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Our work focuses on tuberculosis (TB), which remains the bacterial infection with the highest disease burden worldwide. Approximately one third of the world’s population is latently infected. Although anti-tuberculosis drugs have been available for decades, the success rates of tuberculosis treatment are poor. Overall TB treatment success in the EU is only 74%, and success plateaus near 85% even in countries with very high income (e.g. in Norway or Switzerland), clearly leaving room for improvement. Furthermore the appearance and spread of drug resistant forms of tuberculosis has complicated control efforts based on standardized regimens. When TB acquires resistance to first-line (MDR) or even second-line drugs (XDR), treatment success rates fall to 40% to 34%, respectively.

Part of the problem is that we struggle with finding new antibiotics, also because developing new antibiotics is very expensive and takes a long time. At the very beginning of antibiotic development, thousands of drug candidates are tested, and in each development step from experiments in test tubes to experiments with animals to several phases of clinical trials the majority of these candidates fail, leaving only 1-2 drugs that can be used in patients. Finding ways to reduce the amount of trial-and-error would help save time and money.

We have developed mathematical models that can help reducing trial-and-error in antibiotic development. Together with partners from industry and an international network of university-based researchers (Yale, Harvard, Simon-Fraser University, Division of Clinical Infectious Diseases Research Center Borstel, Task Foundation and University of Cape Town), we will apply these models to improve antibiotic therapy.

Research interests:

Antibiotics, Population Biology, Biochemistry, Pharmacology, Tuberculosis, Infectious Diseases, Public Health, Mathematical Modelling


RESEARCH

Antibiotic resistance poses a substantial global health threat . Leading academics have recently declared that we stand at the precipice of the “post-antibiotic era” . While limiting inappropriate prescribing of existing drugs and accelerating the development of novel antibiotics are key elements of any strategy to circumvent resistance, there is also a clear need to develop better treatment strategies using existing drugs to improve their efficacy and prevent the selection of further resistance.

Although antibiotics have been used for more than 70 years, we are not yet able to predict how antibiotic concentration affects activity (i.e. antibiotic pharmacodynamics) even in the simplest settings, e.g. E. coli growth in vitro. Our inability to design rational treatment strategies is illustrated by the substantial improvements in treatment that have been made solely based on expert opinion even after decades of clinical practice. Currently, most dosing recommendations are based on those regimens that perform best during a lengthy and expensive series of trial-and-error experiments. Many drug candidates fail during this testing process, and for those candidates that do make it through, the best regimen may well be missed. This trial-and-error approach also limits opportunities for the improvement of dosing for existing drugs and may slow down the development of new promising antibiotics. Rational dosing of new combination regimens using multiple drugs is even more complex. Antibiotic synergy and antagonism cannot usually be predicted and the nature of the drug-drug interaction may change depending on drug concentration. Furthermore, differences in the pharmacokinetic and pharmacodynamic profiles of drugs used in combination can facilitate the selection of resistance during multi-drug treatment.



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.

Research interests

Antibiotics

Population Biology 

Tuberculosis

Mathematical Modeling

Biochemistry

Pharmacology

Infectious Diseases