The Molecule That Changed Everything: How Insulin Rewired Drug Manufacturing and Regulatory Thinking

There’s a tendency in our industry to talk about “small molecules versus biologics” as if we woke up one morning and the world had simply divided itself into two neat categories. But the truth is more interesting—and more instructive—than that. The dividing line was drawn by one molecule in particular: insulin. And the story of how insulin moved from animal extraction to recombinant manufacturing didn’t just change how we make one drug. It fundamentally rewired how we think about manufacturing, quality, and regulation across the entire pharmaceutical landscape.

From Pancreases to Plasmids

For the first six decades of its therapeutic life, insulin was an extractive product. Since the 1920s, producing insulin required enormous quantities of animal pancreases—primarily from cows and pigs—sourced from slaughterhouses. Eli Lilly began full-scale animal insulin production in 1923 using isoelectric precipitation to separate and purify the hormone, and that basic approach held for decades. Chromatographic advancements in the 1970s improved purity and reduced the immunogenic reactions that had long plagued patients, but the fundamental dependency on animal tissue remained.

This was, in manufacturing terms, essentially a small-molecule mindset applied to a protein. You sourced your raw material, you extracted, you purified, you tested the final product against a specification, and you released it. The process was relatively well-characterized and reproducible. Quality lived primarily in the finished product testing.

But this model was fragile. Market forces and growing global demand revealed the unsustainable nature of dependency on animal sources. The fear of supply shortages was real. And it was into this gap that recombinant DNA technology arrived.

1982: The Paradigm Breaks Open

In 1978, scientists at City of Hope and Genentech developed a method for producing biosynthetic human insulin (BHI) using recombinant DNA technology, synthesizing the insulin A and B chains separately in E. coli. On October 28, 1982, after only five months of review, the FDA approved Humulin—the first biosynthetic human insulin and the first approved medical product of any kind derived from recombinant DNA technology.

Think about what happened here. Overnight, insulin manufacturing went from:

Animal tissue extraction → Living cell factory production
Sourcing variability tied to agricultural supply chains → Engineered biological systems with defined genetic constructs
Purification of a natural mixture → Directed expression of a specific gene product

The production systems themselves tell the story. Recombinant human insulin is produced predominantly in E. coli (where insulin precursors form inclusion bodies requiring solubilization and refolding) or in Saccharomyces cerevisiae (where soluble precursors are secreted into culture supernatant). Each system brings its own manufacturing challenges—post-translational modification limitations in bacteria, glycosylation considerations in yeast—that simply did not exist in the old extraction paradigm.

This wasn’t just a change in sourcing. It was a change in manufacturing identity.

“The Process Is the Product”

And here is where the real conceptual earthquake happened. With small-molecule drugs, you can fully characterize the molecule. You know every atom, every bond. If two manufacturers produce the same compound by different routes, you can prove equivalence through analytical testing of the finished product. The process matters, but it isn’t definitional.

Biologics are different. As the NIH Regulatory Knowledge Guide puts it directly: “the process is the product”—any changes in the manufacturing process can result in a fundamental change to the biological molecule, impacting the product and its performance, safety, or efficacy. The manufacturing process for biologics—from cell bank to fermentation to purification to formulation—determines the quality of the product in ways that cannot be fully captured by end-product testing alone.

Insulin was the first product to force the industry to confront this reality at commercial scale. When Lilly and Genentech brought Humulin to market, they weren’t just scaling up a chemical reaction. They were scaling up a living system, with all the inherent variability that implies—batch-to-batch differences in cell growth, protein folding, post-translational modifications, and impurity profiles.

This single insight—that for biologics, process control is product control—cascaded through the entire regulatory and quality framework over the next four decades.

The Regulatory Framework Catches Up

Insulin’s journey also exposed a peculiar regulatory gap. Despite being a biologic by any scientific definition, insulin was regulated as a drug under Section 505 of the Federal Food, Drug, and Cosmetic Act (FFDCA), not as a biologic under the Public Health Service Act (PHSA). This was largely a historical accident: when recombinant insulin arrived in 1982, the distinctions between FFDCA and PHSA weren’t particularly consequential, and the relevant FDA expertise happened to reside in the drug review division.

But this classification mismatch had real consequences. Because insulin was regulated as a “drug,” there was no pathway for biosimilar insulins—even after the Hatch-Waxman Act of 1984 created abbreviated pathways for generic small-molecule drugs. The “generic” framework simply doesn’t work for complex biological molecules where “identical” is the wrong standard.

