chassis needs to be developed to fit the demands of a growing arsenal of BGCs that potentially produce novel chemistry. BGC expression is often most successful in strains closely related to the native producer, and, thus, it is important to develop methods for standardized heterologous expression in selected host strains with desirable properties that have not yet been domesticated for the use as regular chassis159.

Chemical space can also be enlarged by using emerging synthetic biology approaches for medium-to-high-throughput genome editing and pathway engineering. These approaches, which are primarily based on CRISPR/Cas9 (refs160,161) and diverse recombination, assembly and integrase systems162,163,164, can be followed up with advanced analytics and screening of the potentially modified natural products, which may be produced in only trace quantities. This technology involves the extensive use of information on genome sequences, enzyme activities and compound structures collected by publications, databases and web tools (such as MIBiG165, antiSMASH166 and PRISM167) over the past few decades. In many cases, the modularity of the BGC composition, which is found in gene clusters, for example, coding for polyketide synthases or non-ribosomal peptide synthetases, can be used to implement a bioinformatics-supported plug-and-play diversification strategy enabling the exchange and recombination of core units, as well as modifying enzymes168,169,170,171. A concomitant refactoring of BGCs, especially from rare microbial sources, often allows high-level heterologous production of the antibiotic compounds in suitable hosts172,173,174,175. However, these methods are still in their infancy and require wider testing with different classes of antimicrobials to define general principles of feasibility and scalability, which, furthermore, necessitates an improved understanding of the complex biosynthetic machineries and their modular evolution.

Advances in analytical chemistry techniques, for example, in mass-spectrometry-based metabolomics and its enhancement by molecular networking and the application of machine learning, support the process of dereplication176 during (secondary) metabolome mining177,178,179,180,181. Known compounds produced in reasonably high yields can be rapidly identified via their high-resolution masses, tandem mass spectrometry fragmentation patterns or structural data in secondary metabolite databases138,182,183,184,185,186,187. However, the remaining bottleneck is to highlight and annotate novel antibiotic compounds, particularly those with low production titres, as early as possible in the discovery process (i.e. from crude extracts if possible, without the need for small-scale fractionation and enrichment). This objective can be supported by innovative extraction methods prior to bioactivity-guided isolation of novel compounds188.

In addition, revisiting known potent antibiotics, previously neglected as a result of unacceptable or non-addressable properties such cytotoxicity or lack of stability, can be a valuable strategy to provide novel leads and candidates. The reassessment of such scaffolds can be based on a variety of efforts, including the improvement of production and purification189, reconsideration of application and effective dose for natural derivatives190, or advantageous scaffold modification by biosynthetic engineering and semi-synthetic approaches191,192 (Box 4).

Further opportunities remain to improve the discovery and development of agents for combination therapy as indicated above, i.e. compounds that act synergistically against multidrug-resistant and/or high-priority pathogens193,194. The discrimination of specific synergistic activities from non-specific antibiotic activities remains a challenge during the discovery process.

Improving bacterial target access, enhancing potency and broadening the antimicrobial spectrum of known and novel antibiotic scaffolds can be achieved by using drug-conjugate strategies, for example, linking of pathogen-specific antibodies195,196, siderophore moieties197,198 or positively charged peptides199,200 to the antibiotic core scaffold. Though these approaches have been proven effective in a number of cases, some of them may also have unintended effects, such as a spontaneously increasing frequency of resistance, which can be problematic, for example, in the case of the Trojan Horse approach201.

Overall, a variety of innovative and complementary technologies is required to improve access to novel natural product scaffolds. Computational methods can provide powerful assistance at different levels in many of the areas indicated above, as recent efforts show202,203. In this context, artificial intelligence might play a game-changing role in the future. The general power of neural networks for detecting new antimicrobial candidates has already been demonstrated202. By using a computational model that screens hundreds of millions of chemical compounds in a few days, potential antibiotics even with new MoA(s) could be proposed rapidly. Given the recent advances in artificial intelligence, these and other models will likely add to the future identification of new candidate drugs.

Interestingly, when looking at compound properties, it appears that there is often more flexibility in the selection of ‘successful’ natural product scaffolds compared with synthetics, for example, regarding Lipinski’s rule of five204,205,206, which natural products frequently ‘disobey’ (such as cyclosporine or macrolides like azithromycin). Thus, antimicrobial drug discovery in ‘beyond rule of five’ chemical space is an opportunity when using natural compound collections or when assembling libraries of de novo designed compounds207,208,209, though the general need for optimizing key pharmacological properties of such hits remains beyond question.

Another major challenge for natural products can be the generation of structurally diverse analogues (particularly if they are not accessible through biosynthesis). Many scaffold positions can be difficult to access by means of semi-synthesis and, thus, broad derivatization of natural-product-based hit and lead compounds is often much more labour-intensive, and establishing synthetic access to these scaffolds with a focus on the ability to systematically diversify their chemical space can require large amounts of resources210. Nevertheless, the modification of natural scaffolds with substituents that are often easier to incorporate by (semi-)synthetic or chemoenzymatic approaches, such as halogens that allow the modulation of solubility, permeability, selectivity, target affinity etc.211,212, proves that multiple opportunities arise when combining synthetic and biological chemistry.

Required access to biological and chemical material and data

Many scientists frequently experience difficulty in accessing and sharing research material from third parties, including microbial strains, cultivation extracts, pure compounds, genome or gene cluster sequences and further background data (of published or even unpublished results). For example, an interesting BGC is identified in publicly accessible databases, but the strain is not specified or not available from the indicated source. Similarly, access to industrial antibiotic overproducers can be impossible, even when a company no longer has a commercial interest in the resulting molecule. This phenomenon has several origins, including legal restraints (for example, imposed by the Nagoya Protocol213) or intellectual property (IP) claims on strains, compounds, biologics or (re)profiling data of already known structures.

In the public interest, standardized procedures are necessary to facilitate access to research materials and to solve IP conflicts, at least within the field of academia, in which it is common practice to share research materials with colleagues by negotiating appropriate cooperation agreements.

Further, the access to in-house compound libraries of pharmaceutical companies (at least subsets of them and especially those that are not intended for antibiotic-related screening) could be very valuable for academic partners who are eager to identify novel antibacterial hits, which could lead to joint drug development programmes. Enabling access to materials can also be extended to strain collections, including clinical isolates representing the diversity of pathogens associated with a certain clinical indication, and advanced compound information based on pre-existing characterization and profiling campaigns. An increased availability of these resources will be of great benefit to the antimicrobial research community worldwide.

Furthermore, comprehensive databases and data-sharing platforms can provide another valuable resource for present and future antibiotic R&D projects and, hence, should be implemented and maintained with care214. There is a growing body of recently initiated and publicly available web-based tools and archives that support accumulation and exchange of data regarding antibacterial compounds in different stages of discovery or therapeutic development, known or predicted antibiotic targets and the diversity of antimicrobial resistance determinants (Box 5). Further connection and integration of such databases is desirable to optimize the output for a specific search request. In addition, initiatives comparable with the European Commission’s manifesto to maximize the public accessibility of research results in the fight against COVID-19 (ref.215) are also highly recommended to support AMR-related scientific research at all levels, including facilitated access to online resources.

Prediction of antimicrobial structure and function from genome sequence data

Driven by breakthroughs in sequencing technologies and genome mining, the identification of BGCs encoding the biosynthesis of natural products has matured to complement the chemistry-driven and bioactivity-driven screening processes for natural product hits. Computational methods are established and continuously improved to identify novel biosynthetic pathways in (meta)genomic sequence data150,151. Recently, third-generation genome sequencing techniques such as PacBio and Oxford Nanopore have been developed that provide high-quality full genome data even for complex microorganisms like filamentous fungi at reasonable cost, which is an ideal prerequisite for large-scale genome mining approaches216.

However, linking the obtained sequence information to possible structural or functional features of the encoded molecules remains a great challenge. Prediction of chemical structures directly from genome data would help to distinguish known from potentially novel scaffolds during a very early stage of dereplication; the training of machine learning algorithms with sufficient quantity of genome data from microbial producers could ultimately lead to fairly accurate predictions of chemical structures linked to specific BGCs and possibly even their biological activities167.

A successful strategy to decipher antibacterial targets of new natural products, without the need to isolate them, is a directed search for known resistance factors in the genomes of antibiotic-producing microbes217,218. These producers may code for resistant variants of the molecular target(s) that interact with the intrinsic antibiotic(s) without damaging the host or conserved class-specific transporters that release the compound(s) into the environment. This approach recently led to the discovery of novel antibiotic scaffolds219. However, most BGCs do not contain apparent or specific drug-resistance genes that could straightforwardly indicate a compound’s function. In the majority of cases, very limited predictions based on genomic data concerning function and potential target(s) of a natural product are currently possible, although advanced automated tools for target-directed genome mining are available220. Thus, there is a high demand for innovative methods to predict the molecular function or target of a natural compound based on genomic data. Such data would be extremely valuable in order to prioritize BGCs for experimental characterization. In the future, artificial intelligence approaches, based on either classical machine learning methods (extracting new knowledge from preprocessed data sets) or on deep learning (drawing conclusions from raw data such as representative examples, often by using multilayer neural networks), may deliver such predictions with increasing accuracy221. However, existing algorithms need to be improved, and new ones have to be developed to specifically address the question of how to assign target-based functions to natural products with confidence during the early stages of discovery and prioritization. These approaches also require a huge amount of validated training data222.

Advancing hits to (pre)clinical status

Regardless of whether antibacterial hits emerge from rationally designed synthetic molecules or from the pool of natural products, the subsequent hit-to-lead and lead-to-candidate optimization phases are very similar for compounds irrespective of origin (‘Y model’, see Fig. 2). We now discuss the most critical obstacles and requirements for delivering those advanced leads that may eventually become the next generation of (pre)clinical candidates.

Drug–target interaction studies as a base for hit development

For hits arising from phenotypic assays, cellular MoA(s) or specific molecular target(s) may not be known at the hit-to-lead stage, and, sometimes, the precise MoA is elucidated years after the approval of a drug, as in the case of daptomycin223. However, detailed insight into the mechanism(s) by which compounds exert their pharmacological activity is highly desirable for further rational optimization of chemical scaffolds, particularly when structurally enabled approaches can be used, for a convincing presentation of preclinical candidate dossiers and for regulatory requirements. Since universally applicable methods for characterizing the MoA(s) of antibiotics do not exist, a full suite of expertise in genetics, genomics, microbiology, chemical biology and biophysics is required. Identification of the molecular target can be achieved by targeted screens of indicator or mutant strains, whole-genome sequencing upon focused resistance development224,225, pattern recognition techniques based on transcriptomics226, imaging227,228, metabolomics229, macromolecular synthesis230,231 or mutant fitness profiles232,233, which can be coupled with machine learning approaches for directed predictions225,233, or chemoproteomics234,235. The latter is specifically useful in the case of non-essential target inhibitors like pathoblockers, since these may not generate resistant mutants (at least under standard laboratory conditions). Additional techniques for MoA studies may include crystallography, a diverse set of spectroscopic and calorimetric analyses236,237,238,239,240, as well as the use of functionalized derivatives (‘tool compounds’)241,242, which can support both target identification and validation and may provide in-depth information of drug–target interactions to drive the rational hit-to-lead optimization process forward. Alternatively, identification of drug–target (or ligand–protein) interactions formed under native (unbiased) conditions by using specialized proteomic approaches is becoming increasingly successful243,244,245,246. Current bioinformatic tools can also combine genome-mining approaches with the prediction of potentially innovative MoA(s) based on the presence of resistant target genes in BGCs encoding novel antibiotics220. These and other examples illustrate how a diverse set of emerging learning methods is steadily enhancing the predictability of drug–target interactions247,248.

In addition to the specific molecular target(s), it is important to understand the impact of the antibiotic compound on the general physiology of the bacterial cell. This includes the sequence of events leading to bacterial death, the time point when killing occurs (based on either individual bacterial cells or their population/colonization level) and the conditions that might enhance or preclude it. Such characterizations may require the application or development of a range of secondary assays. For compounds acting on intracellular bacterial targets (i.e. targets located in the cytoplasm), the processes of compound influx and prevention of efflux (especially so for Gram-negative bacteria as a result of their complex cell envelope and presence of numerous multidrug efflux pumps) are both critical optimization parameters to ensure sufficient target engagement249,250,251,252,253. These factors can be addressed by suitable compound design, which generally remains rather empirical and challenging254,255,256,257. Other possibilities to address this key area would be to use these compounds in combination with outer membrane permeabilizing agents258,259 or efflux inhibitors93,260. Alternative approaches targeting extracellular virulence factors, for example, extracellular lectins required for attachment and biofilm formation or secreted proteolytic enzymes, do not suffer from a possible lack of bacterial uptake261. Often, antibiotics, and particularly natural products, have more than one target and disturb bacterial physiology in several different pathways, a phenomenon referred to as polypharmacology73,262,263, which is beneficial for inflicting severe damage on the bacterial cell and slowing down target-mediated resistance development. Information related to such effects should be acquired for all bacterial species within the spectrum of activity of the potential drug, and it may diverge significantly across phylogenetically distant species.

Apart from the desired biological effects on bacterial pathogens, knowledge about undesired adverse effects on eukaryotic cells (‘off-target effects’264,265,266,267,268,269) should be acquired early on, since toxicity is a major contributor to attrition in the drug development process. However, whilst in vitro cytotoxicity screens are useful during the early discovery process, they are often not predictive of toxicological effects that can become most significant during in vivo studies. Furthermore, collateral damage to the microbiome needs to be considered270,271,272,273 and can be modulated by selective drug design274. For compounds with a novel or particularly complex MoA, it often takes several years to achieve a detailed molecular understanding and the cellular consequences of exposure. Therefore, acquiring this knowledge as early as possible is a key aspect for further rational drug optimization, including SAR studies and structure-guided hit/lead optimization. We recommend investing resources into expanded MoA studies already during the initial stages of the drug development process and, furthermore, building a network of experts who can provide MoA analyses that fulfil the requirements of a preclinical candidate dossier. While these aspects are standard for drug development projects in the pharmaceutical industry, academia usually suffers from insufficient funding to appropriately address such requirements, and, therefore, additional resources need to be secured.

