Draft — Emergent Ventures

MOTIVATION LETTER

The dominant model of addiction treatment is backwards. It waits for the addicted brain to form a reward-memory, then tries to weaken that memory with behavioral therapy or blunt it with substitution drugs. The CCT model, which I have built and validated over the past two years, targets the moment of encoding itself. It asks a different question: what if the memory that drives compulsive reward-seeking never gets consolidated in the first place?

The Conjunctive Consolidation Threshold model is a tripartite pharmacological framework for reward-memory encoding prevention. It couples three axes, dopaminergic reward prediction error, NMDAR-dependent long-term potentiation, and affective contrast, into a single ODE system solved with RK45. I calibrated the model with Bayesian MCMC using PyMC's DEMetropolisZ, fitting 14 free parameters against priors elicited from a systematic screen of 1,847 records. All five pre-registered hypotheses, H1 through H5, were confirmed. The posterior shows super-additivity of 13 to 22 percentage points across model versions, meaning the three axes interact non-additively to gate memory consolidation. This is a falsifiable, quantitative framework with confirmed predictions, currently under review at three peer-reviewed journals: International Addiction Review, Progress in Neuro-Psychopharmacology and Biological Psychiatry, and Neuroscience and Biobehavioral Reviews.

Emergent Ventures funds contrarian, high-impact ideas executed by founders who do not wait for permission. That describes this project exactly. The mainstream addiction research community is heavily invested in retrieval-extinction paradigms and receptor-specific antagonists. The CCT model challenges that consensus by arguing that the threshold for memory consolidation is set by the conjunctive activity of all three axes, and that pharmacological intervention at the encoding stage is both feasible and more efficient than post-hoc extinction. The model is deliberately intervention-oriented. It specifies which receptor targets to hit, in what temporal sequence, and at what predicted effect sizes.

The grant would fund full-time development of a deployable clinical decision-support tool based on the CCT framework. The tool would take patient-specific variables, medication history, and genetic markers, run the calibrated ODE system, and output a recommended peri-exposure pharmacological protocol designed to prevent reward-memory consolidation. I have the full stack to build this: Python, PyMC, ODE solvers, and four independent DuckDB-based data pipelines I built and operate myself. I also have the clinical credibility, as a licensed pharmacist with a B.Pharm from the University of Ibadan, to know what a clinician actually needs at the point of care.

The global burden is massive. Addiction is a leading cause of disability and death worldwide, and Africa has the weakest treatment infrastructure of any region. A tool that prevents memory encoding at the moment of exposure, rather than treating the consolidated addiction months later, is a fundamentally different intervention class. It is also a fundamentally cheaper one.

I am currently enrolled in the M.Sc. Digital Health program at the Hasso Plattner Institute in Germany, which gives me access to European clinical data partnerships. But the core research is independent, self-funded, and already producing confirmed results. Emergent Ventures is the right partner to take it from a validated model to a deployable tool.

RESEARCH STATEMENT

The CCT model emerged from a specific failure in the addiction literature. Preclinical work on reconsolidation blockade shows promise in rodents but translates poorly to humans. Clinical trials of NMDAR antagonists like memantine for addiction have produced mixed results. Dopamine-based interventions target craving but not the memory trace itself. The field lacks a unified, quantitative account of how these systems interact to set the threshold for reward-memory consolidation.

I built that account. The CCT model is a system of coupled ordinary differential equations with three state variables representing dopaminergic reward prediction error, NMDAR-dependent synaptic plasticity, and affective contrast. The threshold for memory consolidation is not set by any single variable but by their conjunctive activity crossing a critical boundary. The model was calibrated using Bayesian MCMC with DEMetropolisZ, 14 free parameters, and priors elicited from a systematic screen of 1,847 records spanning pharmacology, computational neuroscience, and clinical addiction studies. The model was pre-registered with five hypotheses. All five were confirmed. The posterior distribution shows super-additivity of 13 to 22 percentage points, meaning the interaction of the three axes is greater than the sum of their individual contributions.

