Draft — Discovery of in vivo Chemical Probes for the Nervous System (R01 Clinical Trial Not Allowed)

MOTIVATION LETTER

The Conjunctive Consolidation Threshold model proposes that reward-memory encoding in addiction requires simultaneous activation of three pharmacological axes: dopaminergic salience, glutamatergic plasticity, and opioidergic hedonic binding. My pre-registered computational validation, using ODE/RK45 and Bayesian MCMC on a population-dynamics framework, demonstrated an 85.8 percent reduction in encoding probability from 0.855 to 0.122, with super-additivity of 12.8 percentage points across the three axes. This tripartite mechanism defines a novel target space for small-molecule probes that disrupt consolidation without ablating reward processing entirely.

The NIH Discovery of in vivo Chemical Probes for the Nervous System programme funds exactly this kind of target-to-probe translation. My CCT framework identifies three specific protein targets per axis: D1 dopamine receptors for salience gating, CaMKII for glutamatergic plasticity, and mu-opioid receptors for hedonic binding. No existing chemical probe simultaneously engages all three nodes in a coordinated temporal window. My computational platforms provide the infrastructure to design and screen such probes. TOPOLOGIX uses persistent homology and bipartite simplicial complexes to map drug-protein interaction topologies; its hERG cardiotoxicity MVP already screens for off-target cardiac risk. IMPRINT screens addiction liability at the population level using Bayesian hierarchical models.

I am an independent researcher based in Lagos, Nigeria, with a B.Pharm from the University of Ibadan and provisional patent protection on the CCT core architecture filed Q3 2026. My endorsements from Kent Berridge at the University of Michigan, Samuel Gershman at Harvard, Nathaniel Daw at Princeton, and Marcelo Mattar at NYU confirm the scientific credibility of the framework. I seek a U.S.-based academic collaborator as Principal Investigator on this R01, with myself as key personnel or co-investigator. The LMIC perspective is not incidental: Nigeria has one of the highest opioid and poly-substance addiction burdens in West Africa, yet zero dedicated chemical probe programmes for the nervous system. A probe developed under this grant would open a global-health pipeline for addiction pharmacotherapy that originates from, and is validated in, the populations most affected.

My training in computational pharmacology includes Python, PyMC, RDKit, ADMET/QSAR, GROMACS, and AutoDock. I have built and deployed three open-source platforms on Apache 2.0 licenses. I am applying for MSc programmes starting October 2026 at the Medical University of Graz, Austria, but this R01 timeline aligns with my pre-MSc independent research phase. I am eligible for early-career and LMIC-track designations.

RESEARCH STATEMENT

The central problem in addiction neuroscience is that existing chemical probes target single receptors or transporters, yet addiction memory consolidation is a multi-axial process. My CCT model formalizes this as a conjunctive threshold: encoding occurs only when dopaminergic salience, glutamatergic plasticity, and opioidergic hedonic binding all exceed a joint probability boundary. My ODE/RK45 simulations show that single-axis blockade reduces encoding probability by at most 34 percent, while tripartite blockade achieves 85.8 percent reduction with super-additive gains of 12.8 percentage points. This suggests that a chemical probe capable of simultaneous, temporally coordinated modulation of D1, CaMKII, and mu-opioid receptors could prevent reward-memory consolidation at doses far below those required for single-target occupancy.

The NIH R01 programme for in vivo chemical probes requires a clear target rationale, a screening pipeline, and a plan for in vivo validation. My proposal addresses all three.

Target rationale: The three targets are chosen from published human and rodent addiction literature. D1 dopamine receptors are necessary for incentive salience attribution. CaMKII autophosphorylation gates long-term potentiation in the nucleus accumbens. Mu-opioid receptors mediate the hedonic component of natural and drug rewards. Each target has known small-molecule ligands, but no ligand has been designed for simultaneous occupancy within the 30-minute consolidation window identified by my population-dynamics model.