Limited resources to move from hit into lead stage

Once a hit validation has been accomplished, the resources needed to advance the selected compound series into hit-to-lead and lead optimization greatly increase. These stages require a diverse scientific team covering analytical, computational and medicinal chemistry, biochemistry, microbiology, bioinformatics (ideally including machine learning and artificial intelligence methods), drug metabolism and pharmacokinetics, as well as, specifically for natural-product-based compounds, biotechnology and genetic engineering. In industrial projects, typically 5–15 medicinal chemists work on the optimization of a hit (depending on how complex the chemistry of a certain compound is) to create promising leads or preclinical candidates, essentially by generating, testing and advancing SAR-based analogue series in an iterative manner. The challenge is to simultaneously optimize all properties necessary for the drug to be most effective and least toxic. This includes potency, selectivity, physicochemical parameters and cytotoxicity, as well as pharmacokinetics and pharmacodynamics (Fig. 2). The multi-parameter optimization can usually be achieved within a time frame of about 2–4 years, but remains dependent on the human, technological and financial capacities, as well as the particular challenges represented by the chemical series. Such resources are difficult to acquire through classical academic funding schemes, which usually reward new discoveries in fundamental science, rather than subsequent steps of time-consuming and resource-consuming optimization, where there is no guarantee of success.

Academia must, therefore, find new ways to provide suitable resources for early-stage translational research. Since few academic institutions possess the relevant expertise and facilities to carry out lead optimization, they usually require access to high-quality expertise and/or capacities in cooperation with pharmaceutical companies/SMEs or through contract research organizations (CROs), which can only be achieved through additional funding or partnerships. One possible strategy to acquire appropriate resources in future could be the application of alternative reward schemes for evaluation of academic project funding, which might not only be based on high-impact publications but also on verifiable commitment to health research, such as making dedicated contributions to a global antibacterial portfolio. The emergence of centres for translational science in many countries (for example, the German Center for Infection Research; https://www.dzif.de/en) could be an opportunity to develop and implement such measures, possibly at an international level.

The bottleneck of compound supply

The enhanced biological profiling that is mandatory in hit and lead optimization programmes requires a considerable amount of sufficiently pure compounds to be tested. While this can be a problem for chemists in general even with respect to synthetic hits and leads (especially the massive scale-up of typical laboratory test reactions)275,276, the problem of supplying increasingly large quantities of natural products originating from bacteria, fungi or plants is particularly challenging. Indeed, academic projects are often concluded when natural compounds or biotechnologically generated variants thereof are identified at small scale (often <10 mg), with only rudimentary profiling. In many laboratories, there are no additional resources to increase the yields of natural product hits or initial leads, or to scale up production in a pre-pilot plant environment that is capable of carrying out the fermentation (possibly by using heterologous production hosts to achieve attractive yields277,278). In addition, downstream processing has to be established and optimized for every new compound to ensure satisfactory purity at a sufficient quantity for the following stages, including scaffold optimization by medicinal chemistry or extended biological profiling. The fact that sufficient amounts of compounds (multigram-to-kilogram scale) cannot be produced in many cases severely decreases the chances of developing novel therapeutics from natural products. This is particularly unfortunate in the antibiotics field, because about two-thirds of all antibiotic drugs in therapeutic use are derived from natural products44,121. Regrettably, fermentation-independent supply, for example, through the total synthesis of complex natural compounds, can only be achieved for a low percentage of novel hits and leads and requires a tremendous amount of additional capacity and resources279,280,281,282.

Thus, suitable funding instruments are needed to cover the essential processes of natural compound scale-up and supply based on biotechnological methods, including large-scale fermentation and efficient downstream processing283,284,285, towards obtaining high-quality source material for semi-synthesis and further studies. In addition, a robust method for large-scale production and downstream processing of the candidate molecule is a prerequisite for process transfer to good manufacturing practice (GMP) production before entering (pre)clinical stages. Generally, further scientific and technological development is required to make the provision of compound material from various sources a more routine and affordable task, particularly in the non-industrial research environment.

Requirements for in vivo studies and project transfer

The primary assays in most discovery programmes usually address biochemical, biophysical and/or microbiological functionality of newly generated compounds. In order to convert a molecule with in vitro activity into a drug, sufficient exposure at the infection site in vivo must be achieved. To analyse this, a full suite of ADMET assays is required286,287, followed by pharmacokinetics experiments in animals (usually starting with rodent models)288,289, which can be combined with physiologically based PK modelling and in silico ADMET prediction290,291.

A sufficient correlation between in vitro and in vivo data, which is not always achievable for all antimicrobial compounds, should generally be pursued as early as possible in the programme, otherwise, continued lead design might be based on irrelevant or misleading data points (for example, see some case studies with LpxC inhibitors292,293). Furthermore, the availability of PD models294,295 of high translational relevance, i.e. reliably predicting a minimal efficacious dose in humans, is a critical factor of success in order to generate the optimal drug candidate during lead and lead-to-candidate optimization. In the field of antibiotics, in particular, preclinical PK/PD relationships are generally predictive and have a high relevance for regulatory dossiers296,297, for example, for human PK/PD target attainment at therapeutic doses and drug formulation development, and, as such, they have to be evaluated carefully at the earliest possible stages298,299,300. Typically, PK/PD target attainments for antibiotics require relatively high doses compared with other drug classes (particularly to achieve sufficient exposure at the site of infection), limiting the successful application of existing formulation and delivery technologies. This constraint is especially true for oral medications that may present further challenges, for example, to reach an adequate bioavailability of the drug. Hence, a broader array of potential delivery systems should be tested systematically, which may include conventional permeation enhancers301, as well as sophisticated nanoformulations, for example, liposome-based drug delivery systems302,303,304,305. The latter, however, can only be produced based on expert knowledge and infrastructure, which is, once again, not often available in academia, and, thus, specialized CROs or SMEs may be approached based on available funding.

A further obstacle is the need to perform (initially) rather extensive studies in laboratory animals to understand the PK/PD relationship of a novel compound, which, at subsequent stages, allows the number of animal experiments to be minimized according to the 3Rs principle306. However, these studies are generally associated with ethical concerns, high costs and administrative burden. Likewise, these matters are relevant for the in vivo evaluation of toxicology, toxicokinetics and safety pharmacology to cover safety aspects before entering clinical trials307,308. Here, exploratory or early-stage predictive assays using computational models, as well as in vivo systems with minimal ethical concerns, for example, in vertebrates like Danio rerio (zebrafish), insects like Galleria mellonella (the greater wax moth) or worms like Caenorhabditis elegans (a soil-dwelling nematode), are an opportunity to estimate both efficacy and potential toxicity risks before considering standard in vivo experiments in rodents and other mammals309,310,311.

Ultimately, the demonstration of efficacy in a relevant animal model, associated with convincing exposure at the site of infection and a rough estimation of a reasonable safety margin, is often a prerequisite to attract an investor’s interest; typical minimum requirements are a tolerance/dose range finding study in one or two animal species, as well as human dose prediction based on a solid set of PK/PD data, for example, by testing efficacy in the neutropenic thigh infection model in mice312.

Generally, TPPs and the corresponding TCPs should continue to be the base for all further optimization attempts, especially when including in vivo studies, and, hence, should be thoroughly compiled before the development programme starts, with the help of subject matter experts. These data will guide the strategies and decisions for all chemical and biological development processes during the optimization phases, mainly with respect to one (or more) clinical indication(s). In order to specify robust finishing lines, these documents should outline sets of minimum acceptable criteria for each phase, for example, for biochemical assays during early stages and (pre)clinical endpoints at later stages. Such compound progression criteria should be defined for a validated hit, entry into lead optimization, a late lead and a preclinical candidate. There are different TPPs for different bacterial infections. As projects evolve, they may encounter serendipitous discoveries, unsurmountable hurdles or important findings from other groups or competitors, which may affect the TPP that they target. Therefore, the TPP can be critically reviewed and possibly refined or adapted throughout the project, for example, at each transition into the next development stage. Ideally, a pool of commonly accepted TPPs (i.e. approved by the pharmaceutical industry as well as the public health sector) should be available for the multitude of clinical indications to serve as a base for each discovery and development programme of novel therapeutics. The WHO and the GARDP have already started to produce such TPPs for public health concerns, for example, in the field of sexually transmitted infections313. These TPPs need to be regularly reviewed, and, where necessary, updated, to make sure that they reflect the current clinical situation; for example, TPPs addressing indications caused by bacterial infections may be affected by the latest emerging (or anticipated) drug-resistant pathogens of critical relevance. It is important to note that only convincing TPPs together with comprehensive preclinical candidate dossiers (highly informative TCPs) and reliable SOPs for scalable compound supply will allow early partnering and a smooth transfer of the project to an industrial stakeholder to move into (pre)clinical development (Box 6).

The management challenge in hit and lead optimization programmes

As the development of antibacterials requires a multidisciplinary approach, knowledge of a diverse set of techniques and domains (for example, assay development, high-throughput screening, medicinal and computational chemistry, ADMET, PK/PD, drug delivery, clinical background of disease processes etc.) is required in order to develop a compound to the level of a preclinical candidate. Single principal investigators (PIs) will usually not possess the broad base of expertise that is necessary, since academia largely focuses on early-stage discovery and compound optimization at the laboratory scale. Hence, research groups that do not possess the extensive skill set for drug development in its various stages should pursue a team approach by collaborating with organizations that have the relevant experience, be it within the academic or the industrial sector. There is also the possibility of calling on specialized consultancy or outsourcing packages of work (for example, ADMET) to CROs that possess relevant expertise and experimental capabilities. However, in addition to the relatively high costs of such services, PIs often struggle with remaining questions once a CRO assignment ends, and sufficient resources for tailor-made optimizations are often lacking. Moreover, the need to interpret results and devise a clear path forward towards the TPP from multiple data packages remains with the project teams. Hence, partnerships and collaborations are essential if relevant in-house expertise or infrastructure is missing. Therefore, we propose the following solutions for efficient translational project management:

Aligning and collaborating with suitable partners from various sectors or disciplines is crucial for groups with limited know-how in drug discovery and development. Generally, larger project teams can provide or identify expertise much more quickly to sufficiently resolve emerging knowledge gaps. Additionally, project consultants or CROs can be approached at different levels to fulfil remaining tasks, for example, data evaluation or processing defined and highly specific work packages, for example in PK and toxicological studies.

Databases of experts should be available for relevant research areas or services, and the various technical and IP-related aspects need to be elaborated on a case-by-case basis. Unbiased partners have to be identified to host and curate such databases on a regular basis, which could fall into the remit of non-profit health organizations such as the Joint Programming Initiative on Antimicrobial Resistance (JPIAMR) or the GARDP.

Training of PIs on a frequent basis is required to broaden their knowledge and to ensure a high-level understanding of potential barriers and pitfalls at least until projects reach the (pre)clinical stages. A number of renowned institutions already offer regular workshops and seminars (often as interactive webinars, for example, GARDP REVIVE; https://revive.gardp.org/), as well as extended training programmes (for example, the Interdisciplinary Course on Antibiotics and Resistance; https://www.icarecourse.org/, hosted by Institut Pasteur, France), and these are increasingly popular. In addition to PIs from academia, non-academic experts from industry, health and political sectors should share their perspectives on current research and funding aspects more regularly within interdisciplinary settings.

Overall, it is important that the necessary financial and legal frameworks for efficiency-oriented project management are established as early as possible during the course of a development programme to avoid loss of time and resources. The required settings can be implemented either individually at a particular project level or existing management models (for example, available in the industrial sector or in translational research centres) could be used or adapted for the specific purpose.

Conclusions and outlook

To ensure a healthy and vibrant antimicrobial pipeline, considerable efforts are needed not only to develop the next generation of antibacterial drugs but also to safeguard and foster profound expertise in antibiotic drug discovery and development. In the short and medium term, such capacity-building must be performed as a collaborative and iterative process between academia and industry to ensure that the necessary skills are available to translate validated hits into potential drug products. The development of joint initiatives for education in translational sciences will require specific funding, as they are not part of most universities’ standard curricula. Many experienced scientists in the pharmaceutical industry are eager to share their translational and regulatory knowledge, often after retirement or due to change of operations. Thus, pharmaceutical companies could serve as a valuable training ground for acquiring and developing specific skills in the antimicrobial sector. However, limited funding (especially for SMEs) and economic uncertainties negatively affect this premise because it leads to business closures, high employee turnover rates, prevents the recruitment and training of inexperienced staff and deters scientists from embarking on a career in SMEs. In order to achieve transfer of vital expertise, workshops, symposiums and exchanges that foster academic–industrial interactions between students and advanced researchers are required and need financial support. Unfortunately, there are very few market-driven initiatives for such events and, therefore, a connection to already existing education and training programmes, for example, those supported by IMI, European Society of Clinical Microbiology and Infectious Diseases (ESCMID) or British Society for Antimicrobial Chemotherapy (BSAC), can be a valuable option as long as the transition into an era of mutually sustained knowledge transfer between industry and academia continues.

Another critical aspect for all future antibiotic R&D projects is the implementation of a legal framework for IP ownership at project commencement. The multidisciplinary and collaborative nature of antibiotic drug discovery often results in collaborations between different institutions on a national or international level. This creates challenging ownership structures with increasing complexity of such consortia, especially when an antibacterial programme is out-licensed, for example, to an SME. Negotiating ownership agreements among inventor institutions can be lengthy and may discourage industry from in-licensing valuable assets for further development. The increased collaboration between academia and industry requires fair and justifiable guidelines for knowledge and compound transfer outlined in appropriate agreements. The creation of such guidelines should be supported, for example, in the form of templates to settle ownership agreements between project partners or third parties, to facilitate processes for the benefit of researchers with limited experience in these matters. Such a framework will accelerate potential technology and compound transfer towards industrial drug developers, will make the relative commitment for each participant clearer and, thus, their gains more attractive.

Finally, we believe that AMR research requires diligent lobbying at the national and international levels to create entry points for large funders. Many scientists working on antimicrobials in either academia or SMEs are outside the few existing networks that involve decision makers within commercial funding sources, such as venture capitalists, including the newly announced AMR Action Fund, philanthropic organizations, national or regional governments or international bodies. This situation has resulted in an environment in which the challenges of antimicrobial drug developers are either not heard or are even ignored, even as public awareness of AMR steadily increases. It is evident that a strong lobbying position will lead to changes, which has recently been shown by the BEAM Alliance and their interaction and negotiations with diverse political bodies in Europe, leading to increased recognition of the challenges for antibacterial drug developers by the European Commission and Europe’s national governments314,315,316.

