Applying QbD to Upstream Processing

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The concept of quality by design (QbD) has been part of the biopharmaceutical industry for more than a decade. While similar approaches to QbD are used in the development and manufacturing of large-molecule and small-molecule drugs, the complex nature of large-molecule drugs creates more factors that may affect process performances and the critical quality attributes (CQAs) of the drug.

Because biomanufacturing involves living cells, there are more reactions happening in a bioreactor than usually happen in chemical reactions, according to Bill Whitford, strategic solutions leader, bioprocess, GE Healthcare. “While we often understand nearly everything about a small-molecule reactor development pathway, we have only a partial understanding of all the interacting salvage, degradation, and biosynthetic pathways of mammalian cells. This includes even many relevant metabolic pathways, inductions, flux, rates, and turnover.  Finally, we often develop and control bioprocesses using very surrogate key quality indicator (KQI) and critical process parameters (CPPs). Rather than measuring precisely what we know (or suppose) to be relevant, we monitor such general parameters as pH, oxygen, trypan-blue exclusion, and such token or generally representative metabolites as glucose or lactate,” says Whitford.

According to the International Consortium for Innovation and Quality in Pharmaceutical Development (IQ Consortium), a collective of more than 35 biopharmaceutical companies, there are also differences between the use of QbD for large molecules versus small molecules when considering antibody-drug conjugates (ADCs). ADCs are complex molecules composed of an antibody linked to a cytotoxic small-molecule drug. As noted by the IQ Consortium, companies that develop ADCs state that CPPs and CQAs may also differ between the drug-linker (DL) component of the ADC and the related small-molecule drug because the DL represents a small portion of the total mass of the ADC. Unlike small-molecule drugs, the dosing frequency and scheme of the ADCs also may dilute the impurities in DL intermediates. 

Another difference between small-molecule and large-molecule drugs, according to the IQ Consortium, is that in early development, a platform process approach may be applied, which may accelerate timelines and reduce development burden.

QbD in upstream processing

QbD is used in cell-culture expansion, cell harvesting, and cell-culture media development. However, according to the IQ Consortium, many companies find that clone selection and the development of the production-stage bioreactor process benefit most from the application of QbD due to the following: “In clone selection, the final producer cell line is defined, which is then used for the commercial process and throughout the lifecycle of a product, while the N-stage production bioreactor is considered to be the most critical step with respect to the CQAs and essentially determines the final product quality from an upstream perspective. In principle, all upstream process steps can benefit from a QbD approach and examples where companies apply those also depend on the assessment of (business) risks,” a representative of the IQ Consortium says. Predefining development goals for titer ranges and product quality profiles, as well as assessing risk, also benefit from QbD.

The development of robust processes that include good cell culture performance can be done through design of experiments (DoE). In early stage development, QbD can be used to identify a quality target product profile (QTPP), according to the IQ Consortium. Late-stage applications include process characterization studies to decide on one-factor-at-a-time (OFAT) experiments versus DoE-based experiments and the defining and classifying of parameter ranges. “Currently, a claim of a design space is not pursued by most companies. However, achieving in-depth understanding is a goal (e.g., by advanced data analysis approaches). Also, detailed risk assessments can play an important role in late-stage activities. Commonly, the knowledge gained in process characterization studies (PCS) will be included in the control strategy,” the IQ Consortium states.

Performing small-scale DoE by applying verified scale factors and using a risk-based approach are key to implementing QbD in upstream processing, says Whitford. “The final product will be a verified design space, clearly communicated-being mindful of using accepted terms and definitions-to regulators in the submission.  Currently, upstream process development focuses on digitally transformed and intensified biomanufacturing,” Whitford states.

Bioreactor process development has been positively impacted by the use of QbD and empowered by process analytical technology (PAT), according to Whitford, by enabling continuous monitoring of process parameters and product characteristics throughout development and manufacturing. “In the pursuit of process intensification, the influence of altering process parameters on product characteristics can be rapidly assessed.  This contributes to increasing an understanding of what types and ranges of bioreactor parameters provide acceptable product quality. This mapping of the relationship between bioreactor process characteristics and product CQAs will define a bioreactor design space, an important element in both ensuring a robust process as well as current FDA expectations in filings,” says Whitford.

Using QbD to develop cell culture processes

Because cell culture has a direct impact on production efficiency and product quality, using DoE can help determine the relationship between cell-culture process parameters and product quality, which then helps determine the design of the operation space for a process. DoE can also help determine the needed sensor technologies to establish robust closed-loop model predictive controls, according to Whitford. “Unlike many other production processes, biomanufacturing has classically been hindered by a lack of real-time product attribute measurement. Even in nominally identical bioprocesses, we can see diversity in product quality due to variability in (e.g., such raw materials as cell culture media). Using a QbD/PAT strategy directs the ­implementation of advanced near-real time monitoring through the application of newer, automated sampling techniques and multivariate data analysis. This supports advanced in silico modeling that can not only help control the process, but predict product quantity and quality outcomes,” says Whitford. 

Implementing a feedback quality control strategy, as opposed to a recipe-based control strategy, when using QbD in developing cell culture processes may be the best approach, says Brandon Downey, principal engineer, R&D at Lonza, because of the challenges cell-culture processes present. “Many [cell culture] processes have successfully used a recipe-based control strategy, determined using a design space approach, to manufacture quality product. Nevertheless, cell culture processes pose some unique challenges when attempting to employ a recipe-based control strategy,” says Downey. And, the large number of raw materials used in cell culture can make understanding the variation of each component difficult. “Although fundamental understanding of how variations in common raw material attributes impact a cell culture process is increasing, this knowledge is still far from complete enough to encompass the variation that has been observed in the cell culture media,” he states.