It took decades to resolve this. The Biologics Price Competition and Innovation Act (BPCIA), enacted in 2010 as part of the Affordable Care Act, created an abbreviated regulatory pathway for biosimilars and mandated that insulin—along with certain other protein products—would transition from drug status to biologic status. On March 23, 2020, all insulin products were formally “deemed to be” biologics, licensed under Section 351 of the PHSA.

This wasn’t a relabeling exercise. It opened insulin to the biosimilar pathway for the first time, culminating in the July 2021 approval of Semglee (insulin glargine-yfgn) as the first interchangeable biosimilar insulin product. That approval—allowing pharmacy-level substitution of a biologic—was a moment the industry had been building toward for decades.

ICH Q5 and the Quality Architecture for Biologics

The regulatory thinking that insulin forced into existence didn’t stay confined to insulin. It spawned an entire framework of ICH guidelines specifically addressing the quality of biotechnological products:

ICH Q5A – Viral safety evaluation of biotech products derived from cell lines
ICH Q5B – Analysis of the expression construct in cell lines
ICH Q5C – Stability testing of biotechnological/biological products
ICH Q5D – Derivation and characterization of cell substrates
ICH Q5E – Comparability of biotechnological/biological products subject to changes in their manufacturing process

ICH Q5E deserves particular attention because it codifies the “process is the product” principle into an operational framework. It states that changes to manufacturing processes are “normal and expected” but insists that manufacturers demonstrate comparability—proving that post-change product has “highly similar quality attributes” and that no adverse impact on safety or efficacy has occurred. The guideline explicitly acknowledges that even “minor” changes can have unpredictable impacts on quality, safety, and efficacy.

This is fundamentally different from the small-molecule world, where a process change can often be managed through updated specifications and finished-product testing. For biologics, comparability exercises can involve extensive analytical characterization, in-process testing, stability studies, and potentially nonclinical or clinical assessments.

How This Changed Industry Thinking

The ripple effects of insulin’s transition from extraction to biologics manufacturing reshaped the entire pharmaceutical industry in several concrete ways:

1. Process Development Became a Core Competency, Not a Support Function.
When “the process is the product,” process development scientists aren’t just optimizing yield—they’re defining the drug. The extensive process characterization, design space definition, and control strategy work enshrined in ICH Q8 (Pharmaceutical Development) and ICH Q11 (Development and Manufacture of Drug Substances) grew directly from the recognition that biologics manufacturing demands a fundamentally deeper understanding of process-product relationships.

2. Cell Banks Became the Crown Jewels.
The master cell bank concept—maintaining a characterized, qualified starting point for all future production—became the foundational control strategy for biologics. Every batch traces back to a defined, banked cell line. This was a completely new paradigm compared to sourcing animal pancreases from slaughterhouses.

3. Comparability Became a Lifecycle Discipline.
In the small-molecule world, process changes are managed through supplements and updated batch records. In biologics, every significant process change triggers a comparability exercise that can take months and cost millions. This has made change control for biologics a far more rigorous discipline and has elevated the role of quality and regulatory functions in manufacturing decisions.

4. The Biosimilar Paradigm Created New Quality Standards.
Unlike generics, biosimilars cannot be “identical” to the reference product. The FDA requires a demonstration that the biosimilar is “highly similar” with “no clinically meaningful differences” in safety, purity, and potency. This “totality of evidence” approach, developed for the BPCIA pathway, requires sophisticated analytical, functional, and clinical comparisons that go well beyond the bioequivalence studies used for generic drugs.

5. Manufacturing Cost and Complexity Became Strategic Variables.
Biologics manufacturing requires living cell systems, specialized bioreactors, extensive purification trains (including viral clearance steps), and facility designs with stringent contamination controls. The average cost to develop an approved biologic is estimated at $2.6–2.8 billion, compared to significantly lower costs for small molecules. This manufacturing complexity has driven the growth of the CDMO industry and made facility design, tech transfer, and manufacturing strategy central to business planning.

The Broader Industry Shift

Insulin was the leading edge of a massive transformation. By 2023, the global pharmaceutical market was $1.34 trillion, with biologics representing 42% of sales (up from 31% in 2018) and growing three times faster than small molecules. Some analysts predict biologics will outstrip small molecule sales by 2027.

This growth has been enabled by the manufacturing and regulatory infrastructure that insulin’s transition helped build. The expression systems first commercialized for insulin—E. coli and yeast—remain workhorses, while mammalian cell lines (especially CHO cells) now dominate monoclonal antibody production. The quality frameworks (ICH Q5 series, Q6B specifications, Q8–Q11 development and manufacturing guidelines) provide the regulatory architecture that makes all of this possible.

