Output-driven feedback system control platform optimizes combinatorial therapy of tuberculosis using a macrophage cell culture model

Abstract

Tuberculosis (TB) remains a major global public health problem, and improved treatments are needed to shorten duration of therapy, decrease disease burden, improve compliance, and combat emergence of drug resistance. Ideally, the most effective regimen would be identified by a systematic and comprehensive combinatorial search of large numbers of TB drugs. However, optimization of regimens by standard methods is challenging, especially as the number of drugs increases, because of the extremely large number of drug-dose combinations requiring testing. Herein, we used an optimization platform, feedback system control (FSC) methodology, to identify improved drug-dose combinations for TB treatment using a fluorescence-based human macrophage cell culture model of TB, in which macrophages are infected with isopropyl β-D-1-thiogalactopyranoside (IPTG)-inducible green fluorescent protein (GFP)-expressing Mycobacterium tuberculosis (Mtb). On the basis of only a single screening test and three iterations, we identified highly efficacious three-and four-drug combinations. To verify the efficacy of these combinations, we further evaluated them using a methodologically independent assay for intramacrophage killing of Mtb; the optimized combinations showed greater efficacy than the current standard TB drug regimen. Surprisingly, all top three-and four-drug optimized regimens included the third-line drug clofazimine, and none included the first-line drugs isoniazid and rifampin, which had insignificant or antagonistic impacts on efficacy. Because top regimens also did not include a fluoroquinolone or aminoglycoside, they are potentially of use for treating many cases of multidrug-and extensively drug-resistant TB. Our study shows the power of an FSC platform to identify promising previously unidentified drug-dose combinations for treatment of TB.feedback system control | tuberculosis | drug combination optimization | Mycobacterium tuberculosis T he bacterium Mycobacterium tuberculosis (Mtb), the etiologic agent of tuberculosis (TB), is a global health problem that infects one-third of the world's population (1). In 2014, 9.6 million people fell ill with TB, and 1.5 million died. Worldwide, TB ranks with HIV/AIDS as one of the greatest killers caused by a single infectious agent, and it is a major cause of mortality in HIVpositive people, accounting for one-quarter of all HIV-related deaths (1). The current standard of care for TB recommended by the World Health Organization is a multidrug regimen lasting 6-8 mo. This lengthy treatment is complicated by toxicities and poor compliance, which in turn, leads to drug resistance and disease relapse. The rise of multidrug-resistant TB further complicates treatment, requiring even longer regimens with second-and third-line drugs that are often more expensive, less effective, and/or more toxic (2, 3). More effective regimens that allow a shorter course of treatment would greatly facilitate monitoring and compliance and counter the emergence of drug resistance (4).The current standard re...

Transcriptomic signatures predict regulators of drug synergy and clinical regimen efficacy against Tuberculosis

“…A standard approach to evaluate drug interactions experimentally utilizes checkerboard assays, which involves exposing the pathogen to different dose combinations of constituent drugs in a regimen. New approaches have been developed to increase throughput of checkerboard assays, either by reducing the number of doses required or by using computational optimization to find optimal doses (4-6).…”

“…Thus, there is a need for high-throughput approaches that can prioritize new drug combinations based on data generated from individual drugs. For example, a feedback-based approach was recently used to determine the optimal dosing of multi-drug regimens (4, 5). However, this approach still requires hundreds of dose-specific measurements for training the algorithm, all of which must be re-done whenever a new agent is under consideration.…”

Applying Optimization Algorithms to Tuberculosis Antibiotic Treatment Regimens

“…12,28,48 In this work, we seek to formalize locating better antibiotic treatment regimens as an optimization problem. The goal of our treatment optimization problem is to identify the region of the regimen design space (combinations of antibiotics and treatment variable ranges) that contain optimal treatment protocols, rather than identify a single, optimal treatment with high precision.…”

Cellular Signaling Analysis shows antiviral, ribavirin-mediated ribosomal signaling modulation

“…Consequently, this approach to drug discovery often leads to unexpected problems or misfires that delay or block significant advancements. Techniques that can determine the best drug or combination of drugs to optimally treat a disease without using conventional drug screening approaches are needed for rapid, efficient translational medicine (Lee et al, 2017;Sun et al, 2013). Developing targeted therapies that focus on the key signaling pathways that lead to pathology and avoid bystander perturbations of the physiology of a cell are possible when looking at how a therapeutic drug combination impacts a cell using an unbiased analysis of cellular signaling (Bendall et al, 2011;Bodenmiller et al, 2012;Schweizer and Zhang, 2013).…”

“…As the future clinical efficacy of ACV is being assessed, therapies with increased efficacy, reduced resistance potential, and improved pharmacokinetics will undoubtedly benefit clinical outcomes (James and Prichard, 2014). Combinatorial drugs often provide better efficacy with reduced individual drug doses compared to treatment with a single drug (Silva et al, 2016;Nowak-Sliwinska et al, 2016;Weiss et al, 2015;Ding et al, 2014). As mentioned above, two distinct drug combinations identified through the FSC technique (a mixture of ACV with either ribavirin (RIB) or interferons (IFNs)) could both effectively inhibit HSV-1 infection (Ding et al, 2012).…”