Draft — Engineering of Biomedical Systems

MOTIVATION LETTER

The Engineering of Biomedical Systems programme at the U.S. National Science Foundation funds research that integrates engineering principles with biological systems to solve pressing health challenges. My independent work on the Conjunctive Consolidation Threshold (CCT) model represents exactly this convergence: a pharmacological engineering framework that treats addiction as a failure mode in the brain's reward-memory encoding system, then designs a corrective intervention using three drug classes operating on distinct temporal and molecular targets.

I am a 29-year-old Nigerian pharmacist and independent researcher based in Lagos. I hold a B.Pharm from the University of Ibadan (CGPA 5.1/7.0, German equivalent 1.9) and am licensed by the Pharmacists Council of Nigeria. Since 2025, I have developed the CCT model through three sole-authored preprints on OSF and Zenodo, including a formal mathematical specification and a Bayesian population dynamics framework with a clinical trial architecture. I validated the model using ODE/RK45 numerical simulation and Bayesian MCMC, demonstrating an 85.8 percent reduction in encoding probability (from 0.855 to 0.122) and super-additivity of 12.8 percentage points across the three drug classes. All five pre-registered hypotheses (H1 through H5) were confirmed. A review article is under review at Neuroscience and Biobehavioral Reviews, and a co-authored paper is under review at Alcohol (Elsevier).

I built three platforms that demonstrate engineering capability beyond the CCT model. IMPRINT screens individuals for addiction liability using computational pharmacology. TOPOLOGIX applies topological data analysis, persistent homology, and bipartite simplicial complexes to drug-protein interaction networks, with a working MVP for hERG cardiotoxicity screening. GATE evaluates safety parameters for brain-computer interface neural stimulation and is released under Apache 2.0. These platforms are not theoretical exercises; they are functional tools that I designed, coded, and deployed using Python, R, TDA libraries (Ripser, Gudhi), NEURON/Brian2, and a full backend stack including Supabase and Node.js.

My work has received endorsements from leading neuroscientists: Kent Berridge at the University of Michigan, Samuel Gershman at Harvard (who endorsed my arXiv submission), Nathaniel Daw at Princeton, and Marcelo Mattar at New York University. A provisional patent on the CCT core architecture is scheduled for Q3 2026.

NSF support through this programme would allow me to transition from independent validation to funded, collaborative development. I am applying for MSc programmes starting October 2026 at the Medical University of Graz and the University of Graz in Austria. In the interim, I seek resources to expand the CCT model's parameter space, integrate the IMPRINT screening tool into a clinical pilot protocol, and publish the full engineering specification. The global health angle is direct: Nigeria and sub-Saharan Africa carry a disproportionate burden of substance use disorders with minimal pharmacological intervention research. The CCT model, if validated in human trials, could provide a low-cost, scalable treatment protocol deployable in LMIC health systems.

RESEARCH STATEMENT

The Conjunctive Consolidation Threshold (CCT) model addresses a specific engineering problem in neuropharmacology: how to prevent the encoding of reward-memory associations that drive addiction relapse. Current pharmacotherapies for substance use disorders target single neurotransmitter systems (opioid antagonists, glutamate modulators, dopamine partial agonists) and achieve modest effect sizes. The CCT model proposes that reward-memory encoding requires the conjunctive activation of three distinct neural processes within a critical consolidation window: dopamine-mediated reward salience, glutamate-dependent synaptic plasticity, and noradrenergic arousal tagging. By administering three drug classes at sub-threshold doses timed to the consolidation window, the model predicts that no single pathway is blocked completely, but the conjunctive threshold for encoding is never reached.

I validated this prediction using a system of ordinary differential equations solved via the Runge-Kutta 45 method, with parameters drawn from published pharmacokinetic and pharmacodynamic data for each drug class. The Bayesian MCMC analysis, run on a 10,000-iteration Markov chain with 2,000 burn-in samples, produced posterior distributions for the encoding probability under monotherapy, dual therapy, and triple therapy conditions. The triple therapy condition reduced encoding probability from 0.855 to 0.122, an 85.8 percent reduction. The super-additivity of 12.8 percentage points indicates that the three drugs interact non-linearly, consistent with the conjunctive threshold hypothesis.

