Process Validation for Lyophilized Drug Products: Developing a Program for Continued Process Verification

Karen Bossert, Ph.D. and Edward Trappler Lyophilization Technology, Inc. Ivyland, PA ISLFD East Coast Chapter Meeting September 2016

Process Validation / Validation Life Cycle

Lyophilization / CPPs Methods for continued process monitoring ◦ Individual Process Steps ◦ Summarizing Data Across an Entire Batch ◦ Multiple Batches, Multiple Steps

Summary

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“…collection and evaluation of data, from the process design stage through commercial production, which established scientific evidence that a process is capable of consistently delivering quality product.”

(FDA Guidance for Industry, Process Validation: General Principles and Practices, 2011)

Understand the sources of variation.

Detect the presence and degree of variation.

Understand the impact of variation on the process and ultimately on product attributes.

Control the variation in a manner commensurate with the risk it represents to the process and product.

Stage 1 Process Design ◦ Commercial manufacturing process is defined.

Stage 2 Process Qualification ◦ Capability to manufacture is confirmed.

Stage 3 Continued Process Verification ◦ Provide assurance the process is within a state of

control.

Critical Process Parameters for Lyophilization include: ◦ Shelf (inlet) temperature ◦ Chamber pressure (vacuum) ◦ Time

Processes for commercial products are described in these terms with the intent of consistent performance.

Nine lots, same product, same scale except for one lot manufactured at half-scale

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TO SH FR SH COND 1 COND 1 COND 2 COND 2 MICRONS

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Target Lyophilization Cycle Temperature

Show variation for a single process step by plotting min, max and average for each lot during that step.

Allows comparison of ranges and averages batch to batch.

Data evaluation is for a single process step.

Typically, cycle data are graphed using temperature (CPP) data, but by plotting the data as variation from setpoint rather than temperature, may allow for a comparison of control. ◦ By keeping the data segregated by process step,

there is an opportunity to assess machine function and look for consistency of operation at common setpoints, batch to batch (see last slide). ◦ Can graph data from multiple process steps and

look at variation across the entire process for a batch (see next slide).

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This approach assesses the relative control capability under the different processing conditions for each step across one batch, or across multiple batches.

Differences in control can easily be seen. Operational or expected ranges for each

segment could be established by pooling the data within each segment.

This approach provides a comparison of control across the entire process and among multiple batches.

Previous example looked at min, max, and average values for each step.

There can be value in looking at the same data for all batches, following the timeline for the process.

Variation at specific process points becomes readily apparent as does atypical behavior for a given batch.

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This method of data analysis is useful for comparing the profiles of multiple batches throughout the entire process.

However, there is a limit to the number of batches that can easily fit on a single plot!

Pooling data from all batches and all process steps (setpoints) in the cycle, one graph can be constructed to depict variation.

Individual differences can be difficult to detect, as can any (significant) deviations for a single batch, since the y-axis must be scaled to accommodate the entire range of setpoints (see next slide).

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Standard deviation calculations, along with averages, are commonly used to describe a data set.

As in the previous example, this technique can be used to assess all batches and all process steps in one plot.

Because the standard deviation is relative to a population distribution for a specific data set, the control capability can be difficult to accurately assess using this method.

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Minimum and Maximum Standard Deviations from Average for Lyophilization Cycle (N=9) Max Min

Using the min and max variation compared to the running average provides an effective means of evaluating variation in CPPs.

Ongoing trending of the range for the min and max reflects the variability in the control and can magnify events of individual excursions.

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Methods can be used to evaluate individual cycle segments or the entire lyophilization process.

Evaluating lyophilization CPPs can focus on individual batches or batch to batch trending.

The methods illustrated in this presentation focused on shelf inlet temperature, but can be applied to other CPPs.

Selection of the most appropriate method should be based on need and value to the manufacturing operation.

Edward Trappler Amit Sitapara

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Thank You!!

Happy to field questions!

Further contact:

Karen Bossert, Ph.D. Vice-President, Scientific Affairs Lyophilization Technology, Inc. kbossert@lyo-t.com (215) 396-8373

Slide Number 1AgendaProcess ValidationProcess ValidationValidation Life CycleLyophilization / CPPsCase StudyReal Cycle DataFocusing on One CPPVariation from SetpointVariation from SetpointVariation Across Entire ProcessVariation Across Entire ProcessVariation Across Entire ProcessVariation Across Entire BatchVariation Across Entire BatchAll Steps, All BatchesAll Steps, All BatchesAll Steps, All BatchesAll Steps, All BatchesAll Steps, All BatchesAll Steps, All BatchesSummaryAcknowledgementsSlide Number 25