Computational pharmacology of rifampin in mice: an application to dose optimization with conflicting objectives in tuberculosis treatment.

Dose selection for rifampin in the treatment of active pulmonary tuberculosis (TB) illustrates some of the challenges for dose optimization within multidrug therapies. Rifampin-based anti-TB regimens are often combined with antiretroviral therapies to treat human immunodeficiency virus (HIV) coinfection. The potent cytochrome P450 (CYP) enzyme inducing properties of rifampin give rise to significant drug-drug interactions, the minimization of which by limiting the dose, conflicts with the maximization of bacterial killing by increasing the dose. Such multiple and conflicting objectives lead to a set of trade-off optimal solutions for dose optimization rather than a single best solution. Here, we combine pharmacokinetic/pharmacodynamic (PK/PD) modeling with multiobjective optimization to quantitatively explore trade-offs between therapeutic and adverse effects of optimal dosing for the example of rifampin in TB-infected mice. The PK/PD model describes rifampin concentrations in plasma and liver following oral administration together with hepatic CYP enzyme induction and bacterial killing kinetics. We include optimization objectives descriptive of antimicrobial efficacy, CYP-mediated drug-drug interactions, and drug exposure-dependent toxicity. Results show non-conventional dosing scenarios that allow for increased efficacy relative to uniform dosing without increasing drug-drug interactions. Additionally, we find currently employed dosages for rifampin to be nearly optimal with respect to trade-offs between efficacy and toxicity. While limited by the accuracy and applicability of the PK/PD model, these results provide an avenue for experimental investigation of complex dose optimization problems. This method can be extended to include additional drugs and optimization objectives, and may provide a useful tool for individualized medicine.