The three sole-authored preprints are under review at International Addiction Review, Progress in Neuro-Psychopharmacology and Biological Psychiatry, and Neuroscience and Biobehavioral Reviews. A co-authored paper is under review at Alcohol (Elsevier). The model has been endorsed by Kent Berridge at Michigan, Samuel Gershman at Harvard, Nathaniel Daw at Princeton, and Marcelo Mattar at NYU.

The next phase is translation. The CCT framework specifies a set of pharmacological targets and a temporal protocol for intervening at the encoding stage. I want to build a clinical decision-support tool that operationalizes this. The tool will take patient-specific inputs, run the calibrated ODE system, and output a recommended peri-exposure protocol. The technical components are already in place: I have built and operated four independent DuckDB-based ingest-to-analyze pipelines, self-hosted local LLM serving with llama.cpp, and production systems with CI/CD and automated backup. The tool will be validated against retrospective clinical datasets and, where possible, prospective pilot data from collaborators.

The Africa angle is not an afterthought. Nigeria has one of the highest rates of opioid and methamphetamine use in West Africa, and treatment infrastructure is severely limited. A prevention-oriented tool that can be deployed at the point of exposure, in emergency departments, primary care, or even community pharmacies, is a scalable intervention that does not require a psychiatrist or a specialized addiction clinic. I am a licensed pharmacist in Nigeria and have worked as a clinical pharmacist. I know the deployment context.

The Emergent Ventures grant would fund one year of full-time development: building the decision-support tool, validating it against retrospective data, and preparing a prospective pilot protocol. The budget is modest relative to the potential impact. The risk profile is also honest. The model is validated at the computational level, but clinical translation is a separate step. I am not claiming a product exists. I am claiming that a falsifiable, quantitatively confirmed model exists, and that the next step is engineering and clinical validation.

ESSAY: WHY THIS PROJECT IS BOLD AND UNCONVENTIONAL

The addiction field is dominated by two paradigms: agonist substitution and behavioral extinction. Both assume the addiction is already formed. The CCT model rejects that assumption. It argues that the critical intervention point is the moment of encoding, when a reward-associated memory is first consolidated. This is contrarian because it implies that the most effective treatment for addiction is not a treatment at all, but a prevention protocol applied at the time of exposure.

The model is also methodologically unconventional. It is a computational pharmacology framework, not a wet-lab study. It uses dynamical systems theory, Bayesian calibration, and pre-registered hypothesis testing to make quantitative predictions about pharmacological interventions. This is not how most addiction research is done, and it is not how most addiction research is funded. The mainstream prefers RCTs of single drugs or behavioral protocols. The CCT model is a systems-level account that specifies combination protocols and temporal sequences.

The execution has been independent from the start. I am not a postdoc in a well-funded lab. I am an independent researcher with a pharmacy degree, a computational skill set, and a willingness to pre-register hypotheses and report negative results. The hERG cardiotoxicity study is an example. I tested whether bipartite persistent homology predicts hERG cardiotoxicity from protein-ligand interface geometry. The result was negative: topology did not beat a plain descriptor baseline (AUROC 0.8426 vs 0.8782). I reported it as a negative result. That is the same rigor I am applying to the CCT model.

The potential impact is high because the intervention class is fundamentally different. Preventing reward-memory encoding is not a marginal improvement on existing treatments. It is a different mechanism, a different timing, and a different cost structure. If validated clinically, it could reduce the incidence of addiction in high-risk populations, including post-operative patients, chronic pain patients, and individuals with a family history of substance use disorder. The global burden is enormous, and Africa is the most underserved region.

Emergent Ventures explicitly funds projects that challenge consensus and have high expected value. The CCT model is exactly that. It is a contrarian, quantitative, intervention-oriented framework with confirmed pre-registered predictions, and it is ready for the next step.

