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Measuring protective efficacy and quantifying the impact of drug resistance: A novel malaria chemoprevention trial design and methodology [1]
['Andria Mousa', 'Faculty Of Infectious', 'Tropical Diseases', 'London School Of Hygiene', 'Tropical Medicine', 'London', 'United Kingdom', 'Gina Cuomo-Dannenburg', 'Mrc Centre For Global Infectious Disease Analysis', 'Department Of Infectious Disease Epidemiology']
Date: 2024-05
Abstract Background 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. Methods and findings 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. Conclusions 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.
Author summary Why was this study done? In 2022, the World Health Organisation (WHO) released updated guidelines for the implementation of malaria chemoprevention and guidance for undertaking chemoprevention efficacy studies.
We sought to understand how study design and trial setting characteristics influence the ability to assess chemoprevention efficacy and duration of drug protection.
The impact of drug resistance on chemoprevention is not well understood, and there is a need for a method that produces measures of drug resistance effects, which can be applied to different settings. What did the researchers do and find? We fitted a Bayesian Markov Chain Monte Carlo (MCMC) to simulated trial data for a number of trial designs and scenarios.
We find that the WHO recommendation of 28 days of follow-up is insufficient to precisely estimate duration of drug protection or protective efficacy.
In seasonal settings, in the absence of a control arm, it is impossible to distinguish between changing transmission and waning drug protection when estimating protective efficacy. What do these findings mean? Where feasible, particularly in settings of seasonal transmission and/or trials without a control arm, extending follow-up to 42 or 63 days can substantially improve the ability to accurately estimate chemoprevention outcomes.
Although long follow-up can improve precision and power, decisions on study design should also consider costs and trial feasibility.
Citation: Mousa A, Cuomo-Dannenburg G, Thompson HA, Chico RM, Beshir KB, Sutherland CJ, et al. (2024) Measuring protective efficacy and quantifying the impact of drug resistance: A novel malaria chemoprevention trial design and methodology. PLoS Med 21(5): e1004376.
https://doi.org/10.1371/journal.pmed.1004376 Academic Editor: Peter MacPherson, University of Glasgow, UNITED KINGDOM Received: September 8, 2023; Accepted: March 14, 2024; Published: May 9, 2024 Copyright: © 2024 Mousa et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Data Availability: All code and data can be found here:
https://github.com/AndriaMousa/chemoprevention-trial-code.git. Funding: AM, CR, RG, RMC, CJS, KBB and ACP were funded by Unitaid (www.unitaid.org, grant number: 101150IC). GCD and LO acknowledge funding from the UK Royal Society and from the MRC Centre for Global Infectious Disease Analysis (reference MR/R015600/1), jointly funded by the UK Medical Research Council (MRC) and the UK Foreign, Commonwealth & Development Office (FCDO), under the MRC/FCDO Concordat agreement and is also part of the EDCTP2 programme supported by the European Union. WFM and IMA were funded by the Wellcome Trust [
https://wellcome.org/, Grant number: 107741/A/15/Z] and the UK Foreign, Commonwealth & Development Office, with support from the Developing Excellence in Leadership, Training and Science in Africa (DELTAS Africa) programme. For the purpose of open access, the author has applied a CC BY public copyright license to any Author Accepted Manuscript version arising from this submission. The funders had no role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript. Competing interests: LCO has received a research grant from Merck pharmaceutical. RG is funded through a Unitaid grant that funds the Plus Project for his work at the London School of Hygiene and Tropical Medicine and works as an independent consultant paid by USAID, Population Services International and Bill and Melinda Gates Foundation. Abbreviations: ACT, artemisinin-combination therapy; AQ, amodiaquine; CPES, chemoprevention efficacy study; CrI, credible interval; ippy, infections per person per year; IPTp, intermittent preventative treatment in pregnancy; IPTsc, intermittent preventive treatment in school-aged children; MCMC, Markov Chain Monte Carlo; MDA, mass drug administration; PCPI, parasite clearance and protection against infection; PDMC, post-discharge malaria chemoprevention; PMC, perennial malaria chemoprevention; rfMDA, reactive focal MDA; SMC, seasonal malaria chemoprevention; SP, sulfadoxine-pyrimethamine; WHO, World Health Organisation
