using tolerance intervals for setting process validation acceptance criteria richard k. burdick...

Using Tolerance Intervals for Setting Process Validation Acceptance Criteria

Richard K. Burdick —Amgen, Inc. (CO)

Graybill Conference

June, 2008

Using Tolerance Intervals for Setting Process Validation Acceptance Criteria

“A worn-out academician’s adventure in the ‘real word’"

3Operational Excellence

Outline

Life at Amgen

Nonclinical statistics

Definitions for Process Characterization and Validation

Statistical Methods for Setting Process Validation Acceptance Criteria

Future Opportunities


Amgen: A Biotechnology Pioneer

Founded in 1980, Amgen was one of the first biotechnology companies to successfully discover, develop and make protein-based medicines

Today, we’re leading the industry in its next wave of innovation by:

– Developing therapies in multiple modalities

– Driving cutting-edge research and development

– Continuing to advance the science of biotechnological manufacturing


Research and Development at Amgen

Guiding Principles

Focus on serious illness

Be modality independent

Assess efficacy in patients

Seamless integration from research through commercialization

Therapeutic Areas

Inflammation

Oncology

Hematology

Metabolic and bone disease

Neuroscience


Nonclinical Statistics

Chemistry, Manufacturing, Controls (CMC) development establishes the process of manufacturing drug product to meet clinical requirements.

Work in both research and development and manufacturing.


Nonclinical statisticians involved in…

R&D with– Assay validation– Process validation– Method transfer– Stability studies (storage conditions, shelf-life, expiry extensions)– DOE for process characterization– Establishment of specifications and process validation acceptance

limits.

Manufacturing with– Maximization of yields– Control charting– Support in non-conformance reports (identification of assignable

causes)– Raw materials inspection


Timeframe of Characterization and Validation Activities Relative to Clinical Trials

End of Phase II Clinical Trial

Characterization Validation

End of Phase IIIClinical Trial and

Commit to File

Update CVdocuments


Very Simple Process Diagram

(Upstream)

(Downstream)

Diafiltered Medium (DFM)

Filtered Purified Bulk (FPB)


Process Characterization

Process Characterization is a precursor to process validation and is comprised of a set of documented studies in which operating parameters (inputs) are purposely varied to determine the effect on product quality attributes (outputs) and process performance.

Employs Failure Modes and Effects Analysis (FMEA) and Experimental Design


Process Validation Process validation provides the documented evidence that

the process, when operated within established limits, can perform effectively and reproducibly to produce an intermediate, active pharmaceutical ingredient (API) or drug product meeting predetermined criteria and quality attributes.

Final drug product and API have specifications that must be met based on standards mandated by safety concerns and other factors.

However, intermediate process steps (which do not have mandated standards) have a number of acceptance criteria that must be met to demonstrate process consistency and the ability to meet final specifications.


Process Validation Acceptance Criteria

Process Validation Acceptance Criteria (PVAC) A set of numerical limits that when exceeded, signals a significant departure from operating conditions or product quality.

Set prior to initiation of the validation campaign.

Establishing PVAC is one of the greatest challenges in the development of a commercial biopharmaceutical manufacturing process.


Definitions

Operating Parameter (OP): Parameter that can be directly manipulated (input)

Performance Parameter (PP): In-process parameter or measurement used for process performance evaluation (output)

Normal Operating Range (NOR): A range for an operating parameter that is listed in the Manufacturing Procedure. Frequently based on equipment and/or process capability.


Setting PVAC-A personal history

My involvement with the ACO process development (PD) group began as a discussion concerning analysis of one-off studies conducted at 3 times outside the NOR.

Questions concerned how to determine the operating parameters (OPs) that were most important in the process.

I helped them analyze the data in a manner they were comfortable with, and gained their confidence so that I could work with them on future projects.


Setting PVAC-A personal history

My involvement with the ACO process development (PD) group began as a discussion concerning analysis of one-off studies conducted at 3 times outside the normal operating range (NOR).

Questions concerned how to determine the operating parameters (OPs) that were most important in the process.

I helped them analyze the data in a manner they were comfortable with, and gained their confidence so that I could work with them on future projects.

Lesson 1: Sometimes it is best to answer the client’squestion instead of telling them what they are doing wrong.


When the discussion of setting PVAC came up, I researched the history of setting PVAC at ACO:– There was some sentiment for “3 sigma” rules– JMP Prediction Profiler at the extremes of the NOR had been

used with previous projects (these limits are actually the confidence intervals on the average for a given value of the OP).

– Data sets from robustness and edge of range studies were not being combined. In some cases, only centerpoints were being used to determine PVAC.


When the discussion of setting PVAC came up, I researched the history of setting PVAC at ACO:– There was some sentiment for “3 sigma” rules– JMP Prediction Profiler at the extremes of the NOR had been

used with previous projects (these limits are actually the confidence intervals on the average for a given value of the OP).

