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Why DOE Is Essential in the (Bio)Pharma Industry

October 14, 2020

For pharmaceutical and biopharma companies, building quality into your products from an early stage is a key factor in regulatory approval and market success. Design of Experiments (DOE) is an essential tool for achieving both regulatory compliance and faster time to market.

In a highly competitive market, being able to shave months or even years off the research and development process while still delivering quality and stable products can be the key to achieving commercial success.

MODDE DOE software helps to define design space

DOE helps scientists and process specialists confidently create robust and reproducible processes that shorten the time to market for (bio)pharma products.

DOE Shortens Time to Market and Increases Process Robustness

DOE helps support R&D and production in many critical ways. DOE can help you to:

Understand your process.  Investigate many process parameters and their cause-and-effect relationships. For example, evaluate media composition against total cell density (TCD) and viable cell density (VCD).

Perform process optimization. Select the right factors and ranges of process parameters to find process optimums, such as pH range and temperature ranges that optimize a certain key performance indicator (KPI), such as titer.

Build a design space. Define regions where product quality is assured.

Validate and characterize process stability and robustness. Identify critical process parameters (CPPs) that may be sensitive to small factor changes or which need to be controlled to achieve robustness.

Get to market faster. Reduce the number of experiments you need to perform by increasing your knowledge about your process.

Save costs. Identify factors that may reduce material consumption and waiting time.

Fulfill regulatory compliance. Create reproducible and documented processes.

Follow the Quality by Design (QbD) approach. Build quality into your process as outlined by ICH Q8, Q9, Q10.

Watch Recorded Webinar: Robust Optimization Made Easy

Optimize Experimentation Processes with DOE

The traditional approach to process experimentation is to hold all factors constant except for one – known as the “one variable at a time” (OVAT) approach. However, the OVAT approach can suffer from a number of problems:

  • Increased experimental inefficiency – multiple experiments have to be performed in order to understand the cause-and-effect relationships between the variables
  • Not all interactions between variables can be captured – meaning there’s no guide to process behavior

Using a DOE approach can help you better understand the process interactions while running fewer experiments.

With DOE, you enlist a statistical study to show you what the best operating conditions are for a targeted response. For example, DOE can help you identify how factors such as pH, temperature, DO, nutrients, stir speed and flow rates (typically factors are CPPs) affect a specific process outcome or response, such as product amount, glycosylation patterns or molecular-size-distribution (typically responses are CQAs).

The parameters involved in a unit operation, like a bioreactor, or between units, like a filtration and chromatography step, are often interdependent and extremely complex. DOE is an efficient tool to help navigate the study of cause-and-effect relationships between these parameters.

Validate and Characterize Final Product Stability

A big part of Quality by Design is being able to validate and characterize your product or process to ensure that it is stable and robust. This is essential for regulatory compliance, particularly for ICH-Q8 standards.

During process development, process or product characterization means that you have to define the normal operating ranges (NORs) or proven acceptable ranges (PARs).

A critical step in drug development is final formulation development. All pharmaceutical products are formulated to a specific dosage to be effectively delivered to patients. A good formulation must be stable – during both manufacturing and for its product shelf life.

During formulation development, DOE can be used to develop a formulation map, or design space, which allows scientists to choose the optimal formulation conditions. Formulation robustness studies using DOE can help identify:

  • Critical formulation components – ingredients whose levels must be tightly controlled to maintain final drug product stability and efficacy
  • Limits in which variations in the levels of formulation components have maximal, minimal, or no effect of final drug product stability and efficacy
  • The effect on final product stability when the process goes outside the NORs or PARs
  • Interactions among the different formulation components

QbD tools such as DOE provide a thorough understanding of a product’s design space. DOE studies are thus extremely beneficial when used to evaluate and characterize formulation design space. Following the QbD approach can provide a more thorough understanding of your processes as well as the potential for increased process and product robustness.

Quality by Design (QbD) is “a systematic approach to development that begins with predefined objectives and emphasizes product and process understanding and process control, based on sound science and quality risk management.” 

- International Conference on Harmonization (ICH) guidelines Q8 and Q8(R2)

Read more: Correcting the Most Common Causes of Pharma Process Deviations

Use DOE to Determine Design Space

Staying within a process or product design space—the documented, approved process parameters—is a regulatory requirement. Moving outside the design space would require resubmission for approval.

While DOE is not the only method for determining a design space (other methods include using known scientific equations with regression techniques), it is by far the most common and efficient method.

The EUMA defines design space as “the multidimensional combination and interaction of input variables (e.g., material attributes) and process parameters that have been demonstrated to provide assurance of quality.”

