RESEARCH

Antibiotic resistance poses a substantial global health threat (1). Leading academics have recently declared that we stand at the precipice of the “post-antibiotic era” (2). 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 (3-7). 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 (8-12). 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 (13). 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 (14). Furthermore, differences in the pharmacokinetic and pharmacodynamic profiles of drugs used in combination can facilitate the selection of resistance during multi-drug treatment (15,16).

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 (17), 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 (18). 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 (19).

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 (Abel zur Wiesch et al., 2015; Abel zur Wiesch et al., 2017). Our mechanistic models differs from previous models (20), which have explicitly built in these pharmacodynamic effects.

  1. United Nations. http://www.un.org/pga/71/event-latest/high-level-meeting-on-antimicrobial-resistance/. (2016).

  2. Woolhouse, M. & Farrar, J. Policy: An intergovernmental panel on antimicrobial resistance. Nature 509, 555-557 (2014).

  3. Eagle, H., Fleischman, R. & Levy, M. "Continuous" vs. "discontinuous" therapy with penicillin; the effect of the interval between injections on therapeutic efficacy. N Engl J Med 248, 481-488, doi:10.1056/NEJM195303192481201 (1953).

  4. Coates, A. R., Hu, Y., Jindani, A. & Mitchison, D. A. Contradictory results with high-dosage rifamycin in mice and humans. Antimicrob Agents Chemother 57, 1103, doi:10.1128/AAC.01705-12 (2013).

  5. Drusano, G. L. Antimicrobial pharmacodynamics: critical interactions of 'bug and drug'. Nature Publishing Group 2, 289-300, doi:10.1038/nrmicro862 (2004).

  6. Tuntland, T. et al. Implementation of pharmacokinetic and pharmacodynamic strategies in early research phases of drug discovery and development at Novartis Institute of Biomedical Research. Front Pharmacol 5, 174, doi:10.3389/fphar.2014.00174 (2014).

  7. Dooley, K. E. et al. Old Drugs, New Purpose: Retooling Existing Drugs for Optimized Treatment of Resistant Tuberculosis. Clinical Infectious Diseases 55, 572-581, doi:10.1093/cid/cis487 (2012).

  8. Boeree, M. J. et al. A dose-ranging trial to optimize the dose of rifampin in the treatment of tuberculosis. Am J Respir Crit Care Med 191, 1058-1065, doi:10.1164/rccm.201407-1264OC (2015).

  9. Lan, A. J., Colford, J. M. & Colford, J. M., Jr. The impact of dosing frequency on the efficacy of 10-day penicillin or amoxicillin therapy for streptococcal tonsillopharyngitis: A meta-analysis. Pediatrics 105, E19 (2000).

  10. Roord, J. J., Wolf, B. H., Gossens, M. M. & Kimpen, J. L. Prospective open randomized study comparing efficacies and safeties of a 3-day course of azithromycin and a 10-day course of erythromycin in children with community-acquired acute lower respiratory tract infections. Antimicrob Agents Chemother 40, 2765-2768 (1996).

  11. Van Deun, A., Salim, M. A., Das, A. P., Bastian, I. & Portaels, F. Results of a standardised regimen for multidrug-resistant tuberculosis in Bangladesh. Int J Tuberc Lung Dis 8, 560-567 (2004).

  12. WHO. The shorter MDR-TB regimen. http://www.who.int/tb/Short_MDR_regimen_factsheet.pdf?ua=1 (2016).

  13. Horsburgh, C. R., Jr., Barry, C. E., 3rd & Lange, C. Treatment of Tuberculosis. N Engl J Med 373, 2149-2160, doi:10.1056/NEJMra1413919 (2015).

  14. Ankomah, P. & Levin, B. R. Two-drug antimicrobial chemotherapy: a mathematical model and experiments with Mycobacterium marinum. PLoS Pathog 8, e1002487, doi:10.1371/journal.ppat.1002487 (2012).

  15. Zhang, Q. et al. Acceleration of emergence of bacterial antibiotic resistance in connected microenvironments. Science 333, 1764-1767, doi:10.1126/science.1208747 (2011).

  16. Dye, C., Williams, B. G., Espinal, M. A. & Raviglione, M. C. Erasing the world's slow stain: strategies to beat multidrug-resistant tuberculosis. Science 295, 2042-2046, doi:10.1126/science.1063814 (2002).

  17. Gillespie, S. H. et al. Four-month moxifloxacin-based regimens for drug-sensitive tuberculosis. N Engl J Med 371, 1577-1587, doi:10.1056/NEJMoa1407426 (2014).

  18. Norrby, S. R., Nord, C. E., Finch, R., European Society of Clinical, M. & Infectious, D. Lack of development of new antimicrobial drugs: a potential serious threat to public health. Lancet Infect Dis 5, 115-119, doi:10.1016/S1473-3099(05)01283-1 (2005).

  19. Zhang, R. Pharmacodynamics: Which trails are your drugs taking? Nat Chem Biol 11, 382-383, doi:10.1038/nchembio.1795 (2015).

  20. Nielsen, E. I., Cars, O. & Friberg, L. E. Pharmacokinetic/pharmacodynamic (PK/PD) indices of antibiotics predicted by a semimechanistic PKPD model: a step toward model-based dose optimization. Antimicrob Agents Chemother 55, 4619-4630, doi:10.1128/AAC.00182-11 (2011).

Research interests

Antibiotics

Population Biology 

Tuberculosis

Mathematical Modeling

Biochemistry

Pharmacology

Infectious Diseases 

The Abel zur Wiesch Lab

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