The Role of Evolutionary Biology in Resistance Management
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Antibiotics, antivirals, herbicides, insecticides, and anti-cancer drugs are all used to control biological entities, which harm humans or their enterprises. Our use of such control measures has brought enormous health and economic benefits. However, pathogens and pests are highly adaptable and evolve rapidly to changing environments. This may change their ability to spread and their susceptibility to control measures. This article describes commonly used strategies in managing the rate of resistance evolution, as well as the potential of sharing ideas about control measures across different disciplines and industries as a means of improving our ability to preserve existing control measures.
Given time and opportunity, the pests and pathogens we seek to control will inevitably evolve resistance to control measures employed against them. Resistance is not a new phenomenon, and it has not arisen solely because of human interventions. Antibiotic resistance in bacteria evolved as a response to competition between species. Many of the antibiotics in use today are products of fungi and were used as chemical weapons against bacteria, or by one kind of bacteria against another. In nature, the bacteria targeted by these weapons often develop ways to avoid or inactivate such chemical compounds. The ability to evolve mechanisms that allow survival in the face of chemical control measures is at work when; insects evolve resistance to insecticides, parasites, bacteria and viruses evolve resistance to drug treatments, and when cancer cells evolve resistance to chemotherapy (1–6). Though development of new compounds plays an important role in maintaining a chemical arsenal for use in controlling pests and pathogens, evidence suggests that slowing the evolution of resistance is more effective at ensuring an adequate supply of effective drugs than is promoting the rate at which new drugs are developed (7).
UK resistance management policy
Current UK objectives for resistance management focus on antibiotic resistance in both medical and veterinary environments (8,9). Current strategy seeks to reduce the use of antibiotics where it is safe and appropriate to do so, in order to reduce current and future prevalence of antimicrobial resistance. Methods used to achieve this include:
• Improving infection prevention and control to reduce the need for antibiotics.
• Promoting antibiotic stewardship, in order to preserve currently effective therapies, focussing on the appropriate use of these drugs.
• Improving knowledge on resistance mechanisms.
• Facilitating the development of new drugs, vaccines and diagnostics.
Stewardship practices tend to focus on preserving drug efficacy with practical measures such as, use of the most appropriate drug, correct drug dosage and duration of therapy. However, scientists and clinicians from diverse fields of research on resistance could benefit from sharing lessons learned, or considering the possibility that common evolutionary or mechanistic processes may be operating across their different biological systems. At the moment, researchers from different communities publish their research in different journals with little or no citation of common themes between different disciplines (10). A greater exchange of ideas between these disparate groups might increase progress concerning resistance evolution and help in developing a single resistance focused scientific community.
Examples of resistance management strategies
Pest control: Resistance of crop pests in the US is managed using a combination of approaches implemented with the collaboration of farmers, regulators and scientists in academia and industry. Genetically modified (GM) plants are able to produce two or more toxins, which are harmful to insects but not humans, the use of more than one control agent is expected to slow the rate of resistance evolution, as it is less likely that a population will evolve resistance to two chemicals when applied together. Maintaining areas of the crop that do not produce toxins further enhances this control strategy. These toxin free areas known as refuges enable the survival of toxin-susceptible insects, if resistance does arise in the treated area of the crop then these individuals are more likely to mate with nearby susceptible insects, and so slow the rate of resistance evolution.
Drug therapies: Drug combination therapies have been successful in slowing the rate of resistance evolution in HIV, cancer and malaria treatment. These combination treatments work on the same principles as the GM toxins in plants, it being less likely that resistance will evolve when a pathogen is challenged with two or more drugs. In certain cases this effect can be amplified, for instance, if resistance to one drug renders a pathogen more susceptible to the second drug this places the pathogen in an evolutionary trap.
HIV control: In the 1980’s initial treatment of HIV using antiretrovirals was unsuccessful, every newly discovered drug routinely developed resistance. The use of sequential monotherapies in this case promoted the evolution of multidrug resistance. The introduction of drug combination therapies has done much to bring about control of viral loads in HIV infected patients. However, there are still many areas where sequential monotherapies are being applied and are likely to lead to sequential resistance evolution.
