NIH FOA Questionnaire

Drug development and precision medicine still rely predominantly on deterministic ordinary differential equation (ODE) models. These models are computationally convenient but structurally unable to capture the biological reality of the systems they describe: they fail to represent biological variability, rare events, stochastic transitions, and patient-level heterogeneity. This mismatch is a material contributor to the field's high clinical-trial failure rates and to development costs that now exceed $2B per approved therapy.

GoApercu addresses this gap with an AI-orchestrated stochastic intelligence platform that models disease progression and therapeutic response probabilistically rather than as single deterministic trajectories. The core inventive step is the Sparse Laplace Approximation Method (SLAM) — a new technique for evaluating the high-dimensional functional ("path") integrals that arise when a biological system is treated as a latent stochastic (Markov) process. SLAM exploits the sparse (block-tridiagonal) structure of the second- and higher-order derivative tensors of these integrals, reducing the cost of the higher-order correction terms from O(N⁴d⁴) to O(Nd⁴)(N = time points, d = variables), and adds a variance-stabilizing transform that keeps the critical path physically realistic.

Preliminary evidence. The method has been validated on a stochastic SIR infectious-disease model against STAN (the reference Hamiltonian-Monte-Carlo sampler from Andrew Gelman's group), using a real 14-day British boarding-school influenza data set in which only the infected count is observed and the susceptible population is latent. SLAM produced near-identical parameter estimates and marginal likelihoods to STAN while running roughly 6× faster — an advantage expected to widen at scale. The scientific rationale is documented in the manuscript "Beyond Deterministic Models in Drug Discovery and Development"^[1] and the methods manuscript (arXiv:1504.06352)^[2].

Impact. By making rigorous stochastic modeling fast enough for routine use, the platform enables better prediction of drug resistance, tumor evolution, disease progression, and clinical variability — improving trial design and decision-making across pharmacometrics and model-informed drug development.

The platform treats a biological system's true state as a latent Markov process and writes the overall likelihood of observed data as a functional integral over all possible histories of that process. Rather than sampling those histories with Monte Carlo, SLAM:

1. discretizes time and identifies the most likely history (the "critical path") for a given set of dynamical parameters θ;
2. expands the log-likelihood around that critical path and fits a multivariate Gaussian (the Laplace approximation);
3. adds higher-order cumulant correction terms (Shun & McCullagh form), computed efficiently because the derivative tensors are sparse; and
4. applies a variance-stabilizing change of variables so that the approximation does not bias trajectories toward zero.^[2]

Architecturally, the product is a layered pipeline: Data Ingestion → Context Intelligence (knowledge graph + semantic retrieval + multi-agent orchestration) → Stochastic Modeling Engine (state-space models, Bayesian parameter estimation, SLAM, uncertainty quantification) → Prediction (clinical outcome forecasting, drug-response simulation, trial-design optimization) → Clinical Decision Support & Research Dashboard.

• Speed at rigor. Near-STAN accuracy at a fraction of the compute (~6× faster on the validated benchmark), via analytic Laplace approximation instead of long MCMC simulations.^[2]
• Genuinely stochastic. Unlike deterministic ODE fitting, the method allows imperfectly-followed dynamics and handles multiple parameter optima and rare events.
• Scalable complexity. Sparsity reduces higher-order term cost from O(N⁴d⁴) to O(Nd⁴), keeping large biomedical systems tractable.
• Context-aware. An AI orchestration and knowledge-graph layer grounds models in curated biomedical evidence rather than raw data alone.

Existing / proprietary IP

• Proprietary AI orchestration framework
• Proprietary contextual-intelligence architecture
• Proprietary workflow / model-orchestration engine

Planned patent filings

• Patent 1 — AI-Orchestrated Stochastic Biomedical Modeling System
• Patent 2 — Context-Aware Clinical Simulation Engine
• Patent 3 — Sparse Laplace Approximation Workflow for Biomedical Prediction

Trade secrets

• Model training and orchestration workflows
• Data harmonization methods

The underlying SLAM methodology is published in the public domain (arXiv:1504.06352)^[2]; GoApercu's defensible position rests on its applied orchestration, workflow engine, and biomedical-specific implementations.

Stage: feasibility / early prototype (≈ TRL 3–4). Achieved to date:

• Published scientific foundation for stochastic drug-development modeling and for the SLAM method itself.^[1,2]
• Quantitative validation of SLAM against STAN on a real SIR/influenza data set (near-identical estimates, ~6× faster).^[2]
• Existing prototype platform architecture and AI orchestration layer.
• Y Combinator application submitted.

Yes. We anticipate allocating approximately 20–30% of the Phase I budget to external partners for dataset validation and independent benchmarking. Per NIH SBIR requirements, all grant-funded R&D and dataset collection will be performed in the United States with U.S.-based partners (a U.S. academic medical center and/or CRO). Complementary non-grant activities in the EU/India, if any, are managed entirely outside the grant's scope and budget.

GoApercu, Inc. is founder-controlled. The company will confirm and, where needed, structure ownership so that majority ownership (>51%) is held by U.S. citizens or permanent residents to satisfy SBIR eligibility, and will finalize a U.S.-eligible Principal Investigator structure prior to submission.

Target market: pharma, biotech, CROs, and academic medical centers (primary); digital therapeutics, precision-medicine startups, and clinical-AI companies (secondary). TAM $30B+.

Business model: SaaS licensing, enterprise subscriptions, pharma partnerships, and clinical-decision-support licensing.

Competitors: deterministic pharmacometric/PK-PD tools and general Bayesian samplers (e.g., STAN/NONMEM-style workflows). Our advantage is comparable statistical rigor at a fraction of the compute, plus a context-aware AI layer.

Regulatory pathway: the platform is a research and decision-support tool; where outputs inform clinical decisions we will align with FDA guidance on model-informed drug development and, if applicable, Software-as-a-Medical-Device frameworks.

Prospective academic and clinical collaborators have been identified (see Section 5 partnership list). Letters of Support / Intent are being secured from prospective pilot customers and partners; a non-binding LOI template is in circulation to demonstrate market interest. Scientific advisors and KOLs are being finalized.

GoApercu operates primarily as a software company from office / home-office facilities with cloud-based compute; grant-funded validation work will be conducted at, or in partnership with, U.S. academic/clinical facilities. The company's physical U.S. address will be provided with the application.

This is GoApercu's first federal SBIR submission for this project; no prior NIH summary statement applies. The company has applied to Y Combinator and is pursuing early private and non-dilutive funding; specific amounts and competition results will be disclosed in the application.

No. GoApercu has no parent company or subsidiary in the People's Republic of China, and no owner holds a foreign affiliation with a PRC research institution. The company has no ownership, funding, or research links with China.

Direct: deterministic pharmacometric/PK-PD platforms and Bayesian samplers (STAN/NONMEM-style). Indirect: in-house biostatistics teams and general ML modeling tools. Our unique value is MCMC-grade stochastic rigor at a fraction of the compute (SLAM), unified with an AI orchestration and biomedical knowledge layer that turns uncertainty into actionable clinical and R&D intelligence.

The SLAM methodology has been under development and refinement for several years (foundational methods manuscript, arXiv:1504.06352^[2], and subsequent validation). GoApercu is now productizing and applying that methodology to biomedical modeling and precision medicine.

TAM $30B+. Customers: pharma, biotech, CROs, and academic medical centers, with secondary reach into digital therapeutics and precision-medicine startups. Go-to-market via direct enterprise sales and SaaS licensing, pharma R&D partnerships, and clinical-decision-support licensing.

GoApercu, Inc. — website to be provided with the application.