We unite mechanistic pharmacology (QSP, PBPK, PBBM) with modern AI to cut timelines and costs while strengthening regulatory confidence. Built by University of Edinburgh & partners.
Clinical attrition remains >85% and late‑stage failures cost $100M+ each. Regulators and payers expect smarter, evidence‑driven programs; R&D costs keep rising. AI‑MIDD is a structural answer—not an incremental tool.
Phase II is the highest‑risk bottleneck. Each failure destroys value, time, and patient opportunity. We focus where impact is maximal.
Built to fit FDA/EMA MIDD pathways with transparent mechanistic cores and auditable, submission‑ready reports.
We fuse mechanistic models with AI to use more data—omics, imaging, RWE—without sacrificing interpretability.
A unified stack: Mechanistic cores (QSP/PBPK/PBBM) • AI differential equations & Bayesian updating • QSP‑GPT natural‑language interface • Auto‑documentation for regulators.
Every prediction traces to biology—receptor kinetics, pathways, immune‑tumor dynamics—building trust and acceptance.
Faster parameterization, uncertainty handling, and virtual cohorts; continuous learning as data accrues.
Ask: “What if dose ↑50%?” or “Who benefits most?”—no code needed. Insights for clinicians, PMs, and regulators.
Real programs demonstrating speed and decision impact.
Large‑scale QSP with >2,000 ODEs guided Acasunlimab into a 100–200 mg Q3W window, reducing Phase II exploration burden and cost.
Integrated exhaustion, PD‑1, antigen escape, and HDAC effects; matched CRS cytokine patterns (IL‑6, IL‑10, IFN‑γ) and identified rescue combos.
Cut model cycles from months to weeks with AI‑assisted parameterization and continuous learning.
Scale modeling across the portfolio instead of one‑off expert builds.
Protect $300–500M per portfolio by avoiding late failures and reallocating early.
Auto‑generated traceability, validation, and reporting aligned to MIDD expectations.
Natural‑language UX for scientists, clinicians, and regulatory teams.
Federated learning—IP never leaves your boundary; full audit trails.
Oncology • Immunology • Neurology • Metabolic • Cardiovascular • Rare diseases
TME, checkpoint dynamics, combinations (chemo, IO, cell therapy), resistance prediction.
Innate/adaptive interplay, cytokine networks, tissue‑specific inflammation models.
BBB penetration, neuroinflammation, protein aggregation, cognitive endpoints.
Glucose/lipid dynamics, endocrine feedback, GLP‑1 and beyond.
Hemodynamics, energetics, neuro‑hormonal control; endpoints modeling.
Mechanistic orphan models for efficient dose, endpoints, and translation.
Benchmark probabilistic surrogates; target 2× faster qualification with equivalent accuracy.
AI‑ready templates; auto‑reporting aligned with ICH guidance; cross‑agency harmonization.
Unified QSP+PBPK+RWE patient models; continuous post‑market learning.
Trust frameworks, bias control, tiered validation, and streamlined reviews.
Bespoke model builds, embedded co‑development teams, priority access to features, regulatory strategy input, and optional therapeutic exclusivity.
Portfolios with 10–30 active programs, heavy Phase I/II decision load, and strong data assets seeking interpretability without sacrificing speed.
Let’s align on your portfolio, timelines, and regulatory strategy.