A Practical In Silico Method for Predicting Compound Brain Concentration–Time Profiles: Combination of PK Modeling and Machine Learning

• Published in: Molecular pharmaceutics Vol. 21; no. 10; pp. 5182 - 5191
• Main Authors: Handa, Koichi, Fujita, Daichi, Hirano, Mariko, Yoshimura, Saki, Kageyama, Michiharu, Iijima, Takeshi
• Format: Journal Article
• Language: English
• Published: United States American Chemical Society 07.10.2024
• Subjects: Animals; Blood-Brain Barrier - metabolism; Brain - metabolism; Central Nervous System Agents - administration & dosage; Central Nervous System Agents - pharmacokinetics; Computer Simulation; Drug Discovery - methods; Machine Learning; Mice; Models, Biological; Tissue Distribution; modeling and simulation; PK parameters; compound brain concentration−time profiles; QSAR; mouse pharmacokinetics; CNS; random forest; machine learning; compound design
• ISSN: 1543-8384; 1543-8392; 1543-8392
• DOI: 10.1021/acs.molpharmaceut.4c00584

Given the aging populations in advanced countries globally, many pharmaceutical companies have focused on developing central nervous system (CNS) drugs. However, due to the blood–brain barrier, drugs do not easily reach the target area in the brain. Although conventional screening methods for drug discovery involve the measurement of (unbound fraction of drug) brain-to-plasma partition coefficients, it is difficult to consider nonequilibrium between plasma and brain compound concentration–time profiles. To truly understand the pharmacokinetics/pharmacodynamics of CNS drugs, compound concentration–time profiles in the brain are necessary; however, such analyses are costly and time-consuming and require a significant number of animals. Therefore, in this study, we attempted to develop an in silico prediction method that does not require a large amount of experimental data by combining modeling and simulation (M&S) with machine learning (ML). First, we constructed a hybrid model linking plasma concentration–time profile to the brain compartment that takes into account the transit time and brain distribution of each compound. Using mouse plasma and brain time experimental values for 103 compounds, we determined the brain kinetic parameters of the hybrid model for each compound; this case was defined as scenario I (a positive control experiment) and included the full brain concentration–time profile data. Next, we built an ML model using chemical structure descriptors as explanatory variables and rate parameters as the target variable, and we then input the predicted values from 5-fold cross-validation (CV) into the hybrid model; this case was defined as scenario II, in which no brain compound concentration–time profile data exist. Finally, for scenario III, assuming that the brain concentration is obtained at only one time point, we used the brain kinetic parameters from the result of the 5-fold CV in scenario II as the initial values for the hybrid model and performed parameter refitting against the observed brain concentration at that time point. As a result, the RMSE/R2-values of the brain compound concentration–time profiles over time were 0.445/0.517 in scenario II and 0.246/0.805 in scenario III, indicating the method provides high accuracy and suggesting that it is a practical method for predicting brain compound concentration–time profiles.