Do We Build Similar Molecules for Comorbid Diseases? Tevarud in Drug Design, an Analysis for Depression and Inflammation

Joint Decision-Making Model Based on Consensus Modeling Technology for the Prediction of Drug-Induced Liver Injury

Drug-induced liver injury (DILI) is the major cause of clinical trial failure and postmarketing withdrawals of approved drugs. It is very expensive and time-consuming to evaluate hepatotoxicity using animal or cell-based experiments in the early stage of drug development. In this study, an in silico model based on the joint decision-making strategy was developed for DILI assessment using a relatively large dataset of 2608 compounds. Five consensus models were developed with PaDEL descriptors and PubChem, Substructure, Estate, and Klekota–Roth fingerprints, respectively. Submodels for each consensus model were obtained through joint optimization. The parameters and features of each submodel were optimized jointly based on the hybrid quantum particle swarm optimization (HQPSO) algorithm. The application domain (AD) based on the frequency-weighted and distance (FWD)-based method and Tanimoto similarity index showed the wide AD of the qualified consensus models. A joint decision-making model was integrated by the qualified consensus models, and the overwhelming majority principle was used to improve the performance of consensus models. The application scope narrowing caused by the overwhelming majority principle was successfully solved by joint decision-making. The proposed model successfully predicted 99.2% of the compounds in the test set, with an accuracy of 80.0%, a sensitivity of 83.9, and a specificity of 73.3%. For an external validation set containing 390 compounds collected from DILIrank, 98.2% of the compounds were successfully predicted with an accuracy of 79.9%, a sensitivity of 97.1%, and a specificity of 66.0%. Furthermore, 25 privileged substructures responsible for DILI were identified from Substructure, PubChem, and Klekota–Roth fingerprints. These privileged substructures can be regarded as structural alerts in hepatotoxicity evaluation. Compared with the main published studies, our method exhibits certain advantage in data size, transparency, and standardization of the modeling process and accuracy and credibility of prediction results. It is a promising tool for virtual screening in the early stage of drug development.

QSPR model for Caco-2 cell permeability prediction using a combination of HQPSO and dual-RBF neural network

The Caco-2 cell model is widely used to evaluate the in vitro human intestinal permeability of drugs due to its morphological and functional similarity to human enterocytes. Although it is safe and relatively economic, it is time-consuming. A rapid and accurate quantitative structure-property relationship (QSPR) model of Caco-2 permeability is helpful to improve the efficiency of oral drug development. The aim of our study is to explore the predictive ability of the QSPR model, to study its permeation mechanism, and to develop a potential permeability prediction model, for Caco-2 cells. In our study, a relatively large data set was collected and the abnormal data were eliminated using the Monte Carlo regression and hybrid quantum particle swarm optimization (HQPSO) algorithm. Then, the remaining 1827 compounds were used to establish QSPR models. To generate multiple chemically diverse training and test sets, we used a combination of principal component analysis (PCA) and self-organizing mapping (SOM) neural networks to split the modeling data set characterized by PaDEL-descriptors. After preliminary selection of descriptors by the mean decrease impurity (MDI) method, the HQPSO algorithm was used to select the key descriptors. Six different methods, namely, multivariate linear regression (MLR), support vector machine regression (SVR), xgboost, radial basis function (RBF) neural networks, dual-SVR and dual-RBF were employed to develop QSPR models. The best dual-RBF model was obtained finally with R 2 = 0.91, and R cv5 2 = 0.77, for the training set, and R T 2 = 0.77, for the test set. A series of validation methods were used to assess the robustness and predictive ability of the dual-RBF model under OECD principles. A new application domain (AD) definition method based on the descriptor importance-weighted and distance-based (IWD) method was proposed, and the outliers were analyzed carefully. Combined with the importance of the descriptors used in the dual-RBF model, we concluded that the "H E-state" and hydrogen bonds are important factors affecting the permeability of drugs passing through the Caco-2 cell. Compared with the reported studies, our method exhibits certain advantages in data size, transparency of modeling process and prediction accuracy to some extent, and is a promising tool for virtual screening in the early stage of drug development.