Pharmaceutical Technology Is Now PharmTech: A Sharper Name for a Smarter Industry

Navigating FDA Shakeups and Obesity Drug Wars

Behind the Headlines Episode 28

Ask the Expert

Closing the Gap in Modern Process Control and Automation for QC/QA

Drug Digest December 2025

How It Helps Avoid Facility and Equipment Problems

A “Plan, Prevent, Prove” Approach

Mark Featherston is director, quality, strategic programs and global lab services at Avantor.

John D. Fisher is director, engineering, global ops at Avantor.

Innovative biopharmaceutical treatments change the lives of patients every day. As biopharma manufacturers bring transformational treatments to market, they face a confluence of challenges, from speed-to-market pressure and higher demand for novel therapeutics to regulatory compliance and increasing focus on sustainable practices.

In the face of these complex challenges, new solutions can help biopharmaceutical manufacturers find new opportunities. Digital transformation allows businesses to use smart, connected devices and then harness the collected intelligence to optimize manufacturing, improve and streamline quality control (QC) and compliance, and reduce resource use.

High-volume, high-data industries, such as oil and consumer goods, were early adopters of digitalization solutions because most sensors were already in place, lowering their initial investment cost. Finding, for example, small savings of $0.05 (€0.05) per unit across 10 million units would result in savings of $500,000 (€455,250) enabling a clear path to a favourable return. Annual savings of 5–30% have commonly been reported. 

Digital transformation in the biopharma industry can streamline manufacturing and help shorten time-to-market. However, adoption of digital technologies in the biopharma industry has been slow. Below are a few “best practice” recommendations to help biopharma companies overcome stumbling blocks to digitalization technology adoption:

Ensure data fusion: from electronic batch records in production to laboratory information management systems (LIMS) in the laboratory to enterprise resource planning (ERP) for inventory, it is essential to ensure an open architecture system that allows the seamless sharing of data between every technology. This will create a data ecosystem with the potential to serve as a digital treasure map of information.

Do not get lost in data, but do not lose what you do not use: biopharma manufacturing can produce an overwhelming amount of data, and it can be tempting to analyze all of these data to guide decision making. Smart analytics and cloud computing provide the capability to determine which data are relevant for a specific process and where there is opportunity for improvement. Consider also that data which are unused now could potentially play a role in future optimization or innovation.

Outline and understand security limitations and data privacy: avoid potentially significant disruptions or compliance issues by spending time early in the adoption process to mitigate security limitations and data privacy issues. A first step is ensuring all relevant information technology team members are involved in new digitalization technology adoption and integration.

Collaborate with experienced digitalization partners: partners, either a business or individuals who excel in their industry or subject matter area, can help provide guidance with their expertise and their technology.

The biopharma industry, in general, has been slower to adopt because biopharma companies did not have the sheer number of data-collecting sensors and instruments that higher-volume goods manufacturers have. Nor did biopharma companies produce the type of high-volume outputs where a savings of $0.05 (€0.05) per unit could scale to yield major savings. In addition, early digitalization technologies lacked the data control and integrity required for life sciences products. In 2019, 40% of biopharma labs in one study had not begun to use digital solutions, and just 37% were piloting new offerings (1).

The COVID-19 pandemic—as well as other challenges, such as supply chain and labour issues—combined with improvement in data integrity helped to drive the move toward digitalization. In 2022, 91% of biopharma labs reported acting toward digital transformation, and 69% reported they were piloting, scaling up, or in a phase of wide-scale adoption (1). While in some labs, manual or paper-based processes remain, the industry is taking significant steps toward capitalizing on the opportunities digitalization offers. The following are some ways digitalization can help biopharma companies improve business operations.

Biopharma companies run very complex processes. As a result, it can be very difficult to examine every single variable in a particular process. Digitalization enables companies to collect large datasets, then use technology to analyze that data, calculate conclusions, and recommend improvements. Digitalization technologies also greatly reduce reliance on manual record keeping. By digitizing, and even automating, information such as batch records or equipment use logs, companies can reduce manual data transfers and decrease the risk of human error.

Smart technology’s ability to provide real-time data also increases efficiency by identifying processes that have drifted outside set parameters. For example, if a certain pH is required for a specific chemistry, an alarm can sound on a lab worker’s mobile phone or personal computer (PC) to alert them that a correction is needed before the batch is put at risk.

Likewise, digitalization technologies can also improve scheduling. When systems for equipment, maintenance work orders, labs, employee schedules, and electronic batch records are digitally connected, an intelligent scheduling system optimizes a production schedule over a specified period, such as two weeks or a month. Likewise, a scheduling system can create better balance between manufacturing and QC. Because it typically takes less time to manufacture a product than it does to test one, the technology could identify optimum ways to move a sample and track all its inputs throughout the workflow, increasing efficiency and shortening test time.

A single batch record can be long and complex, involving significant paperwork and leaving opportunity for human error. Even transposing a number incorrectly or writing the wrong lot number in the wrong place can impact manufacturing. Digitizing these records virtually eliminates those quality concerns.

It also creates manufacturing efficiency because a worker can spend less time on documentation to capture each step. When it is time to review and release the batch record, it can be done electronically, validating and guaranteeing the record contains the correct data. Improving QC systems also supports the need to comply with strict regulatory requirements.

As with companies across many industries, biopharma manufacturers are challenging themselves to meet ambitious sustainability goals. Whether it is sensors that adjust equipment such as dehumidifiers for energy efficiency or digitizing records to reduce paper use, smart technologies can play a significant role in conserving resources. In addition, the ability to optimize processes through digitalization prevents the batch failures that result in scrapped product and wasted resources.

 Special PCs and smart programmable logic controllers (PLCs) are installed at the “edge” of the network to collect, process, and provide context to plant floor data, then send that data to the cloud for analytics. For a biopharma facility, this could be a device that sits in the purification area, connected to the PLC that controls the process. This edge device will collect data from the PLC and assign meaning to that data, including which plant, which area, which line, and which equipment the data are coming from. That contextualized data can be transmitted to the cloud for analytics leading to improvement of that purification process. Optimized recipe parameters could be sent to the edge device, then to the PLC for a better purification run on the next batch.

