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A method for systematically ranking therapeutic drug candidates using multiple uncertain screening criteria

Multiple different screening tests for candidate leads in drug development may often yield conflicting or ambiguous results, sometimes making the selection of leads a nontrivial maximum-likelihood ranking problem. Here, we employ methods from the field of multiple criteria decision making (MCDM) to...

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Autores principales: Peng, Xubiao, Gibbs, Ebrima, Silverman, Judith M, Cashman, Neil R, Plotkin, Steven S
Formato: Online Artículo Texto
Lenguaje:English
Publicado: SAGE Publications 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8189013/
https://www.ncbi.nlm.nih.gov/pubmed/33847541
http://dx.doi.org/10.1177/09622802211002861
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author Peng, Xubiao
Gibbs, Ebrima
Silverman, Judith M
Cashman, Neil R
Plotkin, Steven S
author_facet Peng, Xubiao
Gibbs, Ebrima
Silverman, Judith M
Cashman, Neil R
Plotkin, Steven S
author_sort Peng, Xubiao
collection PubMed
description Multiple different screening tests for candidate leads in drug development may often yield conflicting or ambiguous results, sometimes making the selection of leads a nontrivial maximum-likelihood ranking problem. Here, we employ methods from the field of multiple criteria decision making (MCDM) to the problem of screening candidate antibody therapeutics. We employ the SMAA-TOPSIS method to rank a large cohort of antibodies using up to eight weighted screening criteria, in order to find lead candidate therapeutics for Alzheimer’s disease, and determine their robustness to both uncertainty in screening measurements, as well as uncertainty in the user-defined weights of importance attributed to each screening criterion. To choose lead candidates and measure the confidence in their ranking, we propose two new quantities, the Retention Probability and the Topness, as robust measures for ranking. This method may enable more systematic screening of candidate therapeutics when it becomes difficult intuitively to process multi-variate screening data that distinguishes candidates, so that additional candidates may be exposed as potential leads, increasing the likelihood of success in downstream clinical trials. The method properly identifies true positives and true negatives from synthetic data, its predictions correlate well with known clinically approved antibodies vs. those still in trials, and it allows for ranking analyses using antibody developability profiles in the literature. We provide a webserver where users can apply the method to their own data: http://bjork.phas.ubc.ca.
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spelling pubmed-81890132021-06-21 A method for systematically ranking therapeutic drug candidates using multiple uncertain screening criteria Peng, Xubiao Gibbs, Ebrima Silverman, Judith M Cashman, Neil R Plotkin, Steven S Stat Methods Med Res Articles Multiple different screening tests for candidate leads in drug development may often yield conflicting or ambiguous results, sometimes making the selection of leads a nontrivial maximum-likelihood ranking problem. Here, we employ methods from the field of multiple criteria decision making (MCDM) to the problem of screening candidate antibody therapeutics. We employ the SMAA-TOPSIS method to rank a large cohort of antibodies using up to eight weighted screening criteria, in order to find lead candidate therapeutics for Alzheimer’s disease, and determine their robustness to both uncertainty in screening measurements, as well as uncertainty in the user-defined weights of importance attributed to each screening criterion. To choose lead candidates and measure the confidence in their ranking, we propose two new quantities, the Retention Probability and the Topness, as robust measures for ranking. This method may enable more systematic screening of candidate therapeutics when it becomes difficult intuitively to process multi-variate screening data that distinguishes candidates, so that additional candidates may be exposed as potential leads, increasing the likelihood of success in downstream clinical trials. The method properly identifies true positives and true negatives from synthetic data, its predictions correlate well with known clinically approved antibodies vs. those still in trials, and it allows for ranking analyses using antibody developability profiles in the literature. We provide a webserver where users can apply the method to their own data: http://bjork.phas.ubc.ca. SAGE Publications 2021-04-13 2021-06 /pmc/articles/PMC8189013/ /pubmed/33847541 http://dx.doi.org/10.1177/09622802211002861 Text en © The Author(s) 2021 https://creativecommons.org/licenses/by-nc/4.0/This article is distributed under the terms of the Creative Commons Attribution-NonCommercial 4.0 License (https://creativecommons.org/licenses/by-nc/4.0/) which permits non-commercial use, reproduction and distribution of the work without further permission provided the original work is attributed as specified on the SAGE and Open Access pages (https://us.sagepub.com/en-us/nam/open-access-at-sage).
spellingShingle Articles
Peng, Xubiao
Gibbs, Ebrima
Silverman, Judith M
Cashman, Neil R
Plotkin, Steven S
A method for systematically ranking therapeutic drug candidates using multiple uncertain screening criteria
title A method for systematically ranking therapeutic drug candidates using multiple uncertain screening criteria
title_full A method for systematically ranking therapeutic drug candidates using multiple uncertain screening criteria
title_fullStr A method for systematically ranking therapeutic drug candidates using multiple uncertain screening criteria
title_full_unstemmed A method for systematically ranking therapeutic drug candidates using multiple uncertain screening criteria
title_short A method for systematically ranking therapeutic drug candidates using multiple uncertain screening criteria
title_sort method for systematically ranking therapeutic drug candidates using multiple uncertain screening criteria
topic Articles
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8189013/
https://www.ncbi.nlm.nih.gov/pubmed/33847541
http://dx.doi.org/10.1177/09622802211002861
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