This document provides an overview of computer-aided formulation development and optimization concepts. It discusses how computer tools are now used in pharmaceutical product formulation and development. Various techniques like design of experiments are implemented to optimize formulation and processing parameters. The document defines key terms related to optimization like variables, effects, interactions, and experimental design. It also discusses response surface methodology and different software used for design of experiment and optimization. The conclusion emphasizes that using design of experiment allows formulation scientists to systematically evaluate factors and optimize formulations and manufacturing processes.

1. 1
COMPUTER-AIDED FORMULATION
DEVELOPMENT & CONCEPT OF OPTIMIZATION
SUBMITTED BY
SHUBHAM KOTNALA
Department of Pharmaceutical
Sciences
BBAU Lucknow

2. CONTENTS
2
 Introduction.
 Advantages.
 Concept and terms.
 Optimization Parameters.
 Design Of Experiments.
 Conclusion.
 References.

3. 3
 Formulation and development is a process of selection of
component and processing.
 Now days computer tools used in the formulation and
development of pharmaceutical product.
 Various technique, such as design of experiment are implemented
for optimization of formulation and processing parameter.
 Many times finding the correct answer is not simple and straight
forward in such cases use of computer tools (optimization
procedure) for best compromise is the smarter way to solve
problem.
INTRODUCTION

4. 4
 The term Optimize is defined as to make perfect , effective, or
functional as possible.
 It is the process of finding the best way of using the existing
resources.
 While taking in to the account of all the factors that influences
decisions in any experiment.
 Traditionally, optimization in pharmaceuticals refers to
changing one variable at a time, so to obtain solution of a
problematic formulation.

5. ADVANTAGES
5

6. BASIC CONCEPT OF OPTIMIZATION &
TERMINOLOGY
6
 Various terminology and concept associated with optimization :-
 1.Variables.
 2.Effect, Interaction & Confounding.
 3.Code transformation.
 4. Factor space.
 5.Experimental design.
 6.Response surface.

7. VARIABLES
7
 Design and development of drug formulation or pharmaceutical
process usually involve several variables.
VARIBLES
QUANTITATIVE
(CAN BE ASSIGNED A
NO.)
Eg-AMOUNT OF
DISINTEGRANT,
QUALITATIVE
(NOT ASSIGNED A NO.
Eg- TYPE OF
EMULGENT,POLYMER
GRADE
INPUT VARIABLES
(INDEPENDENT VARIABLES)
Eg- MIXING TIME,
COMPRESSION FORCE
DEPENDENT
VARIABLES
(RESPONSE
VARIABLES)

8. 8
 INDEPENDENT VARIABLES (INPUT VARIABLES)
 The independent variables, which influence the formulation
characteristics or output of the process, are labeled as
FACTORS.
 Values assigned to the factors are termed as LEVELS, e.g.,
30°and 50°are the levels for the factor, temperature.
 The restrictions placed on the factor levels are known as
CONSTRAINTS.

9. 9
 DEPENDENT VARIABLES (RESPONSE VARIABLES)
 The characteristics of the finished drug product or the in-process
material are known as dependent.
 Variables, e.g., drug release profile, friability, size of tablet granules,
disintegration time, etc.
 These variables are the measured properties of the system to
estimate the outcome of the experiment.
 These variables are the direct function(s) of any change(s) in the
independent variables.

10. 10
 Accordingly, a drug formulation (product) with respect to
optimization techniques can be considered as a system, whose
output (Y) is influenced by a set of controllable (X) and
uncontrollable (U) input variables via a transfer function (T).
 The nomenclature of T depends upon the predictability of the
output as an effect of change of the input variables.
T
System with controllable input variables (X),
uncontrollable input variables (U), transfer function (T) &
output variables (Y).
X
U
Y

11. 11
 If the output is totally unpredictable from the previous studies,
T is termed as black box.
 The term, white box is used for a system with absolutely true
predictability.
 While the term, gray box is used for moderate.

12. EFFECT INTERACTION
&CONFOUNDING.
12
 The magnitude of the change in response caused by varying the
factor level(s) is termed as an EFFECT.
 The main effect is the effect of a factor averaged over all the levels
of other factors.
 An INTERACTION is said to occur, when there is "lack of
additivity of factor effects".
 An interaction may be said to take place when the effect of two or
more factors is dependent on each other, e.g., effect of factor A
depends on the level given to the factor B.
 The term orthogonality is used, if the estimated effects are due to
the main factor of interest and are independent of interactions
(Box et al., 1960; Bolton, 1990). Conversely, lack of orthogonality
(or independence) is termed as CONFOUNDING or .
 The measure of the degree of confounding is known as resolution.