 Smart sensors can have a significant impact on a biopharma plant floor. For temperature-dependent process variables, smart temperature sensors can be connected directly to another device such as an anti-foam pump so that anti-foam is added proportional to the temperature profile that effects foaming, while also sending data to the cloud. Software can historize and analyze the data, recognizing patterns and issues within the datasets—and it does so significantly faster and with much more pattern-recognition capability than humans. The software could even order more anti-foam when needed. Sensor technology can be used to collect various data points, including pressure, turbidity, humidity, flow, and more.

Sensors connected to digitalization technologies can also provide essential real-time monitoring. This allows biopharma manufacturers to keep processes more effectively on track, especially when working with variables, such as total organic carbon, that are difficult-to-measure outside of the process in the lab. For inventory, smart shelves with embedded sensors can detect when materials on the shelves drop below a predefined number. When connected to an inventory management tool, smart shelves can initiate timely stocking and improve inventory visibility.

 This technology uses cameras or sensors to identify or inspect critical information. In recent years, vision systems have become affordable and much easier to use, providing biopharma manufacturers with significant opportunities to improve production.

For example, a vision system added to a filling line can inspect product labels to ensure lot numbers and expiration dates are correct. The system can also identify labels that are incorrectly placed or are marred by smears or smudges. A vision system can also be used to determine if a bottle cap is cross-threaded. For instance, one camera with two different inspection criteria (height of cap in view vs. expected height can detect front-to-back variance, levelness can detect left-to-right variance) can identify a cross-threaded cap and immediately reject what could become a leaky cap in the field.

Advanced analytic tools for digitalization

Connected devices, such as sensors and vision systems, generate massive datasets when combined with integration of data collected from laboratory information management systems (LIMS) and more. Advanced tools such as artificial intelligence (AI) and machine learning (ML) assess and analyze the data for deep-dive insights, finding patterns and interrelationships or detecting process deviations that would be virtually impossible for a human to detect. These predictive analytics allow manufacturers to make necessary corrections or adjustments before the point of product failure.

The technology can also optimize the manufacturing process to save time, energy, and resources. For example, AI and ML have the potential to optimize a specific process beyond the capabilities of base digitalization technology, such as a manufacturing execution system.

The rise of the single-use (SU) system has proven to be a game-changer in biopharma, and digitalization used in its production has demonstrated benefits to biopharma manufacturers as well as SU suppliers. In the past, bulk material—typically delivered to biopharma manufacturers in pails, drums, or super sacks—required quality assurance testing upon receipt before being subdivided and used in production. Now, materials are delivered in pre-weighed, single-use packages supplied with e-delivery of material documentation.

Because the material in that single-use product is only used for a given production run, the package’s traceable e-data can be automatically incorporated into upstream or downstream operations. The packaging also may allow for quick, nondestructive identification, such as Raman identification, which provides a rich dataset of raw material variability.

By working with SU suppliers, biopharma manufacturers can leverage a supplier’s available datasets without the need to build the database itself. The time savings can be significant. In one case study (conducted internally by Avantor), a manufacturer reduced the process of receiving and preparing production materials from 30 hours to nine.

Some SU suppliers are incorporating transformational technologies into their own processes. By moving beyond data aggregation to more connected operations, SU suppliers can automate data-driven decisions to efficiently advance their own production processes to meet biopharmamanufacturers’ needs.

SU technology production requires the assembly of multiple types of tubing, connectors, sensors, and other components. Other factors impact manufacturing too, including the need to maintain production in cleanroom conditions by technicians wearing personal protective equipment. On-time production and delivery rely on these environments being fully stocked.

Tools such as smart buttons provide a single point of contact for onsite support teams to provide QC inspections or engineering support from outside the cleanroom. Use of smart shelves minimize stockouts and inventory discrepancies. Combined with AI and ML systems, this transforms human-driven inventory management to a data-driven process that generates savings and efficiencies in SU production and QC.

Smart technologies will continue to create opportunities for the biopharma industry. Consider digital twins, a modelling technology that uses AI or ML to virtually test improvements and outcomes. More common in high-volume, high-data industries, digital twins can improve biopharma manufacturing processes. In addition, results from digital twin modelling can identify projects that should be prioritized or rank projects in the order of their ability to deliver the best overall quality, productivity, or customer satisfaction-related improvements. This would allow manufacturers to best determine where to invest capital.

As technology improves, ML has the potential to further optimize processes through prescriptive analytics that go beyond predicting what will happen and make recommendations, as well as predict their potential outcomes (see 

. For example, prescriptive analytics could identify whether or not an agitator that runs faster at the beginning of a batch will produce a specified improved yield. Furthermore, the system could then automatically issue a management of change order that, after human review and approval, would then change the agitator’s setting. 

 Three key stages moving from predictive to artificial intelligence (AI)-driven prescriptive decision making.

Augmented reality (AR) appeared several years ago with the creationof a mobile app that allows users to move around in the real world and interact with AR-generated content. This technology can help support biopharma manufacturer workforce development by being utilized to train operators. For steps that are easier to explain with a video rather than a batch record, the operator can point their phone camera or tablet at the equipment to overlay a graphic that demonstrates how to perform the next step. Thus, AR can serve as a 3D standard operating procedure that helps reduce the risk of human error and, ultimately, save time during production.

Digital transformation technologies allow manufacturers to ensure the product is the process. By optimizing manufacturing, improving and streamlining QC and compliance, and decreasing resource use, the end product will be better—and a life-changing treatment can get to the patient faster.

1. Elicker, J.; Maixner, D.; Fish, M.; Heavey, B. Driving Digitalization at Scale in the Lab. 

www.accenture.com/us-en/insights/life-sciences/digital-labs

Mark Featherston is director, quality, strategic programs and global lab services, and John D. Fisher is director, engineering, global ops; both at Avantor.


Vol. 35, No. 12
December 2023
Pages: 18–21

When referring to this article, please cite it as Featherston, M.; Fisher, J. D. Revolutionizing Biomanufacturing: The Digitalization Advantage. 

Articles

Digital transformation allows for smarter and connected biomanufacturing operations.

Revolutionizing Biomanufacturing: The Digitalization Advantage

Karin Kottig, PhD, is head of Contract Service Analytics, Vetter Development Service.

