Awarded Grants

Eric Reiter of the Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE) in France will engineer nanobody-based biologicals to block ovulation as a practical, non-hormonal contraceptive with fewer side effects. Blocking the molecular regulators of ovulation is an attractive contraceptive mechanism. However, it can also affect steroid hormone production, which causes undesirable side-effects. Nanobodies are antigen-binding domains of antibodies that can very selectively modulate signal transduction pathways. They will identify candidate nanobodies that may selectively block ovulation using a phage display approach and functional screens. Specifically, this work will focus on identifying nanobodies that are biased ligands, triggering receptors selectively to yield only the desired downstream responses. These candidates will be engineered to produce long-lasting biologicals that will then be administered to mice and ewes to evaluate their ability to block ovulation as a proof-of-principle.

EVaccination app to ensure each newborn’s vaccination through trained semi-literate local people as incentivized Community Health Workers (CHWs) to find, counsel and accompany new mothers to public hospitals for vaccination

Leo Han of Oregon Health and Science University in the U.S. and colleagues at the University of North Carolina and the Marsico Lung Institute will build a hydration-based drug discovery platform for the cervix to screen drug libraries for long-lasting non-hormonal contraceptives that alter mucus hydration. Contraceptives that thicken cervical mucus to block the movement of sperm and thereby inhibit fertility would be well tolerated and may also protect against pathogens. Identifying nonhormonal drugs that work in this way, however, is difficult because of the lack of relevant cell culture systems for high-throughput testing. They have previously conditionally reprogrammed endocervical cells to grow in culture while retaining relevant physiological characteristics such as hormonal regulation and mucus production. They will adapt this method for high-throughput screens by incorporating particle-based tracking microrheology to quantify hydration of the mucus layer produced by the cells that can then be used to screen drug libraries.

Combining multiple data sets from HBGDKi using ML tools for prediction, classification and topic discovery may yield new insights for adverse birth outcomes and intermediate outcomes of interest. The study is based on a set of epidemiological, clinical and biochemical variables risk stratification algorithms for various adverse outcomes with practical applicability in health programme, and clinical settings may be feasible to develop using ML tools.ML can be used to suitably impute/bin missing values within datasets and merge variables from multiple datasets using robust data triangulation algorithms.

Environmental exposure to antibiotics is correlated well with AMR. The present study proposes the development of high throughput screening (HTS) technique of 125 antibiotics from environmental samples like water (from river, aquifers and food sources like egg and raw meat). A novel in-vitro method will be adopted to correlate AMR with the environmental levels of antibiotics found in Delhi-NCR region and derive safe levels of antibiotics that should be permissible in the environment.

Stephanie Seminara of Massachusetts General Hospital in the U.S. will perform large-scale, human genetic studies to identify gene variants that influence fertility for developing novel non-hormonal contraceptives. Globally, many women do not use contraceptives for reasons including negative side effects of hormonal methods, leading to poor method acceptability. This leads to 88 million unintended pregnancies per year globally. To identify drug targets for developing more acceptable contraceptives, they will analyze whole exome sequences and phenotypes from three existing patient populations with rare forms of infertility, such as primary ovarian insufficiency, and one new cohort with unexplained infertility. This will reveal both single nucleotide and structural variants underlying infertility, and subsequently the associated molecular pathways. They will also perform a large-scale genome-wide association study using over 1.8 million samples from multi-ethnic population biobanks to identify common variants associated with reproductive traits, which could also uncover novel genes involved in infertility.

Viviana Gradinaru of the California Institute of Technology in the U.S. will perform imaging-based, high-throughput screens using adeno-associated virus (AAV) delivery vectors to rapidly identify ovary-specific macromolecules that are essential for fertility and could be used to develop non-hormonal contraceptives. They will compile a comprehensive list of candidate ovary-specific macromolecules, including RNAs and micropeptides, by applying machine learning algorithms and structural analyses to existing datasets and also perform Riboseq on mouse and human ovarian tissues to identify all the proteins being translated. They will then test these candidates by developing an oocyte and follicle cell-based loss-of-function screening platform using AAV to safely, efficiently, and specifically deliver the macromolecule-targeting constructs to the cells. The most promising AAV-based candidates will then be tested directly in mouse follicle cultures and then in vivo to identify those that are critical for female fertility and have reversible effects.

Jeffrey Lee of the University of Toronto in Canada will engineer single-domain camelid antibodies (nanobodies) to block the interaction between two proteins exclusive to the sperm and egg that mediate their fusion and thereby fertilization, as affordable, non-hormonal contraceptives with fewer side effects. Nanobodies are exquisitely specific binding proteins that make attractive therapeutics because of their additional simplicity, stability, and smaller size compared to antibodies, also lowering the cost of their production. They hypothesize that their small size is well adapted to reach the site of sperm-egg binding and block this interaction. To generate specific nanobodies they will immunize alpacas or llamas with the purified sperm and egg proteins and use phage display and ELISA to isolate antigen-specific nanobodies. These will then be tested for their ability to block sperm-egg fusion using biophysical assays, mating, and IVF models.

The study proposes on pregnant women (Garbh-ini) cohort, a multidimensional longitudinal dataset purposely designed to study preterm birth. The study will apply data-driven machine learning approaches to develop an accurate and clinically useful model to predict the risk of preterm births. It will use multiple models for classification, with better objective functions and misclassification penalties that will aid in a higher rate of accurate predictions, and resampling of the data to avert biases arising from class imbalance. The primary deliverable will be dynamic prediction models that can predict, at different periods of gestation, the PTB risk using the clinical, epidemiological and imaging data.