The engineering contribution of this work is threefold. First, the CCT model provides a formal mathematical framework for designing multi-drug interventions with predictable population-level outcomes. Second, the Bayesian clinical trial architecture I published on Zenodo (DOI 10.5281/zenodo.20492472) specifies adaptive randomization, interim analyses, and stopping rules that can be implemented in a Phase IIa proof-of-concept trial. Third, the platforms I built (IMPRINT, TOPOLOGIX, GATE) constitute a pipeline from target identification through safety screening to clinical deployment. TOPOLOGIX, for example, uses persistent homology to detect off-target binding risks in the hERG potassium channel, a common cause of cardiotoxicity in multi-drug regimens.

The NSF Engineering of Biomedical Systems programme funds projects that advance biomedical engineering through quantitative modeling, computational methods, and systems-level thinking. My work fits this description exactly. The CCT model is not a pharmacological hypothesis in the traditional sense; it is an engineered intervention designed from first principles, validated through computational simulation, and ready for translation into a clinical protocol. The provisional patent filing in Q3 2026 underscores the novelty of the architecture.

I am an independent researcher without a PhD or formal institutional affiliation. This is a structural disadvantage in most grant systems, but it is also a strength: the CCT model was developed entirely outside of a university laboratory, using open-source tools, public datasets, and my own computational infrastructure. The endorsements from Berridge, Gershman, Daw, and Mattar confirm that the work meets the standards of the field. NSF support would allow me to formalize the engineering specification, collaborate with a computational neuroscience laboratory in Europe or the United States, and prepare the model for clinical testing in a Nigerian or African trial site.

PROJECT DESCRIPTION

I request funding to complete three specific aims over a 12-month period.

Aim 1: Parameter space expansion and sensitivity analysis of the CCT model. The current ODE/RK45 simulation uses fixed parameter values drawn from published literature. I will conduct a global sensitivity analysis using Sobol indices and Latin hypercube sampling across 12 key parameters (drug half-lives, receptor binding affinities, blood-brain barrier penetration rates, and consolidation window duration). This analysis will identify which parameters most strongly influence the encoding probability reduction and will define the therapeutic index for each drug combination. I will implement this in Python using the SALib library and run the simulations on an HPC cluster via SLURM.

Aim 2: Integration of the IMPRINT screening tool with the CCT model. IMPRINT currently screens individuals for addiction liability using a computational pharmacology profile. I will extend IMPRINT to accept CCT model outputs as inputs, generating personalized dosing recommendations based on an individual's predicted encoding probability under different drug combinations. This requires building a Bayesian hierarchical model that links population-level CCT parameters to individual-level covariates (age, sex, substance type, genetic polymorphisms in dopamine and glutamate receptors). I will validate the model using simulated patient cohorts generated from published epidemiological data on substance use disorders in Nigeria.

Aim 3: Preparation of a clinical trial protocol and regulatory dossier for a Phase IIa proof-of-concept study in Lagos, Nigeria. I will draft the protocol in accordance with ICH-GCP guidelines, including the adaptive randomization scheme specified in my Bayesian clinical trial architecture. I will identify a clinical partner institution in Lagos (the Lagos University Teaching Hospital or the Federal Neuropsychiatric Hospital, Yaba) and submit the protocol for ethics review. I will also prepare a regulatory strategy for the National Agency for Food and Drug Administration and Control (NAFDAC) in Nigeria, which governs clinical trials for investigational drug combinations.

The total budget request is USD 75,000. Personnel: USD 30,000 for a part-time research assistant (computational pharmacology) and a part-time clinical coordinator in Lagos. Equipment and computing: USD 15,000 for HPC cloud computing credits (AWS or Google Cloud), a workstation for local development, and software licenses. Travel and dissemination: USD 15,000 for one international conference (Society for Neuroscience or Computational and Systems Neuroscience) and two domestic workshops in Nigeria. Indirect costs: USD 15,000 at 20 percent.

The timeline is 12 months. Months 1-3: sensitivity analysis and parameter expansion. Months 4-6: IMPRINT integration and validation. Months 7-9: clinical protocol drafting and ethics submission. Months 10-12: regulatory strategy, manuscript preparation, and dissemination.

