Blood Never Lies: The PopPK-Based Lie Detector

Accurate assessment of drug adherence is essential for long-term oral therapies, where self-administration is unsupervised. Conventional tools such as self-reports, electronic monitors, and even therapeutic drug monitoring often fail to detect non-adherence. Population pharmacokinetic modeling improves objectivity but typically relies on a single analyte, which may still miss hidden non-adherence. We propose a bi-analyte population pharmacokinetic-based approach that uses both the parent drug and a metabolite with distinct pharmacokinetics to enhance adherence detection and reconstruct likely dosing histories.

A literature review identified spironolactone and its metabolite canrenone as suitable candidates, based on long-term use and an available population pharmacokinetic model capturing distinct elimination profiles. Virtual patients were simulated under steady-state assumptions, then exposed to various adherence scenarios. A modified Metropolis-Hastings algorithm jointly estimated individual pharmacokinetic parameters and dosing patterns by analyzing simulated drug and metabolite concentrations. The probability of taking 0, 1, or 2 tablets per day was inferred, and a posterior adherence probability was derived from the posterior distribution to quantify adherence likelihood. A receiver operating characteristic analysis was used to evaluate diagnostic performance.

The combined use of parent and metabolite improved non-adherence detection compared with either alone, particularly for recent missed doses. Trough concentrations outperformed peak concentrations, especially when patients did not compensate with extra doses.

This probabilistic bi-analyte approach enhances non-adherence detection and is adaptable to other pharmacokinetic settings where distinct profiles and low model variability are present.