In silico prediction of drug‐induced ototoxicity using machine learning and deep learning methods

Abstract

Drug-induced ototoxicity has become a serious global problem, because of leading to deafness in hundreds of thousands of people every year. It always results from exposure to drugs or environmental chemicals that cause the impairment and degeneration of the inner ear. Herein, we focused on the in silico modeling of drug-induced ototoxicity of chemicals. We collected 1,102 ototoxic medications and 1,705 non-ototoxic drugs. Based on the data set, a series of computational models were developed with different traditional machine learning and deep learning algorithms implemented on an online chemical database and modeling environment. Six ML models performed best on 5-fold cross-validation and test set. A consensus model was developed with the best individual models. These models were further validated with an external validation. The consensus model showed best predictive ability, with high accuracy of 0.95 on test set and 0.90 on validation set. The consensus model and the data sets used for model development are available at https://ochem.eu/model/ 46566321. Besides, 16 structural alerts responsible for drug-induced ototoxicity were identified.We hope the results could provide meaningful knowledge and useful tools for ototoxicity evaluation in drug discovery and environmental risk assessment.

In Silico Prediction and Insights Into the Structural Basis of Drug Induced Nephrotoxicity

“…The model building was performed on the online chemical database and modeling environment (OCHEM), which is a user friendly web-based platform for automatic and simple QSAR modeling ( Sushko et al, 2011 ). OCHEM supports the typical steps of QSAR modeling, and the models can be published and publicly used on the web ( Oprisiu et al, 2013 ; Cui et al, 2019 ; Pawar et al, 2019 ; Cui et al, 2021 ; Hua et al, 2021 ; Huang et al, 2021 ; Ta et al, 2021 ). Among the many state-of-the-art modeling methods available on OCHEM, we applied five widely used traditional machine learning (ML) approaches and five different deep learning (DL) algorithms.…”

SApredictor: An Expert System for Screening Chemicals Against Structural Alerts

“…The data for identification of structural alerts were collected from 1) the databases such as ChEMBL ( Gaulton et al, 2011 ), ChemIDplus ( Tomasulo, 2002 ), Comparative Toxicogenomics Database (CTD) ( Davis et al, 2018 ), Carcinogenic Potency Database (CPDB) ( Gold et al, 1984 ) and DrugBank ( Wishart et al, 2017 ) and 2) peer-reviewed publications through manually filtering and processing. We focused on 22 toxicity endpoints which are of most concern in environmental toxicology and drug discovery, including acute oral toxicity ( Li et al, 2014 ), chemical aquatic toxicity [ Tetrahymena pyriformis ( Cheng et al, 2011 ), Daphnia magna ( Gajewicz-Skretna et al, 2021 ), and fathead minnow ( Sun et al, 2015 )], chemical-induced hematotoxicity ( Hua et al, 2021 ), drug-induced neurotoxicity ( Jiang et al, 2020 ), drug-induced autoimmune diseases ( Wu et al, 2021 ), drug-induced ototoxicity ( Huang et al, 2021 ), drug-induced rhabdomyolysis ( Cui et al, 2019 ), endocrine disruption ( Chen et al, 2014 ), eye irritation ( Wang et al, 2017 ), hepatotoxicity ( Li et al, 2018 ), hERG inhibition ( Li et al, 2017c ), honey bee toxicity ( Li et al, 2017b ), inhalation toxicity ( Cui et al, 2021 ), mitochondrial toxicity ( Nelms et al, 2015 ), mutagenicity ( Yang et al, 2017 ), nephrotoxicity ( Shi et al, 2022 ), non-genotoxic carcinogenicity ( Benigni et al, 2013 ), reproductive and development toxicity ( Fan et al, 2018 ; Jiang et al, 2019 ), skin sensitization ( Di et al, 2019 ), and toxicity on avian species ( Zhang et al, 2015 ). For each toxicity endpoint, we searched the literature separately and included the publications with the same definition of the toxicity endpoint and consistent toxic/non-toxic classification criteria.…”

“…Structural alert (SA) is another widely accepted tool for toxicity prediction in recent years, which can be defined as the key substructure which can cause specific toxicity. SA has been commonly used for assessment of many toxicity endpoints ( Benigni et al, 2013 ; Li et al, 2017a ; Limban et al, 2018 ; Kalgutkar, 2020 ; Cui et al, 2021 ; Huang et al, 2021 ; Shi et al, 2022 ) since Ashby and Tennant (1988) proposed the concept in 1985. The SAs can visually alert the toxicity of chemicals by displaying the key fragments responsible for drug toxicity because of the direct derivation from mechanistic knowledge.…”

Induced Pluripotent Stem Cells, a Stepping Stone to In Vitro Human Models of Hearing Loss

“…Important advancements are being reported in cell culture processes that are based on the use of robotics to achieve an automated reprogramming, maintenance and differentiation of hiPSCs, and that are contributing to the creation of large and reliable repositories of hiPSC lines [ 139 , 140 ]; equally important are the improvements in image analysis techniques and artificial intelligence technologies that enable the processing of large amounts of datasets, cell identification and the elucidation of a cell’s pathological state based on its morphology [ 141 , 142 , 143 , 144 ]. New computational prediction models are being generated, such as those described by Zhang et al (2020) [ 145 ] and Huang et al (2021) [ 146 ] to predict drug-induced ototoxicity, thus contributing to drug design optimization. In addition, new developments in the field of microfluidics will allow for the optimisation of hiPSC-based work in terms of number of cultures and tests that can be reliably processed at a given time while minimizing the amounts of reagents required, of special relevance when searching for promising drug leads [ 139 , 147 ].…”