CHECKLIST

- [ ] Submit Emergent Ventures application at https://koolaidfactory.com/my-emergent-ventures-application/
- [ ] Attach MOTIVATION LETTER (300-500 words, final draft above)
- [ ] Attach RESEARCH STATEMENT (400-600 words, final draft above)
- [ ] Attach ESSAY: WHY THIS PROJECT IS BOLD AND UNCONVENTIONAL (200-350 words, final draft above)
- [ ] Verify current word counts for each section against stated limits
- [ ] Confirm all three CCT preprints are still under review at IART, PNPBP, and NBR; update if status changed
- [ ] Confirm co-authored Alcohol (Elsevier) paper status
- [ ] Verify endorsement emails from Berridge, Gershman, Daw, and Mattar are available as references
- [ ] Prepare a one-page budget breakdown for the $100K request (full-time development, compute, data access, pilot preparation)
- [ ] Prepare a one-page timeline (12 months: months 1-3 tool architecture, months 4-6 retrospective validation, months 7-9 pilot protocol, months 10-12 dissemination and funding continuation)
- [ ] Confirm ORCID (0009-0001-9272-6735) and GitHub (github.com/AmunRaPtah) are linked in application
- [ ] Confirm enrollment status at Hasso Plattner Institute (Winter Semester 2026/27) and include as context if asked

EDITOR NOTES

- Eligibility risk: Emergent Ventures is global and rolling, but the applicant is enrolled in an M.Sc. program starting Winter 2026/27. The application should clarify that the grant funds independent research and tool development, not tuition or academic fees, to avoid any conflict with the program's preference for independent ventures.
- Verification needed: The three journal review statuses (IART, PNPBP, NBR) and the Alcohol paper status must be confirmed immediately before submission. If any paper has been accepted or rejected, the language in the MOTIVATION LETTER and RESEARCH STATEMENT must be updated to reflect the current status.
- Gap to fill: The applicant must insert a specific, personal detail about why addiction prevention matters to them, ideally tied to a clinical experience as a pharmacist in Nigeria. The current draft is strong on evidence but lacks a human anchor. One or two sentences in the MOTIVATION LETTER would suffice.
- Gap to fill: The budget is mentioned but not itemized. The applicant should prepare a simple breakdown (compute, data access, software, travel for pilot collaboration, stipend) to attach if the application portal allows attachments beyond the text fields.
- Risk flag: The CCT model is validated computationally but not clinically. The draft is honest about this, but the applicant should be prepared to answer a follow-up question about what happens if the retrospective validation fails. The answer should reference the ergofluids precedent: report the negative result directly and pivot.

MOTIVATION LETTER

The dominant model of addiction treatment is backwards. It waits for the addicted brain to form a reward-memory, then tries to weaken that memory with behavioral therapy or blunt it with substitution drugs. The CCT model, which I have built and validated over the past two years, targets the moment of encoding itself. It asks a different question: what if the memory that drives compulsive reward-seeking never gets consolidated in the first place?

The Conjunctive Consolidation Threshold model is a tripartite pharmacological framework for reward-memory encoding prevention. It couples three axes, dopaminergic reward prediction error, NMDAR-dependent long-term potentiation, and affective contrast, into a single ODE system solved with RK45. I calibrated the model with Bayesian MCMC using PyMC's DEMetropolisZ, fitting 14 free parameters against priors elicited from a systematic screen of 1,847 records. All five pre-registered hypotheses, H1 through H5, were confirmed. The posterior shows super-additivity of 13 to 22 percentage points across model versions, meaning the three axes interact non-additively to gate memory consolidation. This is a falsifiable, quantitative framework with confirmed predictions, currently under review at three peer-reviewed journals: International Addiction Review, Progress in Neuro-Psychopharmacology and Biological Psychiatry, and Neuroscience and Biobehavioral Reviews.

Emergent Ventures funds contrarian, high-impact ideas executed by founders who do not wait for permission. That describes this project exactly. The mainstream addiction research community is heavily invested in retrieval-extinction paradigms and receptor-specific antagonists. The CCT model challenges that consensus by arguing that the threshold for memory consolidation is set by the conjunctive activity of all three axes, and that pharmacological intervention at the encoding stage is both feasible and more efficient than post-hoc extinction. The model is deliberately intervention-oriented. It specifies which receptor targets to hit, in what temporal sequence, and at what predicted effect sizes.

The grant would fund full-time development of a deployable clinical decision-support tool based on the CCT framework. The tool would take patient-specific variables, medication history, and genetic markers, run the calibrated ODE system, and output a recommended peri-exposure pharmacological protocol designed to prevent reward-memory consolidation. I have the full stack to build this: Python, PyMC, ODE solvers, and four independent DuckDB-based data pipelines I built and operate myself. I also have the clinical credibility, as a licensed pharmacist with a B.Pharm from the University of Ibadan, to know what a clinician actually needs at the point of care.

