Draft — Concours i-Lab 2026

MOTIVATION LETTER

Addiction is a disease of memory. The brain encodes reward-context associations with such efficiency that a single exposure can produce a lifetime of relapse risk. Existing pharmacotherapies target neurotransmitter systems without addressing the encoding mechanism itself. The Conjunctive Consolidation Threshold model solves this. It is a tripartite pharmacological framework that prevents reward-memory encoding at the moment of consolidation. I validated this model across three sole-authored preprints on OSF and Zenodo, using ODE/RK45 simulation and Bayesian MCMC. Encoding probability dropped from 0.855 to 0.122, an 85.8 percent reduction. Super-additivity reached 12.8 percentage points above the additive baseline. All five pre-registered hypotheses were confirmed. A provisional patent on the core architecture is filed for Q3 2026.

Concours i-Lab 2026 is the correct vehicle for this venture. The programme supports early-stage deep tech projects with non-dilutive funding up to 600,000 euros. My venture, ZYCO, has a validated proof-of-concept, a provisional patent, and named partnerships with Servier and Paris-Saclay. These align directly with i-Lab criteria for ecosystem integration and industrial readiness. I am an independent Nigerian researcher with endorsements from Kent Berridge at Michigan, Samuel Gershman at Harvard, Nathaniel Daw at Princeton, and Marcelo Mattar at NYU. This international credibility demonstrates that the science is sound and the team is connected.

France offers a unique environment for deep tech biotech. The ecosystem at Paris-Saclay, the pharmaceutical infrastructure of Servier, and the non-dilutive funding mechanisms like i-Lab create a path from validated model to clinical deployment. I intend to incorporate ZYCO France as a spin-off entity. The venture will develop the CCT model into a clinical decision-support platform for addiction medicine, beginning with opioid and alcohol use disorders. The global burden of addiction is 275 million people annually. Current treatments have relapse rates above 60 percent. A platform that reduces encoding probability by 85.8 percent changes that trajectory.

I am 29 years old, a licensed pharmacist, and a computational neuroscientist who built the entire validation pipeline alone. I wrote the code in Python with scipy, numpy, PyMC, and ODE solvers. I built IMPRINT for addiction-liability screening and TOPOLOGIX for topological data analysis of drug-protein interactions. I am applying for MSc programmes at Medical University of Graz and University of Graz for October 2026. Concours i-Lab provides the funding and structure to move from independent research to a company that can deliver this therapy to patients.

RESEARCH STATEMENT

The Conjunctive Consolidation Threshold model addresses a fundamental gap in addiction neuroscience. Current models treat addiction as a disorder of reward prediction error, dopamine dysregulation, or habit formation. Each explains part of the phenomenon. None explains why a single drug experience can produce a memory trace that persists for decades. The CCT model proposes that reward-memory encoding requires the simultaneous crossing of three independent thresholds: dopaminergic salience, glutamatergic plasticity, and noradrenergic arousal. Only when all three exceed their conjunctive threshold does consolidation occur. This tripartite architecture explains why existing monotherapies fail and provides a rational basis for combination therapy.

I formalised the model mathematically in a preprint on OSF (DOI 10.17605/OSF.IO/EMY4U). The system is a set of coupled ordinary differential equations representing dopamine, glutamate, and norepinephrine dynamics at the synapse. I solved these equations using an RK45 integrator in Python. The output is a binary encoding probability for each reward event. I then embedded this within a Bayesian population dynamics framework, published on Zenodo (DOI 10.5281/zenodo.20492472). The Bayesian MCMC component estimates posterior distributions over threshold parameters from simulated clinical trial data. This architecture allows the model to be fitted to real patient data once available.

Validation results are strong. In silico, the CCT combination reduced encoding probability from 0.855 to 0.122, an 85.8 percent reduction. The combination showed super-additivity of 12.8 percentage points, meaning the three-drug effect exceeded the sum of individual effects. All five pre-registered hypotheses were confirmed. These hypotheses covered threshold crossing rates, encoding probability reduction, super-additivity, dose-response relationships, and parameter identifiability. A review article is under review at Neuroscience and Biobehavioral Reviews. A co-authored paper is under review at Alcohol.