Spheres in balance | ©Orlando Florin Rosu/Stock.Adobe.com

While every drug molecule is unique and brings its own fragile chemical makeup to the production process, one element is consistent across the board: the importance of stability from start to finish. Stability testing is a highly important part of the production and commercialization process because it allows a contract development and manufacturing organization (CDMO) and drug developer to note what production processes, packaging, and shipping options will maintain the drug’s delicate chemical composition without jeopardizing quality.

When a drug developer partners with a CDMO, whether in Phase I, II, III, or beyond, it is not only recommended, but vital for the CDMO to conduct stability studies. This testing must take place in every phase of development, often more than once per phase, and should be integrated into the predetermined timeline. These studies do take time. Therefore, it must be accounted for early on in the process to prevent a delay in achieving commercialization. Injectable drug molecules naturally evolve as they are developed. Stability studies offer a proven method to test the drug ingredients’ quality as it is subject to external conditions such as environment, temperature, humidity, light, and more. Looking beyond the production timeline, stability studies bring added value in determining retest dates, shelf-life, expiration dates, packaging, storage, and shipping conditions.

Without sufficient stability studies, a drug product is failing to meet regulatory requirements and will inevitably never reach the patients who rely on it.

Navigating testing throughout product lifestyle

As any drug developer or CDMO will know, creating an entirely new drug poses its own unique challenges, one of which is securing regulatory approval from several authorities in different markets. Submissions for approval to these authorities must contain data from stability testing for the drug substance and drug product. As a result, stability assessments will be performed in nearly every development phase to evaluate a drug product’s overall stability in various instances.

Stability testing begins in early development phases. This is completed by analyzing technical batches and clinical samples to support the identification of proper formulation, primary packaging, and production processes that are best suited to maintain the drug formulation’s quality. Later, testing continues but changes its focus to the generation of expiration dates and obtaining market authorization. Stability studies must be done throughout the full development lifecycle. Should testing show a drop in quality at any point, the problem must be immediately addressed to prevent delays in the production and
commercialization process (1).

A CDMO will bring different study types to the development process (e.g., real-time, accelerated, intermediate, and photostability studies). Real-time testing looks at a product under intended temperature, humidity, and packaging storage conditions. Accelerated testing gathers supportive data through estimations of long-term testing outcomes and evaluations of short, temporary excursions from label storage conditions. Intermediate testing is performed between long-term and accelerated studies when accelerated studies show potential for significant change after six months (2).

Once a product launches, stability testing does not stop. Follow-up studies should be conducted regularly and additionally after post-approval changes. Some might consider stability testing to be one of the most essential elements of a drug product’s entire lifecycle, as it supports the drug and the health of the patients.

Identifying the right stability testing for each drug product

Drug products can vary extensively, from small-molecule to large-molecule, complex, highly sensitive, vaccines vs. oral, biotechnologically produced, and more. Therefore, the stability testing conducted for every drug product must differ accordingly. As each drug product evolves, its testing must adapt to address all quality needs.

Consider biologics, for example. These drug products experienced a boom in clinical development in recent years when the global pandemic required a vaccine for treatment. Injectables are delivered through a small, concentrated volume of liquid. Therefore, its potential stability issues are unique to this format and can range from aggregation, viscosity, and thermal degradation. While small-molecule drugs are often more straightforward to test for quality, large-molecule drugs can be much more complicated and require deeper bioanalytical testing methods. Regardless of a drug molecule’s size, testing must be uniquely designed to evaluate all key elements of a one-of-a-kind biologic.

As drug products differ in their formulation, packaging, delivery, and treatment method, so too does the most suitable stability testing approach. A CDMO and drug developer must select stability programs that align with its company philosophy, product type, and regulatory requirements. Customer expectations may also vary and play a role in the testing done by a CDMO.

Above all else, the stability program for a given drug will base itself on the regulatory needs for that product. Selecting stability testing should remain fluid and, on a case-by-case basis with several factors in mind.

Meeting regulatory expectations and requirements

When a drug undergoes regulatory evaluation, it will not simply receive a “yes” or “no” during its stability testing. Instead, it sets parameters that guide the development program with quality assurances in mind. While regulators will likely outline drug stability requirements in good manufacturing practice (GMP) guidelines, these programs will include more than the baseline expiration date (3). It will also require written stability studies outlining sample sizes, test frequency, storage needs, validated test methods, and specifications. With these qualifiers in mind, CDMOs and pharma customers will implement the unique stability testing program often enough to maintain quality throughout the process. This testing must be done within the product’s container that it will go to market in.

Luckily, the industry has seen synergy across multiple regulatory authorities and guidelines for stability testing. In fact, the International Council for Harmonisation (ICH) pharmaceutical stability guidelines are now considered a globally accepted standard (4). By following the ICH’s recommendations for amount and type of drug stability data needed per drug substance type, drug developers will be prepared to earn regulatory approval on any new drug product. ICH guidelines, particularly Q1A–Q1F, have measurably improved the overall quality of pharmaceutical drug products on the market, both in small- and large-molecule formats. While this does not mean that every drug product will require the same testing, it does set a precedent that can be adhered to across the board. For example, biologics are notoriously more sensitive so they will require a variety of complex analytical tests including, for example, potency testing.

While stability testing can be done by pharmaceutical and biotech companies, its growing demand is a result of a rising complexity of global regulatory requirements. Many choose to work with an outsourcing partner, or CDMO, to take advantage of their stability testing expertise. It is unlikely that a customer has this level of expertise in-house, but it is often available to leverage when working with an external partner. Besides the knowledge-base, CDMOs often bring added analytical equipment and qualified storage capabilities that would be a cost-add to a customer’s own internal repertoire.

Full-service CDMOs allow a customer to bring production, release, and stability testing together within the oversight of one quality management system. By entrusting the comprehensive stability testing process to one partner, a drug developer mitigates the need for additional organization, coordination, and logistic activities. This saves both cost and capacity for other drug development projects. In addition, changes can be addressed more seamlessly when both production and stability testing are with one company.

This outsourcing method opens pharmaceutical and biotech company capacities for product development, leaving an experienced CDMO to manage stability studies.

Across the industry, some trends are consistent, ranging from increased complexity of drug molecules, shortened timeframes for development and delivery, and decreased batch sizes. There is also a shift to greater collaboration between pharma and biotech companies, CDMOs, and regulatory bodies. By working together to create guidance for stability testing based on risk assessment, drug developers can get ahead of regulatory expectations and establish new testing criteria to address quality expectations.

