Prediction of Antileishmanial Compounds: General Model, Preparation, and Evaluation of 2‑Acylpyrrole Derivatives

In this work, the SOFT.PTML tool has been used to pre-process a ChEMBL dataset of pre-clinical assays of antileishmanial compound candidates. A comparative study of different ML algorithms, such as logistic regression (LOGR), support vector machine (SVM), and random forests (RF), has shown that the...

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Autores: Santiago Alvarez, Carlos, Ortega-Tenezaca, Bernabé, Barbolla Cuadrado, Iratxe, Fundora Ortiz, Brenda, Arrasate Gil, Sonia, Dea-Ayuela, María Auxiliadora, González Díaz, Humberto, Sotomayor Anduiza, María Nuria
Tipo de recurso: artículo
Fecha de publicación:2022
País:España
Institución:Universidad del País Vasco
Repositorio:Addi. Archivo Digital para la Docencia y la Investigación
OAI Identifier:oai:addi.ehu.eus:10810/64181
Acceso en línea:http://hdl.handle.net/10810/64181
Access Level:acceso abierto
Palabra clave:palladium
C-H activation
leishmania
machine learning
cheminformatics
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spelling Prediction of Antileishmanial Compounds: General Model, Preparation, and Evaluation of 2‑Acylpyrrole DerivativesSantiago Alvarez, CarlosOrtega-Tenezaca, BernabéBarbolla Cuadrado, IratxeFundora Ortiz, BrendaArrasate Gil, SoniaDea-Ayuela, María AuxiliadoraGonzález Díaz, HumbertoSotomayor Anduiza, María NuriapalladiumC-H activationleishmaniamachine learningcheminformaticsIn this work, the SOFT.PTML tool has been used to pre-process a ChEMBL dataset of pre-clinical assays of antileishmanial compound candidates. A comparative study of different ML algorithms, such as logistic regression (LOGR), support vector machine (SVM), and random forests (RF), has shown that the IFPTML-LOGR model presents excellent values of specificity and sensitivity (81−98%) in training and validation series. The use of this software has been illustrated with a practical case study focused on a series of 28 derivatives of 2-acylpyrroles 5a,b, obtained through a Pd(II)-catalyzed C−H radical acylation of pyrroles. Their in vitro leishmanicidal activity against visceral (L. donovani) and cutaneous (L. amazonensis) leishmaniasis was evaluated finding that compounds 5bc (IC50 = 30.87 μM, SI > 10.17) and 5bd (IC50 = 16.87 μM, SI > 10.67) were approximately 6-fold more selective than the drug of reference (miltefosine) in in vitro assays against L. amazonensis promastigotes. In addition, most of the compounds showed low cytotoxicity, CC50 > 100 μg/ mL in J774 cells. Interestingly, the IFPMTL-LOGR model predicts correctly the relative biological activity of these series of acylpyrroles. A computational high-throughput screening (cHTS) study of 2-acylpyrroles 5a,b has been performed calculating >20,700 activity scores vs a large space of 647 assays involving multiple Leishmania species, cell lines, and potential target proteins. Overall, the study demonstrates that the SOFT.PTML all-in-one strategy is useful to obtain IFPTML models in a friendly interface making the work easier and faster than before. The present work also points to 2-acylpyrroles as new lead compounds worthy of further optimization as antileishmanial hits.Ministerio de Ciencia e Innovación (PID2019-104148GB-I00), Gobierno Vasco (IT1558-22)ACS202420242022info:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10810/64181reponame:Addi. Archivo Digital para la Docencia y la Investigacióninstname:Universidad del País VascoInglésinfo:eu-repo/grantAgreement/MICINN/PID2019-104148GB-I00/https://doi.org/10.1021/acs.jcim.2c00731info:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by-nc-nd/3.0/es/© 2022 American Chemical Society. This publication is licensed under CC-BY 4.0.oai:addi.ehu.eus:10810/641812026-06-18T09:23:17Z
dc.title.none.fl_str_mv Prediction of Antileishmanial Compounds: General Model, Preparation, and Evaluation of 2‑Acylpyrrole Derivatives
title Prediction of Antileishmanial Compounds: General Model, Preparation, and Evaluation of 2‑Acylpyrrole Derivatives
spellingShingle Prediction of Antileishmanial Compounds: General Model, Preparation, and Evaluation of 2‑Acylpyrrole Derivatives
Santiago Alvarez, Carlos
palladium
C-H activation
leishmania
machine learning
cheminformatics
title_short Prediction of Antileishmanial Compounds: General Model, Preparation, and Evaluation of 2‑Acylpyrrole Derivatives
