Engineering and evaluation of FXa bypassing agents that restore hemostasis following Apixaban associated bleeding.

Direct oral anticoagulants (DOACs) targeting activated factor Xa (FXa) are used to prevent or treat thromboembolic disorders. DOACs reversibly bind to FXa and inhibit its enzymatic activity. However, DOAC treatment carries the risk of anticoagulant-associated bleeding. Currently, only one specific agent, andexanet alfa, is approved to reverse the anticoagulant effects of FXa-targeting DOACs (FXaDOACs) and control life-threatening bleeding. However, because of its mechanism of action, andexanet alfa requires a cumbersome dosing schedule, and its use is associated with the risk of thrombosis. Here, we present the computational design, engineering, and evaluation of FXa-variants that exhibit anticoagulation reversal activity in the presence of FXaDOACs. Our designs demonstrate low DOAC binding affinity, retain FXa-enzymatic activity and reduce the DOAC-associated bleeding by restoring hemostasis in mice treated with apixaban. Importantly, the FXaDOACs reversal agents we designed, unlike andexanet alfa, do not inhibit TFPI, and consequently, may have a safer thrombogenic profile.

Application of machine learning approaches for predicting hemophilia A severity.

BACKGROUND: Hemophilia A (HA) is an X-linked congenital bleeding disorder, which leads to deficiency of clotting factor (F) VIII. It mostly affects males, and females are considered carriers. However, it is now recognized that variants of F8 in females can result in HA. Nonetheless, most females go undiagnosed and untreated for HA, and their bleeding complications are attributed to other causes. Predicting the severity of HA for female patients can provide valuable insights for treating the conditions associated with the disease, such as heavy bleeding. OBJECTIVES: To predict the severity of HA based on F8 genotype using a machine learning (ML) approach. METHODS: Using multiple datasets of variants in the F8 and disease severity from various repositories, we derived the sequence for the FVIII protein. Using the derived sequences, we used ML models to predict the severity of HA in female patients. RESULTS: Utilizing different classification models, we highlight the validity of the datasets and our approach with predictive F1 scores of 0.88, 0.99, 0.93, 0.99, and 0.90 for all the validation sets. CONCLUSION: Although with some limitations, ML-based approaches demonstrated the successful prediction of disease severity in female HA patients based on variants in the F8. This study confirms previous research findings that ML can help predict the severity of hemophilia. These results can be valuable for future studies in achieving better treatment and clinical outcomes for female patients with HA, which is an urgent unmet need.

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 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.

Influence of Hydroxyproline on Mechanical Behavior of Collagen Mimetic Proteins Under Fraying Deformation-Molecular Dynamics Investigations.

Molecular dynamics modeling is used to simulate, model, and analyze mechanical deformation behavior and predictive properties of three different synthetic collagen proteins obtained from RSC-PDB, 1BKV, 3A08, and 2CUO, with varying concentrations of hydroxyproline (HYP). Hydroxyproline is credited with providing structural support for the collagen protein molecules. Hydroxyproline's influence on these three synthetic collagen proteins' mechanical deformation behavior and predictive properties is investigated in this paper. A detailed study and inference of the protein's mechanical characteristics associated with HYP content are investigated through fraying deformation behavior. A calculated Gibbs free energy value (ΔG) of each polypeptide α chain that corresponds with a complete unfolding of a single polypeptide α-chain from a triple-helical protein is obtained with umbrella sampling. The force needed for complete separation of the polypeptide α-chain from the triple-helical protein is analyzed for proteins to understand the influence of HYP concentration and is discussed in this paper. Along with a difference in ΔG, different unfolding pathways for the molecule and individual chains are observed. The correlation between the fraying deformation mechanical characteristics and the collagen proteins' hydroxyproline content is provided in this study via the three collagen proteins' resulting binding energies.