Computers have been used in pharmaceutical research and development since the 1940s. Early computers were large mainframe systems that were expensive and shared between organizations. By the 1960s, some pharmaceutical companies had acquired early computers like the IBM 650 to assist with scientific tasks. Today, computers are essential for tasks across the pharmaceutical industry from drug design and clinical trials to manufacturing, sales, and more. Advanced statistical modeling and software continue to be important tools in pharmaceutical research and development.

1. HISTORY OF COMPUTERS IN
PHARMACEUTICAL RESEARCH &
DEVELOPMENT
PRESENTED BY:
DHANASEKAR J,
II SEMESTER M.PHARM,
DEPARTMENT OF PHARMACEUTICS,
NANDHA COLLEGE OF PHARMACY, ERODE.
30-10-2023
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2. HISTORY OF COMPUTERS IN PHARMACEUTICAL
RESEARCH AND DEVELOPMENT
 Today computers are seen everywhere in Pharmaceutical research and development
that it may be hard to imagine a time when there were no computers to assist the
medicinal chemist or biologist.
 Computers began to be utilized at pharmaceutical companies as early as the 1940s.
 There were several scientific and engineering advances that made possible a
computational approach to design and develop a molecule.
 One fundamental concept understood by chemists was that chemical structure is
related to molecular properties including biological activity.
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3.  A quarter-century ago, the notion of a computer on the desk of every scientist
and company manager was not even contemplated.
 Now, computers are absolutely essential for generating, managing, and
transmitting information.
BRIEF ACCOUNT ON HISTORIC DEVELOPMENT :
 In the late 1950's or early 1960'S, the first computers to have stored programs
of scientific interest were acquired.
 One of these was an IBM 650, it had a rotating magnetic drum memory
consisting of 2000 accessible registers.
 The programs, the data input, and the output were all in the form of IBM
punched cards.
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4. EVOLUTION OF DIGITAL SYSTEM IN
HEALTHCARE SYSTEM
Let's review health information system trends, decade by decade,
1. 1960s :
 The main healthcare drivers in this era were Medicare and Medicaid
 The IT drivers were expensive mainframes and storage
 Because computers and storage were so large and expensive, hospitals
typically shared a Mainframe
 The principal applications emerging in this environment were shared hospital
accounting systems
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5. 2.1970s :
 One of the main healthcare drivers in this era was the need to do a better job
communicating between departments (ADT, order communications, and results
review) and the need for discrete departmental systems (e.g., clinical lab,
pharmacy)
 Computers are now small enough to be installed in a single department without
environmental controls
 As a result, departmental systems proliferated
 Unfortunately, these transactional systems, embedded departments, were
typically islands unto themselves
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6. 3. 1980s :
 Healthcare drivers were heavily tied to DRG's and reimbursement
 For the first time, hospitals needed to pull significant information from both
clinical and financial systems in order to be reimbursed
 At the same time, personal computers, widespread, non-traditional software
applications, and networking solutions entered the market
 As a result, hospitals began integrating applications so financial and clinical
systems could talk to each other in a limited way
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7. 4.1990s :
 In this decade, competition and consolidation drove healthcare, along
with the need to integrate hospitals, providers, and managed care
 From an IT perspective, hospitals now had access to broad, distributed
computing systems and robust networks
 Therefore, creation of Integrated delivery network (IDN)- like
integration, including the impetus to integrate data and reporting
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8. 5.2000s :
 The main healthcare drivers were increased integration and the beginnings
of outcomes-based reimbursement
 We now had enough technology and bedside clinical applications installed
to make a serious run at commercial, real-time clinical decision support
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9. CURRENT APPLICATIONS OF COMPUTERS IN PHARMACY
 1.Usage of computers in the Retail pharmacy
 2. Computer aided design of drugs (CADD)
 3. Use of Computers in Hospital Pharmacy
 4. Data storage and retrieval
 5. Information system in Pharmaceutical Industry
 6. Diagnostic laboratories
 7. Computer aided learning
 8. Clinical trial management
 9. Adverse drug events control
 10. Computers in pharmaceutical formulations
 11. Computers in Toxicology and Risk Assessment
 12. Computational modelling of drug disposition
 13. Recent development in bio computation of drug development
 14. In Research Publication
 15. Digital Libraries
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10. RECENT SOFTWARE'S IN PHARMA INDUSTRY
 Oracle JD Edwards – Manufacturing
 Master Control Quality Management System (QMS)
 NetSuite
 Sapphire One
 Fishbowl Manufacturing
 Lot Tracking
 Interface System and Business-to-Business Trading
 SYSPRO
 MAXLife365
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11. STATISTICAL MODELLING IN PHARMACEUTICAL
RESEARCH AND DEVELOPMENT
DESCRIPTIVE MODELS
 They are based on direct observation, measurement and extensive data
records
 Descriptive model is a generic term for activities that create models by
observation and experiment.