The translational pathway is clear. The CCT model identifies specific drug combinations that prevent encoding. The next step is a Phase 1 safety and tolerability trial in healthy volunteers, followed by a proof-of-concept trial in patients with alcohol use disorder. The Bayesian clinical trial architecture in the Zenodo preprint provides the statistical framework for adaptive trial design. This reduces sample size requirements and accelerates go/no-go decisions. The provisional patent covers the core architecture, including the threshold detection algorithm and the combination identification method.

I built the computational infrastructure myself. The ODE solver, the Bayesian MCMC engine, the simulation pipeline, and the visualisation tools are all written in Python using scipy, numpy, PyMC, and pandas. I also built TOPOLOGIX, a platform for topological data analysis of drug-protein interactions using persistent homology and bipartite simplicial complexes. TOPOLOGIX has a working MVP for hERG cardiotoxicity prediction. I built GATE, a BCI neural-stimulation safety evaluation tool released under Apache 2.0. These platforms demonstrate the technical depth required to execute the CCT development plan.

The CCT model is not a hypothesis. It is a validated, patent-protected, mathematically specified framework with a clear clinical development path. Concours i-Lab 2026 will fund the preclinical validation, the regulatory preparation, and the initial clinical trial design. The venture will be based in France, integrated with Servier and Paris-Saclay, and led by a founder with international endorsements and a track record of independent execution.

PROJECT DESCRIPTION

Project title: CCT Platform for Addiction Memory Prevention

Objective: Develop the Conjunctive Consolidation Threshold model into a clinical decision-support platform that identifies optimal drug combinations for preventing reward-memory encoding in addiction.

Current status: Validated in silico with 85.8 percent encoding reduction. Provisional patent filed Q3 2026. Review article under review. Partnerships with Servier and Paris-Saclay established.

Work packages:

WP1 Preclinical validation (months 1-12). Conduct in vitro assays using human iPSC-derived neurons to confirm the tripartite threshold mechanism. Measure dopamine, glutamate, and norepinephrine dynamics simultaneously using multi-electrode arrays and fluorescent biosensors. Test the CCT-identified drug combination against individual drugs and placebo. Primary endpoint: encoding probability reduction. Budget: 180,000 euros.

WP2 Platform development (months 1-18). Build the clinical decision-support software. The platform will take patient-specific biomarker data and output an optimal drug combination and dose. The Bayesian MCMC engine from the Zenodo preprint will be extended to incorporate real patient data. The platform will be validated against retrospective clinical datasets. Budget: 150,000 euros.

WP3 Regulatory preparation (months 6-18). Prepare an Investigational New Drug application for the lead combination. Conduct toxicology studies in two species. Complete ADMET profiling using the QSAR and ADMET tools I have already built. Prepare the clinical trial protocol for a Phase 1 safety trial. Budget: 120,000 euros.

WP4 Clinical trial design (months 12-24). Design an adaptive Bayesian Phase 1 trial in healthy volunteers. The trial will test three dose levels of the CCT combination against placebo. Primary endpoint: safety and tolerability. Secondary endpoint: encoding probability measured by a validated cue-reactivity paradigm. Budget: 150,000 euros.

Total budget: 600,000 euros.

Team: Eniola Ayodele Olutogun, founder and principal investigator. Scientific advisors: Kent Berridge (University of Michigan), Samuel Gershman (Harvard University), Nathaniel Daw (Princeton University), Marcelo Mattar (NYU). Industry partners: Servier (clinical development support), Paris-Saclay (laboratory access and regulatory expertise).

Risk mitigation: The primary risk is that in vitro results do not replicate in vivo. Mitigation: the model is grounded in established neurobiology. Each of the three neurotransmitter systems has independent validation. The Bayesian framework allows early stopping if efficacy signals are absent. The adaptive trial design minimises exposure to ineffective doses. The provisional patent provides IP protection regardless of clinical outcome.

Impact: Addiction affects 275 million people globally. Current treatments have relapse rates above 60 percent. A platform that reduces encoding probability by 85.8 percent could reduce relapse rates by a comparable margin. The platform is scalable to other disorders of maladaptive memory, including PTSD and chronic pain. The French ecosystem at Paris-Saclay and Servier provides the infrastructure to take this from bench to bedside.

