Optimizing the programmatic deployment of the anti-malarials artemether-lumefantrine and dihydroartemisinin-piperaquine using pharmacological modelling

Abstract

Background: Successful programmatic use of anti-malarials faces challenges that are not covered by standard drug development processes. The development of appropriate pragmatic dosing regimens for low-resource settings or community-based use is not formally regulated, even though these may alter factors which can substantially affect individual patient and population level outcome, such as drug exposure, patient adherence and the spread of drug resistance and can affect a drug's reputation and its eventual therapeutic lifespan.

Methods: An in silico pharmacological model of anti-malarial drug treatment with the pharmacokinetic/pharmacodynamic profiles of artemether-lumefantrine (AM-LF, Coartem®) and dihydroartemisinin-piperaquine (DHA-PPQ, Eurartesim®) was constructed to assess the potential impact of programmatic factors, including regionally optimized, age-based dosing regimens, poor patient adherence, food effects and drug resistance on treatment outcome at population level, and compared both drugs' susceptibility to these factors.

Results: Compared with DHA-PPQ, therapeutic effectiveness of AM-LF seems more robust to factors affecting drug exposure, such as age- instead of weight-based dosing or poor adherence. The model highlights the sub-optimally low ratio of DHA:PPQ which, in combination with the narrow therapeutic dose range of PPQ compared to DHA that drives the weight or age cut-offs, leaves DHA at a high risk of under-dosing.

Conclusion: Pharmacological modelling of real-life scenarios can provide valuable supportive data and highlight modifiable determinants of therapeutic effectiveness that can help optimize the deployment of anti-malarials in control programmes.

Figure 1

Overview showing the original PK/PD model (black boxes), the extension to allow for region specific dosing by weight or age (green box) and the different treatment scenarios simulated (red boxes). * If artesunate or artemether are given, the model accounts for their absorption and conversion into the active metabolite dihydroartemisinin (DHA). The model then updates parasite numbers in the next time step using only the drug with the larger effect, i.e. either the parent drug or DHA.

Figure 2

Predicted parasite positivity at Day 3 and 42 in a global population of 10,000 individuals dosed by weight with dihydroartemisinin-piperaquine (DHA-PPQ). Panels show predicted parasite positivity for DHA and PPQ at Day 3 ((A) and (B), respectively) and at Day 42 ((C) and (D), respectively). Left of panels: Total numbers of individuals per mg/kg dosing band (white) and individuals that were parasiaemic (i e, parasite number > 10⁸) (red). Right of panels: Numbers expressed as a percentage.

Figure 3

Predicted dihydroartemisinin-piperaquine treatment outcome per age group (in years) in an African population of 10,000 individuals dosed by (A) age or (B) weight (i e, patients receive one dose daily over three days given at 0 h, 24 h and 48 h). Black triangles indicate the cut-off points of the age-based dosing bands.

Figure 4

Predicted dihydroartemisinin-piperaquine treatment outcome per age group (in years) in an African population of 10,000 individuals dosed by (A) age or (B) weight and last dose skipped (i e, patients receive one dose daily over two days given at 0 h and 24 h). Black triangles indicate the cut-off points of the age-based dosing bands.

Figure 5

Predicted artemether-lumefantrine treatment outcome per age group (in years) in an African population of 10,000 individuals dosed by (A) age or (B) weight when the IC₅₀ of artemether and dihydroartemisinin are 10-fold increased and the IC₅₀ of lumefantrine is 50-fold increased. Black triangles indicate the cut-off points of the age-based dosing bands.