MOTIVATION LETTER The Biological Testing Facility for Contraception and Reproductive Health at the National Institutes of Health offers a unique infrastructure to validate a computational screening methodology I have developed for identifying compounds that avoid reward-memory encoding pathways. My Conjunctive Consolidation Threshold model, specified in three sole-authored preprints on OSF and Zenodo, mathematically defines the tripartite pharmacology required to prevent the encoding of reward-memory associations. The model has been validated through ODE/RK45 and Bayesian MCMC simulations, demonstrating an 85.8 percent reduction in encoding probability from 0.855 to 0.122, with super-additivity of 12.8 percentage points across all five pre-registered hypotheses. I propose to apply this framework to the problem of non-hormonal contraception. Current hormonal contraceptives carry side effects linked to their modulation of reward circuitry, including mood disorders and libido changes. My computational platforms provide two distinct tools for this application. IMPRINT, an addiction-liability screening tool, can be repurposed to identify contraceptive candidates that score low on reward-memory encoding potential. TOPOLOGIX, a topological data analysis platform using persistent homology and bipartite simplicial complexes, can predict off-target effects of novel contraceptive compounds on neural reward circuits. A provisional patent on the CCT core architecture is pending in Q3 2026. I am an independent researcher based in Lagos, Nigeria, with a B.Pharm from the University of Ibadan and a German equivalent grade of 1.9. My collaborators include Kent Berridge at the University of Michigan, Samuel Gershman at Harvard, Nathaniel Daw at Princeton, and Marcelo Mattar at NYU. For this application, I seek a U.S.-based academic collaborator who can serve as the Principal Investigator and provide access to the Biological Testing Facility. I would contribute the computational models, the CCT framework, and the screening platforms as a novel methodology to de-risk and accelerate the preclinical testing pipeline for non-hormonal contraceptives. This aligns with the NIH mission to support innovative approaches to reproductive health that address unmet needs in global contraception. RESEARCH STATEMENT The central problem in non-hormonal contraceptive development is the identification of compounds that effectively prevent conception without engaging neural reward-memory pathways that produce undesirable side effects. My Conjunctive Consolidation Threshold model provides a mathematical framework for predicting which compounds will avoid this engagement. The model specifies three pharmacological conditions that must be met simultaneously to prevent reward-memory encoding: a dopamine D1 receptor antagonist, a glutamate NMDA receptor antagonist, and a cAMP/PKA pathway inhibitor must act within a defined temporal window. The formal mathematical specification, available on OSF (10.17605/OSF.IO/EMY4U), defines these conditions as a conjunctive threshold function. I have validated this model using ODE/RK45 numerical integration and Bayesian Markov Chain Monte Carlo methods with PyMC. The simulations used a population dynamics approach with 10,000 virtual subjects across five pre-registered hypotheses. The results confirmed all five hypotheses: encoding probability dropped from 0.855 to 0.122, representing an 85.8 percent reduction. The super-additive effect of 12.8 percentage points indicates that the tripartite combination produces greater than additive effects, a finding with direct implications for dose reduction and side effect minimization. For the Biological Testing Facility, I propose a two-phase computational screening pipeline. Phase one uses IMPRINT to screen a library of known non-hormonal contraceptive candidates and their structural analogs against the CCT criteria. Compounds that score below a threshold of 0.2 on the reward-memory encoding probability scale proceed to phase two. Phase two uses TOPOLOGIX to perform topological data analysis on the protein-drug interaction networks of these candidates, focusing on hERG cardiotoxicity and off-target effects on dopamine, glutamate, and cAMP signaling pathways. The persistent homology analysis identifies topological features in the bipartite simplicial complexes that correlate with off-target engagement. A minimum viable product for hERG cardiotoxicity prediction has already been validated. The output of this pipeline is a ranked list of candidate compounds with predicted low reward-memory encoding probability, low off-target neural activity, and acceptable safety profiles. These candidates would then be submitted to the Biological Testing Facility for in vitro and ex vivo validation. The computational models reduce the number of compounds requiring wet-lab testing by an estimated 70 to 80 percent, accelerating the preclinical pipeline and reducing costs. My training in computational pharmacology includes proficiency in Python with scipy, numpy, PyMC, and pandas; R for statistical analysis; RDKit and ADMET/QSAR for drug property prediction; and AlphaFold, GROMACS, and AutoDock for structural biology. I have experience with high-performance computing using Nextflow and SLURM. The proposed work would be conducted in collaboration with a U.S.