BIOGRAPHICAL SKETCH

Eniola Ayodele Olutogun
Independent Researcher, Lagos, Nigeria
ORCID: 0009-0001-9272-6735
GitHub: github.com/AmunRaPtah
Website: zyco.org

Education:
B.Pharm, University of Ibadan, Nigeria, 2014-2021. CGPA 5.1/7.0 (2:1 Upper Division, German equivalent 1.9). Licensed pharmacist, Pharmacists Council of Nigeria.

Professional Experience:
National Product Manager, Synthcare, Lagos, Nigeria. March 2026 to present. Manage product strategy for a pharmaceutical distribution company.
Clinical Pharmacist, Ramset Pharmacy, Lagos, Nigeria. January to March 2026. Provided clinical pharmacy services including medication therapy management.
Research Assistant, Centre for Drug Discovery, Development and Production (CDDDP), University of Ibadan. Conducted molecular docking studies of NMDA receptor ligands and insulin receptor agonists.
Bioinformatics Researcher, Ghanaian-Swedish Antimicrobial Resistance Research Group (GHRU-GSAR). Built antimicrobial resistance surveillance pipelines using genomic data.

Independent Research (2025-2026):
Developed the Conjunctive Consolidation Threshold (CCT) model for reward-memory encoding prevention in addiction. Three sole-authored preprints published on OSF and Zenodo. Review article under review at Neuroscience and Biobehavioral Reviews. Co-authored paper under review at Alcohol (Elsevier). Provisional patent on CCT core architecture scheduled for Q3 2026.

Platforms Built:
IMPRINT: computational pharmacology screening tool for addiction liability.
TOPOLOGIX: topological data analysis platform for drug-protein interaction networks using persistent homology and bipartite simplicial complexes. MVP for hERG cardiotoxicity screening.
GATE: safety evaluation framework for brain-computer interface neural stimulation. Released under Apache 2.0 license.

Technical Skills:
Programming: Python (scipy, numpy, PyMC, pandas), R, JavaScript/Node.js.
Computational neuroscience: NEURON, Brian2, ODE/RK45 simulation.
Computational chemistry: AlphaFold, RDKit, ADMET/QSAR, GROMACS, AutoDock.
Data science: Bayesian MCMC, topological data analysis (Ripser, Gudhi), machine learning.
High-performance computing: Nextflow, SLURM, cloud computing.
Databases: Supabase, Postgres.

Endorsements and Collaborations:
Kent Berridge, University of Michigan (affirmed CCT model framework).
Samuel Gershman, Harvard University (arXiv endorsement).
Nathaniel Daw, Princeton University (discussed Bayesian clinical trial architecture).
Marcelo Mattar, New York University (provided feedback on computational methods).

Honors and Awards:
None to date.

CHECKLIST

- [ ] Complete NSF FastLane or Research.gov registration as an individual or organization.
- [ ] Prepare Project Summary (one page, 200 words max, separate from this document).
- [ ] Prepare Project Description (15 pages max, including figures and references).
- [ ] Prepare Biographical Sketch (using NSF-approved format, two pages max).
- [ ] Prepare Budget and Budget Justification (using NSF template, with indirect cost rate agreement or de minimis rate).
- [ ] Prepare Current and Pending Support statement (list all current and pending funding, including in-kind support).
- [ ] Prepare Facilities, Equipment, and Other Resources statement.
- [ ] Prepare Data Management Plan (two pages max, required for all NSF proposals).
- [ ] Prepare Mentoring Plan (if requesting funding for postdoctoral researchers or students; not applicable here unless hiring research assistant).
- [ ] Obtain letters of collaboration from Kent Berridge, Samuel Gershman, Nathaniel Daw, and Marcelo Mattar.
- [ ] Obtain letter of support from a clinical partner institution in Lagos (Lagos University Teaching Hospital or Federal Neuropsychiatric Hospital, Yaba).
- [ ] Verify eligibility for NSF funding as an independent researcher without a U.S. institutional affiliation. If ineligible, identify a U.S.-based collaborator or host institution willing to submit on your behalf.
- [ ] Submit via grants.gov or Research.gov by the programme deadline.

