Pharmaceuticals (Jan 2023)

The Parasite Reduction Ratio (PRR) Assay Version 2: Standardized Assessment of <i>Plasmodium falciparum</i> Viability after Antimalarial Treatment In Vitro

  • Annabelle Walz,
  • Maëlle Duffey,
  • Ghaith Aljayyoussi,
  • Sibylle Sax,
  • Didier Leroy,
  • Dominique Besson,
  • Jeremy N. Burrows,
  • Mohammed H. Cherkaoui-Rbati,
  • Nathalie Gobeau,
  • Marie-Anne Westwood,
  • Christoph Siethoff,
  • Francisco-Javier Gamo,
  • Pascal Mäser,
  • Sergio Wittlin

DOI
https://doi.org/10.3390/ph16020163
Journal volume & issue
Vol. 16, no. 2
p. 163

Abstract

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With artemisinin-resistant Plasmodium falciparum parasites emerging in Africa, the need for new antimalarial chemotypes is persistently high. The ideal pharmacodynamic parameters of a candidate drug are a rapid onset of action and a fast rate of parasite killing or clearance. To determine these parameters, it is essential to discriminate viable from nonviable parasites, which is complicated by the fact that viable parasites can be metabolically inactive, whilst dying parasites can still be metabolically active and morphologically unaffected. Standard growth inhibition assays, read out via microscopy or [3H] hypoxanthine incorporation, cannot reliably discriminate between viable and nonviable parasites. Conversely, the in vitro parasite reduction ratio (PRR) assay is able to measure viable parasites with high sensitivity. It provides valuable pharmacodynamic parameters, such as PRR, 99.9% parasite clearance time (PCT99.9%) and lag phase. Here we report the development of the PRR assay version 2 (V2), which comes with a shorter assay duration, optimized quality controls and an objective, automated analysis pipeline that systematically estimates PRR, PCT99.9% and lag time and returns meaningful secondary parameters such as the maximal killing rate of a drug (Emax) at the assayed concentration. These parameters can be fed directly into pharmacokinetic/pharmacodynamic models, hence aiding and standardizing lead selection, optimization, and dose prediction.

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