Application of machine learning in combination with mechanistic modeling to predict plasma exposure of small molecules

Abstract

Prediction of a new molecule’s exposure in plasma is a critical first step toward understanding its efficacy/toxicity profile and concluding whether it is a possible first-in-class, best-in-class candidate. For this prediction, traditional pharmacometrics use a variety of scaling methods that are heavily based on pre-clinical pharmacokinetic (PK) data. We here propose a novel framework based on which preclinical exposure prediction is performed by applying machine learning (ML) in tandem with mechanism-based modeling. In our proposed method, a relationship is initially established between molecular structure and physicochemical (PC)/PK properties using ML, and then the ML-driven PC/PK parameters are used as input to mechanistic models that ultimately predict the plasma exposure of new candidates. To understand the feasibility of our proposed framework, we evaluated a number of mechanistic models (1-compartment, physiologically based pharmacokinetic (PBPK)), PBPK distribution models (Berezhkovskiy, PK-Sim standard, Poulin and Theil, Rodgers and Rowland, and Schmidt), and PBPK parameterizations (using in vivo, or in vitro clearance). For most of the scenarios tested, our results demonstrate that PK profiles can be adequately predicted based on the proposed framework. Our analysis further indicates some limitations when liver microsomal intrinsic clearance (CLint) is used as the only clearance pathway and underscores the necessity of investigating the variability emanating from the different distribution models when providing PK predictions. The suggested approach aims at earlier exposure prediction in the drug development process so that critical decisions on molecule screening, chemistry design, or dose selection can be made as early as possible.

Systematic Evaluation of High-Throughput PBK Modelling Strategies for the Prediction of Intravenous and Oral Pharmacokinetics in Humans

“…Potential applications of such HT-PBK modelling strategies are vast, and there have been many efforts recently in both pharmacology and toxicology to establish such strategies for different use cases and based on different approaches. Many of them, however, relied exclusively on rodent data for their validation (Schneckener et al 2019; Kamiya et al 2021; Naga et al 2022; Punt et al 2022b; Obrezanova et al 2022; Mavroudis et al 2023; Handa et al 2023; Führer et al 2024). Others did perform predictions for humans but relied in their validation of prediction quality on summary PK parameters, like Cmax or AUC values (Punt et al 2022a; Miljković et al 2021; Li et al 2023).…”

“…Sometimes, this is pursued by using AI/ML methods to directly predict summary PK/TK parameters, like maximum concentration (Cmax) or area under the curve (AUC) values (Miljković et al 2021; Fagerholm et al 2021; Li et al 2023). Other times, it is done by predicting mechanistically relevant compound properties, like the lipophilicity, solubility or clearance of compounds, which can then be used as inputs for making PK predictions using mechanistic models (Danishuddin et al 2022; Pillai et al 2022; Fagerholm et al 2023; Mavroudis et al 2023; Führer et al 2024). Until now, many in silico -based PK prediction efforts have focused on predicting rodent data (Schneckener et al 2019; Kamiya et al 2021; Naga et al 2022; Punt et al 2022b; Obrezanova et al 2022; Mavroudis et al 2023; Handa et al 2023), presumably due to its greater availability than human data.…”

“…Other times, it is done by predicting mechanistically relevant compound properties, like the lipophilicity, solubility or clearance of compounds, which can then be used as inputs for making PK predictions using mechanistic models (Danishuddin et al 2022; Pillai et al 2022; Fagerholm et al 2023; Mavroudis et al 2023; Führer et al 2024). Until now, many in silico -based PK prediction efforts have focused on predicting rodent data (Schneckener et al 2019; Kamiya et al 2021; Naga et al 2022; Punt et al 2022b; Obrezanova et al 2022; Mavroudis et al 2023; Handa et al 2023), presumably due to its greater availability than human data. While valuable, the ultimate goal in pharmacology and toxicology is to yield human-relevant conclusions.…”

Advancing Drug Safety in Drug Development: Bridging Computational Predictions for Enhanced Toxicity Prediction

“…A significant obstacle in constructing these models is the scarcity of the necessary drug/chemical-specific parameters, for which measurements often remain unavailable, a gap that AI has begun to fill. ,, AI models can be developed by utilizing the available PK data, and their incorporation into PBPK models facilitates the prediction of PK parameters . The inclusion of ML in PBPK models has improved their performance, offering a sophisticated approach for understanding drug kinetics and safety .…”

“…This integration represents a promising framework to overcome the limitations of extrapolating data, particularly for predicting complex biological interactions and individual variability in drug responses. Several reviews on this subject have been published in recent years, containing detailed information on PK parameters, databases, and integration of ML and PBPK, serving as a testament to the growing interest in and potential of this approach. ,,, The drug parameters discussed for drug safety include the maximum plasma concentration ( C max ), which should be substantially lower than the maximum tolerated concentration (MTC), to minimize the risk of adverse effects. However, for efficacy, the drug concentration must remain at or above the minimum effective concentration (MEC) for a sufficient duration.…”