Host pharmacogenetic factors that may affect liver neoplasm incidence upon using direct-acting antivirals for

Ahmad M Zidan1,2, Eman A Saad1, Nasser E Ibrahim1

  • 1Department of Bioinformatics, Genetic Engineering & Biotechnology Research Institute, University of Sadat City, Egypt.

Heliyon
|May 20, 2021
PubMed
Abstract

Insights

This study identified host genetic factors influencing hepatocellular carcinoma (HCC) risk in patients treated with direct-acting antivirals (DAAs) for hepatitis C virus (HCV). These findings aid in precise DAA application to minimize HCC risk.

Area of Science:

  • Hepatology
  • Pharmacogenomics
  • Oncology

Background:

  • Direct-acting antivirals (DAAs) revolutionized hepatitis C virus (HCV) treatment by targeting viral nonstructural proteins.
  • Debate persists regarding the association between DAAs and hepatocellular carcinoma (HCC) incidence.
  • Identifying host genetic factors is crucial for understanding this DAA/HCC paradox.

Purpose of the Study:

  • To identify host pharmacogenetic factors potentially influencing HCC development in patients receiving HCV DAAs.
  • To provide insights into the DAA/HCC controversy through a pharmacogenomic approach.

Main Methods:

  • Collected data on 16 HCV DAAs from literature and DrugBank.
  • Utilized the in silico pharmacogenomics/pharmacovigilance pipeline (PHARMIP) to predict genetic factors.
  • Analyzed digital drug structures to identify potential host genetic associations with HCC.

Main Results:

  • Identified 184 unique genes and 40 unique variants potentially linked to the DAA/HCC paradox.
  • These findings offer a basis for precise DAA application and risk minimization.
  • Results are accessible at https://doi.org/10.17632/8ws8258hn3.2.

Conclusions:

  • Identified host factors are linked to HCC and predicted as DAA targets by PHARMIP.
  • Discussed strategies for addressing the DAA/HCC controversy at individual drug, subclass, and class levels.
  • Further wet laboratory investigation is warranted to validate these in silico findings.

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