PAS-X CMC Innovation Consulting: 
Validate processes and optimize quality with your digital twin factory

Process validation consists of three stages that are interlinked to each other.

The first one, Process Characterization, is the most essential step to establish a Control Strategy in accordance with FDA, EMA and ICH guidelines, ensuring consistent product quality. To achieve this activity, the relationship between process inputs (process parameters, material attributes) and the process output (critical quality attributes, product amount) needs to be understood and controlled. This relationship can be learned from data and prior knowledge.

Our unique workflow guides you through this critical phase, comprising:

1. Risk assessment
2. Scale down model establishment and comparison
3. Experimental design (for statistical and dynamic models)
4. Experimental analysis and criticality assessment (linear regression models, linear mixed models including random effects, dynamic models for up- and downstream)
5. Setting a holistic control strategy using the end-to-end process modelling framework

With over a decade of experience and involvement in more than 20 product commercialization, we've fostered productive exchanges with regulatory authorities and have gained expertise across diverse product areas like monoclonal antibodies, recombinant protein, pDNA, and more.

We have published over 10 peer reviewed papers with large pharmaceutical companies like Boehringer Ingelheim and Takeda that demonstrate the effectiveness of our approach. In a recent peer-reviewed scientific publication, we demonstrated that our method results in a reduction of over 50% in the number of experiments required by leveraging self-learning digital shadows. This method was also demonstrated at many conferences like the 2023 ISPE Biotechnology conference. 

We support you with:

• Faster time-to-market by reducing number of experiments by >50%
• Regulatory accepted workflows and risk assessments according to ICH Q9 / ICH Q12
• Holistic control strategies leading along ICH Q8 to widest possible PARs and design spaces
• Data based criticality assessment of process parameters
• Timely delivery of results

In the second step, we consult you in determining the number of runs needed for a (Process Performance Qualification (PPQ) campaign.

We support you with:

• Drafting a PPQ protocol
• Statistical determination of number of PPQ runs
• PPQ execution

Continued process verification (CPV) is the third step of the process validation life cycle and ensures that the process is in state of control during commercial manufacturing. Within a CPV plan, it is specified which CQAs (Critical Quality Attributes) and which CPPs (Critical Process Parameters) are monitored on a regular basis. The plan also defines which trending rules and control limits will be used, how often sampling must be performed, and how to invest and communicate when trend violations occur.

In general, the establishment of the CPV plan can be divided into two phases:

1. Initial CPV Plan
   1. Number of available data is too small for statistical application
   2. Process knowledge in manufacturing is too low for reduction sampling frequency
   3. List of monitoring CPPs and even CQAs are at maximum
2. Full CPV Plan
   1. Data is sufficient for statistical application
   2. Process knowledge increases
   3. Sampling frequency can be reduced and number of CQA and CPPs can be decreased

We have strong expert knowledge in this area and can facilitate the creation of an efficient and goal oriented CPV plan creation or update of your process.

We support you with:

• Definition of methods for trending
• Selection of CPPs and CQAs for monitoring
• Calculation of control limits
• Support to iteratively reduce the sampling plan

Most of the process steps in biopharmaceutical manufacturing have a time dynamic behavior. Instead of looking at the end values, e.g., end of fermentation or end pool value of chromatography, we can make use of measurements on the time evolution of critical quality attributes, metabolites, and product amount.

First principles, like mass balances or diffusion phenomena, can be combined with data-driven models that describe reaction mechanisms or binding isotherms. 

This methodology brings benefits along the entire process life cycle:

• Process Development for faster optimization
• Process Characterization for reduction of the number of experiments
• CPV for reduction of false alarms

With this, we can generate generalizable process knowledge across scales and products and establish a process knowledge platform that enables fast time to market and reduction of cost of goods in manufacturing via real-time control.

We support you with:

• Establishment of time dynamic models for USP and DSP
• Experimental design to reduce number of experiments
• Offline application to optimize the process performance and set up ICH Q8 relevant control strategies
• Real time application to control the process in closed loop

Biosimilars have emerged as affordable alternatives to approved biologic drugs, meeting the industry's growing demand for cost-effective treatment options. These medicines provide effective and safe therapies while reducing the financial burden on patients and healthcare systems. 

The critical question in this area is: How can we achieve and prove that a biosimilar is similar enough? We have worked for years to develop new statistical methods that enable biosimilar manufacturers to develop a manufacturing process that results in biosimilars that pass the bioequivalence test. For that we have developed a clear workflow that follows a systematic way of experimental design and analysis to establish a control strategy that ensures chances of passing the final analytical biosimilarity assessment and getting drug approval. 

Adopting a regulatory compliant analytical bootstrapping test that controls the Type I error enables companies to establish analytical similarity, leading to faster market entry with a 10 percent higher acceptance rate.

We support you with:

• A systematic approach to ensure analytical biosimilarity 
• Circumventing flawed x-SD approaches and other simple range tests that are questioned in recent regulatory expectations
• Using the correct, scientifically sound, and regulatory accepted analytical biosimilarity test
• Maximizing the chances of drug approval

Digital shadows and digital twins come into play during process development and process validation, but can reach their full potential during reoccurring manufacturing. This is where we use our unique and innovative end-to-end process models to predict the impact of deviations and help to manage and justify them faster. This is a digital shadow application as it does not connect or control the process in real time. However, it has huge benefits as it saves costly investigations and CAPAs. 

We support you with:

• Establishment of end-to-end digital shadows from process development and manufacturing data
• Validation and verification of end-to-end process models along regulatory guidelines and V&V40 standards

Numerous FDA and EMA initiatives advocate automatic real-time control, transitioning away from quality-by-testing to Quality by Design (QbD).

Our unique and innovative end-to-end process models can be used as prescriptive analytics that work like Google Maps. If you deviate from the optimal path in manufacturing (e.g., deviation in process parameters), it acts as recommender system to guide you back on track. 

It predicts optimal changes to the rest of the manufacturing process across multiple process steps to reduce the likelihood of process failures due to quality issues and increases the total yield of the process. This results in a substantial reduction in the cost of goods by more than 20% and provides a recurring revenue stream for our customers.

The PAS-X Savvy digital twin connects with the PAS-X MES system to provide a unique capability to control, monitor and document the process changes introduced.

We support you with:

• Establishment of end-to-end digital twins from process development and manufacturing data
• Validation and verification of end-to-end process models along regulatory guidelines and V&V40 standards
• Connection of end-to-end process digital twins to middleware, SCADA, QMS and MES systems