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Deep process characterization and "lab-on-a-chip" enable SMART bioprocess design.
With thousands of biotechnology companies worldwide, development of biopharmaceutical therapeutics is highly competitive. Revenue, market share, and earnings from sales of ethical biopharmaceutical drugs are crucial financial drivers today, but a robust research and development (R&D) pipeline assures success and longevity. To succeed in the long term, innovator companies must develop and launch new drugs consistently. Most "low-hanging fruit" research targets have been developed, leaving research teams to rely on more innovative discovery and development capabilities to identify novel targets that not only address unmet medical needs, but also demonstrate potential for fulfilling top-line growth objectives. Similarly, follow-on biologics companies must expedite development of biosimilars of existing blockbuster drugs prior to patent expiration or risk losing market share to generic-drug competitors. When a promising drug candidate is identified, a high-performing organization will develop a SMART biomanufacturing process rapidly to address unmet medical needs and achieve performance targets. SMART processes are: Scalable to commercial manufacturing scale, Modeled based on comprehensive and exhaustive data sets, Adaptive to meet acceptance criteria at operating limit thresholds, Rational to justify established process parameters based on performance data, and Tested to assure confidence in process robustness and reproducibility.
Similar to the fields of bioinformatics and genetics in the 1980s, wherein scientists analyzed and mined whole genomes to identify disease-causing therapeutic targets, there is a wealth of historical bioprocess development data in the field of bioprocess engineering. These data can be analyzed and mined to identify SMART bioprocesses for major classes of drugs (e.g., full-length monoclonal antibodies). Best-in-class companies will access their development documentation and data to develop platform processes, requiring only platform process confirmation studies to prepare them for use on novel or biosimilar drug candidates. For more complex drug candidates, these organizations will employ quality-by-design (QbD) principles coupled with multivariate design of experiment (DOE) studies to increase process understanding and enhance predictive capabilities. Ultimately, deep process characterization will enable them to employ SMART bioprocess design principles to increase the speed and likelihood of success of developing clinical and commercial manufacturing processes.
To fully realize the potential of SMART bioprocess design, data, data, and more data are required to drive correlation of the physicochemical properties of a biotherapeutic protein (e.g., primary amino acid sequence, tertiary structure, surface charge distribution, surface hydrophobicity/hydrophilicity, aggregation state, glycosylation patterns) and their effect on fermentation, recovery, formulation, and analytical bioprocess parameters. The challenge is how to execute studies to generate a high volume data for analysis. Multivariate DOE studies are inherently elaborate and require significant data and time for analysis. Even a simple full-factorial DOE study investigating only five factors (e.g., pH, salt, concentrations, buffer, time) of two levels each (e.g., low and high), requires a minimum of 25 =32 experiments and the associated full-time equivalents (FTEs) and materials to complete. Material availability is also an issue, especially early in development when expression titers and yields are generally low, with laboratory- and pilot-scale studies generally performed on liter and hundreds-of-liter scale, respectively.
To overcome these challenges, high-throughput, automated, scale-down systems that accurately mimic at-scale bioprocess behavior can be implemented to expedite study execution that requires minimal FTEs and materials, yet generate a high volume of data. Lab-on-a-chip approaches represent the ultimate scale-down mode for bioprocess development that, if realized, can shrink bioprocess development to the sub-microliter scale. Not only can automated methods be used to execute studies, but also they can be invaluable for sample testing and data collection for the orders of magnitude increase in data output. In this laboratory of the future, fermentation, recovery, and formulation parameters can be developed using SMART bioprocess design to maximize expression titers, recovery yields, and shelf-life stability, respectively. The wealth of data that is generated can serve as a repository for data-mining to develop robust manufacturing processes in silico, based simply on amino- acid sequence and protein structure.
Novel approaches and technologies are needed to meet the evolving challenges of biopharmaceutical R&D to identify blockbuster therapies and develop the bioprocesses required to economically manufacture drugs that consistently meet product quality expectations. SMART design principles enable companies to meet those challenges ahead of competitors and facilitate bottom- and top-line growth targets.
Albert S. Lee is an associate, and Mark A. Mynhier is a partner, both in the health care practice at the global management consulting firm PRTM, email@example.com and firstname.lastname@example.org.