MOTIVATION LETTER The gap between clinical pharmacology research and patient access is widest in low- and middle-income countries. Nigeria has fewer than one pharmacist per 10,000 people, and prescription drug misuse, particularly opioid and stimulant diversion, is rising without the regulatory or clinical infrastructure to track it. My work sits at that gap. I am a licensed pharmacist, a computational modeler, and an incoming M.Sc. Digital Health student at the Hasso Plattner Institute in Potsdam, Germany. I have spent the last two years building quantitative models of addiction pharmacology and drug safety that are designed to be used where clinical trial infrastructure is thin. My primary research line is the Conjunctive Consolidation Threshold (CCT) model, a tripartite pharmacological framework for reward-memory encoding prevention in addiction. The model couples dopaminergic reward prediction error, NMDAR-dependent long-term potentiation, and affective contrast into a system of ordinary differential equations, calibrated with Bayesian MCMC against a literature screen of 1,847 records. All five pre-registered hypotheses were confirmed, with posterior super-additivity of 13 to 22 percentage points across model versions. Three sole-authored preprints are under review at peer-reviewed journals, and a co-authored paper is under review at Alcohol (Elsevier). The CCT model generates testable predictions about which drug combinations, at which doses and timing windows, could block the consolidation of reward memories. That is directly actionable for clinical pharmacology in a country where addiction treatment protocols are imported wholesale from Western contexts without local validation. ASCPT's LMIC Accelerator Program is the right venue for this work because its selection criteria explicitly target applicants from low- and middle-income countries with demonstrated activity in clinical pharmacology or translational science. I meet both. My pharmacist license from the Pharmacy Council of Nigeria is active. My employment history includes clinical pharmacy at Ramset Pharmacy and national product management at Synthcare, where I oversee pharmaceutical product strategy. My research activity is documented across preprints, journal submissions, and open-source code repositories. The ASCPT network and annual meeting would give me two things I cannot get from remote collaboration alone. First, validation: my models need scrutiny from clinical pharmacologists who work with human data daily, not just computational reviewers. Second, dissemination: ASCPT's global audience includes the regulators and clinicians who would ultimately decide whether CCT-informed protocols are tested in Nigerian clinical settings. My LMIC perspective cuts both ways. I bring data and modeling approaches that are underrepresented in ASCPT's typical applicant pool, and I bring a concrete deployment context that most computational pharmacology research lacks. I commit to full participation in quarterly virtual events and the in-person Annual Meeting. My M.Sc. program at HPI is structured around project-based learning, which gives me scheduling flexibility. I am prepared to present my work, to serve as a reviewer for ASCPT's journals, and to contribute to discussions on digital health and pharmacology in LMIC settings. RESEARCH STATEMENT My research program asks one question: can computational models of drug action at the receptor and circuit level be made precise enough to guide clinical decisions in settings where clinical trial data is scarce? I pursue this through three interconnected lines: the CCT model for addiction pharmacology, the neurocascade simulation engine, and a series of falsification studies in protein-ligand topology. The CCT model is my primary contribution. It formalizes the hypothesis that reward-memory encoding requires the simultaneous crossing of a threshold on three axes: dopaminergic reward prediction error, NMDAR-dependent long-term potentiation, and affective contrast. I implemented this as a coupled ODE system solved with RK45, calibrated with PyMC's DEMetropolisZ sampler against 14 free parameters with literature-elicited priors. The prior elicitation drew from a systematic screen of 1,847 records covering dopaminergic, glutamatergic, and affective neuroscience literature. All five pre-registered hypotheses (H1-H5) were confirmed. The key quantitative result is super-additivity: combined interventions across the three axes produce 13 to 22 percentage points greater effect than the sum of individual-axis effects, depending on model version. This predicts that combination pharmacotherapy, timed to the consolidation window, could prevent reward-memory formation more effectively than any single-axis intervention. Three sole-authored preprints are under review at International Addiction Review and Treatment, Progress in Neuro-Psychopharmacology and Biological Psychiatry, and Neuroscience and Biobehavioral Reviews. A co-authored paper is under review at Alcohol. The neurocascade engine extends this work from pharmacology to circuit dynamics. It couples pharmacokinetic models to receptor binding, then to Wilson-Cowan circuit dynamics, then to behavioral readouts, across three literature-calibrated receptor systems: mu-opioid, D2 dopamine, and GABA-A. The full pipeline is Bayesian-calibrated and passes 62 of 62 unit tests. I am explicit that the circuit-layer parameters are illustrative until fitted to real behavioral data. This honesty about validation status is a core principle of my research practice. My falsification studies demonstrate