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Chishti, H. B.

Publications and source records attributed to Chishti, H. B..

2 recordsLinked to original sources

Transiently boosting Vγ9+Vδ2+ γδ T cells early in Mtb coinfection of SIV-infected juvenile macaques does not improve Mtb host resistance.

Children living with HIV have a higher risk of developing tuberculosis (TB), a disease caused by the bacterium Mycobacterium tuberculosis (Mtb). Gamma delta ({gamma}{delta}) T cells in the context of HIV/Mtb coinfection have been understudied in children, despite in vitro evidence suggesting {gamma}{delta} T cells assist with Mtb control. We investigated whether boosting a specific subset of {gamma}{delta} T cells, phosphoantigen-reactive V{gamma}9+V{delta}2+ cells, could improve TB outcome using a nonhuman primate model of pediatric HIV/Mtb coinfection. Juvenile Mauritian cynomolgus macaques (MCM), equivalent to 4-8-year-old children, were infected intravenously (i.v.) with SIV. After 6 months, MCM were coinfected with a low dose of Mtb and then randomized to receive zoledronate (ZOL), a drug that increases phosphoantigen levels, (n=5; i.v.) at 3- and 17-days after Mtb accompanied by recombinant human IL-2 (s.c.) for 5 days following each ZOL injection. A similarly coinfected MCM group (n=5) was injected with saline as a control. V{gamma}9+V{delta}2+ {gamma}{delta} T cell frequencies spiked in the blood, but not airways, of ZOL+IL-2-treated MCM following the first dose, however, were refractory to the second dose. At necropsy eight weeks after Mtb, ZOL+IL-2 treatment did not reduce pathology or bacterial burden. {gamma}{delta} T cell subset frequencies in granulomas did not differ between treatment groups. These data show that transiently boosting peripheral {gamma}{delta} T cells with ZOL+IL-2 soon after Mtb coinfection of SIV-infected MCM did not improve Mtb host defense.

immunology↗

Optimizing tuberculosis treatment efficacy: comparing the standard regimen with Moxifloxacin-containing regimens

Tuberculosis (TB) continues to be one of the deadliest infectious diseases in the world, causing ~1.5 million deaths every year. The World Health Organization initiated an End TB Strategy that aims to reduce TB-related deaths in 2035 by 95%. Recent research goals have focused on discovering more effective and more patient-friendly antibiotic drug regimens to increase patient compliance and decrease emergence of resistant TB. Moxifloxacin is one promising antibiotic that may improve the current standard regimen by shortening treatment time. Clinical trials and in vivo mouse studies suggest that regimens containing moxifloxacin have better bactericidal activity. However, testing every possible combination regimen with moxifloxacin either in vivo or clinically is not feasible due to experimental and clinical limitations. To identify better regimens more systematically, we simulated pharmacokinetics/pharmacodynamics of various regimens (with and without moxifloxacin) to evaluate efficacies, and then compared our predictions to both clinical trials and nonhuman primate studies performed herein. We used GranSim, our well-established hybrid agent-based model that simulates granuloma formation and antibiotic treatment, for this task. In addition, we established a multiple-objective optimization pipeline using GranSim to discover optimized regimens based on treatment objectives of interest, i.e., minimizing total drug dosage and lowering time needed to sterilize granulomas. Our approach can efficiently test many regimens and successfully identify optimal regimens to inform pre-clinical studies or clinical trials and ultimately accelerate the TB regimen discovery process. Author summaryTuberculosis (TB) is a top global health concern and treatment for TB requires multiple antibiotics taken for long periods of time, which is challenging for TB patients. Therefore, identifying regimens that are more effective and more patient-friendly than the standard treatment is urgently needed. It is also known that non-compliance leads to the development of drug resistant TB. In this work, we pair computational and experimental models to predict new regimens for the treatment of TB that optimize how fast bacteria are cleared using minimal dosage. We apply novel approaches to this goal and validate our predictions using a non-human primate model. Our findings suggest that systems pharmacological modeling should be employed as a method to narrow the design space for drug regimens for tuberculosis and other diseases as well.

pharmacology and toxicology↗