New antimalarial medications and vaccines capable of blocking transmission and overcoming antimalarial resistance are vital to continue reducing malaria burden worldwide. Quantitative PCR (qPCR) plays a pivotal role in measurement of low-level infection. Here we report the validation of an 18S rRNA qPCR assay to quantify P. falciparum parasitemia in induced blood stage malaria (IBSM) studies using the guidelines for laboratory-developed molecular assays (Burd 2010 Clin Microbiol Rev 23: 550-76), and the establishment of methodology for other related assays.
Data were sourced from 493 sets of six tenfold dilutions of a 3.19x106 parasites/mL standard and negative control from clinical trials conducted between 2013-15, using the analytical procedure of Rockett et al (2011 Malaria Journal 10: 48). External data for accuracy were sourced from Vietnam for microscopy and USA for external quality assurance. An additional dilution series of nine standards was run on three consecutive days with seven technical replicates and negative control to estimate reportable range, assay variability and analytical sensitivity.
The validation studies established a reportable range of 3.19x101-106parasites/mL; analytical sensitivity of 111 parasites/mL; intra- and inter-assay precision of log10 parasites/mL of 0.456 and 0.658 (SD); specificity of 100%; accuracy comparable with microscopy and standardised by external quality assurance; reference interval as not detect; controls for extraction, inhibition and amplification; and calibration verified by acceptable PCR efficiency > 90%.
We report an accurate and sensitive qPCR assay that performed within the acceptable standards of the Food and Drug Administration, USA. It performed similarly to one reported by Murphy et al (2014 PLoS ONE 9, e97398), and is suitable for use in studies aimed at evaluation of candidate antimalarials under Regulatory review. Validation of related qPCR assays for ring stage parasites and male and female gametocytes is currently underway for use in IBSM transmission models.