Evaluating accuracy of microsatellite markers for classification of recurrent infections during routine monitoring of anti-malarial drug efficacy: A computer modelling approach

Anti-malarial drugs have long half-lives, so clinical trials to monitor their efficacy require long durations of follow-up to capture drug failure that may only become patent weeks after treatment. Reinfections often occur during follow-up so robust methods of distinguishing drug failures (recrudescence) from emerging new infections are needed to produce accurate failure rate estimates. "Molecular correction" aims to achieve this by comparing the genotypes between a patient's pre-treatment (initial) blood sample and any infection that occurs during follow-up, 'matching' genotypes indicating a drug failure. We use an in-silico approach to show that the widely used "match counting" method of molecular correction with microsatellite markers is likely to be highly unreliable and may lead to gross under- or over-estimates of true failure rates depending on the choice of matching criterion. A Bayesian algorithm for molecular correction has been previously developed and utilized for analysis of in vivo efficacy trials. We validated this algorithm using in silico data and showed it had high specificity and generated accurate failure rate estimates. This conclusion was robust for multiple drugs, different levels of drug failure rate, different levels of transmission intensity in the study sites, and microsatellite genetic diversity. The Bayesian algorithm was inherently unable to accurately identify low-density recrudescence that occurred in a small number of patients, but this did not appear to compromise its utility as a highly effective molecular correction method for analysing microsatellite genotypes. Strong consideration should be given to using Bayesian methodology for obtaining accurate failure rate estimates during routine monitoring trials of antimalarial efficacy that use microsatellite markers