Mathematical model and tool to explore shorter multi-drug therapy options for active pulmonary tuberculosis

Abstract

Standard treatment for active tuberculosis (TB) requires drug treatment with at least four drugs over six months. Shorter-duration therapy would mean less need for strict adherence, and reduced risk of bacterial resistance. A system pharmacology model of TB infection, and drug therapy was developed and used to simulate the outcome of different drug therapy scenarios. The model incorporated human immune response, granuloma lesions, multi-drug antimicrobial chemotherapy, and bacterial resistance. A dynamic population pharmacokinetic/pharmacodynamic (PK/PD) simulation model including rifampin, isoniazid, pyrazinamide, and ethambutol was developed and parameters aligned with previous experimental data. Population therapy outcomes for simulations were found to be generally consistent with summary results from previous clinical trials, for a range of drug dose and duration scenarios. An online tool developed from this model is released as open source software. The TB simulation tool could support analysis of new therapy options, novel drug types, and combinations, incorporating factors such as patient adherence behavior.

Bayesian Augmented Clinical Trials in TB Therapeutic Vaccination

“…There are indeed a number of approaches to simulate different aspects of TB, from purely synthetic approaches for designing new multi-epitope subunit vaccine ( 30 ) and molecular dynamics ( 31 ), to contagion dynamics ( 32 ). There are also models able to simulate pharmacokinetics in TB on a population level ( 33 , 34 ), but none of these produce data relevant to the endpoint of the clinical trial considered in this study. UISS-TB is a bespoke ABM capable of simulating cohorts of TB in silico patients treated with RUTI ( 12 , 24 ), which has been through ASME V&V 40-2018 ( 23 ); to the extent of our knowledge, currently there is no available alternative.…”

Model‐Informed Drug Development for Anti‐Infectives: State of the Art and Future

“…These models have the potential to inform selection of the human‐equivalent dose and dosing schedule of a candidate drug used in a combination to then determine the likelihood of achieving treatment durations of 1 to 3 months with efficacy in both drug sensitive and drug resistant patients. Significant recent progress has been realized in the development of such systems pharmacology models (see below) to enable comparative efficacy evaluation and intended treatment‐shortening potential of novel regimens based on preclinical data and optimized translational simulations 166–170 . These model systems seek to integrate: (i) the quantification of the bacterial growth dynamics in the absence of treatment, (ii) the impact of immune system response in the absence and presence of treatment, (iii) the contribution of each drug (concentration–response relationship) to the observed total efficacy of drug combinations, and (iv) the interplay between disease pathology and drug response, including description of tissue penetration.…”

Modelling tuberculosis drug resistance amplification rates in high-burden settings

“…Mathematical modelling is increasingly used to support programmatic optimization for TB [4][5][6]. Accounting appropriately for MDR-TB in mathematical models of disease is critical; as it differs considerably from drug-sensitive TB (DS-TB) in both epidemiological parameters and relevant outcomes.…”