the same rigor. I tested whether bipartite persistent homology predicts hERG cardiotoxicity from protein-ligand interface geometry. The pre-registered, powered replication found that topological features do not beat a plain descriptor baseline (AUROC 0.8426 vs 0.8782). I then tested the same topological constructs for drug-resistance prediction and found they carry almost no signal (AUROC 0.425 and 0.485 on the Platinum benchmark). These negative results are published as preprints and are under peer review. They settled questions the literature had never actually run. The positive result from that pivot is TOPOLOGIX, my current focus: ESM-2 protein language model delta-embeddings combined with Morgan/ECFP drug fingerprints and a Random Forest classifier. TOPOLOGIX predicts drug-resistance mutations from sequence alone with AUROC 0.804 plus or minus 0.025 on the Platinum benchmark (553 mutations) and 0.634 on SKEMPI 2.0. It beats structure-based baselines such as mCSM-lig (approximately 0.70) while covering 100 percent of mutations versus approximately 18 percent for structure-limited tools. This matters for LMIC clinical pharmacology because resistance prediction from sequence data is feasible in settings without crystallography infrastructure. For ASCPT, the translational arc is clear. The CCT model identifies combination pharmacotherapy targets for addiction. neurocascade provides a simulation platform to test those targets at the circuit level. TOPOLOGIX provides sequence-based resistance prediction that works without structural data. All three are computationally reproducible, openly documented, and calibrated against published literature. What they lack is clinical validation, which is precisely what ASCPT's network can provide. SHORT ANSWER: WHY THIS PROGRAM AND WHY NOW ASCPT's LMIC Accelerator Program fits my career stage exactly. I am pre-PhD, early-career, and based in Nigeria, with an incoming M.Sc. in Digital Health at HPI/Potsdam starting Winter Semester 2026/27. I have completed the computational validation phase of my research. The CCT model's hypotheses are confirmed. The falsification studies are done. TOPOLOGIX is benchmarked. The next phase requires clinical pharmacology input, regulatory literacy, and a network that can connect my models to human studies. ASCPT is that network. The program's quarterly virtual events and Annual Meeting would let me present my models to clinical pharmacologists who can critique the translational assumptions and identify pathways toward validation. My LMIC perspective is not incidental. The addiction burden in Nigeria is under-measured and under-treated. My pharmacist license and clinical experience at Ramset Pharmacy give me direct exposure to the prescribing and dispensing realities that my models must address. The timing is right because my M.Sc. program begins in late 2026, giving me the flexibility to travel and participate fully while my research is at its most presentable stage. CHECKLIST - [ ] Confirm World Bank LMIC classification for Nigeria at time of application - [ ] Verify ASCPT LMIC Accelerator Program eligibility for pre-PhD independent researchers - [ ] Confirm whether M.Sc. enrollment status (starting Winter 2026/27) satisfies any student or early-career criteria - [ ] Prepare CV in ASCPT format, including ORCID 0009-0001-9272-6735 and GitHub github.com/AmunRaPtah - [ ] Prepare PDF copies of all three CCT preprints (OSF/Zenodo) and the Alcohol co-authored paper - [ ] Prepare PDF copies of the hERG topology and drug-resistance falsification preprints - [ ] Prepare TOPOLOGIX benchmark documentation (Platinum and SKEMPI 2.0 results) - [ ] Obtain letters of support or endorsement from at least one of: Kent Berridge, Samuel Gershman, Nathaniel Daw, Marcelo Mattar - [ ] Draft and upload statement of purpose per submission portal requirements - [ ] Verify submission portal login and deadline (2026-07-24) at abstractscorecard.com - [ ] Confirm whether the program requires a project proposal or pitch deck in addition to written materials - [ ] Confirm whether the program requires proof of pharmacist licensure from the Pharmacy Council of Nigeria EDITOR NOTES - Eligibility risk: the profile lists the applicant as enrolled in M.Sc. Digital Health starting Winter 2026/27, but the deadline is 2026-07-24. Confirm enrollment status is sufficient for any student-track criteria, or whether the program requires current enrollment at time of application. - The CCT model is the chosen research line for this application because it is the most clinically translatable and has the strongest validation record. The falsification studies are presented as completed negative results, not as current work, to avoid misrepresenting the research arc. - The applicant must insert personal details not in this profile: specific dates of ASCPT membership or prior attendance (if any), any prior conference presentations, and any direct clinical experience with addiction treatment in Nigeria that can be cited concretely. - The venture profile (e-pharmacy platform) is not mentioned in this application because the program's selection criteria emphasize clinical pharmacology and translational science, not venture-stage fit. If the program's application portal asks about commercial ventures, the applicant should disclose the pre-revenue, pre-incorporation status honestly. - Verify the exact word limits for each field in the submission portal. The motivation letter is drafted at approximately 500 words and the research statement at approximately 600 words; both may need trimming to match portal constraints.