Application of machine learning to predict unbound drug bioavailability in the brain

Autores
Morales, Juan Francisco; Ruiz, María Esperanza; Stratford, Robert E.; Talevi, Alan
Año de publicación
2024
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
Purpose: Optimizing brain bioavailability is highly relevant for the development of drugs targeting the central nervous system. Several pharmacokinetic parameters have been used for measuring drug bioavailability in the brain. The most biorelevant among them is possibly the unbound brain-to-plasma partition coefficient, Kpuu,brain,ss, which relates unbound brain and plasma drug concentrations under steady-state conditions. In this study, we developed new in silico models to predict Kpuu,brain,ss.Methods: A manually curated 157-compound dataset was compiled from literature and split into training and test sets using a clustering approach. Additional models were trained with a refined dataset generated by removing known P-gp and/or Breast Cancer Resistance Protein substrates from the original dataset. Different supervised machine learning algorithms have been tested, including Support Vector Machine, Gradient Boosting Machine, k-nearest neighbors, classificatory Partial Least Squares, Random Forest, Extreme Gradient Boosting, Deep Learning and Linear Discriminant Analysis. Good practices of predictive Quantitative Structure-Activity Relationships modeling were followed for the development of the models.Results: The best performance in the complete dataset was achieved by extreme gradient boosting, with an accuracy in the test set of 85.1%. A similar estimation of accuracy was observed in a prospective validation experiment, using a small sample of compounds and comparing predicted unbound brain bioavailability with observed experimental data.Conclusion: New in silico models were developed to predict the Kpuu,brain,ss of drug candidates. The dataset used in this study is publicly disclosed, so that the models may be reproduced, refined, or expanded, as a useful tool to assist drug discovery processes.
Fil: Morales, Juan Francisco. Universidad Nacional de La Plata. Facultad de Ciencas Exactas. Laboratorio de Investigación y Desarrollo de Bioactivos; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata; Argentina
Fil: Ruiz, María Esperanza. Universidad Nacional de La Plata. Facultad de Ciencas Exactas. Laboratorio de Investigación y Desarrollo de Bioactivos; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata; Argentina
Fil: Stratford, Robert E.. Indiana University. School Of Medicine.; Estados Unidos
Fil: Talevi, Alan. Universidad Nacional de La Plata. Facultad de Ciencas Exactas. Laboratorio de Investigación y Desarrollo de Bioactivos; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata; Argentina
Materia
ADME PROPERTIES
BLOOD-BRAIN BARRIER
BRAIN BIOAVAILABILITY
CENTRAL NERVOUS SYSTEM
MACHINE LEARNING
PHARMACOKINETIC MODELING
ARTIFICIAL INTELLIGENCE
UNBOUND PARTITION COEFFICIENT
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/234146
Acceder
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oai_identifier_str oai:ri.conicet.gov.ar:11336/234146
network_acronym_str CONICETDig
repository_id_str 3498
network_name_str CONICET Digital (CONICET)
spelling Application of machine learning to predict unbound drug bioavailability in the brainMorales, Juan FranciscoRuiz, María EsperanzaStratford, Robert E.Talevi, AlanADME PROPERTIESBLOOD-BRAIN BARRIERBRAIN BIOAVAILABILITYCENTRAL NERVOUS SYSTEMMACHINE LEARNINGPHARMACOKINETIC MODELINGARTIFICIAL INTELLIGENCEUNBOUND PARTITION COEFFICIENThttps://purl.org/becyt/ford/1.4https://purl.org/becyt/ford/1Purpose: Optimizing brain bioavailability is highly relevant for the development of drugs targeting the central nervous system. Several pharmacokinetic parameters have been used for measuring drug bioavailability in the brain. The most biorelevant among them is possibly the unbound brain-to-plasma partition coefficient, Kpuu,brain,ss, which relates unbound brain and plasma drug concentrations under steady-state conditions. In this study, we developed new in silico models to predict Kpuu,brain,ss.Methods: A manually curated 157-compound dataset was compiled from literature and split into training and test sets using a clustering approach. Additional models were trained with a refined dataset generated by removing known P-gp and/or Breast Cancer Resistance Protein substrates from the original dataset. Different supervised machine learning algorithms have been tested, including Support Vector Machine, Gradient Boosting Machine, k-nearest neighbors, classificatory Partial Least Squares, Random Forest, Extreme Gradient Boosting, Deep Learning and Linear Discriminant Analysis. Good practices of predictive Quantitative Structure-Activity Relationships modeling were followed for the development of the models.Results: The best performance in the complete dataset was achieved by extreme gradient boosting, with an accuracy in the test set of 85.1%. A similar estimation of accuracy was observed in a prospective validation experiment, using a small sample of compounds and comparing predicted unbound brain bioavailability with observed experimental data.Conclusion: New in silico models were developed to predict the Kpuu,brain,ss of drug candidates. The dataset used in this study is publicly disclosed, so that the models may