World Antimalarial Resistance Network (WARN) IV: clinical pharmacology

Abstract

A World Antimalarial Resistance Network (WARN) database has the potential to improve the treatment of malaria, through informing current drug selection and use and providing a prompt warning of when treatment policies need changing. This manuscript outlines the contribution and structure of the clinical pharmacology component of this database. The determinants of treatment response are multi-factorial, but clearly providing adequate blood concentrations is pivotal to curing malaria. The ability of available antimalarial pharmacokinetic data to inform optimal dosing is constrained by the small number of patients studied, with even fewer (if any) studies conducted in the most vulnerable populations. There are even less data relating blood concentration data to the therapeutic response (pharmacodynamics). By pooling all available pharmacokinetic data, while paying careful attention to the analytical methodologies used, the limitations of small (and thus underpowered) individual studies may be overcome and factors that contribute to inter-individual variability in pharmacokinetic parameters defined. Key variables for pharmacokinetic studies are defined in terms of patient (or study subject) characteristics, the formulation and route of administration of the antimalarial studied, the sampling and assay methodology, and the approach taken to data analysis. Better defining these information needs and criteria of acceptability of pharmacokinetic-pharmacodynamic (PK-PD) studies should contribute to improving the quantity, relevance and quality of these studies. A better understanding of the pharmacokinetic properties of antimalarials and a more clear definition of what constitutes "therapeutic drug levels" would allow more precise use of the term "antimalarial resistance", as it would indicate when treatment failure is not caused by intrinsic parasite resistance but is instead the result of inadequate drug levels. The clinical pharmacology component of the WARN database can play a pivotal role in monitoring accurately for true antimalarial drug resistance and promptly correcting sub-optimal dosage regimens to prevent these contributing to the emergence and spread of antimalarial resistance.

Figure 1

Parameters for modeling calibration curves.