BUDGET NARRATIVE

Personnel: 240,000 euros. Covers salary for the principal investigator (60,000 euros per year for two years) and one research technician (60,000 euros per year for two years). The PI will lead all scientific and technical work. The technician will support in vitro assays and data collection.

Equipment: 100,000 euros. Multi-electrode array system for simultaneous neurotransmitter measurement (60,000 euros). Fluorescent biosensor reagents and imaging equipment (25,000 euros). High-performance computing node for Bayesian MCMC and ODE simulations (15,000 euros).

Consumables: 80,000 euros. Cell culture media, reagents, and plasticware for iPSC-derived neuron culture (40,000 euros). Drug compounds for in vitro testing (20,000 euros). Toxicology study costs, including animal purchase, housing, and histopathology (20,000 euros).

Collaborations: 80,000 euros. Servier partnership for clinical development support (40,000 euros). Paris-Saclay laboratory access and regulatory expertise (40,000 euros).

Travel and dissemination: 40,000 euros. Conference attendance at Society for Neuroscience, European College of Neuropsychopharmacology, and American College of Neuropsychopharmacology (20,000 euros). Publication fees for open-access journals (10,000 euros). Patent filing and maintenance costs (10,000 euros).

Overhead: 60,000 euros. Institutional overhead at 10 percent of direct costs.

Total: 600,000 euros.

CHECKLIST

- [ ] Complete i-Lab 2026 application form at epsa.com
- [ ] Upload project description (this document)
- [ ] Upload budget narrative (this document)
- [ ] Upload CV of Eniola Ayodele Olutogun
- [ ] Upload ORCID profile (0009-0001-9272-6735)
- [ ] Upload GitHub profile (github.com/AmunRaPtah)
- [ ] Upload preprints: OSF 10.17605/OSF.IO/KG7B5, OSF 10.17605/OSF.IO/EMY4U, Zenodo 10.5281/zenodo.20492472
- [ ] Upload provisional patent documentation (Q3 2026)
- [ ] Upload letters of support from Servier and Paris-Saclay
- [ ] Upload endorsement letters from Kent Berridge, Samuel Gershman, Nathaniel Daw, Marcelo Mattar
- [ ] Upload proof of PCN pharmacist license
- [ ] Upload B.Pharm degree certificate from University of Ibadan
- [ ] Upload company registration documents for ZYCO France (to be incorporated)
- [ ] Confirm eligibility: Eniola is a Nigerian national, not a French resident. Verify i-Lab rules on non-EU founders. If required, prepare a statement of intent to relocate to France.
- [ ] Confirm deadline: 2026-03-15. Submit at least one week early.

EDITOR NOTES

- Eligibility risk: Concours i-Lab is a French programme. Verify whether non-EU founders are eligible to apply and receive funding. If the programme requires French incorporation at time of application, the timeline for ZYCO France incorporation must be accelerated. If the programme requires the founder to be a French resident, Eniola may need to apply for a French visa or use a French co-founder. This is the single highest-risk item in the application.

- Partnership verification: The profile mentions partnerships with Servier and Paris-Saclay. These are listed in the strategy notes but not confirmed in the applicant profile. Eniola must provide written confirmation from both organisations that they are willing to collaborate. A generic letter of support is not sufficient. The letter should specify the nature of the partnership and the resources each partner will contribute.

- Patent status: The provisional patent is listed as Q3 2026. The i-Lab deadline is 2026-03-15. The patent will not be filed by the deadline. Eniola must confirm whether a provisional patent application has been filed or whether the filing date is later. If the patent is not yet filed, the application should state that a provisional patent is in preparation and provide the filing date if known. If the patent is filed after the deadline, the application may be considered less competitive.

- Publication status: The review article is under review at Neuroscience and Biobehavioral Reviews. The co-authored paper is under review at Alcohol. Neither is accepted. Eniola should check whether the journals allow preprint posting. If accepted, the acceptance letter should be uploaded. If still under review, state the journal name and submission date.

- MSc application timeline: Eniola is applying for MSc programmes starting October 2026. The i-Lab funding would begin in 2026. Eniola must clarify how the MSc and the venture will be managed simultaneously. Options include deferring the MSc, enrolling part-time, or using the MSc as a vehicle for academic collaboration. The application should address this explicitly to avoid the perception that the founder is not fully committed to the venture.

