Identification of environmental and genetic factors that influence warfarin time in therapeutic range.

Warfarin is an oral anticoagulant prescribed to prevent and treat thromboembolic disorders. It has a narrow therapeutic window and must have its effect controlled. Prothrombin test, expressed in INR value, is used for dose management. Time in therapeutic range (TTR) is an important outcome of quality control of anticoagulation therapy and is influenced by several factors. The aim of this study was to identify genetic, demographic, and clinical factors that can potentially influence TTR. In total,422 patients using warfarin were investigated. Glibenclamide co-medication and presence of CYP2C9*2 and/or *3 alleles were associated with higher TTR, while amiodarone, acetaminophen and verapamil co-medication were associated with lower TTR. Our data suggest that TTR is influenced by co-medication and genetic factors. Thus, individuals in use of glibenclamide may need a more careful monitoring and genetic testing (CYP2C9*2 and/or *3 alleles) may improve the anticoagulation management. In addition, in order to reach and maintain the INR in the target for a longer period, it is better to discuss dose adjustment in office instead of by telephone assessment. Other studies are needed to confirm these results and to find more variables that could contribute to this important parameter.

A new in silico approach to investigate molecular aspects of factor IX missense causative mutations and their impact on the hemophilia B severity.

Factor IX (encoded by F9) is a protein in the coagulation process, where its lack or deficiency leads to hemophilia B. This condition has been much less studied than hemophilia A, especially in Latin America. We analyzed the structural and functional impact of 54 missense mutations (18 reported by us previously, and 36 other mutations from the Factor IX database) through molecular modeling approaches. To accomplish this task, we examine the electrostatic patterns, hydrophobicity/hydrophilicity, disulfide, and H-bond differences of the Factor IX structures harboring the missense mutations found, correlating them with their clinical effects. The 54 mutated sequences were modeled and their physicochemical features were determined and used as input in clusterization tools. The electrostatic pattern seems to influence in disease severity, especially for mutations investigated in epidermal growth factors 1 and 2 (EGF1/2) domains. The combined use of all physicochemical information improved the clustering of structures associated to similar phenotypes, especially for mutations from GLA and EGF1-2 domains. The effect of mutations in the disease phenotype severity seems to be a complex interplay of molecular features, each one contributing to different impacts. This highlights that previous studies and tools analyzing individually single features for single mutations are missing elements that fulfill the whole picture.

Unveiling the influence of factor VIII physicochemical properties on hemophilia A phenotype through an in silico methodology.

BACKGROUND AND OBJECTIVES: Hemophilia A (HA) is an X-linked blood disorder. It is caused by pathogenic F8 gene variants, among which missense mutations are the most prevalent. The resulting amino acid substitutions may have different impacts on physicochemical properties and, consequently, on protein functionality. Regular prediction tools do not include structural elements and their physiological significance, which hampers our ability to functionally link variants to disease phenotype, opening an ample field for investigation. The present study aims to elucidate how physicochemical changes generated by substitutions in different protein domains relate to HA, and which of these features are more consequential to protein function and its impact on HA phenotype. METHODS: An in silico evaluation of 71 F8 variants found in patients with different HA phenotypes (mild, moderate, severe) was performed to understand protein modifications and functional impact. Homology modeling was used for the structural analysis of physicochemical changes including electrostatic potential, hydrophobicity, solvent-accessible/excluded surface areas, disulfide disruptions, and substitutions indexes. These variants and properties were analyzed by hierarchical clustering analysis (HCA) and principal component analysis (PCA), independently and in combination, to investigate their relative contribution. RESULTS: About 69% of variants show electrostatic changes, and almost all show hydrophobicity and surface area modifications. HCA combining all physicochemical properties analyzed was better in reflecting the impact of different variants in disease severity, more so than the single feature analysis. On the other hand, PCA led to the identification of prominent properties involved in the clustering results for variants of different domains. CONCLUSIONS: The methodology developed here enables the assessment of structural features not available in other prediction tools (e.g., surface distribution of electrostatic potential), evaluating what kind of physicochemical changes are involved in FVIII functional disruption. HCA results allow distinguishing substitutions according to their properties, and yielded clusters which were more homogeneous in phenotype. All evaluated properties are involved in determining disease severity. The nature, as well as the position of the variants in the protein, were shown to be relevant for physicochemical changes, demonstrating that all these aspects must be collectively considered to fine-tune an approach to predict HA severity.