MOTIVATION LETTER The Conjunctive Consolidation Threshold model, a tripartite pharmacological framework for reward-memory encoding prevention, predicts that environmental neurotoxins disrupt gut-brain signaling by altering the conjunctive binding of dopamine, glutamate, and norepinephrine signals during memory consolidation. This mechanism is directly relevant to the National Institutes of Health Funding Opportunity Announcement for Impact of Environmental Exposures on Gut-Brain Signaling in Neurological Conditions. My independent research, conducted in Lagos, Nigeria, has produced three sole-authored preprints on OSF and Zenodo that mathematically specify this framework, validated through ODE/RK45 and Bayesian MCMC methods. The model achieved an encoding probability reduction from 0.855 to 0.122, an 85.8 percent reduction, with super-additivity of 12.8 percentage points. All five pre-registered hypotheses H1 through H5 were confirmed. A review article is under review at Neuroscience and Biobehavioral Reviews, and a co-authored paper is under review at Alcohol (Elsevier). My computational toolkit includes Python with scipy, numpy, PyMC for MCMC, and TDA libraries Ripser and Gudhi for topological data analysis. I built IMPRINT, an addiction-liability screening platform, and TOPOLOGIX, which applies persistent homology and bipartite simplicial complexes to drug-protein interaction analysis, with a hERG cardiotoxicity MVP. These tools are directly applicable to modeling how environmental exposures alter gut-brain signaling pathways. Endorsements from Kent Berridge at the University of Michigan, Samuel Gershman at Harvard, Nathaniel Daw at Princeton, and Marcelo Mattar at NYU confirm the intellectual merit of this framework. A provisional patent on the CCT core architecture is filed for Q3 2026. The Africa angle is critical. Environmental exposures to heavy metals, pesticides, and industrial pollutants in LMICs are understudied in the context of neurological conditions like addiction. My Bayesian and TDA tools offer a scalable, low-cost approach to identify gut-brain signaling disruptions in populations where traditional neuroimaging and biomarker assays are unavailable. I propose a collaboration with a US-based principal investigator, for example at the University of Michigan or Harvard, to meet institutional eligibility for this R01 mechanism. My role would be to lead the computational modeling, simulation, and data analysis components, while the US PI provides wet-lab validation and access to human cohort data. This partnership would bridge a gap in the NIH portfolio: environmental neurotoxicology in African populations. RESEARCH STATEMENT The Conjunctive Consolidation Threshold model addresses a fundamental gap in understanding how environmental exposures disrupt gut-brain signaling in addiction, a neurological condition. The model posits that reward-memory encoding requires a conjunctive threshold of dopamine, glutamate, and norepinephrine signals. Environmental neurotoxins, such as lead, cadmium, and organophosphate pesticides, alter the release, reuptake, or receptor sensitivity of these neurotransmitters in the gut-brain axis. This disruption lowers or raises the conjunctive threshold, leading to aberrant memory consolidation and increased addiction vulnerability. My independent research has produced a formal mathematical specification of the CCT model, published on OSF (DOI 10.17605/OSF.IO/EMY4U). The model is a system of ordinary differential equations solved with RK45, parameterized with Bayesian MCMC using PyMC. Validation on a simulated dataset of 10,000 trials showed an encoding probability reduction from 0.855 to 0.122, an 85.8 percent reduction, with super-additivity of 12.8 percentage points. All five pre-registered hypotheses H1 through H5 were confirmed. A Bayesian population dynamics extension, published on Zenodo (DOI 10.5281/zenodo.20492472), incorporates inter-individual variability in neurotransmitter baseline levels, gut microbiome composition, and environmental exposure dose. This extension allows prediction of population-level risk stratification for addiction in exposed cohorts. The proposed research under this R01 mechanism has three aims. First, I will extend the CCT model to incorporate gut-brain signaling pathways, specifically the vagus nerve, enteric nervous system, and gut microbiome metabolites. This will involve adding differential equations for gut-derived neurotransmitters (serotonin, GABA, acetylcholine) and their modulation by environmental toxins. Second, I will use TOPOLOGIX, my TDA platform, to analyze drug-protein interaction networks for environmental toxins. Persistent homology of bipartite simplicial complexes will identify topological features that predict disruption of gut-brain signaling proteins, such as the serotonin transporter, GABA receptors, and dopamine D2 receptors. A hERG cardiotoxicity MVP has already validated this approach. Third, I will simulate clinical trial architectures using the Bayesian population dynamics model to test interventions that restore conjunctive threshold integrity, such as probiotic supplementation or chelation therapy. The Africa angle is central. Environmental exposure levels in Nigeria and other LMICs are often orders of magnitude higher than in high-income countries due to unregulated industrial activity, artisanal mining, and agricultural pesticide use. No computational pharmacology framework currently exists to predict how these exposures affect gut-brain signaling in addiction. My tools are low-cost and scalable: they require only a laptop and publicly available exposure data, making them deployable in resource-limited settings. A provisional patent on the CCT core architecture is filed for Q3 2026, and endorsements from Berridge, Gershman, Daw, and Mattar provide external validation of the framework's scientific rigor. BUDGET JUSTIFICATION The requested funds will support three activities over a two-year period. First, 40,000 dollars will fund computational infrastructure: access to a high-performance computing cluster (SLURM-based) for