 Descriptive model operates on a simple logic: the maker observes a close
correspondence between the behaviour of the model and that of its referent
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12. MECHANISTIC MODELS
 They are based on an understanding of the behavior of a system's
components.
 A mechanistic model assumes that a complex system can be understood by
examining the workings of its individual parts
 Mechanistic models typically have a tangible, physical aspect. In that
system components are real, solid and visible.
 A mechanistic model is one where the basic elements of the model have a
direct correspondence to the underlying mechanisms in the system being
modelled
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13. STATISTICAL PARAMETERS ESTIMATION (SAMPLE
STATISTICS)
 The process by which one makes inferences about a population, based on
information obtained from a sample.
 Inferential statistics are used to determine the likelihood that a conclusion,
based on the analysis of the data from a sample, is true and represents the
population studied
 The two common forms of statistical inference are:
 Estimation
 Null hypothesis tests of significance (NHTS)
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14. ESTIMATION
 A parameter is a statistical constant that describes a feature about a
phenomenon, population etc.
 There are two forms of estimation:
 Point estimation (maximally likely value for parameter)
 Interval estimation (also called confidence interval for parameter)
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15. POINT ESTIMATION
 Point estimation is an estimate of a population parameter given by a single
number.
 They are single points that estimates parameter directly which serve as a
"best guess" or "best estimate" of an unknown population parameter
 Example: Population mean, standard deviation
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16. LIMITATION OF POINT ESTIMATION
 Point estimation does not provide information about sample to sample variability
Point estimates are single points that are used to infer parameters directly.
 For example,
 Sample proportion pˆ(“p hat”) is the point estimator of p
 Sample mean x (“x bar”) is the point estimator of μ
 Sample standard deviation s is the point estimator of σ
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17. CONFIDENCE REGIONS
 It is a set of points in an n-dimensional space, often represented as an ellipsoid
around a point which is an estimated solution to a problem, although other shapes
can occur
 Confidence regions are multivariate extensions of univariate confidence intervals
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18. Interpretation of confidence interval
It provides a range of possible value for the parameter.
Give information about closeness of the sample to unknown population parameter
Provides a measure of the extent to which a sample estimate is likely to differ
from the true population value.
Indicates with a stated level of certainty, the range of values with in which the true
population mean is likely to lie.
CI depends on the level of confidence
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19. NULL HYPOTHESIS TESTS OF SIGNIFICANCE
 It is a method of statistical inference by which an experimental factor is tested
against a hypothesis of no effect or no relationship based on a given
observation.
 Here is a simple example: A school principal claims that students in her school
score an average of seven out of 10 in exams. The null hypothesis is that the
population mean is 7.0.
Null Hypothesis
 It is a statement about population parameter, Denoted by H0
 Tests the likelihood of the statement being true in order to decide whether to
accept or reject the alternative hypothesis.
 It needs to be tested if it’s true.
 Includes signs , =,≤ or ≥
 Ex: Accepted theory, ethanol boils at 73.1 degrees
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20. Test of significance
 It is a formal procedure for comparing observed data with a claim (also called a
hypothesis) whose truth we want to assess.
 Test of significance is used to test a claim about an unknown population parameter.