For the above reasons, we recommend that an international group of experienced AMR lobbyists should be formed that, together, can campaign for funding of early antibacterial drug discovery research along the principles set out in this article. Such a group should include national, regional and global scientific and industry associations that have practice in interacting with relevant stakeholders connected with national parliaments, EC, G7, G20 and further decision-making entities317. The Global AMR R&D Hub (https://globalamrhub.org/) could be a crystallization point to pioneer such developments, which can be supported by various consortia, including the authors of this article: The International Research Alliance for Antibiotic Discovery and Development (IRAADD; https://www.iraadd.eu/), which we have recently established with the support of the JPIAMR Virtual Research Institute (JPIAMR-VRI; https://www.jpiamr.eu/jpiamr-vri/), identifies itself as a part of the mission that is addressed by the current roadmap. The IRAADD aims to improve the situation of novel antibiotic discovery and development by bringing together experts for early drug research from the academic and industrial sectors, who can provide knowledge and advice for diverse projects in the field. A recent example of our activities is the support of the JPIAMR-VRI to create a new online resource (the JPIAMR-VRI Digital Platform ‘DISQOVER’; https://www.jpiamr.eu/activities/jpiamr-vri/digital-platform/), serving as a comprehensive and interlinked database for AMR-related research at multiple levels. Although the IRAADD currently has only a short-term funding perspective, it is one of our main goals to help define and implement interdisciplinary innovative antibiotic development programmes based on sustainable research funding, in order to refill the translational pipeline with new drug candidates in the foreseeable future. In this respect, and as a possible long-term vision, the creation of internationally operating antibiotic research hubs, which may emerge from already existing pre-stage platforms such as the IRAADD, can be a major step forward to engage as many members as possible from academia, industry and public health organizations in antimicrobial R&D collaborations, and to create a strong and path-breaking position that cannot be overlooked. Only a responsible connection of thought leaders and dedicated experts from all relevant sectors of society, joining together now and for the future, will allow suitable rapid responses to globally emerging pathogens. Therefore, taking corrective and preventive action now through concerted and innovative approaches in the field of novel antibiotic drug discovery and development is the essential path forward to be prepared for future pandemics caused by multi-to-pan drug-resistant (so-called superbug) bacteria, which is an aim that deserves our undivided attention.

References

  1. 1.

    Ventola, C. L. The antibiotic resistance crisis: part 1: causes and threats. Pharm. Ther. 40, 277–283 (2015). A comprehensive summary of the emergence of antibiotic resistance and the associated clinical and economic burden.

    Google Scholar 

  2. 2.

    Byrne, M. K. et al. The drivers of antibiotic use and misuse: the development and investigation of a theory driven community measure. BMC Public. Health 19, 1425 (2019).

    PubMed  PubMed Central  Article  Google Scholar 

  3. 3.

    Nadimpalli, M. L. et al. Urban informal settlements as hotspots of antimicrobial resistance and the need to curb environmental transmission. Nat. Microbiol. 5, 787–795 (2020).

    CAS  PubMed  Article  Google Scholar 

  4. 4.

    World Health Organization (WHO). WHO report on surveillance of antibiotic consumption 2016–2018 early implementation (WHO, 2018) https://www.who.int/medicines/areas/rational_use/oms-amr-amc-report-2016-2018/en/

  5. 5.

    Schrader, S. M., Vaubourgeix, J. & Nathan, C. Biology of antimicrobial resistance and approaches to combat it. Sci. Trans. Med. 12, eaaz6992 (2020).

    CAS  Article  Google Scholar 

  6. 6.

    Conlon, B. P. et al. Persister formation in Staphylococcus aureus is associated with ATP depletion. Nat. Microbiol. 1, 16051 (2016).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  7. 7.

    Harms, A., Maisonneuve, E. & Gerdes, K. Mechanisms of bacterial persistence during stress and antibiotic exposure. Science 354, aaf4268 (2016).

    PubMed  Article  CAS  Google Scholar 

  8. 8.

    Blaskovich, M. A. T. Antibiotics special issue: challenges and opportunities in antibiotic discovery and development. ACS Infect. Dis. 6, 1286–1288 (2020). An excellent special issue combining viewpoints, perspectives, reviews and original research to provide a snapshot of the current state of antibiotic discovery and development.

    CAS  Article  Google Scholar 

  9. 9.

    O’Neill, J. Tackling drug-resistant infections globally: final report and recommendations. The Review on Antimicrobial Resistance https://amr-review.org/sites/default/files/160518_Final%20paper_with%20cover.pdf (2016). Ground-breaking report and action plan to tackle present and future threats imposed by drug-resistant infections on a global scale.

  10. 10.

    AMR Industry Alliance. 2020 progress report (AMR Industry Alliance, 2020) https://www.amrindustryalliance.org/wp-content/uploads/2020/01/AMR-2020-Progress-Report.pdf

  11. 11.

    Zhou, P. et al. Bacterial and fungal infections in COVID-19 patients: A matter of concern. Infect. Control Hospital Epidemiol. 41, 1124–1125 (2020).

    Article  CAS  Google Scholar 

  12. 12.

    Chen, X. et al. The microbial coinfection in COVID-19. Appl. Microbiol. Biotechnol. 104, 7777–7785 (2020).

    PubMed  Article  CAS  PubMed Central  Google Scholar 

  13. 13.

    Zhou, F. et al. Clinical course and risk factors for mortality of adult inpatients with COVID-19 in Wuhan, China: a retrospective cohort study. Lancet 395, 1054–1062 (2020).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  14. 14.

    Nori, P. et al. Emerging co-pathogens: New Delhi metallo-beta-lactamase producing Enterobacterales infections in New York City COVID-19 patients. Int. J. Antimicrob. Agents 56, 106179 (2020).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  15. 15.

    Getahun, H., Smith, I., Trivedi, K., Paulin, S. & Balkhy, H. H. Tackling antimicrobial resistance in the COVID-19 pandemic. Bull. World Health Organ. 98, 442–442A (2020).

    PubMed  PubMed Central  Article  Google Scholar 

  16. 16.

    Oldenburg, C. E. & Doan, T. Azithromycin for severe COVID-19. Lancet 396, 936–937 (2020).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  17. 17.

    Yates, P. A. et al. Doxycycline treatment of high-risk COVID-19-positive patients with comorbid pulmonary disease. Ther. Adv. Respir. Dis. 14, 1753466620951053 (2020).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  18. 18.

    Sodhi, M. & Etminan, M. Therapeutic potential for tetracyclines in the treatment of COVID-19. Pharmacotherapy 40, 487–488 (2020).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  19. 19.

    Baron, S. A., Devaux, C., Colson, P., Raoult, D. & Rolain, J.-M. Teicoplanin: an alternative drug for the treatment of COVID-19? Int. J. Antimicrob. Agents 55, 105944 (2020).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  20. 20.

    Contou, D. et al. Bacterial and viral co-infections in patients with severe SARS-CoV-2 pneumonia admitted to a French ICU. Ann. Intensive Care 10, 119 (2020).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  21. 21.

    Sharifipour, E. et al. Evaluation of bacterial co-infections of the respiratory tract in COVID-19 patients admitted to ICU. BMC Infect. Dis. 20, 646 (2020).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  22. 22.

    Langford, B. J. et al. Bacterial co-infection and secondary infection in patients with COVID-19: a living rapid review and meta-analysis. Clin. Microbiol. Infect. 26, 1622–1629 (2020).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  23. 23.

    Rawson, T. M. et al. Bacterial and fungal co-infection in individuals with coronavirus: A rapid review to support COVID-19 antimicrobial prescribing. Clin. Infect. Dis. 71, 2459–2468 (2020).

    CAS  PubMed  PubMed Central  Google Scholar 

  24. 24.

    Vaughn, V. M. et al. Empiric antibacterial therapy and community-onset bacterial co-infection in patients hospitalized with COVID-19: a multi-hospital cohort study. Clin. Infect. Dis. 72, e533–e541 (2020).

    PubMed Central  Article  CAS  Google Scholar 

  25. 25.

    Hsu, J. How covid-19 is accelerating the threat of antimicrobial resistance. BMJ 369, m1983 (2020).

    PubMed  Article  PubMed Central  Google Scholar 

  26. 26.

    Rawson, T. M., Ming, D., Ahmad, R., Moore, L. S. P. & Holmes, A. H. Antimicrobial use, drug-resistant infections and COVID-19. Nat. Rev. Microbiol. 18, 409–410 (2020).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  27. 27.

    Bengoechea, J. A. & Bamford, C. G. SARS-CoV-2, bacterial co-infections, and AMR: the deadly trio in COVID-19? EMBO Mol. Med. 12, e12560 (2020).

    CAS  PubMed  PubMed Central  Google Scholar 

  28. 28.

    Huttner, B. D., Catho, G., Pano-Pardo, J. R., Pulcini, C. & Schouten, J. COVID-19: don’t neglect antimicrobial stewardship principles! Clin. Microbiol. Infect. 26, 808–810 (2020).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  29. 29.

    Buehrle, D. J. et al. Antibiotic consumption and stewardship at a hospital outside of an early coronavirus disease 2019 epicenter. Antimicrob. Agents Chemother. 64, e01011–e01020 (2020).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  30. 30.

    Nieuwlaat, R. et al. COVID-19 and antimicrobial resistance: parallel and interacting health emergencies. Clin. Infect. Dis. 72, 1657–1659 (2020).

    Article  CAS  Google Scholar 

  31. 31.

    Bassetti, M. & Giacobbe, D. R. A look at the clinical, economic, and societal impact of antimicrobial resistance in 2020. Expert Opin. Pharmacother. 21, 2067–2071 (2020).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  32. 32.

    Magiorakos, A.-P. et al. Multidrug-resistant, extensively drug-resistant and pandrug-resistant bacteria: an international expert proposal for interim standard definitions for acquired resistance. Clin. Microbiol. Infect. 18, 268–281 (2012). This article presents the standardized international terminology to describe acquired resistance profiles in bacterial priority pathogens.

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  33. 33.

    Xin Yu, J., Hubbard-Lucey, V. M. & Tang, J. Immuno-oncology drug development goes global. Nat. Rev. Drug Discov. 18, 899–900 (2019).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  34. 34.

    The Pew Charitable Trusts. Tracking the global pipeline of antibiotics in development, April 2020 (The Pew Charitable Trusts, 2020) https://www.pewtrusts.org/en/research-and-analysis/issue-briefs/2020/04/tracking-the-global-pipeline-of-antibiotics-in-development

  35. 35.

    Beyer, P. & Paulin, S. The antibacterial research and development pipeline needs urgent solutions. ACS Infect. Dis. 6, 1289–1291 (2020).

    CAS  Article  Google Scholar 

  36. 36.

    World Health Organization (WHO). 2019 Antibacterial agents in clinical development: an analysis of the antibacterial clinical development pipeline. (WHO, 2019) https://apps.who.int/iris/bitstream/handle/10665/330420/9789240000193-eng.pdf

  37. 37.

    The Pew Charitable Trusts. A scientific roadmap for antibiotic discovery. (The Pew Charitable Trusts, 2016) https://www.pewtrusts.org/en/research-and-analysis/reports/2016/05/a-scientific-roadmap-for-antibiotic-discovery

  38. 38.

    OECD Publishing. Stemming the superbug tide: Just a few dollars more. (OECD, 2018) https://www.oecd.org/health/stemming-the-superbug-tide-9789264307599-en.htm

  39. 39.

    Årdal, C. et al. DRIVE-AB report: revitalizing the antibiotic pipeline: Stimulating innovation while driving sustainable use and global access. (DRIVE AB, 2018) http://drive-ab.eu/wp-content/uploads/2018/01/CHHJ5467-Drive-AB-Main-Report-180319-WEB.pdf

  40. 40.

    Kim, W., Prosen, K. R., Lepore, C. J. & Coukell, A. On the road to discovering urgently needed antibiotics: so close yet so far away. ACS Infect. Dis. 6, 1292–1294 (2020).

    CAS  PubMed  Article  Google Scholar 

  41. 41.

    Theuretzbacher, U., Outterson, K., Engel, A. & Karlén, A. The global preclinical antibacterial pipeline. Nat. Rev. Microbiol. 18, 275–285 (2020). This review summarizes the most recent antibacterial discovery and preclinical development projects in academia and industry on a global scale.

    PubMed  Article  CAS  Google Scholar 

  42. 42.

    O’Neill, J. Securing new drugs for future generations: the pipeline of antibiotics. (The Review on Antimicrobial Resistance, 2015) https://amr-review.org/sites/default/files/SECURING%20NEW%20DRUGS%20FOR%20FUTURE%20GENERATIONS%20FINAL%20WEB_0.pdf

  43. 43.

    Hutchings, M. I., Truman, A. W. & Wilkinson, B. Antibiotics: past, present and future. Curr. Opin. Microbiol. 51, 72–80 (2019).

    CAS  PubMed  Article  Google Scholar 

  44. 44.

    Lewis, K. The science of antibiotic discovery. Cell 181, 29–45 (2020). Outstanding overview of past achievements as well as current perspectives and challenges in the field of antibiotic discovery.

    CAS  PubMed  Article  Google Scholar 

  45. 45.

    Coates, A. R. M., Halls, G. & Hu, Y. Novel classes of antibiotics or more of the same? Br. J. Pharmacol. 163, 184–194 (2011).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  46. 46.

    Hughes, D. & Karlén, A. Discovery and preclinical development of new antibiotics. Upsala J. Med. Sci. 119, 162–169 (2014).

    PubMed  PubMed Central  Article  Google Scholar 

  47. 47.

    Jackson, N., Czaplewski, L. & Piddock, L. J. V. Discovery and development of new antibacterial drugs: learning from experience? J. Antimicrob. Chemother. 73, 1452–1459 (2018).

    CAS  PubMed  Article  Google Scholar 

  48. 48.

    Payne, D. J., Gwynn, M. N., Holmes, D. J. & Pompliano, D. L. Drugs for bad bugs: confronting the challenges of antibacterial discovery. Nat. Rev. Drug Discov. 6, 29–40 (2007).

    CAS  PubMed  Article  Google Scholar 

  49. 49.