– Data sets from robustness and edge of range studies were not being combined. In some cases, only centerpoints were being used to determine PVAC.

Lesson 2: Find out why certain methods were used in the past. Can you use these approaches as a starting point,

and demonstrate continuous improvement?


Construction of PVAC

I suggested we use tolerance intervals for defining PVAC because they describe the long range expected behavior of the process.

Bench data derived from process characterization experimental design studies can be combined with large-scale runs to compute tolerance intervals at set-point conditions (or any other point in the NOR) centered at either commercial or clinical scale.


TI Depends on OP

99% of PPvaluesin this rangewhen OP=+1

Regression Line

Assumed distribution of PP for given OP isnormal.

PP

OP=+1OP=-1

99% of PPvaluesin this rangewhen OP=-1

OP=0


Regression Line


PP

OP=+1OP=-1



Regression Line


PP

OP=+1OP=-1


OP=0


Type of TIs

If all OPs are fixed effects, then exact one-sided tolerance intervals can be constructed based on the non-central t distribution – See, e.g., Graybill (1976, pages 270-275)

Exact two-sided tolerance intervals are available (Eberhardt, Mee, and Reeve, 1989), but computationally complex.– Various two-sided approximations have been suggested

• Weissberg, A. and G. H. Beatty (Technometrics,1960)• Lee, Y. and T. Mathew (JSPI, 2004)• Liao, C. T., Lin, T. Y., and Iyer, H. (Technometrics, 2005).


One other refinement

Many times, the PC models involve random effects such as the raw materials that feed into a process step.

In this case, the fixed effect methods can not be applied for computing tolerance intervals.

Generalized Inference provides an approach for computing tolerance intervals with a random effect.

• Liao, C. T., Lin, T. Y., and Iyer, H. (Technometrics, 2005)• Based on generalized fiducial intervals


One other refinement

Many times, the PC models involve random effects such as the raw materials that feed into a process step.

In this case, the fixed effect methods can not be applied for computing tolerance intervals.

Generalized Inference provides an approach for computing tolerance intervals with a random effect.

• Liao, C. T., Lin, T. Y., and Iyer, H. (Technometrics, 2005)• Based on generalized fiducial intervals

Lesson 3: Continue to make improvements and demonstrate you are willing to continually improve your work.


Example—Purification Column

Purification is used in a biopharmaceutical product to separate desired protein from unwanted materials.

This example considers one such column where the response is modeled as a function of a fixed OP (coded -1 to +1) and the random effect feed material.

Response is a purity measure in %.


12108642

93

92

91

90

89

88

87

86

85

84

OP

PP

Scatterplot of PP vs OP


RSquare

RSquare Adj

Root Mean Square Error

Mean of Response

Observations (or Sum Wgts)

0.363075

0.354105

1.391341

88.86986

73

Summary of Fit

Column Feed Source Lot

Residual

Total

Random Effect

0.4137555

Var Ratio

0.8009605

1.9358307

2.7367912

Var

Component

0.6543545

0.3336

Std Error

0.256919

1.4182415

95% Lower

11.161442

2.8007496

95% Upper

29.266

70.734

100.000

Pct of Total

-2 LogLikelihood = 263.85628974

REML Variance Component Estimates

Intercept

OP

Term

90.725619

-0.201453

Estimate

0.826556

0.092223

Std Error

48.6

68.09

DFDen

109.76

-2.18

t Ratio

<.0001*

0.0324*

Prob>|t|

Parameter Estimates


Using the GCI approach, the computed tolerance interval for the OP=0 (setpoint condition) is from 83.4-95%


Plot of Tolerance Intervals and Runs with OP = 0

95.0

92.5

90.0

87.5

85.0

Resp

onse

(%

)

83.4

95


Future Opportunities

FDA initiative for Quality by Design.

ICH Q8 Appendix on movement within the proven acceptable range (PAR)—also referred to as “Design Space”.


Design Space (ICH Q8)

Unexplored Space

Knowledge Space

“ Design ” Space

NOR

Unexplored Space

Knowledge Space

“ Design ” Space

NOR

PARPAR(Proven Acceptable Range)

Explored with Acceptable

Performance

NOR(Normal Operating Range)

Operating Strategy based on Business/Equipment Requirements

Explored SpaceExplored SpaceDOE DOE ModelingModelingPrior KnowledgePrior KnowledgeFirst PrinciplesFirst Principles

Risk Assessment to

Prioritize Investigation

Control StrategySpecifications

Tolerances

using tolerance intervals for setting process validation acceptance criteria richard k. burdick...

Documents

process performance

operational excellence

simple process diagram

validation statistical

validation activities

experimental design

real word slide

purified bulk fpb slide