Computer simulation in pkpd

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. Computer simulation in pk -pd & computers in clinical development Prepared By: Dolly Sadrani Department of Pharmaceutics II nd 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. 3

4. 4 Theoretical Predictions Simulation Results Experimental Results Model System Real 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. 5

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. 6

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. 7

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 8

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. 9

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. 11

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. 12 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. 13

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 14

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. 15

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. 16

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. 17

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. 18

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. 19

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. 20

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 21

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. 23

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. 26

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. 27

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. 28

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. 29

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” . 30

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. 31

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. 32

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. 33

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) 34

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. 36

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. 37

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. 38

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. 39

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. 44

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. 45

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. 46

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; A graphical user interface components for data entry A validation component to check user data 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: Write a note on regulation of computer system in pharmaceutical industry. Explain the role of computers in clinical Data collection and management. Write a application of computer in pharmaceutical research and development. Discuss computer simulation in pharmacokinetic and pharmacodynamic parameter. Explain computer aided in clinical data collection and management. How to clinical data collection and management regulation of computer system in clinical development. 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 51

52. Thank you For Your Attention! 52