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...

Posttransplant immunosuppressive drugs such as tacrolimus have narrow therapeutic ranges. Inter-and intraindividual variability in dosing requirements conventionally use physician-guided titrated drug administration, which results in frequent deviations from the target trough ranges, particularly during the critical postoperative phase. There is a clear need for personalized management of posttransplant regimens to prevent adverse events and allow the patient to be discharged sooner. We have developed the parabolic personalized dosing (PPD) platform, which is a surface represented by a second-order algebraic equation with experimentally determined coefficients of the equation being unique to each patient. PPD uses clinical data, including blood concentrations of tacrolimus-the primary phenotypic readout for immunosuppression efficacy-to calibrate these coefficients and pinpoint the optimal doses that result in the desired patient-specific response. In this pilot randomized controlled trial, we compared four transplant patients prospectively treated with tacrolimus using PPD with four control patients treated according to the standard of care (physician guidance). Using phenotype to personalize tacrolimus dosing, PPD effectively managed patients by keeping tacrolimus blood trough levels within the target ranges. In a retrospective analysis of the control patients, PPD-optimized prednisone and tacrolimus dosing improved tacrolimus trough-level management and minimized the need to recalibrate dosing after regimen changes. PPD is independent of disease mechanism and is agnostic of indication and could therefore apply beyond transplant medicine to dosing for cancer, infectious diseases, and cardiovascular medicine, where patient response is variable and requires careful adjustments through optimized inputs.

Combination chemotherapy is a cornerstone of cancer treatment. Optimizing its effectiveness requires dose-and time-dependent regulation of drug synergy. In this report, CURATE.AI, an artificial intelligence platform, is used to prospectively guide the dosing of a bromodomain inhibitor (ZEN-3694) and enzalutamide administered in combination to a patient with metastatic castration-resistant prostate cancer to reduce serum prostate-specific antigen (PSA) levels. CURATE.AI successfully identifies substantial ZEN-3694 and enzalutamide dose adjustments, increasing both treatment efficacy and tolerance. CURATE.AI analysis also confirms that the patient's durable response is mediated by ZEN-3694 inclusion in the regimen. Due to CURATE.AI-enhanced efficacy and safety, the patient was able to continue with the combination regimen, resulting in a durable response and no disease progression based on CURATE.AI-sustained control over PSA levels and reduced lesion size.

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