Several advances in scale-down models have made great contributions to accelerating biopharmaceutical process development. Therapeutic biologics have gained considerable momentum in the past three decades by delivering superior clinical performance to common disorders (e.g., rheumatoid arthritis) and disease areas for which traditional small- molecule drugs have proven ineffective. Due to their inherent structural complexity, biologics production is strictly scrutinized from early development stages by regulatory agencies. Both biopharmaceutical manufacturers and regulators heavily emphasize developing science-based, consistent and robust manufacturing process to ensure quality, safety and efficacy of final biological products.
Scale-Down Models – Indispensable For Process Development, Characterization And Validation
This modern biopharmaceutical manufacturing process not only incorporates principles of quality by design (QbD), but also allows implementation of continuous improvement. Such process is built upon thorough understanding of the process and the product, which cannot be achieved without the aid of scale-down models, or small-scale models.
Scale-down models are increasingly adopted by the biopharmaceutical industry and serve as an indispensable tool for process development, characterization, optimization and validation. Due to the complexity of biological products, their manufacturing process needs to be well understood, characterized and controlled. A typical biomanufacturing process development starts as early as in the drug discovery stage (i.e., small cell culture shake flasks), when process knowledge is limited. The process is continuously developed, tested and refined through benchtop bioreactors, pilot-scale bioreactors and commercial-scale production. Throughout this journey, scale-down models are used to allow process specialists to quickly gain process knowledge and trans- late it into optimal operation conditions in a cost-effective and timely manner.
Types Of Scale-Down Models
Scale-down models have been applied to a broad spectrum of process development, from upstream cell line selection and growth medium optimization to down- stream product separation and purification. Multidimensional experimental studies can be conducted in scale-down models to test process parameters. The extent of such studies usually is not feasible or cost- prohibitive at commercial scale.
Based on the purpose of design goals, there are two types of scale-down models:
 Miniaturization of a full-scale unit operation
 Partial, or “worst-case,” model of specific properties1
The miniaturized full-scale model is designed to mimic the whole unit operation (e.g., bioreactor cell cultures) and examine effects of input material and process parameters on process performance and product quality. This type of study is typically conducted in a reduced-size version of the full-scale equipment. The comparison of model performance to full-scale is required to qualify these scale-down models. A qualified scale-down model can, in turn, allow the ease of scaling up the operation.
Partial / worst-case scale-down models are designed to represent a specific physical and / or biochemical environment within a unit operation and test the worst-case conditions of a subset of parameters (e.g., shear force). Miniaturized equipment, or an apparatus imparting a desired force, property or environment is usually used in the study.1 These models are particularly useful in identifying critical process parameters (CPPs), defining their acceptable ranges and understanding the impact of parameter deviations on the manufacturing process.
Regardless of type, the scale-down mod- el needs to be qualified prior to submit- ting scale-down process characterization studies for Biologics License Applications (BLAs). The purpose of qualification is to demonstrate the suitability of a model in evaluating the effect of input material and parameter variation on process performance and a product’s critical quality attributes (CQAs).  Parallel to the gradual increase of production scale, the scale- down model is continuously developed and improved during clinical development. Formal model qualification is typically carried out in Phase III runs when final scale-up is complete and full-scale data is available for comparison. A combination of qualitative and statistical assessments is then performed to determine to what degree the model represents its full- scale counterpart and reliably predicts full-scale manufacturing performance.
The main regulatory guidance for scale- down models is ICH Q11, which recognizes the importance of scientifically justified small-scale models to support process development and “the extrapolation of operating conditions across multiple scales and equipment.” In the process validation package for licensure, both commercial- scale process validation studies and small-scale studies are required. It is expected that the results from commercial- scale batches closely mirror results from small-scale studies. The significance of the data obtained from small-scale studies to support process validation depends on the successful demonstration that the model appropriately represents the pro- posed commercial-scale operation.
