Integrating mechanistic and toxicokinetic information in predictive models of cholestasis
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- dc.contributor.author Rodríguez-Belenguer, Pablo
- dc.contributor.author Mangas Sanjuan, Victor
- dc.contributor.author Soria Olivas, Emilio
- dc.contributor.author Pastor Maeso, Manuel
- dc.date.accessioned 2023-10-16T06:59:44Z
- dc.date.available 2023-10-16T06:59:44Z
- dc.date.issued 2024
- dc.description.abstract Drug development involves the thorough assessment of the candidate's safety and efficacy. In silico toxicology (IST) methods can contribute to the assessment, complementing in vitro and in vivo experimental methods, since they have many advantages in terms of cost and time. Also, they are less demanding concerning the requirements of product and experimental animals. One of these methods, Quantitative Structure-Activity Relationships (QSAR), has been proven successful in predicting simple toxicity end points but has more difficulties in predicting end points involving more complex phenomena. We hypothesize that QSAR models can produce better predictions of these end points by combining multiple QSAR models describing simpler biological phenomena and incorporating pharmacokinetic (PK) information, using quantitative in vitro to in vivo extrapolation (QIVIVE) models. In this study, we applied our methodology to the prediction of cholestasis and compared it with direct QSAR models. Our results show a clear increase in sensitivity. The predictive quality of the models was further assessed to mimic realistic conditions where the query compounds show low similarity with the training series. Again, our methodology shows clear advantages over direct QSAR models in these situations. We conclude that the proposed methodology could improve existing methodologies and could be suitable for being applied to other toxicity end points.
- dc.description.sponsorship The authors received funding from the eTRANSAFE project, Innovative Medicines Initiative 2 Joint Undertaking under grant agreement No 777365, European Union’s Horizon 2020, and EFPIA. The authors declare that this work reflects only the author’s view and that IMI-JU is not responsible for any use that may be made of the information it contains. Also, this project received funding from the European Union’s Horizon 2020 Research and Innovation Programme under Grant Agreement No. 964537 (RISK-HUNT3R), which is part of the ASPIS cluster.
- dc.format.mimetype application/pdf
- dc.identifier.citation Rodríguez-Belenguer P, Mangas-Sanjuan V, Soria-Olivas E, Pastor M. Integrating mechanistic and toxicokinetic information in predictive models of cholestasis. J Chem Inf Model. 2024 Apr 8;64(7):2775-88. DOI: 10.1021/acs.jcim.3c00945
- dc.identifier.doi http://dx.doi.org/10.1021/acs.jcim.3c00945
- dc.identifier.issn 1549-9596
- dc.identifier.uri http://hdl.handle.net/10230/58072
- dc.language.iso eng
- dc.publisher American Chemical Society (ACS)
- dc.relation.ispartof J Chem Inf Model. 2024 Apr 8;64(7):2775-88
- dc.relation.projectID info:eu-repo/grantAgreement/EC/H2020/777365
- dc.relation.projectID info:eu-repo/grantAgreement/EC/H2020/964537
- dc.rights © 2022 The Authors. Published by American Chemical Society. This publication is licensed under CC-BY 4.0.
- dc.rights.accessRights info:eu-repo/semantics/openAccess
- dc.rights.uri http://creativecommons.org/licenses/by/4.0/
- dc.subject.keyword Bioinformatics and computational biology
- dc.subject.keyword Peptides and proteins
- dc.subject.keyword Quality management
- dc.subject.keyword Structure activity relationship
- dc.subject.keyword Therapeutics
- dc.title Integrating mechanistic and toxicokinetic information in predictive models of cholestasis
- dc.type info:eu-repo/semantics/article
- dc.type.version info:eu-repo/semantics/publishedVersion