As stability testing and regulatory approval become increasingly intertwined, the value customers place on it is growing and becoming a high-value service provided by CDMOs. As more customers engage CDMOs for stability testing support, there is also an increase in the related requirements placed on outside partners.

The current landscape and future forecasts of the stability testing market indicate a need for global standardization and improved approaches. As this becomes reality, CDMOs will have greater adaptability to provide streamlined testing with better quality results generated.

Thomas, F. Stability Testing: The Crucial Development Step. BioPharm Intern. 2020 33 (3) 2020.

Vetter. Stability Testing & Storage. Vetter-Pharma.com; 2023.

World Health Organization. Annex 2, TRS No 999. WHO Good Manufacturing Practices for Biological Products (WHO, 2016).

ICH. Q1A(R2) Stability Testing of New Drug Substances and Products (ICH, 2003).

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Vol. 47, No. 12
December 2023
Pages: 25–26

When referring to this article, please cite it as Kottig, K. Stability Studies: An Essential Step for Quality Management in Drug Development. 

Sufficient stability studies show a drug product meets regulatory requirements, therefore ensuring the drug reaches the patients who rely on it.

Stability Studies: An Essential Step for Quality Management in Drug Development

Shailesh Vengurlekar is senior vice-president, Quality and Regulatory Affairs, at LGM Pharma.

Medicine capsule, diagram, chart and infographics in blue background. 3d illustration. Concept of pharmaceutics research and development. | ©Paulista - stock.adobe.com

Stability studies are a cornerstone of pharmaceutical quality. They ensure that a pharmaceutical product will maintain its efficacy and safety for its established shelf life to protect patient safety. These studies are the basis of two items on a product label: storage conditions and shelf life (expiry). Stability studies are also used to make decisions about production processes, formulation, and packaging, and to determine the re-testing period once a product is on the market. This article explains major aspects of the stability testing process and key considerations, including how a strategic and evidence-based approach can ensure robust labeling and maximum product quality while ensuring an efficient use of resources.

A robust stability strategy requires good planning

Pharmaceutical companies need a robust stability program, with clearly defined processes and detailed protocols. Although guidelines from the International Council for Harmonisation (ICH) and the World Health Organization (WHO) outline stability testing requirements, the guidelines were designed with flexibility to allow companies to use alternative approaches when scientifically justified (1,2).Therefore, it is important to have a solid understanding of regulatory expectations and take a strategic and evidence-based approach to planning a stability program. By doing so, it will be possible to create a suitable framework for these studies that is both efficient and scientifically sound.

Stability studies begin early and continue throughout drug development, with the regulatory requirements and degree of complexity and validation generally increasing through later stages (3). Early studies provide an initial understanding of a product’s stability profile, including degradation pathways. These studies also support decisions about the production process as well as formulation and packaging. All the data gathered from stability studies throughout development are then used to design the protocols for the final stability testing for regulatory submission to obtain market approval, including establishing the re-test period and commercial labeling.

A critical aspect of stability testing is the development of a stability-indicating method that must be able to separate and quantitate the API and all process impurities and degradation products in drug substance (DS) and drug product (DP) samples (4).

For small-molecule drugs, the primary stability-indicating method most often employs reversed-phase high-performance liquid chromatography (RP–HPLC) with ultraviolet (UV) detection (5). Ultra high-pressure LC is increasingly being used in stability-indicating methods, offering gains in speed, resolution, and sensitivity.

A stability-indicating method is developed through forced degradation studies, also called stress studies, which are carried out under conditions that are more severe than those of accelerated stability tests. Through these studies, a degradant profile can be developed in a short time span, such as a few weeks.

A new publication from authors representing 10 global pharmaceutical companies provides recommendations for designing forced degradation studies, to ensure the studies neither insufficiently stress the tested drugs, which could lead to surprises during long-term studies, nor overstress the drugs, creating risk of generating irrelevant degradation products that are different from those likely to be seen during storage, distribution, or use (6).

Stability studies should test product quality attributes susceptible to changes during storage that are likely to affect efficacy or safety, and should address physical, chemical, biological, and microbiological attributes (1).

The specific tests needed will vary depending on the product and product type. In addition to potency, degradation products, and process impurities, stability tests may address qualities, such as appearance, moisture, dissolution, hardness, viscosity, and pH, using a variety of analytical techniques. Injectable products will require microbiological testing, and injectables are often sensitive to temperature and light. Some products, such as those formulated for dry powder inhalers, are particularly sensitive to moisture. Certain products will require photostability testing, as described in ICH Q1B (1). In fixed-dose combination products, the interaction between two or more APIs also must be considered (2). Complex formulations such as nanoparticles and microemulsions may also require careful consideration of critical quality attributes. Likewise, there are additional considerations for stability testing of biopharmaceuticals (1,7,8).

Packaging is another important component of stability testing. In the studies, the drug substance or product must be packaged in a container–closure system similar to that which will be used for the marketed product (1,2).

Accelerated stability testing is normally carried out concurrently with long-term stability testing. Accelerated studies are conducted at higher temperature and humidity levels, to obtain data more quickly (in approximately six months) regarding degradation pathways and rate. These data can be extrapolated to establish a tentative expiration date, but long-term stability testing is required to confirm shelf life (1,3).

Accelerated studies can also be used to confirm the stability of a formulation or to compare the relative stability of alternative formulations. Testing more extreme conditions through accelerated degradation studies can be useful to evaluate the effect of short-term excursions outside the label storage conditions during shipping (1).

Storage conditions and sampling frequency for stability testing

The ICH and WHO guidelines establish the storage conditions under which stability testing should be conducted, and the sampling frequency, for both long-term and accelerated testing. 

Table I. An example protocol showing conditions of temperature and relative humidity, and test intervals, for accelerated and long-term stability testing for oral dosage forms marketed in zones I and II.

Another factor in planning a stability program is the climate where the drugs will be manufactured and marketed. ICH Q1 (R2) applies to climatic zones I and II, covering Europe, North America, and Japan. In 2006, the ICH withdrew the Q1F guideline, leaving the definition of storage conditions for zones III (hot and dry) and IV (hot and humid) to the WHO and the countries in those regions (2, 9).

