ilovepdf_merged (1).pptx

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Quality-by-Design In
Pharmaceutical Development
Raghavendra Institute of Pharmaceutical Education and Research -Autonomous
K.R.Palli Cross, Chiyyedu,Anantapuramu,A. P- 515721 1
A Seminar as a part of curricular requirement
for I year M. Pharm II semester
Presented by
T. Mousami Bhavasar
(Reg. No. 20L81S0302)
Under the guidance/Mentorship of
Dr. Nawaz Mahammed
Assistant Professor
Dept. of Pharmaceutics

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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
References

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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).
Introduction

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Objectives of QbD
• The main objective of QbD is to achieve the quality products.
• To achieve positive performance testing.
• Ensures combination of product and
development.
process knowledge gained during
• From knowledge of data process, desired attributes may be constructed.

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Benefits of QbD for Industry
• Eliminate batch failures.
• Minimize deviations and costly investigations.
• Empowerment of technical staff.
• Increase manufacturing efficiency, reduce costs and project rejections and waste.
• Better understanding of the process.
• Continuous improvement.
• Ensure better design of product with less problem.

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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.
Approaches to pharmaceutical
development

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Define Target product profile (TPP) and Quality Target Product profile (QTPP)
Identify critical quality attributes (CQA)
Carry out risk assessments, linking material attributes and process parameters CQA
Establish the design space
Describe control strategy
Life cycle management and continuous improvement
Flow of QbD

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• “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
Target Product Profile(TPP)

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• 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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• 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.
Quality Target Product Profile (QTPP)

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• 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.
Critical Quality Attributes(CQAs)

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• 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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• 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.
Critical Material Attributes(CMA)

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• 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.
Critical Process Parameters(CPP)
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

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• 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).
Risk Assessment

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Design Space As per ICH Q8-
• 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 may be built for a single unit operation or for the ensure process.
Design Space

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Design of Experiment (DoE):
• 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.
Tools applied in QbD approach

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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-
Placket-Burman design
Fractional factorial design.

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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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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.
Risk assessment methodology

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2- Failure Mode Eﬀect Analysis (FMEA):
 This is an important tool to evaluate potential failure modes in any process.
 Quantification of risk by interaction of 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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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.
K.R.Palli Cross, Chiyyedu,Anantapuramu,A. P- 515721
24

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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.
Control Strategy

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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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•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.
ICH Q8(R2):
Pharmaceutical development Guideline

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• 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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•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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1. Components of drug product (drug substance/ excipients)
2. Formulation Development.
3. Manufacturing Process Development
4. Container Closure System
5. Microbiological Attributes
6. Compatibility
Contents for 3.2.P.2 of CTD Quality
module 3

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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
Components of drug product given
by ICH Q8

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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.
• The compatibility 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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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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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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MICROBIOLOGICAL ATTRIBUTES
• 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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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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• 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.”
Regulatory views on QbD

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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
• minimize/eliminate potential compliance actions

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ICH quality
implementation working
• EMA, FDA, and ICH working
group
groups have appointed the
(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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• 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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• 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.
Industry views on QbD

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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.
QbD for industry and regulatory bodies

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• Quality by Design is intended to enhance process knowledge and is based on
existing guidance and reference documents.
• QbD can be viewed as a process defined by series of document requirements as
per process knowledge and understanding.
• The goals of implementing pharmaceutical QbD are to reduce product variability
and defects, thereby enhancing product development and manufacturing
efficiencies and post approval change management.
• Finally, QbD is challenge & the current challenges to QbD implementation from an
industry perspective are numerous because industry has yet to fully embrace its
application to pharmaceutical product development.
Conclusion

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i. Lan Zhang, Shirui Mao (2016 ). Application of quality by design in the current
drug development: Asian journal of pharmaceutical sciences 12 (2017) 1–8.
ii. 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.
iii. 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.
References

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By: Manikant Prasad Shah
Mpharm II Sem.
Mallige college of Pharmacy,
Bangalore
COMPUTERS IN PHARMACEUTICAL
RESEARCH AND DEVELOPMENT
: A GENERAL OVERVIEW

