Computer Simulation in PKPD: - Introduction - Computer Simulation -Whole Organism Simulation - Isolated Tissue Simulation - Organs Simulation - Cells Simulation - Proteins and Genes Simulation Computers in Clinical Development: - Clinical Data Collection and Management - Regulation of Computer System

1. Prepared By: Dolly Sadrani
Department of Pharmaceutics
IInd Sem M.Pharma
1

2.  Computer Simulation in PKPD:
- Introduction
- Computer Simulation
-Whole Organism Simulation
- Isolated Tissue Simulation
- Organs Simulation
- Cells Simulation
- Proteins and Genes Simulation
 Computers in Clinical Development:
- Clinical Data Collection and Management
- Regulation of Computer System
 Questions
 References
2

3. Introduction:
 A computer simulation or a computer model is a computer
program that attempts to simulate an abstract model of a
particular system.
 Computational resources available today, large-scale models
of the body can be used to produce realistic simulations.
 It involves the use of computer simulations of biological
systems, including cellular subsystems (such as the networks
of metabolites and enzymes which comprise metabolism,
signal transduction, pathways and gene regulatory
networks), to both analyze and visualize the complex
connections of these cellular processes.
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4. 4
Theoretical
Predictions
Simulation Results
Experimental
Results
Model SystemReal System Make a Model
Perform
Simulation
Construct
approximate
theories
Compare and
Improve
Theory
Compare and
Improve Model
Perform
Experiments

5. PK PD Simulation:
 Modeling can be considered ‘in numero’ studies: an
investigation carried out with mathematical, statistical and
numerical techniques.
 PK/PD modeling is a scientific mathematical tool which
integrate PK model to that of PD model.
 Simulation is the initiation of the operation of a real-world
process or system.
 Pharmacokinetic simulation is a simulation method used in
determining the safety levels of a drug during its
development.
 Pharmacokinetic simulation gives an insight to drug efficacy
and safety before exposure of individuals to the new drug that
might help to improve the design of a clinical trial.
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6.  The term PK-PD modeling refers to a data driven exploratory
analysis, based on a mathematical/statistical model.
 Pharmacokinetic and Pharmacodynamic modeling and
simulation play a key role in throughout the drug
development process, helping to generate hypotheses,
interpret and understand experimental results and trial
outcomes, to integrate available information and translate it
to clinical implications.
 Thus, modeling and simulation facilitates decision making at
each stage of drug development and helps to efficiently shape
the next step.
 PK-PD models pay an important role in education
simulations.
 The goal is not to describe a system its full complexity, but to
distill those processes that are necessary to understand the
relationship between exposure and effect.
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7. Physiology based Approaches:
 Physiologically based models such as PBPK-PD models are
particularly useful for extrapolation beyond the experimental
conditions.
 In vitro-in vivo extrapolation
 Animal to human translational modeling
 Modeling and simulation of target populations, bridging e.g.,
geriatrics, pediatrics, obese
 PBPK models can suffer greatly in their predictive power if
their parameters are in accurate and poorly specified to the
particular drug.
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8. Population based Approaches:
 Non-compartment, classical pharmacometric methods and
advanced PK-PD modeling are used for statistical analysis
and interpretation of (pre) clinical studies, as well as
modeling support to experimental design:
- Pre-clinical data analysis
- Trial design and simulation
- Statistical hypothesis testing
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9.  In silico modeling is deployed to predict how the
pharmacology and pharmacokinetics of the drug as seen in
laboratory and animal studies might translate to humans.
 Computer simulations of human and animal physiology are
used to for see how a drug substance will behave when
dosed to humans, before any human data exist.
 In silico models of diseases are also developed to explore
their cause and developed to explore their cause and
underlying mechanisms or how patient’s symptoms will
progress over time with or without new treatment.
 These technologies are used to identified drug targets,
predict the relation between dose, drug concentration in the
body and effectiveness of the treatment, identification of
suitable patient populations or markers of drug effects,
optimize the design of clinical studies and analyze data from
clinical trials, on the desired outcome.
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10.  COMPUTER SIMULATON OF…
 Whole Organism
 Isolated Tissues and Organs
 The Cell
 Proteins and Genes
10