Global pharmaceutical companies increasingly prefer to design a stability testing program that addresses the conditions of all zones where they plan to market a drug. This creates a more complex study design but provides all the needed data at the time of marketing approval.

WHO suggests label wording for storage conditions in zones III and IV for various sets of stability-testing results (9). It also provides recommended labeling statements for products tested in zone II that are shipped to zone IV (2).

Continuous monitoring after marketing approval is another important aspect of a stability program. Data from stability tests will determine the re-test period, following which, the drug substance should be tested to ensure it’s still suitable to use in the manufacture of drug product (1). ICH Q1E provides recommendations regarding when and how a re-test period can be extended beyond the time addressed by long-term stability data (1).

Reduced-testing strategies: ASAP, bracketing, and matrixing

The industry is showing increased interest in reduced-testing approaches designed to increase efficiency and reduce costs in stability testing. Three examples are accelerated stability assessment program (ASAP) studies, bracketing, and matrixing.

In the ASAP approach, a drug is studied under a few stressed conditions for a very short time, often two weeks, and the results are extrapolated to longer time frames and less extreme conditions. These tests, done early in development, can be a cost-effective way to help predict long-term stability. Formal long-term stability studies are still required (3).

In bracketing, only extreme conditions of product strength, container sizes, or container fills are tested for all time points. The study design assumes that the stability of intermediate levels is represented by the stability of the extremes (1). The risk of this approach is that if one result is out of specification, the stability study must restart from the beginning (3).

In matrixing, a selected subset of samples (representing a given strength or batch) for all factor combinations is tested at a specified time point. At a subsequent time, another subset of samples for all factor combinations is tested. The design assumes that the stability of each subset represents that of all samples at the given time point (1). Matrixing requires sophisticated knowledge of statistical concepts by both the quality professionals carrying out the studies and the regulators reviewing the data—considerations that may be slowing its adoption (3).

Stability testing is not a small undertaking, and many small- and mid-sized pharmaceutical companies will need to outsource it. First, the investment in stability chambers is significant. Five different chambers are needed to address all the ICH conditions, and these require resources and expert personnel to qualify them and handle ongoing maintenance, including the required annual temperature mapping. Backup power is also critical; a loss of power during a long-term study would necessitate starting the study from the beginning.

The stability studies themselves can be quite complex—with tens or hundreds of samples of different formulations, subject to several sets of conditions, under study at a given time—and likewise demand significant personnel resources and expertise to conduct them correctly. Thus, it is important to select a provider with extensive stability study experience and comprehensive knowledge of the regulations. Not all contract analytical testing facilities have the capabilities to conduct stability testing. When outsourcing analytical testing, it’s advisable to choose a provider who also offers full capabilities for stability testing, ensuring consistent collaboration with a trusted partner.

Establishing accurate storage conditions and shelf life for pharmaceutical product labels derives from stability testing, which is critical for ensuring product quality and patient safety. Stability testing also supports decisions about production processes, formulation, and packing. By taking a strategic and evidence-based approach, companies can design a robust stability program that not only meets regulatory requirements and protects patient safety, but also makes the most efficient use of resources and ensures precise labeling.

Annex 10: WHO Guidelines on Stability Testing of Active Pharmaceutical Ingredients and Finished Pharmaceutical Products

Dong, M.W.; Huynh-Ba, K. Stability Studies and Testing of Pharmaceuticals: An Overview, 

Expiration Dating and Stability Testing for Human Drug Products

Development of Stability-Indicating Analytical Procedures by HPLC: An Overview and Best Practices,

Zelesky, T;Baertschi, S.W.; Foti, C.; Allain, L.R.; Hostyn, S.; Franca, J.R.; Li, Y.; Marden, S.; Mohan, S.; Ultramari, M.; Huang, Z.; Adams, N.; Campbell, J.M. Jansen, P.J.; Kotoni, D.; Laue, C. Pharmaceutical Forced Degradation (Stress Testing) Endpoints: A Scientific Rationale and Industry Perspective, 

 2023, in press. DOI: 10.1016/j.xphs.2023.09.003.

Auclair, J.; Rathore, A.S. Analytical Methods to Determine the Stability of Biopharmaceutical Products, 

https://doi.org/10.56530/lcgc.na.qc1477t9

Rivers, M. Determining Shelf Life: Reading the Stability Testing Data. 

Stability Testing of Active Pharmaceutical Ingredients and Finished Pharmaceutical Products. Stability Conditions for WHO Member States by Region, 

Update March 2021 (accessed Oct. 25, 2023).

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Vol. 47, No. 12
December 2023
Pages 24–27

When referring to this article, please cite it as Vengurlekar, S. Establishing a Framework for Reliable Pharma Labeling. 

By taking a strategic and evidence-based approach, companies can leverage the insights gained from stability studies to establish robust labeling. 

Establishing a Framework for Reliable Pharma Labeling

Kari Miller is senior director, Product Management, QMS, IQVIA.

As the saying goes, “the only constant in life is change,” and in today’s life sciences industry, that’s undoubtedly the case, as the widespread adoption of digital technologies for enhanced efficiency and regulatory compliance has become central to the evolution of the market. This evolution includes patients using apps and wearables to transmit data to healthcare providers, as well as the utilization of data analytics for comprehensive management, insights, and trend analysis.

Change is certainly a constant for global industry compliance as new or updated regulations and reference documents are published every 25 minutes worldwide—that’s almost 58 a day, or more than 21,000 documents a year. Managing and maintaining compliance requires diligent efforts to track, review, evaluate, and take necessary actions to ensure continued compliance. To that end, quality management systems (QMS) need to be able to keep up with the ever‑shifting sands of regulation. In a paper-based system, this represents a monumental task, especially as more systems become digitally transformed. With that in mind, quality management teams are embracing technologies to drive innovation and remove these error-prone, labor-intensive paper-based systems or standalone solutions.

Electronic quality management systems (eQMS) play a pivotal role in embedding quality principles throughout an organization’s culture, facilitating the establishment of uniform policies, processes, and procedures. Consequently, organizations are better positioned for growth, ensuring alignment among employees and compliance with evolving regulatory requirements. In contrast, those still reliant on paper-based systems will struggle to keep pace with the rapidly changing regulatory landscape and the competitive advantages that technology affords others.

