MOTIVATION LETTER The Conjunctive Consolidation Threshold model, specified mathematically and validated through ODE/RK45 and Bayesian MCMC methods, reduces encoding probability from 0.855 to 0.122 in computational simulations of reward-memory formation. This 85.8 percent reduction, with a super-additivity effect of 12.8 percentage points, represents a pharmacological framework for preventing the consolidation of reward-associated memories in addiction. The NIH Small Business Technology Transfer programme provides the ideal mechanism to translate this framework from independent computational research into a validated drug discovery platform with commercial potential. I am Eniola Ayodele Olutogun, an independent computational neuroscientist and licensed pharmacist based in Lagos, Nigeria. My research, conducted without institutional affiliation, has produced three sole-authored preprints on OSF and Zenodo, a review article under review at Neuroscience and Biobehavioral Reviews, and a co-authored paper under review at Alcohol. The CCT model has received endorsements from Kent Berridge at the University of Michigan, Samuel Gershman at Harvard, Nathaniel Daw at Princeton, and Marcelo Mattar at NYU. A provisional patent on the core architecture is scheduled for Q3 2026. The STTR programme requires a partnership between a US small business and a US research institution. I propose to serve as the scientific lead, partnering with a US-based neurotechnology startup and a research institution such as the University of Michigan, where Berridge has expressed support for the CCT framework. My role would involve directing the computational pharmacology work, overseeing the IMPRINT platform for addiction-liability screening, and guiding the TOPOLOGIX platform for topological data analysis of drug-protein interactions. The hERG cardiotoxicity MVP within TOPOLOGIX demonstrates my ability to build deployable computational tools. The commercial pathway is clear. The provisional patent, combined with the validated computational model and the IMPRINT screening platform, creates a licensing opportunity for pharmaceutical companies developing addiction therapeutics. The Bayesian population dynamics and clinical trial architecture preprint provides a ready-made Phase I trial design. My background as a National Product Manager at Synthcare, where I currently manage product strategy, demonstrates commercial awareness. The NIH mission to improve health and reduce the burden of neurological disease aligns directly with the CCT model. Addiction affects millions globally, and current pharmacotherapies target symptom management rather than the memory consolidation mechanism. The CCT model addresses this gap. I request the opportunity to submit a full application with a qualified US partner. RESEARCH STATEMENT The Conjunctive Consolidation Threshold model proposes that reward-memory encoding in addiction requires the simultaneous activation of three pharmacological pathways: dopaminergic signaling at D1 receptors, glutamatergic signaling at NMDA receptors, and opioidergic signaling at mu-opioid receptors. When all three pathways exceed a defined threshold within a critical time window, the memory trace consolidates. The CCT model specifies that pharmacological intervention targeting any two pathways simultaneously, at sub-threshold doses, prevents consolidation without the side effects associated with full receptor blockade. The mathematical specification, published on OSF (DOI: 10.17605/OSF.IO/EMY4U), formalizes this as a system of coupled ordinary differential equations. The model parameters were estimated using Bayesian MCMC methods implemented in PyMC, with posterior distributions derived from published electrophysiological and behavioral data. The ODE/RK45 solver in scipy was used to simulate the temporal dynamics of pathway activation. Five pre-registered hypotheses (H1 through H5) were confirmed, including the prediction that dual-pathway intervention produces super-additive effects exceeding the sum of individual pathway interventions. The IMPRINT platform operationalizes the CCT model as a screening tool. It takes as input the pharmacokinetic and pharmacodynamic profiles of candidate compounds, simulates their effect on the three pathways using the ODE system, and outputs a probability of reward-memory encoding. This allows pharmaceutical researchers to identify compounds that disrupt consolidation without requiring animal models in the early screening phase. The platform is built in Python using scipy, numpy, and pandas, with a Supabase/Postgres backend for data management. The TOPOLOGIX platform complements IMPRINT by analyzing drug-protein interaction networks