11. Computer Simulation of Whole
Organism:
 In a sense, being able to model the whole organism is the
essential goal of biocomputing.
 In drug development, it provides the obligatory handle to
lead to response from exposure.
 In drug development, whole body systems are usually
represented in one of two ways.
 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.
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12.  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.
 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.
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Exposure
• Dosage
Regimen
• Chronic
exposure
•Environmental
Factors
Kinetics
•Route &
absorption
•Demographics
•Serum
Concentration
Dynamics
•Effect & toxicity
•Receptor
interaction
•Biomarkers
Response
•Continuous /
discrete
•Clinical endpoints
•Surrogate
endpoints

13.  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.
 Although the representation of the intact organism
provided by PK-PD and PBPK models is simplified, it
does pose nontraditional challenges.
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14.  For PK-PD, the purpose consists in finding the best model
that can explain the observations.
 model selection is driven by parsimony criterion that
balances model complexity with the actual information
content provided by the measurements.
 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.
 PBPK models can suffer greatly in their predictive power if
their parameterization is inaccurate, poorly specified, or not
well tailored to the particular drug.
RECENT:
 PHYSIOM PROJECT & HUMAN GENOM PROJECT
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15. Computer Simulation in Isolated
Tissue 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 has been extensively researched both in the
biomedical and pharmaceutical literature.
 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.
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16.  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” (more physiologically sensible and plausible)
models of individual organs.
 The main challenge in doing so is the required shift from
lumped to distributed parameter models.
 The jump to partial differential equations is fraught with
difficulties, especially because the average bench biologist
often has a lot of trouble grasping the concepts behind
ordinary differential equations as well.
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17.  A new project funded by the National Institute for General
Medical Sciences at the NIH, the Center for Modeling
Integrated Metabolic Systems (MIMS), has as its mission 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 pathophysiological conditions.
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18. Computer Simulation of the Cell:
 Cellular level computer simulations are complicated by the
fact that 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 among a slew of
competing ones, this approach is rarely taken in cell biology,
understanding the cell, its receptors and channels, and the
modalities of membrane transport may be a worthwhile
endeavor from the scientific point of view, in drug
development this has to be balanced against the constructive
role of this information in accelerating the development
process.
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19.  Because many of these models await independent scientific
validation, their use in drug development is perhaps not as
widespread.
 The Virtual Cell is an online repository of some of these
models, which also makes available a computer simulation
of the whole cell to its users’ network. Another online
repository of biophysical models is at the Cell ML website.
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20. Computer Stimulation of Proteins
and Gene:
 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.
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21.  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 that
shed considerable light on the disease mechanism.
RECENT:
 Quantitative Structure-Pharmacokinetic Relationship
(QSPKR)
 How to predict pharmacokinetics from molecular
information or how to link pharmacokinetic parameters
with molecular features.
 Information theory approaches are being tried to
identify genes that lead to disease susceptibility.
 Mapping of genetic data into ordinary differential
equations
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22. There is number of various software for pharmacokinetic
and pharmacodynamic simulations, available.
Name Manufacturer/ Distributer Website
WinNonlin Pharsight Corporation http://www.pharsight.com
MATLAB- Simulink The Math Works http://www.mathworks.com
GastroPlus Simulation Plus http://www.simulationplus.com
Kinetica ThermoEloctron Corporation http://www.thermo.com
ModelMaker ModelKinetix http://www.modelkinetix.com
Physiolab Entelos http://www.entelos.com
PKBUGS Imperial College, London http://www.mrc-
bsu.cam.ac.uk/bugs
PopKinetix SAAM Institute http://www.saam.com
R The R Project Group http://www.r-project.org
S Plus Insightfull http://www.insightfull.com
22