13. CODE TRANSFORMATION
13
 The process of denoting a natural variable into a dimensionless
coded variable Xi such that the central value of experimental
domain is zero is known as coding or normalization.
 Various salient features of the transformation include:
 Depiction of effects and interaction using signs (+) or (-).
 Allocation of equal significance to each axis.
 Easier calculation of the coefficients.
 Easier calculation of the coefficient variances.
 Easier depiction of the response surfaces.
 Orthogonality of the effects.

14. 14
 Generally, the various levels of a factor are designated as -1, 0
and +1, representing the lowest, intermediate (central) and the
highest factor levels investigated, respectively.
 For instance, if starch, a disintegrating agent, is studied as a factor
in the range of 5 to 10% (w/w), then codes -1 and +1 signify 5%
and 10% concentrations, respectively. The code 0 would represent
the central point at the mean of the two extremes, i.e., 7.5% w/w.

15. FACTOR SPACE
15
 The dimensional space defined by the coded variables is known
as factor space.
 The part of the factor space that is investigated experimentally
for optimization is the experimental domain , Also known as the
region of interest, it is enclosed by the upper and lower levels of
the variables.
 The factor space covers the entire figure area and extends even
beyond it, whereas the design space of the experimental domain
is the square enclosed by X1 = ± 1, X2 = ± 1.
 In Fig. represent the factor space for two factors on a bi-
dimensional (2-D) plane during a typical tablet compression
process.

16. 16

17. Experimental Design
17
 Conduct of an experiment and subsequent interpretation of its
experimental outcome are the twin essential features of the
general scientific methodology.
 This can be accomplished only if the experiments are carried out
in a systematic way and the inferences are drawn accordingly.
 Runs or trials are the experiments conducted as per the selected
experimental design.
 Primarily, the experimental (or statistical) designs are based on
the principles of randomization (the manner of allocations of
treatments to the experimental units), replication (the number of
units employed for each treatment) and error control or local
control (grouping of specific type of experiments to increase the
precision).

18. RESPONSE SURFACE
18
 Conduct of DoE trials, as per the chosen statistical design, yields
a series of data on response variables explored. Such data can be
suitably modeled to generate mathematical relationship between
the independent variables and the dependent variable. Graphical
depiction of the mathematical relationship is known as Response
surface.
 A response surface plot is a 3-D graphical representation of a
response plotted between two independent variables and one
response variable. The use of 3-D response surface plots allows
understanding of the behavior of the system by demonstrating the
contribution of the independent variables.

19. DESIGN OF EXPERIMENT
19
 Design of experiment :- Is a structure organized method used to
determine the factor affecting to process and output of that process.
 Parsimony Principle:-
some of factor are important ,while other factor are not important.
 Few variables are effective or other are not effective.
 Experimental design is an integral part of optimization technique.
 Those technique used in the formulation optimization.

20. VARIOUS EXPERIMENTAL DESIGN
20
9

21. DOE FOR FORMULATION & DEVELOPMENT
21
 All pharmaceutical products are formulated to specific dosage form
drugs to be effectively delivered to patient typical pharmaceutical
dosage form include tablets, capsules, solution suspension, etc.
 Different dosage form required different technology usually
present different technological challenge for formulation &
development .
 Due to complex challenges, formulations scientist used effective
methodology like as design of experiment and statistical analysis
for formulation and development .
 Formulation scientist used this method for process optimization and
process validation .

22. COMPUTER SOFTWARE
22
Software Silent feature
Design Expert Powerful & compressive package used for optimizing
pharmaceutical formulation and process
Minitab Powerful DOE software for automated data analysis
MS-
Excel compatibility. Include almost all design of
RSM.
DOE PRO XL MS-Excel compatible DOE software for automated data
analysis
CARD Powerful DOE software for data analysis include
graphics
and help feature

23. CONCLUSION
23
 Design of experiment & statistical analysis have been
used in the formulation development.
 Using design of experiment formulation scientist evaluate the all
formulation factors in systematically and timely manner to
optimize the formulation and manufacturing process.
 When the pharmaceutical process and product are optimize by
systematic approach then process validation & scale up can be
efficient because of the robustness of the formulation and
manufacturing process.

24. REFERENCES
24
 Singh B, Gupta RK, Ahuja N. Computer-assisted optimization of
pharmaceutical formulations and processes. Pharmaceutical
Product Development (Ed. NK Jain), CBS Publishers, New Delhi.
2006:273-318.
 Cooper L. and Steinberg D., Introduction to methods of
optimization, W.B.Saunders,Philadelphia, 1970, 1st Edition, 301-
305.

25. 25
Thank you
Any query??