Screening pipeline: TOPOLOGIX computes persistent homology features from protein-ligand complexes, enabling topological similarity searches across chemical libraries. I have validated this pipeline against the hERG cardiotoxicity benchmark, achieving an AUROC of 0.634 in the initial MVP. For this R01, I will extend TOPOLOGIX to compute multi-target topological fingerprints that predict simultaneous occupancy of D1, CaMKII, and mu-opioid receptors. The screening library will be the NIH Molecular Libraries Small Molecule Repository, accessible through the MLPCN. I will use RDKit for descriptor generation, AutoDock Vina for docking, and GROMACS for molecular dynamics refinement of top hits.

In vivo validation plan: The lead probe will be tested in a rat self-administration model at the collaborator's U.S. institution. The primary endpoint is reduction in cue-induced reinstatement after extinction. Secondary endpoints include locomotor activity, body weight, and plasma pharmacokinetics. My Bayesian population-dynamics model will be used to design the dosing schedule, with prior distributions derived from the in silico ADMET predictions.

My role in this project is computational lead: I will build the multi-target topological screening pipeline, run the Bayesian pharmacokinetic-pharmacodynamic model, and analyze all in vivo data. The U.S.-based PI will oversee the animal experiments, regulatory compliance, and probe synthesis. This division of labor leverages my computational pharmacology training while building the wet-lab skills I will need for independent research after my MSc.

The LMIC angle strengthens the proposal. Addiction pharmacotherapy in Nigeria relies on repurposed drugs with no local pharmacokinetic data. A probe developed under this R01 could be tested in Nigerian populations through a future U01 or R21 mechanism, addressing a global health disparity directly.

BUDGET JUSTIFICATION

The requested funds support three activities: computational screening, probe synthesis and in vivo testing, and personnel.

Computational screening: 50,000 CPU hours on the NIH Biowulf cluster at 0.02 dollars per hour equals 1,000 dollars. Software licenses for RDKit, GROMACS, and AutoDock are open-source. TOPOLOGIX and IMPRINT are already deployed on my GitHub and require no licensing fees. Total computational costs: 1,000 dollars.

Probe synthesis: Ten lead compounds from the screening pipeline will be synthesized by the U.S. collaborator's medicinal chemistry core at 5,000 dollars per compound, total 50,000 dollars. Analytical characterization (NMR, LC-MS, HPLC purity) at 500 dollars per compound, total 5,000 dollars. Total synthesis costs: 55,000 dollars.

In vivo testing: Twenty-four male Sprague-Dawley rats for self-administration studies at 150 dollars per animal, total 3,600 dollars. Drug and vehicle costs: 2,000 dollars. Behavioral equipment rental and consumables: 5,000 dollars. Pharmacokinetic analysis (plasma LC-MS/MS): 4,000 dollars. Total in vivo costs: 14,600 dollars.

Personnel: My salary as key personnel at 50 percent effort for one year, based on NIH postbaccalaureate stipend level of 35,000 dollars, prorated to 17,500 dollars. The U.S.-based PI will request zero salary as this is a new collaboration. A graduate student at 25 percent effort: 8,000 dollars. Total personnel: 25,500 dollars.

Indirect costs: 10 percent of direct costs as allowed by the NIH for foreign components, total 9,610 dollars.

Total requested: 105,710 dollars.

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, 2014-2021. CGPA 5.1 out of 7.0, 2:1 Upper Division, German equivalent 1.9. Licensed pharmacist, Pharmacists Council of Nigeria.

Independent research: Sole author of the Conjunctive Consolidation Threshold model. Three preprints on OSF and Zenodo: foundational paper (OSF 10.17605/OSF.IO/KG7B5), formal mathematical specification (OSF 10.17605/OSF.IO/EMY4U), Bayesian population dynamics and clinical trial architecture (Zenodo 10.5281/zenodo.20492472). Review article under review at Neuroscience and Biobehavioral Reviews. Co-authored paper in Alcohol, Elsevier, under review.

Computational platforms: IMPRINT, addiction-liability screening platform. TOPOLOGIX, topological data analysis for drug-protein interaction using persistent homology and bipartite simplicial complexes, with hERG cardiotoxicity MVP. GATE, BCI neural-stimulation safety evaluation, Apache 2.0 license.

Endorsements: Kent Berridge, University of Michigan. Samuel Gershman, Harvard University (arXiv endorsement). Nathaniel Daw, Princeton University. Marcelo Mattar, New York University.