23. Computers in Clinical Development:
Introduction:
 Clinical trial data management involves a set of processes
that must be executed successfully to turn out reliable
clinical, control, and administrative data to a central location
such as a coordinating center, a data center, or a resource
center.
 These processes are lumped together under the name clinical
data management or clinical trial data management.
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24. 24
Communications
Before Data
Collection
Data
Collection
Data
Management
After Data
Collection

25.  The development of comprehensive and reliable data
collection and management systems is fundamental for
conducting successful clinical trials.
 The design and implementation of such systems affects all
aspects of conducting clinical trials including data collection,
editing, managing, monitoring, reporting, analyzing,
archiving, and sharing.
 It is thus extremely important that close attention be given to
the development of these systems in terms of their design and
the hardware and software selections made.
 The astonishing advancement in computer hardware and
software technology has had tremendous impact on clinical
trial data collection and management.
25

26.  New developments in computer hardware and software
technology have made clinical trial data collection and
management timely, effective, and reliable, the cornerstones
for conducting a successful clinical trial.
 With the ongoing and rapid advancement in computer
hardware and software technology, and the wide range of
newly available commercial databases, proprietary software
vendors, design tools, and security applications, clinical trial
data collection and management have become widely
attainable, much easier, less time consuming, more reliable,
more secure, and more scaleable than ever.
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27. System is organized into eleven major sections:
(1) Introduction,
(2) Data collection versus management,
(3)Communication in clinical trial data collection and
management,
(4) Pure paper-based data collection and management systems,
(5) Electronic-based data collection and management systems,
(6) Hybrid data collection and management systems,
(7) Acquiring e-clinical software from vendors,
(8) Processes before data collection,
(9) Processes during data collection,
(10) Processes after data collection, and
(11) Final comments.
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28. Data Collection versus Data
Management:
 The term “clinical trial data management” does not fully
describe how computers are used in conducting clinical
trials.
 The two major, and distinct, computer applications in
conducting clinical trials are data collection and data
management.
 Each of these applications has a distinct role in clinical trials.
For that reason, the term “clinical trial data collection and
management” will be used.
 Another aspect that is also integrated in each of these two
aspects is data security. Data security tools and procedures
are necessary during data collection and data management.
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29. Data Collection:
 Data collection in clinical trials consists of the
processes of collecting reliable clinical, control, and
administrative data from the trial’s participating sites
with agreed-upon methods and procedures to record the
collected data and send them to a central location.
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30. Data Management:
 “All the disciplines related to managing data as a valuable
resource, including acquisition, database administration,
storage, backup, security, and quality assurance”
 “work that involves the planning, development,
implementation, and administration of systems for the
acquisition, storage, and retrieval of data”
 The official definition given by the Data Management
Association (DAMA) is “the development and execution of
architectures, policies, practices and procedures that properly
manage the full data lifecycle needs of an enterprise” .
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31.  A layperson’s definition of data management is the
process of accumulating collected data into a master
database in a central location while ensuring their
security, validity, and completeness by generating
quality assurance reports to monitor the progress of the
trial.
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32. Communication:
 Communication is the process of sending information from
one location to another or from one person to another by
means that enable the sender to send the information to the
intended recipient and the intended recipient to receive,
retrieve, and interpret the information.
 Locations and individuals can be geographically dispersed or
within the confines of an organization, where information
can flow between individuals with various types of
communication including direct contact.
 Communication is the backbone of clinical trial data
collection and management.
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33.  Planning, conducting, and ultimately reporting the results of
a clinical trial require that trial personnel be connected
throughout the duration of the trial to ensure successful
completion.
 Efficient communication facilitates the conduct of clinical
trials, especially multicenter clinical trials.
 Clinical trial staff has available many communication tools
that have revolutionized the way they are able to share
comments, exchange ideas, send and receive data, and solve
unexpected problems.
 Trial staff is typically grouped into entities that include a
coordinating center, sponsors, study leadership, resource
centers, and participating sites.
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34. Examples of the types of communications
between these entities:
 Direct contact meetings
 Regular mail carrier
 Telecommunication (voice communication such as
telephones, pagers)
 Teleconferencing (online meetings, a combination of
sound and picture)
 Data communication (digital file sharing and transfer)
 Using fax
 Mail
 Web site posting and
 FTP (file transfer protocol)
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35. Integration:
 Clinical trial data collection and management is, therefore,
the integration of data collection and data management.
 The data management system depends on the data collection
method.
 Data collection methods are of three major types:
- Pure paper-based systems
- Pure electronic-based systems
- Hybrid paper-based
35