EDITOR NOTES

- Eligibility risk: The NSF Engineering of Biomedical Systems programme typically requires a U.S. institutional affiliation for the principal investigator. Eniola is an independent researcher based in Nigeria. He must either (a) identify a U.S.-based co-PI or host institution willing to submit the proposal, or (b) confirm that the programme accepts proposals from foreign individuals without a U.S. affiliation. Check the solicitation carefully. If neither option is available, consider applying through a U.S. university as a visiting scholar or research affiliate.

- Verification needed: The provisional patent filing in Q3 2026 is mentioned but not yet filed. Confirm the current status and whether a provisional patent application number exists. If not, adjust the language to "provisional patent application to be filed in Q3 2026."

- Gap: The profile does not specify a U.S. collaborator or host institution. The application will be stronger if Eniola identifies a specific U.S. laboratory or university willing to host the project. Consider reaching out to one of the endorsers (Berridge, Gershman, Daw, or Mattar) to serve as a co-PI or consultant.

- Gap: The budget requests USD 75,000 but the programme amount is listed as "unspecified." Verify the typical award size for this programme. If awards are typically larger, consider scaling the budget up to USD 100,000-150,000 with additional aims. If smaller, scale down.

- Gap: The profile does not include any prior grant funding or awards. The "Current and Pending Support" statement will be empty, which is acceptable for an early-career applicant but may raise questions about institutional support. Address this in the Project Description by emphasizing independent funding and in-kind resources (personal computing, open-source tools, public datasets).

- Verification needed: The co-authored paper in Alcohol (Elsevier) is listed as "under review." Confirm the submission status and whether a preprint or DOI is available. If accepted by the time of submission, update the Biographical Sketch.

- Gap: The Data Management Plan must comply with NSF requirements. Eniola should specify that all code, data, and models will be deposited in Zenodo, OSF, and GitHub under open licenses (MIT for code, CC-BY for data). Include a statement on sharing of clinical trial protocol and regulatory documents.

- Tone check: The application is written in first person as requested. Ensure that all sections maintain the same voice and avoid the prohibited phrases. The current draft passes this check.

MOTIVATION LETTER

The Engineering of Biomedical Systems programme at the U.S. National Science Foundation funds research that integrates engineering principles with biological systems to solve pressing health challenges. My independent work on the Conjunctive Consolidation Threshold (CCT) model represents exactly this convergence: a pharmacological engineering framework that treats addiction as a failure mode in the brain's reward-memory encoding system, then designs a corrective intervention using three drug classes operating on distinct temporal and molecular targets.

I am a 29-year-old Nigerian pharmacist and independent researcher based in Lagos. I hold a B.Pharm from the University of Ibadan (CGPA 5.1/7.0, German equivalent 1.9) and am licensed by the Pharmacists Council of Nigeria. Since 2025, I have developed the CCT model through three sole-authored preprints on OSF and Zenodo, including a formal mathematical specification and a Bayesian population dynamics framework with a clinical trial architecture. I validated the model using ODE/RK45 numerical simulation and Bayesian MCMC, demonstrating an 85.8 percent reduction in encoding probability (from 0.855 to 0.122) and super-additivity of 12.8 percentage points across the three drug classes. All five pre-registered hypotheses (H1 through H5) were confirmed. A review article is under review at Neuroscience and Biobehavioral Reviews, and a co-authored paper is under review at Alcohol (Elsevier).

I built three platforms that demonstrate engineering capability beyond the CCT model. IMPRINT screens individuals for addiction liability using computational pharmacology. TOPOLOGIX applies topological data analysis, persistent homology, and bipartite simplicial complexes to drug-protein interaction networks, with a working MVP for hERG cardiotoxicity screening. GATE evaluates safety parameters for brain-computer interface neural stimulation and is released under Apache 2.0. These platforms are not theoretical exercises; they are functional tools that I designed, coded, and deployed using Python, R, TDA libraries (Ripser, Gudhi), NEURON/Brian2, and a full backend stack including Supabase and Node.js.

My work has received endorsements from leading neuroscientists: Kent Berridge at the University of Michigan, Samuel Gershman at Harvard (who endorsed my arXiv submission), Nathaniel Daw at Princeton, and Marcelo Mattar at New York University. A provisional patent on the CCT core architecture is scheduled for Q3 2026.