    BEAM Alliance. Supporting financial investments on R&D to de-risk antimicrobial development. (BEAM Alliance, 2019) https://beam-alliance.eu/wp-content/uploads/2019/10/the-beam-push-memo.pdf

  50. 50.

    Shlaes, D. M. Antibacterial drugs: the last frontier. ACS Infect. Dis. 6, 1313–1314 (2020).

    CAS  PubMed  Article  Google Scholar 

  51. 51.

    Rex, J. H. & Outterson, K. Antibiotic reimbursement in a model delinked from sales: a benchmark-based worldwide approach. Lancet Infect. Dis. 16, 500–505 (2016). This article describes how delinkage models should be built to ensure the development of antibiotics with the greatest possible innovative potential.

    PubMed  Article  PubMed Central  Google Scholar 

  52. 52.

    Outterson, K. A shot in the arm for new antibiotics. Nat. Biotechnol. 37, 1110–1112 (2019).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  53. 53.

    Rex, J. H., Fernandez Lynch, H., Cohen, I. G., Darrow, J. J. & Outterson, K. Designing development programs for non-traditional antibacterial agents. Nat. Commun. 10, 3416 (2019). This article discusses strategies focusing on how non-traditional antibacterial products can best be developed.

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  54. 54.

    Årdal, C. et al. Antibiotic development - economic, regulatory and societal challenges. Nat. Rev. Microbiol. 18, 267–274 (2020).

    PubMed  Article  CAS  PubMed Central  Google Scholar 

  55. 55.

    International Federation of Pharmaceutical Manufacturers & Associations (IFPMA) New AMR Action Fund steps in to save collapsing antibiotic pipeline with pharmaceutical industry investment of US$1 billion. (IFPMA, 2020) https://www.ifpma.org/resource-centre/new-amr-action-fund-steps-in-to-save-collapsing-antibiotic-pipeline/

  56. 56.

    The Public Health Agency of Sweden. Availability of antibiotics: Reporting of Government Commission. (The Public Health Agency of Sweden, 2017) https://www.folkhalsomyndigheten.se/contentassets/ab79066a80c7477ca5611ccfb211828e/availability-antibiotics-01229-2017-1.pdf

  57. 57.

    Mullard, A. UK outlines its antibiotic pull incentive plan. Nat. Rev. Drug Discov. 19, 298–299 (2020).

    PubMed  PubMed Central  Google Scholar 

  58. 58.

    Mahase, E. UK launches subscription style model for antibiotics to encourage new development. BMJ 369, m2468 (2020).

    PubMed  Article  PubMed Central  Google Scholar 

  59. 59.

    Bennet, S. M. & Young, S. T. The Pioneering Antimicrobial Subscriptions to End Up Surging Resistance Act of 2020. (The PASTEUR Act. Michael Bennet, U.S. Senator for Colorado); https://www.bennet.senate.gov/public/_cache/files/b/5/b5032465-bf79-4dca-beba-50fb9b0f4aee/C1FD8B8C4143F1332DC6A5E5EC09C83B.bon20468.pdf (2020).

  60. 60.

    Davis, D. K. Developing an Innovative Strategy for Antimicrobial Resistant Microorganisms Act of 2019. DISARM Act of 2019. (Congress.gov, 2019) https://www.congress.gov/116/bills/hr4100/BILLS-116hr4100ih.pdf (2019).

  61. 61.

    Balasegaram, M. & Piddock, L. J. V. The Global Antibiotic Research and Development Partnership (GARDP) not-for-profit model of antibiotic development. ACS Infect. Dis. 6, 1295–1298 (2020).

    CAS  PubMed  Article  Google Scholar 

  62. 62.

    Alm, R. A. & Gallant, K. Innovation in antimicrobial resistance: the CARB-X perspective. ACS Infect. Dis. 6, 1317–1322 (2020).

    CAS  PubMed  Article  Google Scholar 

  63. 63.

    Innovative Medicines Initiative (IMI) AMR Accelerator Programme. Collaboration for prevention and treatment of multi-drug resistant bacterial infections (COMBINE, 2019). https://amr-accelerator.eu/project/combine/

  64. 64.

    BEAM Alliance. BEAM Alliance and global partners call for urgent action on new reimbursement models for life-saving antibiotics. (BEAM Alliance, 2020) https://beam-alliance.eu/wp-content/uploads/2020/09/post-amr-conference-paper.pdf

  65. 65.

    Engel, A. Fostering antibiotic development through impact funding. ACS Infect. Dis. 6, 1311–1312 (2020).

    CAS  PubMed  Article  Google Scholar 

  66. 66.

    Zuegg, J., Hansford, K. A., Elliott, A. G., Cooper, M. A. & Blaskovich, M. A. T. How to stimulate and facilitate early stage antibiotic discovery. ACS Infect. Dis. 6, 1302–1304 (2020).

    CAS  PubMed  Article  Google Scholar 

  67. 67.

    van Hengel, A. J. & Marin, L. Research, innovation, and policy: an alliance combating antimicrobial resistance. Trends Microbiol. 27, 287–289 (2019).

    PubMed  Article  CAS  Google Scholar 

  68. 68.

    Simpkin, V. L., Renwick, M. J., Kelly, R. & Mossialos, E. Incentivising innovation in antibiotic drug discovery and development: progress, challenges and next steps. J. Antibiot. 70, 1087–1096 (2017).

    CAS  Article  Google Scholar 

  69. 69.

    BEAM Alliance. Incentivising a sustainable response to the threat of AMR. (BEAM Alliance, 2019) https://beam-alliance.eu/wp-content/uploads/2019/11/the-beam-pull-memo.pdf

  70. 70.

    European Commission. A European One Health Action Plan against Antimicrobial Resistance. (AMR). (European Commission, 2017) https://ec.europa.eu/health/sites/default/files/antimicrobial_resistance/docs/amr_2017_action-plan.pdf

  71. 71.

    World Health Organization (WHO). Global action plan on antimicrobial resistance. (WHO, 2015) https://www.who.int/antimicrobial-resistance/publications/global-action-plan/en/

  72. 72.

    Hughes, J. P., Rees, S., Kalindjian, S. B. & Philpott, K. L. Principles of early drug discovery. Br. J. Pharmacol. 162, 1239–1249 (2011).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  73. 73.

    Reddy, A. S. & Zhang, S. Polypharmacology: drug discovery for the future. Expert Rev. Clin. Pharmacol. 6, 41–47 (2013). This review outlines the latest progress and challenges in polypharmacology studies.

    CAS  PubMed  Article  Google Scholar 

  74. 74.

    Tyers, M. & Wright, G. D. Drug combinations: a strategy to extend the life of antibiotics in the 21st century. Nat. Rev. Microbiol. 17, 141–155 (2019).

    CAS  Article  Google Scholar 

  75. 75.

    O’Shea, R. & Moser, H. E. Physicochemical properties of antibacterial compounds: implications for drug discovery. J. Med. Chem. 51, 2871–2878 (2008).

    PubMed  Article  CAS  Google Scholar 

  76. 76.

    Reck, F., Jansen, J. M. & Moser, H. E. Challenges of antibacterial drug discovery. Arkivoc 2019, 227–244 (2020). This study highlights challenges in the discovery of antibiotics, with a focus on physicochemical parameters and preferred property space.

    Article  CAS  Google Scholar 

  77. 77.

    AMR Industry Alliance. Declaration by the pharmaceutical, biotechnology and diagnostics industries on combating antimicrobial resistance. (AMR Industry Alliance, 2016) https://www.amrindustryalliance.org/wp-content/uploads/2017/12/AMR-Industry-Declaration.pdf

  78. 78.

    Karawajczyk, A., Orrling, K. M., Vlieger, J. S. B., de, Rijnders, T. & Tzalis, D. The European lead factory: a blueprint for public-private partnerships in early drug discovery. Front. Med. 3, 75 (2016).

    Google Scholar 

  79. 79.

    Morgentin, R. et al. Translation of innovative chemistry into screening libraries: an exemplar partnership from the European Lead Factory. Drug Discov. Today 23, 1578–1583 (2018).

    CAS  PubMed  Article  Google Scholar 

  80. 80.

    Besnard, J., Jones, P. S., Hopkins, A. L. & Pannifer, A. D. The Joint European Compound Library: boosting precompetitive research. Drug Discov. Today 20, 181–186 (2015).

    CAS  PubMed  Article  Google Scholar 

  81. 81.

    Theuretzbacher, U. & Piddock, L. J. V. Non-traditional antibacterial therapeutic options and challenges. Cell Host Microbe 26, 61–72 (2019). Comprehensive overview of non-traditional approaches in antibacterial therapy.

    CAS  PubMed  Article  Google Scholar 

  82. 82.

    Fleitas Martínez, O., Cardoso, M. H., Ribeiro, S. M. & Franco, O. L. Recent advances in anti-virulence therapeutic strategies with a focus on dismantling bacterial membrane microdomains, toxin neutralization, quorum-sensing interference and biofilm inhibition. Front. Cell. Infect. Microbiol. 9, 74 (2019).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  83. 83.

    Kumar, A., Ellermann, M. & Sperandio, V. Taming the beast: Interplay between gut small molecules and enteric pathogens. Infect. Immun. 87, e00131-19 (2019).

    PubMed  PubMed Central  Article  Google Scholar 

  84. 84.

    Laws, M., Shaaban, A. & Rahman, K. M. Antibiotic resistance breakers: current approaches and future directions. FEMS Microbiol. Rev. 43, 490–516 (2019).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  85. 85.

    Annunziato, G. Strategies to overcome antimicrobial resistance (AMR) making use of non-essential target inhibitors: a review. Int. J. Mol. Sci. 20, 5844 (2019).

    CAS  PubMed Central  Article  PubMed  Google Scholar 

  86. 86.

    Calvert, M. B., Jumde, V. R. & Titz, A. Pathoblockers or antivirulence drugs as a new option for the treatment of bacterial infections. Beilstein J. Org. Chem. 14, 2607–2617 (2018).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  87. 87.

    Schütz, C. & Empting, M. Targeting the Pseudomonas quinolone signal quorum sensing system for the discovery of novel anti-infective pathoblockers. Beilstein J. Org. Chem. 14, 2627–2645 (2018).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  88. 88.

    Sommer, R. et al. Glycomimetic, orally bioavailable LecB inhibitors block biofilm formation of pseudomonas aeruginosa. J. Am. Chem. Soc. 140, 2537–2545 (2018). Example of synthetic pathoblockers acting against biofilm formation of Pseudomonas aeruginosa.

    CAS  PubMed  Article  Google Scholar 

  89. 89.

    Boudaher, E. & Shaffer, C. L. Inhibiting bacterial secretion systems in the fight against antibiotic resistance. MedChemComm 10, 682–692 (2019).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  90. 90.

    Duncan, M. C., Linington, R. G. & Auerbuch, V. Chemical inhibitors of the type three secretion system: disarming bacterial pathogens. Antimicrob. Agents Chemother. 56, 5433–5441 (2012).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  91. 91.

    Schönauer, E. et al. Discovery of a potent inhibitor class with high selectivity toward clostridial collagenases. J. Am. Chem. Soc. 139, 12696–12703 (2017).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  92. 92.

    Paolino, M. et al. Development of potent inhibitors of the Mycobacterium tuberculosis virulence factor Zmp1 and evaluation of their effect on mycobacterial survival inside macrophages. ChemMedChem 13, 422–430 (2018).

    CAS  PubMed  Article  Google Scholar 

  93. 93.

    Marshall, R. L. et al. New multidrug efflux inhibitors for Gram-negative bacteria. mBio 11, e01340-20 (2020).

    PubMed  PubMed Central  Google Scholar 

  94. 94.

    Bush, K. & Bradford, P. A. Interplay between β-lactamases and new β-lactamase inhibitors. Nat. Rev. Microbiol. 17, 295–306 (2019).

    CAS  PubMed  Article  Google Scholar 

  95. 95.

    Tooke, C. L. et al. β-Lactamases and β-lactamase inhibitors in the 21st century. J. Mol. Biol. 431, 3472–3500 (2019).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  96. 96.

    Smirnova, G. V. & Oktyabrsky, O. N. Glutathione in bacteria. Biochemistry 70, 1199–1211 (2005).

    CAS  PubMed  Google Scholar 

  97. 97.

    Chen, N. H. et al. A glutathione-dependent detoxification system is required for formaldehyde resistance and optimal survival of Neisseria meningitidis in biofilms. Antioxid. Redox Signal. 18, 743–755 (2013).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  98. 98.

    Lu, P. et al. The anti-mycobacterial activity of the cytochrome bcc inhibitor Q203 can be enhanced by small-molecule inhibition of cytochrome bd. Sci. Rep. 8, 2625 (2018).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  99. 99.

    Iqbal, I. K., Bajeli, S., Akela, A. K. & Kumar, A. Bioenergetics of Mycobacterium: an emerging landscape for drug discovery. Pathogens 7, 24 (2018).

    PubMed Central  Article  CAS  PubMed  Google Scholar 

  100. 100.

    Fol, M., Włodarczyk, M. & Druszczyn´ska, M. Host epigenetics in intracellular pathogen infections. Int. J. Mol. Sci. 21, 4573 (2020).

    CAS  PubMed Central  Article  Google Scholar 

  101. 101.

    Ghosh, D., Veeraraghavan, B., Elangovan, R. & Vivekanandan, P. Antibiotic resistance and epigenetics: more to it than meets the eye. Antimicrob. Agents Chemother. 64, e02225-19 (2020).

    PubMed  PubMed Central  Article  Google Scholar 

  102. 102.

    Venter, H. Reversing resistance to counter antimicrobial resistance in the World Health Organisation’s critical priority of most dangerous pathogens. Biosci. Rep. 39, BSR20180474 (2019).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  103. 103.

    Rezzoagli, C., Archetti, M., Mignot, I., Baumgartner, M. & Kümmerli, R. Combining antibiotics with antivirulence compounds can have synergistic effects and reverse selection for antibiotic resistance in Pseudomonas aeruginosa. PLoS Biol. 18, e3000805 (2020).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  104. 104.

    Strich, J. R. et al. Needs assessment for novel Gram-negative antibiotics in US hospitals: a retrospective cohort study. Lancet Infect. Dis. 20, 1172–1181 (2020).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  105. 105.

