Both Pharmacokinetic Variability and Granuloma Heterogeneity Impact the Ability of the First-Line Antibiotics to Sterilize Tuberculosis Granulomas

Abstract

Tuberculosis (TB) remains as one of the world's deadliest infectious diseases despite the use of standardized antibiotic therapies. Recommended therapy for drug-susceptible TB is up to 6 months of antibiotics. Factors that contribute to lengthy regimens include antibiotic underexposure in lesions due to poor pharmacokinetics (PK) and complex granuloma compositions, but it is difficult to quantify how individual antibiotics are affected by these factors and to what extent these impact treatments. We use our next-generation multi-scale computational model to simulate granuloma formation and function together with antibiotic pharmacokinetics and pharmacodynamics, allowing us to predict conditions leading to granuloma sterilization. In this work, we focus on how PK variability, determined from human PK data, and granuloma heterogeneity each quantitatively impact granuloma sterilization. We focus on treatment with the standard regimen for TB of four first-line antibiotics: isoniazid, rifampin, ethambutol, and pyrazinamide. We find that low levels of antibiotic concentration due to naturally occurring PK variability and complex granulomas leads to longer granuloma sterilization times. Additionally, the ability of antibiotics to distribute in granulomas and kill different subpopulations of bacteria contributes to their specialization in the more efficacious combination therapy. These results can inform strategies to improve antibiotic therapy for TB.

A multi-scale pipeline linking drug transcriptomics with pharmacokinetics predictsin vivointeractions of tuberculosis drugs

“…Even for a given simulated granuloma and drug regimen, antibiotic concentrations can vary due to host-to-host PK variability 31 . This can also result in changes in iDIS.…”

“…Growth rates of extracellular Mtb are also slowed by the presence of caseum (dead cell debris), as a way to estimate the effect of lack of oxygen 44 . The parameter values describing rules and interactions are based on previous GranSim studies and evidence from experimental literature and datasets on non-human primates 22,31,45 .…”

“…We simulate antibiotic delivery within the GranSim computational framework as previously described 22,29,31 . Briefly, a plasma PK model simulates absorption into plasma following an oral dose, exchange with peripheral tissue, and first-order elimination from plasma.…”

“…Hence the dynamic binding and partitioning of these drugs are modeled using ordinary differential equations 29 . We determined plasma PK parameters by calibration to human data as previously described 20,31,46 . We calibrated tissue PK parameters based on concentrations in rabbit or human lesions 20,29,31,39 .…”

“…GranSim uses a hybrid agent-based model to describe interactions between immune cells and cytokines with bacteria and antibiotic delivery to granulomas, and provides a dynamic picture of pathogen clearance leading to granuloma sterilization . Previously, we modeled PK/PD in GranSim and explored regimens with isoniazid and rifampin, three fluoroquinolones, and more recently HRZE [29][30][31] . However, in these studies we assumed no interaction between antibiotics.…”