Quality by Design - SAAPIsaapi.org.za/wp-content/uploads/2014/05/Dr-Zahn-Quality-by-Design.… · Quality by Design SAAPI Conference Pretoria, ... Target the product profile;

Quality by Design

SAAPI Conference

Pretoria, 13 April 2016

Manuel Zahn

Road Map to Robust Manufacturing

Perform risk assessment;

Target the product profile;

Identify critical quality attributes;

Define a design space for manufacturing;

Implement a control strategy;

Manage product lifecycle / continuous improvement.

© 3R Pharma Consulting GmbH – Slide # 2

Principles

Risk Based Orientation

Quality should be built into the product;

Testing alone cannot be relied on to ensure product quality.

"Quality should be produced - not tested"


Today: Low Process Capability

Scrap and rework at 5-10% is accepted;

Time to ‘effectiveness’ is not challenged;

Cost of Quality > 20%

On time supply 60-80%

Right First Time 85-95%

2.5 sigma processes.


Product Quality and Process Sigma

Sigma (σ) ppm Defect Yield Cost of Quality

2 308537 69.2% 25-35%

3 66807 93.4% 20-25%

4 6210 99.4% 12-18%

5 233 99.98% 4-8%

6 3.4 99.99966% 1-3%

Source: Ali Afnan, Office of Pharmaceutical Science, CDER, FDAConcept Heidelberg, 24 Oct 2007


PROCESS ANALYTICAL TECHNOLOGY (PAT)

FDA Definition

A system for designing, analyzing, and controlling manufacturing

through timely measurements (i.e., during processing) of critical quality and performance attributes of raw and in-process materials and processes,

with the goal of ensuring final product quality.


The Desired State of Manufacturing

Product quality and performance are ensured through the design of effective and efficient manufacturing processes;

Product and process specifications are based on a mechanistic understanding of how formulation and process factors affect product performance;

Continuous real time quality assurance.


PAT Approach

Source: Dr. Gabriele Reich, IPMB – University of Heidelberg


PAT is a Philosophy ...

... rather than a set of instruments or a new measurement technology.

Continuous quality assurance;

Flexibility based on science;

Process understanding / risk management;

Part of GMP for the 21st century

Source: Dr. Gabriele Reich, IPMB – University of Heidelberg


DESIGN OF EXPERIMENTS

Sir Ronald Aylmer Fisher

Born 17 Feb 1890 in London

Invented "Design of Experiments" in the 1920s and 1930s at an agricultural research station north of London.

Agricultural experiments with natural fluctuations (temperature, soil condition, rain fall)

1935: 1st edition of his textbook "The Design of Experiments"

Died 29 July 1962 in Australia

DoE Applications

1948-1956, George E. P. Box optimized chemical processes at ICI (married one of Ron Fisher's five daugthers);

In the 1950s, Genichi Taguchi (Toyota) developed the use of an outer arrayfor factors that are uncontrollable in real life to improve the quality of manufactured goods ;

late 1970s: "Total Quality", "Six Sigma" programs(Motorola, GE, etc.).


DoE Definition

= A series of tests in which purposeful changes are made to the input variables of a system or process, and the effects on response variables are measured.

Maximizing the amount of information gained from a study while minimizing the amount of data to be collected.

Source:

Jacqueline K. Telford, Johns Hopkins APL Technical Digest, Vol 27 (3), 224-232 (2007)


DoE Benefits

Reduce time to design/develop new products & processes;

Increase process understanding;

Improve performance of processes;

Achieve product & process robustness;

Improve reliability and performance of products;

etc.


General Model of a Process or System


Examples

Process:

Granulation

Inputs:

API + excipients

Factors (variables):

Mixing time, stirrer speed, water temperature

Outputs (responses):

Content uniformity, degradation, water content


Factorial Designs

Investigate the effects of many different factors by varying them simultaneously;(p = number of factors)

Collect data at two levels (high & low);(+1 and ‒1)

Full factorial design requiring 2p runs.


Full & Fractional Factorial Designs

p = 3runs: 23 = 8

p = 3runs: 23 ∙ (½) = 4

full 1/2

Runs required: 2p-q


Example

Process:

Crystallisation

Inputs:

Substance A, Water, n-Heptane

Factors (variables):

Equivalents of Substance A

Concentration of A in Water

Ratio of Water to n-Heptane

Temperature during isolation/washing

Outputs (responses):

Yield, assay, impurities


Example: Fractional Factorial Design

Run Substance A

Concentration of A in Water

Ratio of Water to n-Heptane

Temperature

1 −1 −1 −1 −1

2 −1 −1 +1 +1

3 −1 +1 −1 +1

4 −1 +1 +1 +1

5 +1 −1 −1 −1

6 +1 −1 +1 +1

7 +1 +1 −1 −1

8 +1 +1 +1 +1

p = 4runs: 24 ∙ (½) = 8


Plot of Yield vs. Concentration of Substance A in Water

Substance A

Water

Optimum Yield


Plot of Yield vs. Solvent Ratio and Amount of Substance A

Substance A

Optimum Yield


Plot of Yield vs. Temperature and Solvent Ratio

Optimum Yield


Results of the DoE

Proven Acceptable Range (PAR)useful knowledge about the process,operation within this range will result in producing a material meeting relevant quality criteria;

Critical Process Parameter (CPP)should be monitored or controlled to ensure the process produces the desired quality;

Critical Quality Attribute (CQA)should be within an appropriate limit, range, or distribution to ensure the desired product quality.

ICH Q8(R2) - Pharmaceutical Development - Aug 2009


"The statistician cannot evade the responsibility for understanding the process

he applies or recommends."Ron Fisher


DESIGN SPACE

Design Space

=The multidimensional combination and interaction of input variables (e.g., material attributes) and process parameters that have been demonstrated to provide assurance of quality (ICH Q8).

Working within the design space is not considered as a change. Movement out of the design space is considered to be a change and would normally initiate a regulatory post approval change process.

Design space is proposed by the applicant and is subject to regulatory assessment and approval.


Knowledge SpaceWhere we have experience

Design SpaceWhere we are good


Presentations of Design Space

Source: ICH Q8

Parameters 1 and 2 are factors of a granulation operation that affect the dissolution rate of a tablet (e.g., excipient attribute, water amount, granule size.)

Surface plot Contour plot


Design space determined from the common region of multiple CQAs

Source: ICH Q8© 3R Pharma Consulting GmbH – Slide # 31

The drying operation is dependent upon the path of temperature over time. The end point for moisture content is 1-2%.

Source: ICH Q8

Design Space for a Drying Operation



Thank You

3R Pharma Consulting GmbHDr. Manuel ZahnWildbader Str. 375335 Dobel Germanywww.3rpc.com


http://[email protected]/

Documents

Quality by Design - SAAPIsaapi.org.za/wp-content/uploads/2014/05/Dr-Zahn-Quality-by-Design.… · Quality by Design SAAPI Conference Pretoria, ... Target the product profile;