Analyses

In 2014, the Illuminating the Druggable Genome programme was launched to promote the exploration of currently understudied but potentially druggable proteins. This article discusses how the systematic collection and processing of a wide array of biological and chemical data as part of this programme has enabled the development of evidence-based criteria for tracking the target development level of human proteins, which indicates a substantial knowledge deficit for approximately one out of three proteins in the human proteome. It also highlights the nature of the unexplored therapeutic opportunities for major protein families.

Shih and colleagues analyse comprehensive industry-wide data on drug development projects pursued during the past 20 years, classified according to the mechanism and indication for each project. Their findings indicate several points and trends that may be useful in understanding and improving the productivity of the pharmaceutical industry, including areas with substantial success or failure and the relative extent of novelty in completed and ongoing projects.

G protein-coupled receptors (GPCRs) are the most intensively studied class of drug targets. This article presents a pioneering analysis of all GPCR-targeted drugs and agents that are currently in clinical trials, and discusses the trends across molecule types, drug targets and therapeutic indications.

The success of mechanism-based drug discovery depends on the definition of the drug target, but targets are often poorly defined in the literature. Here, Overington and colleagues present a comprehensive map of the molecular targets of approved drugs, and explore aspects including the footprint of target classes across disease areas, the success of privileged target families and drug target orthologues across standard model organisms.

Attempts to reduce the number of efficacy- and safety-related failures that may be linked to the physicochemical properties of small-molecule drug candidates have been inconclusive owing to the limited size of data sets from individual companies. Waring and colleagues analyse the largest data set compiled so far on the causes of attrition for oral, small-molecule drug candidates, derived from a pioneering data-sharing effort by AstraZeneca, Eli Lilly and Company, GlaxoSmithKline and Pfizer.

Previous analyses of new drug approvals have suggested that phenotypic screening strategies have been more productive than target-based approaches in the discovery of first-in-class small-molecule drugs. Eder and colleagues analysed the origins of the first-in-class drugs approved by the US Food and Drug Administration from 1999 to 2013, and found that target-based approaches have had a substantial impact in more recent years. They discuss the implications for drug discovery strategies, including viewing phenotypic screening as a novel discipline rather than as a neoclassical approach.

There has been a resurgence of interest in the use of phenotypic screens in drug discovery as an alternative to target-focused approaches. Moffat and colleagues investigated the contribution of phenotypic assays in oncology by analysing the origins of the new small-molecule cancer drugs approved by the US Food and Drug Administration over the past 15 years. They also discuss technical and biological advances that could empower phenotypic drug discovery in oncology by enabling the development of mechanistically informed phenotypic screens.

Ligand efficiency metrics quantify the molecular properties required to gain binding affinity for a drug target. This article discusses the application of such metrics in the selection and optimization of fragments, hits and leads, highlighting how optimizing ligand efficiency metrics based on both molecular mass and lipophilicity, when set in the context of the specific target, has the potential to increase the quality of drug candidates.

Effectively selecting therapeutic targets from the sizeable lists that are emerging from large-scale multi-omics initiatives is a key challenge in drug discovery. This article describes an objective, systematic computational assessment of biological and chemical space that can be applied to any human gene set to prioritize targets for further evaluation, and demonstrates its use on a set of 479 cancer-associated genes to identify new opportunities for drug discovery and repurposing.

Co-developing a drug with a diagnostic to create a stratified medicine — a therapy that is targeted to a specific patient population on the basis of a clinical biomarker — presents challenges for product developers, regulators, payers and physicians. With the aim of developing a shared framework and tools for addressing these challenges, this article presents an analysis using data from case studies in oncology and Alzheimer's disease, coupled with integrated computational modelling of clinical outcomes and economic value, to quantify the effects of decisions on key issues such as the design of clinical trials.

In the past 15 years, it has become clear that physicochemical properties of drug candidates, such as lipophilicity and molecular mass, have an important influence on the likelihood of compound-related attrition during development. By analysing the properties of compounds described in patents from leading pharmaceutical companies between 2000 and 2010, this article indicates that a substantial part of the industry has not modified its drug design practices accordingly and is still producing compounds with suboptimal physicochemical profiles.

Schiöth and colleagues examine the drugs approved by the US Food and Drug Administration over the past 30 years and analyse the interactions of these drugs with therapeutic targets encoded by the human genome, identifying 435 effect-mediating drug targets. They also analyse trends in the introduction of drugs that modulate previously unexploited targets, and discuss the network pharmacology of the drugs in the data set.

To investigate whether some strategies have been more successful than others in the discovery of new drugs, this article analyses the discovery strategies and the molecular mechanism of action for 259 new drugs that were approved by the US Food and Drug Administration between 1999 and 2008. Observations from this analysis — such as the fact that the contribution of phenotypic screening to the discovery of first-in-class small-molecule drugs exceeded that of target-based approaches in an era in which the major focus was on target-based approaches — could have important implications for efforts to increase the productivity of drug research and development.

Although investment in pharmaceutical research and development (R&D) has increased substantially in recent decades, the lack of a corresponding increase in the output in terms of new drugs being approved indicates that therapeutic innovation has become more challenging. Here, using a large database that contains information on R&D projects for more than 28,000 compounds investigated since 1990, Riccaboni and colleagues examine the factors underlying the decline in R&D productivity, which include an increasing concentration of R&D investments in areas in which the risk of failure is high.

A common assumption in current drug discovery strategies is that compounds with highin vitro potency at their target(s) have a greater potential to translate into successful, low-dose therapeutics, which is reflected in screening cascades with in vitro potency embedded as an early filter. This analysis of the publicly available ChEMBL database, which includes more than 500,000 drug discovery and marketed oral drug compounds, suggests that the perceived benefit of high in vitropotency may be negated by poorer absorption, distribution, metabolism, elimination and toxicity (ADMET) properties.

Understanding the factors that promote drug innovation is important both for improvements in health care and the future of organizations engaged in the field. To investigate these factors, Kneller identifies the inventors of 252 new drugs approved by the US Food and Drug Administration from 1998 to 2007 and their places of work, and classifies these drugs according to innovativeness. This article presents a comprehensive analysis of these data, which highlight the strong contribution of biotechnology companies, particularly in the United States, to innovative drug discovery, and discusses potential contributing factors to the trends observed.

Improving R&D productivity is crucial to ensuring the future viability of the pharmaceutical industry and advances in health care. This article presents a detailed analysis, based on comprehensive, recent, industry-wide data, to identify the relative contributions of each of the steps in the drug discovery and development process to overall R&D productivity, and proposes strategies that could have the most substantial impact in enhancing R&D productivity.

This article investigates pharmaceutical innovation by analysing data on the companies that introduced the ∼1,200 new drugs that have been approved by the US FDA since 1950. Implications of this analysis — which shows that the rate of new drug output in this period has essentially been constant despite the huge increases in R&D investment — are discussed, as well as options to achieve sustainability for the pharmaceutical industry.

Here, the authors use bibliometrics and related data-mining methods to analyse PubMed abstracts, literature citation data and patent filings. The analyses are used to identify trends in disease-related scientific activity that are likely to give new therapeutic opportunities.

The recent determination of several crystal structures of G protein-coupled receptors (GPCRs) is providing new opportunities for structure-based drug design. This article analyses the state of the art in the prediction of GPCR structure and the docking of potential ligands on the basis of a community-wide, blind prediction assessment — GPCR Dock 2008 — that was carried out in coordination with the publication of the human adenosine A_2Areceptor structure in 2008 and public release of the three-dimensional coordinates.