Drug-resistance management results presented at two symposia at MIM in Senegal

Maciek Boni Drug Resistance, Global Health, Malaria

We presented some of our most recent modeling results on how to effectively manage resistance to multiple artemisinin combination therapies (ACTs) at two symposia at the 7th Multilateral Initiative on Malaria (MIM) Pan-African Malaria Conference.  One symposium was run by our partners at Medicines for Malaria Venture (MMV) and a second one was run by Guilin Pharmaceuticals who are awaiting WHO pre-qualification of their DHA-PPQ formulation.

At the MMV symposium, we discussed how deployment of multiple first-line therapies (MFT) against malaria may be able to provide an extra layer of protection by delaying the emergence and onset of drug resistance. We discussed the principle of environmental variation in evolution, and how evolution is generally slowed down in variable environments. For malaria treatment, this translates to creating a “variable drug environment” for malaria parasites. The most straightforward way to do this is to treat a patient with combination therapy; this way, the parasites are experiencing the action of both drugs simultaneously. The second best option is to deploy MFT. Under MFT, a parasite may experience a constant (non-variable) drug environment during a single patient treatment, but the parasite will likely experience a different environment the next time it is treated. The least efficient way to achieve environmental variation is to cycle drugs on a long time scale. Under this type of cycling or rotation policy, the parasites’ environment is constant for the duration of each individual cycling period (and thus evolution is easier).

We reviewed the three main reasons that MFT outperforms cycling policies (see here), and we showed how private-market drug use can affect evolutionary trajectories of drug-resistant alleles.

At the Guilin symposium, we showed what drug-resistance management might look like in a high-transmission setting. Specifically, we modeled the drug-coverage and drug-usage patterns in Burkina Faso, where province by province prevalence estimates range from 4% to 82%. This animation shows that artemisinin resistance evolves rather slowly under Burkina Faso’s current treatment scenario (60% coverage), mainly because a low proportion (20%-30%) of malaria cases are treated with ACTs.

( In the animation above username=mboni and password=malaria. Set to 50 frames per second for best playback. )

This animation shows the same scenario under 90% coverage. Artemisinin resistance evolution is much more rapid. You can refresh your browser window to get three new randomly chosen simulations.

The priority in high-transmission areas should be improving access to ACTs. However, once access and usage rates are high. Initiatives to control and slow down drug resistance must immediately follow.

Thanks to Patrick Dudas for putting together these great animations.