As AI-driven discovery floods the pipeline with candidates, L7 Informatics argues the constraint on speed to patients has moved from the lab to Chemistry, Manufacturing, and Controls, and that orchestrating the work across internal labs and external partners on one governed data model is how precision science scales.
AUSTIN, Texas, September 23, 2026 — Artificial intelligence is accelerating drug discovery faster than the systems meant to turn candidates into medicines can absorb. According to L7 Informatics, that mismatch has exposed a new constraint on how quickly a portfolio reaches patients, and it sits in Chemistry, Manufacturing, and Controls (CMC), the work that turns a research molecule into a reproducible, releasable product.
CMC is where timelines and cost concentrate, and it no longer happens inside one company's walls. A modern development program runs across dozens of external partners, including Contract Research Organizations, Contract Development and Manufacturing Organizations, analytical laboratories, and fill-finish sites, often connected by little more than batch records re-keyed by hand, certificates of analysis sent as PDFs, and status tracked in email. As AI-driven discovery pushes more candidates into that network, those document handoffs become a rate-limiting step.
The company argues that adding more point tools makes the problem worse, because each new system introduces another translation burden at the boundaries between them and compounds integration debt. That view echoes industry analysts. In its 2025 Market Guide for Laboratory Information Management Systems, Gartner observed that point solutions can expand laboratory capability while adding technical debt, and pointed to unified platforms as an emerging architectural direction.
L7 Informatics argues the industry must now shift to CMC-in-the-Loop: coordinating the work across R&D, Manufacturing Science and Technology, manufacturing, and external partners through a single, governed data model, so process knowledge and quality data follow each molecule from research through clinical manufacturing and technology transfer. Expressed in the ISA-88 and ISA-95 standards used across process industries, a development recipe and a commercial manufacturing recipe rest on the same structural foundation, which reduces the translation required at each partner boundary.
The approach also sets a standard for how AI operates in regulated work. People and AI agents work within the same governed environment, with attributable identities, defined permissions, and a common audit framework, while agents operate under additional boundaries and risk-based validation appropriate to their use. This is the foundation behind L7|ESP®, the company's agentic operating system for precision science.
Regulators are moving the same way. The FDA's Knowledge-Aided Assessment and Structured Application program and its Pharmaceutical Quality/CMC data standards effort are shifting quality information toward structured, machine-readable formats, which favors organizations that capture quality data as structured records at the point of execution.
"For years, the industry has asked how to automate the next instrument or the next lab," said Vasu Rangadass, Ph.D., Founder and CEO of L7 Informatics. "That question is useful, but it cannot address the larger problem. The question that scales is how to orchestrate an entire scientific outcome across internal labs and external partners under one governed model."
"AI did not create this problem. It exposed one that was already there," Rangadass added. "The silos and manual handoffs were tolerable when timelines had slack. AI takes the slack away, and the missing coordination layer becomes the thing holding the pipeline back."
The full argument is available in Rangadass's article, "Beyond Lab-in-the-Loop: Orchestrating CMC-in-the-Loop," at https://l7informatics.com/blog/orchestrating-cmc-in-the-loop/
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About L7 Informatics:
L7 Informatics delivers the foundational software infrastructure that connects data, intelligence, and operations for precision sciences and healthcare. Its enterprise platform, L7|ESP, serves as an Agentic Operating System that unifies scientific workflows, equipment, and teams into a single composable ecosystem.
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