Skip to main content

The population biology of drug resistance:
Key principles for a more sustainable use of drugs

Final Report Summary - PBDR (The population biology of drug resistance: Key principles for a more sustainable use of drugs)

Modern medicine as well as modern agriculture relies heavily on the availability of effective drugs to control and treat diseases ranging from viral, bacterial, fungal, and parasitic infections to cancer. However, looking back at the history of drug discovery shows that essentially wherever drugs have been used for any prolonged period, resistance has eventually evolved, and in some cases resistance seriously limits our treatment options. The scientific communities working on the evolution of drug resistance for different diseases are disconnected, although many of the fundamental questions are related. Therefore, in the context of the ERC Grant Population Biology of Drug Resistance (PBDR) I assembled a research team working on drug resistance across a range of differing disease systems. The project was divided into five parts: (i) resistance evolution in viruses, (ii) resistance evolution in human bacterial infections, (iii) resistance evolution in parasites (malaria); (iv) resistance evolution in cancer; and (v) resistance evolution in fungal plant pathogens. For each of these disease systems a number of disease specific question regarding were addressed such as: what are the relative contributions of de novo and transmitted resistance in HIV? How does population structure affect resistance evolution in cancer? What is the relative benefit of cycling, mixing or combination therapy in antibiotic therapy? What is the role of feedbacks between within-host competition and epidemiology in malaria? What is the role of population structure for resistance evolution in cancer? What is the effect of spatial structure on resistance evolution in plant pathogens?
To address these questions, we first developed population biological models specific to a particular diseases system, investigating the effect of key factors on resistance evolution in a disease specific manner. However, as many of the question listed above are not unique only one disease or pathogen type, we adapted the models to other diseases in order develop a more general understanding how these key factors affect resistance evolution across different disease systems. For example, we developed population dynamical models that incorporate both the within and the between host level of competition between drug-sensitive and resistant strains, and applied these models to HIV and malaria to investigate how the epidemiological and the intra-host level feed back onto each other. Moreover, we studied the benefits and limitations of combination therapy in bacterial diseases, plant fungal infections, and cancer. Our work argues that under a wide range of conditions combination therapy performs best in preventing the emergence of drug resistance. We studied the respective contributions of standing genetic variation and de novo emergence to drug resistance, finding again that under a broad range of conditions standing genetic variation is expected to be the main contributor to drug resistance. We found that methods, models, and concepts can be successfully adapted to study resistance evolution in different disease systems and that scientific exchange across disease systems leads to considerable synergy.