Development of a 2D-QSAR Model for Tissue-to-Plasma Partition Coefficient Value with High Accuracy Using Machine Learning Method, Minimum Required Experimental Values, and Physicochemical Descriptors

Authors: Handa K, Sakamoto S, Kageyama M, Iijima T
Publication: Eur J Drug Metab Pharmacokinet
Keywords: admet predictor, atomic descriptors, machine learning, machine learning PBPK QST(P) marriage, molecular descriptors, PBPK modeling, QSAR
Software: ADMET Predictor®

Abstract

Background

The demand for physiologically based pharmacokinetic (PBPK) model is increasing currently. New drug application (NDA) of many compounds is submitted with PBPK models for efficient drug development. Tissue-to-plasma partition coefficient (Kp) is a key parameter for the PBPK model to describe differential equations. However, it is difficult to obtain the Kp value experimentally because the measurement of drug concentration in the tissue is much harder than that in plasma.

Objective

Instead of experiments, many researchers have sought in silico methods. Today, most of the models for Kp prediction are using in vitro and in vivo parameters as explanatory variables. We thought of physicochemical descriptors that could improve the predictability. Therefore, we aimed to develop the two-dimensional quantitative structure-activity relationship (2D-QSAR) model for Kp using physicochemical descriptors instead of in vivo experimental data as explanatory variables.

Methods

We compared our model with the conventional models using 20-fold cross-validation according to the published method (Yun et al. J Pharmacokinet Pharmacodyn 41:1–14, 2014). We used random forest algorithm, which is known to be one of the best predictors for the 2D-QSAR model. Finally, we combined minimum in vitro experimental values and physiochemical descriptors. Thus, the prediction method for Kp value using a few in vitro parameters and physicochemical descriptors was developed; this is a multimodal model.

Results

Its accuracy was found to be superior to that of the conventional models. Results of this research suggest that multimodality is useful for the 2D-QSAR model [RMSE and % of two-fold error: 0.66 and 42.2% (Berezohkovsky), 0.52 and 52.2% (Rodgers), 0.65 and 34.6% (Schmitt), 0.44 and 61.1% (published model), 0.41 and 62.1% (traditional model), 0.39 and 64.5% (multimodal model)].

Conclusion

We could develop a 2D-QSAR model for Kp value with the highest accuracy using a few in vitro experimental data and physicochemical descriptors.

By Koichi Handa, Seishiro Sakamoto, Michiharu Kageyama & Takeshi Iijima