    Lakemeyer, M., Zhao, W., Mandl, F. A., Hammann, P. & Sieber, S. A. Thinking outside the box — novel antibacterials to tackle the resistance crisis. Angew. Chem. Int. Ed. 57, 14440–14475 (2018). Extensive and interdisciplinary overview of methods for mining novel antibiotics and strategies to unravel their modes of action.

    CAS  Article  Google Scholar 

  106. 106.

    Hennessen, F. et al. Amidochelocardin overcomes resistance mechanisms exerted on tetracyclines and natural chelocardin. Antibiotics 9, 619 (2020).

    CAS  PubMed Central  Article  Google Scholar 

  107. 107.

    Sarigul, N., Korkmaz, F. & Kurultak, I∙. A new artificial urine protocol to better imitate human urine. Sci. Rep. 9, 20159 (2019).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  108. 108.

    Ganz, T. & Nemeth, E. Iron homeostasis in host defence and inflammation. Nat. Rev. Immunol. 15, 500–510 (2015).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  109. 109.

    Martins, A. C., Almeida, J. I., Lima, I. S., Kapitão, A. S. & Gozzelino, R. Iron metabolism and the inflammatory response. IUBMB Life 69, 442–450 (2017).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  110. 110.

    Krismer, B., Weidenmaier, C., Zipperer, A. & Peschel, A. The commensal lifestyle of Staphylococcus aureus and its interactions with the nasal microbiota. Nat. Rev. Microbiol. 15, 675–687 (2017).

    CAS  PubMed  Article  Google Scholar 

  111. 111.

    Ayaz, M. et al. Synergistic interactions of phytochemicals with antimicrobial agents: Potential strategy to counteract drug resistance. Chem. Biol. Interact. 308, 294–303 (2019).

    CAS  PubMed  Article  Google Scholar 

  112. 112.

    Mahomoodally, M. F. & Sadeer, N. B. Antibiotic potentiation of natural products: A promising target to fight pathogenic bacteria. Curr. Drug Targets 22, 555–572 (2020).

    Google Scholar 

  113. 113.

    Parkinson, E. I. et al. Deoxynybomycins inhibit mutant DNA gyrase and rescue mice infected with fluoroquinolone-resistant bacteria. Nat. Commun. 6, 6947 (2015).

    CAS  PubMed  Article  Google Scholar 

  114. 114.

    King, A. M. et al. Aspergillomarasmine A overcomes metallo-β-lactamase antibiotic resistance. Nature 510, 503–506 (2014).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  115. 115.

    Park, S. W. et al. Target-based identification of whole-cell active inhibitors of biotin biosynthesis in Mycobacterium tuberculosis. Chem. Biol. 22, 76–86 (2015).

    CAS  PubMed  Article  Google Scholar 

  116. 116.

    Verma, S. & Prabhakar, Y. S. Target based drug design - a reality in virtual sphere. Curr. Med. Chem. 22, 1603–1630 (2015).

    CAS  PubMed  Article  Google Scholar 

  117. 117.

    Graef, F. et al. In vitro model of the Gram-negative bacterial cell envelope for investigation of anti-infective permeation kinetics. ACS Infect. Dis. 4, 1188–1196 (2018).

    CAS  PubMed  Article  Google Scholar 

  118. 118.

    Richter, R. et al. A hydrogel-based in vitro assay for the fast prediction of antibiotic accumulation in Gram-negative bacteria. Mater. Today Bio 8, 100084 (2020).

    PubMed  PubMed Central  Article  Google Scholar 

  119. 119.

    Martin, E. J. et al. All-Assay-Max2 pQSAR: activity predictions as accurate as four-concentration IC50s for 8558 Novartis assays. J. Chem. Inf. Model. 59, 4450–4459 (2019).

    CAS  PubMed  Article  Google Scholar 

  120. 120.

    Durrant, J. D. & Amaro, R. E. Machine-learning techniques applied to antibacterial drug discovery. Chem. Biol. Drug Des. 85, 14–21 (2015).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  121. 121.

    Newman, D. J. & Cragg, G. M. Natural products as sources of new drugs over the nearly four decades from 01/1981 to 09/2019. J. Nat. Prod. 83, 770–803 (2020). Fundamental review addressing the role of natural products in drug discovery during the past 40 years.

    CAS  PubMed  Article  Google Scholar 

  122. 122.

    Locey, K. J. & Lennon, J. T. Scaling laws predict global microbial diversity. Proc. Natl Acad. Sci. USA 113, 5970–5975 (2016).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  123. 123.

    Thompson, L. R. et al. A communal catalogue reveals Earth’s multiscale microbial diversity. Nature 551, 457–463 (2017).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  124. 124.

    Mousa, W. K., Athar, B., Merwin, N. J. & Magarvey, N. A. Antibiotics and specialized metabolites from the human microbiota. Nat. Prod. Rep. 34, 1302–1331 (2017).

    CAS  PubMed  Article  Google Scholar 

  125. 125.

    Zipperer, A. et al. Human commensals producing a novel antibiotic impair pathogen colonization. Nature 535, 511–516 (2016). Research article describing the discovery of the novel antibiotic lugdunin produced by commensals of the human nasal microbiome.

    CAS  PubMed  Article  Google Scholar 

  126. 126.

    Imai, Y. et al. A new antibiotic selectively kills Gram-negative pathogens. Nature 576, 459–464 (2019). This study describes the discovery of the new antibiotic darobactin that is active against Gram-negative pathogens.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  127. 127.

    Adnani, N., Rajski, S. R. & Bugni, T. S. Symbiosis-inspired approaches to antibiotic discovery. Nat. Prod. Rep. 34, 784–814 (2017).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  128. 128.

    Lodhi, A. F., Zhang, Y., Adil, M. & Deng, Y. Antibiotic discovery: combining isolation chip (iChip) technology and co-culture technique. Appl. Microbiol. Biotechnol. 102, 7333–7341 (2018).

    CAS  PubMed  Article  Google Scholar 

  129. 129.

    Kealey, C., Creaven, C. A., Murphy, C. D. & Brady, C. B. New approaches to antibiotic discovery. Biotechnol. Lett. 39, 805–817 (2017).

    CAS  PubMed  Article  Google Scholar 

  130. 130.

    Ling, L. L. et al. A new antibiotic kills pathogens without detectable resistance. Nature 517, 455–459 (2015). An intriguing example of discovering a new antibiotic (teixobactin) from uncultured bacteria by using innovative cultivation techniques (iChip).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  131. 131.

    Ma, L. et al. Gene-targeted microfluidic cultivation validated by isolation of a gut bacterium listed in Human Microbiome Project’s Most Wanted taxa. Proc. Natl Acad. Sci. USA 111, 9768–9773 (2014).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  132. 132.

    Mahler, L. et al. Highly parallelized microfluidic droplet cultivation and prioritization on antibiotic producers from complex natural microbial communities. eLife 10, e64774 (2021).

    PubMed  PubMed Central  Article  Google Scholar 

  133. 133.

    Jiang, C.-Y. et al. High-throughput single-cell cultivation on microfluidic streak plates. Appl. Environ. Microbiol. 82, 2210–2218 (2016).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  134. 134.

    Seyedsayamdost, M. R. High-throughput platform for the discovery of elicitors of silent bacterial gene clusters. Proc. Natl Acad. Sci. USA 111, 7266–7271 (2014).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  135. 135.

    Pishchany, G. et al. Amycomicin is a potent and specific antibiotic discovered with a targeted interaction screen. Proc. Natl Acad. Sci. USA 115, 10124–10129 (2018).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  136. 136.

    Chaudhary, D. K., Khulan, A. & Kim, J. Development of a novel cultivation technique for uncultured soil bacteria. Sci. Rep. 9, 6666 (2019).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  137. 137.

    Masschelein, J., Jenner, M. & Challis, G. L. Antibiotics from Gram-negative bacteria: a comprehensive overview and selected biosynthetic highlights. Nat. Prod. Rep. 34, 712–783 (2017).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  138. 138.

    Hoffmann, T. et al. Correlating chemical diversity with taxonomic distance for discovery of natural products in myxobacteria. Nat. Commun. 9, 803 (2018). This study underpins the ‘taxonomy paradigm’, suggesting to explore new genera of microorganisms to increase the chance of finding novel antibiotic chemotypes.

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  139. 139.

    Hughes, C. C. & Fenical, W. Antibacterials from the sea. Chem. Eur. J. 16, 12512–12525 (2010).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  140. 140.

    Abrudan, M. I. et al. Socially mediated induction and suppression of antibiosis during bacterial coexistence. Proc. Natl Acad. Sci. USA 112, 11054–11059 (2015).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  141. 141.

    Granato, E. T., Meiller-Legrand, T. A. & Foster, K. R. The evolution and ecology of bacterial warfare. Curr. Biol. 29, R521–R537 (2019).

    CAS  PubMed  Article  Google Scholar 

  142. 142.

    Kraemer, S. A., Ramachandran, A. & Perron, G. G. Antibiotic pollution in the environment: from microbial ecology to public policy. Microorganisms 7, 180 (2019).

    CAS  PubMed Central  Article  PubMed  Google Scholar 

  143. 143.

    Ju, F. et al. Wastewater treatment plant resistomes are shaped by bacterial composition, genetic exchange, and upregulated expression in the effluent microbiomes. ISME J. 13, 346–360 (2019).

    PubMed  Article  Google Scholar 

  144. 144.

    Sierra-Zapata, L. et al. Inducible antibacterial activity in the bacillales by triphenyl tetrazolium chloride. Sci. Rep. 10, 5563 (2020).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  145. 145.

    Charusanti, P. et al. Exploiting adaptive laboratory evolution of Streptomyces clavuligerus for antibiotic discovery and overproduction. PLoS One 7, e33727 (2012).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  146. 146.

    Cowan, M. M. Plant products as antimicrobial agents. Clin. Microbiol. Rev. 12, 564–582 (1999).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  147. 147.

    Newman, D. J. & Cragg, G. M. Plant endophytes and epiphytes: Burgeoning sources of known and “unknown” cytotoxic and antibiotic agents? Planta Medica 86, 891–905 (2020).

    CAS  PubMed  Article  Google Scholar 

  148. 148.

    Hyde, K. D. et al. The amazing potential of fungi: 50 ways we can exploit fungi industrially. Fungal Divers. 97, 1–136 (2019).

    Article  Google Scholar 

  149. 149.

    Silva, O. N. et al. Repurposing a peptide toxin from wasp venom into antiinfectives with dual antimicrobial and immunomodulatory properties. Proc. Natl Acad. Sci. USA 117, 26936–26945 (2020).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  150. 150.

    Medema, M. H. & Fischbach, M. A. Computational approaches to natural product discovery. Nat. Chem. Biol. 11, 639–648 (2015).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  151. 151.

    Katz, M., Hover, B. M. & Brady, S. F. Culture-independent discovery of natural products from soil metagenomes. J. Ind. Microbiol. Biotechnol. 43, 129–141 (2016).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  152. 152.

    Crits-Christoph, A., Diamond, S., Butterfield, C. N., Thomas, B. C. & Banfield, J. F. Novel soil bacteria possess diverse genes for secondary metabolite biosynthesis. Nature 558, 440–444 (2018).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  153. 153.

    Milshteyn, A., Schneider, J. S. & Brady, S. F. Mining the metabiome: identifying novel natural products from microbial communities. Chem. Biol. 21, 1211–1223 (2014).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  154. 154.

    Myronovskyi, M. et al. Generation of a cluster-free Streptomyces albus chassis strains for improved heterologous expression of secondary metabolite clusters. Metab. Eng. 49, 316–324 (2018).

    CAS  PubMed  Article  Google Scholar 

  155. 155.

    Gomez-Escribano, J. P. & Bibb, M. J. Engineering Streptomyces coelicolor for heterologous expression of secondary metabolite gene clusters. Microb. Biotechnol. 4, 207–215 (2011).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  156. 156.

    Komatsu, M. et al. Engineered Streptomyces avermitilis host for heterologous expression of biosynthetic gene cluster for secondary metabolites. ACS Synth. Biol. 2, 384–396 (2013).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  157. 157.

    Sherwood, E. J., Hesketh, A. R. & Bibb, M. J. Cloning and analysis of the planosporicin lantibiotic biosynthetic gene cluster of Planomonospora alba. J. Bacteriol. 195, 2309–2321 (2013).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  158. 158.

    Ahmed, Y. et al. Engineering of Streptomyces lividans for heterologous expression of secondary metabolite gene clusters. Microb. Cell Fact. 19, 5 (2020).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  159. 159.

    Wang, G. et al. CRAGE enables rapid activation of biosynthetic gene clusters in undomesticated bacteria. Nat. Microbiol. 4, 2498–2510 (2019).

    PubMed  Article  CAS  Google Scholar 

  160. 160.

    Tong, Y., Weber, T. & Lee, S. Y. CRISPR/Cas-based genome engineering in natural product discovery. Nat. Prod. Rep. 36, 1262–1280 (2019). This review summarizes the current knowledge about the CRISPR/Cas system and how it became one of the most important tools for genome editing.

    CAS  PubMed  Article  Google Scholar 

  161. 161.

    La Fuente-Núñez, C. de & Lu, T. K. CRISPR-Cas9 technology: applications in genome engineering, development of sequence-specific antimicrobials, and future prospects. Integr. Biol. 9, 109–122 (2017).

    Article  Google Scholar 

  162. 162.

    Wang, H. et al. ExoCET: exonuclease in vitro assembly combined with RecET recombination for highly efficient direct DNA cloning from complex genomes. Nucleic Acids Res. 46, e28 (2018).

    PubMed  Article  CAS  Google Scholar 

  163. 163.

    Zeng, F. et al. AFEAP cloning: a precise and efficient method for large DNA sequence assembly. BMC Biotechnol. 17, 81 (2017).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  164. 164.

    Snoeck, N. et al. Serine integrase recombinational engineering (SIRE): A versatile toolbox for genome editing. Biotechnol. Bioeng. 116, 364–374 (2019).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  165. 165.

    Kautsar, S. A. et al. MIBiG 2.0: a repository for biosynthetic gene clusters of known function. Nucleic Acids Res. 48, D454–D458 (2020). This article presents a major update of the ‘Minimum Information about a Biosynthetic Gene cluster’ (MIBiG) data repository.

    PubMed  PubMed Central  Google Scholar 

  166. 166.

