A Visual Representation of the Drug Input and Disposition Based on a Bayesian Approach

Abstract

Compliance to a drug prescription describes the degree to which a patient correctly follows medical advice. Poor compliance significantly impacts on the efficacy and safety of a planned therapy, which can be summed up by the dictum: “a drug only works if it’s taken”. However, the relationship between drug intake and pharmacokinetics (PK) is only partially known, especially the so-called inverse problem, concerned with the issue of retracing the patient compliance scenario using limited clinical knowledge. Based on the Bayesian theory, we develop a decision rule to solve this problem. Given an observed concentration, we determine, among all possible compliance scenarios, which is the most probable one. Using a simulation approach, we are able to judge the quality of this retracing process by measuring its global performance. Since the sampling concentration is the result of both patient compliance (drug input) and patient PK characteristics (drug disposition), two natural questions arise here: first, given two different sampling concentration values, can we expect the same performance of the retracing process? Second, how is this performance affected by the PK variability between individuals? For this, we here design an heatmap-style image, called Compliance Spectrum, that provides an intuitive and interactive way to evaluate the relationship between drug input and drug disposition along with their consequences on PK profile. The current work provides a solution to this inverse problem of compliance determination from a probability viewpoint and uses it as a base to build a visual representation of drug input and disposition.