CLC-Pred 2.0: A Freely Available Web Application for In Silico Prediction of Human Cell Line Cytotoxicity and Molecular Mechanisms of Action for Druglike Compounds

• Published in: International journal of molecular sciences Vol. 24; no. 2; p. 1689
• Main Authors: Lagunin, Alexey A, Rudik, Anastasia V, Pogodin, Pavel V, Savosina, Polina I, Tarasova, Olga A, Dmitriev, Alexander V, Ivanov, Sergey M, Biziukova, Nadezhda Y, Druzhilovskiy, Dmitry S, Filimonov, Dmitry A, Poroikov, Vladimir V
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 14.01.2023; MDPI
• Subjects: Accuracy; Antineoplastic Agents - chemistry; Antineoplastic Agents - pharmacology; Applications programs; Bayes Theorem; Bayesian analysis; Cancer; Cell Line, Tumor; cell lines; Classification; Cytotoxicity; Drug development; Enzymes; Humans; in silico prediction; Kidneys; mechanism of action; Melanoma; Molecular modelling; PASS; Predictions; Prednisone; Probability; Proteins; SAR; Software; Tumor cell lines; Tumors; PASS; SAR; cell lines; mechanism of action; CLC-Pred; in silico prediction; cytotoxicity
• ISSN: 1422-0067; 1661-6596; 1422-0067
• DOI: 10.3390/ijms24021689

In vitro cell-line cytotoxicity is widely used in the experimental studies of potential antineoplastic agents and evaluation of safety in drug discovery. In silico estimation of cytotoxicity against hundreds of tumor cell lines and dozens of normal cell lines considerably reduces the time and costs of drug development and the assessment of new pharmaceutical agent perspectives. In 2018, we developed the first freely available web application (CLC-Pred) for the qualitative prediction of cytotoxicity against 278 tumor and 27 normal cell lines based on structural formulas of 59,882 compounds. Here, we present a new version of this web application: CLC-Pred 2.0. It also employs the PASS (Prediction of Activity Spectra for Substance) approach based on substructural atom centric MNA descriptors and a Bayesian algorithm. CLC-Pred 2.0 provides three types of qualitative prediction: (1) cytotoxicity against 391 tumor and 47 normal human cell lines based on ChEMBL and PubChem data (128,545 structures) with a mean accuracy of prediction (AUC), calculated by the leave-one-out (LOO CV) and the 20-fold cross-validation (20F CV) procedures, of 0.925 and 0.923, respectively; (2) cytotoxicity against an NCI60 tumor cell-line panel based on the Developmental Therapeutics Program's NCI60 data (22,726 structures) with different thresholds of IG data (100, 10 and 1 nM) and a mean accuracy of prediction from 0.870 to 0.945 (LOO CV) and from 0.869 to 0.942 (20F CV), respectively; (3) 2170 molecular mechanisms of actions based on ChEMBL and PubChem data (656,011 structures) with a mean accuracy of prediction 0.979 (LOO CV) and 0.978 (20F CV). Therefore, CLC-Pred 2.0 is a significant extension of the capabilities of the initial web application.