Gourav Pandey

Jaenisch, Muraru, and Pandey discuss new skills for QC and regulatory teams, plus patient trust, as new approach methodologies reshape pharma.

Panelists on PharmTech AI Pulse Check debate SURPASS, a new digital-twin trial program, regulatory acceptance, and the data-cleanup burden it will create.

The deadline to comment on FDA's generative AI paper is Oct 19, 2026. We explain why professionals in pharma manufacturing should weigh in.

Jaenisch, Muraru, and Pandey join PharmTech's Cole to discuss FDA's generative AI paper, ARPA-H's SURPASS trials, and AI accountability.

PharmTech AI Pulse Check panelists explain why attribution, audit trails, and sign-off matter more than AI watermark detection for GxP quality teams.

API-gated journals may skew pharma regulatory research as AI agents favor accessible sources, raising citation integrity and pharmacovigilance risks.

Shadow AI and vendor data retention put GMP records and trade secrets at risk. AI Pulse Check panelists outline what pharma AI policies require.

PharmTech AI Pulse Check episode 2 covers AI data governance, API-gated journals, watermarking limits, and why attribution beats detection in GxP.

Episode 1, Part 3 of PharmTech AI Pulse Check covers why INTerpath-001's AI isn't ChatGPT and names pharma manufacturing's biggest AI governance gaps.

Florin Muraru, Gourav Pandey, and Richard Jaenisch explain where human sign-off belongs as multi-agent AI and document-parsing models reach GxP.

Jaenisch, Muraru, and Pandey debate whether AI "slop" is infiltrating pharma SOPs and deviation reports and who should catch it first.

PharmTech AI Pulse Check debuts: pharma experts tackle GxP 'slop,' multi-agent validation risk, OCR document mining, personalized mRNA cancer therapy.

An FDA warning letter to Purolea Cosmetics Lab shows that AI-assisted drafting can support documentation work only when the resulting document is subject to quality-system controls.

What pharma’s regulatory writers can borrow from clinical AI before the next inspection.

FDA’s 2024-2025 guidances and policy signals, when read together, point to a digital-by-default oversight model built on structured data exchange, more routine remote engagement, and clearer expectations for lifecycle control of automated systems used in regulated decisions. This article translates those signals into practical steps for chemistry, manufacturing, and control, as well as quality management systems.