The document discusses computer-aided drug design using statistical modeling, explaining the differences between descriptive and mechanistic modeling, as well as their applications in predicting drug behavior and efficacy. It covers statistical parameter estimation, sensitivity analysis, and optimal experimental design while emphasizing quality by design (QbD) principles and their importance in pharmaceutical development. The QbD approach aims to enhance product quality through systematic understanding and continuous improvement in drug manufacturing processes.

1. Computer aided drug design:
Statistical modeling:
Statistical modeling is the use of mathematical models and statistical
assumptions to generate sample data and make predictions about the real
world. A statistical model is a collection of probability distributions on a set of
all possible outcomes of an experiment.
 According to Breiman , there are two cultures in the use of statistical modeling to
reach conclusions from data.
i. First culture, namely, the data modeling culture.( DMC)
ii. Second culture ,algorithmic modeling culture.(AMC)
Two of the main goals of performing statistical investigations are to be able to predict
what the responses are going to be to future input variables and to extract some
information about how nature is associating the response variables to the input
variables.

2. Descriptive(prescriptive/empirical ) Versus Mechanistic Modeling:
 Descriptive models aim to summarize and describe observed
data without necessarily understanding the underlying
mechanisms.(describes how a system behaves)
 Empirical modelling are based on direct observation,
measurement and extensive data records
 Mechanistic models, on the other hand, incorporate
knowledge of the underlying biological or chemical
processes to explain observed phenomena.
 Mechanistic models are based on an understanding of the
behavior of a system’s components.
 It is based on the fundamental law of neutral
sciences,physics, and biochemical principles.
It contain:
Mechanism of action
Scientist &specallist in the field
statisticians

3. Descriptive model Mechanistic model
1.It describes the overall
behaviour of the system in
question,without making any
claim about the nature of the
underlying mechanisms that
produce this behaviour.
1.It is one where the basic
elements of the model have a
direct correspondance being
modelled.
2. Descriptive model is a
geniric term for activities that
create models by observation
and experment
2.It assumes that a complex
systems can be understood by
examining the workings of its
individual parts and the
manner in which they are
coupled.
Statistical Parameters Estimation:
Statistical parameters describe the distribution of values within a data set.
The various statistical parameters are :
a. Measures of central tendency
b. Dispersion (also called variablity,scatter, spread)
c. Coeeficient of dispersion(COD)
d. Variance

4. e. Standard deviation
f. Residuals
g. Factor analysis
h. Absolute error(AE)
i. Mean absolute error(MAE)
j. Percentage error of estimate(PE).
In pharmaceutical research, statistical methods are
used to estimate parameters related to drug efficacy, safety, and
pharmacokinetics.
These parameters include maximum concentration
(C_max), area under the concentration-time curve
(AUC), half-life (t_1/2), and clearance (CL).
Estimation of of statistical parameters:
 Point estimation
 Interval estimation
Point estimation:a statistical intended for estimating single
parameter is called a point estimator.The standard deviation of
this estimator is called its standard error or SE.
Confidence Regions:(Inference regions)
In statistic,confidential region is a multi-dimensional
generalization of a confidence interval.
Confidence intervals provide a range of values
within which a parameter is likely to fall.
It covers the complete range of data that went into
the model,and incorporate both uncertainty in the parameter
estimates and predication error.
Confidence regions extend this concept to multiple
parameters simultaneously.

5. The proposed interval will contain the true value
with a specified high probability.
This probability called the confidence interval is
typically taken as 90%,95%,for any confidence level the
corresponding confidence interval is computed as :
C I=(Mean-EM,mean +EM).
It is a set of points in a n dimensional space,
Non-Linearity at the Optimum:

6. Nonlinearity is a statistical term used to describe a situation where there is not
a straight-line or direct relationship between an independent variable and a
dependent variable.
Some drug responses exhibit non-linear behavior,
especially near the optimal dose.
There are several tests to detect non linearity in
pharmacokinetics but the simple onec are:
FIRST TEST:Determination of steady state plasma
concentration at different doses.
SECOND TEST:Determination of some imporatnt
phramacokinitic parameters such as friction bioavailability,
elimination half life or total system clearance at different doses
of drug.
Regression analysis is also very usefull for the estimation of
nonlinear analysis.
Statistical modeling helps identify the optimal dose and
understand non-linear effects.
Sensitivity Analysis:

7. The technique used to determine how
independent variable values will impact a particular
dependent variable under a given set of assumptions is defined
as sensitivity analysis.
It is extensively used by economists and financial
analyst.
It is used within specific boundaries on one or
more input variables.it is fairly simple to understand.
(EMV) Expected monetary value is a statistical technique used in
risk management.EMV= probalility × impact.
Methods of sensitivity analysis:
1. Local sensitivity analysis(derivatives are taken at a single
point)
2. Global sensitivity analysis(this approach uses a global set of
samples to explore the design space.)
The various techniques widely applied include;
1. Differential sensitivity analysis
2. One at a time sensitivity measures
3. Factorial analysis method
DR. FOR SC

