This document discusses the use of computer simulation in pharmacokinetics and pharmacodynamics at four different levels: whole organism, isolated tissues/organs, cellular, and protein/gene levels. At each level, mathematical models are used to represent biological processes and predict behavior over time. The goal is to better understand drug behavior and improve drug development by replacing animal and human trials with computer simulations. Challenges include integrating data from different structural levels and ensuring high quality input data.

1. COMPUTER SIMULATION IN
PHARMACOKINETICS AND
PHARMACODYNAMICS
Presented by:
sujitha mary
M.pharm
pharmaceutics
St joseph college of pharmacy
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INTRODUCTION
• Computer simulation is the usage of a computer for the imitation of a
real world process or system.
• This is used in determining the safety levels of a drug during it’s
development i.e in therapy planning , design of a clinical trial.
• It helps to rapid development of dosage forms with cheaper price and
by using less manpower.

3. LEVEL 1 : COMPUTER SIMULATION OF THE
WHOLE ORGANISM
• Whole organism is modelled, this is the essential goal of the bio
computing.
• Whole organism can be mathematically represented and series of
possibilities can be brought into practice, such as simulation of clinical
trials and prospective behaviour of entire population.
• Here whole body systems are represented in two ways
• First approach – formalization of a lumped parameter PK-PD model
coupled with a model of the disease process, A relatively small number of
differential equations, between one and ten, is used to predict the system’s
behaviour over time . Often, but not always, some variation of population
PK-PD , predicated on nonlinear regression and nonlinear mixed-effects
models , is used to estimate both the population parameter values and
their statistical distribution.
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4. • The other approach to whole organism models is based on
physiological modeling brought into practice by physiologically based
pharmacokinetic (PBPK) models . These models are still based on
ordinary differential equations, but they attempt to describe the
organism and especially the interacting organs with more detail, often
by increasing the number of differential equations (from 10 to
perhaps 30) and building appropriate interactions between the
organs that resemble their physical arrangement in the organism
being studied.
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5. LEVEL 2 : COMPUTER SIMULATION OF
ISOLATED TISSUES AND ORGANS
• Here the behaviour of drug molecules in isolated organs is the subject of
extensive investigation.
• The heart and the liver were historically the organs most extensively
investigated, although the kidney and brain have also been the subjects of
mathematical modeling research. The liver in particular has been extensively
researched.
• Many of the computer simulations for the heart and liver were carried out with
distributed blood tissue exchange models , because the increased level of detail
and temporal resolution certainly makes the good mixing and uniformity
hypotheses at the basis of lumped parameter models less tenable.
• the integration of organ-specifi c modeling with the above whole-organism
models would result in improvements for the PBPK approach through “better”
(i.e., more physiologically sensible and plausible) models of individual organ
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6. LEVEL 3 : COMPUTER SIMULATIONS OF THE
CELL
• Cellular level computer simulations are complicated because there is
no universal accord as to how several of the intracellular and
membrane processes actually take place.
• Although the use of competing computer models would be an
efficient way to select the best hypothesis.
• the models that focus on the cellular environment. Clearly,
interactions between cells, or also within the intracellular milieu, can
be viewed as complex networks of signals.
• ‘nd thus the computer implementation of oriented networks is a
straightforward approach to modeling this kind of systems.
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7. LEVEL 4 : PROTIENS AND GENES
• Computational protein design is an area of ever-increasing interest [56]. Its most
intriguing feature is that it can lead to the designand laboratory creation of
structures that are not present in nature,
• From the standpoint of pharmacokinetics and pharmacodynamics computer
simulations, the challenge is once again to achieve the blending of very
heterogeneous information at many structural levels.
• Eg: development of models of HIV viral loadvior of new molecules designed to
have specific physicochemical properties. The success story of antiretrovirals [58]
testifies to that concept. At the same time, one of the most interesting
contributions of computer simulation to pharmacotherapy was also in the field of
HIV/AIDS treatment, through the development of models of HIV viral load [59]
based on clinical data [60] that shed considerable light on the disease mechanism
• quantitative structure-pharmacokinetic relationships (QSPKR). Reports on how to
predict pharmacokinetics from molecular information, or how to link
pharmacokinetic parameters with molecular features
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8. • field of in silico computer simulation field to improve the product trials
and replacement of animals and human during clinical trials. Subject
specific model studies are recommended in which living models can be
easily replaced by in silico models. The major challenge in the field of in
silico pharmacokinetic and Pharmacodynamic studies is the harmony of
understanding ,
• Computer simulation methods are based on availability of literature and
studies regarding pharmacokinetic and pharmacodynamics parameters of
the selected drugs [4].
• quality of data inputs available [5]. Previous studies are taken as a
reference to predict the simulation; and computer simulations
demonstrate the pharmacokinetic parameters (i.e. half lives) of different
drugs [6]. Computer simulations can give atomic details which are not
accessible from experiments and help to elucidate the mechanism
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10. CONCLUSION
• Drug discovery has focused almost exclusively on efficacy and
selectivity against the biological target.
• Half of drug candidates fail at phase II and phase III clinical trials
because of undesirable drug pharmacokinetics properties, including
ADMET.
• The major recent advancement in ADMET modeling is in elucidating
the role and successful modeling of various transporters.
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11. Reference:
1. Ekins S. Computer applications in pharmaceutical research and
development. New Jersey: John’ Wiley & Sons, Inc;2006. 513-524.
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