Even the regulatory structure itself—the distinction between 21 CFR Parts 210/211 (drug CGMP) and 21 CFR Parts 600–680 (biologics)—reflects this historical evolution. Biologics manufacturers must often comply with both frameworks simultaneously, maintaining drug CGMP baselines while layering on biologics-specific controls for establishment licensing, lot release, and biological product deviation reporting.

Where We Are Now

Today, insulin sits at a fascinating intersection. It’s a relatively small, well-characterized protein—analytically simpler than a monoclonal antibody—but it carries the full regulatory weight of a biologic. The USP maintains five drug substance monographs and thirteen drug product monographs for insulin. Manufacturers must hold Biologics License Applications, comply with CGMP for both drugs and biologics, and submit to pre-approval inspections.

Meanwhile, the manufacturing technology continues to evolve. Animal-free recombinant insulin is now a critical component of cell culture media used in the production of other biologics, supporting CHO cell growth in monoclonal antibody manufacturing—a kind of recursive loop where the first recombinant biologic enables the manufacture of subsequent generations.

And the biosimilar pathway that insulin’s reclassification finally opened is beginning to deliver on its promise. Multiple biosimilar and interchangeable insulin products are now reaching patients at lower costs. The framework developed for insulin biosimilars is being applied across the biologics landscape—from adalimumab to trastuzumab to bevacizumab.

The Lesson for Quality Professionals

If there’s a single takeaway from insulin’s manufacturing evolution, it’s this: the way we make a drug is inseparable from what the drug is. This was always true for biologics, but it took insulin—the first recombinant product to reach commercial scale—to force the industry and regulators to internalize that principle.

Every comparability study you run, every cell bank qualification you perform, every process validation protocol you execute for a biologic product exists because of the conceptual framework that insulin’s journey established. The ICH Q5E comparability exercise, the Q5D cell substrate characterization, the Q5A viral safety evaluation—these aren’t bureaucratic requirements imposed from outside. They’re the rational response to a fundamental truth about biological manufacturing that insulin made impossible to ignore.

The molecule that changed everything didn’t just save millions of lives. It rewired how an entire industry thinks about the relationship between process and product. And in doing so, it set the stage for every biologic that followed.

Residence Time Distribution

Residence Time Distribution (RTD) is a critical concept in continuous manufacturing (CM) of biologics. It provides valuable insights into how material flows through a process, enabling manufacturers to predict and control product quality.

The Importance of RTD in Continuous Manufacturing

RTD characterizes how long materials spend in a process system and is influenced by factors such as equipment design, material properties, and operating conditions. Understanding RTD is vital for tracking material flow, ensuring consistent product quality, and mitigating the impact of transient events. For biologics, where process dynamics can significantly affect critical quality attributes (CQAs), RTD serves as a cornerstone for process control and optimization.

By analyzing RTD, manufacturers can develop robust sampling and diversion strategies to manage variability in input materials or unexpected process disturbances. For example, changes in process dynamics may influence conversion rates or yield. Thus, characterizing RTD across the planned operating range helps anticipate variability and maintain process performance.

Methodologies for RTD Characterization

Several methodologies are employed to study RTD, each tailored to the specific needs of the process:

Tracer Studies: Tracers with properties similar to the material being processed are introduced into the system. These tracers should not interact with equipment surfaces or alter the process dynamics. For instance, a tracer could replace a constituent of the liquid or solid feed stream while maintaining similar flow properties.
In Silico Modeling: Computational models simulate RTD based on equipment geometry and flow dynamics. These models are validated against experimental data to ensure accuracy.
Step-Change Testing: Quantitative changes in feed composition (e.g., altering a constituent) are used to study how material flows through the system without introducing external tracers.

The chosen methodology must align with the commercial process and avoid interfering with its normal operation. Additionally, any approach taken should be scientifically justified and documented.

Applications of RTD in Biologics Manufacturing Process Control

RTD data enables real-time monitoring and control of continuous processes. By integrating RTD models with Process Analytical Technology (PAT), manufacturers can predict CQAs and adjust operating conditions proactively. This is particularly important for biologics, where minor deviations can have significant impacts on product quality.

Material Traceability

In continuous processes, material traceability is crucial for regulatory compliance and quality assurance. RTD models help track the movement of materials through the system, enabling precise identification of affected batches during deviations or equipment failures.

Process Validation

RTD studies are integral to process validation under ICH Q13 guidelines. They support lifecycle validation by demonstrating that the process operates within defined parameters across its entire range. This ensures consistent product quality during commercial manufacturing.