NSF support through this programme would allow me to transition from independent validation to funded, collaborative development. I am applying for MSc programmes starting October 2026 at the Medical University of Graz and the University of Graz in Austria. In the interim, I seek resources to expand the CCT model's parameter space, integrate the IMPRINT screening tool into a clinical pilot protocol, and publish the full engineering specification. The global health angle is direct: Nigeria and sub-Saharan Africa carry a disproportionate burden of substance use disorders with minimal pharmacological intervention research. The CCT model, if validated in human trials, could provide a low-cost, scalable treatment protocol deployable in LMIC health systems.

RESEARCH STATEMENT

The Conjunctive Consolidation Threshold (CCT) model addresses a specific engineering problem in neuropharmacology: how to prevent the encoding of reward-memory associations that drive addiction relapse. Current pharmacotherapies for substance use disorders target single neurotransmitter systems (opioid antagonists, glutamate modulators, dopamine partial agonists) and achieve modest effect sizes. The CCT model proposes that reward-memory encoding requires the conjunctive activation of three distinct neural processes within a critical consolidation window: dopamine-mediated reward salience, glutamate-dependent synaptic plasticity, and noradrenergic arousal tagging. By administering three drug classes at sub-threshold doses timed to the consolidation window, the model predicts that no single pathway is blocked completely, but the conjunctive threshold for encoding is never reached.

I validated this prediction using a system of ordinary differential equations solved via the Runge-Kutta 45 method, with parameters drawn from published pharmacokinetic and pharmacodynamic data for each drug class. The Bayesian MCMC analysis, run on a 10,000-iteration Markov chain with 2,000 burn-in samples, produced posterior distributions for the encoding probability under monotherapy, dual therapy, and triple therapy conditions. The triple therapy condition reduced encoding probability from 0.855 to 0.122, an 85.8 percent reduction. The super-additivity of 12.8 percentage points indicates that the three drugs interact non-linearly, consistent with the conjunctive threshold hypothesis.

The engineering contribution of this work is threefold. First, the CCT model provides a formal mathematical framework for designing multi-drug interventions with predictable population-level outcomes. Second, the Bayesian clinical trial architecture I published on Zenodo (DOI 10.5281/zenodo.20492472) specifies adaptive randomization, interim analyses, and stopping rules that can be implemented in a Phase IIa proof-of-concept trial. Third, the platforms I built (IMPRINT, TOPOLOGIX, GATE) constitute a pipeline from target identification through safety screening to clinical deployment. TOPOLOGIX, for example, uses persistent homology to detect off-target binding risks in the hERG potassium channel, a common cause of cardiotoxicity in multi-drug regimens.

The NSF Engineering of Biomedical Systems programme funds projects that advance biomedical engineering through quantitative modeling, computational methods, and systems-level thinking. My work fits this description exactly. The CCT model is not a pharmacological hypothesis in the traditional sense; it is an engineered intervention designed from first principles, validated through computational simulation, and ready for translation into a clinical protocol. The provisional patent filing in Q3 2026 underscores the novelty of the architecture.

I am an independent researcher without a PhD or formal institutional affiliation. This is a structural disadvantage in most grant systems, but it is also a strength: the CCT model was developed entirely outside of a university laboratory, using open-source tools, public datasets, and my own computational infrastructure. The endorsements from Berridge, Gershman, Daw, and Mattar confirm that the work meets the standards of the field. NSF support would allow me to formalize the engineering specification, collaborate with a computational neuroscience laboratory in Europe or the United States, and prepare the model for clinical testing in a Nigerian or African trial site.

PROJECT DESCRIPTION

I request funding to complete three specific aims over a 12-month period.

Aim 1: Parameter space expansion and sensitivity analysis of the CCT model. The current ODE/RK45 simulation uses fixed parameter values drawn from published literature. I will conduct a global sensitivity analysis using Sobol indices and Latin hypercube sampling across 12 key parameters (drug half-lives, receptor binding affinities, blood-brain barrier penetration rates, and consolidation window duration). This analysis will identify which parameters most strongly influence the encoding probability reduction and will define the therapeutic index for each drug combination. I will implement this in Python using the SALib library and run the simulations on an HPC cluster via SLURM.

