The document discusses statistical modeling in pharmaceutical research and development. It begins with definitions of statistics and pharmaceutical statistics. It then discusses the history and concepts of statistical modeling, noting that models help reduce drug development costs and time while improving quality. Models can be descriptive, modeling real-world events, or mechanistic, based on natural science principles. The objective of models is to improve understanding of experiments and drugs through representation of reality.

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Statistical modeling in Pharmaceutical research
and development
Computer aided drug development
RR COLLEGE OF PHARMACY
SUBMITTED BY:
PAWAN DHAMALA
2nd sem M.Pharm
Dept. of Pharmaceutics
SUBMITTED TO:
Mr. Mahalingan, M.Pharm.
Associate professor
Dept. of Pharmaceutics

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Contents
• Definition
• Introduction
• History
• Objective
• Concept of
statistical
modeling

3. Definition
Statistics:
• Statistics is a scientific study of numerical data based on natural phenomena.
• It is also the science of collecting, organizing, interpreting and reporting data.
Pharmaceutical statistics:
• Pharmaceutical statistics is the application of statistics to matters concerning the
pharmaceutical industry.
• This can be from issues of design of experiments, to analysis of drug trials, to issue of
commercialization of a medicine.
• Evaluate the activity of a drug; e.g., effect of caffein on attention, compare the analgesic
effect of plant extract and NSAID.
• To explore weather the changes produced by the drug are due to the action of drug or by
chance.
• To compare the action of two or more different drugs or different dosage of same drug are
studied using statistical method.
• To find an association between disease and risk factor such as coronary artery disease and
smoking.
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4. Introduction
• The new major challenge that the pharmaceutical industry is facing in the
discovery and development of the new drugs is to reduce costs and time
needed from discovery to market, while at the same time raising standards
of quality.
• If the pharmaceutical industry cannot find a solution to reduce both the
costs and time, then its whole business model will be jeopardized.
• The market will hardly be able, even in the near future, to afford
excessively expensive drugs, regardless of their quality.
• In parallel to this growing challenge, technology are also dramatically
evolving, opening doors to opportunities never seen before.
• This standard way to discover new drugs is essentially by trial and error.
• The “ new technologies ” approach has given rise to new hope in that it has
permitted many more attempts per unit time, increasing proportionally,
however, also the number of errors.
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5. • The development of the models in the pharmaceutical industry is
certainly one of the significant breakthroughs proposed to face the
challenges of cost, speed and quality.
• The concept, however, is that of adopting just another new
technology, known as “ modeling ”.
• The purpose of model is essentially for that of translating the known
properties as well as some new hypothesis into a mathematical
representation.
• In this way a model is a simplifying representation of the data
generating mechanism under investigation.
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6. Objective
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The use of models in experiment cycle to reduce cost
and time improve quality.
Without models the final purpose of experiment was
one single drug or its behavior, with the use of
models, the objective of experiments will be the drug
and the model at the same level.
Improving the model will help understanding the
experiments or successive drugs and improving the
models ability will help to represent reality.

7. History
• The origins of statistical theory lie in the 18th century advances in
probability, the modern field of statistics only emerged in the late 19th and
early 20th century.
• A theory of statistical inference was developed by Charles S. Peirce in
"Illustrations of the Logic of Science" (1877–1878) and "A Theory of
Probable Inference" (1883), two publications that emphasized the
importance of randomization-based inference in statistics.
• Fisher initiated the principles of design of experiments and elaborated on
his studies of “ analysis of variance ”
• Fisher began to pay particular attention to the labour involved in the
necessary computations performed by hand, as the calculation was very
tedious and time consuming.
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8. • Through this, there came an era of development of Computational
Statistical Modelling.
• Computational statistics, or statistical computing, is the interface
between statistics and computer science.
• With this there was development of many software to solve the
problems regarding the statistical calculation.
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9. Concept
• According to Breiman, there are two cultures in the use of statistical
modelling to reach conclusions from data.
• The first culture, namely, the data modeling culture, assume that the data
are generated by a given stochastic data model.
• Whereas the other, the algorithmic modeling culture, uses algorithmic
models and treats the data mechanism as unknown.
• To understand the mechanism, the use of modeling concepts is essential.
• In this way, a model is a simplifying representation of the data-generating
mechanism under investigation.
• The identification of an appropriate model is often not easy and may
require thorough investigation.
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10. Statistical modeling
• Standard way to discover new drugs is essentially by trial and error.
New technologies approach permitted many more attempts per unit
time.
• In pharmaceutical research, pharmacokinetic and pharmacodynamics
(PK/PD), models are built to characterize the kinetics and action of
new compounds.
• Models are also developed in drug design, e.g., QSAR related models
Based on this, the models can broadly classified into:
• Descriptive modeling
• Mechanistic modeling
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11. • Descriptive modelling is a mathematical process that describes real
world events and the relationship between factors responsible for
them.
• It describes a system or other entity and its relationship to its
environment.
• Mechanistic models are based on the fundamental laws of natural
sciences, Physical & biochemical principles constitute the model
equations.
• Whenever the interest lies in the understanding of the mechanisms of
action, it is critical to be able to count on a strong collaboration
between scientists, specialists in the field, and statisticians or
mathematicians.
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12. Reference
• Computer applications in pharmaceutical research and development,
Sean Ekins, John Wiley & sons
• https://www.slideshare.net/paviviji/Statistical modelling in
pharmaceutical research and development
• Textbook of computer aided drug development, PV publications.
• www.slideshare.net
• www.google.com
• www.youtube.in
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