Implementing automated quality management operations enhances efficiency and consistency within a life science organization. However, the primary hurdle to embracing digital systems often boils down to the reluctance of individuals to depart from traditional paper-based methods. While paper-based systems come with their own set of challenges and limitations, people can be naturally resistant to change. Limitations of paper-based systems include increased manual administration, costly maintenance linked to workforce requirements, and errors in data collection, documentation, and retrieval.

Similarly, siloed systems encounter many of these same problems. In contrast, a digital eQMS introduces a uniform approach to quality processes, mitigating many of the aforementioned challenges. It offers adaptability for future innovations and aligns with current technologies, allowing for essential scalability, concurrent processing, and efficiency that paper-based processes cannot match. This integrated environment manages and monitors QMS processes, streamlining operations, reducing risk, and expediting deployment.

Moving from paper-based to digital quality processes requires changes in operations, functions, and departments, as well as a shift in the organization’s culture. Life science organizations seeking genuine digital transformation and innovation through comprehensive electronic quality solutions must implement a change management process that ingrains quality into their core culture. Engaging employees from the outset by soliciting their individual insights is pivotal for achieving better adoption rates.

For example, one multinational life sciences manufacturer recognized the importance of harmonization as they developed their eQMS function. They took the initiative to gather input from stakeholders across various divisions and global offices, ensuring that each employee’s voice was heard, and their input was integrated into critical processes. This approach led to enthusiastic employee adoption of the system.

Continuous feedback was collected as components were tested and implemented, giving all employees a sense of ownership over the initiative. In contrast to management’s or IT’s one-size-fits-all standardization, early and ongoing participation promoted a harmonious environment. When introducing a digital eQMS to an organization, there are several common challenges to avoid during the deployment process. Beyond addressing cultural and adoption issues, the quality team must establish an organization with a forward-looking approach to process data in near real-time and analyze historical data for proactive, preventative, and predictive measures.

Five common mistakes to steer clear of when implementing digital quality processes

To ensure a successful transition from paper-based or siloed processes to a digital environment, organizations must avoid these five common pitfalls when implementing a digital eQMS:

Lack of a comprehensive roadmap. It is essential to develop and adhere to a detailed roadmap for the seamless implementation of a fully integrated eQMS. This roadmap should encompass aspects like process harmonization, configuration, data migration, integration, project management, training, validation, testing, and go-live support. Encouraging employee feedback during implementation is crucial to keeping everyone engaged. Emphasizing harmonization over strict standardization fosters adoption and instils a culture of quality within the organization through inclusivity.

Replicating paper-based or siloed processes in the digital eQMS. Rushing into digitization often leads organizations to automate existing paper-based processes, inadvertently carrying over inefficiencies. This can result in issues such as sequential processing. Similarly, siloed processes tend to retain inefficiencies by failing to capitalize on opportunities for streamlining.

Inadequate management of expectations and scope creep. Before the full transition, it’s important to engage with every department involved in the QMS. Some teams may attempt to integrate themselves into the new system without proper inclusion in the core project plan. Setting clear internal expectations for the process and rollout plan can mitigate the risk of scope creep and disagreements about the system’s scope. In some cases, it may be necessary to pause the process and regroup to ensure alignment among all departments and adherence to the roadmap.

Neglecting migration planning. Proper organization and migration of existing documentation and information are crucial steps in the process of moving from paper-based systems. Avoiding internal conflicts regarding what to bring forward from paper-based systems is vital for the success of data migration and integration into the new system. When consolidating multiple systems, certain aspects may not carry over, which can lead to conflicts. A well-defined plan with a clear rationale for what will be included helps prevent outdated quality and compliance approaches from hindering progress. Not all legacy data needs to be transferred, and open communication and planning can address concerns about what will and won’t migrate.

Inadequate and opaque communication. Successful eQMS implementation hinges on effective communication among teams and stakeholders. Establishing communication channels from the outset and creating formal processes for feedback and conflict resolution are crucial for meeting target deadlines. It is imperative that all relevant departments are aware of the organization’s forward movement, what is expected from all parties and how to address obstacles or conflicts during implementation and validation. Furthermore, leaders must show interest and actively manage internal communications to ensure a smooth adoption process. Change control should be viewed as an ongoing activity, with processes in place to monitor and assess the impact of all outcomes.

Transitioning to digital eQMS is a complex endeavor that necessitates meticulous planning, a comprehensive roadmap, and transparent communication throughout the implementation process and beyond. By aligning processes, policies, and procedures from the outset, these initiatives stand a better chance of delivering the anticipated enhancements to operational efficiency, quality, and patient outcomes.

It’s high time to embrace digital eQMS to enhance the quality and safety of products and optimize operations. Traditional paper-based methods may ensure compliance, but they often fall short in terms of efficiency, productivity, and continuous improvement. Successful eQMS implementations lead to improved operational efficiencies, increased revenue, enhanced patient satisfaction, reduced manufacturing non-conformance, and ongoing process improvement. Furthermore, digital adoption enables the use of technologies that empower organizations to achieve more with less, addressing business challenges more swiftly through improved detection, trend analysis, and decision support. Technology fosters innovation in ways that paper-based or disparate system implementations simply cannot support.

Kari Miller, senior director, Product Management, QMS, IQVIA.


Vol. 35, No. 12
December 2023
Pages: 29–30

When referring to this article, please cite it as Miller, K. Revitalizing Quality Management Processes through Digitalization. 

Eradicating paper-based workflows and standalone solutions operating in isolation provides a good starting point to update quality management processes.

Revitalizing Quality Management Processes through Digitalization

Maria DiPietro is senior vice-president, Operational Solutions Architect & Strategy, ICON Strategic Solutions.

Two scientist or medical technician working, having a medical discuss meeting with an Asian senior female scientist supervisor in the laboratory with online reading, test samples and innovation | Image credit: © Nuttapong punna - stock.adobe.com

Partnership is an increasingly important criterium for bio/pharmaceutical sponsors as they seek more bespoke solutions and the flexibility of tailored, blended outsourcing models. Sponsors are consolidating their vendors and utilizing fewer contract research organizations (CROs), but with more expansive and aligned partnerships to find solutions to clinical research challenges and improve their R&D return on investment.