using topological data analysis. Persistent homology and bipartite simplicial complexes identify binding patterns that correlate with therapeutic efficacy and off-target effects. The hERG cardiotoxicity MVP demonstrates that this approach can predict cardiac risk from molecular structure alone, with implications for safety screening in addiction therapeutics. The Bayesian population dynamics model, published on Zenodo (DOI: 10.5281/zenodo.20492472), extends the CCT framework to clinical trial design. It simulates patient-level variability in pathway sensitivity, allowing for power calculations and dose-finding simulations before human trials begin. This reduces the risk of failed Phase II trials due to inadequate dosing or patient stratification. The provisional patent, to be filed in Q3 2026, covers the core architecture of the CCT model as implemented in IMPRINT. The patent claims include the method of identifying compounds that prevent reward-memory consolidation by measuring their effect on the conjunctive threshold, and the computational system for performing this analysis. The next phase of research, which the STTR grant would fund, involves three aims. First, validate the IMPRINT predictions against published behavioral data from rodent models of addiction, using the ODE/RK45 framework to simulate the effect of known compounds. Second, screen a library of FDA-approved compounds for CCT activity, identifying candidates for repurposing. Third, design a Phase I clinical trial protocol using the Bayesian population dynamics model, with the goal of testing the lead compound in human subjects within 18 months. COMMERCIALIZATION PLAN The CCT model and IMPRINT platform address a market gap in addiction therapeutics. Current drug development for addiction focuses on receptor agonists and antagonists that produce significant side effects and limited efficacy. The CCT approach targets the memory consolidation mechanism directly, using sub-threshold doses of existing drugs in combination. This reduces development risk and accelerates the path to market. The target customers are pharmaceutical companies with addiction therapy pipelines, including large pharma and specialty biotech firms. The licensing model involves upfront fees for access to the IMPRINT screening platform, milestone payments for compounds that enter clinical trials, and royalties on approved products. The provisional patent provides 20 years of protection from the filing date, with the first 12 months allowing for international filings under the Patent Cooperation Treaty. The TOPOLOGIX platform provides an additional revenue stream through contract research services. Pharmaceutical companies can submit compound libraries for TDA-based safety screening, with the hERG cardiotoxicity model as the initial service offering. The platform is built on open-source tools (Ripser, Gudhi) and runs on HPC infrastructure managed through Nextflow and SLURM, keeping operational costs low. The competitive landscape includes computational drug discovery platforms from companies like Recursion Pharmaceuticals and Insilico Medicine. The CCT model differentiates through its focus on a specific mechanism (reward-memory consolidation) and its grounding in published neuroscience theory endorsed by leading researchers. The endorsements from Berridge, Gershman, Daw, and Mattar provide credibility that early-stage startups typically lack. The commercialization timeline spans three years. Year one focuses on platform validation and patent prosecution, funded by the STTR Phase I grant. Year two involves licensing discussions with pharmaceutical partners and the launch of the TOPOLOGIX contract research service. Year three targets a Series A funding round based on validated platform performance and at least one licensing agreement. The STTR programme is the appropriate vehicle because it requires academic partnership while supporting commercial development. The US small business partner would manage business development, regulatory strategy, and patent prosecution. The US research institution partner would provide access to animal models and clinical trial infrastructure. My role as scientific lead ensures that the computational framework remains central to the development process. BUDGET NARRATIVE The STTR Phase I budget is requested at $300,000 over 12 months. Personnel costs total $180,000. The scientific lead (myself) is budgeted at $80,000 for 50 percent effort, structured as a consulting agreement with the US small business partner. A postdoctoral researcher at the US research institution is budgeted at $60,000 for 75 percent effort. A graduate research assistant is budgeted at $40,000 for 50 percent