Articles producció científica> Enginyeria Química

Pharmacophore Modeling Using Machine Learning for Screening the Blood–Brain Barrier Permeation of Xenobiotics

  • Datos identificativos

    Identificador: imarina:9285935
    Handle: https://hdl.handle.net/20.500.11797/imarina9285935
    Autores:
    Kumar SDeepika DKumar V
    Resumen:
    Daily exposure to xenobiotics affects human health, especially the nervous system, causing neurodegenerative diseases. The nervous system is protected by tight junctions present at the blood–brain barrier (BBB), but only molecules with desirable physicochemical properties can permeate it. This is why permeation is a decisive step in avoiding unwanted brain toxicity and also in developing neuronal drugs. In silico methods are being implemented as an initial step to reduce animal testing and the time complexity of the in vitro screening process. However, most in silico methods are ligand based, and consider only the physiochemical properties of ligands. However, these ligand-based methods have their own limitations and sometimes fail to predict the BBB permeation of xenobiotics. The objective of this work was to investigate the influence of the pharmacophoric features of protein–ligand interactions on BBB permeation. For these purposes, receptor-based pharmacophore and ligand-based pharmacophore fingerprints were developed using docking and Rdkit, respectively. Then, these fingerprints were trained on classical machine-learning models and compared with classical fingerprints. Among the tested footprints, the ligand-based pharmacophore fingerprint achieved slightly better (77% accuracy) performance compared to the classical fingerprint method. In contrast, receptor-based pharmacophores did not lead to much improvement compared to classical descriptors. The performance can be further improved by considering efflux proteins such as BCRP (breast cancer resistance protein), as well as P-gp (P-glycoprotein). However, the limited data availability for other proteins regarding their pharmacophoric interactions is a bottleneck to its improvement. Nonetheless, the developed models

  • Otros:

    Autor según el artículo: Kumar S; Deepika D; Kumar V
    Departamento: Enginyeria Química
    Autor/es de la URV: , Deepika / Kumar, Vikas
    Palabras clave: Pharmacophore P-glycoprotein Neurotoxicity Machine learning Graph neural network Cns permeability Blood–brain barrier Blood-brain barrier prediction pharmacophore p-glycoprotein neurotoxicity machine learning graph neural network drugs accuracy
    Resumen: Daily exposure to xenobiotics affects human health, especially the nervous system, causing neurodegenerative diseases. The nervous system is protected by tight junctions present at the blood–brain barrier (BBB), but only molecules with desirable physicochemical properties can permeate it. This is why permeation is a decisive step in avoiding unwanted brain toxicity and also in developing neuronal drugs. In silico methods are being implemented as an initial step to reduce animal testing and the time complexity of the in vitro screening process. However, most in silico methods are ligand based, and consider only the physiochemical properties of ligands. However, these ligand-based methods have their own limitations and sometimes fail to predict the BBB permeation of xenobiotics. The objective of this work was to investigate the influence of the pharmacophoric features of protein–ligand interactions on BBB permeation. For these purposes, receptor-based pharmacophore and ligand-based pharmacophore fingerprints were developed using docking and Rdkit, respectively. Then, these fingerprints were trained on classical machine-learning models and compared with classical fingerprints. Among the tested footprints, the ligand-based pharmacophore fingerprint achieved slightly better (77% accuracy) performance compared to the classical fingerprint method. In contrast, receptor-based pharmacophores did not lead to much improvement compared to classical descriptors. The performance can be further improved by considering efflux proteins such as BCRP (breast cancer resistance protein), as well as P-gp (P-glycoprotein). However, the limited data availability for other proteins regarding their pharmacophoric interactions is a bottleneck to its improvement. Nonetheless, the developed models and exploratory analysis provide a path to extend the same framework for environmental chemicals, which, like drugs, are also xenobiotics. This research can help in human health risk assessment by a priori screening for neurotoxicity-causing agents.
    Áreas temáticas: Zootecnia / recursos pesqueiros Serviço social Saúde coletiva Química Public, environmental & occupational health Public health, environmental and occupational health Psicología Pollution Odontología Nutrição Medicina iii Medicina ii Medicina i Materiais Interdisciplinar Health, toxicology and mutagenesis Geografía Geociências Farmacia Environmental studies Environmental sciences Ensino Engenharias ii Engenharias i Enfermagem Educação física Educação Ciências biológicas iii Ciências biológicas ii Ciências biológicas i Ciências ambientais Ciências agrárias i Ciência da computação Biotecnología Biodiversidade Astronomia / física Administração pública e de empresas, ciências contábeis e turismo
    Acceso a la licencia de uso: https://creativecommons.org/licenses/by/3.0/es/
    Direcció de correo del autor: vikas.kumar@urv.cat deepika@urv.cat deepika@urv.cat
    Identificador del autor: 0000-0002-9795-5967
    Fecha de alta del registro: 2024-09-07
    Versión del articulo depositado: info:eu-repo/semantics/publishedVersion
    Enlace a la fuente original: https://www.mdpi.com/1660-4601/19/20/13471
    URL Documento de licencia: https://repositori.urv.cat/ca/proteccio-de-dades/
    Referencia al articulo segun fuente origial: International Journal Of Environmental Research And Public Health. 19 (20):
    Referencia de l'ítem segons les normes APA: Kumar S; Deepika D; Kumar V (2022). Pharmacophore Modeling Using Machine Learning for Screening the Blood–Brain Barrier Permeation of Xenobiotics. International Journal Of Environmental Research And Public Health, 19(20), -. DOI: 10.3390/ijerph192013471
    DOI del artículo: 10.3390/ijerph192013471
    Entidad: Universitat Rovira i Virgili
    Año de publicación de la revista: 2022
    Tipo de publicación: Journal Publications

  • Palabras clave:

    Environmental Sciences,Health, Toxicology and Mutagenesis,Pollution,Public Health, Environmental and Occupational Health,Public, Environmental & Occupational Health
    Pharmacophore
    P-glycoprotein
    Neurotoxicity
    Machine learning
    Graph neural network
    Cns permeability
    Blood–brain barrier
    Blood-brain barrier
    prediction
    pharmacophore
    p-glycoprotein
    neurotoxicity
    machine learning
    graph neural network
    drugs
    accuracy
    Zootecnia / recursos pesqueiros
    Serviço social
    Saúde coletiva
    Química
    Public, environmental & occupational health
    Public health, environmental and occupational health
    Psicología
    Pollution
    Odontología
    Nutrição
    Medicina iii
    Medicina ii
    Medicina i
    Materiais
    Interdisciplinar
    Health, toxicology and mutagenesis
    Geografía
    Geociências
    Farmacia
    Environmental studies
    Environmental sciences
    Ensino
    Engenharias ii
    Engenharias i
    Enfermagem
    Educação física
    Educação
    Ciências biológicas iii
    Ciências biológicas ii
    Ciências biológicas i
    Ciências ambientais
    Ciências agrárias i
    Ciência da computação
    Biotecnología
    Biodiversidade
    Astronomia / física
    Administração pública e de empresas, ciências contábeis e turismo

  • Documentos:

    DocumentPrincipal

  • Cerca a google

    Search to google scholar