Prediction of Drug Distribution to Each Tissue Using Random Forest Twice with In Vitro Experimental Values and Predicted Kp Values of Other Tissues

AI is expected to help identify excellent candidates in drug discovery. However, we face a lack of data as it is time consuming and expensive to acquire raw data perfectly for many compounds. The tissue-to-plasma partition coefficient (Kp), which is an important parameter for understanding drug distribution in tissues and building the physiologically based pharmacokinetic (PBPK) model, is a representative of small and sparse datasets. Hence, we tried to develop a novel QSAR method to predict a parameter more precisely from an incomplete dataset via optimizing data handling by making use of predicted explanatory variables. In this study, we focused on predicting the Kp values of 119 compounds in nine tissues (adipose, brain, gut, heart, kidney, liver, lung, muscle, and skin), while some of these were not available. To fill the missing values in Kp for each tissue, firstly we predicted those Kp values by the non-missing dataset using a random forest (RF) model with in vitro parameters (log P, fu, Drug Class, and fi) like a classical prediction by a QSAR model. Next, to predict the tissue-specific Kp values in a test dataset, we constructed a second RF model with not only in vitro parameters but also the Kp values of other tissues (i.e. other than target tissues) predicted by the first RF model as explanatory variables. Furthermore, we tested all possible combinations of explanatory variables and selected the model with the highest predictability from the test dataset as the final model. The evaluation of Kp prediction accuracy based on the root-mean-square error and R2-value revealed that the proposed models outperformed other machine learning methods, such as the conventional RF and message-passing neural networks. Significant improvements were observed in the Kp values of adipose tissue, brain, kidney, liver, and skin. These improvements indicated that the Kp information of other tissues can be used to predict the same for a specific tissue. Additionally, we found a novel relationship between each tissue by evaluating all combinations of explanatory variables. For example, Kp values in no other tissues were needed for the prediction of adipose Kp, and liver Kp was not needed for the prediction of Kp in other tissues. In conclusion, we developed a novel RF model to predict Kp values. We hope that this method will be applied to various problems in the field of experimental biology which often contains missing values in the near future.