Pharmacophore an International Research Journal
Antimicrobial dosing is a high-stakes clinical task where insufficient exposure can lead to therapeutic failure, while excessive exposure increases the risk of toxicity. Conventional dosing tools often reduce this complexity to broad rules that fail to capture patient-specific pharmacokinetics, pathogen susceptibility, or dynamic changes in renal function. Many clinical decision support systems provide recommended doses without explaining how these recommendations might vary under plausible alternative patient states, limiting clinicians’ ability to assess robustness against uncertainties in renal function, MIC interpretation, or PK/PD target selection. This article proposes a counterfactual explainable AI (XAI) framework for antimicrobial dose optimization, designed not only to recommend a dose but also to generate interpretable “what-if” scenarios illustrating how dosing would change if renal function, MIC, or PK/PD goals differed. The framework integrates a predictive dosing model with a counterfactual generation engine guided by pharmacometric reasoning: the predictive component estimates antimicrobial doses based on renal function, MIC, patient covariates, and target-attainment objectives, while the counterfactual component perturbs clinically relevant inputs within plausible ranges. Conceptually, the system would return a recommended dose alongside alternative dosing scenarios linked to changes in renal clearance, pathogen susceptibility, or exposure targets, each accompanied by a rationale connecting the output to pharmacokinetic and pharmacodynamic principles rather than presenting the recommendation as an unexplained prediction. By providing transparent, clinically interpretable alternatives, this counterfactual XAI approach could support individualized pharmacotherapy, promote antimicrobial stewardship, and enhance the safe implementation of AI-assisted prescribing.
