A PopPBPK-RL approach for precision dosing of benazepril in renal impaired patients

Current treatment recommendations mainly rely on rule-based protocols defined from evidence-based clinical guidelines, which are difficult to adapt for high-risk patients such as those with renal impairment. Consequently, unsuccessful therapies and the occurrence of adverse drug reactions are common. Within the context of personalized medicine, that tries to deliver the right treatment dose to maximize efficacy and minimize toxicity, the concept of model-informed precision dosing proposes the use of mechanistic models, like physiologically based pharmacokinetic (PBPK) modeling, to predict drug regimes outcomes. Nonetheless, PBPK models have limited capability when computing patients' centric optimized drug doses. Consequently, reinforcement learning (RL) has been previously used to personalize drug dosage. In this work we propose the first PBPK and RL-based precision dosing system for an orally taken drug (benazepril) considering a virtual population of patients with renal disease. Population based PBPK modeling is used in combination with RL for obtaining patient tailored dose regimes. We also perform patient stratification and feature selection to better handle dose tailoring problems. Based on patients' characteristics with best predictive capabilities, benazepril dose regimes are obtained for a population with features' diversity. Obtained regimes are evaluated based on PK parameters considered. Results show that the proof-of-concept approach herein is capable of learning good dosing regimes for most patients. The use of a PopPBPK model allowed to account for intervariability of patient characteristics and be more inclusive considering also non-frequent patients. Impact analysis of patients' features reveals that renal impairment is the main driver affecting RL capabilities.