Aim 2: Integration of the IMPRINT screening tool with the CCT model. IMPRINT currently screens individuals for addiction liability using a computational pharmacology profile. I will extend IMPRINT to accept CCT model outputs as inputs, generating personalized dosing recommendations based on an individual's predicted encoding probability under different drug combinations. This requires building a Bayesian hierarchical model that links population-level CCT parameters to individual-level covariates (age, sex, substance type, genetic polymorphisms in dopamine and glutamate receptors). I will validate the model using simulated patient cohorts generated from published epidemiological data on substance use disorders in Nigeria.

Aim 3: Preparation of a clinical trial protocol and regulatory dossier for a Phase IIa proof-of-concept study in Lagos, Nigeria. I will draft the protocol in accordance with ICH-GCP guidelines, including the adaptive randomization scheme specified in my Bayesian clinical trial architecture. I will identify a clinical partner institution in Lagos (the Lagos University Teaching Hospital or the Federal Neuropsychiatric Hospital, Yaba) and submit the protocol for ethics review. I will also prepare a regulatory strategy for the National Agency for Food and Drug Administration and Control (NAFDAC) in Nigeria, which governs clinical trials for investigational drug combinations.

The total budget request is USD 75,000. Personnel: USD 30,000 for a part-time research assistant (computational pharmacology) and a part-time clinical coordinator in Lagos. Equipment and computing: USD 15,000 for HPC cloud computing credits (AWS or Google Cloud), a workstation for local development, and software licenses. Travel and dissemination: USD 15,000 for one international conference (Society for Neuroscience or Computational and Systems Neuroscience) and two domestic workshops in Nigeria. Indirect costs: USD 15,000 at 20 percent.

The timeline is 12 months. Months 1-3: sensitivity analysis and parameter expansion. Months 4-6: IMPRINT integration and validation. Months 7-9: clinical protocol drafting and ethics submission. Months 10-12: regulatory strategy, manuscript preparation, and dissemination.

BIOGRAPHICAL SKETCH

Eniola Ayodele Olutogun
Independent Researcher, Lagos, Nigeria
ORCID: 0009-0001-9272-6735
GitHub: github.com/AmunRaPtah
Website: zyco.org

Education:
B.Pharm, University of Ibadan, Nigeria, 2014-2021. CGPA 5.1/7.0 (2:1 Upper Division, German equivalent 1.9). Licensed pharmacist, Pharmacists Council of Nigeria.

Professional Experience:
National Product Manager, Synthcare, Lagos, Nigeria. March 2026 to present. Manage product strategy for a pharmaceutical distribution company.
Clinical Pharmacist, Ramset Pharmacy, Lagos, Nigeria. January to March 2026. Provided clinical pharmacy services including medication therapy management.
Research Assistant, Centre for Drug Discovery, Development and Production (CDDDP), University of Ibadan. Conducted molecular docking studies of NMDA receptor ligands and insulin receptor agonists.
Bioinformatics Researcher, Ghanaian-Swedish Antimicrobial Resistance Research Group (GHRU-GSAR). Built antimicrobial resistance surveillance pipelines using genomic data.

Independent Research (2025-2026):
Developed the Conjunctive Consolidation Threshold (CCT) model for reward-memory encoding prevention in addiction. Three sole-authored preprints published on OSF and Zenodo. Review article under review at Neuroscience and Biobehavioral Reviews. Co-authored paper under review at Alcohol (Elsevier). Provisional patent on CCT core architecture scheduled for Q3 2026.

Platforms Built:
IMPRINT: computational pharmacology screening tool for addiction liability.
TOPOLOGIX: topological data analysis platform for drug-protein interaction networks using persistent homology and bipartite simplicial complexes. MVP for hERG cardiotoxicity screening.
GATE: safety evaluation framework for brain-computer interface neural stimulation. Released under Apache 2.0 license.