8. 4. Correlation analysis method
5. Regression analysis method
6. Subjective sensitivity analysis
Used to provide an appropriate insight into the problems
assosiated with the model under reference.
Finally the decision taker gets idea about how sensitive is the
optimum solution chosen by him to any changes in the input
values of one or more parameters.
 If there is variables deviate from expectations, what will the
effect on (model,system or whatever is being analyzed)and
which variables are causing the largest deviations.
Optimal Design:
Optimal experimental design aims to maximize
information gained from a limited number of experiments.
Statistical techniques guide the selection of optimal
experimental conditions.
Optimal design is usually considered as the design process that seeks the “best”
possible solution(s) for a mechanical structure, device, or system, satisfying the requirements
and leading to the “best” performance, through optimization techniques.
Types of optimal design:
1. A Optimality
2. C Optimality
3. D Optimality
4. E Optimality
5. S Optimality
6. T Optimality
7. G Optimality
8. I Optimality
9. V Optimality
Population Modeling:

9. It is a complex process requiring robust underlying procedures for ensuring clean data, appropriate
computing platforms, adequate resources, and effective communication.
Population modeling is a tool to identify and describe relationships between a subjects physiologic
and observed drug expousure or response.
Althrough this approach was initially developed to deal withsparse PK data collected uring therapeutic
drug monitoring, it was soon expanded to include models linking drug concentration to response.
# population parameters were originally estimated either by fitting the combined data from
all the individuals , ignoring individual differences or by fitting each individuals data separately
and combining individual parameter estimates to generate mean parameters.
Components of model
 PM requies accurate information on dosing, measurements,and covariates.
 Pm are comprised of several componens:
Strural, stochastic models
Covariate models.
Types of model:
PK MODELS
PK/PD MODEL
DISEASE PROGRESSION MODEL
B. QUALITY BY DESIGN in
Pharmaceutical Development:
It is a systamatic approch to product development that
begins with predefined objectives and emphasized
product and process understanding and controls based on
sound science and quality risk management(ICH Q8)
 QbD has been adopted by US food and drug
administration (FDA)for the discovery, development
and manufacture of drugs.

10.  Quality by design (QbD) is a concept introduces by
the international conference on hormonization (ICH)
Q 8 guidelines.
Objects:
 The main object is to achive the quality products
 To achive positive performance testing
 Ensures combination of product and process
knowledge gained during development.
 From knowledge of data process desired attributes
may be constructed.
 Continuous improment
 Reduce batch failure
 Minimuze deviations
Key asscpects of ICH 9
Pharmaceutical Development:
Quality Risk Management
Design Space:
Quality Target Product Profile (QTPP):
Control Strategy:
Process Analytical Technology (PAT):
Design of Experiments (DoE):
Regulatory Flexibility:
TOOLS APPLIED IN QBD APPROACH:
1. Design of Experiment(DoE)
software like Minitab and Statistica
(it done by two methods: Screening
Optimization)
2. Failure Mode Effect Analysis(FMEA)
3. PAT(process analytical technology)

11. Help to analys the Risk assessment methodology:
Cause and Effect Diagrams (fish bone/Ishikawa): This is
very basic methodology to identify multiple possible
factors for a single effect (Figure 3). Various cause
associated with single effect like man, machine,
material, method, system, and environment need to be
considered to identify root cause.
Primary branch represents effect, whereas major braches
in diagram are associated with major causes and minor
branches supports the possible detailed cause.
Overview of qbd:
Quality target product profile
Product design and understanding
Process design and understanding
Control strategy
Continuous improvement
4 M

12. Parameters of qbd:
 Target product profile(TPP)
 Quality target product profile(QTPP)
 Critical quality attributes(CQA)
 Critical material attributes(CMA)
 Critical process parameters(cpp)
 Risk assessment
 Design space
ICH Q8 pharmaceutical development guideline
 The ICH Q8 guideline describes GOOD PRATICES
FOR PHARMACEUTICAL PRODUCT
DEVELOPMENT.
 It is often emphasized that the Quality of a
pharmaceutical product should be built in by design
rather than by testing alone.
 PFIZER was one of the first companies to implement
Qbd and PAT concepts.

13. REGULATORY VIEWS ON QbD:
The QbD concept represents product and process perforrmance charecterists
scientifically designed to meet specific objectives.
FDA
 It states QbD concept can lead to cost saving and
efficiency improvement for both industry and
regulators.
 Provides implementation of QBD in abbreviated
new drug application(ANDA) for both immediate
and modified release dosage form.
QbD facilities:

14. QbD can facilitate innovation, increase manufacturing efficiency, reduce cost/product rejections,
minimize /elements potential compliance actions, enhance opportunities for first cycle approval,
streamline post approval changes and regulatory process, enable more focused inspections, and
provide oppurtunities for continual improvement
Examples for QbD:
1. Implementation of QbD for the development of a vaccine candidate.
2. QbD based process development for biotherapeutic purification.
3. QbD to analytical method:
For chromatographic technique:
 In determination of impurity
 In screening of column used for chromatography
 In development of column used for chromatiography.
 In capillary electrophorosis
 In stability studies
 In UHPLC
4. Other applications of QbD or elementa of QBd
A. Pharmaceuticals:
In modified release products
In sterial manufacture
In solid oral dosage form
In gel manufacuturing
QbD for ANDAs
B. Biopharmaceutiical
In manufacturing of proteins
In the production and charetirization of monoclonal antibodies
For chromatographic techiques used for purification
C. Clinicals
D. Genetics.