Making the Most of Clinical Data: Reviewing the Role of Pharmacokinetic-Pharmacodynamic Models of Anti-malarial Drugs
Mechanistic within-host models integrating blood anti-malarial drug concentrations with the parasite-time profile provide a valuable decision tool for determining dosing regimens for anti-malarial treatments, as well as a formative component of population-level drug resistance models. We reviewed published anti-malarial pharmacokinetic-pharmacodynamic models to identify the challenges for these complex models where parameter estimation from clinical field data is limited. The inclusion of key pharmacodynamic processes in the mechanistic structure adopted varies considerably. These include the life cycle of the parasite within the red blood cell, the action of the anti-malarial on a specific stage of the life cycle, and the reduction in parasite growth associated with immunity. With regard to estimation of the pharmacodynamic parameters, the majority of studies simply compared descriptive summaries of the simulated outputs to published observations of host and parasite responses from clinical studies. Few studies formally estimated the pharmacodynamic parameters within a rigorous statistical framework using observed individual patient data. We recommend three steps in the development and evaluation of these models. Firstly, exploration through simulation to assess how the different parameters influence the parasite dynamics. Secondly, application of a simulation-estimation approach to determine whether the model parameters can be estimated with reasonable precision based on sampling designs that mimic clinical efficacy studies. Thirdly, fitting the mechanistic model to the clinical data within a Bayesian framework. We propose that authors present the model both schematically and in equation form and give a detailed description of each parameter, including a biological interpretation of the parameter estimates.
KEY WORDSanti-malarial treatment Bayesian methods parameter estimation pharmacokinetic-pharmacodynamic model Plasmodium falciparum
The work was supported by the Victorian Centre for Biostatistics (ViCBiostat), which is funded by the National Health and Medical Research Centre of Australia (NHMRC) Centre of Research Excellence 1035261, and by NHMRC Project Grant 1025319. JMcC is supported by an Australian Research Council Future Fellowship 1101002580. RNP is a Wellcome Trust Senior Fellow in Clinical Science (091625).
- 1.WHO. World Malaria Report. Geneva: Available: http://whqlibdoc.who.int/publications/2010/9789241564106_eng.pdf. 2008.
- 2.WHO. Guidelines for the treatment of malaria. Geneva. Available from http://www.who.int/malaria/publications/atoz/9789241547925/en/index.html. 2010.
- 3.Barnes KI, Watkins WM, White NJ. Antimalarial dosing regimens and drug resistance. Trends Parasitol. 2008;24(3):127–34.Google Scholar
- 12.Hoshen MB, Na-Bangchang K, Stein WD, Ginsburg H. Mathematical modelling of the chemotherapy of Plasmodium falciparum malaria with artesunate: postulation of ‘dormancy’, a partial cytostatic effect of the drug, and its implication for treatment regimens. Parasitology. 2000;121(Pt 3):237–46.PubMedCrossRefGoogle Scholar
- 21.Hietala SF, Martensson A, Ngasala B, Dahlstrom S, Lindegardh N, Annerberg A, et al. Population pharmacokinetics and pharmacodynamics of artemether and lumefantrine during combination treatment in children with uncomplicated falciparum malaria in Tanzania. Antimicrob Agents Chemother. 2010;54(11):4780–8.PubMedCentralPubMedCrossRefGoogle Scholar
- 27.Dietz K, Raddatz G, Molineaux L. Mathematical model of the first wave of Plasmodium falciparum asexual parasitemia in non-immune and vaccinated individuals. AmJTrop Med Hyg. 2006;75(2 Suppl):46–55.Google Scholar
- 29.Geary TG, Divo AA, Jensen JB. Stage specific actions of antimalarial drugs on Plasmodium falciparum in culture. AmJTrop Med Hyg. 1989;40(3):240–4.Google Scholar
- 31.Price RN, Simpson JA, Davis TME. Artemisinins. Kucers’ the use of antibiotics. 6th ed: Hodder Arnold; 2010. pp. 2090–104.Google Scholar
- 32.Evans M, Hastings N, Peacock B. “von Mises distribution”. Statistical distributions. 3rd ed. Wiley: New York; 2000. p. 189–91.Google Scholar
- 34.Jamsen KM, Duffull SB, Tarning J, Lindegardh N, White NJ, Simpson JA. Optimal designs for population pharmacokinetic studies of the partner drugs co-administered with artemisinin derivatives in patients with uncomplicated falciparum malaria. Malar J. 2012;11:143.PubMedCentralPubMedCrossRefGoogle Scholar
- 36.Price R, Nosten F, Simpson JA, Luxemburger C, Phaipun L, ter Kuile F, et al. Risk factors for gametocyte carriage in uncomplicated falciparum malaria. AmJTrop Med Hyg. 1999;60(6):1019–23.Google Scholar
- 37.Price RN, Simpson JA, Nosten F, Luxemburger C, Hkirjaroen L, ter Kuile F, et al. Factors contributing to anemia after uncomplicated falciparum malaria. AmJTrop Med Hyg. 2001;65(5):614–22.Google Scholar
- 40.Kamau E, Tolbert LS, Kortepeter L, Pratt M, Nyakoe N, Muringo L, et al. Development of a highly sensitive genus-specific quantitative reverse transcriptase real-time PCR assay for detection and quantitation of plasmodium by amplifying RNA and DNA of the 18S rRNA genes. J Clin Microbiol. 2011;49(8):2946–53.PubMedCentralPubMedCrossRefGoogle Scholar