 A significance test uses data to evaluate a hypothesis by comparing sample point
estimates of parameters to values predicted by the hypothesis.
Null Hypothesis
True False
Accept if p>=0.05 (non
significant)
conclusion- negative
1-α (confidence level) β (type 2 error)
Reject if p< 0.05
(significant)
conclusion- Positive
α (type 1 error) 1-β (power of the test)
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21. STATISTICAL PARAMETERS
NONLINEARITY AT THE OPTIMUM
 It is useful to study the degree of nonlinearity of our model in a
neighborhood of the forecast
 Briefly, there exist methods of assessing the maximum degree of intrinsic
nonlinearity that the model exhibits around the optimum found.
 If maximum nonlinearity is excessive, for one or more parameters the
confidence regions obtained applying the results of the classic theory are
not to be trusted.
 In this case, alternative simulation procedures may be employed to
provide empirical confidence regions.
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22. SENSITIVITY ANALYSIS
 It is the process by which the robustness of a cost- utility analysis (CUA) is
assessed by examining the changes in the results of the analysis when key
variables are varied.
 Sensitivity analysis is a way to predict the outcome of a decision if a situation
turns out to be different compared to the key prediction
Steps to perform sensitivity analysis
• Create A Model
• Write A Set Of Requirements
• Design A System
• Make A Decision
• Do A Trade off Study
• Originate A Risk Analysis
• Want To Discover The Cost Drivers
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23. OPTIMAL DESIGN
 Introduction - 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.
 The objective of designing quality formulation is achieved by various
optimization techniques. In Pharmacy word “optimization” is found
in the literature referring to study of the formula.
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24. ADVANTAGES OF OPTIMAL DESIGNS
• Optimal designs reduce the costs of experimentation by allowing statistical
models to be estimated with fewer experimental runs.
• Optimal designs can accommodate multiple types of factors, such as process
and discrete factors.
• Designs can be optimized when the design-space is constrained, for example,
when the mathematical process-space contains factor-settings that are
practically infeasible (e.g- due to safety concern)
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25. TYPES OF OPTIMAL DESIGN
1. Plackett-burman designs.
2. Factorial designs.
3. Fractional factorial design (FFD).
4. Response surface methodology.
5. Central composite design (box-wilson design).
6. Box-behnken designs
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26. STATISTICAL PARAMETERS-
POPULATION MODELING
Introduction
• Modeling and simulation have emerged as important tools
for integrating data, knowledge and mechanisms to aid in
arriving at rational decisions regarding drug use and
development
• Population modeling methods provide a framework for
quantitating and explaining variability in drug exposure
and response
• Population modeling is a tool to identify and describe
relationships between a subject's physiologic characteristics
and observed drug exposure or response
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28. Traditional Standard Two Stage Method
 It is a traditional method.
 It involves study of relatively small number of individuals subjected to intense
sampling.
 The period of the study is short, since the individuals are usually categorised.
Advantages
 It provides reliable and robust estimates when extensive numbers of samples are
available for each individual.
 It is a simple method.
 It is a well tried and straightforward method to implement.
 Many software packages are available for this method.
Disadvantages
 Being a controlled study design, it is very expensive and requires careful planning
and implementation.
 It gives unreliable results in case of sparse data.
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29. Native Pooling Method
It is a traditional method.
In this method, the data from all individuals are pooled and analysed
simultaneously without consideration of the individual from whom the
specific data were obtained
Advantages
It may be the only viable approach in certain situations, for
e.g. in case on animal data, where each animal provides only one data point.
Disadvantages
This method is generally considered the least favourable. It is
susceptible to bias. It produces inaccurate estimates of
pharmacokinetic parameters
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30. PARAMETRIC METHODS
Mixed Effect modelling
 Mixed effect modelling is a parametric method which assumes a specific distribution
of pharmacokinetic parameters prior to estimation.
 It is considered as the optimum population model method.
 They are of two types:
 Fixed
 Random
Fixed effects are components of the structural pharmacokinetic model
They do not include any unexplainable variation either between or within
individuals. Fixed effect parameters are represented by the symbol theta.