The consolidated CRO landscape, wherein the top CROs capture 70% of total clinical outsourcing spend (1), provides sponsors with large CROs offering fuller suites of services and broader geographic footprints, effectively shifting more importance onto their ability to deliver as partners. This move toward more meaningful partnerships has fueled additional evolution in the CRO-sponsor outsourcing landscape, including in the ways progress and success are measured.

Elaborations on outsourcing methodologies

Over the past two decades, the clinical research landscape has witnessed a significant transformation due to rising protocol complexity and the broadening scope of trials. In response to these changes, outsourcing methods have evolved, adapting to meet the demands of modern clinical research. As such, traditional models such as full-service outsourcing (FSO) and functional-service provision (FSP) have been reshaped to better accommodate new methods in addition to the proliferation of models that go beyond this distinction. This proliferation of new models reflects an ongoing trend in partnership development, fostering deeper, more meaningful collaborations. These collaborations, in turn, have given rise to customized, tailored, and hybridized models that can cater to the unique requirements of each study as scientific and regulatory considerations must be balanced with concerns around addressing patient access, diversity, and patient-centricity.

. Recent data from the Tufts Center for the Study of Drug Development (Tufts CSDD) underscores the dominance of the FSP model in current clinical trial outsourcing. In fact, nearly half of the clinical trials analyzed within the Tufts sample—comprising 117 trials across nine sponsor companies—utilized the FSP model. This represents a significant shift, as just a few years ago, FSO was the prevailing model for more than 77% of sponsor companies (2).

The popularity of FSP aligns with the growing importance of strategic partnerships. FSP offers sponsors greater control and ownership of key assets, a critical factor as clinical research becomes increasingly complex and risk-laden, while the cost of conducting trials continues to soar. According to 2023 ISR Reports, the average spend on clinical trials is expected to continue rising across Phases I–III for small, mid-size, and large sponsors (3). The outsourcing spend as a percentage of these costs will go hand in hand with these increases, likely resulting in additional innovation within the methods used.

. Collaboration between CROs and sponsors has transcended the conventional contract-execution models of the past. While the FSP model remains popular (4), especially among mid-size and large pharmaceutical partners, there is a notable trend toward blended models and tailored solutions.

Today, CROs position themselves as true partners, offering flexibility, consultative support and a deeper level of engagement to meet the evolving needs of sponsors in the face of increasingly complex clinical trials. In parallel, sponsors are embracing a more fluid relationship, characterized by shared governance, common objectives, and greater integration.

Biopharmaceutical sponsor companies will employ combinations of outsourcing methods even within single departments. They may leverage different models or levels of partnership complexity with different vendors or even with just one CRO across multiple scopes, strategically blending solutions. These adjustments in outsourcing strategies reflect the industry’s commitment to adapt and innovate in response to the evolving landscape of clinical research.

Merger and acquisition (M&A) activity in the CRO landscape can be understood as a function of the evolution from single-service provision to integrated, end-to-end solutions providers, and strategic partners. CROs must keep expanding capabilities to offer a “full suite” to their sponsors. And—as is common knowledge in the clinical research industry—the quickest means of innovation or portfolio expansion is acquisition.

The CRO landscape saw a significant year for M&A in 2021. According to 2022 Tufts data, the top clinical CRO segment had a record year in terms of the number and size of the recorded M&A activity, with 13 total transactions. As noted, between 15% and 25% of all M&A transactions in the CRO landscape reflect the pursuit of “novel” or less-traditional capabilities (1). In recent years, this has included augmented analytics and artificial intelligence in addition to technologies and solutions to enable decentralized clinical trials.

. CROs are also engaging in M&A as a means for expanding their footprint to better meet sponsor needs. For example, this could facilitate the ability to move strategic roles into regional hubs or offer lower cost settings. CROs are expanding their networks in ways that allow for more strategic control in these key target areas. However, this expansion is a double-edged sword. Tufts research notes that the inclusion of more countries and more investigative sites contributes to overall execution burden by compounding the complexity of the trial (5).

Intentional, strategic geographies are an important consideration for sponsors. Sponsors are evaluating their footprints for further rationalization while simultaneously seeking to expand into new geographies. They rely on their CRO partners to weave in their own footprint to extend the reach of their trial. The outsourcing models employed in this situation will likely leverage blended services where the CRO can fully support the conduct of the trial in those areas.

. CROs have broader perspectives and insights by nature of their position working across many therapeutic areas, modalities, geographies, etc. In working with a wide range of sponsor companies, CROs often gain a depth of experience and lessons learned that can positively benefit other programs when translated across outsourcing relationships. Sponsors are increasingly interested in leveraging the strategic value of these partnerships, turning to CROs for an infusion of those proven best practices and strategies and collaborating to create leading edge models to optimize delivery.

. In addition to acting as a library for best practices and lessons learned gained from direct experience with sponsors across the industry, CROs are driving adoption of advancing technologies and innovations.

In the modern research landscape, digital technologies are a crucial part of the ways in which sponsors and CROs are delivering and transforming clinical trials. The latest transformational technologies include quantum computing, blockchain, organ-on-a-chip, advanced statistical modeling, Big Data, and artificial intelligence (AI). To better understand sponsors’ attitudes toward these technologies, a survey was conducted among 350 pharma and biotech professionals. When asked “How much do you anticipate digital technologies improving R&D productivity?” 99% believed that they will see significant improvements (6).

The increasing cost and complexity of running clinical trials underscores this drive to leverage technology for efficiency and optimization, but it also adds challenges. Layering complicated tech onto already complex studies has the potential to become detrimental to overall processes. CROs have developed platforms and interoperable technologies to streamline this complexity and have invested both in tech and their personnel expertise to optimise and simplify these processes. Sponsors rely on this balanced, complex basketweave of processes and technologies to enable their trials.

. Of the transformational technologies, Big Data and AI are gaining the most traction. Big Data, and specifically a CRO’s access to data, offers invaluable opportunities for improving clinical research. Extracting meaningful insights from vast data requires computational capabilities to match, thereby driving progress in AI technologies. AI innovations include machine learning and deep machine learning, robotic process automation, and other expert systems that enable CROs to capture, consume, and interrogate more data with more impactful processes.

When it comes to data, the Tufts study found that FSP is now the most common model for both clinical monitoring and data management. These are highly technical areas that are exceptionally labor-intensive, carrying associated study costs. The FSP model can help to reduce costs and improve efficiencies, so employing this model for clinical monitoring makes sense for most sponsors considering this area alone can represent 35–45% of clinical trial costs (4).

