A simulation-based framework illustrates why a drug concentration cannot reveal adherence without assumptions about the patient and dosing history.
What the source reports
The researchers modeled fourteen antiseizure medicines, including clobazam, under full adherence and several missed-dose patterns. Population pharmacokinetic models, patient characteristics, sampling times, co-medications, and prior probabilities were combined to estimate the relative likelihood of dosing histories. Performance was strongest under idealized low-variability conditions.
Limits and uncertainty
These were computational experiments rather than direct validation against observed real-world missed doses in every modeled setting. Posterior probabilities depend on model fit and prior probabilities. Similar concentrations may arise from different histories or physiological conditions.
Implications for forensic review
For forensic adherence opinions, disclose the candidate histories and assumptions rather than presenting one reconstructed history as fact. A low result can warrant investigation, but it does not independently prove a missed dose. The framework highlights the need for individualized uncertainty rather than a universal adherence cutoff.
Source and related reading
Source reviewed: Bayesian Based Pharmacokinetic Framework Integrated with Therapeutic Drug Monitoring for Assessing A (PMC12772940). Forensic applications above are interpretive analysis of the supplied paper.
Related reading: Clobazam–Stiripentol Modeling Predicted a Genotype-Dependent Metabolite Interaction; Clobazam Interaction Review Shows Why Measuring the Active Metabolite Matters.
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