About

I'm Varun Debbeti, a statistical programmer with over 13 years of experience in pharma, biotech, and CRO. I have regulatory submission experience for NDA, BLA, and EMA applications.

I founded clinstandards.org — a hub for clinical data standards, AI tools, blogs, and educational content built for the global clinical programming community.

Varun Debbeti

Project Maintainer

Contributions to PhUSE & Industry

PhUSE 2018 / SI12 PhUSE 2020 / DS03 PHUSE Blog / USDM, ARS & CORE

Connect on LinkedIn

As Featured In

InterviewThe AI Journal · March 2026

“The AI Tools Clinical Programmers Actually Need Don't Exist Yet, So He Built Them.”

Varun Debbeti noticed that the AI tools being marketed to pharma were either too risky to use with real patient data, too generic to understand CDISC standards, or too expensive for programmers globally. So he built his own — and what he learned in the process says a lot about where AI in drug development is actually headed.

Code Generator & Converter

SAS ↔ R code from plain-language descriptions

CDISC Chatbot

RAG model trained on CDISC standards & FDA guidance

UAT Data Generator

Realistic test datasets — no production data needed

Spec Generator

Variable-level specs for NDA/BLA from your code

ArticleBiopharmaTrend · May 2026

“AI Won't Replace Clinical Programmers. It Will Split Them Into Two Groups.”

Statistical programmers are sorting into two camps: those who learn to direct AI toward regulatory-grade output, and those writing every line by hand. The first group will work on more studies, take on higher-complexity problems, and move into leadership roles faster. This piece explores where AI adds speed, where it introduces risk in a GxP environment, and why the FDA's trajectory toward Dataset-JSON creates a structural advantage for programmers who build AI-assisted, audit-ready pipelines.

ArticlePHUSE Blog

“Three Standards, One Pipeline: Why USDM, ARS, and CORE Belong Together.”

This PHUSE Blog article explains why USDM, ARS, and CORE should be treated as connected parts of one clinical data pipeline rather than separate standards conversations. It frames study design, analysis intent, and conformance checks as a practical path toward clearer, more automatable submissions.

ArticleClinical Leader · June 2026

“How The Quiet Data Standards Revolution Is Impacting Drug Approval.”

The data infrastructure behind every FDA submission is getting its most significant overhaul in two decades. This piece breaks down what SDTM v3.0 changes — from replacing the brittle SUPPQUAL construct with non-standard variables to machine-readable metadata — alongside the FDA's move toward Dataset-JSON as a replacement for the 1980s-era XPORT format and CORE, the open-source conformance engine closing the interpretation gap between sponsors, CROs, and reviewers. Drawing on Phase 1–4 oncology experience, Varun Debbeti explains why these standards shifts translate directly into shorter approval timelines for patients with serious and rare conditions.

EbookAmazon Kindle

“The Regulated Machine.”

A compact reference for clinical programming and data standards professionals exploring AI, automation, validation, and human accountability in regulated environments.

Mission Statement

“To streamline access to critical regulatory and technical data, empowering the clinical research community to stay informed and excel in an ever-evolving field.”