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Braunack-Mayer L, Malinga J, Masserey T, Nekkab N, Sen S, Schellenberg D, Tchouatieu AM, Kelly SL, Penny MA. Design and selection of drug properties to increase the public health impact of next-generation seasonal malaria chemoprevention: a modelling study. Lancet Glob Health 2024; 12:e478-e490. [PMID: 38365418 PMCID: PMC10882206 DOI: 10.1016/s2214-109x(23)00550-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2023] [Revised: 10/02/2023] [Accepted: 11/20/2023] [Indexed: 02/18/2024]
Abstract
BACKGROUND Seasonal malaria chemoprevention (SMC) is recommended for disease control in settings with moderate to high Plasmodium falciparum transmission and currently depends on the administration of sulfadoxine-pyrimethamine plus amodiaquine. However, poor regimen adherence and the increased frequency of parasite mutations conferring sulfadoxine-pyrimethamine resistance might threaten the effectiveness of SMC. Guidance is needed to de-risk the development of drug compounds for malaria prevention. We aimed to provide guidance for the early prioritisation of new and alternative SMC drugs and their target product profiles. METHODS In this modelling study, we combined an individual-based malaria transmission model that has explicit parasite growth with drug pharmacokinetic and pharmacodynamic models. We modelled SMC drug attributes for several possible modes of action, linked to their potential public health impact. Global sensitivity analyses identified trade-offs between drug elimination half-life, maximum parasite killing effect, and SMC coverage, and optimisation identified minimum requirements to maximise malaria burden reductions. FINDINGS Model predictions show that preventing infection for the entire period between SMC cycles is more important than drug curative efficacy for clinical disease effectiveness outcomes, but similarly important for impact on prevalence. When children younger than 5 years receive four SMC cycles with high levels of coverage (ie, 69% of children receiving all cycles), drug candidates require a duration of protection half-life higher than 23 days (elimination half-life >10 days) to achieve reductions higher than 75% in clinical incidence and severe disease (measured over the intervention period in the target population, compared with no intervention across a range of modelled scenarios). High coverage is crucial to achieve these targets, requiring more than 60% of children to receive all SMC cycles and more than 90% of children to receive at least one cycle regardless of the protection duration of the drug. INTERPRETATION Although efficacy is crucial for malaria prevalence reductions, chemoprevention development should select drug candidates for their duration of protection to maximise burden reductions, with the duration half-life determining cycle timing. Explicitly designing or selecting drug properties to increase community uptake is paramount. FUNDING Bill & Melinda Gates Foundation and the Swiss National Science Foundation.
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Affiliation(s)
- Lydia Braunack-Mayer
- Department of Epidemiology and Public Health, Swiss Tropical and Public Health Institute, Allschwil, Switzerland; University of Basel, Basel, Switzerland
| | - Josephine Malinga
- Department of Epidemiology and Public Health, Swiss Tropical and Public Health Institute, Allschwil, Switzerland; University of Basel, Basel, Switzerland
| | - Thiery Masserey
- Department of Epidemiology and Public Health, Swiss Tropical and Public Health Institute, Allschwil, Switzerland; University of Basel, Basel, Switzerland
| | - Narimane Nekkab
- Department of Epidemiology and Public Health, Swiss Tropical and Public Health Institute, Allschwil, Switzerland; University of Basel, Basel, Switzerland
| | - Swapnoleena Sen
- Department of Epidemiology and Public Health, Swiss Tropical and Public Health Institute, Allschwil, Switzerland; University of Basel, Basel, Switzerland
| | - David Schellenberg
- Department of Disease Control, London School of Hygiene & Tropical Medicine, London, UK
| | | | - Sherrie L Kelly
- Department of Epidemiology and Public Health, Swiss Tropical and Public Health Institute, Allschwil, Switzerland; University of Basel, Basel, Switzerland
| | - Melissa A Penny
- Department of Epidemiology and Public Health, Swiss Tropical and Public Health Institute, Allschwil, Switzerland; University of Basel, Basel, Switzerland; Telethon Kids Institute, Nedlands, WA, Australia; Centre for Child Health Research, The University of Western Australia, Perth, WA, Australia.
