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A machine learning approach for identifying variables associated with risk of developing neutralizing antidrug antibodies to factor VIII

A key unmet need in the management of hemophilia A (HA) is the lack of clinically validated markers that are associated with the development of neutralizing antibodies to Factor VIII (FVIII) (commonly referred to as inhibitors). This study aimed to identify relevant biomarkers for FVIII inhibition u...

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Autores principales: Rawal, Atul, Kidchob, Christopher, Ou, Jiayi, Yogurtcu, Osman N., Yang, Hong, Sauna, Zuben E.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10220358/
https://www.ncbi.nlm.nih.gov/pubmed/37251488
http://dx.doi.org/10.1016/j.heliyon.2023.e16331
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author Rawal, Atul
Kidchob, Christopher
Ou, Jiayi
Yogurtcu, Osman N.
Yang, Hong
Sauna, Zuben E.
author_facet Rawal, Atul
Kidchob, Christopher
Ou, Jiayi
Yogurtcu, Osman N.
Yang, Hong
Sauna, Zuben E.
author_sort Rawal, Atul
collection PubMed
description A key unmet need in the management of hemophilia A (HA) is the lack of clinically validated markers that are associated with the development of neutralizing antibodies to Factor VIII (FVIII) (commonly referred to as inhibitors). This study aimed to identify relevant biomarkers for FVIII inhibition using Machine Learning (ML) and Explainable AI (XAI) using the My Life Our Future (MLOF) research repository. The dataset includes biologically relevant variables such as age, race, sex, ethnicity, and the variants in the F8 gene. In addition, we previously carried out Human Leukocyte Antigen Class II (HLA-II) typing on samples obtained from the MLOF repository. Using this information, we derived other patient-specific biologically and genetically important variables. These included identifying the number of foreign FVIII derived peptides, based on the alignment of the endogenous FVIII and infused drug sequences, and the foreign-peptide HLA-II molecule binding affinity calculated using NetMHCIIpan. The data were processed and trained with multiple ML classification models to identify the top performing models. The top performing model was then chosen to apply XAI via SHAP, (SHapley Additive exPlanations) to identify the variables critical for the prediction of FVIII inhibitor development in a hemophilia A patient. Using XAI we provide a robust and ranked identification of variables that could be predictive for developing inhibitors to FVIII drugs in hemophilia A patients. These variables could be validated as biomarkers and used in making clinical decisions and during drug development. The top five variables for predicting inhibitor development based on SHAP values are: (i) the baseline activity of the FVIII protein, (ii) mean affinity of all foreign peptides for HLA DRB 3, 4, & 5 alleles, (iii) mean affinity of all foreign peptides for HLA DRB1 alleles), (iv) the minimum affinity among all foreign peptides for HLA DRB1 alleles, and (v) F8 mutation type.
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spelling pubmed-102203582023-05-28 A machine learning approach for identifying variables associated with risk of developing neutralizing antidrug antibodies to factor VIII Rawal, Atul Kidchob, Christopher Ou, Jiayi Yogurtcu, Osman N. Yang, Hong Sauna, Zuben E. Heliyon Research Article A key unmet need in the management of hemophilia A (HA) is the lack of clinically validated markers that are associated with the development of neutralizing antibodies to Factor VIII (FVIII) (commonly referred to as inhibitors). This study aimed to identify relevant biomarkers for FVIII inhibition using Machine Learning (ML) and Explainable AI (XAI) using the My Life Our Future (MLOF) research repository. The dataset includes biologically relevant variables such as age, race, sex, ethnicity, and the variants in the F8 gene. In addition, we previously carried out Human Leukocyte Antigen Class II (HLA-II) typing on samples obtained from the MLOF repository. Using this information, we derived other patient-specific biologically and genetically important variables. These included identifying the number of foreign FVIII derived peptides, based on the alignment of the endogenous FVIII and infused drug sequences, and the foreign-peptide HLA-II molecule binding affinity calculated using NetMHCIIpan. The data were processed and trained with multiple ML classification models to identify the top performing models. The top performing model was then chosen to apply XAI via SHAP, (SHapley Additive exPlanations) to identify the variables critical for the prediction of FVIII inhibitor development in a hemophilia A patient. Using XAI we provide a robust and ranked identification of variables that could be predictive for developing inhibitors to FVIII drugs in hemophilia A patients. These variables could be validated as biomarkers and used in making clinical decisions and during drug development. The top five variables for predicting inhibitor development based on SHAP values are: (i) the baseline activity of the FVIII protein, (ii) mean affinity of all foreign peptides for HLA DRB 3, 4, & 5 alleles, (iii) mean affinity of all foreign peptides for HLA DRB1 alleles), (iv) the minimum affinity among all foreign peptides for HLA DRB1 alleles, and (v) F8 mutation type. Elsevier 2023-05-23 /pmc/articles/PMC10220358/ /pubmed/37251488 http://dx.doi.org/10.1016/j.heliyon.2023.e16331 Text en https://creativecommons.org/licenses/by-nc-nd/4.0/This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
spellingShingle Research Article
Rawal, Atul
Kidchob, Christopher
Ou, Jiayi
Yogurtcu, Osman N.
Yang, Hong
Sauna, Zuben E.
A machine learning approach for identifying variables associated with risk of developing neutralizing antidrug antibodies to factor VIII
title A machine learning approach for identifying variables associated with risk of developing neutralizing antidrug antibodies to factor VIII
title_full A machine learning approach for identifying variables associated with risk of developing neutralizing antidrug antibodies to factor VIII
title_fullStr A machine learning approach for identifying variables associated with risk of developing neutralizing antidrug antibodies to factor VIII
title_full_unstemmed A machine learning approach for identifying variables associated with risk of developing neutralizing antidrug antibodies to factor VIII
title_short A machine learning approach for identifying variables associated with risk of developing neutralizing antidrug antibodies to factor VIII
title_sort machine learning approach for identifying variables associated with risk of developing neutralizing antidrug antibodies to factor viii
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10220358/
https://www.ncbi.nlm.nih.gov/pubmed/37251488
http://dx.doi.org/10.1016/j.heliyon.2023.e16331
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