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Optimizing antimicrobial use: challenges, advances and opportunities

Nature Reviews Microbiology volume 19, pages 747–758 (2021)Cite this article

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Abstract

An optimal antimicrobial dose provides enough drug to achieve a clinical response while minimizing toxicity and development of drug resistance. There can be considerable variability in pharmacokinetics, for example, owing to comorbidities or other medications, which affects antimicrobial pharmacodynamics and, thus, treatment success. Although current approaches to antimicrobial dose optimization address fixed variability, better methods to monitor and rapidly adjust antimicrobial dosing are required to understand and react to residual variability that occurs within and between individuals. We review current challenges to the wider implementation of antimicrobial dose optimization and highlight novel solutions, including biosensor-based, real-time therapeutic drug monitoring and computer-controlled, closed-loop control systems. Precision antimicrobial dosing promises to improve patient outcome and is important for antimicrobial stewardship and the prevention of antimicrobial resistance.

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Fig. 1: Antimicrobial pharmacokinetics, pharmacodynamics and factors driving variation.
Fig. 2: Barriers to implementation of therapeutic drug monitoring of antimicrobials.
Fig. 3: Aptamer-based antimicrobial detection.
Fig. 4: Microneedle-based biosensors for real-time drug monitoring in humans.
Fig. 5: Closed-loop control for automated, precision drug delivery.

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Acknowledgements

The authors acknowledge the National Institute for Health Research Health Protection Research Unit (NIHR HPRU) in Healthcare Associated Infection and Antimicrobial Resistance at Imperial College London in partnership with Public Health England and the NIHR Imperial Patient Safety Translational Research Centre. The Department of Health and Social Care-funded Centre for Antimicrobial Optimization (CAMO), Imperial College London provides state-of-the-art research facilities and consolidates multidisciplinary academic excellence, clinical expertise, Imperial’s NIHR/Wellcome-funded Clinical Research Facility (CRF) and partnerships with the National Health Service (NHS) to support and deliver innovative research on antimicrobial optimization and precision prescribing. The authors also thank the Department of Health and Social Care-funded Centre of Excellence in Infectious Diseases Research (CEIDR), Liverpool University, which focuses on infection therapeutics and the NIHR HPRU. The views expressed in this publication are those of the authors and not necessarily those of the NHS, the NIHR or the UK Department of Health.

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Authors and Affiliations

  1. National Institute for Health Research Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance, Imperial College London, London, UK

    Timothy M. Rawson, Richard C. Wilson, Matthew Ellington & Alison H. Holmes

  2. Centre for Antimicrobial Optimisation, Imperial College London, London, UK

    Timothy M. Rawson, Richard C. Wilson, Danny O’Hare, Pau Herrero, Pantelis Georgiou, Anthony Cass & Alison H. Holmes

  3. Department of Bioengineering, Imperial College London, London, UK

    Danny O’Hare

  4. Centre for Bio-Inspired Technology, Department of Electrical and Electronic Engineering, Imperial College London, London, UK

    Pau Herrero & Pantelis Georgiou

  5. Infectious Diseases Institute, College of Health Sciences, Makerere University, Kampala, Uganda

    Andrew Kambugu & Mohammed Lamorde

  6. National Infections Service, Public Health England, London, UK

    Matthew Ellington

  7. Department of Chemistry, Imperial College London, London, UK

    Anthony Cass

  8. Department of Molecular and Clinical Pharmacology, University of Liverpool, Liverpool, UK

    William W. Hope

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Contributions

T.M.R. and A.H.H. drafted the initial manuscript with support from other authors on their areas of expertise (R.C.W., D.O’H., P.H., A.K., M.L., M.E., P.G., A.C., W.W.H.). All authors contributed significantly to subsequent iterations and finalization of the manuscript for submission to the journal. T.M.R., R.C.W., P.H., A.C. and A.H.H. produced the figures, finalizing them for submission to the journal.

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Correspondence to Alison H. Holmes.

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Glossary

Antimicrobial resistance

The development of resistance to the effects of an antimicrobial drug that once was effective against a bacterium, fungus or other microorganism.

Antimicrobial stewardship

An approach to optimize antimicrobial use to ensure optimal therapeutic outcomes while minimizing the harmful consequences of therapy on the individual (toxicity) and the wider population (antimicrobial resistance).

Pharmacokinetics

The movement of a drug into, through and out of the body (what the body does to a drug). Pharmacokinetics can be described by, for example, concentration over time.

Pharmacodynamics

The relationship between drug concentration and an observed effect (what the drug does to the body).

Biofilm

A collection of microorganisms in which the cells stick to each other and/or a surface. The adherent cells are protected by an extracellular matrix of polymeric substances.

Hetero-resistance

Resistance to an antimicrobial occurring in a subset of an otherwise susceptible microbial population.

Therapeutic index

A relative measure of drug safety. Defined as a ratio between the amount of drug that causes toxicity and the amount of drug that leads to a therapeutic effect. A narrow therapeutic index (low ratio) therefore has a small window between therapeutic success and toxicity.

Volume of distribution

A theoretical volume that would be required to contain the total amount of an administered drug at the concentration that is observed in the plasma.

Minimum inhibitory concentration

(MIC). The minimum antimicrobial concentration required to prevent the visible growth of bacteria in vitro. Measurement is performed under standardized conditions.

C-reactive protein

(CRP). An acute-phase response protein produced by the liver in response to inflammation, including that caused by infection.

Galactomannan

A polysaccharide component of the cell wall of certain fungi, such as Aspergillus spp.

Breakpoints

Chosen concentrations (milligrams per litre) of antimicrobial that define whether an organism is sensitive or resistant based on the minimum inhibitory concentration. Defined based on the highest drug concentration that can likely be achieved in a patient.

Area under the concentration–time curve

(AUC). The total integrated area under a drug concentration–time curve.

EC50

The concentration of drug that gives a half-maximal response.

Bayesian posterior estimate

An estimate that enables the inference of a value (for example, drug concentration at a certain time), derived using a prior assumption and a likelihood function, forming a statistical model for observed data.

Nomogram

A diagram that represents the relationship between three or more variables, designed so that one variable can be estimated, for example, by drawing a straight line intersecting the other variables at measured values.

Aptamer

An oligonucleotide or peptide molecule that is selected to bind to a specific target molecule.

Square wave voltammetry

An electrochemical technique that is a type of linear potential sweep voltammetry, using combined square wave and staircase potentials applied to a stationary electrode.

Chronoamperometry

An electrochemical technique that involves stepping the potential of an electrode. The step in potential results in a current that can be monitored as a function of time.

Artificial intelligence

The theory and development of computer systems that can perform tasks that normally require human intelligence.

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Rawson, T.M., Wilson, R.C., O’Hare, D. et al. Optimizing antimicrobial use: challenges, advances and opportunities. Nat Rev Microbiol 19, 747–758 (2021). https://doi.org/10.1038/s41579-021-00578-9

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