pmxstudio.ai was started by pharmacometricians who had spent careers in pharma and CRO settings and wanted to work differently. Smaller teams, fixed prices, modern tooling, and analyses that hold up at the regulatory desk.
Modelling decisions are scientific judgements: structural form, covariate inclusion, error model, how to handle BLQ. They are made by named scientists, not by tools. Our agents accelerate execution; they do not select models.
Every deliverable is built to the standard of a regulatory submission. Datasets are CDISC-aligned. Runs are reproducible. The path from source data to final number is traceable. We assume the FDA will ask.
Time-and-materials engagements reward scope drift. We write the scope and the price up front, and we live with both. Re-scoping happens explicitly and in writing, when the science demands it. Never by attrition.
Our AI agents are internal. They were built because the off-the-shelf options were either generic or research-grade. They run on your data, in our environment, under our review. And they have made our small team substantially more productive.
The team that scopes your project is the team that runs it. No partner-pitched, junior-delivered engagements. You speak to the modeller every week.
Full names and credentials are shared during scoping.
Across the team we have led pharmacometric components of submissions to FDA, EMA, PMDA, Health Canada and MHRA, including IND / CTA briefing documents, end-of-Phase-2 packages, NDA / BLA / MAA Modules 5.3.3 and 5.3.4, response-to-information replies, and post-marketing commitments.
Therapeutic exposure spans oncology (solid tumours, haematologic malignancies), immunology, rare and metabolic disease, infectious disease, and neuroscience.
Tell us about the molecule, the study, and the question. We will set up a 30-minute scoping call with the pharmacometrician who would run the project.