Automation Driving Productivity in Biologics Drug Development


There are several regulatory frameworks that guide quality standards in drug development, including Process Analytical Technology (PAT) and quality by design (QBD), to meet critical quality attributes (CQA). PAT is the overarching model for quality in drug development and describes “a system for designing, analyzing, and controlling manufacturing through the timely measurements (i.e., during processing) of critical quality attributes of raw materials and active ingredients.

Automation Driving Production in Biologics Drug Development

Drug development technology has evolved rapidly in recent years, and with the industry advancement comes an increased capability of producing high-quality targeted large molecule drugs such as biologics and biosimilars. The significance of biologics is far-reaching, including treatment of rheumatologic diseases, cancer, diabetes and anemia. The range of therapy modalities is ever-expanding and at present spans the spectrum from monoclonal antibodies to nanobodies, fusion proteins, immunotherapies, receptors, and synthetic vaccines.

Since most biologics are large molecules, they may prove challenging to develop and manufacture on the large scale. However, adoption of automation in the lab setting can set the stage for a pilot phase to drive optimization and efficiency into a new era of drug development and manufacturing. This transformation offers the added benefits of increased agility and compliance with regulatory guidelines, particularly during a pandemic era requiring social distancing and remote monitoring.

Biologic products and biosimilars

The growing market for biologics and their therapeutic specificity has resulted in a number of modality options, which are now being considered to combat the novel coronavirus. In addition to having more precise targeting capabilities in situ compared to traditional small molecule drugs, large molecule medications like biologics also offer a more profitable financial return on development costs.

Following the success of biologics, biosimilars are being developed to offer a cost-effective alternative to the high price point of patent-protected first-generation drugs. The FDA requires that biosimilar products must demonstrate a high degree of relevance to their biologic counterpart in regard to biochemical profile, mechanism of action and safety.

There are several regulatory frameworks that guide quality standards in drug development, including Process Analytical Technology (PAT) and quality by design (QBD), to meet critical quality attributes (CQA). PAT is the overarching model for quality in drug development, and describes “a system for designing, analyzing, and controlling manufacturing through the timely measurements (i.e., during processing) of critical quality attributes of raw materials and active ingredients.”(9)

Quality by design was a concept first introduced to the field by Dr. Joseph M. Juran(10), who suggested that the majority of quality issues for a specific product could be avoided through intentional design, rather than increased testing and surveillance. Overarching goals of the modern QBD approach include development of meaningful quality specifications measured through clinical performance; reduction in product variability and quality issues through better understanding and control of the design process; increased efficiency; and use of evidence-based root cause analysis and change management. By considering upstream factors in drug design, companies can predict and prevent downstream issues that might lead to delays in drug approval or costly recalls. Critical quality attributes are a key component of the upstream considerations in the QBD model, and represent the “physical, chemical, biological, or microbiological property or characteristic of an output material”(9) that should fall within predetermined limits, ranges or other variable distributions as a measure of product quality. Relevant attributes include everything from drug identity to its degradation products, mechanism of release or dissolution, moisture and microbial limits, and descriptors like its color, shape, size, and odor. The degree to which these attributes are considered critical is determined by the anticipated degree of harm to a patient if the respective attribute falls outside of its acceptable range. The CQA does not take into account factors such as probability of occurrence or methods of controlling such outcomes.

CQAs are particularly relevant to the approval of biosimilars, just as they are for patented biologic drugs. Biosimilars should not be considered “generic” versions of specific reference biologics. Although they must meet certain similar CQAs to biologic drug models, as well as safety and efficacy measures, biosimilars do not constitute “true” biologics. It is nearly impossible to completely replicate complex large molecule biologics, so the goals of biosimilar products are aimed at meeting the CQAs determined to be most critical for a particular drug or product. Similar to the approval process of biologics, the vetting of biosimilar products involves careful analytical comparability testing.

The pharmaceutical industry’s focus on safety and consistency of product has led to an increased adoption of automation in the drug development setting, based on recognition of its many potential benefits.

Benefits of automation in drug development

While the value of automation in the lab setting have become even more visible during the COVID-19 pandemic, certain attributes of this approach were recognized in the existing bioprocess environment. In recent years, automation has already begun to be implemented for use in development of both small- and large molecule products. The push for automation was driven by the increasing demand for biologics, particularly monoclonal antibodies but also vaccines, peptides and other large biomolecules. Prior to the introduction of automated processes, biologics developers were relying primarily on in-person staffing and use of conventional fermenters, batch process manufacturing and traditional monitoring. Influenced by factors ranging from cost savings to tightening audit requirements in the drug development industry, biotherapeutics developers began to realize the value of smart manufacturing based on digitization and continuous manufacturing. Indeed, this era of large drug development and manufacturing, referred to as “Bioprocess 4.0,” focuses on application of machine learning and artificial intelligence to enable predictive modeling, automation, and optimization of the process from raw materials through the entire operation.

When COVID-19 was designated as a pandemic in late 2019, biologics gained global attention as a result of their potential role as therapeutics. At the time, biologic drug manufacturers were primarily focused on production of monoclonal antibodies. However, as the COVID-19 death toll soared, the pharmaceutical industry unified efforts toward timely vaccine development and repurposing of existing drug products into antiviral therapies. Faced with staffing restrictions and supply chain constraints, pharmaceutical companies accelerated the adoption of automation to enable remote monitoring as well as scale production in shifts to maintain a socially distanced safe environment. Automation allows for continuous manufacturing to be run around the clock and with less in-person monitoring; in addition, increased consistency has been shown to help with conservation of key components, including adjuvants and other components.

The intense push for production efficiency and timely results also encouraged a transition to modular, single-use reactors, which offer increased agility when trying to manufacture different drug products, compared to conventional hardware such as stainless-steel fermenters. The new modular systems also reduce the need for such intensive in-person staffing, so that employees may be re-deployed into new roles. Although the transition to automation and digitization can be costly, the long-term ROI resulting from increased efficiency and production appears to support the migration.

Finally, automation and digitization allow for the collection of vast amounts of quality assurance data, which can facilitate a robust audit trail and lend itself to predictive modeling efforts. Enhanced data collection assists in monitoring quality measures in real time and increases the likelihood that a product will remain on schedule throughout the drug development process, via achievement of CQA and QBD targets. This presents an obvious financial benefit in the form of cost savings and helps to ensure safety and reliability of the final product.

Challenges of automation

The use of automation in the biologic drug development environment presents many benefits, but it also comes with several unique challenges.

The most readily apparent challenge to most drug developers is the initial financial investment. Automated processes require software support, and subsequent configurations can be expensive and time-consuming. Industry experts tend to recommend a thorough evaluation of processing needs as well as a careful assessment of all relevant automation options, in the interest of avoiding overspending.

An evaluation of specific drug development needs will also help prevent insufficient gains, by considering required PAT and CQA targets.

Ensuring optimal restructuring of process workflow is particularly important, as it can help remedy any immediate backlogs during the transition to a full- or partially automated system. Beyond this, staff will likely need to be re-deployed into new roles. As with any form of change, employers may encounter initial cultural resistance, which will require effective leadership and communication.


The introduction of automation into the bioprocess environment has created a new frontier in the development and production of large-molecule drugs. While this shift comes with certain unavoidable challenges, the benefits have thus far been seen to greatly outweigh any potential drawbacks. As the healthcare industry needs to evolve under increasingly uncertain conditions, pharmaceutical companies have begun to recognize the value of automation in remaining agile and responsive to unforeseen circumstances.


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