Random Effects: Each individual in a population will have a specific value
for their pharmacokinetic parameter, which will differ from the population
typical value due to unexplainable variability
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31. NON-PARAMETRIC METHODS
 Non parametric methods do not assume any specific distribution of parameters
about the population values, but rather allow for many possible distributions.
 In this method, the entire population distribution of each parameter is
estimated from the population data.
 This permits visual inspection of distribution before committing to one.
 Different non parametric methods are:
 Non parametric maximum likelihood [NPML]
 Non parametric expectation maximization [NPEM]
 Nonparametric Semi/ smooth non parametric method [SNP]
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32. NON PARAMETRIC MAXIMUM LIKELIHOOD [NPML]
 This method permits all forms of distributions including those containing sharp
changes, such as discontinuities and kinks.
 It uses maximum likelihood as estimator.
NON PARAMETRIC EXPECTATION MAXIMIZATION [NPEM]
 This method is preferred to any parametric method when there is an unexpected
multimodal or non-normal distribution of at least one of the nodal parameters.
 It eliminates the need for initial guesses which are required for nonlinear least
square procedure. It is preferable to traditional method in case of sparse data. It uses
expectation maximization as the estimator.
SEMI/ SMOOTH NON PARAMETRIC METHOD [SNP]
This method places some restrictions on the type of parametric distributions considered.
Functions that are not permitted include those containing sharp edges and discontinuities.
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33. CONTENTS
Introduction
Approaches to pharmaceutical development
Flow of QbD
Tools applied in QbD approach
ICH Q8 Pharmaceutical Development Guideline
Regulatory and Industry views on QbD
Conclusion
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34. INTRODUCTION
DEFINITION
Quality by design (QbD) is a systematic approach to product development
that begins with predefined objectives and emphasizes product and process
understanding and controls based on sound science and quality risk
management (ICH Q8).
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35. OBJECTIVES OF QbD
 The main objective of QbD is to achieve the quality products.
 To achieve positive performance testing.
 Ensures combination of product and process knowledge gained during
development.
 From knowledgeof data process, desired attributes may be constructed.
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36. Benefits of QbD for Industry
o Eliminate batch failures.
o Minimize deviations and costly investigations.
o Empowerment of technical staff.
o Increase manufacturing efficiency, reduce costs and project rejections and
waste.
o Better understanding of the process.
o Continuous improvement.
o Ensure better design of product with less problem.
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37. APPROACHES TO PHARMACEUTICAL DEVELOPMENT
Aspects Traditional QbD
Pharmaceutical development Empirical (Experimental) Systematic and multivariate
experiments.
Manufacturing process Fixed Adjustable with experiment
design space.
Process control Offline and has wide or slow
response
PAT (Process Analytical
Technique) utilized for feed
back.
Product specification Based on batch data Based on the desired product
performance.
Control strategy By end product testing Risk based, controlled shifted up
stream, real time release.
Life cycle management Post approval changes
needed
Continual improvement
enable within design space.
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38. TARGET PRODUCT PROFILE(TPP)
 “A prospective summary of the quality characteristics of drug product that ideally
will be achieved to ensure the desired quality, taking into account safety & efficacy
of drug product.”(ICH Q8)
Target product profile should includes-
 Dosage form
 Route of administration
 Dosage strength
 Pharmacokinetics
 Stability
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39.  The TPP is a patient & labeling centered concepts, because it identifies the
desired performance characteristics of the product, related to the patient’s
need & it is organized according to the key section in the drug labeling.
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40. QUALITY TARGET PRODUCT PROFILE (QTPP)
• QTPP is a quantitative substitute for aspects of scientific safety & efficacy
that can be used to design and optimize a formulation and manufacturing
process.
• QTPP should only include patient relevant product performance.
• The Quality Target product profile is a term that is an ordinary addition of
TPP for product quality.
• QTPP is related to identity, assay, dosage form, purity, stability in the label.
