ObjectivesMycobacterium tuberculosis can exist in different states in vitro, which can be denoted as fast multiplying, slow multiplying and non-multiplying. Characterizing the natural growth of M. tuberculosis could provide a framework for accurate characterization of drug effects on the different bacterial states.MethodsThe natural growth data of M. tuberculosis H37Rv used in this study consisted of viability defined as cfu versus time based on data from an in vitro hypoxia system. External validation of the natural growth model was conducted using data representing the rate of incorporation of radiolabelled methionine into proteins by the bacteria. Rifampicin time–kill curves from log-phase (0.25–16 mg/L) and stationary-phase (0.5–64 mg/L) cultures were used to assess the model's ability to describe drug effects by evaluating different linear and non-linear exposure–response relationships.ResultsThe final pharmacometric model consisted of a three-compartment differential equation system representing fast-, slow- and non-multiplying bacteria. Model predictions correlated well with the external data (R2 = 0.98). The rifampicin effects on log-phase and stationary-phase cultures were separately and simultaneously described by including the drug effect on the different bacterial states. The predicted reduction in log10 cfu after 14 days and at 0.5 mg/L was 2.2 and 0.8 in the log-phase and stationary-phase systems, respectively.ConclusionsThe model provides predictions of the change in bacterial numbers for the different bacterial states with and without drug effect and could thus be used as a framework for studying anti-tubercular drug effects in vitro.

Assessment of pharmacodynamic (PD) drug interactions is a cornerstone of the development of combination drug therapies. To guide this venture, we derive a general pharmacodynamic interaction (GPDI) model for ≥2 interacting drugs that is compatible with common additivity criteria. We propose a PD interaction to be quantifiable as multidirectional shifts in drug efficacy or potency and explicate the drugs’ role as victim, perpetrator or even both at the same time. We evaluate the GPDI model against conventional approaches in a data set of 200 combination experiments in Saccharomyces cerevisiae: 22% interact additively, a minority of the interactions (11%) are bidirectional antagonistic or synergistic, whereas the majority (67%) are monodirectional, i.e., asymmetric with distinct perpetrators and victims, which is not classifiable by conventional methods. The GPDI model excellently reflects the observed interaction data, and hence represents an attractive approach for quantitative assessment of novel combination therapies along the drug development process.

A crucial step for accelerating tuberculosis drug development is bridging the gap between preclinical and clinical trials. In this study, we developed a preclinical model‐informed translational approach to predict drug effects across preclinical systems and early clinical trials using the in vitro‐based Multistate Tuberculosis Pharmacometric (MTP) model using rifampicin as an example. The MTP model predicted rifampicin biomarker response observed in 1) a hollow‐fiber infection model, 2) a murine study to determine pharmacokinetic/pharmacodynamic indices, and 3) several clinical phase IIa early bactericidal activity (EBA) studies. In addition, we predicted rifampicin biomarker response at high doses of up to 50 mg/kg, leading to an increased median EBA0‐2 days (90% prediction interval) of 0.513 log CFU/mL/day (0.310; 0.701) compared to the standard dose of 10 mg/kg of 0.181 log/CFU/mL/day (0.076; 0.483). These results suggest that the translational approach could assist in the selection of drugs and doses in early‐phase clinical tuberculosis trials.

PurposeThe purpose of the study was to develop a drug-unspecific approach to pharmacometric modeling for predicting the rate and extent of distribution from plasma to epithelial lining fluid (ELF) and alveolar cells (AC) for data emanating from studies involving bronchoalveolar lavage (BAL) sampling, using rifampicin (RIF) as an example.MethodsData consisting of RIF plasma concentrations sampled at approximately 2 and 4 h postdose and ELF and AC concentrations quantified from one BAL sample, taken at approximately 4 h postdose, in 40 adult subjects without tuberculosis was used as an example for model development.ResultsThis study emphasized the usage of drug-specific plasma pharmacokinetics (PK) for a correct characterization of plasma to pulmonary distribution. As such, RIF PK was described using absorption transit compartments and a one compartment distribution model coupled with an enzyme turn-over model. The ELF and AC distribution model consisted of characterization of the rate of distribution of drug from plasma to ELF and AC by two distribution rate constant, kELF and kAC, respectively. The extent of distribution to ELF and AC was described by unbound ELF/plasma concentration ratio (RELF/unbound-plasma) and unbound AC/plasma concentration ratio (RAC/unbound-plasma) which typical values were predicted to be 1.28 and 5.5, respectively.ConclusionsThe model together with a drug-specific plasma PK description provides a tool for handling data from both single and multiple BAL sampling designs and directly predicts the rate and extent of distribution from plasma to ELF and AC. The model can be further used to investigate design aspects of optimized BAL studies.

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.