The global burden is massive. Addiction is a leading cause of disability and death worldwide, and Africa has the weakest treatment infrastructure of any region. A tool that prevents memory encoding at the moment of exposure, rather than treating the consolidated addiction months later, is a fundamentally different intervention class. It is also a fundamentally cheaper one.

I am currently enrolled in the M.Sc. Digital Health program at the Hasso Plattner Institute in Germany, which gives me access to European clinical data partnerships. But the core research is independent, self-funded, and already producing confirmed results. Emergent Ventures is the right partner to take it from a validated model to a deployable tool.

RESEARCH STATEMENT

The CCT model emerged from a specific failure in the addiction literature. Preclinical work on reconsolidation blockade shows promise in rodents but translates poorly to humans. Clinical trials of NMDAR antagonists like memantine for addiction have produced mixed results. Dopamine-based interventions target craving but not the memory trace itself. The field lacks a unified, quantitative account of how these systems interact to set the threshold for reward-memory consolidation.

I built that account. The CCT model is a system of coupled ordinary differential equations with three state variables representing dopaminergic reward prediction error, NMDAR-dependent synaptic plasticity, and affective contrast. The threshold for memory consolidation is not set by any single variable but by their conjunctive activity crossing a critical boundary. The model was calibrated using Bayesian MCMC with DEMetropolisZ, 14 free parameters, and priors elicited from a systematic screen of 1,847 records spanning pharmacology, computational neuroscience, and clinical addiction studies. The model was pre-registered with five hypotheses. All five were confirmed. The posterior distribution shows super-additivity of 13 to 22 percentage points, meaning the interaction of the three axes is greater than the sum of their individual contributions.

The three sole-authored preprints are under review at International Addiction Review, Progress in Neuro-Psychopharmacology and Biological Psychiatry, and Neuroscience and Biobehavioral Reviews. A co-authored paper is under review at Alcohol (Elsevier). The model has been endorsed by Kent Berridge at Michigan, Samuel Gershman at Harvard, Nathaniel Daw at Princeton, and Marcelo Mattar at NYU.

The next phase is translation. The CCT framework specifies a set of pharmacological targets and a temporal protocol for intervening at the encoding stage. I want to build a clinical decision-support tool that operationalizes this. The tool will take patient-specific inputs, run the calibrated ODE system, and output a recommended peri-exposure protocol. The technical components are already in place: I have built and operated four independent DuckDB-based ingest-to-analyze pipelines, self-hosted local LLM serving with llama.cpp, and production systems with CI/CD and automated backup. The tool will be validated against retrospective clinical datasets and, where possible, prospective pilot data from collaborators.

The Africa angle is not an afterthought. Nigeria has one of the highest rates of opioid and methamphetamine use in West Africa, and treatment infrastructure is severely limited. A prevention-oriented tool that can be deployed at the point of exposure, in emergency departments, primary care, or even community pharmacies, is a scalable intervention that does not require a psychiatrist or a specialized addiction clinic. I am a licensed pharmacist in Nigeria and have worked as a clinical pharmacist. I know the deployment context.

The Emergent Ventures grant would fund one year of full-time development: building the decision-support tool, validating it against retrospective data, and preparing a prospective pilot protocol. The budget is modest relative to the potential impact. The risk profile is also honest. The model is validated at the computational level, but clinical translation is a separate step. I am not claiming a product exists. I am claiming that a falsifiable, quantitatively confirmed model exists, and that the next step is engineering and clinical validation.

ESSAY: WHY THIS PROJECT IS BOLD AND UNCONVENTIONAL

The addiction field is dominated by two paradigms: agonist substitution and behavioral extinction. Both assume the addiction is already formed. The CCT model rejects that assumption. It argues that the critical intervention point is the moment of encoding, when a reward-associated memory is first consolidated. This is contrarian because it implies that the most effective treatment for addiction is not a treatment at all, but a prevention protocol applied at the time of exposure.

