In silico functionality prediction analysis of pharmacogenes and development of a pharmacogenetic score for Brazilian familial hypercholesterolemia patients

• Grant number: 19/19009-4
• Support Opportunities: Scholarships abroad - Research Internship - Doctorate (Direct)
• Effective date (Start): January 03, 2020
• Effective date (End): December 17, 2020
• Field of knowledge: Health Sciences - Pharmacy
• Principal Investigator: Rosario Dominguez Crespo Hirata
• Grantee: Carolina Dagli Hernandez
• Supervisor: Volker Lauschke
• Host Institution: Faculdade de Ciências Farmacêuticas (FCF). Universidade de São Paulo (USP). São Paulo , SP, Brazil
• Research place: Karolinska Institutet, Sweden
• Associated to the scholarship: 16/25637-0 - Pharmacogenomic and pharmacoepigenomic analysis in individuals with familial hypercholesterolemia, BP.DD

Abstract

Familial hypercholesterolemia (FH) is a primary dyslipidemia resulting from functional mutations in cholesterol homeostasis genes. Statins are the first-line treatment, but the response is highly variable among individuals. Pharmacogenetic scores have been developed to predict this variability, but none is specific to the Brazilian FH population. Also, the impact of variants found on product functionality is not precisely known. Recently, Dr. Volker M. Lauschke's group of the Karolinska Institute, Sweden, developed a framework for predicting in silico functionality of variants in pharmacogenes. This study aims 1) to predict the functionality of variants in genes associated with the response to statins; and 2) to develop a pharmacogenetic score for statin response in Brazilian FH patients for LDL-c reduction and statin-related adverse events (SRAE). The in silico functionality prediction will be performed using the framework developed by Dr. Volker M. Lauschke's group for variants identified in genes involved in the pharmacokinetics and pharmacodynamics of statins in our study population. Using this result, a pharmacogenetic algorithm for dose prediction will be developed for better therapeutic response and reducing SRAE. FH patients will be grouped into a generation cohort (G) (80% of patients) and a validation cohort (V) (20% of patients). The analysis will be performed by univariate linear regressions using G cohort and significant variables will be tested by multiple linear regression to predict the patient's therapeutic response. The algorithm will be validated in the cohort V. A correlation analysis will be performed to determine the correlation between the actual statin dose and that predicted with the algorithm. With this study, we hope to develop a pharmacogenetic score that could help Brazilian FH patients to achieve the best response to statin treatment. (AU)

Scientific publications

(References retrieved automatically from Web of Science and SciELO through information on FAPESP grants and their corresponding numbers as mentioned in the publications by the authors)

ZHOU, YITIAN; DAGLI HERNANDEZ, CAROLINA; LAUSCHKE, VOLKER M.. Population-scale predictions of DPD and TPMT phenotypes using a quantitative pharmacogene-specific ensemble classifier. BRITISH JOURNAL OF CANCER, v. 123, n. 12, p. 1782-1789, DEC 8 2020. (19/19009-4)