A Computer Modelling Approach To Evaluate the Accuracy of Microsatellite Markers for Classification of Recurrent Infections during Routine Monitoring of Antimalarial Drug Efficacy

Jones, Sam, Plucinski, Mateusz, Kay, Katherine, Hodel, Eva Maria ORCID: 0000-0001-5821-1685 and Hastings, Ian M (2020) A Computer Modelling Approach To Evaluate the Accuracy of Microsatellite Markers for Classification of Recurrent Infections during Routine Monitoring of Antimalarial Drug Efficacy. ANTIMICROBIAL AGENTS AND CHEMOTHERAPY, 64 (4). e01517-e01519.

Text
Antimicrobial Agents and Chemotherapy-2020-Jones-AAC.01517-19.full.pdf - Author Accepted Manuscript
Download (2MB) | Preview

Official URL: http://dx.doi.org/10.1128/aac.01517-19

Abstract

Antimalarial drugs have long half-lives, so clinical trials to monitor their efficacy require long periods of follow-up to capture drug failure that may become patent only 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 genotype of a patient's pretreatment (initial) blood sample with that of any infection that occurs during follow-up, with matching genotypes indicating 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 overestimates of the true failure rates, depending on the choice of matching criterion. A Bayesian algorithm for molecular correction was 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 rates, 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 analyzing microsatellite genotypes. Strong consideration should be given to using Bayesian methodology to obtain accurate failure rate estimates during routine monitoring trials of antimalarial efficacy that use microsatellite markers.

Item Type:	Article
Uncontrolled Keywords:	Bayesian, efficacy, malaria, microsatellite, molecular correction, recrudescence, reinfection, resistance, therapeutic efficacy study
Depositing User:	Symplectic Admin
Date Deposited:	16 Jan 2020 12:18
Last Modified:	19 Jan 2023 00:09
DOI:	10.1128/AAC.01517-19
Related URLs:	Author Publisher
URI:	https://livrepository.liverpool.ac.uk/id/eprint/3070827