MOTIVATION LETTER The National Institutes of Health Biomedical Technology Optimization and Dissemination Center programme funds the translation of computational tools into clinical research environments. The Conjunctive Consolidation Threshold model, a tripartite pharmacological framework for reward-memory encoding prevention in addiction, has been validated through ODE/RK45 and Bayesian MCMC methods across five pre-registered hypotheses. Encoding probability dropped from 0.855 to 0.122, an 85.8 percent reduction, with super-additivity of 12.8 percentage points. These results were produced independently in Lagos, Nigeria, without institutional research infrastructure. The CCT model now requires optimization for deployment at U.S. clinical research sites. The IMPRINT platform, a computational screening tool for addiction liability, has been built and tested on the same mathematical core. This application proposes a collaboration between my independent research group and a U.S.-based principal investigator at the University of Michigan, Harvard, or New York University, where existing collaborators Kent Berridge, Samuel Gershman, and Nathaniel Daw have endorsed the framework. The BTOD programme is the appropriate mechanism because it specifically funds the optimization and dissemination of biomedical technologies, not clinical trials. My role would be as key personnel providing the computational pharmacology expertise, Bayesian modeling pipeline, and platform architecture that cannot be replicated from published methods alone. The provisional patent filed in Q3 2026 on the CCT core architecture protects the intellectual property for U.S. partners. Nigeria has the highest burden of substance use disorders in West Africa, yet zero computational pharmacology tools for addiction liability screening exist in the region. This project bridges that gap by developing a technology in a low-resource setting and disseminating it through U.S. clinical research infrastructure. The BTOD programme requires a U.S. institutional applicant. I am seeking a PI who can host this technology optimization project and supervise its dissemination to addiction research centers. My preprints on OSF and Zenodo, the review article under review at Neuroscience and Biobehavioral Reviews, and the co-authored paper in Alcohol under review at Elsevier demonstrate the scientific rigor of the framework. The grant would fund the optimization of IMPRINT for U.S. clinical data formats, the integration of the Bayesian population dynamics model into a deployable software package, and the training of U.S. research staff on the CCT methodology. I am available for a remote or hybrid collaboration from Lagos, with quarterly visits to the U.S. host institution. RESEARCH STATEMENT The Conjunctive Consolidation Threshold model addresses a fundamental gap in addiction neuroscience: no existing pharmacological framework predicts the encoding probability of reward-memory associations before they become consolidated. Current treatments intervene after addiction is established. The CCT model specifies three concurrent conditions that must be met for reward-memory encoding to occur: dopaminergic salience exceeding a threshold, glutamatergic plasticity permissiveness, and opioidergic hedonic gating. When any one condition is suppressed below its threshold, encoding probability drops. The mathematical specification, published on OSF at 10.17605/OSF.IO/EMY4U, formalizes this as a system of coupled ordinary differential equations solved with RK45 integration. The Bayesian population dynamics model, archived on Zenodo at 10.5281/zenodo.20492472, uses Markov Chain Monte Carlo sampling to estimate threshold distributions across a simulated population of 10,000 virtual subjects. All five pre-registered hypotheses H1 through H5 were confirmed. The super-additive effect of 12.8 percentage points indicates that targeting two conditions simultaneously produces greater suppression than the sum of individual effects. The IMPRINT platform operationalizes this model as a screening tool. It takes patient-level pharmacogenetic, neuroimaging, and behavioral data, runs the ODE system forward in time, and outputs a predicted encoding probability score. The platform is built in Python with scipy for numerical integration, PyMC for Bayesian inference, and Supabase for data storage. It has been tested on synthetic data with known ground truth. The next step is optimization for real clinical data from U.S. addiction research cohorts, which requires adapting the input pipeline to handle heterogeneous data formats, missing values, and variable sampling rates. The BTOD programme is the correct mechanism because it funds technology optimization and dissemination, not clinical trials. The CCT model is not ready for a clinical trial. It needs validation against real patient data, calibration of threshold parameters, and deployment as a user-accessible software tool. The dissemination component would train U.S. clinical researchers on the CCT methodology through workshops, documentation, and a cloud-based instance of IMPRINT that can be accessed by collaborating sites. The computational pharmacology skills required for this optimization include Bayesian hierarchical modeling, differential equation solving, and software engineering. I have built these skills through independent research in Lagos, using open-source tools and HPC resources accessed through Nextflow and SLURM. The TOPOLOGIX platform for topological data analysis of drug-protein interactions, using persistent homology and bipartite simplicial complexes, demonstrates my ability to build computational tools from mathematical foundations. The GATE platform for BCI neural-stimulation safety evaluation, released under Apache 2.0, shows experience with open-source dissemination. The provisional patent on the CCT core architecture, filed in Q3 2026, protects the commercial and academic use of the framework. The patent covers the tripartite threshold computation method and the super-additivity detection algorithm. This intellectual property can be licensed to the U.S. host institution for the duration of the BTOD project. The Nigeria angle is not incidental. Addiction research in Africa is virtually nonexistent. No Nigerian institution has a computational pharmacology group focused on addiction. The CCT model was developed in this vacuum, using only publicly available data and open-source tools. Disseminating the technology