Required concentration index quantifies effective drug combinations against hepatitis C virus infection

Yusuke Kakizoe, Yoshiki Koizumi, Yukino Ikoma, Hirofumi Ohashi, Takaji Wakita, Shingo Iwami, Koichi Watashi

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)


Successful clinical drug development requires rational design of combination treatments based on preclinical data. Anti-hepatitis C virus (HCV) drugs exhibit significant diversity in antiviral effect. Dose-response assessments can be used to determine parameters profiling the diverse antiviral effect during combination treatment. In the current study, a combined experimental and mathematical approaches were used to compare and score different combinations of anti-HCV treatments. A “required concentration index” was generated and used to rank the antiviral profile of possible double- and triple-drug combinations against HCV genotype 1b and 2a. Rankings varied based on target HCV genotype. Interestingly, multidrug (double and triple) treatment not only augmented antiviral activity, but also reduced genotype-specific efficacy, suggesting another advantage of multidrug treatment. The current study provides a quantitative method for profiling drug combinations against viral genotypes, to better inform clinical drug development.

Original languageEnglish
Article number4
JournalTheoretical Biology and Medical Modelling
Issue number1
Publication statusPublished - Dec 2021
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • Modelling and Simulation
  • Health Informatics


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