Measuring protective efficacy and quantifying the impact of drug resistance: A novel malaria chemoprevention trial design and methodology

• Published in: PLoS medicine Vol. 21; no. 5; p. e1004376
• Main Authors: Mousa, Andria, Cuomo-Dannenburg, Gina, Thompson, Hayley A, Chico, R Matthew, Beshir, Khalid B, Sutherland, Colin J, Schellenberg, David, Gosling, Roly, Alifrangis, Michael, Hocke, Emma Filtenborg, Hansson, Helle, Chopo-Pizarro, Ana, Mbacham, Wilfred F, Ali, Innocent M, Chaponda, Mike, Roper, Cally, Okell, Lucy C
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 09-05-2024; Public Library of Science (PLoS)
• Subjects: Antimalarials; Antimalarials - therapeutic use; Asymptomatic; Bayes Theorem; Bayesian analysis; Chemoprevention - methods; Clinical trials; Disease transmission; Drug development; Drug resistance; Drug Resistance - genetics; Drug therapy; Genotype; Genotype & phenotype; Genotypes; Humans; Infections; Influence; Malaria; Malaria - epidemiology; Malaria - prevention & control; Malaria - transmission; Medicine and Health Sciences; Methods; Microscopy; Mutation; Parasites; Pharmaceutical research; Physical Sciences; Physiological aspects; Prevention; Research and Analysis Methods; Research Design; Simulation; Testing; United Kingdom
• ISSN: 1549-1676; 1549-1277; 1549-1676
• DOI: 10.1371/journal.pmed.1004376

Recently revised WHO guidelines on malaria chemoprevention have opened the door to more tailored implementation. Countries face choices on whether to replace old drugs, target additional age groups, and adapt delivery schedules according to local drug resistance levels and malaria transmission patterns. Regular routine assessment of protective efficacy of chemoprevention is key. Here, we apply a novel modelling approach to aid the design and analysis of chemoprevention trials and generate measures of protection that can be applied across a range of transmission settings.
We developed a model of genotype-specific drug protection, which accounts for underlying risk of infection and circulating genotypes. Using a Bayesian framework, we fitted the model to multiple simulated scenarios to explore variations in study design, setting, and participant characteristics. We find that a placebo or control group with no drug protection is valuable but not always feasible. An alternative approach is a single-arm trial with an extended follow-up (>42 days), which allows measurement of the underlying infection risk after drug protection wanes, as long as transmission is relatively constant. We show that the currently recommended 28-day follow-up in a single-arm trial results in low precision of estimated 30-day chemoprevention efficacy and low power in determining genotype differences of 12 days in the duration of protection (power = 1.4%). Extending follow-up to 42 days increased precision and power (71.5%) in settings with constant transmission over this time period. However, in settings of unstable transmission, protective efficacy in a single-arm trial was overestimated by 24.3% if recruitment occurred during increasing transmission and underestimated by 15.8% when recruitment occurred during declining transmission. Protective efficacy was estimated with greater precision in high transmission settings, and power to detect differences by resistance genotype was lower in scenarios where the resistant genotype was either rare or too common.
These findings have important implications for the current guidelines on chemoprevention efficacy studies and will be valuable for informing where these studies should be optimally placed. The results underscore the need for a comparator group in seasonal settings and provide evidence that the extension of follow-up in single-arm trials improves the accuracy of measures of protective efficacy in settings with more stable transmission. Extension of follow-up may pose logistical challenges to trial feasibility and associated costs. However, these studies may not need to be repeated multiple times, as the estimates of drug protection against different genotypes can be applied to different settings by adjusting for transmission intensity and frequency of resistance.