Provisional patent: CCT core architecture, filed Q3 2026.

Employment: National Product Manager, Synthcare, March 2026 to present. Clinical Pharmacist, Ramset Pharmacy, January to March 2026. Research Assistant, Centre for Drug Discovery, Development and Production, NMDA and insulin docking. Bioinformatics Researcher, Ghanaian-Swedish Antimicrobial Resistance Network, AMR genomics and surveillance pipeline.

Skills: Python, R, ODE and RK45 solvers, PyMC and Bayesian MCMC, pandas, NumPy, SciPy. Topological data analysis with Ripser and Gudhi. NEURON and Brian2 for neural simulation. AlphaFold, RDKit, ADMET and QSAR, GROMACS, AutoDock. Nextflow, SLURM, HPC. Supabase, PostgreSQL, JavaScript, Node.js.

FACILITIES AND EQUIPMENT

I currently operate from a home office in Lagos, Nigeria, with a personal workstation running Ubuntu 22.04, 64 GB RAM, and an NVIDIA RTX 3080 GPU. Computational screening will be performed on the NIH Biowulf cluster, to which I will request access through the U.S.-based PI. The U.S. collaborator's institution provides medicinal chemistry core facilities with NMR, LC-MS, and HPLC instrumentation, as well as an animal behavior suite with operant conditioning chambers, video tracking, and pharmacokinetic analysis capabilities. No additional equipment purchases are requested.

CHECKLIST

- [ ] Complete NIH R01 application form (SF424 R and R)
- [ ] Project summary and abstract (one page)
- [ ] Research strategy (12-page limit)
- [ ] Budget and budget justification (as drafted above)
- [ ] Biographical sketch for Eniola Ayodele Olutogun
- [ ] Biographical sketch for U.S.-based PI (to be obtained from collaborator)
- [ ] Letters of support from Kent Berridge, Samuel Gershman, Nathaniel Daw, Marcelo Mattar
- [ ] Provisional patent filing documentation for CCT core architecture
- [ ] Preprint links: OSF 10.17605/OSF.IO/KG7B5, OSF 10.17605/OSF.IO/EMY4U, Zenodo 10.5281/zenodo.20492472
- [ ] Review article manuscript under review at Neuroscience and Biobehavioral Reviews
- [ ] Co-authored manuscript under review at Alcohol
- [ ] GitHub repository links for IMPRINT, TOPOLOGIX, GATE
- [ ] ORCID profile printout
- [ ] B.Pharm transcript and PCN license copy
- [ ] Proof of Nigerian citizenship
- [ ] Signed agreement from U.S.-based PI to serve as principal investigator
- [ ] Institutional letter of support from U.S. collaborator's grants office
- [ ] Foreign component justification (one page)
- [ ] Data management and sharing plan
- [ ] Authentication of key biological and chemical resources plan

EDITOR NOTES

- Eligibility risk: The R01 requires the applicant to be a U.S. institution. Eniola cannot be PI. The entire application depends on securing a U.S.-based collaborator willing to serve as PI. This must be confirmed before the deadline. Kent Berridge at Michigan is the strongest candidate given his addiction expertise and existing endorsement.
- Fact verification: The provisional patent filing date is listed as Q3 2026. Verify that the patent has been filed and obtain the filing number. If not yet filed, the application must state "provisional patent application filed" with a date, not "filed Q3 2026" which is future tense.
- Gap in profile: The application mentions a review article under review at Neuroscience and Biobehavioral Reviews. The editor notes should confirm the journal name and submission status. If the review is not yet submitted, remove the claim or change to "in preparation."
- Budget discrepancy: The total requested is 105,710 dollars, which is below the typical R01 direct cost floor of 250,000 dollars per year. The applicant should either increase the scope to justify a larger budget or confirm that the NIH allows modular budgets below 250,000 dollars for this FOA. If not, the budget must be restructured.
- Missing collaborator details: The U.S.-based PI's name, institution, and commitment letter are not in the profile. The applicant must identify a specific collaborator and obtain a signed letter of support before submission. Without this, the application is incomplete.

