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How to Define Design Space
How to Define Design Space
Overview
Overview
Why is this Important
Why is this Important
Regulatory Impact
Regulatory Impact
Potential Benefits
Potential Benefits
ICH Q8 Definition
ICH Q8 Definition
Deconstructing the Definition
Deconstructing the Definition
Multidimensional
Multidimensional
Interaction
Interaction
Example Interaction
Example Interaction
Input Variables
Input Variables
Assurance of Quality
Assurance of Quality
Cause Effect
Cause Effect
Critical Cause and Effect
Critical Cause and Effect
Design Space
Design Space
Design Space
Design Space
Factor Space
Factor Space
Response Space
Response Space
Conceptual Design Space
Conceptual Design Space
Tablet Process Example
Tablet Process Example
Chemical Process Example
Chemical Process Example
Statistical Design Space
Statistical Design Space
Modeling the World
Modeling the World
Model Prediction
Model Prediction
S.I.P.O.C. Model
S.I.P.O.C. Model
Macro View
Macro View
Mid-Level View
Mid-Level View
Micro Level View: Design Space
Micro Level View: Design Space
Existing Products
Existing Products
Factor Space
Factor Space
Quick Dry Example
Quick Dry Example
Quick Dry Example
Quick Dry Example
Factor Space
Factor Space
Design Space
Design Space
2 Factor Interaction Effects to Consider
2 Factor Interaction Effects to Consider
Time*Temp Interaction Plot
Time*Temp Interaction Plot
Time* Moisture Interaction Plot
Time* Moisture Interaction Plot
Temp*Moisture Interaction Plot
Temp*Moisture Interaction Plot
Time*Temp Contour Plot
Time*Temp Contour Plot
Time*Moisture Contour Plot
Time*Moisture Contour Plot
Temp*Moisture Contour Plot
Temp*Moisture Contour Plot
Time*Temp Surface
Time*Temp Surface
Time*Moisture Surface
Time*Moisture Surface
Temp*Moisture Surface
Temp*Moisture Surface
Quick Dry Example
Quick Dry Example
Conclusions
Conclusions
f(Xi) Design Space
f(Xi) Design Space
Goal
Goal
Predictive Equation
Predictive Equation
Predictive Equation
Predictive Equation
Design Space
Design Space
Design Space
Design Space
Multidimensional Specifications
Multidimensional Specifications
Scale-Up
Scale-Up
Design Space Conclusions
Design Space Conclusions
Design Space Conclusions
Design Space Conclusions

: How to Define Design Space. : Torbeck & Assoc.. : How to Define Design Space.ppt. zip-: 810 .

How to Define Design Space

How to Define Design Space.ppt
1 How to Define Design Space

How to Define Design Space

Lynn Torbeck

2 Overview

Overview

Why is a definition important? Definitions of Design Space. Deconstructing Q8 Definition. Basic science, Cause and Effect SIPOC Process Analysis Three Levels of Application. Case Study with Example.

3 Why is this Important

Why is this Important

ICH Q8 is in its final version. Design Space is defined in Q8. Many presenters are using the term. All are repeating the same definition. Many presenters dont understand the statistical implications of the issue. Need for a detailed Operational Definition

4 Regulatory Impact

Regulatory Impact

Design space is proposed by the applicant and is subject to regulatory assessment and approval. Working within the design space is not considered a change. Movement out of the design space is considered to be a change and would normally initiate a regulatory post approval change process. This is a big deal, it needs to be done correctly ! The economic impact of this can be huge.

5 Potential Benefits

Potential Benefits

Real process understanding and knowledge, not just tables of raw data. Reduced rejects, deviations, discrepancies, lost time, scrap and rework. Fewer 483 citations and warning letters. Fewer investigations and CAPA. Freedom to operate with design space

6 ICH Q8 Definition

ICH Q8 Definition

The multidimensional combination and interaction of input variables and process parameters that have been demonstrated to provide assurance of quality. This is not universally understood by all parties involved. We need to harmonize several viewpoints, statistical, scientific, engineering and regulatory.

