Cascade search for HSV-1 combinatorial drugs with high antiviral efficacy and low toxicity

Xianting Ding1 Department of Mechanical and Aerospace Engineering, UCLA, Los Angeles, CA, USA

David Jesse Sanchez2 Department of Microbiology, Immunology, and Molecular Genetics, UCLA, Los Angeles, CA, USA;3 Department of Pharmaceutical Sciences, College of Pharmacy, Western University of Health Sciences, Pomona, CA, USA

Arash Shahangian2 Department of Microbiology, Immunology, and Molecular Genetics, UCLA, Los Angeles, CA, USA

Ibrahim Al-Shyoukh1 Department of Mechanical and Aerospace Engineering, UCLA, Los Angeles, CA, USA;4 Molecular and Medical Pharmacology Department, David Geffen School of Medicine, UCLA, Los Angeles, CA, USA

Genhong Cheng2 Department of Microbiology, Immunology, and Molecular Genetics, UCLA, Los Angeles, CA, USA

Chih-Ming Ho1 Department of Mechanical and Aerospace Engineering, UCLA, Los Angeles, CA, USACorrespondence[email protected]

Abstract

Background

Infectious diseases cause many molecular assemblies and pathways within cellular signaling networks to function aberrantly. The most effective way to treat complex, diseased cellular networks is to apply multiple drugs that attack the problem from many fronts. However, determining the optimal combination of several drugs at specific dosages to reach an endpoint objective is a daunting task.

Methods

In this study, we applied an experimental feedback system control (FSC) method and rapidly identified optimal drug combinations that inhibit herpes simplex virus-1 infection, by only testing less than 0.1% of the total possible drug combinations.

Results

Using antiviral efficacy as the criterion, FSC quickly identified a highly efficacious drug cocktail. This cocktail contained high dose ribavirin. Ribavirin, while being an effective antiviral drug, often induces toxic side effects that are not desirable in a therapeutic drug combination. To screen for less toxic drug combinations, we applied a second FSC search in cascade and used both high antiviral efficacy and low toxicity as criteria. Surprisingly, the new drug combination eliminated the need for ribavirin, but still blocked viral infection in nearly 100% of cases.

Conclusion

This cascade search provides a versatile platform for rapid discovery of new drug combinations that satisfy multiple criteria.

Keywords:

Acknowledgments

Presented in part: NIH Nanomedicine Development Centers Awardee Meeting, Washington, DC, March 2011. The work was supported by National Institutes of Health Nanomedicine Development Center, grant number PN2EY018228.

Disclosure

The authors have no conflict of interest to declare.

Supplementary figures

Figure S1 Illustration of differential evolution (DE) search algorithm. DE is divided into four main steps, which can me summarized as production of the original drug combinations, mutation stage, crossover stage, and production of the new drug combinations.

Abbreviation: TNF, tumor necrosis factor.

Figure S2 Long-term test between optimized drug combinations and individual drugs. Both optimal drug combinations DE1 and DE2 show low percentage of infection from day 1 to day 4, while individual drugs in general lost their antiviral efficacy after day 3.

Abbreviations: ACV, acyclovir; IFN, interferon; TNF, tumor necrosis factor.

Figure S3 Plaque assay analysis of the viral titer in the supernatant. The supernatant of each sample from Figure S2 was tested for the absolute viral titer using plaque assay. Viral titer gradually clears up by optimized drug combination DE1 and DE2 after 2 days.

Abbreviations: ACV, acyclovir; IFN, interferon; TNF, tumor necrosis factor.