    Blin, K. et al. antiSMASH 5.0: updates to the secondary metabolite genome mining pipeline. Nucleic Acids Res. 47, W81–W87 (2019). This article presents an entirely redesigned and extended version of the ‘antibiotics and secondary metabolite analysis shell’: antiSMASH.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  167. 167.

    Skinnider, M. A. et al. Comprehensive prediction of secondary metabolite structure and biological activity from microbial genome sequences. Nat. Commun. 11, 6058 (2020).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  168. 168.

    Alanjary, M., Cano-Prieto, C., Gross, H. & Medema, M. H. Computer-aided re-engineering of nonribosomal peptide and polyketide biosynthetic assembly lines. Nat. Prod. Rep. 36, 1249–1261 (2019).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  169. 169.

    Cummings, M. et al. Assembling a plug-and-play production line for combinatorial biosynthesis of aromatic polyketides in Escherichia coli. PLoS Biol. 17, e3000347 (2019).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  170. 170.

    Hwang, S., Lee, N., Cho, S., Palsson, B. & Cho, B.-K. Repurposing modular polyketide synthases and non-ribosomal peptide synthetases for novel chemical biosynthesis. Front. Mol. Biosci. 7, 87 (2020).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  171. 171.

    Bozhüyük, K. A. J. et al. Modification and de novo design of non-ribosomal peptide synthetases using specific assembly points within condensation domains. Nat. Chem. 11, 653–661 (2019).

    PubMed  Article  CAS  PubMed Central  Google Scholar 

  172. 172.

    Luo, Y., Enghiad, B. & Zhao, H. New tools for reconstruction and heterologous expression of natural product biosynthetic gene clusters. Nat. Prod. Rep. 33, 174–182 (2016).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  173. 173.

    Tan, G.-Y. & Liu, T. Rational synthetic pathway refactoring of natural products biosynthesis in actinobacteria. Metab. Eng. 39, 228–236 (2017).

    CAS  PubMed  Article  Google Scholar 

  174. 174.

    Zhang, J. J., Tang, X. & Moore, B. S. Genetic platforms for heterologous expression of microbial natural products. Nat. Prod. Rep. 36, 1313–1332 (2019). This review highlights the present arsenal of genetic platforms to identify, evaluate and engineer biosynthetic gene clusters from diverse microbial sources.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  175. 175.

    Huo, L. et al. Heterologous expression of bacterial natural product biosynthetic pathways. Nat. Prod. Rep. 36, 1412–1436 (2019).

    CAS  PubMed  Article  Google Scholar 

  176. 176.

    Ito, T. & Masubuchi, M. Dereplication of microbial extracts and related analytical technologies. J. Antibiot. 67, 353–360 (2014).

    CAS  Article  Google Scholar 

  177. 177.

    Quinn, R. A. et al. Molecular networking as a drug discovery, drug metabolism, and precision medicine strategy. Trends Pharmacol. Sci. 38, 143–154 (2017).

    CAS  PubMed  Article  Google Scholar 

  178. 178.

    Ernst, M. et al. MolNetEnhancer: Enhanced molecular networks by integrating metabolome mining and annotation tools. Metabolites 9, 144 (2019).

    CAS  PubMed Central  Article  PubMed  Google Scholar 

  179. 179.

    Liebal, U. W., Phan, A. N. T., Sudhakar, M., Raman, K. & Blank, L. M. Machine learning applications for mass spectrometry-based metabolomics. Metabolites 10, 243 (2020).

    CAS  PubMed Central  Article  PubMed  Google Scholar 

  180. 180.

    Hubert, J., Nuzillard, J.-M. & Renault, J.-H. Dereplication strategies in natural product research: How many tools and methodologies behind the same concept? Phytochem. Rev. 16, 55–95 (2017).

    CAS  Article  Google Scholar 

  181. 181.

    Li, Y., Kuhn, M., Gavin, A.-C. & Bork, P. Identification of metabolites from tandem mass spectra with a machine learning approach utilizing structural features. Bioinformatics 36, 1213–1218 (2020).

    CAS  PubMed  Google Scholar 

  182. 182.

    Zani, C. L. & Carroll, A. R. Database for rapid dereplication of known natural products using data from MS and Fast NMR experiments. J. Nat. Prod. 80, 1758–1766 (2017).

    CAS  PubMed  Article  Google Scholar 

  183. 183.

    van Santen, J. A. et al. The natural products atlas: an open access knowledge base for microbial natural products discovery. ACS Cent. Sci. 5, 1824–1833 (2019).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  184. 184.

    Wang, M. et al. Sharing and community curation of mass spectrometry data with global natural products social molecular networking. Nat. Biotechnol. 34, 828–837 (2016).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  185. 185.

    Guijas, C. et al. METLIN: a technology platform for identifying knowns and unknowns. Anal. Chem. 90, 3156–3164 (2018).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  186. 186.

    Kuhn, S. & Schlörer, N. E. Facilitating quality control for spectra assignments of small organic molecules: nmrshiftdb2–a free in-house NMR database with integrated LIMS for academic service laboratories. Magn. Reson. Chem. 53, 582–589 (2015).

    CAS  PubMed  Article  Google Scholar 

  187. 187.

    López-Pérez, J. L., Therón, R., del Olmo, E. & Díaz, D. NAPROC-13: a database for the dereplication of natural product mixtures in bioassay-guided protocols. Bioinformatics 23, 3256–3257 (2007).

    PubMed  Article  CAS  Google Scholar 

  188. 188.

    Bader, C. D., Neuber, M., Panter, F., Krug, D. & Müller, R. Supercritical fluid extraction enhances discovery of secondary metabolites from myxobacteria. Anal. Chem. 92, 15403–15411 (2020).

    CAS  PubMed  Article  Google Scholar 

  189. 189.

    Nass, N. M. et al. Revisiting unexploited antibiotics in search of new antibacterial drug candidates: the case of γ-actinorhodin. Sci. Rep. 7, 17419 (2017).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  190. 190.

    Ishikawa, M. et al. Lower ototoxicity and absence of hidden hearing loss point to gentamicin C1a and apramycin as promising antibiotics for clinical use. Sci. Rep. 9, 2410 (2019).

    PubMed  PubMed Central  Article  Google Scholar 

  191. 191.

    Lešnik, U. et al. Construction of a new class of tetracycline lead structures with potent antibacterial activity through biosynthetic engineering. Angew. Chem. Int. Ed. 54, 3937–3940 (2015).

    Article  CAS  Google Scholar 

  192. 192.

    Grandclaudon, C. et al. Semisynthesis and biological evaluation of amidochelocardin derivatives as broad-spectrum antibiotics. Eur. J. Med. Chem. 188, 112005 (2020).

    CAS  PubMed  Article  Google Scholar 

  193. 193.

    Omollo, C. et al. Developing synergistic drug combinations to restore antibiotic sensitivity in drug-resistant Mycobacterium tuberculosis. Antimicrob. Agents Chemother. 65, e02554-20 (2021).

    Article  Google Scholar 

  194. 194.

    Tan, J. H. et al. In vitro and in vivo efficacy of an LpxC inhibitor, CHIR-090, alone or combined with colistin against Pseudomonas aeruginosa biofilm. Antimicrob. Agents Chemother. 61, e02223-16 (2017).

    PubMed  PubMed Central  Article  Google Scholar 

  195. 195.

    Mariathasan, S. & Tan, M.-W. Antibody–antibiotic conjugates: a novel therapeutic platform against bacterial infections. Trends Mol. Med. 23, 135–149 (2017).

    CAS  PubMed  Article  Google Scholar 

  196. 196.

    Koenig, S. G. & Pillow, T. H. in Complete Accounts of Integrated Drug Discovery and Development: Recent Examples from the Pharmaceutical Industry Volume 2 Vol. 1332 (eds Pesti, J. A., Abdel-Magid, A. F. & Vaidyanathan, R.) 85–105 (American Chemical Society, 2019).

  197. 197.

    Negash, K. H., Norris, J. K. S. & Hodgkinson, J. T. Siderophore–antibiotic conjugate design: New drugs for bad bugs? Molecules 24, 3314 (2019).

    CAS  PubMed Central  Article  PubMed  Google Scholar 

  198. 198.

    Schalk, I. J. Siderophore–antibiotic conjugates: exploiting iron uptake to deliver drugs into bacteria. Clin. Microbiol. Infect. 24, 801–802 (2018).

    CAS  PubMed  Article  Google Scholar 

  199. 199.

    Blaskovich, M. A. T. et al. Protein-inspired antibiotics active against vancomycin- and daptomycin-resistant bacteria. Nat. Commun. 9, 22 (2018). This study shows how positively charged effector sequences can ‘revitalize’ antibiotics that have lost effectiveness against recalcitrant bacteria.

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  200. 200.

    Umstätter, F. et al. Vancomycin resistance is overcome by conjugation of polycationic peptides. Angew. Chem. Int. Ed. 59, 8823–8827 (2020).

    Article  CAS  Google Scholar 

  201. 201.

    Kim, A. et al. Pharmacodynamic profiling of a siderophore-conjugated monocarbam in Pseudomonas aeruginosa: assessing the risk for resistance and attenuated efficacy. Antimicrob. Agents Chemother. 59, 7743–7752 (2015).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  202. 202.

    Stokes, J. M. et al. A deep learning approach to antibiotic discovery. Cell 180, 688–702.e13 (2020). This work demonstrates how deep learning approaches can expand the antibiotic arsenal through the discovery of structurally distinct hit compounds.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  203. 203.

    de la Fuente-Nunez, C. Toward autonomous antibiotic discovery. mSystems 4, e00151-19 (2019).

    PubMed  PubMed Central  Google Scholar 

  204. 204.

    Lipinski, C. A., Lombardo, F., Dominy, B. W. & Feeney, P. J. Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings. Adv. Drug Deliv. Rev. 23, 3–25 (1997). This study presents, for the first time, ‘the rule of five’, defining five key physiochemical parameters for orally active drugs.

    CAS  Article  Google Scholar 

  205. 205.

    Lipinski, C. A. Rule of five in 2015 and beyond: Target and ligand structural limitations, ligand chemistry structure and drug discovery project decisions. Adv. Drug Deliv. Rev. 101, 34–41 (2016).

    CAS  Article  Google Scholar 

  206. 206.

    Benet, L. Z., Hosey, C. M., Ursu, O. & Oprea, T. I. BDDCS, the Rule of 5 and drugability. Adv. Drug Deliv. Rev. 101, 89–98 (2016).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  207. 207.

    DeGoey, D. A., Chen, H.-J., Cox, P. B. & Wendt, M. D. Beyond the rule of 5: lessons learned from AbbVie’s drugs and compound collection. J. Med. Chem. 61, 2636–2651 (2018).

    CAS  PubMed  Article  Google Scholar 

  208. 208.

    Doak, B. C., Over, B., Giordanetto, F. & Kihlberg, J. Oral druggable space beyond the rule of 5: insights from drugs and clinical candidates. Chem. Biol. 21, 1115–1142 (2014).

    CAS  PubMed  Article  Google Scholar 

  209. 209.

    Tyagi, M., Begnini, F., Poongavanam, V., Doak, B. C. & Kihlberg, J. Drug syntheses beyond the rule of 5. Chem. Eur. J. 26, 49–88 (2020).

    CAS  PubMed  Article  Google Scholar 

  210. 210.

    Wright, P. M., Seiple, I. B. & Myers, A. G. The evolving role of chemical synthesis in antibacterial drug discovery. Angew. Chem. Int. Ed. 53, 8840–8869 (2014).

    CAS  Article  Google Scholar 

  211. 211.

    Agarwal, V. et al. Enzymatic halogenation and dehalogenation reactions: pervasive and mechanistically diverse. Chem. Rev. 117, 5619–5674 (2017).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  212. 212.

    Wilcken, R., Zimmermann, M. O., Lange, A., Joerger, A. C. & Boeckler, F. M. Principles and applications of halogen bonding in medicinal chemistry and chemical biology. J. Med. Chem. 56, 1363–1388 (2013).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  213. 213.

    Convention on Biological Diversity United Nations. Nagoya Protocol on access to genetic resources and the fair and equitable sharing of benefits arising from their utilization to the Convention on Biological Diversity. (Convention on Biological Diversity, 2011) https://www.cbd.int/abs/doc/protocol/nagoya-protocol-en.pdf

  214. 214.

    Sorokina, M. & Steinbeck, C. Review on natural products databases: where to find data in 2020. J. Cheminform. 12, 20 (2020).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  215. 215.

    European Commission. Manifesto for EU COVID-19 research: maximising the accessibility of research results in the fight against COVID-19. (European Commission, 2020) https://ec.europa.eu/info/research-and-innovation/research-area/health-research-and-innovation/coronavirus-research-and-innovation/covid-research-manifesto_en

  216. 216.

    Wibberg, D. et al. High quality genome sequences of thirteen Hypoxylaceae (Ascomycota) strengthen the phylogenetic family backbone and enable the discovery of new taxa. Fungal Divers. 106, 7–28 (2021).

    Article  Google Scholar 

  217. 217.

    Kling, A. et al. Antibiotics. targeting DnaN for tuberculosis therapy using novel griselimycins. Science 348, 1106–1112 (2015). This study presents a primary example of discovering a new mode of action by self-resistant target identification.

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  218. 218.

    Johnston, C. W. et al. Assembly and clustering of natural antibiotics guides target identification. Nat. Chem. Biol. 12, 233–239 (2016).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  219. 219.

    Panter, F., Krug, D., Baumann, S. & Müller, R. Self-resistance guided genome mining uncovers new topoisomerase inhibitors from myxobacteria. Chem. Sci. 9, 4898–4908 (2018).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  220. 220.

    Mungan, M. D. et al. ARTS 2.0: feature updates and expansion of the Antibiotic Resistant Target Seeker for comparative genome mining. Nucleic Acids Res. 48, W546–W552 (2020). This study presents the update and expansion of the Antibiotic Resistant Target Seeker (ARTS).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  221. 221.

    Emmert-Streib, F., Yang, Z., Feng, H., Tripathi, S. & Dehmer, M. An introductory review of deep learning for prediction models with big data. Front. Artif. Intell. 3, 4 (2020). Comprehensive overview of deep learning models and future developments in artificial intelligence.

    PubMed  PubMed Central  Article  Google Scholar 

  222. 222.

    Schneider, P. et al. Rethinking drug design in the artificial intelligence era. Nat. Rev. Drug. Discov. 19, 353–364 (2020).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  223. 223.

