Experimental designon

product development

Introduction

What is the traditional developing method?

What is experimental design?

What do we need and what kind of possibilities do we have for designing?

Why is it useful during product development?

Where and how can we use or apply the design results?

Traditional method

Developing a hypothesis

Conducting an experiment to test the hypothesis

Modifying the hypothesis on the basis of the experiment results

Conducting an additional experiment based on the modified hypothesis

Drawbacks of the traditional method

Limited understanding of variable effect

No information about interaction with other

components

The approach is extremely inefficient

Requires too many experiments and

too much time

Expensive

The developer must work until ...

a satisfactory result is found

all possibilities are exhausted

all available time and money are exhausted ...

Because the ...

data processing is slow

the number of errors is high

What kind of possibilities do we have?

Data from previous investigations

Experience, knowledge and intuition

Computer software

neural networks

designing models

Biological neuron structure

Cell body

Axon

Dendrites

Synapses

Electrical signal

Signal transport between neurons

Eletrical signalAxon

Electrical signal

Presynaptic membrane

Postsynapticmembrane

Synaptic gap

Neurotransmittermaterial

Relationship between biological and artificial neural network system (ANN)

Treshold value

LinearTresholdgate

What is artificial neural network?

A modelling tool that discovers a relationship from a database of examples

An automatic mathematical construction method for modelling preparations directly from data

A cost-effective possibility for designing a new preparation

What is needed for design?

Pre-examination results of the substances to be used

Know the final dose or the final pharmaceutical form

Determination of preparation parameters

Test results of the preparation

Application areas of ANN

Pattern recognition Economic and social models Financial sector

Marketing modeling Optimization of investments

Telecommunication Signal Analysis Data compression

Environmental Protection Weather forecast

Biology

Industry Quality control Manufacturing Planning Fault Diagnosis Combine multiple source data

General characteristics of ANN1. It consists of nodes and links between the knots

the weighted input of the input signals is calculated

the amount is compared with the threshold(s)

linear or nonlinear transmission function

their "behavior" changes their behavior and the links between the knots

2. It can be divided into three main parts an interconnected network of nodes

the node activation rule

the learning rule for nodes

Applied ANNsAssociating networksFeature extracting networksNonadaptive networks

Back-propagation model

One and more "hidden" layers of neural systems

Bayesian network

(Generalized Regression Neural Network)

Input layer

Hidden layerRegression layer

Output layer

Data modeling systemStuttgart Neural Network Simulator

Other applied ANNsBox-BehnkenCentral compositePseudo-randomResilient Propagation (Rprop)Resilient Propagation with MAximum-Posterior (Rprop-MAP)

Algorithm: back-propagation

Determining the number of hidden neurons

M.N. Jadid et al: Eng.Appl.Artif.Intell. 9 (1996) 303-319

J.C. Carpenter et al: AI Expert 10 (1995) 31-33

The optimization of the node number of the "hidden" layer depends on:

the number of inbound and outbound neurons

the number of data used to teach

the "noise level" of the desired value

the complexity of the function you want to teach

the type of neural network

from the activation value of hidden nodes

from the teaching algorithm

How does a neural network work?

Data collection

Determination of variables and parameters

Data input into the computer

Teaching and checking

Application of knowledge

What do we need?

Input parameters Output parameters

effective materials

disintegrants

fillers

binders

glidants

lubricants

etc.

particle size

flow properties

hardness

friability

disintegration time

dissolution rate

etc.

Predicted and experimental dissolution valuesY1 Y2 Y3 Y4

P E P E P E P E

F1 47,00 47,03 74,66 74,69 92,61 92,62 103,4 103,4

F2 21,38 21,36 47,17 47,16 91,38 91,42 102,3 102,3

F3 13,81 13,81 21,61 21,61 33,95 33,94 47,49 47,48

F4 17,03 17,03 26,23 26,22 47,02 46,99 67,21 67,19

F5 14,06 14,06 21,81 21,81 32,86 32,85 49,55 49,54

F6 23,12 23,12 50,35 50,36 80,40 80,44 99,16 99,19

F7 17,02 16,66 30,08 29,81 49,79 49,87 67,44 68,76

F8 17,02 17,37 30,08 30,35 49,79 49,71 67,44 66,11

F9 14,47 14,46 26,46 26,44 42,44 42,41 62,08 62,06

F10 36,11 36,11 65,80 65,80 77,18 77,17 99,43 99,43

Release profiles of ASA from model formulations

Response surfaces of the influence of the percentage of Eudragit L 100 and tablet hardness on the percentage of ASA released after (A) 1 hour, (B) 2 hours, (C) 4 hours, and (D) 8 hours, predicted using the GRNN.

Contour plots of the influence of percentage of Eudragit L 100 and tablet hardness on percentage of ASA released after (A) 1 hour, (B) 2 hours, (C) 4 hours, and (D) 8 hours, predicted using the GRNN.

Predicted and experimental observed Aspirin release from optimal formulation.

Crack velocity and film opacity responsesurfaces

Experimental design

What does experimental designinvolve?

A practice that employs statistical tools and

methods in scientific experimenting

Variables via which we are attempting to make a

correlation or regression with a measurable input

and output we are trying to predict

What is necessary for designing

Preliminary investigations of the materials

The dosage and the dosage form of the final product

Determination of the parameters of the preparation process

Results of the investigations of the preparations

Preliminary investigations

Designing a pellet

Designing the experiment

Design method selection

Central compositeFace centeredBox-BehnkenSimplexEquiradialRandom

Simplex centroidSimplex LatticeHybrid

Detailing of the experiments

Parameterizing the ANN

Setting the control parameters

Setting up the parameters

Investigated compositions

Results of investigations 1.

