Designing antifilarial drug trials using clinical trial simulators

Walker, Martin; Hamley, Jonathan I. D.; Milton, Philip; Monnot, Frédéric; Pedrique, Belén; Basáñez, María‐Gloria

doi:10.1038/s41467-020-16442-y

Cited by 9 publications

(15 citation statements)

References 71 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Such models can consider detailed population processes such as movement between towns or children mixing in schools, in addition to tracking the infection history of individuals and their associated morbidity over time. Individual-based models have been developed for macroparasitic diseases, including onchocerciasis and lymphatic filariasis, which have been applied to clinical trial design for new anthelmintics [6].…”

Section: Modelling For Parasitologistsmentioning

confidence: 99%

“…calculations, and relevant outcomes by considering the transmission process and how it responds to perturbations [5]. For instance, Walker and colleagues simulated clinical trials to compare ivermectin, the existing treatment for onchocerciasis and lymphatic filariasis which kills microfilarial (juvenile) stages and temporarily reduces fecundity in Onchocerca volvulus, against hypothetical anthelmintic drugs that kill adult worms [6]. The study showed the importance of follow-up timeframes, as the benefits of anthelmintic drugs are most prominent 12-24 months after treatment, and calculated the required sample sizes under different scenarios.…”

mentioning

confidence: 99%

See 1 more Smart Citation

Towards Evidence-based Control of Opisthorchis viverrini

Crellen

Sithithaworn

Pitaksakulrat

et al. 2021

Trends in Parasitology

View full text Add to dashboard Cite

Transmission of the carcinogenic liver fluke Opisthorchis viverrini is ongoing across Southeast Asia. Endemic countries within the region are in different stages of achieving control. However, evidence on which interventions are the most effective for reducing parasite transmission, and the resulting liver cancer, is currently lacking. Quantitative modelling can be used to evaluate different control measures against O. viverrini and assist the design of clinical trials. In this article we evaluate the epidemiological parameters that underpin models of O. viverrini and the data necessary for their estimation, with the aim of developing evidence-based strategies for parasite control at a national or regional level. Assessing InterventionsControl initiatives against the liver fluke O. viverrini are driven by two related aims. Firstly, to reduce transmission of the parasite, leading to its elimination (see Glossary). Secondly, to halt the progression of O. viverrini-induced liver pathology, and thus prevent new cases of cholangiocarcinoma [1]. In endemic regions, definitive hosts become infected through consumption of raw or insufficiently cooked freshwater fish encysted with metacercariae and perpetuate the life cycle by defecating into water sources containing snails of the Bithynia genus. Programmes to control O. viverrini therefore have a number of interventions available to them, including health education to promote safe eating habits; case diagnosis and anthelmintic treatment; improvements to sanitation; and food safety controls [2]. Parasite-induced liver pathology can be tackled with ultrasound screening to detect periductal fibrosis, an early warning sign of cholangiocarcinoma; and operative surgery to improve survival rates [3]. With such a variety of tools on offer, policy makers, international health organisations, and affected communities are entitled to ask: what works? Which measures are most effective at interrupting transmission of the parasite and reducing mortality in a cost-effective, sustainable manner?The traditional method for answering this question is through randomised controlled trials, which are the gold standard for assessing interventions [4]. Such trials, however, are costly, take many years, and require substantial manpower and expertise. It is also unethical to conduct trials in contexts where public health initiatives are ongoing and would have to be withdrawn for participants allocated into the control arm of a trial. An alternative approach is to use quantitative models to make predictions on the effectiveness of different control strategies. These models take myriad forms, can be parameterised from routinely collected field data, and provide considerable insight for a fraction of the cost of a trial. Different types of models that can capture macroparasite dynamics are summarised in Box 1.When the need to conduct a clinical trial is unavoidable, for example on novel therapeutics or diagnostics, dynamic simulations can provide powerful insights into trial design, power HighlightsH...

show abstract

Section: Modelling For Parasitologistsmentioning

confidence: 99%

mentioning

confidence: 99%

Towards Evidence-based Control of Opisthorchis viverrini

Crellen

Sithithaworn

Pitaksakulrat

et al. 2021

Trends in Parasitology

View full text Add to dashboard Cite

show abstract

“…Onchocerciasis and LF have recently benefited from pharmacokinetic-pharmacodynamics modelling, translating pre-clinical non-human experimental results into quantitative insights relevant to human treatment 79 . Clinical trial simulations are designed to include all aspects of a clinical trial protocol including (but not limited to) recruitment criteria, drug properties/ effectiveness and follow-up times 80 , providing valuable guidance that translates into more effective, efficient, cost-efficient and robust clinical trials. In addition to providing insight into the optimal distribution of new drugs 81 , rethinking the distribution of existing drugs to achieve public health targets can also be guided by modelling 37,48 .…”

