QbD Strategies to Secure the Scale-Up of Semi-Solid Topical Formulations

Once a formulation has been selected for a given API, its manufacture needs to be scaled up to supply toxicology, clinical, and commercial batches. Scaling up may appear simple, but most pharmaceutical companies have now learnt the value of a well thoughtout strategy for process development. With the ever-increasing costs and timelines associated with drug-development programs, risk mitigation invariably pays dividends. Process development requires modest investment in time and money compared to the risk of batch failure and the consequent adverse effects on timings, costs, and project success if they were to happen. Furthermore, it is not only development programs for new chemical entities (NCEs) that can benefit from process development. It is a step that generic-drug companies omit at their peril as it is often not realized that originator products may not be based on optimized formulations, and that the correct process parameters needed for their manufacture may not be well defined. 

Quality by design (QbD) is an approach that aims to ensure the quality of medicines by employing statistical, analytical, and risk-management methodology in the design, development, and manufacturing of medicines (1). The practice has now been thoroughly embraced by the majority of the pharmaceutical industry as it has become clear that, far from being a hindrance (2), a QbD approach in combination with six sigma tools, provides a methodology from which a robust processing control strategy can be derived. The QbD strategy, given the appropriate scale-up considerations, lives with a product throughout its entire life, not just for the initial scale-up for toxicology or clinical batches but also when moving to commercial scale and future plant-to-plant transfers. The benefits of this approach are multiple. Above all, the building-in of quality as opposed to the determination of quality by testing clearly demonstrates to regulatory authorities that control over consistent manufacturing has been established. This increased level of assurance in manufacture reinforces the commercial viability of the product.

In addition to experimental design to challenge critical process parameters (CPP), six sigma tools such as failure mode effect analysis (FMEA), input-output diagram, voice of the customer, benchmarking, and evaluation criteria add to the power of the QbD approach.

Applying QbD to topical formulations

Defining a control strategy around CPPs and critical material attributes (CMAs) is of particular importance with semi-solid products. The composition of such products is carefully derived by taking in every aspect of the target product profile while acknowledging the scientific and technical constraints imposed by the API, excipients alone, and in combination. The attributes of each material used can have a profound impact on the resultant formulation in terms of both safety and efficacy, but they can also dictate or heavily influence the manufacturing process especially in areas such as dissolution, mixing parameters, and heating or cooling processes. The safety and efficacy of topical pharmaceutical formulations, such as creams, gels, foams, and ointments are clearly intimately related to the composition of the product; however, there is often a lack of appreciation for the relationship between clinical performance and the microstructure of the formulation, which is highly dependent upon manufacturing process parameters. 

Any process development program aimed at product optimization must, therefore, take into account a broad range of processing parameters to ensure consistent manufacture of pharmaceutical product to the chosen specification. Creams tend to be one of the more complex topical pharmaceutical formulations (3) where the combining of two immiscible phases requires a defined order and speed of addition of materials; defined speeds and times for the mixing and homogenisation steps; and more often than not, a controlled heating and cooling rate needs to be applied during the process (4). The more complex the processing requirements, the more it is likely that the CPP will have an influence upon the control strategy and complexity of the scale-up program.

Generation of the CQAs 

A key tool from the start of any pharmaceutical development program is the quality target product profile (QTPP). The QTPP ensures that the broad objectives of the project are captured, including the patient and prescriber requirements and the attributes needed to ensure a safe, effective, and commercially viable treatment. The QTPP should always be referred back to when determining the impact of material attributes and process parameters on product quality. From the QTPP, the critical quality attributes (CQAs) of the product may be determined together with an estimation of the impact on product quality of individual raw material attributes and processing parameters (5). Benchmarking against competitor products, especially in the generic-drug market, is a useful guide to the QTPP where it can highlight key commercially relevant differentiators for the new product.

Voice of the customer (VOC) is a six-sigma tool to aid the compilation of the QTPP. It allows the end user and the company sponsoring the development to provide clarity on what they need and want, or do not want in some cases. For a topical pharmaceutical product, the patient voice is paramount, and for orphan products, this is often efficiently captured through internet-connected patient groups. Other significant “voices” are the prescribers and investors. Simple surveys of patients or key opinion leaders (KOLs) are a highly effective means of acquiring valuable VOC data. VOC and the QTPP play a crucial role in the determination of CQAs. 

