There are many reasons that the development of new drugs is slow and expensive. While many of these are unavoidable and associated with ensuring safety and efficacy, delays and additional costs are partially attributable to bottlenecks that occur as candidate compounds are weeded out during library screening, pre-clinical research, and clinical trials. Late failures of drug candidates are especially problematic given the time and financial investments made during the long development process. These late failures are often due to relapse, either because of persisting bacteria or resistance evolution, an outcome that early trials of drug efficacy are not designed to assess. For example, in the recent trial assessing moxifloxacin for TB, despite excellent early success, patients were more likely to relapse with the new regimen compared to standard therapy.

We require new tools to rationally design dosing regimens to maximize the benefits of existing antibiotics and to shorten the development process for new antibiotics . The development of models that can inform optimal dosing strategies from data collected in early phases of antibiotic development (e.g. drug-target binding and transmembrane permeability) could accelerate the drug development process and help to identify promising compounds that should be prioritized. In particular, mathematical models that predict relapse from pre-clinical and early clinical data would be tremendously helpful.

We use a novel mechanistic modeling framework that makes explicit links between chemical reaction kinetics (i.e. drug-target association and dissociation), effects on bacterial growth and death, and the population dynamics of bacteria within infected hosts. This modeling framework has been able to reproduce (and mechanistically explain) observed differences in antibiotic pharmacodynamics. Our mechanistic models differs from previous models, which have explicitly built in these pharmacodynamic effects.