COMPUTER AIDED DRUG DESIGN.pptx

Computer Simulation In
Pharmacokinetics And
Pharmacodynamics
1
Presented By:
Shubham Ahir
M.Pharm 1st year
(Pharmaceutics)
Roll no: MPH08
DRGCOP, Malkapur
Guided By:
DR. More Sir,
Asst. Professor,
DRGCOP, Malkapur

CONTENTS
Introduction .
Computer Simulation of the Whole Organism .
Computer Simulation of Isolated Tissues and Organs
.
Computer Simulations of the Cell .
Proteins and Genes .
References .
Computer Simulation In Pharmacokinetics & Pharmacodynamics
2

INTRODUCTION
What the joint application of these enabling technologies
allows us to do instantaneously and efficiently exchange
robust verifiable , and consistent information .
It is also called as bio - computation or bio - simulation .
Now extending computer modeling to virtually every aspect
of the biomedical enterprise " from bench to bedside " .
All the way from clinical record management to computer -
aided drug design , through clinical trial simulation ,
therapeutic drug monitoring , pharmacogenomics , and
molecular engineering .
3

The information revolution in biology has been facilitated ,
and in a very real sense motivated , by the emphasis placed
on " discovery science ".
 According to Guyton and other holistic physiologists , a living
homeostatic system was thought of as being comprised of a
series of interacting parts , or sub systems , an understanding
of which was deemed essential to comprehension of the
complex dynamics of the whole .
It was believed that only through information gathered on the
macroscopic behavior of the whole could one understand the
inner workings of the parts .
4

Aristotle's proposal that " the whole is more than the sum of
the parts , " direct investigation of the living system was
essential .
The approach was " top to bottom . "
pharmaceutical sciences from clinical pharmacology to
molecular pharmacology . This is the " bottom to top " * two
different emphases are both used to lead to the creation of
better therapeutics .
The FDA , -advances in bio computation and has introduced
recent developments in computational modeling in the
development process through the issue of guidances and
consensus documents .
5

The EPA - aggressively using computational representations
of complex systems to predict likely system behavior , or at
least narrow down the field of possibilities .
DARPA has started a project , termed Virtual Soldier , to
achieve the rather ambitious goal of creating physiological ,
mathematical , and software representations of individual
soldiers .
We focus on clinical sciences in particular , because we feel
that simplified , but useful representations of
pharmacological interventions have the greatest potential for
shortening the development process and weeding out
potentially unsatisfactory candidates .
6

COMPUTER SIMULATION OF THE
WHOLE ORGANISM
whole organism is the essential goal of bio - computing .
Provided the intact organism can be mathematically
represented , a whole series of possibilities can be brought
into practice , such as the simulation of clinical trials and of
the prospective behavior of entire populations .
In drug development , whole body systems are usually
represented in one of two ways
7

The first approach is through the formalization of a lumped
parameter PK - PD model , often coupled with a model of the
disease process , whose parameters can be estimated from
data .
A relatively small number of differential equations , between
one and ten , is used to predict the system's behavior 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 .
8

The same approach can be taken in reverse by using models to
generate synthetic data , ultimately performing a full clinical trial
simulation from first principles .
Second 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.
9

The exposure - response road map passes through
pharmacokinetics and pharmacodynamics .
This sequence of events is essentially the same as that
which informs computer simulation of clinical trials , with
the addition of complicating , but important , factors such
as protocol adherence and dropouts .
Although the representation of the intact organism
provided by PK - PD and PBPK models is simplified , it
does pose nontraditional challenges
For PK - PD , the purpose consists in finding the best (
simplest ? ) model that can explain the observations.
10

A consensus workshop developed some time ago a set of "
good practices " that can serve as guidance to model
development , selection , and application .
PBPK models come at the problem from a different angle.
Because they embed previous knowledge about the organ
kinetics , their arrangements , and their specific parameter
values , the process of tailoring the model to the specific
measurements at hand is not as crucial .
 On the other hand , PBPK models can suffer greatly in their
predictive power if their parameterization is in accurate ,
poorly specified , or not well tailored to the particular drug .
Many researchers split PBPK model parameters and
structures into " drug specific " and " not drug specific , " thus
implying that the model can indeed capture some underlying
dynamics that are general for all drugs,
11

