Background Identification 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).

Results All 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.

Conclusions With 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.

Additional Metadata
Keywords rifampin ethambutol drug combinations infectious mononucleosis isoniazid mycobacterium tuberculosis tuberculosis drug development pharmacodynamic interaction
Persistent URL dx.doi.org/10.1093/jac/dkx380, hdl.handle.net/1765/110250
Journal Journal of Antimicrobial Chemotherapy
Citation
Clewe, O, Wicha, S.G, de Vogel, C.P, de Steenwinkel, J.E.M, & Simonsson, Ulrika. (2017). A model-informed preclinical approach for prediction of clinical pharmacodynamic interactions of anti-TB drug combinations. Journal of Antimicrobial Chemotherapy, 73(2), 437–447. doi:10.1093/jac/dkx380