A multistate tuberculosis pharmacometric model: a framework for studying anti-tubercular drug effectsin vitro

Abstract

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.

Translational PK/PD of anti-infective therapeutics

“…Complicating factors such as resistance development, combination therapy, or host responses can be incorporated in the modeling approach. In situations where only limited data are available, system specific parameters describing for example growth characteristics may also be implemented based on prior knowledge [4]. With these tools at hand, translational PK/PD modelling and simulation may play a pivotal role in identifying the right balance between bacterial killing, adverse effects, and appearance of resistance, and may help identifying and optimizing dosing regimens for novel and established antibacterial agents.…”

“…However, there is still a knowledge gap regarding translation of PK/PD parameters from preclinical to clinical settings. For example, recently a multistate tuberculosis pharmacometric model describing different bacterial states of Mycobacterium tuberculosis was developed based on in vitro data [4]. For clinical implementation of this model [46], most of the parameters pertaining to the natural bacterial growth were fixed to the in vitro estimates; however the exposure-response parameters related to drug effect had to be estimated from clinical data and were different from the in vitro drug effect parameters.…”

“…For example, infections with Mycobacterium tuberculosis are usually characterized by the presence of both multiplying and non-multiplying bacteria, and most antibiotics are usually more effective in killing multiplying rather than non-multiplying microorganisms. Mechanistic PK/PD models have successfully been applied to delineate the independent drug effects associated with killing of the multiplying and the non-multiplying bacterial populations [4]. In another example, different dosing regimens with similar exposure to colistin resulted in greater emergence of resistance with longer dosing intervals.…”

The multistate tuberculosis pharmacometric model: a semi-mechanistic pharmacokinetic-pharmacodynamic model for studying drug effects in an acute tuberculosis mouse model

“…A rifampicin population PK model including auto-induction earlier developed in the mouse [12] was linked to the MTP model [6], using a population pharmacokinetic parameter (PPP) approach [13], since PK data was not obtained in this study. This allowed predicting typical rifampicin blood concentrations over time, based on the rifampicin drug regimens applied in this study, as the input to the MTP model.…”

“…The Multistate Tuberculosis Pharmacometric (MTP) model [6] is a semi-mechanistic PK–PD model for studying anti-tubercular drug effects which was developed using in vitro data. It has further been extended to use for clinical data and proven to be able to be used for clinical trial simulations [7].…”