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Digital Research Environment(DRE)-enabled Artificial Intelligence (AI) to facilitate early stage drug development

Early-stage drug discovery is highly dependent upon drug target evaluation, understanding of disease progression and identification of patient characteristics linked to disease progression overlaid upon chemical libraries of potential drug candidates. Artificial intelligence (AI) has become a credib...

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Autores principales: Barrett, Jeffrey S., Oskoui, Solmaz Eradat, Russell, Scott, Borens, Amanda
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10079992/
https://www.ncbi.nlm.nih.gov/pubmed/37033647
http://dx.doi.org/10.3389/fphar.2023.1115356
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author Barrett, Jeffrey S.
Oskoui, Solmaz Eradat
Russell, Scott
Borens, Amanda
author_facet Barrett, Jeffrey S.
Oskoui, Solmaz Eradat
Russell, Scott
Borens, Amanda
author_sort Barrett, Jeffrey S.
collection PubMed
description Early-stage drug discovery is highly dependent upon drug target evaluation, understanding of disease progression and identification of patient characteristics linked to disease progression overlaid upon chemical libraries of potential drug candidates. Artificial intelligence (AI) has become a credible approach towards dealing with the diversity and volume of data in the modern drug development phase. There are a growing number of services and solutions available to pharmaceutical sponsors though most prefer to constrain their own data to closed solutions given the intellectual property considerations. Newer platforms offer an alternative, outsourced solution leveraging sponsors data with other, external open-source data to anchor predictions (often proprietary algorithms) which are refined given data indexed upon the sponsor’s own chemical libraries. Digital research environments (DREs) provide a mechanism to ingest, curate, integrate and otherwise manage the diverse data types relevant for drug discovery activities and also provide workspace services from which target sharing and collaboration can occur providing yet another alternative with sponsors being in control of the platform, data and predictive algorithms. Regulatory engagement will be essential in the operationalizing of the various solutions and alternatives; current treatment of drug discovery data may not be adequate with respect to both quality and useability in the future. More sophisticated AI/ML algorithms are likely based on current performance metrics and diverse data types (e.g., imaging and genomic data) will certainly be a more consistent part of the myriad of data types that fuel future AI-based algorithms. This favors a dynamic DRE-enabled environment to support drug discovery.
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spelling pubmed-100799922023-04-08 Digital Research Environment(DRE)-enabled Artificial Intelligence (AI) to facilitate early stage drug development Barrett, Jeffrey S. Oskoui, Solmaz Eradat Russell, Scott Borens, Amanda Front Pharmacol Pharmacology Early-stage drug discovery is highly dependent upon drug target evaluation, understanding of disease progression and identification of patient characteristics linked to disease progression overlaid upon chemical libraries of potential drug candidates. Artificial intelligence (AI) has become a credible approach towards dealing with the diversity and volume of data in the modern drug development phase. There are a growing number of services and solutions available to pharmaceutical sponsors though most prefer to constrain their own data to closed solutions given the intellectual property considerations. Newer platforms offer an alternative, outsourced solution leveraging sponsors data with other, external open-source data to anchor predictions (often proprietary algorithms) which are refined given data indexed upon the sponsor’s own chemical libraries. Digital research environments (DREs) provide a mechanism to ingest, curate, integrate and otherwise manage the diverse data types relevant for drug discovery activities and also provide workspace services from which target sharing and collaboration can occur providing yet another alternative with sponsors being in control of the platform, data and predictive algorithms. Regulatory engagement will be essential in the operationalizing of the various solutions and alternatives; current treatment of drug discovery data may not be adequate with respect to both quality and useability in the future. More sophisticated AI/ML algorithms are likely based on current performance metrics and diverse data types (e.g., imaging and genomic data) will certainly be a more consistent part of the myriad of data types that fuel future AI-based algorithms. This favors a dynamic DRE-enabled environment to support drug discovery. Frontiers Media S.A. 2023-03-24 /pmc/articles/PMC10079992/ /pubmed/37033647 http://dx.doi.org/10.3389/fphar.2023.1115356 Text en Copyright © 2023 Barrett, Oskoui, Russell and Borens. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Pharmacology
Barrett, Jeffrey S.
Oskoui, Solmaz Eradat
Russell, Scott
Borens, Amanda
Digital Research Environment(DRE)-enabled Artificial Intelligence (AI) to facilitate early stage drug development
title Digital Research Environment(DRE)-enabled Artificial Intelligence (AI) to facilitate early stage drug development
title_full Digital Research Environment(DRE)-enabled Artificial Intelligence (AI) to facilitate early stage drug development
title_fullStr Digital Research Environment(DRE)-enabled Artificial Intelligence (AI) to facilitate early stage drug development
title_full_unstemmed Digital Research Environment(DRE)-enabled Artificial Intelligence (AI) to facilitate early stage drug development
title_short Digital Research Environment(DRE)-enabled Artificial Intelligence (AI) to facilitate early stage drug development
title_sort digital research environment(dre)-enabled artificial intelligence (ai) to facilitate early stage drug development
topic Pharmacology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10079992/
https://www.ncbi.nlm.nih.gov/pubmed/37033647
http://dx.doi.org/10.3389/fphar.2023.1115356
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