Evaluation of pharmacokinetic‐pharmacodynamic relationships and selection of drug combinations for tuberculosis

Abstract

Despite evidence of the efficacy of anti-tubercular drug regimens in clinical practice, the rationale underpinning the selection of doses and companion drugs for combination therapy remains empirical. Novel methods are needed to optimise the antibacterial activity in combination therapies. A drug-disease modelling framework for rational selection of dose and drug combinations in tuberculosis is presented here. Methods: A model-based meta-analysis was performed to assess the antibacterial activity of different combinations in infected mice. Data retrieved from the published literature were analysed using a two-state bacterial growth dynamics model, including fast-and slow-growing bacterial populations. The contribution of each drug to the overall antibacterial activity of the combination was parameterised as relative change to the potency of the backbone drug (EC 50-F and/or EC 50-S). Rifampicin and bedaquiline were selected as paradigm drugs to evaluate the predictive performance of the modelling approach. Results: Pyrazinamide increased the potency (EC 50-F and EC 50-S) of rifampicin (RZ) and bedaquiline (BZ) by almost twofold. By contrast, pretomanid and isoniazid were found to worsen the antibacterial activity of BZ and RZ, respectively. Following extrapolation of in vivo pharmacokinetic-pharmacodynamic relationships, the dose of rifampicin showing maximum bactericidal effect in tuberculosis patients was predicted to be 70 mgÁkg −1 when given in combination with pyrazinamide. Conclusions: The use of a drug-disease modelling framework may provide a more robust rationale for extrapolation and selection of dose and companion drugs in humans. Our analysis demonstrates that RZ and BZ should be considered as a backbone therapy in prospective novel combination regimens against tuberculosis.

Applications of Model-Based Meta-Analysis in Drug Development

“…Additional collaborations are also possible with the health economics and outcomes research (HEOR) function, with which outputs from the MBMA could be integrated into other, non-clinically focused models, such as cost-effectiveness models or financial forecasting models, potentially leading to improved commercial and financial strategies [45]. Therefore, continued education and communication among project stakeholders, team collaborators, and [19,[107][108][109][110] Correlation between early and late endpoints Allow the use of a biomarker or an early clinical efficacy time point to detect a signal of the treatment effect [38,42,63,111] Pharmacokinetic and pharmacodynamic relationship Establishing the relationship between exposure and an efficacy or safety biomarker, possibly a less frequently reported one and would need data from a large population to be detected [41,[112][113][114][115][116][117] Simulation of established MBMA models Simulate various scenarios using established MBMA models to optimize clinical trial design [62,118] Pharmacoeconomics Incorporate cost-effectiveness into a MBMA model [119] other pharmacometric specialty experts regarding MBMA and MIDD approaches in general are warranted [1]. Another important ally of MIDD approaches is from statistics.…”

Bacterial growth dynamics and pharmacokinetic–pharmacodynamic relationships of rifampicin and bedaquiline in BALB/c mice

“…The differences in the potency of bedaquiline and rifampicin on slow-growing bacteria provide further insight into the requirements for higher rifampicin doses and relatively long treatment periods with drug combinations. We anticipate that such an integrated model will facilitate the ranking of compounds during screening, including improved selection of the doses and partner drugs for combination therapy in patients(Muliaditan & Della Pasqua, 2020).…”

Profiling Pretomanid as a Therapeutic Option for TB Infection: Evidence to Date

“… 50 , 51 In a recent exposure-response model-based meta-analysis in mice, the addition of pretomanid to rifampicin and bedaquiline reduced the simulated bacterial activity of bedaquiline in multi-drug regimens; however, predicted MICs were still within the range of susceptibility. 52 …”