Real-Time Release Testing (RTRT)

While not mandatory, RTRT aligns well with continuous manufacturing principles. By combining RTD models with PAT tools, manufacturers can replace traditional end-product testing with real-time quality assessments.

Regulatory Considerations: Aligning with ICH Q13

ICH Q13 emphasizes a science- and risk-based approach to CM. RTD characterization supports several key aspects of this guideline:

Control Strategy Development: RTD data informs strategies for monitoring input materials, controlling process parameters, and diverting non-conforming materials.
Process Understanding: Comprehensive RTD studies enhance understanding of material flow and its impact on CQAs.
Lifecycle Management: RTD models facilitate continuous process verification (CPV) by providing real-time insights into process performance.
Regulatory Submissions: Detailed documentation of RTD studies is essential for regulatory approval, especially when proposing RTRT or other innovative approaches.

Challenges and Future Directions

Despite its benefits, implementing RTD in CM poses challenges:

Complexity of Biologics: Large molecules like mAbs require sophisticated modeling techniques to capture their unique flow characteristics.
Integration Across Unit Operations: Synchronizing RTD data across interconnected processes remains a technical hurdle.
Regulatory Acceptance: While ICH Q13 encourages innovation, gaining regulatory approval for novel applications like RTRT requires robust justification and data.

Future developments in computational modeling, advanced sensors, and machine learning are expected to enhance RTD applications further. These innovations will enable more precise control over continuous processes, paving the way for broader adoption of CM in biologics manufacturing.

Residence Time Distribution is a foundational tool for advancing continuous manufacturing of biologics. By aligning with ICH Q13 guidelines and leveraging cutting-edge technologies, manufacturers can achieve greater efficiency, consistency, and quality in producing life-saving therapies like monoclonal antibodies.

Deviation Review for CxO – Best Practice

Regulatory agencies have continually continued to make it clear that when a Contract Manufacturing Organization (CMO) or Contract Research Organization (CRO) experiences a deviation, the sponsor/Marketing Authorization Holder (MAH) has several key responsibilities:

Review the deviation: The sponsor must thoroughly review the deviation to ensure it was appropriately defined and investigated. This review is crucial as the sponsor cannot delegate their responsibility to ensure the drug product is safe, effective, and conforms to specifications and regulatory commitments.
Assess product impact: The sponsor should ensure that the CMO has properly assessed the impact of the deviation on the product. This includes evaluating whether the deviation affected material quality, safety, or efficacy.
Verify appropriate material control: It’s the sponsor’s responsibility to ensure the CMO has appropriately controlled the affected material and extended this control to any other potentially affected materials.
Make disposition decisions: Ultimately, the sponsor is responsible for deciding whether the product should be released, reprocessed, or rejected. This decision is especially critical if the deviation affected material in clinical trials.
Oversee corrective and preventive actions: The sponsor should understand how the CMO’s corrective and preventive action (CAPA) system operates and ensure appropriate measures are taken to prevent recurrence of the deviation.
Maintain oversight: While the quality agreement defines the CMO’s responsibilities, the sponsor retains 100% oversight, including executed batch record review, change control, and deviation review and approval.
Risk-based approach: For major or critical deviations, sponsors should employ a risk-based approach to assess the severity and potential impact.

To simplify the deviation notification process with a Contract Organization (CxO), sponsors and can implement several strategies:

Clear Communication and Documentation

Establish a Well-Defined Quality Agreement: Create a comprehensive quality agreement that clearly outlines the deviation notification process, including timelines, classification criteria, and reporting requirements.
Implement Standardized Templates: Develop and provide standardized templates for deviation reporting to ensure consistency and completeness of information.
Set Clear Notification Timelines: Agree on specific timelines for different deviation categories. For example, critical and major deviations should be reported within one business day.

Risk-Based Approach

Adopt a Quality Risk Management (QRM) Mindset: Approach the partnership with a focus on risk management, ensuring that both parties understand the potential impact of deviations on product quality and patient safety.
Calibrate Risk Classification: Align the deviation classification system between the sponsor and CxO to avoid discrepancies in severity assessment.

Streamlined Processes

Utilize Electronic Quality Management Systems: Implement digital tools to facilitate real-time reporting and tracking of deviations, improving efficiency and transparency. Yes, the sponsor should be taking a risk based approach to tracking deviations in their eQMS that captures the important sponsor/MAH decision making.
Define Clear Roles and Responsibilities: Clearly delineate who is responsible for each step of the deviation management process, from identification to reporting and investigation.

Training and Support

Provide Comprehensive Training: Ensure that CxO staff are well-trained on the sponsor’s quality expectations, deviation reporting procedures, and the use of any specific tools or systems.
Offer Ongoing Support: Establish a dedicated point of contact or support team to assist the CxO with questions or issues related to deviation reporting.