    Grein, F. et al. Ca2+-Daptomycin targets cell wall biosynthesis by forming a tripartite complex with undecaprenyl-coupled intermediates and membrane lipids. Nat. Commun. 11, 1455 (2020). Describes the detailed mode of action of daptomycin for the first time.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  224. 224.

    Cardona, S. T., Selin, C. & Gislason, A. S. Genomic tools to profile antibiotic mode of action. Crit. Rev. Microbiol. 41, 465–472 (2015).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  225. 225.

    van Camp, P.-J., Haslam, D. B. & Porollo, A. Bioinformatics approaches to the understanding of molecular mechanisms in antimicrobial resistance. Int. J. Mol. Sci. 21, 1363 (2020).

    PubMed Central  Article  CAS  Google Scholar 

  226. 226.

    O’Rourke, A. et al. Mechanism-of-action classification of antibiotics by global transcriptome profiling. Antimicrob. Agents Chemother. 64, e01207–e01219 (2020).

    PubMed  PubMed Central  Article  Google Scholar 

  227. 227.

    Nonejuie, P., Burkart, M., Pogliano, K. & Pogliano, J. Bacterial cytological profiling rapidly identifies the cellular pathways targeted by antibacterial molecules. Proc. Natl Acad. Sci. USA 110, 16169–16174 (2013).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  228. 228.

    Lamsa, A. et al. Rapid inhibition profiling in Bacillus subtilis to identify the mechanism of action of new antimicrobials. ACS Chem. Biol. 11, 2222–2231 (2016).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  229. 229.

    Ribeiro da Cunha, B., Fonseca, L. P. & Calado, C. R. C. Metabolic fingerprinting with fourier-transform infrared (FTIR) spectroscopy: towards a high-throughput screening assay for antibiotic discovery and mechanism-of-action elucidation. Metabolites 10, 145 (2020).

    PubMed Central  Article  CAS  Google Scholar 

  230. 230.

    Eustice, D. C., Feldman, P. A. & Slee, A. M. The mechanism of action of DuP 721, a new antibacterial agent: effects on macromolecular synthesis. Biochem. Biophys. Res. Commun. 150, 965–971 (1988).

    CAS  PubMed  Article  Google Scholar 

  231. 231.

    Sugimoto, A., Maeda, A., Itto, K. & Arimoto, H. Deciphering the mode of action of cell wall-inhibiting antibiotics using metabolic labeling of growing peptidoglycan in Streptococcus pyogenes. Sci. Rep. 7, 1129 (2017).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  232. 232.

    Cacace, E., Kritikos, G. & Typas, A. Chemical genetics in drug discovery. Curr. Opin. Syst. Biol. 4, 35–42 (2017).

    PubMed  PubMed Central  Article  Google Scholar 

  233. 233.

    Santiago, M. et al. Genome-wide mutant profiling predicts the mechanism of a Lipid II binding antibiotic. Nat. Chem. Biol. 14, 601–608 (2018). This study demonstrates how machine learning approaches can be used to predict the mode of action of antibiotics via mutant fitness profiles.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  234. 234.

    Wright, M. H. & Sieber, S. A. Chemical proteomics approaches for identifying the cellular targets of natural products. Nat. Prod. Rep. 33, 681–708 (2016).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  235. 235.

    Drewes, G. & Knapp, S. Chemoproteomics and chemical probes for target discovery. Trends Biotechnol. 36, 1275–1286 (2018).

    CAS  PubMed  Article  Google Scholar 

  236. 236.

    Hoerr, V. et al. Characterization and prediction of the mechanism of action of antibiotics through NMR metabolomics. BMC Microbiol. 16, 82 (2016).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  237. 237.

    Medeiros-Silva, J. et al. High-resolution NMR studies of antibiotics in cellular membranes. Nat. Commun. 9, 3963 (2018).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  238. 238.

    Tietze, L. F., Krewer, B., Major, F. & Schuberth, I. CD-spectroscopy as a powerful tool for investigating the mode of action of unmodified drugs in live cells. J. Am. Chem. Soc. 131, 13031–13036 (2009).

    CAS  PubMed  Article  Google Scholar 

  239. 239.

    Altharawi, A., Rahman, K. M. & Chan, K. L. A. Towards identifying the mode of action of drugs using live-cell FTIR spectroscopy. Analyst 144, 2725–2735 (2019).

    CAS  PubMed  Article  Google Scholar 

  240. 240.

    Cirnski, K., Coetzee, J., Herrmann, J. & Müller, R. Metabolic profiling to determine bactericidal or bacteriostatic effects of new natural products using isothermal microcalorimetry. J. Vis. Exp. 164, e61703 (2020).

    Google Scholar 

  241. 241.

    Arrowsmith, C. H. et al. The promise and peril of chemical probes. Nat. Chem. Biol. 11, 536–541 (2015).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  242. 242.

    Wang, Y. et al. Evidence-based and quantitative prioritization of tool compounds in phenotypic drug discovery. Cell Chem. Biol. 23, 862–874 (2016).

    CAS  PubMed  Article  Google Scholar 

  243. 243.

    Franken, H. et al. Thermal proteome profiling for unbiased identification of direct and indirect drug targets using multiplexed quantitative mass spectrometry. Nat. Protoc. 10, 1567–1593 (2015).

    CAS  PubMed  Article  Google Scholar 

  244. 244.

    Schopper, S. et al. Measuring protein structural changes on a proteome-wide scale using limited proteolysis-coupled mass spectrometry. Nat. Protoc. 12, 2391–2410 (2017).

    CAS  PubMed  Article  Google Scholar 

  245. 245.

    Piazza, I. et al. A map of protein-metabolite interactions reveals principles of chemical communication. Cell 172, 358–372 (2018).

    CAS  PubMed  Article  Google Scholar 

  246. 246.

    Dai, L. et al. Horizontal cell biology: monitoring global changes of protein interaction states with the proteome-wide cellular thermal shift assay (CETSA). Ann. Rev. Biochem. 88, 383–408 (2019).

    CAS  PubMed  Article  Google Scholar 

  247. 247.

    Bagherian, M. et al. Machine learning approaches and databases for prediction of drug–target interaction: a survey paper. Brief. Bioinform. 22, 247–269 (2021).

    PubMed  Article  Google Scholar 

  248. 248.

    Pliakos, K. & Vens, C. Drug-target interaction prediction with tree-ensemble learning and output space reconstruction. BMC Bioinform. 21, 49 (2020).

    CAS  Article  Google Scholar 

  249. 249.

    Davin-Regli, A. et al. Membrane permeability and regulation of drug “influx and efflux” in enterobacterial pathogens. Curr. Drug Targets 9, 750–759 (2008).

    CAS  PubMed  Article  Google Scholar 

  250. 250.

    Louw, G. E. et al. A balancing act: efflux/influx in mycobacterial drug resistance. Antimicrob. Agents Chemother. 53, 3181–3189 (2009).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  251. 251.

    Chitsaz, M. & Brown, M. H. The role played by drug efflux pumps in bacterial multidrug resistance. Essays Biochem. 61, 127–139 (2017).

    PubMed  Article  Google Scholar 

  252. 252.

    Weston, N., Sharma, P., Ricci, V. & Piddock, L. J. V. Regulation of the AcrAB-TolC efflux pump in Enterobacteriaceae. Res. Microbiol. 169, 425–431 (2018).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  253. 253.

    Ropponen, H.-K., Richter, R., Hirsch, A. K. H. & Lehr, C.-M. Mastering the Gram-negative bacterial barrier – Chemical approaches to increase bacterial bioavailability of antibiotics. Adv. Drug Deliv. Rev. 172, 339–360 (2021).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  254. 254.

    Richter, M. F. et al. Predictive compound accumulation rules yield a broad-spectrum antibiotic. Nature 545, 299–304 (2017). This study highlights which physicochemical properties enforce the accumulation of small molecules in Gram-negative bacteria.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  255. 255.

    Jukic˅, M., Gobec, S. & Sova, M. Reaching toward underexplored targets in antibacterial drug design. Drug Dev. Res. 80, 6–10 (2019).

    PubMed  Article  CAS  PubMed Central  Google Scholar 

  256. 256.

    Manchester, J. I., Buurman, E. T., Bisacchi, G. S. & McLaughlin, R. E. Molecular determinants of AcrB-mediated bacterial efflux implications for drug discovery. J. Med. Chem. 55, 2532–2537 (2012).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  257. 257.

    Hevener, K. E. et al. Chapter Eighteen-Special challenges to the rational design of antibacterial agents. Ann. Rep. Med. Chem. 48, 283–298 (2013).

    CAS  Google Scholar 

  258. 258.

    Zavascki, A. P., Goldani, L. Z., Li, J. & Nation, R. L. Polymyxin B for the treatment of multidrug-resistant pathogens: a critical review. J. Antimicrob. Chemother. 60, 1206–1215 (2007).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  259. 259.

    Ferrer-Espada, R. et al. A permeability-increasing drug synergizes with bacterial efflux pump inhibitors and restores susceptibility to antibiotics in multi-drug resistant Pseudomonas aeruginosa strains. Sci. Rep. 9, 3452 (2019).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  260. 260.

    Schweizer, H. P. Understanding efflux in Gram-negative bacteria: opportunities for drug discovery. Exp. Opin. Drug Discov. 7, 633–642 (2012).

    CAS  Article  Google Scholar 

  261. 261.

    Wagner, S. et al. Novel strategies for the treatment of Pseudomonas aeruginosa infections. J. Med. Chem. 59, 5929–5969 (2016).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  262. 262.

    Zhang, W., Bai, Y., Wang, Y. & Xiao, W. Polypharmacology in drug discovery: a review from systems pharmacology perspective. Curr. Pharm. Des. 22, 3171–3181 (2016).

    CAS  PubMed  Article  Google Scholar 

  263. 263.

    Proschak, E., Stark, H. & Merk, D. Polypharmacology by design: a medicinal chemist’s perspective on multitargeting compounds. J. Med. Chem. 62, 420–444 (2019).

    CAS  PubMed  Article  Google Scholar 

  264. 264.

    Cunningham, M. L., Kwan, B. P., Nelson, K. J., Bensen, D. C. & Shaw, K. J. Distinguishing on-target versus off-target activity in early antibacterial drug discovery using a macromolecular synthesis assay. J. Biomol. Screen. 18, 1018–1026 (2013).

    CAS  PubMed  Article  Google Scholar 

  265. 265.

    Singh, R., Sripada, L. & Singh, R. Side effects of antibiotics during bacterial infection: mitochondria, the main target in host cell. Mitochondrion 16, 50–54 (2014).

    PubMed  Article  CAS  Google Scholar 

  266. 266.

    Young, M. J. Off-target effects of drugs that disrupt human mitochondrial DNA maintenance. Front. Mol. Biosci. 4, 74 (2017).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  267. 267.

    Moullan, N. et al. Tetracyclines disturb mitochondrial function across eukaryotic models: a call for caution in biomedical research. Cell Rep. 10, 1681–1691 (2015).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  268. 268.

    Pasternak, B., Inghammar, M. & Svanström, H. Fluoroquinolone use and risk of aortic aneurysm and dissection: nationwide cohort study. BMJ 360, k678 (2018).

    PubMed  PubMed Central  Article  Google Scholar 

  269. 269.

    Fief, C. A., Hoang, K. G., Phipps, S. D., Wallace, J. L. & Deweese, J. E. Examining the impact of antimicrobial fluoroquinolones on human DNA topoisomerase IIα and IIβ. ACS Omega 4, 4049–4055 (2019).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  270. 270.

    Becattini, S., Taur, Y. & Pamer, E. G. Antibiotic-induced changes in the intestinal microbiota and disease. Trends Mol. Med. 22, 458–478 (2016).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  271. 271.

    Bhalodi, A. A., van Engelen, T. S. R., Virk, H. S. & Wiersinga, W. J. Impact of antimicrobial therapy on the gut microbiome. J. Antimicrob. Chemother. 74, i6–i15 (2019).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  272. 272.

    Wipperman, M. F. et al. Antibiotic treatment for Tuberculosis induces a profound dysbiosis of the microbiome that persists long after therapy is completed. Sci. Rep. 7, 10767 (2017).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  273. 273.

    Xu, L. et al. The effect of antibiotics on the gut microbiome: a metagenomics analysis of microbial shift and gut antibiotic resistance in antibiotic treated mice. BMC Genomics 21, 263 (2020).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  274. 274.

    Evans, L. E. et al. Exploitation of antibiotic resistance as a novel drug target: development of a β-lactamase-activated antibacterial prodrug. J. Med. Chem. 62, 4411–4425 (2019).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  275. 275.

    Hong, J. Natural product synthesis at the interface of chemistry and biology. Chem. Eur. J. 20, 10204–10212 (2014).

    CAS  PubMed  Article  Google Scholar 

  276. 276.

    Bauer, A. & Brönstrup, M. Industrial natural product chemistry for drug discovery and development. Nat. Prod. Rep. 31, 35–60 (2014).

    CAS  PubMed  Article  Google Scholar 

  277. 277.

    Sucipto, H., Pogorevc, D., Luxenburger, E., Wenzel, S. C. & Müller, R. Heterologous production of myxobacterial α-pyrone antibiotics in Myxococcus xanthus. Metab. Eng. 44, 160–170 (2017).

    CAS  PubMed  Article  Google Scholar 

  278. 278.

    Pogorevc, D. et al. Production optimization and biosynthesis revision of corallopyronin A, a potent anti-filarial antibiotic. Metab. Eng. 55, 201–211 (2019).

    CAS  PubMed  Article  Google Scholar 

  279. 279.

    Sun, C. et al. A robust platform for the synthesis of new tetracycline antibiotics. J. Am. Chem. Soc. 130, 17913–17927 (2008).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  280. 280.

    Hogan, P. C. et al. Large-scale preparation of key building blocks for the manufacture of fully synthetic macrolide antibiotics. J. Antibiot. 71, 318–325 (2018).

    CAS  Article  Google Scholar 

  281. 281.

    Hüttel, S. et al. Discovery and total synthesis of natural cystobactamid derivatives with superior activity against Gram-negative pathogens. Angew. Chem. Int. Ed. 56, 12760–12764 (2017).

    Article  CAS  Google Scholar 

  282. 282.