Introduction Chemopreventive treatment of vulnerable groups is an essential component of malaria control. Its aim is to clear existing asymptomatic infections and provide ongoing protection against new infections and clinical disease [1]. Chemoprevention interventions include intermittent preventative treatment in pregnancy (IPTp), perennial malaria chemoprevention (PMC), seasonal malaria chemoprevention (SMC) in children, intermittent preventive treatment in school-aged children (IPTsc), post-discharge malaria chemoprevention (PDMC), mass drug administration (MDA), or reactive focal MDA (rfMDA) [1]. In children, SMC and PMC involve the administration of sulfadoxine-pyrimethamine (SP) alone or in combination with other antimalarials, such as amodiaquine (SP-AQ). Until recently, SMC was limited to the Sahel region, as the initial recommendation was restricted to areas of Africa with highly endemic, very seasonal malaria transmission and high SP-drug susceptibility [2]. Similarly, the initial recommendation of PMC (previously called IPTi) stated that the intervention should only be given in areas where resistance markers were below a specified threshold [3]. In June 2022, the World Health Organisation (WHO) published new, less prescriptive chemoprevention guidelines that encourage chemoprevention programmes to consider a wider variety of drugs and delivery in new geographies, with locally tailored strategies [1]. The WHO Global Malaria Programme published guidelines for the design and analysis of chemoprevention efficacy studies (CPES) [4]. In their standardised approach, chemoprevention failure is defined as asexual parasitaemia by microscopy within 28 days post-dosing. This approach helps harmonise surveillance across countries, but the main challenge is that findings will be context-specific, varying with underlying transmission intensity and circulating resistance genotypes. In programmes where SP-based chemoprevention is deployed, a priority is to understand how parasite resistance to SP might impact protection, particularly in parts of Eastern and Southern Africa where resistance-associated mutations in the dhps and dhfr genes are common [5]. Ethical considerations preclude the inclusion of a control group in settings where chemoprevention is the standard of care. Without a control group, it is difficult to distinguish the effect of drug protection from the effect of the local level of malaria incidence. Current guidelines on sample size calculations [4] propose predicting the proportion infected based on microscopy prevalence on the day of treatment (day 0) and the relationship between prevalence and incidence estimated from previous studies [6]. However, in previous analyses, EIR estimates greatly deviated from those predicted from prevalence data [7]. Therefore, when analysing a study, estimating infection rates during the trial may produce more accurate results on protective efficacy. A second challenge associated with the current WHO-proposed guidelines for chemoprevention trials is that, although they include the reporting of resistance-associated mutation prevalence, there are no guidelines on incorporating resistance genotypes in the analysis to estimate their impact on protective efficacy. Certain parasite mutations are strongly associated with treatment failure in symptomatic patients and may also reduce the duration of chemoprevention (i.e., resistant parasites can reinfect earlier after chemoprevention) [7–9], although, to date, this has been largely uncharacterised [10]. Previous studies have quantified the interval of protection against malaria infection by analysing reinfection data from randomised controlled treatment or chemoprevention trials of lumefantrine, piperaquine, and amodiaquine (the long-acting partner drugs of artemisinin-combination therapies (ACTs)) [7,11] as well as SP-AQ [12]. In these models, the probability of a new infection is a function of both the transmission level in a given area and drug protection. Estimation of the infection rate in a particular setting is informed by data either from a control group or from treated cohorts as the drug protection wanes. Here, we extend previous modelling approaches to explore considerations and challenges in chemoprevention study design by developing a novel method to quantify protective efficacy, which can be used for the design and analysis of future malaria chemoprevention trials. To the best of our knowledge, this is the first method that produces estimates of protection that can be transposed to other settings with different transmission and frequency of resistance, enabling estimation of chemoprevention efficacy in areas where these trials are not conducted. In our modelling approach, we consider participant characteristics, transmission intensity, seasonality, drug resistance frequency, genotype-specific efficacy estimation, and study-design characteristics, i.e., presence/absence of a control arm and length of follow-up.
