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

Xianting Ding1, David Jesse Sanchez2,3, Arash Shahangian2, Ibrahim Al-Shyoukh1,4, Genhong Cheng2, Chih-Ming Ho11Department of Mechanical and Aerospace Engineering, UCLA, Los Angeles, CA, USA; 2Department of Microbiology, Immunology, and Molecular Genetics, UCLA, Los Angeles, CA, USA; 3Department of Pharmaceutical Sciences, College of Pharmacy, Western University of Health Sciences, Pomona, CA, USA; 4Molecular and Medical Pharmacology Department, David Geffen School of Medicine, UCLA, Los Angeles, CA, USABackground: 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: drug combination, HSV-1, combinatorial drug optimization, feedback system control, FSC, drug screenin

Drug regimens identified and optimized by output-driven platform markedly reduce tuberculosis treatment time

The current drug regimens for treating tuberculosis are lengthy and onerous, and hence complicated by poor adherence leading to drug resistance and disease relapse. Previously, using an output-driven optimization platform and an in vitro macrophage model of Mycobacterium tuberculosis infection, we identified several experimental drug regimens among billions of possible drug-dose combinations that outperform the current standard regimen. Here we use this platform to optimize the in vivo drug doses of two of these regimens in a mouse model of pulmonary tuberculosis. The experimental regimens kill M. tuberculosis much more rapidly than the standard regimen and reduce treatment time to relapse-free cure by 75%. Thus, these regimens have the potential to provide a markedly shorter course of treatment for tuberculosis in humans. As these regimens omit isoniazid, rifampicin, fluoroquinolones and injectable aminoglycosides, they would be suitable for treating many cases of multidrug and extensively drug-resistant tuberculosis

Mechanism-Independent Optimization of Combinatorial Nanodiamond and Unmodified Drug Delivery Using a Phenotypically Driven Platform Technology

Combination chemotherapy can mediate drug synergy to improve treatment efficacy against a broad spectrum of cancers. However, conventional multidrug regimens are often additively determined, which have long been believed to enable good cancer-killing efficiency but are insufficient to address the nonlinearity in dosing. Despite improved clinical outcomes by combination treatment, multi-objective combination optimization, which takes into account tumor heterogeneity and balance of efficacy and toxicity, remains challenging given the sheer magnitude of the combinatorial dosing space. To enhance the properties of the therapeutic agents, the field of nanomedicine has realized novel drug delivery platforms that can enhance therapeutic efficacy and safety. However, optimal combination design that incorporates nanomedicine agents still faces the same hurdles as unmodified drug administration. The work reported here applied a powerful phenotypically driven platform, termed feedback system control (FSC), that systematically and rapidly converges upon a combination consisting of three nanodiamond-modified drugs and one unmodified drug that is simultaneously optimized for efficacy against multiple breast cancer cell lines and safety against multiple control cell lines. Specifically, the therapeutic window achieved from an optimally efficacious and safe nanomedicine combination was markedly higher compared to that of an optimized unmodified drug combination and nanodiamond monotherapy or unmodified drug administration. The phenotypically driven foundation of FSC implementation does not require any cellular signaling pathway data and innately accounts for population heterogeneity and nonlinear biological processes. Therefore, FSC is a broadly applicable platform for both nanotechnology-modified and unmodified therapeutic optimizations that represent a promising path toward phenotypic personalized medicine