Awards

Jessica Manning of the National Institute of Allergy and Infectious Diseases and Daniel Parker of the University of California, Irvine in the U.S. are leveraging metagenomic next-generation sequencing technology to control vector-borne and enteric diseases in Cambodia. In Phase I, which coincided with the country's worst ever recorded dengue epidemic, they documented the full range of pathogens carried by wild mosquitoes and in serum samples from around 400 febrile patients in a peri-urban hospital in Kampong Speu Province. They also measured antibody reactivity against mosquito saliva in these patients to locate disease hotspots, which were targeted by control efforts. In Phase II, they will expand their approach to 3,000 patients across three urban hospitals (adult, pediatric and maternity) in the capital, and characterize the prevalence and spread of multi-drug resistant Salmonella typhi to better manage the use of antibiotics. They will also train local laboratory technicians, and use open-source tools to produce maps of the data for health officials to track outbreaks.

Do-yeon Pi of PiQuant in the Republic of Korea is developing a low-cost spectroscopic device and monitoring system, the Water Scanner, that can be nationally deployed to rapidly detect and map Escherichia coli contamination in drinking water in low-resource settings. Water pollution causes up to 90% of diarrheal diseases, which kill 500,000 children under the age of five each year. Water quality is currently measured using spectroscopic devices that are expensive and time-consuming. In Phase I, they developed a new device incorporating a noise-canceling algorithm that can accurately measure water quality within an hour at 1% of the cost of traditional devices. They also set up a GIS-based monitoring system to create water quality maps that enable a rapid response to any contamination. In Phase II, they will finalize the development and validation of their technology, and work towards commercialization in India and Vietnam.

Firat Guder and Tony Cass of Imperial College London in the United Kingdom along with George Mahuku and James Legg at the International Institute of Tropical Agriculture in Tanzania are developing a low-cost, disposable electrochemical lateral flow assay for smartphones to rapidly detect crop viruses in the field and enable broad crop disease surveillance in low-income regions. Most diagnostic tests are laboratory-based, expensive, and slow. Their approach is based on chemical amplification to increase sensitivity, and detection by nucleic acid aptamers, which are more stable and less costly than antibodies. In Phase I, they selected aptamers for viruses that cause maize lethal necrosis and cassava brown streak disease, optimized the silicon ink for printing in nitrocellulose membranes, developed mobile and cloud/web applications for data storage and visualization, and laboratory-tested the assay. In Phase II, Firat Guder and colleagues will complete and validate the assay, refine the applications, and manufacture 1,000 prototypes and test them in the field in East Africa.

Casey Brown of the University of Massachusetts in the U.S. is building a water distribution network with digital platform to provide affordable access to safe drinking water for poor urban populations. Public water infrastructure in low- and middle-income areas is often poorly maintained and insufficient for rapidly growing cities. This has led to additional water being provided in tankers by private companies, which is expensive and the water quality is often poor. In Phase I, they built prototype mobile applications, and engaged nine high-quality water vendors to set up online profiles and 300 customers to place orders selecting from quantity, price, distance, and water quality. Once orders were placed, a rider was mobilized with an optimized delivery route. In Phase II, they will optimize the prototypes, implement a real-time water quality measurement system to reduce costs, and expand their platform to target low-income households in Mombasa and Nairobi in Kenya.

Julius Lucks of Northwestern University in the U.S. is developing a low-cost field test that can detect multiple plant pathogens and produce simple visual outputs for farmers in low-income countries to better monitor their crops. Current diagnostic field tests only detect one disease and are generally costly and difficult to use. In Phase I, they developed a sensitive, multiplexed assay that can detect multiple pathogens using biosensors and produce colorimetric outputs, and performed successful field-testing in several countries. In Phase II, they will generate a flexible diagnostic platform that uses computational models to rapidly optimize and formulate tests for a range of plant pathogens by combining nucleic acid sequence-based amplification with CRISPR Cas13 detection. They will also optimize cost, usability, and stability, and test performance in the laboratory and in the field in New Zealand, Kenya, and Uganda.