Incorporating risk management

An important element of any efficient process development program is the identification and mitigation of risks. It is widely recognized that the deployment of FMEA as a risk management tool, alongside an appropriate design of experiments, lead to control strategies that reliably produce drug products of the appropriate quality, whether they be based on NCEs or generic drugs. FMEA is a step-by-step approach for estimating the potential risk arising from all possible failures in the design or processing of the topical product. It also highlights where efforts should be deployed in risk mitigation-often defining the most suitable process controls. Many liquid and semi-solid topical pharmaceutical products are, by design or necessity, highly complex systems, often involving multiple phases (e.g., oil and water emulsions) with a defined range of droplet sizes. As such failures can arise in a number of different processing areas, including heterogeneity of drug content, or consistency and physical, chemical, or microbial instability. The efficacy, uniformity of dose, and safety of topical pharmaceuticals rely upon these formulations being homogenous, stable, safe, and easy to use.

Focusing in on the CPPs

The CPPs are derived by establishing the relationship between the processing parameters, the CMAs of both the API and excipients, and the CQAs of the product. A concise way of expressing the process parameters is the six-sigma input–process–output (IPO) diagram (see example in Table I). The IPO diagram highlights unit operations and which operational parameters should be investigated in an example manufacturing process.

Table I: Input–

Process–Output (IPO) diagram showing the unit operations of a sample manufacturing process.

The first experimental design (DoE), in the form of screening study, is then derived from the outputs of the FMEA (see example in Table II) and the IPO diagram. The objective of this pre-screen is to confirm the output from the FMEA, uncover any interactions between key parameters, and to determine processing parameters that are truly critical. Both the experience and expertise of the technical project team and efficient experimental design software are crucial for this approach. Pramod et al. noted that “though design of experiments is not a substitute for experience, expertise, or intelligence, it is a valuable tool for choosing experiments efficiently and systematically to give reliable and coherent information” (6). The FMEA allows the operator to capture the knowledge and decide which areas of the process are most critical and require experimental investigation; the DoE will then validate this thinking statistically and quantitatively. 

Table II: The failure mode effect analysis (FMEA) is used to highlight unit process operations that pose the most risk to product quality. IVRT is in-vitro release testing.

Key factors in the design of experiments 

A crucial factor in the pre-screen and full experiment design for topical formulations is that experiments are conducted using equipment that is representative of larger-scale equipment in order to derive meaningful qualitative CPP data. At MedPharm, IKA LR1000 lab reactors are used, which allow for the control of all typical processing parameters. This approach is crucial to avoid generating “noise” and ensure the quality of the output and the robustness of the resultant control strategy. The understanding of the influence of scale on the CPP from high-quality experimental work conducted on small scale forms the basis of any future scale-up work and technical-transfer activities. 

For a complex cream, typically 12 experiments should be targeted to cover two to four CPPs in a pre-screening study in preparation for the manufacture of toxicity or clinical batches. The actual number of experiments may have to be expanded depending on the outputs and the associated risks.

Another important factor is that experimental design in both the pre-screen and full study should attempt to push the product to failure to allow for the understanding of the design and control space boundaries. If the developer is conservative in experimentation, they can misunderstand the boundaries between success and failure, and the design space is limited to knowledge space (see Figure 1).

Figure 1: A graphical representation of the relationship between knowledge space, design space, and control space [figure courtesy of the authors]. 

A third factor is the identification of what six sigma calls the key process output variable(s) (KPOV), the variables that determine success, and using evaluation criteria to establish whether the method employed to measure the KPOV will detect critical failure. If the answer is no, there needs to be a mitigation plan to change this. The output of any experimental work will only be as good as the analytical method allows. Topical pharmaceutical products require an array of analytical techniques to evaluate their quality ranging from the commonplace such as high-performance liquid chromatography (HPLC) and viscosity testing to more sophisticated methodology such as rheological evaluation, accelerated stressing to show the potential for separation (7), and in-vitro release testing to check that there is no change in the release/thermodynamic activity of the drug from the formulation. 