ICH Q11 also requires studies to demonstrate process ability to remove product- related impurities (e.g., intermediates, degradants), process-related impurities (e.g., host cell DNA and proteins), and potential contaminants (e.g., viruses). In the case of viral clearance, ICH Q5A (R1) provides specific guidance on this matter.  The viral clearance studies can only be performed using qualified scale-down platforms, including chromatography and nanofiltration, in a virology lab located outside the cGMP facility. The viral clearance data is required for both Investigational New Drug (IND) Applications and BLAs, though the scope of data is reduced in IND applications.  The difference in data requirement reflects regulatory agencies recognition of the status of biomanufacturing process at the beginning of clinical trials, when process parameter ranges are not well established.
Limitations Of Scale-Down Models
Despite a scale-down model’s ability to test various input parameters at conditions even outside normal operating ranges, it cannot fully represent a physically larger, more complicated and expensive system. It is important for biological manufacturers to understand the limitations of their scale-down models. As pointed out in the FDA’s recently updated Process Validation Guidance, any differences that exist between small-scale and commercial process “may have an impact on the relevance of information derived from the models.” 
Furthermore, scalability varies among scale-down models for different unit operations. Generally, chromatography and filtration scale well. For chromatography, it is widely accepted that operation of a smaller diameter column at the same bed height and linear velocity is an acceptable scaled- down model for larger-scale columns. For filtration, once operating parameters are normalized, filtration scales rather well. On the other hand, small-scale models for harvest/centrifugation (e.g., disk-stack centrifugation) are poorly scalable. This unit operation is best characterized at scale.
Another challenge for scale-down models is prediction of filter performance in the presence of a viral spike during viral clearance assessments. The virus and/or impurities often cause filter fouling, which does not commonly occur with protein streams. Therefore, it is difficult to predict maximum viral filter loading using a non-virus-containing feedstream. The best means to overcome these limitations is to develop processes that no longer re- quire viral clearance from chromatography. Many well-established techniques to inactivate viruses (such as low pH, detergent, UVC) and / or to remove viruses (such as nanofiltration) can be utilized to eliminate the need for clearing viruses by chromatography.
Technology Advancement In Scale-Down Models
Several advances in scale-down models have made great contributions to accelerating biopharmaceutical process development, including automated scale-down bioreactor systems for cell culture (e.g., ambr® 250, Sartorius Stedim Biotech), microcolumns for chromatography, and mechanistic modeling and simulations for chromatographic performance prediction. These technologies allow process scientists to better understand critical process parameters and optimize processes rap- idly without the need for pilot-scale runs and extensive experimentation. Addition- ally, they require significantly less material than traditional scale-down models.
- McKnight, N.,“Scale-down Model Qualification and Usein Process Characterization.” CMC Strategy Forum. Jan 28, 2013. http://c.ymcdn.com/sites/www.casss.org/resource/ resmgr/CMC_No_Am_Jan_Spkr_Slds/2013_CMCJ_McKnight- Nathan.pdf.
- ICH Q11, “Development and Manufacture of Drug Substances (Chemical Entities and Biotechnological/Biologi- cal Entities).” May 1, 2012. http://www.ich.org/fileadmin/ Public_Web_Site/ICH_Products/Guidelines/Quality/Q11/ Q11_Step_4.pdf.
- ICH Q5A(R1), “Viral Safety Evaluation of Biotechnology Products Derived from Cell Lines of Human or Animal Origin.” Sept 23, 1999. http://ichguideline.weebly.com/ uploads/2/6/2/1/26210522/q5a_r1__step4.pdf.
- Remington, K.M., “Fundamental Strategies for Viral Clear- ance Part 2: Technical Approaches.” BioProcess International. May 12, 2015.http://www.bioprocessintl.com/downstream- processing/viral-clearance/fundamental-strategies-for-viral- clearance-part-2-technical-approaches/.
- CBER/CDER/CVM, “Guidance for Industry: Process Valida- tion — General Principles and Practices.” US Food and Drug Administration. Jan 2011. http://www.fda.gov/downloads/ Drugs/.../Guidances/UCM070336.pdf.