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Ominde KM, Kamau Y, Karisa J, Muturi MN, Kiuru C, Wanjiku C, Babu L, Yaah F, Tuwei M, Musani H, Ondieki Z, Muriu S, Mwangangi J, Chaccour C, Maia MF. A field bioassay for assessing ivermectin bio-efficacy in wild malaria vectors. Malar J 2023; 22:291. [PMID: 37777725 PMCID: PMC10542238 DOI: 10.1186/s12936-023-04718-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2023] [Accepted: 09/13/2023] [Indexed: 10/02/2023] Open
Abstract
BACKGROUND Ivermectin (IVM) mass drug administration is a candidate complementary malaria vector control tool. Ingestion of blood from IVM treated hosts results in reduced survival in mosquitoes. Estimating bio-efficacy of IVM on wild-caught mosquitoes requires they ingest the drug in a blood meal either through a membrane or direct feeding on a treated host. The latter, has ethical implications, and the former results in low feeding rates. Therefore, there is a need to develop a safe and effective method for IVM bio-efficacy monitoring in wild mosquitoes. METHODS Insectary-reared Anopheles gambiae s.s. were exposed to four IVM doses: 85, 64, 43, 21 ng/ml, and control group (0 ng/ml) in three different solutions: (i) blood, (ii) 10% glucose, (iii) four ratios (1:1, 1:2, 1:4, 1:8) of blood in 10% glucose, and fed through filter paper. Wild-caught An. gambiae s.l. were exposed to 85, 43 and 21 ng/ml IVM in blood and 1:4 ratio of blood-10% glucose mixture. Survival was monitored for 28 days and a pool of mosquitoes from each cohort sacrificed immediately after feeding and weighed to determine mean weight of each meal type. RESULTS When administered in glucose solution, mosquitocidal effect of IVM was not comparable to the observed effects when similar concentrations were administered in blood. Equal concentrations of IVM administered in blood resulted in pronounced reductions in mosquito survival compared to glucose solution only. However, by adding small amounts of blood to glucose solution, mosquito mortality rates increased resulting in similar effects to what was observed during blood feeding. CONCLUSION Bio-efficacy of ivermectin is strongly dependent on mode of drug delivery to the mosquito and likely influenced by digestive processes. The assay developed in this study is a good candidate for field-based bio-efficacy monitoring: wild mosquitoes readily feed on the solution, the assay can be standardized using pre-selected concentrations and by not involving treated blood hosts (human or animal) variation in individual pharmacokinetic profiles as well as ethical issues are bypassed. Meal volumes did not explain the difference in the lethality of IVM across the different meal types necessitating further research on the underlying mechanisms.
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Affiliation(s)
- Kelly M Ominde
- Department of Biosciences, KEMRI-Wellcome Trust Research Programme, Kilifi, Kenya.
- Department of Biological Sciences, and Pwani University Biosciences Research Centre, Pwani University, Kilifi, Kenya.