be reproduced, refined, or expanded, as a useful tool to assist drug discovery processes.Fil: Morales, Juan Francisco. Universidad Nacional de La Plata. Facultad de Ciencas Exactas. Laboratorio de Investigación y Desarrollo de Bioactivos; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata; ArgentinaFil: Ruiz, María Esperanza. Universidad Nacional de La Plata. Facultad de Ciencas Exactas. Laboratorio de Investigación y Desarrollo de Bioactivos; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata; ArgentinaFil: Stratford, Robert E.. Indiana University. School Of Medicine.; Estados UnidosFil: Talevi, Alan. Universidad Nacional de La Plata. Facultad de Ciencas Exactas. Laboratorio de Investigación y Desarrollo de Bioactivos; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata; ArgentinaFrontiers Media2024-04info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/234146Morales, Juan Francisco; Ruiz, María Esperanza; Stratford, Robert E.; Talevi, Alan; Application of machine learning to predict unbound drug bioavailability in the brain; Frontiers Media; Frontiers in Drug Discovery; 4; 4-2024; 1-142674-0338CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/url/https://www.frontiersin.org/articles/10.3389/fddsv.2024.1360732/fullinfo:eu-repo/semantics/altIdentifier/doi/10.3389/fddsv.2024.1360732info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2026-01-08T12:59:14Zoai:ri.conicet.gov.ar:11336/234146instacron:CONICETInstitucionalhttp://ri.conicet.gov.ar/Organismo científico-tecnológicoNo correspondehttp://ri.conicet.gov.ar/oai/requestdasensio@conicet.gov.ar; lcarlino@conicet.gov.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:34982026-01-08 12:59:15.164CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Application of machine learning to predict unbound drug bioavailability in the brain
title Application of machine learning to predict unbound drug bioavailability in the brain
spellingShingle Application of machine learning to predict unbound drug bioavailability in the brain
Morales, Juan Francisco
ADME PROPERTIES
BLOOD-BRAIN BARRIER
BRAIN BIOAVAILABILITY
CENTRAL NERVOUS SYSTEM
MACHINE LEARNING
PHARMACOKINETIC MODELING
ARTIFICIAL INTELLIGENCE
UNBOUND PARTITION COEFFICIENT
title_short Application of machine learning to predict unbound drug bioavailability in the brain
title_full Application of machine learning to predict unbound drug bioavailability in the brain
title_fullStr Application of machine learning to predict unbound drug bioavailability in the brain
title_full_unstemmed Application of machine learning to predict unbound drug bioavailability in the brain
title_sort Application of machine learning to predict unbound drug bioavailability in the brain
dc.creator.none.fl_str_mv Morales, Juan Francisco
Ruiz, María Esperanza
Stratford, Robert E.
Talevi, Alan
author Morales, Juan Francisco
author_facet Morales, Juan Francisco
Ruiz, María Esperanza
Stratford, Robert E.
Talevi, Alan
author_role author
author2 Ruiz, María Esperanza
Stratford, Robert E.
Talevi, Alan
author2_role author
author
author
dc.subject.none.fl_str_mv ADME PROPERTIES
BLOOD-BRAIN BARRIER
BRAIN BIOAVAILABILITY
CENTRAL NERVOUS SYSTEM
MACHINE LEARNING
PHARMACOKINETIC MODELING
ARTIFICIAL INTELLIGENCE
UNBOUND PARTITION COEFFICIENT
topic ADME PROPERTIES
BLOOD-BRAIN BARRIER
BRAIN BIOAVAILABILITY
CENTRAL NERVOUS SYSTEM
MACHINE LEARNING
PHARMACOKINETIC MODELING
ARTIFICIAL INTELLIGENCE
UNBOUND PARTITION COEFFICIENT
purl_subject.fl_str_mv https://purl.org/becyt/ford/1.4
https://purl.org/becyt/ford/1
dc.description.none.fl_txt_mv Purpose: Optimizing brain bioavailability is highly relevant for the development of drugs targeting the central nervous system. Several pharmacokinetic parameters have been used for measuring drug bioavailability in the brain. The most biorelevant among them is possibly the unbound brain-to-plasma partition coefficient, Kpuu,brain,ss, which relates unbound brain and plasma drug concentrations under steady-state conditions. In this study, we developed new in silico models to predict Kpuu,brain,ss.Methods: A manually curated 157-compound dataset was compiled from literature and split into training and test sets using a clustering approach. Additional models were trained with a refined dataset generated by removing known P-gp and/or Breast Cancer Resistance Protein substrates from the original dataset. Different supervised machine learning algorithms have been tested, including Support Vector Machine, Gradient Boosting Machine, k-nearest neighbors, classificatory Partial Least Squares, Random Forest, Extreme Gradient Boosting, Deep Learning and Linear Discriminant Analysis. Good practices of predictive Quantitative Structure-Activity Relationships modeling were followed for the development of the models.Results: The best performance in the complete dataset was achieved by extreme gradient boosting, with an accuracy in the test set of 85.1%. A similar estimation of accuracy was observed in a prospective validation experiment, using a small sample of compounds and comparing predicted unbound brain bioavailability with observed experimental data.Conclusion: New in silico models were developed to predict the Kpuu,brain,ss of drug candidates. The dataset used in this study is publicly disclosed, so that the models may be reproduced, refined, or expanded, as a useful tool to assist drug discovery processes.