Regular Review and Improvement

Conduct Periodic Reviews: Schedule regular meetings to review the deviation notification process, discuss any challenges, and identify areas for improvement.
Encourage Open Dialogue: Foster an environment where the CMO feels comfortable reporting issues promptly without fear of punitive action.

I strongly believe that a CxO needs to implement these strategies (do not put it only on the MAH’s shoulders) as part of their client onboarding and management process to create a more efficient and effective deviation notification process. This approach not only simplifies the process but also ensures that critical quality information is communicated promptly and accurately, ultimately contributing to better product quality and regulatory compliance. Add some value and don’t make the sponsor beg for information.

Batch and the Batch Record

Inevitably, in biotech, with our manufacturing processes such as cell culture, fermentation, and purification, we ask the question (especially with continuous manufacturing), “Just what is a batch anyway.” Luckily for us, the ISA S88.01 provides a standard, with models and terminology, to give us a structured framework to define, control, and automate batch processes effectively

ISA S88.01 (ANSI/ISA-88) standardizes batch control terminology by providing a consistent set of models and terminology for describing all the aspects of batch processing. This standardization helps improve communication between all parties involved in batch control, including users, vendors, and engineers.

Models and Terminology: ISA S88.01 defines a set of models and terminology to describe batch control’s physical and procedural aspects. This includes the physical model, which outlines the hierarchical structure of equipment, and the procedural control model, which details the sequence of operations and phases involved in batch processing.
Physical Model: The physical model begins at the enterprise level and includes sites, areas, process cells, units, equipment, and control modules. This hierarchical structure ensures that all physical components involved in batch processing are consistently described.
Procedural Control Model: This model consists of recipe procedures, unit procedures, operations, and phases. Each level in this hierarchy represents a different level of detail in the batch process, from high-level procedures to specific actions performed by equipment.
Recipe Types and Contents: ISA S88.01 standardizes the types of recipes (general, site, master, and control) and their contents, which include the header, formula, equipment requirements, procedure, and other necessary information. This ensures recipes are consistently structured and understood across different systems and organizations.
State Definitions: The standard defines various states that units or phases can transition through during their operation, such as idle, running, held, paused, aborted, and completed. These states provide a standardized framework for interaction between recipe phases and control system equipment.
Data Structures and Guidelines: ISA S88.01 provides guidelines for data structures and batch control languages, simplifying programming, configuration tasks, and communication between system components. This helps ensure that data is consistently managed and communicated within the batch control system.

The Batch Record

Batch records are the primary documentation that captures the real-time performance of production records. Batch records are crucial to confirming that all expected and required actions have been completed within parameters to produce a product that meets specifications and complies with quality standards.

The Master Batch Record (MBR) is the version-controlled documentation necessary to trace the complete cycle of manufacture of a batch of product, from the dispensing of materials through all processing, testing, and subsequent packaging to the dispatch for sale or supply of the finished product. This documentation includes quality control, quality assurance, and environmental data relevant to the intended manufacturing.

The MBR may be segmented on intended manufacturing and testing stages, each part controlled separately.

The Production Batch Record (PBR) is issued for manufacturing one (or more) batches from the MBR and is compiled during manufacturing.

The MBR and PBR may be controlled in the document management system, within a manufacturing execution system (MES)/electronic batch record (EBR) platform, or some hybrid. Parts may also be found within the LIMS, data historian, and other electronic systems. A critical part of building the MBR is ensuring the correct connections between it and data in specific electronic platforms.

	Electronic System	Description
Master Production Record	Master Recipe	Contains product name or designation, recipe designation or version, formulas, equipment requirements or classes, sequence of activities, procedures, normalized bill of materials (quantity per unit volume to produce)
	Work Instructions	Additional detailed instructions – may include electronic SOPs or SOP references
	Critical Process Parameters	Required Process Parameters that are to be checked or monitored or are to be downloaded to other systems such as automation
Production Batch Record	Control Recipe	A Master Recipe dispatched or otherwise made available in manufacturing-related areas for Execution. Includes Master Recipe information with the addition of schedule, specific quantity to make, actual target bill of materials quantities, and other data for the batch and production instance
	Electronic Production Record	A store of data and information created by systems or entered by personnel during execution of Control Recipes May be located in one or more systems or databases Data may or may not be stored in human readable format
	Production Report	Data and information in human-readable format, presented either in electronic or paper format for activities such as review, disposition, investigation, audit, and analysis.

Comparison of the MBR and PBR Paper to Electronic