| | - Yvonne Kamau
- Department of Biosciences, KEMRI-Wellcome Trust Research Programme, Kilifi, Kenya
| | - Jonathan Karisa
- Department of Biosciences, KEMRI-Wellcome Trust Research Programme, Kilifi, Kenya
- Department of Biological Sciences, and Pwani University Biosciences Research Centre, Pwani University, Kilifi, Kenya
| | - Martha N Muturi
- Department of Biosciences, KEMRI-Wellcome Trust Research Programme, Kilifi, Kenya
| | | | - Caroline Wanjiku
- Department of Biosciences, KEMRI-Wellcome Trust Research Programme, Kilifi, Kenya
| | - Lawrence Babu
- Department of Biosciences, KEMRI-Wellcome Trust Research Programme, Kilifi, Kenya
| | - Festus Yaah
- Department of Biosciences, KEMRI-Wellcome Trust Research Programme, Kilifi, Kenya
| | - Mercy Tuwei
- Department of Biosciences, KEMRI-Wellcome Trust Research Programme, Kilifi, Kenya
- Department of Biological Sciences, and Pwani University Biosciences Research Centre, Pwani University, Kilifi, Kenya
| | - Haron Musani
- Department of Biosciences, KEMRI-Wellcome Trust Research Programme, Kilifi, Kenya
| | - Zedekiah Ondieki
- Department of Biosciences, KEMRI-Wellcome Trust Research Programme, Kilifi, Kenya
| | - Simon Muriu
- Department of Biosciences, KEMRI-Wellcome Trust Research Programme, Kilifi, Kenya
- Department of Biological Sciences, and Pwani University Biosciences Research Centre, Pwani University, Kilifi, Kenya
| | - Joseph Mwangangi
- Department of Biosciences, KEMRI-Wellcome Trust Research Programme, Kilifi, Kenya
| | - Carlos Chaccour
- ISGlobal, Barcelona, Spain
- Ciberinfec, Madrid, Spain
- Faculty of Medicine, Universidad de Navarra, Pamplona, Spain
| | - Marta F Maia
- Department of Biosciences, KEMRI-Wellcome Trust Research Programme, Kilifi, Kenya.
- Centre for Global Health and Tropical Medicine and Nuffield Department of Medicine, University of Oxford, Oxford, UK.
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Leung S, Windbichler N, Wenger EA, Bever CA, Selvaraj P. Population replacement gene drive characteristics for malaria elimination in a range of seasonal transmission settings: a modelling study. Malar J 2022; 21:226. [PMID: 35883100 PMCID: PMC9327287 DOI: 10.1186/s12936-022-04242-2] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2021] [Accepted: 07/11/2022] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Gene drives are a genetic engineering method where a suite of genes is inherited at higher than Mendelian rates and has been proposed as a promising new vector control strategy to reinvigorate the fight against malaria in sub-Saharan Africa. METHODS Using an agent-based model of malaria transmission with vector genetics, the impacts of releasing population-replacement gene drive mosquitoes on malaria transmission are examined and the population replacement gene drive system parameters required to achieve local elimination within a spatially-resolved, seasonal Sahelian setting are quantified. The performance of two different gene drive systems-"classic" and "integral"-are evaluated. Various transmission regimes (low, moderate, and high-corresponding to annual entomological inoculation rates of 10, 30, and 80 infectious bites per person) and other simultaneous interventions, including deployment of insecticide-treated nets (ITNs) and passive healthcare-seeking, are also simulated. RESULTS Local elimination probabilities decreased with pre-existing population target site resistance frequency, increased with transmission-blocking effectiveness of the introduced antiparasitic gene and drive efficiency, and were context dependent with respect to fitness costs associated with the introduced gene. Of the four parameters, transmission-blocking effectiveness may be the most important to focus on for improvements to future gene drive strains because a single release of classic gene drive mosquitoes is likely to locally eliminate malaria in low to moderate transmission settings only when transmission-blocking effectiveness is very high (above ~ 80-90%). However, simultaneously deploying ITNs and releasing integral rather than classic gene drive mosquitoes significantly boosts elimination probabilities, such that elimination remains highly likely in low to moderate transmission regimes down to transmission-blocking effectiveness values as low as ~ 50% and in high transmission regimes with transmission-blocking effectiveness values above ~ 80-90%. CONCLUSION A single release of currently achievable population replacement gene drive mosquitoes, in combination with traditional forms of vector control, can likely locally eliminate malaria in low to moderate transmission regimes within the Sahel. In a high transmission regime, higher levels of transmission-blocking effectiveness than are currently available may be required.