David Hughes of Pennsylvania State University in the U.S. is leveraging real-time, high-resolution satellite imagery of smallholder farms along with artificial intelligence to automatically detect crop pests and diseases in Africa. In Phase I, together with Nita Bharti also of Penn State University in the U.S. and James Legg at the International Institute of Tropical Agriculture in Tanzania and the Charity, Self Help Africa, they recruited 10 agricultural graduates to collect detailed field data on crop pests across Busia County in Kenya together with a large network of farmers supplied with smartphones carrying an artificial intelligence-based visual diagnostic tool. They also trained machine learning models using satellite data to accurately predict future crop growth. In Phase II, they will combine different remote-sensing observations from satellites and other relevant data, such as soil quality and water stress, with crowdsourced observations from 400,000 farmers across Kenya together with machine learning models to build a low-cost sentinel surveillance system with enhanced accuracy. They will test the ability of this system to detect the top five diseases for the top five crops and to provide accurate pest forecasts that can help smallholder African farmers grow more food and make greater profits.

Drew Arenth, Benjamin Fels, and Suvrit Sra of Macro-Eyes in the U.S. are applying a statistical machine learning approach to the immunization supply chains of health facilities in Tanzania that accurately and continuously predicts demand to ensure the right vaccines and levels are being stocked. Currently, vaccine supply is largely fixed or driven by depleted stocks. This leaves children unable to be vaccinated due to stock outs at clinics, as well as often high levels of waste, which could both be overcome by better forecasting vaccination needs for individual clinics. In Phase I, they worked with an NGO and the Ministry of Health to access routinely-collected daily vaccination data from 710 health facilities spread across Tanzania. These data were then used to train algorithms to identify predictive patterns that were tested on independent datasets. This led to a model that could accurately forecast future vaccine consumption. In Phase II, they will work out how best to integrate their approach as an automated component within the existing supply chain infrastructure in Tanzania and develop tools and train advocates to demonstrate its value and encourage implementation and adoption.

Darryl Russell of the University of Adelaide in Australia is seeking safer contraceptives that block ovulation without altering hormone levels and cause fewer side effects using an automated in-vitro screening platform that measures cell adhesion in the cumulus-oocyte complex, which is required to release the oocyte from the ovary. In Phase I, they built the screening platform by isolating cumulus-oocyte complexes from mice, culturing them in fibronectin-coated multi-well plates, and quantifying adhesion in a 96-well plate format using an automated assay. In a first run, they screened a library of 129 FDA-approved chemical compounds over four months and identified seven candidate contraceptives with known protein targets, one of which showed a strong reduction in ovulation when tested in mice. In Phase II, they will study whether one of the main target proteins identified in their first screen is a key target for blocking ovulation. They will also test whether the other candidates from their first screen can block ovulation in mice and screen larger and more diverse libraries to identify new candidate contraceptives and prioritize them for further drug development and testing.

Jianjun Sun of the University of Connecticut in the U.S. is developing non-hormonal contraceptives using a fly-based ovulation assay to identify compounds that specifically block the rupture of follicles, which is required to release eggs for fertilization also in mammals. The popular female contraceptive "pill" alters the hormonal cycle and is widely used throughout the Western world. However, it can have undesirable side effects. In Phase I, they developed a medium-throughput follicle rupture assay using Drosophila follicles and showed that three out of four drugs inhibiting Drosophila follicle rupture had the same effect in mice. They discovered that these drugs also inhibited the production of superoxide, which promotes follicle rupture. Building on this new knowledge, in Phase II, they will develop a high-throughput luminescence-based superoxide-detection assay in Drosophila and screen 13,000 compounds. Top hits will again be validated for activity also in mice, and they will further identify their cellular mechanisms of action using available genetic tools in Drosophila.

Kevin Osteen of Vanderbilt University Medical Center in the U.S. is developing a three-dimensional cell model that mimics the lining of the human uterus (endometrium), including different cell types and a vascular system, that can be used for affordable medium-to-high-throughput compound screening to discover new contraceptives with minimal adverse side effects. The endometrium is a multi-layered tissue that supports embryo implantation and maintains pregnancy and responds to hormonal cues to undergo renewal during each menstrual cycle. Recapitulating this environment in vitro could provide a valuable, contraceptive screening platform to identify new contraceptives. In Phase I, they built a compartmentalized two-chamber device to model the cross-talk between two endometrial cell layers. They showed that this EndoChip was able to mimic the process of decidualization that is critical for establishing pregnancy. In Phase II, they will refine a second-generation EndoChip to include an epithelial component in the form of organoids by incorporating a three-dimensional synthetic hydrogel and complete endometrial microenvironment for in vitro contraceptive screens. This will be validated with existing drugs known to affect endometrial function. They will also incorporate microfluidic platforms and existing in vitro organ models to establish a multi-organ system that mimics the effect of the liver or gut for analyzing candidate drug efficacy and safety.