Feedback System Control: optimizing drug combinations for tuberculosis treatment

Over the past years, numerous reports have surfaced demonstrating the outstanding superiority of combinatorial therapies over single drug treatments, one such example was the successful treatment of the human immunodeficiency virus with a combination therapy. The main problem faced when designing a multi-drug therapy is that combining a set of drugs at different possible concentrations yields a large testing parametric space, and thus the search of an optimal combination becomes a major challenge. To solve this issue, the Feedback System Control (FSC) optimization scheme has emerged as a better alternative for achieving a therapeutic goal when compared to the typical trial and error methods; FSC's primary advantage is its ability to circumvent the need for detailed information of the cellular functions of the system of interest. It has been demonstrated that only tens of iterations out of a large number of possible combinations are needed to achieve a desired response, as opposed to testing the entire search space. This effort-saving approach actively manipulates the complex biological systems as a whole, rather than controlling the system's individual intrinsic pathways. To further exploit the capabilities of this platform, FSC has now taken advantage of the benefits offered by multivariable experimental designs such as orthogonal array composite designs; these designs are intended to draw valid correlation conclusions from an experimental data set while further minimizing the number of tests performed. In the context of FSC, they provide the initial conditions to be tested, which facilitate the development of quadratic models describing the relationship between the drug combinations and their efficacies with a reliable statistic correlation. This method is known as FSC.II.In this project, the FSC.II methodology was used to find a drug combination for tuberculosis treatment. In six iterations, several three and four drug combinations were found to be superior to the standard regimen, which represented a drastic decrease in the number of experiments needed to find the optimal combinations for inhibiting tuberculosis infection on cell based assays. The results obtained were then verified through a colony forming unit cell based assay to verify tuberculosis killing.These results will provide a basis of drug combinations to be tested on an animal model, where only a small number of subjects will be needed to find the optimal drug combination. Furthermore, future efforts will focus on using the FSC scheme to model the drug combination efficacy as a temporal function of a drug combination, which would allow the optimization of a drug combination efficacy over time on a single individual subject; this method would be suitable for both animal and human clinical tests and will an outstanding step towards personalized medicine

When Medicine Meets Engineering—Paradigm Shifts in Diagnostics and Therapeutics

During the last two decades, the manufacturing techniques of microfluidics-based devices have been phenomenally advanced, offering unlimited potential for bio-medical technologies. However, the direct applications of these technologies toward diagnostics and therapeutics are still far from maturity. The present challenges lay at the interfaces between the engineering systems and the biocomplex systems. A precisely designed engineering system with narrow dynamic range is hard to seamlessly integrate with the adaptive biological system in order to achieve the design goals. These differences remain as the roadblock between two fundamentally non-compatible systems. This paper will not extensively review the existing microfluidic sensors and actuators; rather, we will discuss the sources of the gaps for integration. We will also introduce system interface technologies for bridging the differences to lead toward paradigm shifts in diagnostics and therapeutics

When Medicine Meets Engineering-Paradigm Shifts in Diagnostics and Therapeutics.

During the last two decades, the manufacturing techniques of microfluidics-based devices have been phenomenally advanced, offering unlimited potential for bio-medical technologies. However, the direct applications of these technologies toward diagnostics and therapeutics are still far from maturity. The present challenges lay at the interfaces between the engineering systems and the biocomplex systems. A precisely designed engineering system with narrow dynamic range is hard to seamlessly integrate with the adaptive biological system in order to achieve the design goals. These differences remain as the roadblock between two fundamentally non-compatible systems. This paper will not extensively review the existing microfluidic sensors and actuators; rather, we will discuss the sources of the gaps for integration. We will also introduce system interface technologies for bridging the differences to lead toward paradigm shifts in diagnostics and therapeutics

Neuropathological aspects of SARS-CoV-2 infection: significance for both Alzheimer’s and Parkinson’s disease

"Evidence suggests that SARS-CoV-2 entry into the central nervous system can result in neurological and/or neurodegenerative diseases. In this review, routes of SARS-Cov-2 entry into the brain via neuroinvasive pathways such as transcribrial, ocular surface or hematogenous system are discussed. It is argued that SARS-Cov-2-induced cytokine storm, neuroinflammation and oxidative stress increase the risk of developing neurodegenerative diseases such as Alzheimer’s disease and Parkinson’s disease. Further studies on the effects of SARS-CoV-2 and its variants on protein aggregation, glia or microglia activation, and blood-brain barrier are warranted"

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

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

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

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

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

Identification and Optimization of Combinatorial Glucose Metabolism Inhibitors in Hepatocellular Carcinomas.

Hepatocellular carcinoma (HCC) is the fifth most common cancer worldwide. The expression of glucose transporter isoform 1, a key factor in transporting glucose into cancer cells, is overexpressed in several human cancers, including HCC. In addition, this has been shown to correlate with a higher proliferation index and more advanced stages in HCC, suggesting that inhibition of glucose metabolism is a promising therapeutic strategy. Our study used high-content screening (HCS) for compounds that target glucose metabolism and effect cell death in HCC cells. Specifically, we showed that a fluorescent 2-deoxyglucose analog, 2-[N-(7-nitrobenz-2- oxa-1,3-diazol-4-yl)amino]-2-deoxyglucose, and CellTrace Calcein Red-Orange AM can be used reliably as readouts for glucose uptake and proliferative index, respectively, to identify drug candidates that simultaneously reduce glucose uptake and induce cell death in HCC cells. Thus, fluorescent glucose uptake bioprobes can be implemented in HCS assays to identify previously unknown regulators of glucose metabolism in HCC. In addition, our study also employs the use of feedback system control (FSC.II), a platform that optimizes the combinations of drugs identified through HCS. The coordinated use of HCS and FSC.II can improve the development of drug combinations and uncover previously unidentified signaling pathways that govern HCC as well as other cancers

Identification and Optimization of Combinatorial Glucose Metabolism Inhibitors in Hepatocellular Carcinomas.

Hepatocellular carcinoma (HCC) is the fifth most common cancer worldwide. The expression of glucose transporter isoform 1, a key factor in transporting glucose into cancer cells, is overexpressed in several human cancers, including HCC. In addition, this has been shown to correlate with a higher proliferation index and more advanced stages in HCC, suggesting that inhibition of glucose metabolism is a promising therapeutic strategy. Our study used high-content screening (HCS) for compounds that target glucose metabolism and effect cell death in HCC cells. Specifically, we showed that a fluorescent 2-deoxyglucose analog, 2-[N-(7-nitrobenz-2- oxa-1,3-diazol-4-yl)amino]-2-deoxyglucose, and CellTrace Calcein Red-Orange AM can be used reliably as readouts for glucose uptake and proliferative index, respectively, to identify drug candidates that simultaneously reduce glucose uptake and induce cell death in HCC cells. Thus, fluorescent glucose uptake bioprobes can be implemented in HCS assays to identify previously unknown regulators of glucose metabolism in HCC. In addition, our study also employs the use of feedback system control (FSC.II), a platform that optimizes the combinations of drugs identified through HCS. The coordinated use of HCS and FSC.II can improve the development of drug combinations and uncover previously unidentified signaling pathways that govern HCC as well as other cancers