7 Deconstructing the Definition

Deconstructing the Definition

Need to deconstruct the definition to get to a day to day working Operational Definition that can be implemented. Need enough detail to write a Standard Operating Procedure or SOP. Need to see an example of what it looks like.

8 Multidimensional

Multidimensional

Also called multivariable or multivariate More than one variable at a time is considered. The practice of holding the world constant while only considering one-factor-at-a-time has been shown to be grossly inefficient and ineffective.

9 Interaction

Interaction

Defined in the PAT guidance Interactions essentially are the inability of one factor to produce the same effect on the response at different levels of another factor. Interactions are the joint action of two or more factors working together.

10 Example Interaction

Example Interaction

11 Input Variables

Input Variables

Input Variables: The cause Independent variable Factor Output Variables The effect Dependent variable Responses

12 Assurance of Quality

Assurance of Quality

Assurance is a high probability of meeting: Safety Strength Quality Identity Purity For all measured quality characteristics.

13 Cause Effect

Cause Effect

?

Basic Science

14 Critical Cause and Effect

Critical Cause and Effect

Multiple Causes

Effects

Dependent

Independent

Responses

Factors

15 Design Space

Design Space

?

Dependent Response Space

Independent Factor Space

16 Design Space

Design Space

FACTOR SPACE N dimension Xs X1 X2 X3 X4 X5 XN

RESPONSE SPACE M dimension Ys Y1 Y2 Y3 Y4 Y5 YM

17 Factor Space

Factor Space

Potential Space Areas that could be investigated Uncertain Space Insufficient data for a decision. Unacceptable Space Factors and ranges have been shown to not provide assurance of SSQuIP. Acceptable Space Data to demonstrate assurance of SSQuIP. Production Space Factors and ranges that are selected for routine use.

18 Response Space

Response Space

Potential space or Region of Interest Uncertain Space, unknown responses Unacceptable Space unacceptable responses Region of Operability, acceptable responses Production Space for manufacturing Optimal Conditions or Control Space

19 Conceptual Design Space

Conceptual Design Space

Region of operability

Uncertain space

Design Space

Opt

Region of Interest

20 Tablet Process Example

Tablet Process Example

Filler Lactose Mannitol Lubricant Steraric Acid Mag Stearate Disintegrant Maze Starch Microcrystalline Cell Binder PVP Gelatine

Intact drug % Content uniformity Impurities Moisture Disintegration Dissolution Weight Hardness Friability Stability

21 Chemical Process Example

Chemical Process Example

Catalyst 10-15 lbs Temperature 220-240 degrees Pressure 50-80 lbs Concentration 10-12%

Yield Percent converted Impurity pH Color Turbidity Viscosity Stability

22 Statistical Design Space

Statistical Design Space

The mathematically and statistically defined combination of Factor Space and Response Space that results in a system, product or process that consistently meets its quality characteristics, SSQuIP, with a high degree of assurance. LDT

23 Modeling the World

Modeling the World

All Models are wrong, but some are useful. G. E. P. Box Empirical Models: Simple linear, y = a + bx Quadric equation, y = a + bx + cx2 Mechanistic Models: A physical or chemical equation.

24 Model Prediction

Model Prediction

Equations for critical factors and the mechanistic connection with the critical responses allow for the prediction of the quality characteristics in quantitative terms. Multidimensional in factors and responses.

25 S.I.P.O.C. Model

S.I.P.O.C. Model

26 Macro View

Macro View

Product Process Design

The Whole New Product Development Cycle

Unknown

Controllable Factors

Controlled Responses

Uncontrolled Responses

Concomitant

Uncontrollable Factors

27 Mid-Level View

Mid-Level View

Pre-formulation / formulation studies Pharmacology / toxicology Animal studies Product development Process development Clinical trials Validation and process improvement

28 Micro Level View: Design Space

Micro Level View: Design Space

Independent Factor Space

Dependent Response space

29 Existing Products

Existing Products

Design Space can be inferred by using existing information and historical data . Retrospective process capability studies. Annual Product Review analysis Comparison of historical data to specs Risk management and assessment, Q9

30 Factor Space

Factor Space

ASTM E1325-2002 That portion of the experiment space restricted to the range of levels of the factors to be studied in the experiment AKA, Design Regions The Cambridge Dictionary of Statistics. B. S. Everitt, Cambridge University Press

31 Quick Dry Example

Quick Dry Example

Five batches of product had been lost to an impurity exceeding the criteria The criteria for impurity 1 was NMT 1.0% Four factors studied. Four responses.