    Zong, Y. et al. Gram-scale total synthesis of teixobactin promoting binding mode study and discovery of more potent antibiotics. Nat. Commun. 10, 3268 (2019).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  283. 283.

    Crater, J. S. & Lievense, J. C. Scale-up of industrial microbial processes. FEMS Microbiol. Lett. 365, fny138 (2018).

    CAS  PubMed Central  Article  PubMed  Google Scholar 

  284. 284.

    Nielsen, J. et al. (eds) Metabolic Engineering. Advances in Biochemical Engineering/Biotechnology Vol 73 (Springer, 2001).

  285. 285.

    Pham, J. V. et al. A review of the microbial production of bioactive natural products and biologics. Front. Microbiol. 10, 1404 (2019).

    PubMed  PubMed Central  Article  Google Scholar 

  286. 286.

    Balani, S. K., Miwa, G. T., Gan, L.-S., Wu, J.-T. & Lee, F. W. Strategy of utilizing in vitro and in vivo ADME tools for lead optimization and drug candidate selection. Curr. Top. Med. Chem. 5, 1033–1038 (2005).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  287. 287.

    Chung, T. D. Y., Terry, D. B. & Smith, L. H. in Assay Guidance Manual (eds Markossian, S. et al.) (Eli Lilly & Company and the National Center for Advancing Translational Sciences, 2015).

  288. 288.

    Reichel, A. & Lienau, P. Pharmacokinetics in drug discovery: an exposure-centred approach to optimising and predicting drug efficacy and safety. Handb. Exp. Pharmacol. 232, 235–260 (2016). This article highlights the central role of pharmacokinetics in drug discovery.

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  289. 289.

    Singh, S. S. Preclinical pharmacokinetics: an approach towards safer and efficacious drugs. Curr. Drug Metab. 7, 165–182 (2006).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  290. 290.

    Shi, J. & Zha, W. Predicting human pharmacokinetics: physiologically based pharmacokinetic modeling and in silico ADME prediction in early drug discovery. Eur. J. Drug. Metab. Pharmacokinet. 44, 135–137 (2019).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  291. 291.

    Xiong, G. et al. ADMETlab 2.0: an integrated online platform for accurate and comprehensive predictions of ADMET properties. Nucleic Acids Res. 49, W5–W14 (2021). Redesigned version of the widely used ADMETlab web server for predictions of pharmacokinetics and toxicity properties of chemicals.

    PubMed  PubMed Central  Article  Google Scholar 

  292. 292.

    Krause, K. M. et al. Potent LpxC inhibitors with in vitro activity against multidrug-resistant Pseudomonas aeruginosa. Antimicrob. Agents Chemother. 63, e00977-19 (2019).

    PubMed  PubMed Central  Article  Google Scholar 

  293. 293.

    Cohen, F. et al. Optimization of LpxC inhibitors for antibacterial activity and cardiovascular safety. ChemMedChem 14, 1560–1572 (2019).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  294. 294.

    Felmlee, M. A., Morris, M. E. & Mager, D. E. Mechanism-based pharmacodynamic modeling. Meth. Mol. Biol. 929, 583–600 (2012).

    CAS  Article  Google Scholar 

  295. 295.

    Lepak, A. J. & Andes, D. R. in Antibiotic Pharmacodynamics Vol. 191 (eds Rotschafer, J. C., Andes, D. R. & Rodvold, K. A.) 59–87 (Springer, 2016).

  296. 296.

    van Peer, A., Snoeck, E., Huang, M. L. & Heykants, J. Pharmacokinetic-pharmacodynamic relationships in phase I/phase II of drug development. Eur. J. Drug Metab. Pharmacokinet. 18, 49–59 (1993).

    PubMed  Article  Google Scholar 

  297. 297.

    Zhao, M., Lepak, A. J. & Andes, D. R. Animal models in the pharmacokinetic/pharmacodynamic evaluation of antimicrobial agents. Bioorg. Med. Chem. 24, 6390–6400 (2016).

    CAS  PubMed  Article  Google Scholar 

  298. 298.

    McGinnity, D. F., Collington, J., Austin, R. P. & Riley, R. J. Evaluation of human pharmacokinetics, therapeutic dose and exposure predictions using marketed oral drugs. Curr. Drug Metab. 8, 463–479 (2007).

    CAS  PubMed  Article  Google Scholar 

  299. 299.

    Zou, P. et al. Applications of human pharmacokinetic prediction in first-in-human dose estimation. AAPS J. 14, 262–281 (2012).

    PubMed  PubMed Central  Article  Google Scholar 

  300. 300.

    Page, K. M. Validation of early human dose prediction: a key metric for compound progression in drug discovery. Mol. Pharmaceutics 13, 609–620 (2016).

    CAS  Article  Google Scholar 

  301. 301.

    Maher, S., Brayden, D. J., Casettari, L. & Illum, L. Application of permeation enhancers in oral delivery of macromolecules: an update. Pharmaceutics 11, 41 (2019).

    CAS  PubMed Central  Article  PubMed  Google Scholar 

  302. 302.

    Ma, X. & Williams, R. O. Polymeric nanomedicines for poorly soluble drugs in oral delivery systems: an update. J. Pharm. Invest. 48, 61–75 (2018).

    CAS  Google Scholar 

  303. 303.

    Pridgen, E. M., Alexis, F. & Farokhzad, O. C. Polymeric nanoparticle technologies for oral drug delivery. Clin. Gastroenterol. Hepatol. 12, 1605–1610 (2014).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  304. 304.

    Bassetti, M., Vena, A., Russo, A. & Peghin, M. Inhaled liposomal antimicrobial delivery in lung infections. Drugs 80, 1309–1318 (2020).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  305. 305.

    Bulbake, U., Doppalapudi, S., Kommineni, N. & Khan, W. Liposomal formulations in clinical use: an updated review. Pharmaceutics 9, 12 (2017).

    PubMed Central  Article  CAS  PubMed  Google Scholar 

  306. 306.

    Russell, W. M. S. & Burch, R. L. The Principles of Humane Experimental Technique (Methuen & Co. Limited, 1959). This work defines, for the first time, the 3Rs principle as the present ethical standard in animal research.

  307. 307.

    Jaeger, A., Sauder, P., Kopferschmitt & Dahlet, M. Toxicokinetics in clinical toxicology. Acta Clin. Belg. 45, 1–12 (1990).

    Article  Google Scholar 

  308. 308.

    Goehl, T. J. Toxicokinetics in the national toxicology program. NIDA Res. Monogr. 173, 273–304 (1997).

    CAS  PubMed  Google Scholar 

  309. 309.

    Low, Y. S., Sedykh, A. Y., Rusyn, I. & Tropsha, A. Integrative approaches for predicting in vivo effects of chemicals from their structural descriptors and the results of short-term biological assays. Curr. Top. Med. Chem. 14, 1356–1364 (2014).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  310. 310.

    Cox, L. A. T., Popken, D. A., Kaplan, A. M., Plunkett, L. M. & Becker, R. A. How well can in vitro data predict in vivo effects of chemicals? Rodent carcinogenicity as a case study. Regul. Toxicol. Pharmacol. 77, 54–64 (2016).

    CAS  Article  Google Scholar 

  311. 311.

    Zhang, X. et al. Zebrafish and Galleria mellonella: Models to identify the subsequent infection and evaluate the immunological differences in different Klebsiella pneumoniae intestinal colonization strains. Front. Microbiol. 10, 2750 (2019).

    PubMed  PubMed Central  Article  Google Scholar 

  312. 312.

    Herlich, J. A. et al. The non-GLP toleration/dose range finding study: design and methodology used in an early toxicology screening program. Proc. West. Pharmacol. Soc. 52, 94–98 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

  313. 313.

    Alirol, E. et al. Multidrug-resistant gonorrhea: A research and development roadmap to discover new medicines. PLoS Med. 14, e1002366 (2017).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  314. 314.

    BEAM Alliance. Position Paper 2017: Key Guidelines to implement effective measures toward SMEs to revive the antibacterial R&D field. (BEAM Alliance, 2017) https://beam-alliance.eu/wp-content/uploads/2019/03/2017-11-16-beam-alliance-position-paper.pdf

  315. 315.

    BEAM Alliance. A new vision for AMR innovation to support medical care. (BEAM Alliance, 2019) https://beam-alliance.eu/wp-content/uploads/2019/10/beam-alliance-a-new-vision-to-support-amr-innovation.pdf

  316. 316.

    BEAM Alliance. Reflexion paper on the EU pharmaceutical strategy roadmap. (BEAM Alliance, 2020) https://beam-alliance.eu/wp-content/uploads/2020/07/roadmap-reflexion-paper.pdf

  317. 317.

    ReAct Europe. AMR stakeholder mapping. (ReAct, 2016) https://www.reactgroup.org/uploads/Stakeholder%20Analysis_ReActForWHO.pdf

  318. 318.

    Fisher, J. F. & Mobashery, S. Endless resistance. Endless antibiotics? MedChemComm 7, 37–49 (2016).

    CAS  PubMed  Article  Google Scholar 

  319. 319.

    DiMasi, J. A., Grabowski, H. G. & Hansen, R. W. Innovation in the pharmaceutical industry: New estimates of R&D costs. J. Health Econ. 47, 20–33 (2016).

    PubMed  Article  Google Scholar 

  320. 320.

    The Boston Consulting Group. Breaking through the wall – A call for concerted action on antibiotics research and development. (Bundesministerium für Gesundheit, 2017) https://www.bundesgesundheitsministerium.de/fileadmin/Dateien/5_Publikationen/Gesundheit/Berichte/GUARD_Follow_Up_Report_Full_Report_final.pdf

  321. 321.

    Access to Medicine Foundation. Antimicrobial resistance benchmark 2018. (Access to Medicine Foundation, 2018) https://accesstomedicinefoundation.org/media/uploads/downloads/5f292e3996b58_Antimicrobial-Resistance-Benchmark-2018.pdf

  322. 322.

    Paul, S. M. et al. How to improve R&D productivity: the pharmaceutical industry’s grand challenge. Nat. Rev. Drug Discov. 9, 203–214 (2010).

    CAS  PubMed  Article  Google Scholar 

  323. 323.

    Pieroni, M., Wan, B., Cho, S., Franzblau, S. G. & Costantino, G. Design, synthesis and investigation on the structure–activity relationships of N-substituted 2-aminothiazole derivatives as antitubercular agents. Eur. J. Med. Chem. 72, 26–34 (2014).

    CAS  PubMed  Article  Google Scholar 

  324. 324.

    Azzali, E. et al. Substituted N-phenyl-5-(2-(phenylamino)thiazol-4-yl)isoxazole-3-carboxamides are valuable antitubercular candidates that evade innate efflux machinery. J. Med. Chem. 60, 7108–7122 (2017).

    CAS  PubMed  Article  Google Scholar 

  325. 325.

    Bohacek, R. S., McMartin, C. & Guida, W. C. The art and practice of structure-based drug design: a molecular modeling perspective. Med. Res. Rev. 16, 3–50 (1996).

    CAS  PubMed  Article  Google Scholar 

  326. 326.

    Hartwig, J. F. Evolution of a fourth generation catalyst for the amination and thioetherification of aryl halides. Acc. Chem. Res. 41, 1534–1544 (2008).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  327. 327.

    Abdel-Magid, A. F. & Mehrman, S. J. A review on the use of sodium triacetoxyborohydride in the reductive amination of ketones and aldehydes. Org. Process. Res. Dev. 10, 971–1031 (2006).

    CAS  Article  Google Scholar 

  328. 328.

    Gedeck, P. et al. Benefit of retraining pKa models studied using internally measured data. J. Chem. Inf. Model. 55, 1449–1459 (2015).

    CAS  PubMed  Article  Google Scholar 

  329. 329.

    Lovering, F., Bikker, J. & Humblet, C. Escape from flatland: increasing saturation as an approach to improving clinical success. J. Med. Chem. 52, 6752–6756 (2009).

    CAS  PubMed  Article  Google Scholar 

  330. 330.

    Zender, M. et al. Discovery and biophysical characterization of 2-amino-oxadiazoles as novel antagonists of PqsR, an important regulator of Pseudomonas aeruginosa virulence. J. Med. Chem. 56, 6761–6774 (2013).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  331. 331.

    Zender, M. et al. Flexible fragment growing boosts potency of quorum-sensing inhibitors against Pseudomonas aeruginosa virulence. ChemMedChem 15, 188–194 (2020).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  332. 332.

    Ahmed, A. S. A. et al. PqsR inverse agonists. Patent WO2020007938A1 (2020).

  333. 333.

    Schütz, C. et al. A new PqsR inverse agonist potentiates tobramycin efficacy to eradicate Pseudomonas aeruginosa biofilms. Adv. Sci. 8, 2004369 (2021). Demonstrates the synergistic effect of a quorum sensing-targeting pathoblocker with a standard-of-care antibiotic in a Pseudomonas aeruginosa lung infection mouse model.

    Article  Google Scholar 

  334. 334.

    Oliver, T. J. & Sinclair, A. C. Antibiotic M-319. US Patent US3155582A (1964).

  335. 335.

    Mitscher, L. A. et al. Structure of chelocardin, a novel tetracycline antibiotic. J. Am. Chem. Soc. 92, 6070–6071 (1970).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  336. 336.

    Petkovic, H., Raspor, P. & Lesnik, U. Genes for biosynthesis of tetracycline compounds and uses thereof. US Patent US8361777B2 (2013).

  337. 337.

    Lukežic˅, T. et al. Identification of the chelocardin biosynthetic gene cluster from Amycolatopsis sulphurea: a platform for producing novel tetracycline antibiotics. Microbiology 159, 2524–2532 (2013).

    PubMed  Article  CAS  PubMed Central  Google Scholar 

  338. 338.

    Molnar, V., Matković, Z., Tambić, T. & Kozma, C. Klinicko-farmakolosko ispitivanje kelokardina u bolesnika s infekcijom mokraćnih putova. Lijec. Vjesn. 99, 560–562 (1977).

    CAS  PubMed  PubMed Central  Google Scholar 

  339. 339.

    Lukežic˅, T. et al. Engineering atypical tetracycline formation in Amycolatopsis sulphurea for the production of modified chelocardin antibiotics. ACS Chem. Biol. 14, 468–477 (2019).

    PubMed  Article  CAS  PubMed Central  Google Scholar 

  340. 340.

    Flores-Mireles, A.