Results of investigations 2.

Finding the best or worst sample

Importance of ingredients

Querying a new sample

Predicting the properties 1.

Predicting the properties 2.

Material and method

API: Dilthiazem HCl (Dilt)Filler: Vivapur 101 (V101)Disintegrant: Era-Tab (Era-T)Binder: Pharmacoat 603 (P603)

Granule:

Desing mode: Hybrid design

Preparation: Freund CF-360 granulator

Design the „core”

Min. 100 g

Max. 500 g

Min. 300 g

Max. 500 g

Min. 100 g

Max. 500 g

Min. 10 g

Max. 40 gP603

Dilt

Era-T

V101

Designed compositions

Dilt Era-T V101 P603

(g) (g) (g) (g)

MST-1 315 339 346 23

MST-2 144 440 417 36

MST-3 471 413 116 17

MST-4 426 408 166 23

MST-5 374 453 173 19

MST-6 342 378 280 23

MST-7 365 420 215 21

Granule preparation

Preparation conditions

Liqu. add. Rotor Temp

ml/min rpm °C

MST-1 10 140 45

MST-2 10 140 45

MST-3 15 160 45

MST-4 15 160 55

MST-5 20 200 55

MST-6 20 200 65

MST-7 20 200 65

Investigated properties

1,649 1,19 0,751 0,15

1,753 1,17 0,747 0,12

1,583 1,29 0,729 0,18

1,651 1,16 0,746 0,23

1,678 1,25 0,757 0,31

1,731 1,22 0,781 0,17

1,939 1,19 0,731 0,19

Density Roundness Hardness Friability

(N) (%)

MST-1

MST-2

MST-3

MST-4

MST-5

MST-6

MST-7

Shape of granules

Parameterizing the ANN

Editing the training parameters

Structure of ANN

Control parameters

Prediction of density

Prediction of hardness

Prediction of friability

Prediction of roundness

Searching for samples or parameters

Directed search

Area Application Applied DoE TYPE

Oral drug delivery Tablet formulation development Multivariate design (fractional factorial design in14 variables, 214–9 design, 35 experiments)

Multivariate design + simplex optimization byModde Optimizer

Fractional factorial designs (two studies); design space definition using a simplified BayesianMonte Carlo simulation

Mixture design

Oral drug delivery –immediate release (IR)

Dispersible tablets development Several factorial experiments at 2–3 factors, 2–3 levels

Immediate release tablet platform Resolution V 25–1 fractional factorial design

Fast dissolving pellets 25–1 fractional factorial design, five factors (fournumeric and one categorical), two levels

Oral drug delivery –modified release (MR)

Gastroretentive dosage form 3-level-3-factor, Box–Behnken design

Areas where it is useful

Area Application Applied DoE TYPE

Inhalation drug delivery Powder for inhalation(formulation and process development)

Half-fractional factorial design with five factors attwo levels with resolution V

Face centered central Composite Design withthree factors at three levels

Risk assessment by Lean QbD Software

Transdermal drug delivery Patch development 24 full factorial design

Iontophoretic delivery Face-centered central composite design (totalnumber of experimental combinations 2k +2k + n0, with k = number of independent variables and n0

= number of repetitions of the experiments at thecenter point)

Cutaneous drug delivery (Topical) Nanoemulsion for leishmaniasis (formulation development)

22 full factorial design

Microsponge-based gel for surgical wounds (development)

3-factor, 3-level Box–Behnken design

Ocular drug delivery PEGylated PLGA nanospheres (optimization and

characterization)

Central composite factorial design

Liquid crystalline nanoparticles (formulation

optimization)

Fractional factorial design 25–1; simplex-lattice

experimental design

Area Application Applied DoE TYPE

Injections Formulation for parenteral nutrition

(development)

D-optimal experimental design (mixture design)

Biopharmaceuticals Toward QbD implementation in the

biopharmaceutical industry

QbD for biopharmaceuticals

QbD for risk assessment and management (Case

Study on Monoclonal Antibody)

Antibody Formulation Robustness Multivariate study (full factorial) including three

factors at two levels

Protein formulation

Nanopharmaceutics Development and industrialization of polymeric

targeted nanoparticle drug delivery platforms

Solid Lipid Nanoparticles for Inhalation (process

development)

Two-level full factorial design (with no center

points and three repetitions for each level)

Area Application Applied DoE TYPE

Pharmaceutical processes Dry powder inhaler capsule filling D-optimal model with design statistics G-

efficiency with three replicates

Excipient micronization Full factorial (three variables at two levels, eight

runs)

Freeze-drying of injectables Design space calculation (according to a cited

model)

Film-formation by spraying Rechtschaffner Res V two-level fractional design

for four variables with center point

Nano-precipitation and nanospray-drying Design model based on integrated-variance

optimal design for surface response

Analytical methods Method (e.g. chromatographic) development

Test methods Adhesion test (for patches) Randomized response surface design, five

factors, 38 runs

In vitro aerosol deposition in human cast (for

inhaled products)

Half-factorial design

Generic drug products Generic product equivalence Fractional factorial design with triplicate center

points

Conclusions

It is necessary to know the properties of the materials

The dosage form and the final dosage of the preparation must be known for the preliminary investigations

It is possible to determine the optimal parameters of the preparation process

It can generally be used for the development of new products

The benefits ofcomputerized design

Short data-processing time

Economical design

Fewer errors

Better preparation

More profit