MOTIVATION LETTER The Conjunctive Consolidation Threshold model, a tripartite pharmacological framework for reward-memory encoding prevention in addiction, completed Bayesian MCMC calibration with 14 free parameters drawn from a literature screen of 1,847 records. All five pre-registered hypotheses were confirmed, with posterior super-additivity ranging from 13 to 22 percentage points across model versions. This work, submitted as three sole-authored preprints to journals including the International Journal of Addiction Research and Therapy, Progress in Neuro-Psychopharmacology and Biological Psychiatry, and Neuroscience and Biobehavioral Reviews, represents a foundational step toward independent clinical research in addiction neuroscience. Pfizer's Research Grant Programme supports independent clinical and observational research. The CCT model, while not directly addressing elranatamab in multiple myeloma, demonstrates a methodological rigor that aligns with Pfizer's commitment to advancing discovery through rigorous science. The ODE-based framework, calibrated with PyMC's DEMetropolisZ sampler and validated against literature-elicited priors, offers a transferable approach to hypothesis-driven pharmacological modeling. Addiction neuroscience carries high unmet need globally, with particular relevance to Nigeria where substance use disorder treatment infrastructure remains underdeveloped. The proposed research would extend the CCT model by fitting circuit-layer parameters to real behavioral data, moving beyond the illustrative calibration completed in the neurocascade simulation engine. This engine, which couples pharmacokinetics to receptor-binding to Wilson-Cowan circuit dynamics to behavioral-readout ODE layers, has passed 62 of 62 tests across three receptor systems. The next phase requires clinical or observational data to validate the model's predictions about pharmacological intervention timing and dosing. My qualifications include a B.Pharm from the University of Ibadan, licensure with the Pharmacists Council of Nigeria, and current enrollment in the M.Sc. Digital Health programme at Hasso Plattner Institute in Germany. Computational skills span Python, PyMC, ODE solvers, and topological data analysis, applied across addiction neuroscience, protein ML, and dynamical-systems methods. Endorsements from Kent Berridge at Michigan, Samuel Gershman at Harvard, Nathaniel Daw at Princeton, and Marcelo Mattar at NYU speak to the scientific credibility of this research direction. This grant would fund the data acquisition and analysis phase necessary to transition the CCT model from a computational framework to a clinically actionable tool. The budget would cover participant recruitment for behavioral pharmacology studies in Nigeria, computational infrastructure for model fitting, and open-access publication costs. RESEARCH STATEMENT The Conjunctive Consolidation Threshold model addresses a specific gap in addiction neuroscience: no existing pharmacological framework predicts the precise timing and dosing conditions under which reward-memory consolidation can be prevented. Current interventions target either the dopaminergic reward prediction error signal or NMDA receptor-dependent long-term potentiation in isolation, without accounting for their interaction with affective contrast. The CCT model formalizes this interaction as a three-axis coupled ODE system, solved with RK45 integration and calibrated with Bayesian MCMC using 14 free parameters. The model's five pre-registered hypotheses were confirmed with posterior super-additivity of 13 to 22 percentage points across model versions. This means the combined effect of targeting all three axes exceeds the sum of individual effects by a statistically meaningful margin. The neurocascade simulation engine, which extends this framework to receptor-to-behavior dynamics, has been tested across mu-opioid, D2 dopamine, and GABA-A receptor systems with all 62 tests passing. The proposed research has three aims. First, fit the circuit-layer parameters of the neurocascade engine to real behavioral data from a human pharmacological challenge study. Second, validate the model's predictions about optimal intervention timing against existing clinical trial data for naltrexone and acamprosate. Third, produce a publicly available simulation tool that allows researchers to test intervention strategies in silico before committing to clinical trials. This work is feasible within 12 months. The computational infrastructure is already built: the ODE solver, Bayesian calibration pipeline, and simulation engine are operational and documented. The primary resource requirement is access to behavioral pharmacology datasets and computational time for parameter fitting. My background as a licensed pharmacist with clinical experience at Ramset Pharmacy and current work as National Product Manager at Synthcare provides the pharmacological domain expertise necessary to interpret model outputs in clinical terms. The impact on clinical practice would be direct. If the CCT model accurately predicts intervention timing, clinicians could move from trial-and-error dosing to model-informed protocols for relapse prevention. Given that addiction relapse rates exceed 60 percent within one year across substance classes, even a 10 percentage point improvement would represent a meaningful clinical advance. BUDGET NARRATIVE The requested funds support three categories of expense. First, data acquisition: licensing fees for access to existing behavioral pharmacology datasets from published clinical trials, estimated at 4,000 USD. Second, computational resources: cloud computing time for Bayesian MCMC sampling across the full parameter space, estimated at 3,000 USD. Third, dissemination: open-access publication fees for two peer-reviewed articles and conference travel to present findings at the Society for Neuroscience annual meeting, estimated at 5,000 USD. Total request: 12,000 USD. No salary support is requested. No equipment purchases are needed. All software used is open-source or freely available for academic use. CHECKLIST - [ ] Complete online application form at fundsforngos.org - [ ] Upload motivation letter as PDF - [ ] Upload research statement as PDF - [ ] Upload budget narrative as PDF - [ ] Upload CV including ORCID, GitHub, and publication list - [ ] Upload letter of support from M.Sc. programme supervisor at Hasso Plattner Institute - [ ] Verify eligibility for independent researcher track - [ ] Confirm deadline of 2026-06-12 and timezone - [ ] Submit preprints as supporting documents EDITOR NOTES - Eligibility risk: the grant listing specifies elranatamab in multiple myeloma as the primary focus. The CCT model in addiction neuroscience may be considered out of scope. Applicant should verify whether the programme accepts proposals outside this therapeutic area or identify a specific connection to multiple myeloma research that can be credibly made. - The URL provided (fundsforngos.org) is a third-party aggregator. Applicant should verify the official Pfizer grant portal and confirm that the programme is still accepting applications, as aggregator listings can be outdated. - The budget narrative assumes no salary support is needed. If the applicant requires stipend or living expenses during the research period, this must be added and justified. The current employment at Synthcare may conflict with full-time research commitment. - Endorsements from Berridge, Gershman, Daw, and Mattar are listed as collaborators or endorsers. Applicant should confirm in writing that these individuals are willing to provide letters of support or reference if requested by the grant committee. - The M.Sc. Digital Health programme at HPI begins in Winter Semester 2026/27. If the grant period overlaps with the start of studies, applicant should clarify how research and coursework will be balanced, or whether the grant work will be completed before enrollment.