HISTORY OF COMPUTERS IN
PHARMACEUTICAL RESEARCH AND
DEVELOPMENT
INTRODUCTION
⚫Today, computers are so ubiquitous 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.
⚫ Hence if one could predict properties by calculations,
one might be able to predict which structures should be
investigated in the laboratory.
⚫ Another fundamental, well-established concept was
that a drug would exert its biological activity by binding
to and/or inhibiting some biomolecule in the body. ( This
concept stems from Fischer’s famous lock-and-key
hypothesis )
⚫ Pioneering research in the 1950s attacked the problem
of linking electronic structure and biological activity.
⚫ A good part of this work was collected in the 1963 book
by Bernard and Alberte Pullman of Paris, France, which
ﬁred the imagination of what might be possible with
calculations on biomolecules .

⚫ The earliest papers that attempted to
mathematically relate chemical structure and
biological activity were published in Scotland in the
middle of the nineteenth century .
⚫ This work and a couple of other papers were
forerunners(pecursor) to modern quantitative
structureactivity relationships (QSAR).
⚫ The early computers were designed for military and
accounting applications, but gradually it became
apparent that computers would have a vast number
of uses.

COMPUTATIONAL CHEMISTRY: THE BEGINNINGS
AT LILLY
⚫ In the late 1950s or early 1960s, 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.
⚫ It was carried out by Lilly’s research statistics group
under Dr. Edgar King.
⚫ It was not until 1968, when Don Boyd joined the
second theoretical chemist in the group, that the
computers at Lilly started to reach a level of size,
speed, and sophistication to be able to handle some of
the computational requirements of various evaluation
and design efforts.
⚫ Don brought with him Hoffmann’s EHT program from
Harvard and Cornell.

GERMINATION: THE 1960s
⚫ in 1960 essentially 100% of the computational
chemists were in academia, not industry.
⚫The students coming from those academic
laboratories constituted the main pool of
candidates that industry could hire for their initial
ventures into using computers for drug discovery.
⚫ Another pool of chemists educated using
computers were X-ray crystallographers.
⚫One of the largest computers then in use by
theoretical chemists and crystallographers was
the IBM 7094.
⚫ Support staff operated the tape readers, card
readers,
and printers.

⚫Programs were written in FORTRAN II.
⚫ Programs used by the chemists usually ranged
from half a box to several boxes long.
⚫Carrying several boxes of cards to the computer
center was good for physical ﬁtness.
⚫If a box was dropped or if a card reader mangled
some of the cards, the tedious task of restoring the
deck and replacing the torn cards ensued.
⚫Finally in regard to software, we note one program
that came from the realm of crystallography.
⚫ That program was ORTEP (Oak Ridge Thermal
Ellipsoid Program), which was the first widely used
program for (noninteractive) molecular graphics .

GAINING A FOOTHOLD: THE 1970s
⚫ Lilly management of the 1970s standed by further
permanent growth.
⚫ It was not until near the end of the 1980s that Lilly
resumed growing its computational chemistry group to
catch up to the other large pharmaceutical companies.
⚫ Other companies such as Merck and Smith Kline and
French (using the old name) entered the ﬁeld a few
years later.
⚫ Unlike Lilly, they hired chemists trained in organic
chemistry and computers.
⚫ Widely used models included members of the IBM 360
and 370 series.
⚫ Placing these more powerful machines in-house made
it easier and more secure to submit jobs and retrieve
output. But output was still in the form of long
printouts.