title_full Prediction of Antileishmanial Compounds: General Model, Preparation, and Evaluation of 2‑Acylpyrrole Derivatives
title_fullStr Prediction of Antileishmanial Compounds: General Model, Preparation, and Evaluation of 2‑Acylpyrrole Derivatives
title_full_unstemmed Prediction of Antileishmanial Compounds: General Model, Preparation, and Evaluation of 2‑Acylpyrrole Derivatives
title_sort Prediction of Antileishmanial Compounds: General Model, Preparation, and Evaluation of 2‑Acylpyrrole Derivatives
dc.creator.none.fl_str_mv Santiago Alvarez, Carlos
Ortega-Tenezaca, Bernabé
Barbolla Cuadrado, Iratxe
Fundora Ortiz, Brenda
Arrasate Gil, Sonia
Dea-Ayuela, María Auxiliadora
González Díaz, Humberto
Sotomayor Anduiza, María Nuria
author Santiago Alvarez, Carlos
author_facet Santiago Alvarez, Carlos
Ortega-Tenezaca, Bernabé
Barbolla Cuadrado, Iratxe
Fundora Ortiz, Brenda
Arrasate Gil, Sonia
Dea-Ayuela, María Auxiliadora
González Díaz, Humberto
Sotomayor Anduiza, María Nuria
author_role author
author2 Ortega-Tenezaca, Bernabé
Barbolla Cuadrado, Iratxe
Fundora Ortiz, Brenda
Arrasate Gil, Sonia
Dea-Ayuela, María Auxiliadora
González Díaz, Humberto
Sotomayor Anduiza, María Nuria
author2_role author
author
author
author
author
author
author
dc.subject.none.fl_str_mv palladium
C-H activation
leishmania
machine learning
cheminformatics
topic palladium
C-H activation
leishmania
machine learning
cheminformatics
description In this work, the SOFT.PTML tool has been used to pre-process a ChEMBL dataset of pre-clinical assays of antileishmanial compound candidates. A comparative study of different ML algorithms, such as logistic regression (LOGR), support vector machine (SVM), and random forests (RF), has shown that the IFPTML-LOGR model presents excellent values of specificity and sensitivity (81−98%) in training and validation series. The use of this software has been illustrated with a practical case study focused on a series of 28 derivatives of 2-acylpyrroles 5a,b, obtained through a Pd(II)-catalyzed C−H radical acylation of pyrroles. Their in vitro leishmanicidal activity against visceral (L. donovani) and cutaneous (L. amazonensis) leishmaniasis was evaluated finding that compounds 5bc (IC50 = 30.87 μM, SI > 10.17) and 5bd (IC50 = 16.87 μM, SI > 10.67) were approximately 6-fold more selective than the drug of reference (miltefosine) in in vitro assays against L. amazonensis promastigotes. In addition, most of the compounds showed low cytotoxicity, CC50 > 100 μg/ mL in J774 cells. Interestingly, the IFPMTL-LOGR model predicts correctly the relative biological activity of these series of acylpyrroles. A computational high-throughput screening (cHTS) study of 2-acylpyrroles 5a,b has been performed calculating >20,700 activity scores vs a large space of 647 assays involving multiple Leishmania species, cell lines, and potential target proteins. Overall, the study demonstrates that the SOFT.PTML all-in-one strategy is useful to obtain IFPTML models in a friendly interface making the work easier and faster than before. The present work also points to 2-acylpyrroles as new lead compounds worthy of further optimization as antileishmanial hits.
publishDate 2022
dc.date.none.fl_str_mv 2022
2024
2024
dc.type.none.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv http://hdl.handle.net/10810/64181
url http://hdl.handle.net/10810/64181
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv info:eu-repo/grantAgreement/MICINN/PID2019-104148GB-I00/
https://doi.org/10.1021/acs.jcim.2c00731
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
http://creativecommons.org/licenses/by-nc-nd/3.0/es/
© 2022 American Chemical Society. This publication is licensed under CC-BY 4.0.
eu_rights_str_mv openAccess
rights_invalid_str_mv http://creativecommons.org/licenses/by-nc-nd/3.0/es/
© 2022 American Chemical Society. This publication is licensed under CC-BY 4.0.
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv ACS
publisher.none.fl_str_mv ACS
dc.source.none.fl_str_mv reponame:Addi. Archivo Digital para la Docencia y la Investigación
instname:Universidad del País Vasco
instname_str Universidad del País Vasco
reponame_str Addi. Archivo Digital para la Docencia y la Investigación
collection Addi. Archivo Digital para la Docencia y la Investigación
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15.300724

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