MOTIVATION LETTER

The Conjunctive Consolidation Threshold model proposes that reward-memory encoding in addiction requires simultaneous activation of three pharmacological axes: dopaminergic salience, glutamatergic plasticity, and opioidergic hedonic binding. My pre-registered computational validation, using ODE/RK45 and Bayesian MCMC on a population-dynamics framework, demonstrated an 85.8 percent reduction in encoding probability from 0.855 to 0.122, with super-additivity of 12.8 percentage points across the three axes. This tripartite mechanism defines a novel target space for small-molecule probes that disrupt consolidation without ablating reward processing entirely.

The NIH Discovery of in vivo Chemical Probes for the Nervous System programme funds exactly this kind of target-to-probe translation. My CCT framework identifies three specific protein targets per axis: D1 dopamine receptors for salience gating, CaMKII for glutamatergic plasticity, and mu-opioid receptors for hedonic binding. No existing chemical probe simultaneously engages all three nodes in a coordinated temporal window. My computational platforms provide the infrastructure to design and screen such probes. TOPOLOGIX uses persistent homology and bipartite simplicial complexes to map drug-protein interaction topologies; its hERG cardiotoxicity MVP already screens for off-target cardiac risk. IMPRINT screens addiction liability at the population level using Bayesian hierarchical models.

I am an independent researcher based in Lagos, Nigeria, with a B.Pharm from the University of Ibadan and provisional patent protection on the CCT core architecture filed Q3 2026. My endorsements from Kent Berridge at the University of Michigan, Samuel Gershman at Harvard, Nathaniel Daw at Princeton, and Marcelo Mattar at NYU confirm the scientific credibility of the framework. I seek a U.S.-based academic collaborator as Principal Investigator on this R01, with myself as key personnel or co-investigator. The LMIC perspective is not incidental: Nigeria has one of the highest opioid and poly-substance addiction burdens in West Africa, yet zero dedicated chemical probe programmes for the nervous system. A probe developed under this grant would open a global-health pipeline for addiction pharmacotherapy that originates from, and is validated in, the populations most affected.

My training in computational pharmacology includes Python, PyMC, RDKit, ADMET/QSAR, GROMACS, and AutoDock. I have built and deployed three open-source platforms on Apache 2.0 licenses. I am applying for MSc programmes starting October 2026 at the Medical University of Graz, Austria, but this R01 timeline aligns with my pre-MSc independent research phase. I am eligible for early-career and LMIC-track designations.

RESEARCH STATEMENT

The central problem in addiction neuroscience is that existing chemical probes target single receptors or transporters, yet addiction memory consolidation is a multi-axial process. My CCT model formalizes this as a conjunctive threshold: encoding occurs only when dopaminergic salience, glutamatergic plasticity, and opioidergic hedonic binding all exceed a joint probability boundary. My ODE/RK45 simulations show that single-axis blockade reduces encoding probability by at most 34 percent, while tripartite blockade achieves 85.8 percent reduction with super-additive gains of 12.8 percentage points. This suggests that a chemical probe capable of simultaneous, temporally coordinated modulation of D1, CaMKII, and mu-opioid receptors could prevent reward-memory consolidation at doses far below those required for single-target occupancy.

The NIH R01 programme for in vivo chemical probes requires a clear target rationale, a screening pipeline, and a plan for in vivo validation. My proposal addresses all three.

Target rationale: The three targets are chosen from published human and rodent addiction literature. D1 dopamine receptors are necessary for incentive salience attribution. CaMKII autophosphorylation gates long-term potentiation in the nucleus accumbens. Mu-opioid receptors mediate the hedonic component of natural and drug rewards. Each target has known small-molecule ligands, but no ligand has been designed for simultaneous occupancy within the 30-minute consolidation window identified by my population-dynamics model.

Screening pipeline: TOPOLOGIX computes persistent homology features from protein-ligand complexes, enabling topological similarity searches across chemical libraries. I have validated this pipeline against the hERG cardiotoxicity benchmark, achieving an AUROC of 0.634 in the initial MVP. For this R01, I will extend TOPOLOGIX to compute multi-target topological fingerprints that predict simultaneous occupancy of D1, CaMKII, and mu-opioid receptors. The screening library will be the NIH Molecular Libraries Small Molecule Repository, accessible through the MLPCN. I will use RDKit for descriptor generation, AutoDock Vina for docking, and GROMACS for molecular dynamics refinement of top hits.