32 Quick Dry Example

Quick Dry Example

FACTOR SPACE Drying time 3-9 mins Drying Temperature 40-100 Excipients Moisture 1.2-5 % %Solvent 1-14 %

RESPONSE SPACE Impurity-1 % Impurity-2 % Intact drug % Final moisture %

33 Factor Space

Factor Space

34 Design Space

Design Space

f(x)=?

Independent Factor Space

Dependent Response space

Process understanding is cause and effect quantitated. We find a mathematical and statistical formula that describes the relationship between factor space and response space.

35 2 Factor Interaction Effects to Consider

2 Factor Interaction Effects to Consider

Time * Temperature Time * Moisture Time * Solvent Temperature * Moisture Temperature * Solvent Moisture * Solvent

36 Time*Temp Interaction Plot

Time*Temp Interaction Plot

37 Time* Moisture Interaction Plot

Time* Moisture Interaction Plot

38 Temp*Moisture Interaction Plot

Temp*Moisture Interaction Plot

39 Time*Temp Contour Plot

Time*Temp Contour Plot

Temp

Time

40 Time*Moisture Contour Plot

Time*Moisture Contour Plot

Moisture

Time

41 Temp*Moisture Contour Plot

Temp*Moisture Contour Plot

Moisture

Temp

42 Time*Temp Surface

Time*Temp Surface

43 Time*Moisture Surface

Time*Moisture Surface

44 Temp*Moisture Surface

Temp*Moisture Surface

45 Quick Dry Example

Quick Dry Example

FACTOR SPACE Drying time 3-9 mins Drying Temperature 40-100 Excipients Moisture 1.2-5 % %Solvent 1-14 %

RESPONSE SPACE Impurity-1 % Impurity-2 % Intact drug % Final moisture %

46 Conclusions

Conclusions

FACTOR SPACE Solvent, no effect Time, decrease Temp, decrease Moisture, decrease

RESPONSE SPACE Impurity 1 Less than 1% R2 = 0.95

47 f(Xi) Design Space

f(Xi) Design Space

Impurity = +0.6079 +Time * -0.0057 +Temperature * -0.0058 +Moisture * +0.1994 +Time*Temp * +0.00061 +Time*Moist * -0.29386 +Temp*Moist * -0.00502 +T*T*M * +0.00713

48 Goal

Goal

Find a set of levels for Time, Temperature, and Moisture that will predict impurity of less than 1 percent. (Solvent doesnt matter.) The combination of levels is the design space for impurity 1.

49 Predictive Equation

Predictive Equation

50 Predictive Equation

Predictive Equation

51 Design Space

Design Space

52 Design Space

Design Space

53 Multidimensional Specifications

Multidimensional Specifications

Specifications should not be set one factor at a time. We need to consider all responses together. We need to do the same analysis for impurity 2, intact drug and final moisture and then overlay the four solutions to find the design space that will meet all of the criteria at the same time.

54 Scale-Up

Scale-Up

Scale-up may not be linear Assume that the basic equations will apply Assume the design space will be somewhat robust and rugged. Need to do confirmation experiments to confirm assumptions. Or reestablish the design space.

55 Design Space Conclusions

Design Space Conclusions

ICH Q8 and the FDA are asking for designed experiments and predictive equations for each aspect of a new product. Descriptions need to be mathematical and statistical equations. Empirical equations are the most common, but a few mechanistic equations may be possible.

56 Design Space Conclusions

Design Space Conclusions

This is a new and perhaps confusing issue for the pharmaceutical industry. To implement this approach will require designed experiments with overlays of multiple responses for each new product. Sometimes retrospective studies of existing products can be done with historical data.

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