Section: Drug Development and Clinical Study Designmentioning

confidence: 99%

How modelling can help steer the course set by the World Health Organization 2021-2030 roadmap on neglected tropical diseases

et al. 2021

View full text Add to dashboard Cite

The World Health Organization recently launched its 2021-2030 roadmap, Ending the Neglect to Attain the Sustainable Development Goals, an updated call to arms to end the suffering caused by neglected tropical diseases. Modelling and quantitative analyses played a significant role in forming these latest goals. In this collection, we discuss the insights, the resulting recommendations and identified challenges of public health modelling for 13 of the target diseases: Chagas disease, dengue, gambiense human African trypanosomiasis (gHAT), lymphatic filariasis (LF), onchocerciasis, rabies, scabies, schistosomiasis, soil-transmitted helminthiases (STH), Taenia solium taeniasis/ cysticercosis, trachoma, visceral leishmaniasis (VL) and yaws. This piece reflects the three cross-cutting themes identified across the collection, regarding the contribution that modelling can make to timelines, programme design, drug development and clinical trials.

show abstract

“…1). However, models have been mainly used at late stages during the development of a new intervention; for example, to predict likely impact or cost-effectiveness from data collected in Phase 3 clinical trials (10)(11)(12)(13)(14)(15). Model investigations are usually informed by scenario analysis accounting for the delivery and target age groups, as well as properties of the new intervention pre-defined or informed by late clinical trials (16)(17)(18)(19).…”

Section: Introductionmentioning

confidence: 99%

Leveraging mathematical models of disease dynamics and machine learning to improve development of novel malaria interventions

Golumbeanu

Yang

Stuckey

et al. 2021

Preprint

View full text Add to dashboard Cite

The development of novel interventions against a disease entails optimising their specifications to achieve desired health goals such as disease reduction. As testing is limited early in development, it is difficult to predefine these optimal specifications, prioritize or continue investment in candidate interventions. Mathematical models of disease can provide quantitative evidence as they can simulate deployment and predict impact of a new intervention considering deployment, health-system, population and disease characteristics. However, due to large uncertainty early in development, as well as model complexity, testing all possible combinations of interventions and deployments becomes infeasible. As a result, mathematical models have been only marginally used during intervention development to date. Here, we present a new approach where machine learning enables the use of detailed disease models to identify optimal properties of candidate interventions to reach a desired health goal and guide development. We demonstrate the power of our approach by application to five novel malaria interventions under development. For various targeted reductions of malaria prevalence, we quantify and rank intervention characteristics which are key determinants of health impact. Furthermore, we identify minimal requirements and tradeoffs between operational factors, intervention efficacy and duration to achieve different levels of impact and show how these vary across disease transmission settings. When single interventions cannot achieve significant impact, our method allows finding optimal combinations of interventions fulfilling the desired health goals. By enabling efficient use of disease models, our approach supports decision-making and resource investment in the development of new interventions for infectious diseases.Significance StatementDuring development of novel disease interventions (e.g. vaccines), a target product profile (TPP) document defines intervention characteristics required to meet health goals. As clinical trials are limited early in development, mathematical models simulating disease dynamics can help define TPPs. However, testing all combinations of intervention, delivery and environment characteristics is infeasible and so complex mathematical models have not been used until now. We introduce a new approach to define TPPs, combining models of disease with machine learning. We examined several novel malaria interventions, identifying key characteristics, minimum efficacy and duration of effect that ensure significant reductions in malaria prevalence. This approach therefore enabled mathematical models of disease to support intervention development, by identifying intervention requirements that ensure public health impact.One Sentence SummaryDefining quantitative profiles of novel disease interventions by combining machine learning with mathematical models of disease transmission

show abstract

Designing antifilarial drug trials using clinical trial simulators

Cited by 9 publications

References 71 publications

Towards Evidence-based Control of Opisthorchis viverrini

Towards Evidence-based Control of Opisthorchis viverrini

How modelling can help steer the course set by the World Health Organization 2021-2030 roadmap on neglected tropical diseases

Leveraging mathematical models of disease dynamics and machine learning to improve development of novel malaria interventions

Contact Info

Product

Resources

About