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41. CRITICAL QUALITYATTRIBUTES(CQAS)
• A CQA has been defined as “a physical, chemical, biological or
microbiological property or characteristics that should be within an
appropriate limit, range, or distribution to ensure the desired product quality”.
• Critical Quality Attributes are generally associated with the drug substance,
excipients, intermediates and drug product.
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42.  The quality attributes of a drug product may include identity, assay, content
uniformity, degradation products, residual solvents, drug release, moisture
content, microbial limits.
 Physical attributes such as color, shape, size, odor, and friability.
 These attributes can be critical or not critical.
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43. CRITICAL MATERIAL ATTRIBUTES(CMA)
• A CMA of a drug substance, excipient, and in-process materials is a physical,
chemical, biological, or microbiological characteristic of an input material that
should be consistently, within an appropriate limit to ensure the desired
quality of that drug substance, excipient, or in-process material.
• The CMA is likely to have an impact on CQA of the drug product.
• A material attributes can be an excipients, raw material, drug substances,
reagents, solvents, packaging & labeling materials.
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44. CRITICAL PROCESS PARAMETERS(CPP)
 A CPP of manufacturing process are the parameters which, when changed,
can potentially impact product CQA and may result in failure to meet the
limit of the CQA.
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Operations during tableting CPP
Wet granulation Mixing time, temperature, method of
binder addition
Drying Drying time, Inert air flow
Milling Milling speed, screen size, feeding rate
Compression Pre compression force, main compression
force, dwell time, hopper design, ejection
force
Coating Inert air flow, time, temperature, spray
pattern and rate

45. RISK ASSESSMENT
• Risk assessment is the linkages between material attributes & process
parameters.
• It is performed during the lifecycle of the product to identify the critical
material attributes (CMA) & critical process parameters (CPP).
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46. DESIGN SPACE
 This is the multidimensional combination and interaction of input
variables (e.g., material attributes) and process parameters that have
been demonstrated to provide assurance of quality.
 A design space maybe built for a single unit operation or for the
ensure process.
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48. TOOLS APPLIED IN QBD APPROACH
 This is a systematic approach applied to conduct experiments to
obtain maximum output.
 We have capability and expertize to perform DoE in product
development using software like Minitab and Statistica.
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49. Design of experiments done by 2 methods
1. SCREENING:
 Designs applied to screen large number of factors in minimal number
of experiments to identify the significant ones.
 Main purpose of these designs is to identify main effects and not the
interaction effects.
 For such studies common designs used are
1. Placket-Burman design
2. Fractional factorial design.
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50. 2. OPTIMIZATION:
 Experimental designs considered to carry out optimization are mainly full
factorial design, surface response methodology .
e.g. Central composite
Box-Behnken and
Mixture designs.
 These designs include main effects and interactions and may also have
quadratic and cubic terms require to obtain curvature.
 These designs are only applied once selected factors are identified, which
seem to be contributing in process or formulation.
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51. RISK ASSESSMENT METHODOLOGY
1.Cause and Effect Diagrams (Fish bone/Ishikawa):
 This is very basic methodology to identify multiple possible factors for a
single effect.
 Various cause associated with single effect like man, machine, material,
method, system, and environment need to be considered to identify root cause.
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52. 30-10-2023
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53. 2.Failure mode effect analysis (FMEA):
 This is an important tool to evaluate potential failure modes in any process.
 Quantification of risk byinteractionof probability functions of severity,
occurrence, and detectability of any event can be done.
 Fmea can be effectively performed with good understanding of process.
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54. 3 PAT (Process Analytical Technology) :
 Assurance of product quality during intermittent steps using Process
Analytical Technology (PAT) is recommended by regulatory authorities,
which is yet to be extensively accepted by the pharmaceutical industry over
conservative methodologies.
 It involves advanced online monitoring systems like NIR (Near IR),
Handheld Raman Spectrometer, Online Particle Size Analyzer etc.,
 These technologies further make assurance of continuous improvement in
process and product quality through its life cycle.
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55. CONTROLSTRATEGY
 Control strategy Based on process and product understanding, during
product development sources of variability are identified.