Malaria vector control: Although resistance of mosquitoes to pyrethroid insecticides is not yet universal, this method of controlling malaria may be subject to imminent failure. Pyrethroids are used in agriculture, bed nets and household sprays, providing such a steep selection pressure on mosquito populations is likely to lead to the evolution of resistance.
The following measures should be considered to augment current policy with regard to the control resistance evolution:
• Promotion of collaborations between farmers, health workers, regulators and scientists in academia and industry, which would facilitate the sharing of ideas and implementation of strategies that effectively slow the rate of resistance evolution.
• All chemical control agents should have a specific management plan designed to prolong the period of effective use.
• The use of model experimental evolution systems to screen chemical compounds for their propensity to elicit resistance in target organisms.
Article by Alan Reynolds
1. Greene BYSE, Reid ANN, Riley MA, Ph D, Read A, Phil D. MOVING TARGETS : FIGHTING THE EVOLUTION OF RESISTANCE IN INFECTIONS , PESTS , AND CANCER. [Internet]. The American Academy of Microbiology Colloquim. Philadelphia; 2012. Available from: http://academy.asm.org/index.php/browse-all-reports/666-moving-targets-fighting-the-evolution-of-resistance-in-infections-pests-and-cancer
2. Read AF, Day T, Huijben S. The evolution of drug resistance and the curious orthodoxy of aggressive chemotherapy. Proc Natl Acad Sci U S A [Internet]. 2011 Jun 28 [cited 2014 Jul 23];108 Suppl 10871–7. Available from: http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=3131826&tool=pmcentrez&rendertype=abstract
3. Read AF, Lynch P a, Thomas MB. How to make evolution-proof insecticides for malaria control. PLoS Biol [Internet]. 2009 Apr 7 [cited 2013 Mar 4];7(4):e1000058. Available from: http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=3279047&tool=pmcentrez&rendertype=abstract
4. Thomas MB, Godfray HCJ, Read AF, van den Berg H, Tabashnik BE, van Lenteren JC, et al. Lessons from agriculture for the sustainable management of malaria vectors. PLoS Med [Internet]. 2012 Jan [cited 2013 Apr 29];9(7):e1001262. Available from: http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=3393651&tool=pmcentrez&rendertype=abstract
Rivero A, Vézilier J, Weill M, Read AF, Gandon S. Insecticide control of vector-borne diseases: when is insecticide resistance a problem?
5. PLoS Pathog [Internet]. 2010 Jan [cited 2013 Mar 5];6(8):e1001000. Available from: http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=2916878&tool=pmcentrez&rendertype=abstract
Hastings IM. Modelling parasite drug resistance: lessons for management and control strategies. Trop Med Int Health [Internet]. 2001 Nov;6(11):883–90. Available from: http://www.ncbi.nlm.nih.gov/pubmed/11703842
McClure NS, Day T. Slowing evolution is more effective than enhancing drug development for managing resistance [Internet]. 2013 Apr p. 1–32. Available from: http://arxiv.org/abs/1304.7715
6. Department of Health, Department of Environment F& RA. UK Five Year Antimicrobial Resistance Strategy 2013 to 2018 [Internet]. London; 2013. Available from: https://www.gov.uk/government/publications/uk-5-year-antimicrobial-resistance-strategy-2013-to-2018
Resistance AC on AR and HAI. “ START SMART – THEN FOCUS ” ANTIMICROBIAL STEWARDSHIP : Guidance for antimicrobial stewardship [Internet]. London; 2011. Available from: https://www.gov.uk/government/publications/antimicrobial-stewardship-start-smart-then-focus
7. Consortium REX. Structure of the Scientific Community Modelling the Evolution of Resistance. Futrelle R, editor. PLoS One [Internet]. 2007 Dec 5 [cited 2012 Nov 8];2(12):e1275. Available from: http://dx-stage.plos.org/10.1371/journal.pone.0001275