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antimicrobial-and-tdm.md

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1# Antimicrobial PK/PD and research TDM
2

Antimicrobial PK/PD and research TDM

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3## Define the target before computing attainment
4
5A target needs the drug, organism, infection, endpoint, patient population, assay/MIC method,
6exposure interval and free/total concentration basis. AUC/MIC has time units unless the convention
7explicitly normalizes them; Cmax/MIC is a ratio, and fT>MIC is a time fraction or percent.
8A preclinical stasis target, clinical efficacy target and toxicity threshold are different endpoints.
9Do not transfer a numeric target between those settings without evidence.
11For serious MRSA, the 2020 ASHP/IDSA/PIDS/SIDP vancomycin consensus recommends total-drug AUC24
12400–600 mg·h/L assuming broth-microdilution MIC 1 mg/L. That is not a universal antimicrobial target,
13not a free-AUC target, and not an instruction to increase exposure indefinitely with a higher MIC.
14[IDSA guideline](https://www.idsociety.org/practice-guideline/vancomycin/).

Define the target before computing attainment

A target needs the drug, organism, infection, endpoint, patient population, assay/MIC method, exposure interval and free/total concentration basis. AUC/MIC has time units unless the convention explicitly normalizes them; Cmax/MIC is a ratio, and fT>MIC is a time fraction or percent. A preclinical stasis target, clinical efficacy target and toxicity threshold are different endpoints. Do not transfer a numeric target between those settings without evidence.

For serious MRSA, the 2020 ASHP/IDSA/PIDS/SIDP vancomycin consensus recommends total-drug AUC24 400–600 mg·h/L assuming broth-microdilution MIC 1 mg/L. That is not a universal antimicrobial target, not a free-AUC target, and not an instruction to increase exposure indefinitely with a higher MIC. IDSA guideline.

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16## Bayesian estimation
18MAP estimation combines the population prior with the observation likelihood. The local helper
19uses log CL and log V normal priors (omega inputs are log SDs) and a combined Gaussian residual
20model whose objective includes log variance. It reports a point estimate, not a posterior credible
21interval. One level does not independently identify both CL and V; the prior can dominate.
23The vancomycin-adult parameterization is illustrative, not a validated clinical population model.
24Before applying any published model, check population, assay, parameter units, covariates,
25renal-function equation, dosing history, external validation and relevant clinical workflow.
26A numerically converged fit to one or two levels does not validate those assumptions.

Bayesian estimation

MAP estimation combines the population prior with the observation likelihood. The local helper uses log CL and log V normal priors (omega inputs are log SDs) and a combined Gaussian residual model whose objective includes log variance. It reports a point estimate, not a posterior credible interval. One level does not independently identify both CL and V; the prior can dominate.

The vancomycin-adult parameterization is illustrative, not a validated clinical population model. Before applying any published model, check population, assay, parameter units, covariates, renal-function equation, dosing history, external validation and relevant clinical workflow. A numerically converged fit to one or two levels does not validate those assumptions.

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28## Bundled dose history and exposure
30Levels are entered as concentration@hours after the latest dose start. The helper assumes identical
31doses spaced by the stated interval and duration. `--doses-given 20` is a finite history, not a
32steady-state guarantee. Missed doses, variable intervals, changing renal function and dialysis are
33outside this implementation. Use full event-based modeling for those data.
35For a linear one-compartment steady-state system, `AUC24=24*D/(CL*tau)`; peak/trough account for
36infusion duration. These exact steady-state calculations are distinct from the finite-dose
37concentrations used to fit observations. The model-target dose output is an amount that matches
38the requested model AUC, not an individualized clinical recommendation.

Bundled dose history and exposure

Levels are entered as concentration@hours after the latest dose start. The helper assumes identical doses spaced by the stated interval and duration. --doses-given 20 is a finite history, not a steady-state guarantee. Missed doses, variable intervals, changing renal function and dialysis are outside this implementation. Use full event-based modeling for those data.

For a linear one-compartment steady-state system, AUC24=24*D/(CL*tau); peak/trough account for infusion duration. These exact steady-state calculations are distinct from the finite-dose concentrations used to fit observations. The model-target dose output is an amount that matches the requested model AUC, not an individualized clinical recommendation.

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40## Simulation and target attainment
42The simulation helper draws CL and V independently from lognormal distributions specified by CV.
43Its omega convention differs from TDM's log SD. It does not propagate population-parameter
44uncertainty, correlations, changing physiology or between-occasion variability. Their omission can
45raise or lower attainment. Assay error affects observed levels, not the underlying true exposure.
47Check multiple intervals for convergence, event-boundary behavior, reproducibility and numerical
48integration tolerance. Report the fraction meeting a prespecified target together with sampling
49uncertainty and model limitations; simulated attainment is not evidence of patient benefit.

Simulation and target attainment

The simulation helper draws CL and V independently from lognormal distributions specified by CV. Its omega convention differs from TDM's log SD. It does not propagate population-parameter uncertainty, correlations, changing physiology or between-occasion variability. Their omission can raise or lower attainment. Assay error affects observed levels, not the underlying true exposure.

Check multiple intervals for convergence, event-boundary behavior, reproducibility and numerical integration tolerance. Report the fraction meeting a prespecified target together with sampling uncertainty and model limitations; simulated attainment is not evidence of patient benefit.

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