 Understanding the sources of variability and their impact on processes, in-
process materials, and drug product quality can enable appropriate controls to
ensure consistent quality of the drug product during the product life cycle.
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56. Elements of a Control Strategy
 Procedural controls
 In-process controls
 Batch release testing
 Process monitoring
Characterization testing
 Comparability testing
 Consistency testing
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57. ICH Q8(R2): PHARMACEUTICAL DEVELOPMENT
GUIDELINE
The ICH Q8 guideline describes GOOD PRACTICES FOR
PHARMACEUTICAL PRODUCT DEVELOPMENT.
ICH Q8 Pharmaceutical Development describes the principles of QbD,
outlines the key elements, and provides illustrative examples for
pharmaceutical drug products.
It is often emphasized that the QUALITY of a pharmaceutical product should
be BUILT IN BY DESIGN RATHER THAN BY TESTING ALONE.
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58.  The ICH Q8 guideline suggests that those aspects of drug substances,
excipients, container closure systems, and manufacturing processes that are
critical to product quality, should be DETERMINED AND CONTROL
STRATEGIES justified.
 Some of the tools that should be applied during the design space appointment
include experimental designs, PAT, prior knowledge, quality risk management
principles, etc.
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59.  PAT(Process Analytical Technology) is 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 .
 Pfizer was one of the first companies to implement QbD and PAT concepts
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60. CONTENTS FOR 3.2.P.2 OF CTD QUALITY MODULE 3
1. Components of drug product (drug substance/ excipients)
2. Formulation Development.
3. Manufacturing Process Development
4. Container Closure System
5. Microbiological Attributes
6. Compatibility
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61. COMPONENTS OF DRUG PRODUCT GIVEN BY ICH
Q8
DRUG SUBSTANCES-
• “The physicochemical and biological properties of the drug substance that
can influence the performance of the drug product and its manufacturability.”
• Examples of physicochemical and biological properties that might need
to be examined include-
Solubility, Water content, Particle size, Crystal properties, Biological activity,
Permeability
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62. EXCIPIENTS
 The excipients chosen, their concentration, and the characteristics
that can influence the drug product performance or manufacturability
should be discussed relative to the respective function of each
excipients.
 Thecompatibility of the drug substance with excipients should be
evaluated.
 For products that contain more than one drug substance, the
compatibility of the drug substances with each other should also be
evaluated.
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63. FORMULATION DEVELOPMENT
• A summary should be provided describing the development of the formulation,
including identification of those attributes that are, critical to the quality of the drug
product and also highlight the evolution of the formulation design from initial
concept up to the final design.
• Information from comparative in vitro studies (e.g., dissolution) or comparative in
vivo studies (e.g., BE) that links clinical formulations to the proposed commercial
formulation.
• A successful correlation can assist in the selection of appropriate dissolution
acceptance criteria, and can potentially reduce the need for further bioequivalence
studies following changes to the product or its manufacturing process.
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64. CONTAINER AND CLOSURE SYSTEM
• The choice for selection of the container closure system for the
commercial product should be discussed.
• The choice of materials for primary packaging and secondary packaging
should be justified.
• A possible interaction between product and container or label should
be considered.
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65. MICROBIOLOGICALATTRIBUTES
• The selection and effectiveness of preservative systems in products containing
antimicrobial preservative or the antimicrobial effectiveness of products that
are inherently antimicrobial.
• For sterile products, the integrity of the container closure system as it
relates to preventing microbial contamination.
• The lowest specified concentration of antimicrobial preservative should
be justified in terms of efficacy and safety,
• such that the minimum concentration of preservative that gives the required
level of efficacy throughout the intended shelf life of the product is used.
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66. COMPATIBILITY
• The compatibility of the drug product with reconstitution diluents (e.g.,
precipitation, stability) should be addressed to provide appropriate and
supportive information for the labelling.
• This information should cover the recommended in-use shelf life, at the
recommended storage temperature and at the likely extremes of concentration.
• Similarly, admixture or dilution of products prior to administration (e.g.,
product added to large volume infusion containers) might need to be
addressed.
