New QSAR models to predict chromosome damaging potential based on the in vivo micronucleus test

Bossuyt, Melissa Van; Raitano, Giuseppa; Honma, Masamitsu; Hoeck, Els Van; Vanhaecke, Tamara; Rogiers, Vera; Mertens, Birgit; Benfenati, Emilio

doi:10.1016/j.toxlet.2020.04.016

Cited by 8 publications

(6 citation statements)

References 16 publications

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“…Conversely to MN in vitro, a number of in vivo prediction models exist [ 40 , 41 , 58 , 59 ]. Using commercial CASE Ultra software for MN in vivo prediction, Morita et al [ 25 ] on the external dataset of 337 chemicals reported Acc, SE and SP of 0.72, 0.91 and 0.57.…”

Section: Resultsmentioning

confidence: 99%

Datasets Construction and Development of QSAR Models for Predicting Micronucleus In Vitro and In Vivo Assay Outcomes

Khondkaryan,

Tevosyan,

Navasardyan

et al. 2023

Toxics

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In silico (quantitative) structure–activity relationship modeling is an approach that provides a fast and cost-effective alternative to assess the genotoxic potential of chemicals. However, one of the limiting factors for model development is the availability of consolidated experimental datasets. In the present study, we collected experimental data on micronuclei in vitro and in vivo, utilizing databases and conducting a PubMed search, aided by text mining using the BioBERT large language model. Chemotype enrichment analysis on the updated datasets was performed to identify enriched substructures. Additionally, chemotypes common for both endpoints were found. Five machine learning models in combination with molecular descriptors, twelve fingerprints and two data balancing techniques were applied to construct individual models. The best-performing individual models were selected for the ensemble construction. The curated final dataset consists of 981 chemicals for micronuclei in vitro and 1309 for mouse micronuclei in vivo, respectively. Out of 18 chemotypes enriched in micronuclei in vitro, only 7 were found to be relevant for in vivo prediction. The ensemble model exhibited high accuracy and sensitivity when applied to an external test set of in vitro data. A good balanced predictive performance was also achieved for the micronucleus in vivo endpoint.

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Section: Resultsmentioning

confidence: 99%

Datasets Construction and Development of QSAR Models for Predicting Micronucleus In Vitro and In Vivo Assay Outcomes

Khondkaryan,

Tevosyan,

Navasardyan

et al. 2023

Toxics

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“…SARpy is a python based standalone software programme for automated QSAR model development 41 , 42 , 43 , 44 . The software uses user-defined SMILEs notations for generating substructures in the set and tries to correlate between the particular molecular structures and their biological activity using three different steps.…”

Section: Methodsmentioning

confidence: 99%

Unmasking of crucial structural fragments for coronavirus protease inhibitors and its implications in COVID-19 drug discovery

Ghosh

Amin

Gayen

et al. 2021

Journal of Molecular Structure

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Fragment based drug discovery (FBDD) by the aid of different modelling techniques have been emerged as a key drug discovery tool in the area of pharmaceutical science and technology. The merits of employing these methods, in place of other conventional molecular modelling techniques, endorsed clear detection of the possible structural fragments present in diverse set of investigated compounds and can create alternate possibilities of lead optimization in drug discovery. In this work, two fragment identification tools namely SARpy and Laplacian-corrected Bayesian analysis were used for previous SARS-CoV PLpro and 3CLpro protease inhibitors. A robust and predictive SARpy based fragments identification was performed which have been validated further by Laplacian-corrected Bayesian model. These comprehensive approaches have advantages since fragments are straight forward to interpret. Moreover, distinguishing the key molecular features (with respect to ECFP_6 fingerprint) revealed good or bad influences for the SARS-CoV protease inhibitory activities. Furthermore, the identified fragments could be implemented in the medicinal chemistry endeavors of COVID-19 drug discovery.

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“…Nfragment total indicates the number of compounds containing the fragments in their structure [11]. The following settings were fixed to identify the toxic substructures [4]:…”

Section: Analysis Of Structural Alertsmentioning

confidence: 99%

“…In general, animal assays are regarded as the fate of chemicals and are precise than in vitro studies. For instance, in vivo assays are unethical, expensive, and time-consuming [4]. Moreover, some chemicals, including cosmetic constituents, are already banned for animal testing [5].…”

Section: Introductionmentioning

confidence: 99%

“…Hence developing a successful QSAR model with high accuracy has become challenging. For instance, the QSAR model developed by Bossuyt et al using 718 micronucleus assay data from five different resources achieved the highest accuracy of about 0.68 for the test set [4]. Thus, incorporating machine learning techniques to replace this method, as proposed by Lavecchia et al, should overcome such limits in forecasting the toxicity of chemicals [9].…”

Section: Introductionmentioning

confidence: 99%

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Prediction of Micronucleus Assay Outcome Using In Vivo Activity Data and Molecular Structure Features

Ramesh

Shanthi

2021

Appl Biochem Biotechnol

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In vivo micronucleus assay is the widely used genotoxic test to determine the extent of chromosomal aberrations caused by the chemical compounds in human beings, which plays a significant role in the drug discovery paradigm. To reduce the uncertainties of the in vivo experiments and the expenses, we intended to develop novel machine learning-based tools to predict the toxicity of the compounds with high precision. A total of 472 compounds with known toxicity information were retrieved from the PubChem Bioassay database and literature. The fingerprints and descriptors of the compounds were generated using PaDEL and ChemSAR for the analysis. The performance of the models was assessed using three tires of evaluation strategies such as 5-fold, 10-fold, and external validation. The accuracy of the models during external validation lay between 0.57 and 0.86. Note that a combination of fingerprints and random forest showed reliable predictive capability. In essence, structural alerts causing genotoxicity of the compounds were identified using the structural activity relationship model of SARpy tool. This study highlights that the structural alerts such as chlorocyclohexane and trimethylamine are likely to be the leading cause of toxicity in humans, further validated using the Toxtree application.Indeed, the results from our study will assist in scrutinizing the genotoxicity of the compounds with high precision by replacing extensive sacrifice of animal models.

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New QSAR models to predict chromosome damaging potential based on the in vivo micronucleus test

Cited by 8 publications

References 16 publications

Datasets Construction and Development of QSAR Models for Predicting Micronucleus In Vitro and In Vivo Assay Outcomes

Datasets Construction and Development of QSAR Models for Predicting Micronucleus In Vitro and In Vivo Assay Outcomes

Unmasking of crucial structural fragments for coronavirus protease inhibitors and its implications in COVID-19 drug discovery

Prediction of Micronucleus Assay Outcome Using In Vivo Activity Data and Molecular Structure Features

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