The biopharma industry has invested heavily in accelerating discovery. AI, machine learning (ML), high-throughput experimentation and advanced modelling are helping scientists identify promising therapeutic candidates faster than ever.
But there is a problem.
Before any therapy can reach patients, it must pass through Chemistry, Manufacturing and Controls (CMC). And that pathway is struggling to keep pace.
In its new position paper, The Billion-Dollar Bottleneck: Why CMC is Strangling the Biopharma Pipeline’, Zifo argues that CMC has become one of the industry’s most consequential operational constraints. The paper examines how scientific complexity, fragmented data, manual documentation, and poorly connected tech transfer processes are delaying commercialisation and consuming valuable scientific capacity.
The stakes are significant. CMC deficiencies have contributed to 74% of FDA Complete Response Letters (CRLs), while every day of delayed prescription sales may represent about $800,000 in lost value, according to the paper. For therapies approaching fixed patent-expiry dates, those delays cannot always be recovered later. They can permanently reduce an asset’s commercial life.
Yet the impact reaches well beyond revenue.
A modern scientific pipeline running on fractured infrastructure
The challenge has intensified as biopharma pipelines have shifted from comparatively predictable small-molecule processes toward biologics, cell and gene therapies, nucleic-acid therapeutics and other advanced modalities.
These therapies introduce hundreds, sometimes thousands, of potentially influential process, material, equipment and environmental variables. Small changes in mixing, oxygen transfer, pH, temperature, shear or raw materials can affect the behaviour and quality of the final product.
The science has changed. Much of the operating model has not.
Critical information still moves between process development, analytical teams, quality functions, manufacturing sites, and external partners through spreadsheets, PDFs, paper batch records, slide decks and email. Context is lost at every handoff. Scientists then spend hours finding data, reconciling sample identifiers and manually rebuilding experimental histories.
The paper describes this as the ‘data janitor’ epidemic, with current operational analyses indicating that PhD-level scientists may spend between 30% and 40% of their working time on data cleanup and reconciliation rather than scientific problem-solving.
Digitising paper is not transformation
The viewpoint also challenges a familiar response to CMC friction: purchasing another platform.
Replicating an inefficient paper process on a screen does not remove the underlying problem. It can make the process more rigid. In dynamic scientific environments, systems built around fixed templates and isolated workflows can restrict experimentation and encourage scientists to return to spreadsheets or paper simply to get the work done.
Instead, Zifo proposes a scientist-centered CMC operating model built around six connected moves:
• Liberate evidence trapped in batch records, certificates of analysis, PDFs and legacy reports.
• Connect the most valuable data choke points across instruments and scientific systems.
• Rewire design-make-test-analyse (DMTA) workflows so context travels with every sample and result.
• Create an orchestration layer that adapts to scientists without replacing validated systems of record.
• Apply governed scientific language models grounded in proprietary CMC evidence.
• Treat tech transfer as a continuous knowledge flow, not a late-stage baton pass.
The ambition is substantial: reduce overall CMC cycle times by as much as 40%, with phase acceleration of up to 60%. Achieving that goal will require more than incremental optimisation. It will demand a joint operating model across CMC, IT, Quality and data teams, supported by targeted lighthouse projects that demonstrate value quickly and scale under appropriate governance.
The full viewpoint examines the causes and costs of the CMC bottleneck and presents a practical blueprint for building a connected, AI-enabled and scientist-centred CMC ecosystem. Click to access the report.
