The development of optimal treatment regimens in tuberculosis (TB) remains challenging due to the need of combination therapy and possibility of pharmacodynamic (PD) interactions. Preclinical information about PD interactions needs to be used more optimally when designing early bactericidal activity (EBA) studies. In this work, we developed a translational approach which can allow for forward translation to predict efficacy of drug combination in EBA studies using the Multistate Tuberculosis Pharmacometric (MTP) and the General Pharmacodynamic Interaction (GPDI) models informed by in vitro static time-kill data. These models were linked with translational factors to account for differences between the in vitro system and humans. Our translational MTP-GPDI model approach was able to predict the EBA 0-2 days , EBA 0-5 days , and EBA 0-14 days from different EBA studies of rifampicin and isoniazid in monotherapy and combination. Our translational model approach can contribute to an optimal dose selection of drug combinations in early TB clinical trials.A more effective regimen with a shorter treatment duration is an urgent need to provide more efficient treatment options for patients with pulmonary tuberculosis (TB). Since TB treatment requires multidrug regimens, pharmacodynamics (PD) interactions can be a challenge for developing optimal regimens. The typical early bactericidal activity (EBA) study in a TB phase IIa trial acts as the first "proof-of-concept" study of microbiological activity in humans when the drug is given as monotherapy for 2 weeks. Few EBA studies have explored combinations of drugs, 1,2 but traditionally the EBA studies have played a role in dose selection for the phase IIb trials where the results from the EBA study informs the combination regimen to be evaluated. However, the most optimal dose in monotherapy may not be the optimal dose for combination treatment. Therefore, it is not possible to study the most optimal dose combinations in humans due to the many possible combinations. An alternative is to use preclinical in vitro information where it is possible to study a large set of combinations in order to define the PD interaction space. However, prediction of clinical efficacy based on in vitro information needs to account for translational factors such as human pharmacokinetics (PK), target site exposure, and mycobacterial factors, such as bacterial growth phase, postantibiotic effect (PAE), and minimum inhibitory concentration (MIC) distribution. 3

Background The weight-band dosing in tuberculosis treatment regimen has been implemented in clinical practice for decades. Patients will receive different number of fixed dose combination tablets according to their weight-band. However, some analysis has shown that weight was not the best covariate to explain variability of rifampicin exposure. Furthermore, the rationale for using weight-band dosing instead of flat-dosing becomes questionable. Therefore, this study aimed to compare the average and the variability of rifampicin exposure after weight-band dosing and flat-dosing. Methods Rifampicin exposure were simulated using previously published population pharmacokinetics model at dose 10–40 mg/kg for weight-band dosing and dose 600–2400 mg for flat-dosing. The median area under the curve (AUC0–24 h) after day 7 and 14 were compared as well as the variability of each dose group between weight-band and flat-dosing. Results The difference of median AUC0–24 h of all dose groups between flat-dosing and weight-band dosing were considered low (< 20%) except for the lowest dose. At the dose of 10 mg/kg (600 mg for flat-dosing), flat-dosing resulted in higher median AUC0–24h compared to the weight-band dosing. A marginal decrease in between-patient variability was predicted for weight-band dosing compared to flat-dosing. Conclusions Weight-band dosing yields a small and non-clinically relevant decrease in variability of AUC0–24h.

Mycobacterium avium complex pulmonary disease shows limited efficacy in an open source hollow fiber system that simulates human plasma and epithelial lining fluid pharmacokinetics, Clinical Microbiology and Infection,