Antimicrobial PK/PD and therapeutic drug monitoring

PK/PD indices

Antimicrobial efficacy correlates with one of three exposure indices, determined by whether killing is concentration-dependent or time-dependent. The index is a property of the drug class, and using the wrong one leads to the wrong dosing strategy.

Targets commonly cited from preclinical and clinical work — targets, not regulation, and they vary by organism and endpoint:

The free (unbound) fraction is what matters. For a highly bound agent such as ceftriaxone or daptomycin, total concentrations overstate the active exposure substantially.

Probability of target attainment and cumulative fraction of response

• PTA — for a fixed MIC, the fraction of a simulated population reaching the PK/PD target at a given regimen. Plotted against MIC, the PTA curve gives the PK/PD breakpoint: the highest MIC at which the regimen achieves (conventionally) ≥ 90% attainment.
• CFR — PTA integrated over the MIC distribution of the actual pathogen population, giving a single expected success probability for empirical therapy against that organism.

Both need a population PK model with realistic variability. simulate_regimen.py --simulate with --target-auc or --target-trough gives the machinery; note it includes between-subject variability only, so real attainment is lower once residual and between-occasion variability are added.

Critically ill patients are the population where this matters most and where standard models fail: augmented renal clearance (creatinine clearance above 130 mL/min, common in young trauma and sepsis patients) can put a standard beta-lactam regimen well below target, while acute kidney injury and renal replacement therapy move it the other way.

Vancomycin: AUC-guided dosing

The 2020 consensus guideline (ASHP/IDSA/PIDS/SIDP) moved the target from trough-guided to AUC₂₄/MIC of 400-600, assuming an MIC of 1 mg/L, for serious MRSA infections.

Why troughs were abandoned: trough concentration is a poor surrogate for AUC. Achieving the historical 15-20 mg/L trough target frequently produces AUC₂₄ well above 600 and is associated with more nephrotoxicity, without better efficacy. Two patients with the same trough can have AUCs differing by 50% depending on their volume and interval.

Two accepted methods for estimating AUC:

1. Bayesian estimation from one or two levels against a population model. Works with a single level, tolerates levels drawn at imprecise times, and is the preferred approach.
2. First-order equations from a peak and a trough within the same interval, both drawn at steady state, with the peak at least 1-2 hours after the end of the infusion so that distribution is complete.

tdm_bayes.py --model vancomycin-adult implements method 1. Its bundled parameterisation is explicitly illustrative — substitute a model validated in your population, because vancomycin population models differ substantially between general ward, ICU, obese, paediatric and dialysis populations.

Model-informed precision dosing

MAP Bayesian forecasting combines a population prior with a patient's measured concentrations:

minimise   sum_j (obs_j - pred_j)^2 / var_j  +  sum_k (eta_k / omega_k)^2

The second term is the prior penalty. Its consequences:

• A single level is enough to be useful but cannot separate clearance from volume. Whichever parameter the sample is uninformative about returns essentially its population value; the reported "individual" estimate for it is the prior.
• Sample timing determines what is learned. Troughs are informative about clearance; a peak (after distribution) is informative about volume. All-trough sampling leaves volume weakly identified.
• A large eta is a data-quality signal first. An individual clearance three-fold the population value is more often a mis-recorded sampling or infusion time than a genuinely unusual patient. Check the times before acting on the estimate.
• The prior must be appropriate to the patient. A model built in general medical inpatients applied to a patient on continuous renal replacement therapy will shrink towards the wrong place, and the fit statistics will not reveal it.

Other drug classes where MIPD is established: aminoglycosides, busulfan (AUC-targeted conditioning), methotrexate rescue, immunosuppressants (tacrolimus, ciclosporin, mycophenolate), antiepileptics, infliximab and other anti-TNF biologics, and increasingly beta-lactams in critical care.

Reporting a TDM calculation

State the population model and its source, the assay and matrix, the actual (not scheduled) dose and sampling times, whether steady state was reached, the estimated individual parameters with the etas, the predicted exposure metric, and the target with its justification. Without the actual times, the calculation cannot be reproduced or audited.

Any change to a patient's regimen is a clinical decision that depends on the organism, the site of infection, renal trajectory, concomitant nephrotoxins and local protocol. The model provides an exposure estimate; it does not provide the decision.