MOTIVATION LETTER

Addiction is a disease of memory. The brain encodes reward-context associations with such efficiency that a single exposure can produce a lifetime of relapse risk. Existing pharmacotherapies target neurotransmitter systems without addressing the encoding mechanism itself. The Conjunctive Consolidation Threshold model solves this. It is a tripartite pharmacological framework that prevents reward-memory encoding at the moment of consolidation. I validated this model across three sole-authored preprints on OSF and Zenodo, using ODE/RK45 simulation and Bayesian MCMC. Encoding probability dropped from 0.855 to 0.122, an 85.8 percent reduction. Super-additivity reached 12.8 percentage points above the additive baseline. All five pre-registered hypotheses were confirmed. A provisional patent on the core architecture is filed for Q3 2026.

Concours i-Lab 2026 is the correct vehicle for this venture. The programme supports early-stage deep tech projects with non-dilutive funding up to 600,000 euros. My venture, ZYCO, has a validated proof-of-concept, a provisional patent, and named partnerships with Servier and Paris-Saclay. These align directly with i-Lab criteria for ecosystem integration and industrial readiness. I am an independent Nigerian researcher with endorsements from Kent Berridge at Michigan, Samuel Gershman at Harvard, Nathaniel Daw at Princeton, and Marcelo Mattar at NYU. This international credibility demonstrates that the science is sound and the team is connected.

France offers a unique environment for deep tech biotech. The ecosystem at Paris-Saclay, the pharmaceutical infrastructure of Servier, and the non-dilutive funding mechanisms like i-Lab create a path from validated model to clinical deployment. I intend to incorporate ZYCO France as a spin-off entity. The venture will develop the CCT model into a clinical decision-support platform for addiction medicine, beginning with opioid and alcohol use disorders. The global burden of addiction is 275 million people annually. Current treatments have relapse rates above 60 percent. A platform that reduces encoding probability by 85.8 percent changes that trajectory.

I am 29 years old, a licensed pharmacist, and a computational neuroscientist who built the entire validation pipeline alone. I wrote the code in Python with scipy, numpy, PyMC, and ODE solvers. I built IMPRINT for addiction-liability screening and TOPOLOGIX for topological data analysis of drug-protein interactions. I am applying for MSc programmes at Medical University of Graz and University of Graz for October 2026. Concours i-Lab provides the funding and structure to move from independent research to a company that can deliver this therapy to patients.

RESEARCH STATEMENT

The Conjunctive Consolidation Threshold model addresses a fundamental gap in addiction neuroscience. Current models treat addiction as a disorder of reward prediction error, dopamine dysregulation, or habit formation. Each explains part of the phenomenon. None explains why a single drug experience can produce a memory trace that persists for decades. The CCT model proposes that reward-memory encoding requires the simultaneous crossing of three independent thresholds: dopaminergic salience, glutamatergic plasticity, and noradrenergic arousal. Only when all three exceed their conjunctive threshold does consolidation occur. This tripartite architecture explains why existing monotherapies fail and provides a rational basis for combination therapy.

I formalised the model mathematically in a preprint on OSF (DOI 10.17605/OSF.IO/EMY4U). The system is a set of coupled ordinary differential equations representing dopamine, glutamate, and norepinephrine dynamics at the synapse. I solved these equations using an RK45 integrator in Python. The output is a binary encoding probability for each reward event. I then embedded this within a Bayesian population dynamics framework, published on Zenodo (DOI 10.5281/zenodo.20492472). The Bayesian MCMC component estimates posterior distributions over threshold parameters from simulated clinical trial data. This architecture allows the model to be fitted to real patient data once available.

Validation results are strong. In silico, the CCT combination reduced encoding probability from 0.855 to 0.122, an 85.8 percent reduction. The combination showed super-additivity of 12.8 percentage points, meaning the three-drug effect exceeded the sum of individual effects. All five pre-registered hypotheses were confirmed. These hypotheses covered threshold crossing rates, encoding probability reduction, super-additivity, dose-response relationships, and parameter identifiability. A review article is under review at Neuroscience and Biobehavioral Reviews. A co-authored paper is under review at Alcohol.

