A model-informed preclinical approach for prediction of clinical pharmacodynamic interactions of anti-TB drug combinations

Abstract

BackgroundIdentification of pharmacodynamic interactions is not reasonable to carry out in a clinical setting for many reasons. The aim of this work was to develop a model-informed preclinical approach for prediction of clinical pharmacodynamic drug interactions in order to inform early anti-TB drug development.Methods In vitro time–kill experiments were performed with Mycobacterium tuberculosis using rifampicin, isoniazid or ethambutol alone as well as in different combinations at clinically relevant concentrations. The multistate TB pharmacometric (MTP) model was used to characterize the natural growth and exposure–response relationships of each drug after mono exposure. Pharmacodynamic interactions during combination exposure were characterized by linking the MTP model to the general pharmacodynamic interaction (GPDI) model with successful separation of the potential effect on each drug’s potency (EC50) by the combining drug(s).ResultsAll combinations showed pharmacodynamic interactions at cfu level, where all combinations, except isoniazid plus ethambutol, showed more effect (synergy) than any of the drugs alone. Using preclinical information, the MTP-GPDI modelling approach was shown to correctly predict clinically observed pharmacodynamic interactions, as deviations from expected additivity.ConclusionsWith the ability to predict clinical pharmacodynamic interactions, using preclinical information, the MTP-GPDI model approach outlined in this study constitutes groundwork for model-informed input to the development of new and enhancement of existing anti-TB combination regimens.

Forecasting Clinical Dose–Response From Preclinical Studies in Tuberculosis Research: Translational Predictions With Rifampicin

“…4 As such, the estimation and prediction of drug effects on this phenotypic resistant subpopulation is crucial in order to develop and predict a successful treatment regimen. The MTP model was developed using in vitro information from classical time-kill experiments and has been successful in describing the effects after exposure to rifampicin, not only for in vitro in monotherapy but also for assessing efficacy of drug combinations in vitro together with the General Pharmacodynamic Interaction model, 5,6 in vivo monotherapy, 7 in vivo assessment of drug combinations, 8 and clinical settings 9 suggesting its value for describing drug effects as well as for translational applications.…”

Translational Assessment of Drug‐Induced Proximal Tubule Injury Using a Kidney Microphysiological System

“…The expected drug concentration–effect relationship (pharmacodynamics) of rifampicin was simulated using a maximum effect sigmoidal model ( E max ). For each dosing regimen, the effect E of rifampicin on inhibiting the growth of mycobacteria tuberculosis was calculated as a function of mean drug concentration after 2 and 10 days:where EC 50 is the drug concentration leading to 50% inhibition, C is the drug concentration, and the steepness of the curve (Hill coefficient; Methods ). Subsequently, the area under the effect curve was calculated and normalized to maximum effect to assess the efficacy differences of the simulated dosing regimens.…”

Model-Informed Drug Discovery and Development Strategy for the Rapid Development of Anti-Tuberculosis Drug Combinations

“…The GPDI model-based approach proposes a PD interaction to be quantifiable, as multidirectional shifts in drug efficacy (E max ) or potency (EC 50 ) and explicates the drugs' role as victim, perpetrator or even both at the same time. The GPDI model has been utilized along with the MTP model [121] to develop a model-informed preclinical approach for the prediction of PD interactions [144]. The MTP-GPDI model has been further employed to successfully evaluate and quantify the PD interactions of anti-TB drug combinations in mice [145].…”