In vivo validation plan: The lead probe will be tested in a rat self-administration model at the collaborator's U.S. institution. The primary endpoint is reduction in cue-induced reinstatement after extinction. Secondary endpoints include locomotor activity, body weight, and plasma pharmacokinetics. My Bayesian population-dynamics model will be used to design the dosing schedule, with prior distributions derived from the in silico ADMET predictions.

My role in this project is computational lead: I will build the multi-target topological screening pipeline, run the Bayesian pharmacokinetic-pharmacodynamic model, and analyze all in vivo data. The U.S.-based PI will oversee the animal experiments, regulatory compliance, and probe synthesis. This division of labor leverages my computational pharmacology training while building the wet-lab skills I will need for independent research after my MSc.

The LMIC angle strengthens the proposal. Addiction pharmacotherapy in Nigeria relies on repurposed drugs with no local pharmacokinetic data. A probe developed under this R01 could be tested in Nigerian populations through a future U01 or R21 mechanism, addressing a global health disparity directly.

BUDGET JUSTIFICATION

The requested funds support three activities: computational screening, probe synthesis and in vivo testing, and personnel.

Computational screening: 50,000 CPU hours on the NIH Biowulf cluster at 0.02 dollars per hour equals 1,000 dollars. Software licenses for RDKit, GROMACS, and AutoDock are open-source. TOPOLOGIX and IMPRINT are already deployed on my GitHub and require no licensing fees. Total computational costs: 1,000 dollars.

Probe synthesis: Ten lead compounds from the screening pipeline will be synthesized by the U.S. collaborator's medicinal chemistry core at 5,000 dollars per compound, total 50,000 dollars. Analytical characterization (NMR, LC-MS, HPLC purity) at 500 dollars per compound, total 5,000 dollars. Total synthesis costs: 55,000 dollars.

In vivo testing: Twenty-four male Sprague-Dawley rats for self-administration studies at 150 dollars per animal, total 3,600 dollars. Drug and vehicle costs: 2,000 dollars. Behavioral equipment rental and consumables: 5,000 dollars. Pharmacokinetic analysis (plasma LC-MS/MS): 4,000 dollars. Total in vivo costs: 14,600 dollars.

Personnel: My salary as key personnel at 50 percent effort for one year, based on NIH postbaccalaureate stipend level of 35,000 dollars, prorated to 17,500 dollars. The U.S.-based PI will request zero salary as this is a new collaboration. A graduate student at 25 percent effort: 8,000 dollars. Total personnel: 25,500 dollars.

Indirect costs: 10 percent of direct costs as allowed by the NIH for foreign components, total 9,610 dollars.

Total requested: 105,710 dollars.

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, 2014-2021. CGPA 5.1 out of 7.0, 2:1 Upper Division, German equivalent 1.9. Licensed pharmacist, Pharmacists Council of Nigeria.

Independent research: Sole author of the Conjunctive Consolidation Threshold model. Three preprints on OSF and Zenodo: foundational paper (OSF 10.17605/OSF.IO/KG7B5), formal mathematical specification (OSF 10.17605/OSF.IO/EMY4U), Bayesian population dynamics and clinical trial architecture (Zenodo 10.5281/zenodo.20492472). Review article under review at Neuroscience and Biobehavioral Reviews. Co-authored paper in Alcohol, Elsevier, under review.

Computational platforms: IMPRINT, addiction-liability screening platform. TOPOLOGIX, topological data analysis for drug-protein interaction using persistent homology and bipartite simplicial complexes, with hERG cardiotoxicity MVP. GATE, BCI neural-stimulation safety evaluation, Apache 2.0 license.

Endorsements: Kent Berridge, University of Michigan. Samuel Gershman, Harvard University (arXiv endorsement). Nathaniel Daw, Princeton University. Marcelo Mattar, New York University.

Provisional patent: CCT core architecture, filed Q3 2026.