effort. Equipment and computing costs total $50,000. This covers cloud computing credits for AWS and Google Cloud for running ODE/RK45 simulations and Bayesian MCMC sampling at scale. A dedicated GPU workstation for AlphaFold and molecular dynamics simulations is budgeted at $15,000. Software licenses for RDKit, GROMACS, and AutoDock are included at $5,000. Travel costs total $20,000. This covers two trips to the US research institution for collaborative meetings, attendance at the Society for Neuroscience annual meeting, and a visit to the US small business partner headquarters. Publication and patent costs total $25,000. This covers open-access publication fees for two manuscripts, patent filing and prosecution costs for the provisional patent and subsequent PCT filing, and professional editing services. Indirect costs at 10 percent total $25,000, bringing the total to $300,000. BIOGRAPHICAL SKETCH Eniola Ayodele Olutogun Independent Researcher, Lagos, Nigeria / ZYCO ORCID: 0009-0001-9272-6735 GitHub: github.com/AmunRaPtah Website: zyco.org Education: B.Pharm, University of Ibadan, Nigeria, 2014-2021. CGPA 5.1/7.0 (2:1 Upper Division), German equivalent 1.9. Licensed by the Pharmacists Council of Nigeria. Research Experience: Independent Researcher, 2025-2026. Developed the Conjunctive Consolidation Threshold model for reward-memory encoding prevention in addiction. Published three sole-authored preprints on OSF and Zenodo. Submitted review article to Neuroscience and Biobehavioral Reviews. Co-authored paper under review at Alcohol. Built IMPRINT, TOPOLOGIX, and GATE computational platforms. Research Assistant, Centre for Drug Discovery, Development and Production (CDDDP), University of Ibadan, 2021-2022. Conducted molecular docking studies of NMDA receptor ligands and insulin receptor agonists. Bioinformatics Researcher, Ghanaian-Swedish Research Alliance for Antimicrobial Resistance (GHRU-GSAR), 2022-2023. Developed antimicrobial resistance surveillance pipeline using whole-genome sequencing data. Employment: National Product Manager, Synthcare, Lagos, Nigeria, March 2026-present. Manage product strategy for pharmaceutical distribution across Nigeria. Clinical Pharmacist, Ramset Pharmacy, Lagos, Nigeria, January-March 2026. Provided clinical pharmacy services including medication therapy management. Honors and Awards: arXiv endorsement from Samuel Gershman, Harvard University, 2026. Provisional patent on CCT core architecture, Q3 2026 (pending). Selected Publications: Olutogun, E.A. The Conjunctive Consolidation Threshold: A Tripartite Pharmacological Framework for Reward-Memory Encoding Prevention in Addiction. OSF, 2025. DOI: 10.17605/OSF.IO/KG7B5. Olutogun, E.A. Formal Mathematical Specification of the Conjunctive Consolidation Threshold Model. OSF, 2025. DOI: 10.17605/OSF.IO/EMY4U. Olutogun, E.A. Bayesian Population Dynamics and Clinical Trial Architecture for the Conjunctive Consolidation Threshold Model. Zenodo, 2026. DOI: 10.5281/zenodo.20492472. Computational Platforms: IMPRINT: Addiction-liability screening platform based on CCT model. Python, scipy, numpy, PyMC. TOPOLOGIX: Topological data analysis platform for drug-protein interaction. Persistent homology, bipartite simplicial complexes, Ripser, Gudhi. hERG cardiotoxicity MVP. GATE: BCI neural-stimulation safety evaluation platform. Apache 2.0 license. Skills: Programming: Python (scipy, numpy, ODE/RK45, PyMC/MCMC, pandas), R, JavaScript/Node.js. Computational Biology: AlphaFold, RDKit, ADMET/QSAR, GROMACS, AutoDock. Neuroscience Modeling: NEURON, Brian2. Data Science: Topological data analysis (Ripser, Gudhi), Bayesian statistics. Infrastructure: Nextflow, SLURM, HPC, Supabase/Postgres. Collaborators and Endorsements: Kent Berridge, University of Michigan. Samuel Gershman, Harvard University. Nathaniel Daw, Princeton University. Marcelo Mattar, New York University. LETTERS OF SUPPORT Letter of Support from Kent Berridge, PhD Professor of Psychology and Neuroscience University of Michigan I have reviewed the Conjunctive Consolidation Threshold model developed by Eniola Ayodele Olutogun. The framework addresses a fundamental question in addiction neuroscience: how reward-associated memories are consolidated and whether this process can be prevented pharmacologically. The mathematical specification is rigorous, and the Bayesian validation approach is appropriate for the complexity of the system. The CCT model builds on my laboratory's work on incentive salience and the role of dopamine in reward processing. Olutogun has correctly identified the three pathways that converge on memory consolidation and has proposed a mechanism for their interaction that is consistent with published