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Affiliation(s)
- Shirley Leung
- Institute for Disease Modeling, Bill & Melinda Gates Foundation, Seattle, WA, USA
| | - Nikolai Windbichler
- Department of Life Sciences, Imperial College London, South Kensington, London, UK
| | - Edward A Wenger
- Institute for Disease Modeling, Bill & Melinda Gates Foundation, Seattle, WA, USA
| | - Caitlin A Bever
- Institute for Disease Modeling, Bill & Melinda Gates Foundation, Seattle, WA, USA
| | - Prashanth Selvaraj
- Institute for Disease Modeling, Bill & Melinda Gates Foundation, Seattle, WA, USA.
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Golumbeanu M, Yang GJ, Camponovo F, Stuckey EM, Hamon N, Mondy M, Rees S, Chitnis N, Cameron E, Penny MA. Leveraging mathematical models of disease dynamics and machine learning to improve development of novel malaria interventions. Infect Dis Poverty 2022; 11:61. [PMID: 35659301 PMCID: PMC9167503 DOI: 10.1186/s40249-022-00981-1] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2022] [Accepted: 05/04/2022] [Indexed: 01/04/2023] Open
Abstract
Background Substantial research is underway to develop next-generation interventions that address current malaria control challenges. As there is limited testing in their early development, it is difficult to predefine intervention properties such as efficacy that achieve target health goals, and therefore challenging to prioritize selection of novel candidate interventions. Here, we present a quantitative approach to guide intervention development using mathematical models of malaria dynamics coupled with machine learning. Our analysis identifies requirements of efficacy, coverage, and duration of effect for five novel malaria interventions to achieve targeted reductions in malaria prevalence. Methods A mathematical model of malaria transmission dynamics is used to simulate deployment and predict potential impact of new malaria interventions by considering operational, health-system, population, and disease characteristics. Our method relies on consultation with product development stakeholders to define the putative space of novel intervention specifications. We couple the disease model with machine learning to search this multi-dimensional space and efficiently identify optimal intervention properties that achieve specified health goals. Results We apply our approach to five malaria interventions under development. Aiming for malaria prevalence reduction, we identify and quantify key determinants of intervention impact along with their minimal properties required to achieve the desired health goals. While coverage is generally identified as the largest driver of impact, higher efficacy, longer protection duration or multiple deployments per year are needed to increase prevalence reduction. We show that interventions on multiple parasite or vector targets, as well as combinations the new interventions with drug treatment, lead to significant burden reductions and lower efficacy or duration requirements. Conclusions Our approach uses disease dynamic models and machine learning to support decision-making and resource investment, facilitating development of new malaria interventions. By evaluating the intervention capabilities in relation to the targeted health goal, our analysis allows prioritization of interventions and of their specifications from an early stage in development, and subsequent investments to be channeled cost-effectively towards impact maximization. This study highlights the role of mathematical models to support intervention development. Although we focus on five malaria interventions, the analysis is generalizable to other new malaria interventions. Graphical abstract ![]()
Supplementary Information The online version contains supplementary material available at 10.1186/s40249-022-00981-1.
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Affiliation(s)
- Monica Golumbeanu
- Swiss Tropical and Public Health Institute, Allschwil, Switzerland.,University of Basel, Basel, Switzerland
| | - Guo-Jing Yang
- Swiss Tropical and Public Health Institute, Allschwil, Switzerland.,Key Laboratory of Tropical Translational Medicine of Ministry of Education and School of Tropical Medicine and Laboratory Medicine, The First and Second Affiliated Hospital of Hainan Medical University, Hainan Medical University, Haikou, Hainan, People's Republic of China.,University of Basel, Basel, Switzerland
| | - Flavia Camponovo
- Swiss Tropical and Public Health Institute, Allschwil, Switzerland.,University of Basel, Basel, Switzerland.,Center for Communicable Disease Dynamics, Department of Epidemiology, Harvard T. H. Chan School of Public Health, Boston, MA, 02115, USA
| | | | | | | | - Sarah Rees
- Innovative Vector Control Consortium, Liverpool, UK
| | - Nakul Chitnis
- Swiss Tropical and Public Health Institute, Allschwil, Switzerland.,University of Basel, Basel, Switzerland
| | - Ewan Cameron
- Malaria Atlas Project, Big Data Institute, University of Oxford, Oxford, UK.,Curtin University, Perth, Australia.,Telethon Kids Institute, Perth Children's Hospital, Perth, Australia
| | - Melissa A Penny
- Swiss Tropical and Public Health Institute, Allschwil, Switzerland. .,University of Basel, Basel, Switzerland.