36. Pure Paper-based System:
 pure paper-based data collection systems have predominated
in clinical trials.
 they are still being used by many contract research
organizations (CROs).
 Pure paper-based data collection systems are most suitable
for small and short-term studies.
 Their advantages are that no computer hardware or software
is needed at the participating sites because data are recorded
manually on paper forms that are transferred to the
centralized location in batches.
 A major drawback is that participating sites do not have real-
time access to their data because no database is created
locally.
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37. Suitability and Hardware / Software
Requirements:
 Both hardware and software are needed at the centralized
location for the data management system.
 The type of hardware and software used is determined by the
configuration of the centralized computer.
 The most commonly used platforms include Open VMS,
Unix, or PC, and one of the most widely used software
packages is SAS®
Design and Implementation:
 Pure paper-based data collection systems use paper forms
that can be designed with any graphical or word processing
software such as Adobe PageMaker, Microsoft Word, or MS
PowerPoint.
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38. Electronic based approach:
 Electronic-based data collection and management systems
have revolutionized data collection and management.
 The advantages of such systems over the traditional pure
paper-based data collection and management systems:
(1) provide cleaner data faster, thus significantly reducing
query rates and eliminating double data entry,
(2) provide up-to-date interim progress reports in a timely
fashion,
(3) dramatically reduce the time from last patient visit to
final database lock out.
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39. Types of System:
 Centralized Systems
 Distributed Systems
 Wireless Systems
 PDF-Based Systems
 Web-Based Systems
 Direct Systems
Hybrid System:
 Hybrid systems are those systems that employ various
strategies to collect data.
 data may be collected on paper forms as patient self
administered questionnaires, while additional data may be
downloaded from centralized databases.
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40. Strategies:
 Paper Data Collection with Centralized Interactive Data
Entry
 Paper Data Collection with Centralized Batch Data Entry
 Paper Data Collection with Direct Data Transfer to
Centralized DMS
 Integration of Distributed Systems with Remote Servers over
the Internet
40

41. Acquiring Proprietary E- Clinical
Software:
The New Trend
 Contract research organizations (CROs) may choose to
acquire one of the many already established and well-
developed proprietary data collection and management
systems known as e-clinical software from various vendors
in the field as an alternative to developing their own systems
in-house.
 These systems tend to be comprised of integrated
components using various technologies that allow flexibility
in the methods of data entry, data submission, and data
management.
 Some vendors indicate that their products comply with FDA
regulations for computerized systems, including 21 CFR Part
11
41

42. Processes Before Data Collection:
 All processes for data collection and management are
defined, addressed, and accomplished before data collection
begins.
 These processes include determining the type of the data
collection and management systems, developing the
systems, defining procedures for subject recruitment,
registration, screening, randomization, and treatment
dispensing.
 Choosing a Data Collection and Management System
 Hardware and Software Selection
 Form and System Design
 Protocol and System Rules
 System Development
 System Validation
 Staff Training 42

43. Processes During Data Collection:
 System Evaluation
 Subject Management
 Data Quality Assurance
 Treatment Dispensing
 Handling Unexpected Events
 Data Transformation
Processes After Data Collection:
 Data Lockout
 Data Retention
 Data Archiving
 Data Sharing
43