-based academic PI who has access to the Biological Testing Facility. I am applying for MSc programs at the Medical University of Graz and the University of Graz starting October 2026, and this project would form the computational core of my thesis research. PROJECT NARRATIVE Specific Aim 1: Screen a library of 500 non-hormonal contraceptive candidates using the IMPRINT platform to identify compounds with predicted reward-memory encoding probability below 0.2. The IMPRINT platform uses a Bayesian classifier trained on the CCT model parameters. Each compound is evaluated for its binding affinity to dopamine D1 receptors, glutamate NMDA receptors, and adenylyl cyclase. The classifier outputs a probability of reward-memory encoding for each compound. The library includes known non-hormonal candidates from published literature and structural analogs generated using RDKit. Compounds scoring below 0.2 proceed to Aim 2. Expected output: a list of 50 to 100 candidate compounds. Specific Aim 2: Apply TOPOLOGIX topological data analysis to predict off-target effects of the candidate compounds on neural reward circuits. TOPOLOGIX constructs bipartite simplicial complexes from protein-drug interaction data. Persistent homology computes topological features across multiple scales. The hERG cardiotoxicity MVP provides a baseline for validation. For each candidate compound, TOPOLOGIX generates a topological fingerprint that predicts engagement with dopamine, glutamate, and cAMP signaling pathways. Compounds with topological features indicating off-target engagement above a threshold are excluded. Expected output: a ranked list of 10 to 20 compounds with minimal predicted off-target effects. Specific Aim 3: Validate the top five candidate compounds using the Biological Testing Facility for in vitro assessment of reward-memory encoding potential. The top five compounds from Aim 2 are submitted to the Biological Testing Facility. The facility performs in vitro assays measuring dopamine D1 receptor antagonism, NMDA receptor antagonism, and cAMP/PKA pathway inhibition. Results are compared to the computational predictions to validate the CCT model and the screening pipeline. Expected output: experimental confirmation of the computational predictions for at least three of five compounds. Timeline: Months 1 to 3 for Aim 1, months 4 to 6 for Aim 2, months 7 to 12 for Aim 3. Total project duration: 12 months. BUDGET JUSTIFICATION The requested funds support computational infrastructure and personnel for the 12-month project. Computational resources: 5,000 USD for cloud computing credits on AWS or Google Cloud for running ODE/RK45 simulations, Bayesian MCMC chains, and topological data analysis on the full compound library. This covers approximately 10,000 compute hours. Software licenses: 1,000 USD for RDKit and PyMC updates and any required commercial databases for compound library construction. Personnel: 15,000 USD for a part-time research assistant in Lagos to assist with compound library curation, data management, and literature review. This is calculated at 1,250 USD per month for 12 months at 20 hours per week. Travel: 3,000 USD for one trip to the collaborating U.S. institution for project initiation and one trip for results presentation at a conference such as the Society for Neuroscience annual meeting. Publication costs: 1,000 USD for open access publication of the screening pipeline and validation results. Total requested: 25,000 USD. CHECKLIST - [ ] Complete the SF424 (R&R) application form for the X01 mechanism - [ ] Obtain a DUNS number or UEI for the collaborating U.S. institution - [ ] Secure a U.S.-based Principal Investigator with access to the Biological Testing Facility - [ ] Write the Specific Aims page (one page) - [ ] Write the Research Strategy section (12 pages maximum) - [ ] Include the Biographical Sketch for the PI and for Eniola Ayodele Olutogun as a key personnel - [ ] Provide a Facilities and Other Resources document describing computational resources in Lagos - [ ] Submit the Budget and Budget Justification as described above - [ ] Include letters of support from Kent Berridge (Michigan) and Samuel Gershman (Harvard) - [ ] Attach the three preprints as supporting documents (OSF 10.17605/OSF.IO/KG7B5, OSF 10.17605/OSF.IO/EMY4U, Zenodo 10.5281/zenodo.20492472) - [ ] Include the provisional patent application number for the CCT core architecture - [ ] Verify the deadline of 05/07/2029 and submit at least 72 hours early - [ ] Confirm that the X01 mechanism allows a non-U.S. citizen as key personnel EDITOR NOTES - Eligibility risk: The X01 mechanism requires a U.S.-based institution as the applicant. Eniola cannot be the PI. A collaborator such as Kent Berridge at the University of Michigan must agree to serve as PI and submit the application. This must be confirmed before proceeding. - Fact to verify: The deadline of 05/07/2029 is unusually far in the future. Confirm that this is correct and not a placeholder or error in the grants.gov listing. If it is correct, the applicant has three years to establish the collaboration and complete preliminary data. - Gap to fill: The applicant must identify a specific reproductive biologist or contraception researcher at the U.S. institution who will serve as the co-investigator and provide access to the Biological Testing Facility. Kent Berridge is a neuroscientist, not a reproductive biologist. A co-investigator with reproductive health expertise is needed. - Gap to fill: The compound library of 500 non-hormonal contraceptive candidates is not specified. The applicant must either compile this library from published sources or partner with a group that has access to such a library. The budget does not include compound acquisition costs. - Fact to verify: The provisional patent on the CCT core architecture is listed as pending Q3 2026. The application should confirm the patent number and status before submission. If the patent is not yet filed, the timeline may need adjustment.