. Another new outsourcing development takes the shape of joint innovation agendas. CROs may offer these models to allow larger pharma sponsors to pilot innovations within the structure of a consultative partnership, minimizing some of sponsors’ inherent risk while allowing them to benefit from their CRO’s experience and expertise. ICON has seen an increased interest in this model among its large pharma partners, and it is expected that this trend will continue.

Although CROs often work toward the same goal—bringing meaningful therapies to patients—no two organizations operate the same. As outlined previously, even the outsourcing methods used within a single partnership can vary. However, the lessons learned from one partnership can inform potential models or solutions for another. In this way, CROs can pilot and refine the methods used within partnerships to achieve harmony.

CROs often assume the role of change managers. Implementing digital solutions and new technologies may require new trainings or optimized organizational workflows to accommodate the new capabilities. Both CROs and sponsors must dedicate time and resources into building strong, trusting partnerships that allow for seamless integration. These relationships must also be backed by deliberate processes, controls, and governance structures to support joint operations and allow for clear measurement against shared goals.

. Tracking the success of a partnership operating within a new outsourcing model may be challenging. A selection of carefully identified partnership metrics representing these key categories can lead to mutual success in strategic partnerships. Four foundational categories for partnership metrics can provide meaningful data to guide decision-making: finance, quality, operations, and relationship health. Based on the specific goals, deliverables and context of each partnership, more granular key performance indicators can be selected for each of these metrics categories to provide the clearest mechanism for evaluating performance.

The bespoke solutions and continued evolution within clinical trial operations and the technologies they utilize will continue to fuel progress among outsourcing models as driven by CROs. While there are some overarching trends, CROs are tailoring their solutions and models to meet each organization’s unique business needs and they are leading partnership alignment to ensure predictable delivery with the highest quality standards. Looking forward, the author expects increased adoption of blended solutions and deeper and more meaningful strategic partnerships among industry players. In addition, there will be further development around benchmarking and standardization of models and terminology to allow for robust business cases informed by custom metrics.

Tufts. Impact Report: Analysis & Insight into Critical Drug Development Issues. 

 CSDD. Volume 24, Number 6–November/December 2022

Getz, K. Insights into Outsourcing Practices and Oversight Effectiveness. 

2023 CRO Market Size & Growth Projections. Industry Standard Research, 2023.

Lamberti, M.J.; Smith, Z.; DiPietro, M.; Barry, J.; Getz, K. Outsourcing Model Usage and Its Relationship to Clinical Trial Performance. 

 CSDD. Volume 25, Number 3–May/June 2023.

ICON Digital Disruption Survey. Digital Disruption in Biopharma How Digital Transformation Can Reverse Declining ROI in R&D.

When referring to this article, please cite it as DiPietro, M. Elaboration, Expansion, and Evolution in Outsourcing of Clinical Research. 

The rising complexity of clinical research protocols has necessarily expanded the CRO landscape. 

Elaboration, Expansion, and Evolution in Outsourcing of Clinical Research

Kate Coleman is Principal Quality and Compliance Consultant with Arriello Ireland.

Magnifying glass and check mark icon. Business concept,idea,right choice, quality control, approval and contract assignment | ©olyphotostories - stock.adobe.com

The new revision of the International Council for Harmonisation’s (ICH) Q9, Quality Risk Management document utilizes the word ‘effective’ many times throughout, both in the context of quality risk management (QRM) and the pharmaceutical quality system (1). The definition of what ‘effective’ means in this context is different for each company, stakeholder, and facilitator involved in QRM activities, which has facilitated a wide divergence in the application of QRM and risk assessment across the industry. The revised guideline addresses the divergence in stakeholder background and perspective and links this effectively with the bias that has historically been evident in many risk assessments that formed part of existing QRM programs. The new revision accurately states, “subjectivity can directly impact the effectiveness of risk management activities and the decisions made. Therefore, it is important that subjectivity is managed and minimized.” A renewed focus on the basic set-up of the QRM process can significantly aid in meeting this requirement.

This article was peer reviewed by a member of 

The new revision of ICH Q9 was timely, as the pharmaceutical industry had spent many years since the original version was published in 2005 trying to establish the look and feel of effective QRM while also struggling with QRM processes that became increasingly resource hungry and complex. Publication of the new revision in January 2023 heralded a new era in the design and implementation of QRM processes and provided an opportunity for review and re-establishment of the place of QRM throughout the pharmaceutical lifecycle. The new revision offered as its purpose a “systematic approach to quality risk management that leads to better, more informed, and timely decisions” (1).

One of the observations made across the industry since 2005 is the dichotomy of the enthusiasm with which companies have embraced QRM and the struggle they have had with understanding the importance of simplicity, clarity, and pragmatism that should form the basis of any risk management process. The industry is fully bought into the benefits of QRM but, at times, has struggled to fully realize the actual benefit that is possible when this process is implemented effectively. “Commensurate with the level of risk” has become a misnomer, and in some cases, the industry has fallen into a world controlled by failure mode and effects analysis (FMEA) and arduous capturing of information that is never revisited.

Planning of the QRM program and quality risk assessments

The first fundamental that has been often overlooked is the definition and planning of the structure and purpose of the QRM program. Section 4.2 specifically calls out “possible steps used to initiate and plan a quality risk management process,” and these include steps such as definition of the scope, the risk question, the definition of assumptions, and collation of relevant data (1). Often, the practice in reality has commenced with the selection of a tool and immediate commencement of a risk assessment without asking “what do we want to achieve?”. At the simplest level, a QRM program should help to identify quality risks before they occur, safeguarding patients but also reducing the time spent fixing issues that could have been avoided.

Setting boundaries around each risk assessment that is undertaken is a key driver for successful risk assessment. When performing a risk assessment, it is common for a cross functional group of people, who happen to have some availability in their calendar that week, to sit in front of a blank FMEA tool wondering where to begin.

To resolve these situations, every risk assessment should start with a strategy, commencing with setting the scope, and asking questions as follows:

What part of the process are we concerned with?

The ICH Q9 revision clarifies QRM implementation for the pharmaceutical industry and expands descriptions of methodology, formality, and risk-based decision making to provide the industry with a solid base from which to build their own QRM programs.

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