44. Clinical Data Management:
 Clinical Data Management System or CDMS is used in
clinical research to manage the data of a clinical trial.
 The clinical trial Data gathered at the investigators site in the
form of case report forms are stored in the CDMS.
 By storing research data within a clinical data management
software system, researcher can ensure that human error will
be kept to a minimum.
 Software systems of this type also file data and screen data
for any illogical patterns.
 To reduce the possibility of errors due to human entry, the
systems employ different means to verify the entry.
 The most popular method is Double data entry.
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45.  In that , two members of the data entry team are provided
with identical copies of a CRF.
 The First person enters the data which the software accepts.
 If the second person enters the same data, the software again
accept them, but if there is any discrepancy in data, the
software raises a red flag.
 When a red flag is raised, the supervisors check the entries
with the original.
 Thus, any discrepancy in the data entry is promptly
identified and resolved.
 Another function that the CDMS can perform is the coding
of data.
 Currently, coding is generally centred around two areas;
adverse event terms and medication names.
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46.  With the variance on the number of reference that can be
made for adverse event terms or medication names, standard
dictionaries of these terms can be loaded in to CDMS.
 The data items containing the adverse event terms or
medication names can be linked to one of these dictionaries.
 The system can check the data in the CDMS and compare it
to the dictionaries.
 Items that do not match can be flagged for further checking.
 Some systems allow for the storage of synonyms to permit
the system to match common abbreviations and map them to
the correct them.
 E.g. ASA could be mapped to Aspirin, a common notation.
 Popular adverse event dictionaries are MedDra and
WHOART and popular medication dictionaries are
COSTART and WHO Drug Dictionary.
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47. Software's for CDM:
 Clinical data management software system may used to store
vital patient information.
 When it comes to managing patient medications, these
systems can be life-saving.
 Frequently, patient medications become mixed resulting in a
dangerous drug levels or unforeseen side effects.
 These common errors rarely occur when specialized patient
software has been utilized.
 Another advantages of data management software is that
they help in the management of the trial too.
 The software helps in scheduling visits, planning projects
and even keeps track of payment schedules.
 These versatile software are expensive but the make the life
of trial managers easy, by providing them skilled assistance.
47

48. Electronic Data Capture (EDC):
 Newer method of data collection are known as Electronic
Data Capture (EDC).
 An Electronic Data Capture (EDC) system is a computerized
system designed for the collection of clinical data in
electronic format for use mainly in human clinical trials.
Typically, EDC system provide;
1. A graphical user interface components for data entry
2. A validation component to check user data
3. A reporting tool for analysis of the collected data
48

49.  EDC systems are used by life science organizations, broadly
define as the pharmaceutical, medical device and
biotechnology industries in all aspects of clinical research,
but are particularly beneficial for late-phase (phase III, IV)
studies and pharmacovigilance and post-market safety
surveillance.
 EDC can increase the data accuracy and decrease the time to
collect data for studies of drug and medical devices.
49

50. Questions:
1. Write a note on regulation of computer system in
pharmaceutical industry.
2. Explain the role of computers in clinical Data collection
and management.
3. Write a application of computer in pharmaceutical research
and development.
4. Discuss computer simulation in pharmacokinetic and
pharmacodynamic parameter.
5. Explain computer aided in clinical data collection and
management.
6. How to clinical data collection and management regulation
of computer system in clinical development.
7. Write a note on computer simulation using whole organism,
isolated tissues and organs.
50

51. References:
 Computer Applications in Pharmaceutical Research and
Development, Sean Ekins, 2006, John Wiley 7 Sons.
 Essential of Clinical Research by Dr. Ravindra B.
Ghooi and Sachin C. Itkar, Nirali Prakashan, First
edition June 2010
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52. Thank you
For
Your
Attention!
52