• Sauna, Zuben E.
• Yogurtcu, Osman N.
• Ou, Jiayi
• Rawal, Atul
• Yang, Hong
• Kidchob, Christopher

Abstract

<jats:title>Abstract</jats:title><jats:p>Lack of clinically validated markers associated with the development of neutralizing antibodies (inhibitors) to the replacement protein-drug, Factor VIII (FVIII) is a major challenge in managing Hemophilia A (HA). By utilizing Machine Learning (ML) and Explainable AI (XAI) we identified and ranked relevant variables (biomarkers) that could be predictive for developing inhibitors to FVIII drugs in hemophilia A patients. The dataset used for the study was derived from the My Life Our Future (MLOF) repository and included variables such as age, race, ethnicity, mutations in the F8 gene and the Human Leucocyte Antigen Class II (HLA-II) type. We computed 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 (using the NetMHCIIpan algorithm). The complete dataset with the added variables was trained and validated using multiple ML classification models to identify the top performing model, which was chosen for further processing with XAI via SHAP (SHapley Additive exPlanations) to identify the variables critical for the prediction of FVIII inhibitor development in a hemophilia A patients. 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, &amp; 5 alleles, (iii) mean affinity of all foreign peptides for HLA DRB1 alleles, (iv) the maximum affinity among all foreign peptides for HLA DRB1 alleles, and (v) F8 mutation type.</jats:p><jats:p>This research was supported by funds from the Hemostasis Branch/Division of Plasma Protein 344 Therapeutics/Office of Tissues and Advanced Therapies/Center for Biologics Evaluation and Research 345 of the U.S. Food and Drug Administration and in part by an appointment to the Research Participation 346 Program at the Center for Biologics Evaluation and Research administered by the Oak Ridge Institute 347 for Science and Education through an interagency agreement between the U.S. Department of Energy 348 and the U.S. Food and Drug Administration. The MLOF program was developed as a partnership 349 between NHF, ATHN, Bloodworks Northwest, and Bioverativ and supported financially by 350 Bioverativ, NHF, Bloodworks Northwest, and ATHN.</jats:p>

Topics

• impedance spectroscopy
• machine learning