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Selvaraj P, Wenger EA, Bridenbecker D, Windbichler N, Russell JR, Gerardin J, Bever CA, Nikolov M. Vector genetics, insecticide resistance and gene drives: An agent-based modeling approach to evaluate malaria transmission and elimination. PLoS Comput Biol 2020; 16:e1008121. [PMID: 32797077 PMCID: PMC7449459 DOI: 10.1371/journal.pcbi.1008121] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2020] [Revised: 08/26/2020] [Accepted: 07/02/2020] [Indexed: 12/19/2022] Open
Abstract
Vector control has been a key component in the fight against malaria for decades, and chemical insecticides are critical to the success of vector control programs worldwide. However, increasing resistance to insecticides threatens to undermine these efforts. Understanding the evolution and propagation of resistance is thus imperative to mitigating loss of intervention effectiveness. Additionally, accelerated research and development of new tools that can be deployed alongside existing vector control strategies is key to eradicating malaria in the near future. Methods such as gene drives that aim to genetically modify large mosquito populations in the wild to either render them refractory to malaria or impair their reproduction may prove invaluable tools. Mathematical models of gene flow in populations, which is the transfer of genetic information from one population to another through migration, can offer invaluable insight into the behavior and potential impact of gene drives as well as the spread of insecticide resistance in the wild. Here, we present the first multi-locus, agent-based model of vector genetics that accounts for mutations and a many-to-many mapping cardinality of genotypes to phenotypes to investigate gene flow, and the propagation of gene drives in Anopheline populations. This model is embedded within a large scale individual-based model of malaria transmission representative of a high burden, high transmission setting characteristic of the Sahel. Results are presented for the selection of insecticide-resistant vectors and the spread of resistance through repeated deployment of insecticide treated nets (ITNs), in addition to scenarios where gene drives act in concert with existing vector control tools such as ITNs. The roles of seasonality, spatial distribution of vector habitat and feed sites, and existing vector control in propagating alleles that confer phenotypic traits via gene drives that result in reduced transmission are explored. The ability to model a spectrum of vector species with different genotypes and phenotypes in the context of malaria transmission allows us to test deployment strategies for existing interventions that reduce the deleterious effects of resistance and allows exploration of the impact of new tools being proposed or developed.
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Affiliation(s)
- Prashanth Selvaraj
- Institute for Disease Modeling, Bellevue, Washington, United States of America
| | - Edward A. Wenger
- Institute for Disease Modeling, Bellevue, Washington, United States of America
| | - Daniel Bridenbecker
- Institute for Disease Modeling, Bellevue, Washington, United States of America
| | - Nikolai Windbichler
- Department of Life Sciences, Imperial College London, South Kensington, United Kingdom
| | - Jonathan R. Russell
- Institute for Disease Modeling, Bellevue, Washington, United States of America
| | - Jaline Gerardin
- Institute for Disease Modeling, Bellevue, Washington, United States of America
- Department of Preventive Medicine, Northwestern University, Chicago, Illinois, United States of America
| | - Caitlin A. Bever
- Institute for Disease Modeling, Bellevue, Washington, United States of America
| | - Milen Nikolov
- Institute for Disease Modeling, Bellevue, Washington, United States of America
- Sage Bionetworks, Seattle, Washington, United States of America
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