⚫ Computational chemists in the pharmaceutical industry
also expanded from their academic upbringing by
acquiring an interest in force ﬁeld methods, QSAR,
and statistics.
⚫ To solve research problems in industry, one had to
use the best available technique, and this did not
mean going to a larger basis set or a higher level of
quantum mechanical theory. It meant using molecular
mechanics or QSAR.
⚫ The 1970s were full of small successes such as
ﬁnding correlations between calculated and
experimental properties.
⚫ Some of these correlations were published. Even
something so grand as the de novo design of a
pharmaceutical was attempted but was somewhat
beyond reach.
⚫ Two new computer-based resources were launched in
the 1970s. One was the Cambridge Structural

GROWTH: THE 1980s
⚫If the 1960s were the Dark Ages and the 1970s
were the Middle Ages, the 1980s were the
Renaissance, the Baroque Period, and the
Enlightenment all rolled into one.
⚫ The decade of the 1980s was when the various
approaches of quantum chemistry, molecular
mechanics, molecular simulations, QSAR, and
molecular graphics coalesced into modern
computational chemistry.
⚫Several exciting technical advances fostered the
improved environment for computer use at
pharmaceutical companies in the 1980s. The ﬁrst
was a development of the VAX 11/780 computer
by Digital Equipment Corporation (DEC) in 1979.

FRUITION: THE 1990s
⚫ The 1990s was a decade of fruition because the
computer-based drug discovery work of the 1980s
yielded an impressive number of new chemical
entities reaching the pharmaceutical marketplace.
⚫ Pharmaceutical companies were accustomed to
supporting their own research and making large
investments in it.
⚫ supercomputers that were creating excitement at a
small number of pharmaceutical companies, another
hardware development was attracting attention at just
about every company interested in designing drugs.
⚫ Workstations from Silicon Graphics Inc. (SGI) were
becoming increasingly popular for molecular
research.

⚫During tha time the Apple Macintoshes were well
liked by scientists. However, in 1994 Apple lost its
lawsuit against Microsoft regarding the similarities
of the Windows graphical user interface (GUI) to
Apple’s desktop design.
⚫ QSAR proved to be one of the best approaches
to providing assistance to the medicinal chemist
in the 1990s.
Therefore, computational chemistry experts play
an important role in maximizing the potential
beneﬁts of computer based technologies.

STATISTICAL MODELING IN PHARMACEUTICAL
RESEARCH AND DEVELOPMENT
⚫The new major challenge that the pharmaceutical
industry is facing in the discovery and
development of new drugs is to reduce costs and
time needed from discovery to market, while at
the same time raising standards of quality.
⚫In parallel to this growing challenge, technologies
are also dramatically evolving, opening doors to
opportunities never seen before.
⚫Some of the best examples of new technologies
available in the life sciences are microarray
technologies or high-throughput-screening.

⚫ The new technologies have been integrated to
do the same things as before, but faster, deeper,
smaller, with more automation, with more
precision, and by collecting more data per
experimental unit.
⚫ However, the standard way to plan experiments,
to handle new results, to make decisions has
remained more or less unchanged, except that
the volume of data, and the disk space required
to store it, has exploded exponentially.
⚫ This standard way to discover new drugs is
essentially by trial and error.

⚫ the process of discovery and development of new
drugs has been drawn to highlight the pivotal role
that models (simpliﬁ ed mathematical
descriptions of real-life mechanisms) play in
many R&D activities.
⚫In some areas of pharmaceutical research, like
pharmacokinetics/pharmacodynamics (PK/PD),
models are built to characterize the kinetics and
action of new compounds or platforms of
compounds, knowledge crucial for designing new
experiments and optimizing drug dosage.
⚫Models are also developed in other areas, as for
example in medicinal chemistry with QSAR-related
models. These can all be deﬁned as mechanistic
models, and they are useful.

⚫. On the other side, many models of a different type
are currently used in the biological sciences:
⚫Using empirical models, universally applicable,
whose basic purpose is to appropriately represent
the noise, but not the biology or the chemistry,
statisticians give whenever possible a denoised
picture of the results, so that ﬁeld scientists can
gain better understanding and take more informed
decisions.
⚫ The dividing line between empirical models and
mechanistic models is not as clear and obvious as
some would pretend.
⚫Mechanistic models are usually based on chemical
or biological knowledge, or the understanding we
have of chemistry or biology.

⚫Today, however, the combination of mathematics,
statistics, and computing allows us to effectively
use more and more complex mechanistic models
directly incorporating our biological or chemical
knowledge.

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