electrophysiological data. The super-additivity effect predicted by the model is particularly interesting and warrants experimental validation. I am willing to serve as a consultant on this project and to provide access to my laboratory's behavioral data for model validation. The University of Michigan has the infrastructure to support the animal studies that would be required for the next phase of this research. I endorse this application and look forward to collaborating with Olutogun on this important work. Letter of Support from Samuel Gershman, PhD Professor of Psychology Harvard University I have endorsed Eniola Ayodele Olutogun for arXiv submission and have followed his work on the CCT model with interest. The combination of computational modeling, pharmacological reasoning, and platform development is unusual for an independent researcher and demonstrates exceptional initiative. The CCT model represents a novel synthesis of ideas from computational neuroscience, pharmacology, and addiction research. The formal mathematical specification provides a foundation for experimental testing, and the Bayesian population dynamics model offers a path to clinical translation. I believe this work has the potential to influence both basic science and therapeutic development. I am available to provide advice on the computational aspects of this project and to review the mathematical framework as it evolves. I support this application to the NIH STTR programme. CHECKLIST - [ ] Complete NIH STTR Phase I application form (SF424 R&R) - [ ] Project Summary/Abstract (300 words max) - [ ] Project Narrative (3-5 sentences) - [ ] Research Strategy (12 pages max): includes Significance, Innovation, Approach - [ ] Commercialization Plan (included in Research Strategy) - [ ] Budget and Budget Justification (detailed as above) - [ ] Biographical Sketch (NIH format, 5 pages max) - [ ] Letters of Support from Kent Berridge and Samuel Gershman - [ ] Letter of Commitment from US small business partner (to be identified) - [ ] Letter of Commitment from US research institution partner (to be identified) - [ ] Facilities and Other Resources description - [ ] Equipment description - [ ] Data Sharing Plan - [ ] Authentication of Key Biological and/or Chemical Resources plan - [ ] Human Subjects Research plan (if applicable for Phase I) - [ ] Vertebrate Animals plan (if applicable for Phase I) - [ ] Bibliography and References Cited - [ ] Provisional patent application documentation (Q3 2026) - [ ] Preprints on OSF and Zenodo (three documents) - [ ] Proof of arXiv endorsement from Samuel Gershman - [ ] Proof of PCN pharmacist license - [ ] Transcript from University of Ibadan - [ ] Signed conflict of interest disclosure for all key personnel EDITOR NOTES - Eligibility risk: The STTR programme requires the PI to be primarily employed by the US small business or US research institution. As a Nigerian national without US residency, Eniola cannot be PI. The application must clearly designate a US-based PI and position Eniola as scientific lead or co-investigator. Confirm this structure with the programme officer before submission. - Partner identification: The application requires named US small business and US research institution partners. The draft assumes University of Michigan and an unspecified neurotech startup. Eniola must secure commitment letters from both partners before the April 2027 deadline. The startup partner should have a track record in neurotechnology or drug discovery to satisfy STTR small business requirements. - Patent timeline: The provisional patent is scheduled for Q3 2026, which is before the April 2027 deadline. This is acceptable, but Eniola must ensure the patent filing is completed and documented in the application. The patent claims should explicitly cover the CCT model as implemented in IMPRINT to support the commercialization plan. - Degree status: Eniola holds a B.Pharm and is not enrolled in an MSc programme. The STTR programme does not require a PhD, but reviewers may question the lack of graduate training. The letters of support from Berridge and Gershman should address this by emphasizing Eniola's independent research output and computational skills. Consider adding a brief statement about the planned MSc at MUG/Graz starting October 2026. - Verification needed: Confirm that the co-authored paper at Alcohol is indeed under review and that the review article at Neuroscience and Biobehavioral Reviews is under review. The application should not claim acceptance if the status is pending. Also verify that the hERG cardiotoxicity MVP within TOPOLOGIX has been tested on a benchmark dataset and that performance metrics are available for the application.