The design space is defined as the multidimensional combination and interaction of input variables and process parameters that have been demonstrated to provide assurance of quality (8). It is the boundary of the process parameters to which a product can be made and satisfies the QTPP and CQAs for the product. It is important to stress that there is not a hard border (9) with the knowledge space and that defined process inputs are as important as the measured output. Interpretation of the DoE should point to CPPs and any key interactions between these parameters. Analysis of variants (ANOVA) plots, which show both the mean and the distribution of date around each mean, are particularly useful in this respect as they will clearly show the significance of a parameter or interaction and associated variability. Many experimental design software packages (e.g., SPSS, Minitab, JMP, and Design Expert) are now sophisticated enough to identify statistically significant effects across a range of parameters and present them in a concise graphic that decision makers find easy to understand. 

An important point is that any process change made within the design space is not considered a regulatory change (10), and hence, has a direct bearing on the flexibility of any future manufacturing process. The more that is known about the boundaries of success and failure, the better the control strategy will be and the less impact that changes in excipient suppliers and specifications will have upon a product over its lifetime. 

Ensuring a robust process

The control space represents a range of critical parameters within which the process will yield an assured output to meet the CQAs and target specification at all times. Getting from the design space to the control space can be achieved through the further use of experimental design; typically, two to five factors in full, or fractional factorial, or other surface response design informed by the data from previous experiment design work. A useful guide to DoE can be found online in the form of the engineering statistics handbook (10). 

Clearly, the control space for the CPP must sit well within the design space and sufficiently away from the edge of failure to ensure robustness. The interpretation and conclusions from an optimization design will show “best” settings to achieve a product that meets the QTPP and CQAs. A confirmation batch using the optimal settings will demonstrate that the response values from the DoE are close to their predicted values. 

Conclusion

Using a stepwise and methodical QbD approach during the development and late-stage formulation of topical products provides a sound and robust platform in establishing the design space for process development and will ultimately enable the developer to provide a robust control strategy for manufacturing. Missing this step can lead to poor processing and physical instability in topical products, which directly impacts product performance and the patients who need them.

The often complex liquid and semi-solid processing for topical products cannot be underestimated, and the marriage of experience, QbD, six-sigma tools, and experimental design ensure the manufacturing scale-up of complex topical products that can be conducted with minimum risk. Above all, experience has shown that a well thoughtout QbD-based process development strategy with in-depth knowledge of the products and processes can save not only time and money, but also embarrassment from having to explain the lack of understanding behind a critical batch failure that is holding up a development program for an important company asset.

References

1. EMA, Quality by Design, accessed June 26, 2018. 
2. Eliot et al., Pharm. Bioprocess. 1 (1) 105–122 (2013).
3. M.E. Aulton and K.M. Taylor, Pharmaceutics: the Design and Manufacture of Medicines (Churchill Livingstone Elsevier, Amsterdam, Netherlands, 4th ed., 2013) p. 449. 
4. Editors of Pharmaceutical Technology, “A Troubleshooting Guide for Topical Drug Manufacturing: Consider critical process parameters and strategies to optimize the manufacturing process,” Pharmaceutical Technology 36 (11) (2012).
5. R.K. Chang et al., AAPS J. 15 (3) 674–683 (2013). 
6. K. Pramod et al., Int J Pharm Investig. 6 (3) 129–138 (2016). 
7. D. Lerche and T. Sobisch, Journal of Dispersion Science and Technology 32, 1799-1822 (2011).
8. ICH, Q8 (R2) Pharmaceutical Development, Step 4 (August 2009).
9. Quality by Design & Design for Six Sigma ISPE Breakfast Seminar Toronto (May 2009) Murray Adams GSK.
10. NIST/SEMATECH, NIST/SEMATECH e-Handbook of Statistical Methods, accessed July 12, 2018.

About the Authors

Christopher Harrison, is head of Process Development and Manufacture; Sarah Pratt is chief operating officer; and Professor Marc Brown, PhD, is chief scientific officer and co-founder, all at MedPharm.