https://doi.org/10.1016/j.xphs.2023.10.035
Gruber, A., Führer, F., Menz, S., Diedam, H., Göller, A. H., & Schneckener, S. (2023). Prediction of human pharmacokinetics from chemical structure: combining mechanistic modeling with machine learning. Journal of Pharmaceutical Sciences [ePub ahead of print].
Abstract
Pharmacokinetics (PK) is the result of a complex interplay between compound properties and physiology, and a detailed characterization of a molecule's PK during preclinical research is key to understanding the relationship between applied dose, exposure, and pharmacological effect. Predictions of human PK based on the chemical structure of a compound are highly desirable to avoid advancing compounds with unfavorable properties early on and to reduce animal testing, but data to train such models are scarce. To address this problem, we combine well-established Physiologically Based Pharmacokinetic models with Deep Learning models for molecular property prediction into a hybrid model to predict PK parameters for small molecules directly from chemical structure. Our model predicts exposure after oral and intravenous administration with fold change errors of 1.87 and 1.86, respectively, in healthy subjects and 2.32 and 2.23, respectively, in patients with various diseases. Unlike pure Deep Learning models, the hybrid model can predict endpoints on which it was not trained. We validate this extrapolation capability by predicting full concentration-time profiles for compounds with published PK data. Our model enables early selection and prioritization of the most promising drug candidates, which can lead to a reduction in animal testing during drug discovery and development.
Keywords
Pharmacokinetics; Machine learning; Physiologically Based Pharmacokinetic (PBPK) modeling; In silico modeling; Neural network(s); Simulation(s)
https://doi.org/10.1016/j.xphs.2023.10.035 Gruber, A., Führer, F., Menz, S., Diedam, H., Göller, A. H., & Schneckener, S. (2023). Prediction of human pharmacokinetics from chemical structure: combining mechanistic modeling with machine learning. Journal of Pharmaceutical Sciences [ePub ahead of print].
Abstract Pharmacokinetics (PK) is the result of a complex interplay between compound properties and physiology, and a detailed characterization of a molecule's PK during preclinical research is key to understanding the relationship between applied dose, exposure, and pharmacological effect. Predictions of human PK based on the chemical structure of a compound are highly desirable to avoid advancing compounds with unfavorable properties early on and to reduce animal testing, but data to train such models are scarce. To address this problem, we combine well-established Physiologically Based Pharmacokinetic models with Deep Learning models for molecular property prediction into a hybrid model to predict PK parameters for small molecules directly from chemical structure. Our model predicts exposure after oral and intravenous administration with fold change errors of 1.87 and 1.86, respectively, in healthy subjects and 2.32 and 2.23, respectively, in patients with various diseases. Unlike pure Deep Learning models, the hybrid model can predict endpoints on which it was not trained. We validate this extrapolation capability by predicting full concentration-time profiles for compounds with published PK data. Our model enables early selection and prioritization of the most promising drug candidates, which can lead to a reduction in animal testing during drug discovery and development.
Keywords Pharmacokinetics; Machine learning; Physiologically Based Pharmacokinetic (PBPK) modeling; In silico modeling; Neural network(s); Simulation(s)