Discussion Chemoprevention has saved an estimated three-quarters of a million lives over the last decade [19], and its impact will grow following the WHO recommendation to scale-up programmes to a larger number of endemic countries. Accurately monitoring chemoprevention efficacy in trials, particularly in settings with drug resistance, is essential to maintaining this impact, but there are challenges in designing these trials. The novel analysis framework presented in this study identifies strengths and weaknesses in current chemoprevention trial designs and proposes methods to improve future protocols and analysis. Our findings highlight the importance of determining the underlying incidence in the absence of drug protection during chemoprevention trials to avoid mistaking naturally occurring fluctuations in transmission levels for drug effect. In all scenarios, we confirm that protective efficacy can be estimated with greater precision in settings with higher transmission. An untreated control group is the most robust study design to measure background transmission, but withholding chemoprevention poses ethical issues. Alternatively, in areas of stable, constant transmission, we show that a long follow-up of ≥42 days allows measurement of underlying incidence when the drug is no longer active in preventing infection. For this reason, the current recommendation of 28-day follow-up in a single-arm trial may be insufficient to accurately determine protective efficacy against new infections, providing low precision. Nonetheless, the benefits of extending trial follow-up should be balanced against associated costs and feasibility, and long follow-up designs may prove demanding for participating children and their caregivers. However, once protective efficacy for different genotype profiles has been quantified, trials would not need to be repeated other than for validation, as genotype-specific protection parameters can be fixed and applied to different settings when modelling intervention impact. Even with longer follow-up, trial results could still prove inaccurate as infection risk can vary greatly over small distances or time-frames. In seasonal areas, depending on whether follow-up occurs during the start or the end of a transmission season, we show that protective efficacy and mean duration of protection may be over- or underestimated, respectively. In settings with unstable transmission, the current model can be extended to include time-varying risk of infection in analyses of controlled chemoprevention trials [12,20]. Given the ethical challenges of including a control group in a trial, WHO recommends an alternative option of a 2-arm trial including a chemoprevention group and an artesunate-lumefantrine comparator group. However, lumefantrine is expected to provide approximately 13 days protection against a new infection [7], which is likely to reduce power to detect protective efficacy in the chemoprevention group. Another option could be to use a comparator drug that is efficacious at clearing parasitaemia but is short-lived, such as 7 days of artesunate monotherapy administered before the start of follow-up [18]. Alternatively, chemoprevention could be trialled in age groups not covered by local chemoprevention strategies (i.e., >5 years for SMC or >2 years for PMC), which is likely to give a reasonable indication of efficacy in the target age group, although differences in pharmacokinetics or immunity in these groups may affect results. Drug resistance poses one of the greatest challenges to chemoprevention, yet there are no guidelines on quantifying the impact of drug resistance on chemoprevention. Our analysis provides a method to estimate duration of protection against specific parasite genotypes, disaggregating the effects of transmission intensity, and frequency of resistant strains. This enables prediction of chemoprevention efficacy in settings with different frequencies of resistance. We find that placing a trial where both resistant and sensitive infections are common allows more precise estimation of genotype-specific protective efficacy and molecular surveillance in the trial area can provide robust estimates of the frequency of different strains [21]. A limitation of this analysis is that additional potential confounders affecting chemoprevention efficacy, such as drug absorption, metabolism, drug quality, nonadherence, coverage/access, acquired immunity, and level of parasitaemia were not considered [10,22–26]. For instance, a placebo-controlled chemoprevention trial of SP-AQ showed higher efficacy in children aged under 2 compared to children aged 2 to 5 [27], though this age effect was not significant in other studies [28]. Another challenge of this work is that sample size estimations for estimating duration of drug protection require high-performance computing, particularly when a broad range of sample sizes/scenarios need to be assessed. This analysis focuses on incidence of malaria infection rather than clinical malaria incidence, which is an important outcome in quantifying malaria burden and assessing the value of chemoprevention. The probability of becoming symptomatic depends on the levels of immunity acquired after repeated exposure to malaria and therefore varies with respect to the age-range of participants, transmission level, and malaria intervention coverage [29,30]. If only 50% of new malaria infections become symptomatic, the power to detect genotype differences in the duration of protection from a clinical infection would reduce to 79.5%, compared to 93.5% for any malaria infection, assuming that the probability of an infection becoming symptomatic is unaffected by chemoprevention [31]. In the approach modelled, the power is reduced by excluding those who are positive on day 0. Retaining these individuals will preserve statistical power but may introduce bias due to inability to differentiate between existing and new infections. Standard PCR amplification of mixtures of alleles lacks the sensitivity for assessing treated asymptomatic infections and cannot reliably detect alleles present at low frequencies, leading to potential misclassification of recurrent infections [32,33]. This is particularly important for SP, where recrudescence may be common in many areas of high SP-resistance. The approach presented here can inform the design and analysis of chemoprevention trials to drive national policy decision-making, such as informing selection of sites with particular characteristics that optimise power for a given sample size in the presence of budget constraints or other logistical challenges. This modelling approach has been applied in the design of parasite clearance and protection against infection (PCPI) studies aiming to look at genotype specific effects on chemoprevention efficacy of SP [13]. Additionally, our method can produce essential inputs for decision-making relevant to malaria chemoprevention. Quantifying protective efficacy provided by different implementations can help inform policy decisions that are tailored to specific countries or subnational areas. For instance, estimates of protective efficacy by genotype (in combination with up-to-date area-surveillance of mutation prevalence) can be incorporated into tools that national malaria control programmes can use to predict the likely impact of these interventions.
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