The behavior of the cull culture process may also be impacted by variations in unmeasured impurities in raw materials, says Downey, making it difficult to determine the total impact of raw material variation on the cell culture process. “Finally, the cells in the production cell culture step uniquely determine many of the structural attributes of biologic molecules which could (and in some cases have been clinically shown to) influence the function of the molecule.”

QbD in cell line selection

When it comes to cell line selection, QbD can be used in the selection of a final clone during early-stage development by using prior knowledge to predefine clone selection criteria including cell-line stability, culture performance, and product quality attributes. “As the selection process progresses, more clone-specific knowledge is gained, and the process continues until the clone that best fulfills the selection criteria is identified,” according to the IQ Consortium.

Whitford stresses that even though many bioprocessing platforms have been around for decades, individual process optimization is beneficial to individual cell strains, recombination techniques, cloning approaches, and culture systems. Continuous monitoring is being enabled by PAT-derived systems, says Whitford. In addition, highly accelerated bioprocess development is supported by new fully-automated, single-use multi-parallel bioreactors. “Employing some of the process monitoring and design approaches … in the context of high throughput and automated systems for process development is now very popular.  For example, some vendors offer as many as 24 fully featured, single-use, 250 mL mini-bioreactors supporting process development and optimization as well as selection or development in scale-down studies,” says Whitford. 

Medium selection, however, varies based on practices and needs, according to the IQ Consortium. “For example, companies that employ a platform process approach, including a platform cell culture media system, typically do not require media selection for molecules that fit the platform process. However, for atypical modalities that do not fit the predefined selection criteria, media screening can be done to select the condition which yields attributes that best fit the criteria,” says the IQ Consortium.

QbD in equipment selection

Using a QbD approach to define objectives for establishing a new ­manufacturing facility may be beneficial. Risk assessments can be used to evaluate different equipment against the predefined objectives. The knowledge gained through this process can show how different equipment may impact the objectives, according to the IQ Consortium.

QbD can also be applied to the evaluation of equipment performance. The suitability of equipment can be established through experimental designs, says Whitford. “This is generally accomplished through either limited experimentation at scale, or through studies in scale-down models, employing prior understanding of the scale-factors involved in extrapolating results to full-scale manufacturing.  Premier single-use system vendors have for years been supplying much information to aid in such studies. One example here is CFD data on single-use mixers and various scales, fill volumes, impeller speeds, and viscosity of diluents.  We have seen such techniques applied to the selection of single-use bioreactors and mixers, perfusion apparatus and even dynamically feed-back controlled inline buffer conditioning systems.”

When it comes to late-stage development, the IQ Consortium agrees that small-scale models (SSMs) are essential to QbD when scale-down models must be qualified to show they represent large-scale operations. “SSMs are used in PCS to understand the process and determine parameter ranges and classifications. Given that the PCS experiments are often large and complex, it is important to balance between representativeness and throughput of the SSMs,” according to the IQ Consortium.

Challenges in QbD in early development

Designing quality into the biopharmaceutical process early on can be difficult because the nature of the protein may not be fully understood, says Whitford. There is also a range of variants, such as glycosylation in monoclonal antibodies (mAbs), that occurs when biologics are generated, which can affect both biological activity and/or the secondary or tertiary structure of the molecule. “This can influence not only the entity’s activity but its stability in storage,” says Whitford.  “Secondly, we don’t yet fully understand the conditions of cell culture that will control those molecular properties. Often, they are discovered more empirically than by pathway and structure understanding. Finally, we are only beginning to implement comprehensive monitoring of the bioreactor process, metabolites, and products that can provide robust specific, relevant, and predictive process parameter values and product quality assessment,” Whitford adds.

A robust cell culture platform process can ensure that CQA profiles are consistent from the early stages of development through commercialization, according to the IQ Consortium. It may be difficult, however, for companies without a platform process in place to incorporate quality in early-stage development without performing additional experimentation to determine the robustness of their process. This experimentation can be done using a QbD approach and DoE methodology, says the IQ Consortium, when “molecules cannot conform to the platform.”

This work, however, could have an impact on investigational new drug application submission timelines. “There is a limitation to how much quality can be designed into the process during early-stage development, and in some cases, target quality characteristics may not be clear. Some companies question what effort associated with this should be taken early in development. For example, early-stage CQAs are more focused on the safety aspect of the drugs and as clinical data and process experience are gathered over the course of the project, this informs the efficacy aspect thus modifying the CQAs in late-stage development,” according to the IQ Consortium. They also warn that using in-licensing molecules may present unanticipated challenges. “This might, depending on the development stage of the in-licensed molecule, shift focus from ‘design’ to comparability.”

Conclusion

The implementation of QbD into the bio/pharmaceutical industry aligns with regulatory and industry efforts to ensure the efficacy, safety, and quality of medications. The complex nature of biologics adds additional complexity to developing and manufacturing these innovative therapies. Using a QbD approach from early-stage development through commercialization can ensure that upstream processes are efficient and reliable.

“As biologics increasingly take the form of novel modalities (e.g., not mAbs), it will become increasingly important to be able to control the quality of biologics so that the structure-functional relationships of these new classes of compounds can be better understood in the clinic. Furthermore, as medicines become more personalized, the ability to tailor the structural attributes of biologics molecules that are dictated by the process will become increasingly important. QbD will likely continue to play a foundational role in creating the processes which can robustly deliver these compounds,” concludes Downey.

Article Details

BioPharm International
Vol. 32, No. 7
July 2019
Pages: 17-22

Citation

When referring to this article, please cite it as S. Haigney, "Applying QbD to Upstream Processing," BioPharm International 32 (7) 2019.