Awards

Denali Dahl of Kalia Health, Inc. in the U.S. will evaluate Activin A and Inhibin A as urinary biomarkers for prediction and detection of preeclampsia early in pregnancy. This work builds on an ongoing biomarker validation study in Bloemfontein, South Africa. Through collaborations, clinical studies will be performed with blood and urine sampling in cohorts of pregnant women. Studies in Stellenbosch, South Africa will assess how levels of the two proteins vary in urine during pregnancy, and studies in Bloemfontein, South Africa will assess how early in pregnancy they can serve to predict preeclampsia risk. Activin A and Inhibin A levels in urine will be measured by MSD, and their diagnostic value will be compared to a standard assay for the biomarker protein ratio sFlt1/PIGF in blood and to clinical diagnosis by the treating physician.

Neha Lasure of Intignus Biotech Pvt. Ltd. in India will develop an affordable point-of-care diagnostic platform for prediction and detection of preeclampsia early in pregnancy. The diagnostic test is a lateral flow immunoassay that detects two key preeclampsia biomarker proteins in blood: sENG and PIGF. They will generate monoclonal antibodies against these proteins, manufacture test kits, and train frontline health care workers to administer and interpret the test. They will then perform a pilot study with 2,000 pregnant women in the Indian states of Pune and Mumbai, evaluating prediction accuracy compared to clinical outcomes and standard existing clinical tests.

Annie McDougall of the Burnet Institute in Australia will develop a digital tool for point-of-care prediction of preeclampsia risk early in pregnancy, using data from clinical trials in Sub-Saharan Africa. A predictive model will be developed and validated using data from an ongoing set of clinical treatment studies in Ghana, Kenya, and South Africa: the PEARLS trial (Preventing Preeclampsia: Evaluating Aspirin Low-Dose Regimens Following Risk Screening). This model will be used to develop a tool for automated preeclampsia risk stratification to support clinical decision making by antenatal care workers. It will be designed for integration into existing digital health platforms, including real-time patient data entry. The tool will be evaluated for usability, feasibility, and acceptability through interviews and workshops with patients and care workers in two of the PEARLS trial countries.

Javan Esfandiari of Chembio Diagnostics, Inc. in the U.S. will develop an affordable point-of-care diagnostic platform for prediction and detection of preeclampsia early in pregnancy. The diagnostic test is a semi-quantitative lateral flow immunoassay to monitor the level of the key preeclampsia biomarker protein sFlt1 in whole blood from a finger prick. The test will discriminate between two levels of the biomarker, identifying patients at either low, medium, or high risk of developing preeclampsia, and it will be integrated into a low-cost, portable reader device. Through local collaboration, the prototype device will be tested in France, Nigeria, and Benin. In each country, 100 women with identified risk of preeclampsia will participate. For comparison with the diagnostic test results and predicted preeclampsia risk, patient clinical outcomes will be recorded, and serum samples will be tested at a central laboratory, using existing tests to measure sFlt1 and the sFlt1/PIGF biomarker ratio.

Mathias Wipf of MOMM Diagnostics GmbH will improve their préXclude test to better enable its use in low- and middle-income countries (LMICs) as an affordable point-of-care diagnostic platform for prediction and detection of preeclampsia early in pregnancy. The existing test is contained in a single-use cartridge connected to an inexpensive handheld reader. It is based on an electrochemical enzyme-linked lateral flow immunoassay that quantifies the levels and ratio of two key preeclampsia biomarker proteins, sFlt1 and PIGF, in whole blood from a finger prick. Multiple aspects of the test will be improved to enhance usability and analytical performance, with the goal of developing a prototype platform that meets a target product profile appropriate for LMIC settings.

Sébastien Mazzuri of the EspeRare Foundation in Switzerland and its partners will reposition an oral drug previously evaluated in cardiovascular patients as a treatment candidate for early-onset preeclampsia. The drug has an extensive data package and advanced to Phase 3 trials before discontinuation for lack of superiority over standard of care. Leveraging preliminary research suggesting it could beneficially rebalance key physiological disruptions underlying preeclampsia, EspeRare will drive translational proof-of-concept studies in established preclinical models and coordinate advisory board consultations to guide the clinical trial design. The goal is to confirm the drug's therapeutic potential in preeclampsia and accelerate regulatory clearance for clinical evaluation. Ultimately this new therapeutic approach aims to significantly improve survival and health outcomes for pregnant women and their unborn children, with a focus on accessibility in high-burden regions.

Offer Erez with Gil Shalev of Ben-Gurion University of the Negev in Israel, in collaboration with Diomede Ntasumbumuyange of the University of Rwanda in Rwanda, will develop an affordable point-of-care diagnostic platform for prediction and detection of women at risk for preeclampsia early in pregnancy. The sensor system is an electronic biochip composed of biological field-effect transistors (bio-FETs) incorporating antibodies to sFlt1 and PIGF, two key preeclampsia biomarker proteins. It will be designed for simultaneous (multiplex) monitoring of blood and urine levels of these two biomarkers, accommodating whole blood and not requiring pre-measurement processing of the sample or the sensor, making it suitable for out-of-hospital use. They will evaluate the system with an existing inexpensive read-out device, testing it with samples of whole blood and urine spiked with the biomarker proteins, as well as samples collected from pregnant women in Israel and in Rwanda, and compare these clinical sample test results to those using a conventional FDA approved ELISA.

Liat Shenhav of New York University Grossman School of Medicine in the U.S. will develop a diagnostic platform, based on non-invasive retinal imaging, for prediction and detection of preeclampsia early in pregnancy. Previous results with a U.S.-based cohort of 1,400 pregnant women showed promise in using retinal vasculature features in the first trimester to predict the risk of preeclampsia. With local collaborators, the current study will recruit 2,000 pregnant women in clinical research centers in Belagavi and Nagpur in India, part of the Global Network for Women’s and Children’s Health Research. Each participant will get a retinal scan in the first and second trimester and be followed through pregnancy to determine clinical outcomes. An AI-based model will be developed to predict preeclampsia risk from the retinal scans, guided by parameters used in modeling for the U.S.-based cohort to help ensure generalizability.

Young Kim of Purdue University in the U.S. will develop a diagnostic platform for prediction and detection of preeclampsia early in pregnancy. The project is based on non-invasive imaging with an unmodified smartphone of the conjunctiva of the eye, and it will be a collaboration with Edwin Were of the Moi University Teaching and Referral Hospital (MTRH) in Kenya and Martin Were of Vanderbilt University Medical Center in the U.S. A cohort of 1,600 pregnant women at 12-15 weeks of gestation will be recruited from MTRH antenatal clinics, and they will be followed through pregnancy and delivery to determine clinical outcomes. Participants will get a conjunctival photo taken at enrollment, using a custom-made color chart to standardize images, and an AI-based model will be developed to predict preeclampsia risk from the vasculature features in the imaging data.

Ling-Jun Li of the National University of Singapore in Singapore will develop a diagnostic platform for prediction of preeclampsia early in pregnancy for women at high-risk of developing the condition. Two, existing, Singapore-based pregnancy cohorts will be used to develop and validate an AI-based risk prediction model for hypertensive disorders of pregnancy including preeclampsia. A non-invasive, preeclampsia screening test will then be developed, combining retinal imaging to identify vasculature features plus epidermal detection of the preeclampsia biomarker proteins PIGF and PAPP-A using a wearable forearm patch with a solid-state sensing system. The feasibility and acceptability of this test will be assessed with 60 pregnant women recruited at National University Hospital in Singapore, integrating the clinical data with the AI-based risk prediction model to guide further evaluation of the diagnostic platform.