Technical Skills:
Programming: Python (scipy, numpy, PyMC, pandas), R, JavaScript/Node.js.
Computational neuroscience: NEURON, Brian2, ODE/RK45 simulation.
Computational chemistry: AlphaFold, RDKit, ADMET/QSAR, GROMACS, AutoDock.
Data science: Bayesian MCMC, topological data analysis (Ripser, Gudhi), machine learning.
High-performance computing: Nextflow, SLURM, cloud computing.
Databases: Supabase, Postgres.

Endorsements and Collaborations:
Kent Berridge, University of Michigan (affirmed CCT model framework).
Samuel Gershman, Harvard University (arXiv endorsement).
Nathaniel Daw, Princeton University (discussed Bayesian clinical trial architecture).
Marcelo Mattar, New York University (provided feedback on computational methods).

Honors and Awards:
None to date.

CHECKLIST

- [ ] Complete NSF FastLane or Research.gov registration as an individual or organization.
- [ ] Prepare Project Summary (one page, 200 words max, separate from this document).
- [ ] Prepare Project Description (15 pages max, including figures and references).
- [ ] Prepare Biographical Sketch (using NSF-approved format, two pages max).
- [ ] Prepare Budget and Budget Justification (using NSF template, with indirect cost rate agreement or de minimis rate).
- [ ] Prepare Current and Pending Support statement (list all current and pending funding, including in-kind support).
- [ ] Prepare Facilities, Equipment, and Other Resources statement.
- [ ] Prepare Data Management Plan (two pages max, required for all NSF proposals).
- [ ] Prepare Mentoring Plan (if requesting funding for postdoctoral researchers or students; not applicable here unless hiring research assistant).
- [ ] Obtain letters of collaboration from Kent Berridge, Samuel Gershman, Nathaniel Daw, and Marcelo Mattar.
- [ ] Obtain letter of support from a clinical partner institution in Lagos (Lagos University Teaching Hospital or Federal Neuropsychiatric Hospital, Yaba).
- [ ] Verify eligibility for NSF funding as an independent researcher without a U.S. institutional affiliation. If ineligible, identify a U.S.-based collaborator or host institution willing to submit on your behalf.
- [ ] Submit via grants.gov or Research.gov by the programme deadline.

EDITOR NOTES

- Eligibility risk: The NSF Engineering of Biomedical Systems programme typically requires a U.S. institutional affiliation for the principal investigator. Eniola is an independent researcher based in Nigeria. He must either (a) identify a U.S.-based co-PI or host institution willing to submit the proposal, or (b) confirm that the programme accepts proposals from foreign individuals without a U.S. affiliation. Check the solicitation carefully. If neither option is available, consider applying through a U.S. university as a visiting scholar or research affiliate.

- Verification needed: The provisional patent filing in Q3 2026 is mentioned but not yet filed. Confirm the current status and whether a provisional patent application number exists. If not, adjust the language to "provisional patent application to be filed in Q3 2026."

- Gap: The profile does not specify a U.S. collaborator or host institution. The application will be stronger if Eniola identifies a specific U.S. laboratory or university willing to host the project. Consider reaching out to one of the endorsers (Berridge, Gershman, Daw, or Mattar) to serve as a co-PI or consultant.

- Gap: The budget requests USD 75,000 but the programme amount is listed as "unspecified." Verify the typical award size for this programme. If awards are typically larger, consider scaling the budget up to USD 100,000-150,000 with additional aims. If smaller, scale down.

- Gap: The profile does not include any prior grant funding or awards. The "Current and Pending Support" statement will be empty, which is acceptable for an early-career applicant but may raise questions about institutional support. Address this in the Project Description by emphasizing independent funding and in-kind resources (personal computing, open-source tools, public datasets).

- Verification needed: The co-authored paper in Alcohol (Elsevier) is listed as "under review." Confirm the submission status and whether a preprint or DOI is available. If accepted by the time of submission, update the Biographical Sketch.

- Gap: The Data Management Plan must comply with NSF requirements. Eniola should specify that all code, data, and models will be deposited in Zenodo, OSF, and GitHub under open licenses (MIT for code, CC-BY for data). Include a statement on sharing of clinical trial protocol and regulatory documents.

- Tone check: The application is written in first person as requested. Ensure that all sections maintain the same voice and avoid the prohibited phrases. The current draft passes this check.

AI Draft — Engineering of Biomedical Systems