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Antimicrobial pharmacodynamics: critical interactions of 'bug and drug'

Nature Reviews Microbiology volume 2, pages 289–300 (2004)Cite this article

Key Points

  • Antimicrobial pharmacodynamics attempts to link measures of drug exposure to the observed effect. This differs from other areas of pharmacodynamics because the main indicator of effect and the site of action of the drug is the organism that causes the pathological process.

  • Understanding antimicrobial pharmacodynamics requires the acceptance of four important ideas. First, the drug exposure achieved with a fixed drug dose varies greatly in the infected population of interest. Second, the shape of the concentration–time curve can sometimes affect the outcome. Third, only non-protein-bound drug is microbiologically active. Finally, as the measure of potency increases, the effect any fixed drug dose will cause decreases.

  • All these ideas can be integrated by use of the Monte Carlo simulation to determine the potential use of a drug and dose for the intended population and to estimate susceptibility breakpoints.

  • These techniques can also be used to help suppress the amplification of resistant subpopulations by identifying the drug exposure that will cause this effect and then evaluating the use of different doses for attaining the exposure target in the population of interest.

  • These ideas can be transferred to the clinical arena. The use of optimal sampling techniques allows informative times for blood sample acquisition to be identified. Population modelling followed by Bayesian estimation allows robust estimation of the exposure achieved in a specific patient. Exposure measures relative to the MIC of the pathogen (peak/MIC ratio, AUC/MIC ratio and time > MIC) can then be linked to the desired clinical or microbiological outcome through common statistical techniques, such as logistic regression analysis, classification and regression tree (CART) analysis and Cox proportional hazards modelling.

Abstract

Antimicrobial pharmacodynamics is the discipline that integrates microbiology and pharmacology, with the aim of linking a measure of drug exposure, relative to a measure of drug potency for the pathogen in question, to the microbiological or clinical effect achieved. The delineation of such relationships allows the drug dose to be chosen in a rational manner, so that the desired effect (for example, the maximal bactericidal effect) can be achieved in a large proportion of the intended patient population. Ultimately, the goal of any anti-infective therapy is to administer a dose of drug that has an acceptably high probability of achieving the desired therapeutic effect balanced with an acceptably low probability of toxicity. Appropriate use of the latest pharmacodynamic modelling approaches can minimize the emergence of resistance and optimize the outcome for patients.

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Figure 1: True between-patient variance.
Figure 2: Different pharmacodynamic variables are important for different drugs.
Figure 3: Only non-protein-bound drug is microbiologically active.
Figure 4: The higher the measure of potency, the lower the measure of drug exposure relative to the measure of potency.
Figure 5: The relationship between three pharmacodynamic parameters: peak concentration/MIC ratio, AUC/MIC ratio and time > MIC.
Figure 6: Suppressing the emergence of resistance.
Figure 7: Transferring to the clinic: linking the AUC/MIC ratio to clinical and microbiological outcomes.

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Acknowledgements

The author benefitted from insightful discussions with F. Bloom.

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Authors and Affiliations

  1. Ordway Research Institute, 150 New Scotland Avenue, Albany, 12208, New York, USA

    George L. Drusano

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DATABASES

Entrez

Pseudomonas aeruginosa

Staphylococcus aureus

Streptococcus pneumoniae

FURTHER INFORMATION

European Agency for the Evaluation of Medicinal Products

International Society of Anti-infective Pharmacology

Molecular Monte Carlo Home Page

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US Food and Drug Administration

Glossary

PHARMACODYNAMICS

The study of the biochemical and physiological effects of drugs and the mechanisms of their actions.

MINIMAL INHIBITORY CONCENTRATION

(MIC). The lowest drug concentration that results in stasis.

EFFECTIVE CONCENTRATION90

(EC90). Usually applied to virus susceptibility — the drug concentration resulting in a decrease in replication of 90%.

PHARMACOKINETIC

The study of the bodily absorption, distribution, metabolism and excretion of drugs.

DRUG CLEARANCE

The volume of plasma that is completely cleared of drug per unit time.

VOLUME OF DISTRIBUTION

The apparent volume in the patient relating dose and observed plasma concentration.

AREA UNDER THE CONCENTRATION–TIME CURVE

(AUC). A measure of the total exposure to drug — the integral of the concentration–time curve.

PEAK CONCENTRATION

The highest concentration attained in a dosing interval.

TIME > MIC

The time that drug concentrations exceed the MIC, often stated as a percentage of the dosing interval.

BACTERIOSTATIC

An antibiotic that inhibits the growth of a bacterial population.

BACTERICIDAL

An antibiotic that kills 99.99% of a bacterial population.

AUC/MIC

Ratio of the area under the concentration–time curve to the MIC.

POST-ANTIBIOTIC EFFECT

(PAE). Persistent inhibitory effect on a microorganism that results from drug exposure after the drug has been completely removed.

POST-ANTIBIOTIC SUB-MIC EFFECT

(PA-SME). Persistent inhibitory effect on a microorganism that results from drug exposure after the drug has been diluted to a fraction of the MIC.

PROTECTIVE DRUG DOSE

(PD50 or CD50). The drug dose resulting in protection of 50% of challenged animals.

PEAK CONCENTRATION/MIC

The ratio of the peak concentration to the MIC.

GRANULOCYTOPENIC

A blood disorder that is characterized by a severe reduction in the number of granulocytes in blood.

NEUTROPENIC

An abnormal decrease in the number of white cells in the blood.

MONTE CARLO SIMULATION

An analytical technique for solving a problem by performing a large number of simulations and inferring a solution from the collective results that can be used to calculate the probability distribution of possible outcomes.

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Drusano, G. Antimicrobial pharmacodynamics: critical interactions of 'bug and drug'. Nat Rev Microbiol 2, 289–300 (2004). https://doi.org/10.1038/nrmicro862

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