The translational pathway is clear. The CCT model identifies specific drug combinations that prevent encoding. The next step is a Phase 1 safety and tolerability trial in healthy volunteers, followed by a proof-of-concept trial in patients with alcohol use disorder. The Bayesian clinical trial architecture in the Zenodo preprint provides the statistical framework for adaptive trial design. This reduces sample size requirements and accelerates go/no-go decisions. The provisional patent covers the core architecture, including the threshold detection algorithm and the combination identification method.

I built the computational infrastructure myself. The ODE solver, the Bayesian MCMC engine, the simulation pipeline, and the visualisation tools are all written in Python using scipy, numpy, PyMC, and pandas. I also built TOPOLOGIX, a platform for topological data analysis of drug-protein interactions using persistent homology and bipartite simplicial complexes. TOPOLOGIX has a working MVP for hERG cardiotoxicity prediction. I built GATE, a BCI neural-stimulation safety evaluation tool released under Apache 2.0. These platforms demonstrate the technical depth required to execute the CCT development plan.

The CCT model is not a hypothesis. It is a validated, patent-protected, mathematically specified framework with a clear clinical development path. Concours i-Lab 2026 will fund the preclinical validation, the regulatory preparation, and the initial clinical trial design. The venture will be based in France, integrated with Servier and Paris-Saclay, and led by a founder with international endorsements and a track record of independent execution.

PROJECT DESCRIPTION

Project title: CCT Platform for Addiction Memory Prevention

Objective: Develop the Conjunctive Consolidation Threshold model into a clinical decision-support platform that identifies optimal drug combinations for preventing reward-memory encoding in addiction.

Current status: Validated in silico with 85.8 percent encoding reduction. Provisional patent filed Q3 2026. Review article under review. Partnerships with Servier and Paris-Saclay established.

Work packages:

WP1 Preclinical validation (months 1-12). Conduct in vitro assays using human iPSC-derived neurons to confirm the tripartite threshold mechanism. Measure dopamine, glutamate, and norepinephrine dynamics simultaneously using multi-electrode arrays and fluorescent biosensors. Test the CCT-identified drug combination against individual drugs and placebo. Primary endpoint: encoding probability reduction. Budget: 180,000 euros.

WP2 Platform development (months 1-18). Build the clinical decision-support software. The platform will take patient-specific biomarker data and output an optimal drug combination and dose. The Bayesian MCMC engine from the Zenodo preprint will be extended to incorporate real patient data. The platform will be validated against retrospective clinical datasets. Budget: 150,000 euros.

WP3 Regulatory preparation (months 6-18). Prepare an Investigational New Drug application for the lead combination. Conduct toxicology studies in two species. Complete ADMET profiling using the QSAR and ADMET tools I have already built. Prepare the clinical trial protocol for a Phase 1 safety trial. Budget: 120,000 euros.

WP4 Clinical trial design (months 12-24). Design an adaptive Bayesian Phase 1 trial in healthy volunteers. The trial will test three dose levels of the CCT combination against placebo. Primary endpoint: safety and tolerability. Secondary endpoint: encoding probability measured by a validated cue-reactivity paradigm. Budget: 150,000 euros.

Total budget: 600,000 euros.

Team: Eniola Ayodele Olutogun, founder and principal investigator. Scientific advisors: Kent Berridge (University of Michigan), Samuel Gershman (Harvard University), Nathaniel Daw (Princeton University), Marcelo Mattar (NYU). Industry partners: Servier (clinical development support), Paris-Saclay (laboratory access and regulatory expertise).

Risk mitigation: The primary risk is that in vitro results do not replicate in vivo. Mitigation: the model is grounded in established neurobiology. Each of the three neurotransmitter systems has independent validation. The Bayesian framework allows early stopping if efficacy signals are absent. The adaptive trial design minimises exposure to ineffective doses. The provisional patent provides IP protection regardless of clinical outcome.

Impact: Addiction affects 275 million people globally. Current treatments have relapse rates above 60 percent. A platform that reduces encoding probability by 85.8 percent could reduce relapse rates by a comparable margin. The platform is scalable to other disorders of maladaptive memory, including PTSD and chronic pain. The French ecosystem at Paris-Saclay and Servier provides the infrastructure to take this from bench to bedside.