Fil: Morales, Juan Francisco. Universidad Nacional de La Plata. Facultad de Ciencas Exactas. Laboratorio de Investigación y Desarrollo de Bioactivos; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata; Argentina
Fil: Ruiz, María Esperanza. Universidad Nacional de La Plata. Facultad de Ciencas Exactas. Laboratorio de Investigación y Desarrollo de Bioactivos; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata; Argentina
Fil: Stratford, Robert E.. Indiana University. School Of Medicine.; Estados Unidos
Fil: Talevi, Alan. Universidad Nacional de La Plata. Facultad de Ciencas Exactas. Laboratorio de Investigación y Desarrollo de Bioactivos; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata; Argentina
description Purpose: Optimizing brain bioavailability is highly relevant for the development of drugs targeting the central nervous system. Several pharmacokinetic parameters have been used for measuring drug bioavailability in the brain. The most biorelevant among them is possibly the unbound brain-to-plasma partition coefficient, Kpuu,brain,ss, which relates unbound brain and plasma drug concentrations under steady-state conditions. In this study, we developed new in silico models to predict Kpuu,brain,ss.Methods: A manually curated 157-compound dataset was compiled from literature and split into training and test sets using a clustering approach. Additional models were trained with a refined dataset generated by removing known P-gp and/or Breast Cancer Resistance Protein substrates from the original dataset. Different supervised machine learning algorithms have been tested, including Support Vector Machine, Gradient Boosting Machine, k-nearest neighbors, classificatory Partial Least Squares, Random Forest, Extreme Gradient Boosting, Deep Learning and Linear Discriminant Analysis. Good practices of predictive Quantitative Structure-Activity Relationships modeling were followed for the development of the models.Results: The best performance in the complete dataset was achieved by extreme gradient boosting, with an accuracy in the test set of 85.1%. A similar estimation of accuracy was observed in a prospective validation experiment, using a small sample of compounds and comparing predicted unbound brain bioavailability with observed experimental data.Conclusion: New in silico models were developed to predict the Kpuu,brain,ss of drug candidates. The dataset used in this study is publicly disclosed, so that the models may be reproduced, refined, or expanded, as a useful tool to assist drug discovery processes.
publishDate 2024
dc.date.none.fl_str_mv 2024-04
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
http://purl.org/coar/resource_type/c_6501
info:ar-repo/semantics/articulo
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/11336/234146
Morales, Juan Francisco; Ruiz, María Esperanza; Stratford, Robert E.; Talevi, Alan; Application of machine learning to predict unbound drug bioavailability in the brain; Frontiers Media; Frontiers in Drug Discovery; 4; 4-2024; 1-14
2674-0338
CONICET Digital
CONICET
url http://hdl.handle.net/11336/234146
identifier_str_mv Morales, Juan Francisco; Ruiz, María Esperanza; Stratford, Robert E.; Talevi, Alan; Application of machine learning to predict unbound drug bioavailability in the brain; Frontiers Media; Frontiers in Drug Discovery; 4; 4-2024; 1-14
2674-0338
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/url/https://www.frontiersin.org/articles/10.3389/fddsv.2024.1360732/full
info:eu-repo/semantics/altIdentifier/doi/10.3389/fddsv.2024.1360732
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
application/pdf
dc.publisher.none.fl_str_mv Frontiers Media
publisher.none.fl_str_mv Frontiers Media
dc.source.none.fl_str_mv reponame:CONICET Digital (CONICET)
instname:Consejo Nacional de Investigaciones Científicas y Técnicas
reponame_str CONICET Digital (CONICET)
collection CONICET Digital (CONICET)
instname_str Consejo Nacional de Investigaciones Científicas y Técnicas
repository.name.fl_str_mv CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicas
repository.mail.fl_str_mv dasensio@conicet.gov.ar; lcarlino@conicet.gov.ar
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score 12.747614

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