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67. REGULATORYVIEWS ON QBD
• As defined by an FDA official (Woodcock, 2004)
• “The QbD concept represents product and process performance characteristics
scientifically designed to meet specific objectives, not merely empirically derived
from performance of test batches.”
• Another FDA representative (Shah, 2009) states that “introduction of the QbD
concept can lead to cost savings and efficiency improvements for both industry
and regulators.”
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68. QBD FACILITATES
enhance opportunities for first cycle approval
streamline post approval changes and regulatory processes
enable more focused inspections
provide opportunities for continual improvement
innovation
increase manufacturing efficiency
reduce cost/product rejects compliance
minimize/eliminate potential actions
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69.  EMA, FDA, and ICH working groups have appointed the ICH quality
implementation working group (Q-IWG), which prepared various templates,
workshop training materials, questions and answers, as well as a points- to
consider document (issued in 2011) that covers ICH Q8(R2), ICH Q9, and
ICH Q10 guidelines.
 This document provides an interesting overview on the use of different
modelling techniques in QbD
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70.  There were several EMA marketing authorization applications (MAA)
with QbD and PAT elements for the following products: Avamys®, Torisel® ,
Tyverb® , Norvir®, Exjade® , Revolade® , Votrient® , etc.).
 Up to 2011, there was a total of 26 QbD submissions to EMA (for the new
chemical entities)
 18 of them were initial MAAs (4 including the real time release), 6 of them
were concerning post- authorization, and 2 were scientific advice requests.
 An additional two MAAs were submitted for biological products, but none of
the submissions were related to the generics industry.
 Up to 2011, there were approximately 50 QbD related applications to the FDA
(Miksinski, 2011). FDA authorities state that QbD is to be fully implemented
by January 2013 (Miksinski, 2011).
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71. INDUSTRY VIEWS ON QBD
• Pfizer was one of the first companies to implement QbD and PAT concepts.
• Through these concepts, the company gained enhanced process
understanding, higher process capability, better product quality, and increased
flexibility to implement continuous improvement change.
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72. QBD FOR INDUSTRYAND REGULATORY BODIES
Industry Regulatory agency
Development of scientific understanding of
critical process and product attributes.
Scientifically based assessment of product
and manufacturing process design and
development.
Controls and testing are designed based on
limits of scientific understanding at
development stage.
Evaluation and approval of product quality
specifications in light of established standards
(e.g: purity, stability, content uniformity, etc.,)
Utilization of knowledge gained over the
product’s lifecycle for continuous
improvement.
Evaluation of post-approval changes based on
risk and sciences.
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73. REFERENCES
o History of Computers in Pharmaceutical Research and Development by
ClinSkill | Dec 19, 2017 | Pharmaceutical Research (Review Article)
o Mannam A, Mubeen H “Review Article Digitalisation And Automation In
Pharmaceuticals From Drug Discovery To Drug Administration”
international Journal of Pharmacy and Pharmaceutical Science 10(6), 2018
May 8, 1-10.
o Hoffmann A, IGihny-Simonius J, Marcel Plattner , Vanja Schmidli-
Vckovski , Kronseder e C “Computer system validation: An overview of
official requirements and standards” Pharmaceutics Acta Helvetiae, 72,
1998, 317-325.
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74. o Zhang, Shirui Mao (2016 ). Application of quality by design in the current
drug development: Asian journal of pharmaceutical sciences 12 (2017) 1–8.
o Sushila D. Chavan, Nayana V. Pimpodkar, Amruta S. Kadam, Puja
S.Gaikwad. Quality by Design : Research and Reviews: Journal of
Pharmaceutical Quality Assurance|Volume 1 | Issue 2 | October- December,
2015.
o D. M. Patwardhan, S. S. Amrutkar , T. S. Kotwal and M. P. Wagh ,
APPLICATION OF QUALITY BY DESIGN TO DIFFERENT ASPECTS
OF PHARMACEUTICAL TECHNOLOGIES : IJPSR, 2017; Vol. 8(9):
3649-3662.
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75. Thank you
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