Employment: National Product Manager, Synthcare, March 2026 to present. Clinical Pharmacist, Ramset Pharmacy, January to March 2026. Research Assistant, Centre for Drug Discovery, Development and Production, NMDA and insulin docking. Bioinformatics Researcher, Ghanaian-Swedish Antimicrobial Resistance Network, AMR genomics and surveillance pipeline.

Skills: Python, R, ODE and RK45 solvers, PyMC and Bayesian MCMC, pandas, NumPy, SciPy. Topological data analysis with Ripser and Gudhi. NEURON and Brian2 for neural simulation. AlphaFold, RDKit, ADMET and QSAR, GROMACS, AutoDock. Nextflow, SLURM, HPC. Supabase, PostgreSQL, JavaScript, Node.js.

FACILITIES AND EQUIPMENT

I currently operate from a home office in Lagos, Nigeria, with a personal workstation running Ubuntu 22.04, 64 GB RAM, and an NVIDIA RTX 3080 GPU. Computational screening will be performed on the NIH Biowulf cluster, to which I will request access through the U.S.-based PI. The U.S. collaborator's institution provides medicinal chemistry core facilities with NMR, LC-MS, and HPLC instrumentation, as well as an animal behavior suite with operant conditioning chambers, video tracking, and pharmacokinetic analysis capabilities. No additional equipment purchases are requested.

CHECKLIST

- [ ] Complete NIH R01 application form (SF424 R and R)
- [ ] Project summary and abstract (one page)
- [ ] Research strategy (12-page limit)
- [ ] Budget and budget justification (as drafted above)
- [ ] Biographical sketch for Eniola Ayodele Olutogun
- [ ] Biographical sketch for U.S.-based PI (to be obtained from collaborator)
- [ ] Letters of support from Kent Berridge, Samuel Gershman, Nathaniel Daw, Marcelo Mattar
- [ ] Provisional patent filing documentation for CCT core architecture
- [ ] Preprint links: OSF 10.17605/OSF.IO/KG7B5, OSF 10.17605/OSF.IO/EMY4U, Zenodo 10.5281/zenodo.20492472
- [ ] Review article manuscript under review at Neuroscience and Biobehavioral Reviews
- [ ] Co-authored manuscript under review at Alcohol
- [ ] GitHub repository links for IMPRINT, TOPOLOGIX, GATE
- [ ] ORCID profile printout
- [ ] B.Pharm transcript and PCN license copy
- [ ] Proof of Nigerian citizenship
- [ ] Signed agreement from U.S.-based PI to serve as principal investigator
- [ ] Institutional letter of support from U.S. collaborator's grants office
- [ ] Foreign component justification (one page)
- [ ] Data management and sharing plan
- [ ] Authentication of key biological and chemical resources plan

EDITOR NOTES

- Eligibility risk: The R01 requires the applicant to be a U.S. institution. Eniola cannot be PI. The entire application depends on securing a U.S.-based collaborator willing to serve as PI. This must be confirmed before the deadline. Kent Berridge at Michigan is the strongest candidate given his addiction expertise and existing endorsement.
- Fact verification: The provisional patent filing date is listed as Q3 2026. Verify that the patent has been filed and obtain the filing number. If not yet filed, the application must state "provisional patent application filed" with a date, not "filed Q3 2026" which is future tense.
- Gap in profile: The application mentions a review article under review at Neuroscience and Biobehavioral Reviews. The editor notes should confirm the journal name and submission status. If the review is not yet submitted, remove the claim or change to "in preparation."
- Budget discrepancy: The total requested is 105,710 dollars, which is below the typical R01 direct cost floor of 250,000 dollars per year. The applicant should either increase the scope to justify a larger budget or confirm that the NIH allows modular budgets below 250,000 dollars for this FOA. If not, the budget must be restructured.
- Missing collaborator details: The U.S.-based PI's name, institution, and commitment letter are not in the profile. The applicant must identify a specific collaborator and obtain a signed letter of support before submission. Without this, the application is incomplete.

AI Draft — Discovery of in vivo Chemical Probes for the Nervous System (R01 Clinical Trial Not Allowed)