Predicting human exposure of active drug after oral prodrug administration, using a joined in vitro/in silico–in vivo extrapolation and physiologically-based pharmacokinetic modeling approach

Introduction: Predicting the pharmacokinetics (PK) of prodrugs and their corresponding active drugs is challenging, as there are many variables to consider. Prodrug conversion characteristics in different tissues are generally measured, but integrating these variables to a PK profile is not a common practice. In this paper, a joined in vitro/in silico–in vivo extrapolation (IVIVE) and physiologically-based pharmacokinetic (PBPK) modeling approach is presented to predict active drug exposure in human after oral prodrug administration. Methods: Physico-chemical and in vitro assays as well as in silico predictions were proposed to characterize key pharmacokinetic properties (e.g. clearance, volume of distribution, conversion rates) of three marketed prodrugs. These data were used to parameterize a PBPK model for simulating human PK profiles of the active drugs after prodrug administration, which were compared to literature data by evaluating the accuracy and uncertainty of the predictions. Results: For mycophenate mofetil and midodrine the PK of their active moieties could be adequately predicted. The assumptions of the PBPK–IVIVE approach were valid, i.e. being hepatically cleared, converted in the gut lumen, blood and liver and not metabolized in the gut wall. However, the observed profiles after oral bambuterol administration clearly fell outside the prediction interval as the PBPK model failed to predict the observed bioavailability. Discussion: Adding quantitative information about prodrug conversion in the gut, liver and blood to a PBPK model for the absorption, distribution, metabolism and excretion (ADME) properties of prodrugs and their active moieties resulted, retrospectively, in reasonable predictions of the human PK when the ADME properties are well understood. Also in a prospective compound selection process, this integrative approach can improve decision making on prodrug candidates by putting relative differences in prodrug conversion of a large number of candidates into the perspective of their human PK profile, before conducting any in vivo experiments.