The steps to creating a design space with DOE are as follows:

  1. Determine your experiment need and what knowledge deficit will it fill (i.e Design space to understand the relationship between a particular CPP and CQA)
  2. Decide if you want to take an isolated process step or multiple-process step approach
  3. Do your risk assessment – this is done to rationalize the factor/response selection in step 1
  4. DOE Design Space Analysis – analyze the data, eliminate outliers, demine statistical significant factors, and quantify the effect of CPPs on CQAs
  5. Robustness Optimization – once the DOE has been generated and analyzed, optimize the set points to find the most robust area within the design space
  6. Determine NORs and PARs – finalize the design space
  7. Design Space Verification – compare values from verification runs to help assure the design space has reasonable predictive power
  8. Determine final process control strategy – design spaces determined by DOE can help define control parameters for manufacturing
Another important aspect of DOE is how it can help scientists get answers to important questions or prove theories in a specific way that starts from their place of knowledge. It lets them skip experiments that cover known factors and do the interesting ones.

 Incorporating Prior Knowledge into Experimental Designs

DOE Means Faster Time to Market

The cost of pharma R&D has been steadily increasing over the last decade. According to Deloitte, the cost to bring a new drug to market in 2018 was approximately $2.17 billion USD, nearly double the cost calculated in 2010 ($1.18 billion). Yet, the number of new drugs being brought to market hasn’t been increasing, and sales forecasts have decreased more than 8% in the last decade.

One of the reasons for this decline is that it is taking longer to bring new drugs to market. The average time for a new drug development project increased from 7-9 years in the 1990s to 10-15 years in the 2000s and 2010s.²,³ Unnecessary expenditures on testing, which increases the time it takes to study drugs and leads to lower return on investment, is a factor.

DOE can be used to shorten time to market by creating experimental efficiency and greater confidence in the processes.

An effective DOE can greatly reduce the number of experiments needed to define your design space and ensure robust and stable products. In some cases, it could cut the number of experiments needed by half.

The importance of time to market in the pharmaceutical world can be attributed to a number of factors:

  • Importance of delivering lifesaving drugs faster
  • Decreasing costs of development
  • Commercial disadvantage of being late

In order to get products to market first and derive maximum revenue, companies should adopt DOE as a way to shorten drug development timelines.

The chart below shows an estimate of the financial advantages pharmaceutical companies might gain over the lifespan of a product through earlier commercialization.

drug development diagram DOE

The potential gain in revenues when a drug is brought to market before competitors is significant (up to44 million).

DOE Is the Key to Regulatory Approval

Advocated by regulatory agencies, DOE is one of the most effective tools to achieve an efficient and cost-effective approach to improve process development

DOE is the backbone for efficient implementation of QbD and for creating an optimal design space. Applying DOE therefore not only saves time and effort, it also paves the way towards faster regulatory approval.

Software Tools Make It Easier

Creating an optimized process using DOE is easier when you use tools designed specifically for DOE. One example is MODDE Pro software, which includes such essential features as:

  • Wizard for guided DOE studies - designed for the process specialists 
  • Guided workflow for data evaluation and modeling
  • Support for more advanced design methods

Want to know more?

Watch this webinar about MODDE  advanced functionality for robust optimization and design space analysis.

Watch Recorded Webinar: Robust Optimization Made Easy

Get MODDE free trial

 

References

1. “Ten years on, Measuring the return from pharmaceutical innovation 2019.” Deloitte. 2019.

2. Norman, Gail A. Van. “Drugs, Devices, and the FDA: Part 1: An Overview of Approval Processes for Drugs.” JACC: Basic to Translational Science. Elsevier, April 25, 2016.

3. Pammolli, Fabio, Laura Magazzini, and Massimo Riccaboni. “The Productivity Crisis in Pharmaceutical R&D.” Nature News. Nature Publishing Group, June 1, 2011.

4. European Medicines Agency, 2017. Questions and answers: Improving the understanding of NORs, PARs, DSp and normal variability of process parameters, 6 June 2017

 

Topics: Design of Experiments (DOE), Quality by Design (QbD), MODDE, Pharmaceutical manufacturing

Tiffany McLeod

Written by Tiffany McLeod

Tiffany McLeod is the (Bio)pharma Market Manager within the Sartorius Stedim Data Analytics marketing team. She has been employed by Sartorius since 2017. Within this function Tiffany acts as the teams’ subject matter expert for life science market trends and requirements. She also works to addresses and develop data analytics solutions to solve industry challenges. She is passionate about biopharma 4.0 and helping businesses pursue digital transformations. Tiffany holds a degree in Bioengineering and Bioinformatics from the University of California, San Diego.

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