Optimal designs for pharmacokinetic-pharmacodynamic studies of dihydroartemisinin following oral artesunate

Background: Artemisinin derivatives are the main drugs used for the treatment of uncomplicated malaria.  Although these drugs remain the most powerful anti-parasitic agents available, there is now evidence for parasite resistance to artesunate, the most widely used artemisinin derivative [1].  This finding provides motivation for conducting more pharmacokinetic-pharmacodynamic (PK-PD) studies of artesunate to monitor its effectiveness and re-assess current dosing regimens.  Designing these studies is challenging since it is unacceptable and logistically difficult to take a large number of blood samples from patients presenting with uncomplicated malaria in the initial 48 hours of treatment.  In this work optimal design methodology was used to determine sampling designs for both the measurement of the drug concentrations and parasite counts of typical future PK-PD studies of artesunate.
Methods: The following optimal designs were derived: (i) a T-optimal design for the estimation of the parasite clearance curve (PCC), which is based on the characterisation of the parasitema-time course for an individual (PD only; [2]), and (ii) a D-optimal design for a sequential population PK-PD model, where the killing effect of dihydroartemisinin (DHA, the active metabolite of artesunate) is described by a sigmoid Emax equation [3].  Key sampling constraints were a maximum of four samples per patient for a given dose of artesunate and at least 15 minutes between consecutive samples.  The derived optimal designs were then evaluated via a simulation-estimation procedure.

Results (preliminary): The T-optimal design for the PCC provided sufficient means for discriminating among linear, quadratic and cubic parasitemia-time profiles.  The D-optimal design for the sequential population PK-PD model provided acceptable precision for estimates of the PK and PD fixed-effect parameters, PK between-subject variability parameters and between subject variance for the maximal killing effect of DHA.

Conclusions: The sampling schedules proposed in this work will be further developed to be considered for future PK-PD studies of DHA following oral artesunate where intensive sampling is not possible.  The D-optimal design will be extended to account for the killing effect of DHA at different stages of the parasite life-cycle.

1. Dondorp AM, Nosten F, Yi P, Das D, Phyo AP, Tarning J, Lwin KM, Ariey F, Hanpithakpong W, Lee SJ, Ringwald P, Silamut K, Imwong M, Chotivanich K, Lim P, Herdman T, An S, Yeung S, Singhasivanon P, Day NPJ, Lindegardh N, Socheat D, White NJ: Artemisinin Resistance in Plasmodium falciparum Malaria. N Engl J Med 2009, 361:455-67.

2. Flegg JA, Guerin PJ, White NJ, Stepniewska K.  Standardizing the measurement of parasite clearance in falciparum malaria: the parasite clearance estimator. Malar J 2011, 10:339

3. Simpson JA, Watkins ER, Price RN, Aarons L, Kyle DE, White NJ. Mefloquine Pharmacokinetic-Pharmacodynamic Models: Implications for Dosing and Resistance. Antimicrob Agents Chemother 2000, 44(12), 3414-24.

Kris Jamsen

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