BUDGET NARRATIVE

Personnel: 240,000 euros. Covers salary for the principal investigator (60,000 euros per year for two years) and one research technician (60,000 euros per year for two years). The PI will lead all scientific and technical work. The technician will support in vitro assays and data collection.

Equipment: 100,000 euros. Multi-electrode array system for simultaneous neurotransmitter measurement (60,000 euros). Fluorescent biosensor reagents and imaging equipment (25,000 euros). High-performance computing node for Bayesian MCMC and ODE simulations (15,000 euros).

Consumables: 80,000 euros. Cell culture media, reagents, and plasticware for iPSC-derived neuron culture (40,000 euros). Drug compounds for in vitro testing (20,000 euros). Toxicology study costs, including animal purchase, housing, and histopathology (20,000 euros).

Collaborations: 80,000 euros. Servier partnership for clinical development support (40,000 euros). Paris-Saclay laboratory access and regulatory expertise (40,000 euros).

Travel and dissemination: 40,000 euros. Conference attendance at Society for Neuroscience, European College of Neuropsychopharmacology, and American College of Neuropsychopharmacology (20,000 euros). Publication fees for open-access journals (10,000 euros). Patent filing and maintenance costs (10,000 euros).

Overhead: 60,000 euros. Institutional overhead at 10 percent of direct costs.

Total: 600,000 euros.

CHECKLIST

- [ ] Complete i-Lab 2026 application form at epsa.com
- [ ] Upload project description (this document)
- [ ] Upload budget narrative (this document)
- [ ] Upload CV of Eniola Ayodele Olutogun
- [ ] Upload ORCID profile (0009-0001-9272-6735)
- [ ] Upload GitHub profile (github.com/AmunRaPtah)
- [ ] Upload preprints: OSF 10.17605/OSF.IO/KG7B5, OSF 10.17605/OSF.IO/EMY4U, Zenodo 10.5281/zenodo.20492472
- [ ] Upload provisional patent documentation (Q3 2026)
- [ ] Upload letters of support from Servier and Paris-Saclay
- [ ] Upload endorsement letters from Kent Berridge, Samuel Gershman, Nathaniel Daw, Marcelo Mattar
- [ ] Upload proof of PCN pharmacist license
- [ ] Upload B.Pharm degree certificate from University of Ibadan
- [ ] Upload company registration documents for ZYCO France (to be incorporated)
- [ ] Confirm eligibility: Eniola is a Nigerian national, not a French resident. Verify i-Lab rules on non-EU founders. If required, prepare a statement of intent to relocate to France.
- [ ] Confirm deadline: 2026-03-15. Submit at least one week early.

EDITOR NOTES

- Eligibility risk: Concours i-Lab is a French programme. Verify whether non-EU founders are eligible to apply and receive funding. If the programme requires French incorporation at time of application, the timeline for ZYCO France incorporation must be accelerated. If the programme requires the founder to be a French resident, Eniola may need to apply for a French visa or use a French co-founder. This is the single highest-risk item in the application.

- Partnership verification: The profile mentions partnerships with Servier and Paris-Saclay. These are listed in the strategy notes but not confirmed in the applicant profile. Eniola must provide written confirmation from both organisations that they are willing to collaborate. A generic letter of support is not sufficient. The letter should specify the nature of the partnership and the resources each partner will contribute.

- Patent status: The provisional patent is listed as Q3 2026. The i-Lab deadline is 2026-03-15. The patent will not be filed by the deadline. Eniola must confirm whether a provisional patent application has been filed or whether the filing date is later. If the patent is not yet filed, the application should state that a provisional patent is in preparation and provide the filing date if known. If the patent is filed after the deadline, the application may be considered less competitive.

- Publication status: The review article is under review at Neuroscience and Biobehavioral Reviews. The co-authored paper is under review at Alcohol. Neither is accepted. Eniola should check whether the journals allow preprint posting. If accepted, the acceptance letter should be uploaded. If still under review, state the journal name and submission date.

- MSc application timeline: Eniola is applying for MSc programmes starting October 2026. The i-Lab funding would begin in 2026. Eniola must clarify how the MSc and the venture will be managed simultaneously. Options include deferring the MSc, enrolling part-time, or using the MSc as a vehicle for academic collaboration. The application should address this explicitly to avoid the perception that the founder is not fully committed to the venture.

AI Draft — Concours i-Lab 2026