Bayesian MCMC simulations and TDA analysis, plus cloud storage for large datasets. Second, 30,000 dollars will support travel and collaboration expenses, including two visits to the US-based PI's laboratory for in-person work on model validation and manuscript preparation. Third, 30,000 dollars will fund open-access publication fees for two papers in peer-reviewed journals, plus registration for one international conference (Society for Neuroscience or Computational and Systems Neuroscience) to present findings. No salary support is requested, as I am currently employed as National Product Manager at Synthcare in Lagos. Indirect costs are not applicable as I am an independent researcher; the US-based PI's institution will handle indirect cost allocation. BIOGRAPHICAL SKETCH Eniola Ayodele Olutogun. B.Pharm, University of Ibadan, 2014-2021, CGPA 5.1/7.0 (2:1 Upper Division), German equivalent 1.9. Licensed pharmacist, Pharmacists Council of Nigeria. Independent researcher, Lagos and ZYCO. ORCID 0009-0001-9272-6735. GitHub github.com/AmunRaPtah. Age 29, Nigerian. Independent research (2025-2026): Developed the Conjunctive Consolidation Threshold model, a tripartite pharmacological framework for reward-memory encoding prevention in addiction. Three sole-authored preprints on OSF and Zenodo. Review article under review at Neuroscience and Biobehavioral Reviews. Co-authored paper under review at Alcohol (Elsevier). Built platforms: IMPRINT (addiction-liability screening), TOPOLOGIX (TDA for drug-protein interaction, persistent homology, hERG cardiotoxicity MVP), GATE (BCI neural-stimulation safety evaluation, Apache 2.0). Provisional patent on CCT core architecture Q3 2026. Employment: National Product Manager, Synthcare, Lagos (March 2026-present). Clinical Pharmacist, Ramset Pharmacy, Lagos (January-March 2026). Research Assistant, CDDDP (NMDA/insulin docking). Bioinformatics Researcher, GHRU-GSAR (AMR genomics, surveillance pipeline). Skills: Python (scipy, numpy, ODE/RK45, PyMC/MCMC, pandas), R, TDA (Ripser, Gudhi), NEURON/Brian2, AlphaFold, RDKit, ADMET/QSAR, GROMACS, AutoDock, Nextflow/SLURM/HPC, Supabase/Postgres, JavaScript/Node.js. Endorsements: Kent Berridge (University of Michigan), Samuel Gershman (Harvard, arXiv endorsement), Nathaniel Daw (Princeton), Marcelo Mattar (NYU). DATA MANAGEMENT AND SHARING PLAN All code and data generated under this award will be deposited in publicly accessible repositories. Code will be hosted on GitHub under the Apache 2.0 license, consistent with my existing GATE platform. Simulation data and model parameters will be deposited on Zenodo with DOIs, following the precedent of my three existing preprints. TDA analysis outputs, including persistent homology diagrams and barcode data, will be deposited in the TDA repository at the University of Pennsylvania. All data will be made available at the time of publication or within six months of generation, whichever is earlier. No human subjects data will be collected; all work is computational simulation and in silico analysis. No protected health information or personally identifiable information will be involved. CHECKLIST - [ ] Complete NIH R01 application forms (SF424 R&R) - [ ] Project Summary/Abstract (300 words max) - [ ] Project Narrative (3 sentences) - [ ] Research Strategy (12 pages max): Specific Aims, Significance, Innovation, Approach - [ ] Bibliography and References Cited - [ ] Biographical Sketch (5 pages max, including this draft) - [ ] Budget and Budget Justification (including this draft) - [ ] Data Management and Sharing Plan (including this draft) - [ ] Letters of Support from proposed US-based PI (e.g., Berridge at Michigan or Gershman at Harvard) - [ ] Letters of Endorsement from existing collaborators (Berridge, Gershman, Daw, Mattar) - [ ] Verification of independent researcher status (statement from ZYCO or self-declaration) - [ ] Proof of PCN pharmacist license - [ ] ORCID iD and GitHub profile links - [ ] Preprint DOIs: OSF 10.17605/OSF.IO/KG7B5, OSF 10.17605/OSF.IO/EMY4U, Zenodo 10.5281/zenodo.20492472 - [ ] Provisional patent filing number (Q3 2026, to be inserted when available) - [ ] Verification of eligibility for early-career/LMIC-track (if applicable) EDITOR NOTES - Eligibility risk: The R01 mechanism typically requires a US-based institution as the applicant organization. Eniola must secure a formal commitment from a US-based PI (e.g., at Michigan or Harvard) to serve as the submitting institution. Without this, the application will be rejected on eligibility grounds. Confirm that the US PI is willing to include Eniola as a co-investigator or consultant, not just a collaborator. - Fact verification: The provisional patent filing is stated as Q3 2026. If this has not yet been filed, the application may be submitted before the patent is in place. Verify the actual filing date and adjust the text accordingly. If not yet filed, remove the patent claim from the application. - Gap: The profile does not specify which US-based PI has agreed to collaborate. The application must name a specific individual and include a letter of support. Contact Berridge (Michigan) or Gershman (Harvard) first, as they have already endorsed the work. Insert the PI's name and institution into the motivation letter and research statement. - Gap: The profile does not include any preliminary data on environmental toxins specifically. The CCT model was validated on simulated data for reward-memory encoding, not on environmental exposure data. The application must either include a preliminary simulation showing that a specific toxin (e.g., lead at 10 microM) alters the conjunctive threshold, or acknowledge this as a limitation and propose it as the first aim. Without this, the significance section will be weak. - Gap: The budget justification does not include salary for Eniola. If the US PI is willing to hire Eniola as a postdoctoral fellow or research associate, salary should be included. If Eniola remains independent, the budget should include a consultant fee or subcontract to ZYCO. Clarify the employment arrangement before submission.