Integrating drug concentrations and minimum inhibitory concentrations with Bayesian-dose optimisation for multidrug-resistant tuberculosis.

The problems of multidrug-resistant (MDR) tuberculosis (TB), extensively drug-resistant (XDR) TB, and even ‘‘totally drug-resistant’’ TB (a term that is still debated), have exposed two important clinical issues [1, 2]. The first issue involves figuring out a way to prevent these difficult-to-treat diseases. The second issue is treating patients with drug-resistant TB [3]. In the meantime even the current susceptibility breakpoints have been challenged as being too high, so that potentially more drug-resistant TB exists than anticipated [4]. This latter aspect suggests that we need a better idea of the minimum inhibitory concentrations (MICs) to both first-line drugs and second-line drugs. Here we propose the use of MICs and drug concentrations to calculate pharmacokinetic/pharmacodynamics (PK/PD) target exposures and then to use Bayesian-feedback dosing as an integrated solution that could improve treatment outcomes.