through U.S. clinical research sites creates a pipeline for eventual deployment in African clinical settings, where the burden of substance use disorders is rising fastest. The collaborators who have endorsed this work include Kent Berridge at the University of Michigan, whose incentive salience theory provides the dopaminergic foundation for the CCT model; Samuel Gershman at Harvard, who provided the arXiv endorsement for the mathematical specification; Nathaniel Daw at Princeton, whose work on reinforcement learning and dopamine informs the temporal dynamics of the model; and Marcelo Mattar at NYU, whose computational memory framework aligns with the consolidation prevention mechanism. Any of these researchers could serve as the U.S. PI for this BTOD application. The specific deliverables for the BTOD project are: a validated IMPRINT software package compatible with U.S. clinical data standards; a Bayesian calibration pipeline that estimates population-level threshold distributions from existing cohort data; a user manual and training curriculum for clinical researchers; and a dissemination plan for at least three U.S. addiction research centers. The timeline is 24 months. Months 1 through 6 would focus on data format adaptation and software refactoring. Months 7 through 12 would run the Bayesian calibration on historical cohort data. Months 13 through 18 would build the cloud deployment and user interface. Months 19 through 24 would conduct training workshops and publish the optimized model. BUDGET NARRATIVE The BTOD programme does not specify a maximum award amount. The requested budget is 100,000 USD total costs over 24 months. This covers the following categories. Personnel: 60,000 USD. Eniola Ayodele Olutogun as key personnel at 30,000 USD per year for two years at 50 percent effort. This is below the NIH postdoctoral salary cap and reflects the independent researcher status without U.S. institutional appointment. The U.S. PI would receive 10,000 USD per year for 10 percent effort to supervise the project. A graduate student at the U.S. host institution would receive 5,000 USD per year for 10 percent effort to assist with software testing and documentation. Equipment: 5,000 USD. A dedicated workstation in Lagos with 64 GB RAM and an NVIDIA RTX 4090 GPU for running Bayesian MCMC chains and ODE simulations. Current hardware in Lagos is a consumer laptop that limits chain length and parallelization. Travel: 15,000 USD. Four round-trip flights from Lagos to the U.S. host institution at 2,500 USD each, plus 5,000 USD for accommodation and per diem during two-week visits each quarter. Virtual collaboration via Slack and weekly video calls will handle the remaining coordination. Software and computing: 10,000 USD. Cloud computing credits on AWS or Google Cloud for running large-scale Bayesian calibration on historical cohort data. Open-source software licenses are free. The 10,000 USD covers 24 months of compute at 400 USD per month. Publication and dissemination: 5,000 USD. Open-access publication fees for two papers in journals indexed in PubMed Central. Documentation hosting and domain registration for the IMPRINT platform website. Indirect costs: 5,000 USD. The U.S. host institution may charge indirect costs at its negotiated rate. This line item assumes a 10 percent rate on total direct costs, which is below the typical NIH rate but reflects the minimal administrative burden of a small technology optimization project. Total direct costs: 95,000 USD. Total indirect costs: 5,000 USD. Total requested: 100,000 USD. CHECKLIST - [ ] Identify and secure a U.S.-based principal investigator at University of Michigan, Harvard, or NYU who agrees to submit the application as the applicant institution. - [ ] Obtain a letter of support from the U.S. PI confirming institutional commitment and facilities. - [ ] Obtain letters of collaboration from Kent Berridge, Samuel Gershman, Nathaniel Daw, and Marcelo Mattar. - [ ] Prepare a biographical sketch for Eniola Ayodele Olutogun in NIH format, including ORCID 0009-0001-9272-6735 and GitHub github.com/AmunRaPtah. - [ ] Prepare a biographical sketch for the U.S. PI in NIH format. - [ ] Write the specific aims page (one page) summarizing the CCT model, IMPRINT platform, and optimization goals. - [ ] Write the research strategy section (12 pages maximum) expanding the research statement above with figures showing the ODE system, Bayesian calibration results, and IMPRINT architecture. - [ ] Include the preprints as citations: OSF 10.17605/OSF.IO/KG7B5, OSF 10.17605/OSF.IO/EMY4U, Zenodo 10.5281/zenodo.20492472. - [ ] Include the provisional patent filing number and date from Q3 2026. - [ ] Prepare a data management and sharing plan describing deposition of code on GitHub and data on Zenodo. - [ ] Prepare a human subjects protection section noting that the project uses only de-identified historical data and does not involve new data collection. - [ ] Prepare a resource sharing plan for the IMPRINT software under Apache 2.0 license. - [ ] Submit the application through grants.gov by the deadline of 01/29/2029. EDITOR NOTES - Eligibility risk: Eniola cannot be the PI. The application must be submitted by a U.S. institution. The collaborator letters from Berridge, Gershman, Daw, and Mattar are critical but none have confirmed willingness to serve as PI. This must be secured before writing the full application. - Fact verification needed: The provisional patent filing date is listed as Q3 2026. This is a future date relative to the current timeline. Confirm whether the patent has been filed or is planned. If not yet filed, remove the claim or mark it as pending. - Gap in profile: The application mentions training U.S. clinical researchers on the CCT methodology. The profile does not list any teaching, mentoring, or workshop experience. Eniola should insert a paragraph about any experience training colleagues, presenting at conferences, or writing documentation that demonstrates ability to disseminate methods to non-computational audiences. - Budget realism: 30,000 USD per year for key personnel is below the NIH postdoctoral minimum. Some reviewers may question whether this is sufficient to retain Eniola for two years. Consider increasing to 40,000 USD and reducing travel or computing costs. - Missing element: The profile does not mention any previous grant funding. The application should include a section on prior support, even if it is zero, to demonstrate transparency.