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