The approach taken by PBPK modeling is not very dissimilar
from the recently proposed Physiome Project , a " parts list " of
the human organism whose development follows the broad
strokes of the Human Genome Project .
 More often than not , the rate - limiting step for development of
PBPK models is the availability of information on single - organ
parameters , such as clearance rates and partition coefficients .
It is interesting to note that the foremost challenges for the
detailed modeling of the intact organism ( computing time
complexity of interactions , model selection ) are very similar to
those entailed by the analysis of proteomic or genomic data.
12

COMPUTER SIMULATION OF ISOLATED
TISSUES AND ORGANS
The behavior of molecules in isolated organs has been 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 both
in the biomedical and pharmaceutical literature.
13

Many of the computer simulations for the heart and liver were
carried out with distributed blood tissue exchange ( BTEX )
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 .
It can be speculated that the integration of organ - specific
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 organs .
14

As an example of infrastructure endeavors , a new project
funded by the NIH , the Center for MIMS .
The development and integration of in vivo , organ - specific
mathematical models that can successfully predict behaviors
for a range of parameters , including rest and exercise and
various patho physiological conditions .
The microcirculation Physiome and the Cardiome are other
multicenter projects focused on particular aspects of the
Physiome undertaking .
15

COMPUTER SIMULATIONS OF THE CELL
Although the use of competing computer models would be an
efficient way to select the best hypothesis among a slew of
competing ones , this approach is rarely taken in cell biology ,
where experimental verification dominates the literature by
and large .
The idea of " network " is very widespread in 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 , and thus the computer implementation of oriented
networks is a straightforward approach to modeling this kind
of systems .
16

The system is then studied at steady state , because the
dynamic parameters determining the time - varying
biochemistry are largely unknown and the stoichiometry of the
reactions , in contrast , is reasonably well identified .
this system has allowed us to learn a great deal about the
long term behavior of simple organisms exposed to variable
environmental conditions .
It has provided new avenues of investigation for the optimal
design of bioreactors.
 more in general , for how biological systems may choose to
adapt in the face of changing environments by redistributing
energy to various sub locations of the overall reaction
network.
17

> This has been described for simple organisms by models that
integrate data at many levels , from gene to biochemistry to
physiology .
In the pharmacokinetics literature , there are still not that many
examples of tight integration between cellular , in vitro
information and whole system prediction .
One example regarding a mechanistic model of the intracellular
metabolism of methotrexate , which was then merged in an
integrated model of in vitro and in vivo information , may serve
as a possible case in point for the gains that can be reaped
from the synergistic amalgamation ( with predictive purposes )
of cellular and whole - body models .
18

PROTEINS AND GENES
Computational protein design is an area of ever - increasing
interest .
Its most intriguing feature is that it can lead to the design and
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 .
There is no doubt that drug design can be accomplished through
computer simulation of the expected behavior of new molecules
designed to have specific physicochemical properties
19

The success story of antiretrovirals 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 based on clinical data .
One wonders how much stronger the impact would have
been if such models could have been augmented with cellular
and molecular quantitative information .
20

It seems , however , that tight collaboration between clinical
and preclinical departments in industry , or between
clinicians and bench biologists in academia , is essential to
make significant progress in the development and
applications of in silico biomedicine .
> One example of such constructive cross talk can be found
in the growing literature on quantitative structure -
pharmacokinetic relationships ( QSPKR ) .
Reports on how to predict pharmacokinetics from molecular
information , or how to link pharmacokinetic parameters with
molecular features , have appeared in both the
pharmacokinetic and the toxicological literature .
21

Others are extending this to pharmacodynamics as well , and
the approaches look promising .
well - defined method for approaching the integration problem
at the basis of preclinical to clinical simulations .
It can also be said , however , that many different
methodological developments are being aggressively tried .
For example , information theory approaches are being tried to
identify genes that lead to disease susceptibility , in a sense
merging the smallest with the largest information items .
 Some recent contributions allow the mapping of genetic data
onto a queryable network based on ordinary differential
equations
22

REFERENCES
Computer applications in pharmaceutical research and
development , sean ekins , 2006 , john wiley & sons Inc. ,
Hoboken , New Jersey.p.p : 513-25 .
 Computer aided applications in pharmaceutical technology ,
1st Edition , Jelana Djuris , Woodhead publishing .
23

24

COMPUTER AIDED DRUG DESIGN.pptx

Recommended

Recommended

More Related Content

What's hot

What's hot (20)

Similar to COMPUTER AIDED DRUG DESIGN.pptx

Similar to COMPUTER AIDED DRUG DESIGN.pptx (20)

Recently uploaded

Recently uploaded (20)

COMPUTER AIDED DRUG DESIGN.pptx