The model is also methodologically unconventional. It is a computational pharmacology framework, not a wet-lab study. It uses dynamical systems theory, Bayesian calibration, and pre-registered hypothesis testing to make quantitative predictions about pharmacological interventions. This is not how most addiction research is done, and it is not how most addiction research is funded. The mainstream prefers RCTs of single drugs or behavioral protocols. The CCT model is a systems-level account that specifies combination protocols and temporal sequences.

The execution has been independent from the start. I am not a postdoc in a well-funded lab. I am an independent researcher with a pharmacy degree, a computational skill set, and a willingness to pre-register hypotheses and report negative results. The hERG cardiotoxicity study is an example. I tested whether bipartite persistent homology predicts hERG cardiotoxicity from protein-ligand interface geometry. The result was negative: topology did not beat a plain descriptor baseline (AUROC 0.8426 vs 0.8782). I reported it as a negative result. That is the same rigor I am applying to the CCT model.

The potential impact is high because the intervention class is fundamentally different. Preventing reward-memory encoding is not a marginal improvement on existing treatments. It is a different mechanism, a different timing, and a different cost structure. If validated clinically, it could reduce the incidence of addiction in high-risk populations, including post-operative patients, chronic pain patients, and individuals with a family history of substance use disorder. The global burden is enormous, and Africa is the most underserved region.

Emergent Ventures explicitly funds projects that challenge consensus and have high expected value. The CCT model is exactly that. It is a contrarian, quantitative, intervention-oriented framework with confirmed pre-registered predictions, and it is ready for the next step.

CHECKLIST

- [ ] Submit Emergent Ventures application at https://koolaidfactory.com/my-emergent-ventures-application/
- [ ] Attach MOTIVATION LETTER (300-500 words, final draft above)
- [ ] Attach RESEARCH STATEMENT (400-600 words, final draft above)
- [ ] Attach ESSAY: WHY THIS PROJECT IS BOLD AND UNCONVENTIONAL (200-350 words, final draft above)
- [ ] Verify current word counts for each section against stated limits
- [ ] Confirm all three CCT preprints are still under review at IART, PNPBP, and NBR; update if status changed
- [ ] Confirm co-authored Alcohol (Elsevier) paper status
- [ ] Verify endorsement emails from Berridge, Gershman, Daw, and Mattar are available as references
- [ ] Prepare a one-page budget breakdown for the $100K request (full-time development, compute, data access, pilot preparation)
- [ ] Prepare a one-page timeline (12 months: months 1-3 tool architecture, months 4-6 retrospective validation, months 7-9 pilot protocol, months 10-12 dissemination and funding continuation)
- [ ] Confirm ORCID (0009-0001-9272-6735) and GitHub (github.com/AmunRaPtah) are linked in application
- [ ] Confirm enrollment status at Hasso Plattner Institute (Winter Semester 2026/27) and include as context if asked

EDITOR NOTES

- Eligibility risk: Emergent Ventures is global and rolling, but the applicant is enrolled in an M.Sc. program starting Winter 2026/27. The application should clarify that the grant funds independent research and tool development, not tuition or academic fees, to avoid any conflict with the program's preference for independent ventures.
- Verification needed: The three journal review statuses (IART, PNPBP, NBR) and the Alcohol paper status must be confirmed immediately before submission. If any paper has been accepted or rejected, the language in the MOTIVATION LETTER and RESEARCH STATEMENT must be updated to reflect the current status.
- Gap to fill: The applicant must insert a specific, personal detail about why addiction prevention matters to them, ideally tied to a clinical experience as a pharmacist in Nigeria. The current draft is strong on evidence but lacks a human anchor. One or two sentences in the MOTIVATION LETTER would suffice.
- Gap to fill: The budget is mentioned but not itemized. The applicant should prepare a simple breakdown (compute, data access, software, travel for pilot collaboration, stipend) to attach if the application portal allows attachments beyond the text fields.
- Risk flag: The CCT model is validated computationally but not clinically. The draft is honest about this, but the applicant should be prepared to answer a follow-up question about what happens if the retrospective validation fails. The answer should reference the ergofluids precedent: report the negative result directly and pivot.

AI Draft — Emergent Ventures