Clinical Data Management, Clinical research, clinical trial

CLINICAL DATA MANGEMENT (CDM)

1. CLINICAL DATA MANGEMENT (CDM) Sourabh Kosey Department of Pharmacy Practice I S F College of Pharmacy, Moga Punjab

2. Drug Candidate safety testing Animal Studies - relevant species - transgenic KO/KI mice - conditional KOs - agonists/antagonists - antibodies - antisense - RNAi Studies of Disease Mechanisms Human Studies Phases I,II, III Target -receptor; -ion channel; -transporter; -enzyme; - signalling molecule Lead Search -Develop assays (use of automation) -Chemical diversity -Highly iterative process Molecular Studies The Drug Discovery Process Lead optimization -selectivity -efficacy in animal models -tolerability: AEs mechanism- based or structure-based? -pharmacokinetics -highly iterative process Drug Approval and Registration Target selection & validation Discovery Development

3. Target Selection & Validation Define the unmet medical need (disease) Understand the molecular mechanism of the disease Identify a therapeutic target in that pathway (e.g gene, key enzyme, receptor, ion-channel, nuclear receptor) Demonstrate that target is relevant to disease mechanism using genetics, animal models, lead compounds, antibodies, RNAi, etc.

4. Discovery Develop an assay to evaluate activity of compounds on the target - in vitro (e.g. enzyme assay) - in vivo (animal model or pharmacodynamic assay) Identify a lead compound screen collection of compounds (“compound library”) compound from published literature screen Natural Products structure-based design (“rational drug design”) Optimize to give a “proof-of-concept” molecule—one that shows efficacy in an animal disease model Optimize to give drug-like properties—pharmacokinetics, metabolism, off-target activities Safety assessment, Preclinical Candidate!!!

5. Development Pharmaceutical R&D Formulation Clinical Investigator & patient Clinical Pharmacology Clinical Research Statistics & Epidemiology Data Coordination Research Information Systems Information Services Regulatory Affairs Project Planning & Management Marketing Process R&D Chem Eng. R&D Manufacturing Bio Process R&D Safety Assessment Toxicology Drug Metabolism (ADME) Pharmacology Pre-Clinical Clinical

6. Clinical Trials Product Profile Marketing SOI Information Learned 1. Absorption and metabolism 2. Effects on organs and tissue 3. Side effects as dosage is increased Information Learned 1. Effectiveness in treating disease 2. Short-term side effects in health -impaired patients 3. Dose range Information Learned 1. Benefit/risk relationship of drug 2. Less common and longer term side effects 3. Labeling information Compassionate Use Phase II Several hundred health-impaired patients Treatment Group Control Group Phase III Hundreds or thousands of health-impaired patients Investigational New Drug application IND Phase I 20 - 100 healthy volunteers take drug for about one month Remote data entry

7. Clinical Trials Continued APPROVAL PROCESS (Ex. FDA) Reviews, comments, and discussions Drug Co./Regulatory liaison activities APPROVAL Submit to Regulatory Agencies Advisory Committee Regulatory Review Team New Drug Application (NDA) Worldwide Marketing Authorization (WMA) in other countries

8. Drug Discovery—Convergence of Disciplines Patent Law Combinatorial Chemistry Synthetic Chemistry Physical Chemistry Physiology Biochemistry DMPK Enzymology Immunology Pharmacology Information Technology Mo d elling Physiology Safety Assessment Metabolism Pharmacology Pathology Behavior Novel Molecule Intellectual Property Structural Activity Pharmacokinetic Properties In Vivo activity Safety Design Pharmaco- dynamics Physiology Physiology Physiology

9. INTRODUCTION Data with reference to CDM means the patient information which is collected during clinical trial. data is collected to establish whether the objective of the trial is met.

10. CDM is a critical process in the clinical research, which leads to generation of high quality, reliable and statistically sound data from clinical trials. it is the art and science of creating a computer database of clinical trial data for efficient and comprehensive analysis.

11. NECESSITY OF CDM CDM ensures collection, intigration and availabilty of data at appropriate quality and cost. it also supports the conduct , mnagement and analysis of studies across the spectrum of clinical research as defined by the national institute of health (NIH).

12. CDM PROCESS CDM process is designed to deliver an error-free, valid, and statistically sound database. The CDM process starts early, even before the finalization of the study protocol.

13. GOAL OF CDM to ensure that conclusion drawn from reaseach are well supported by th data achieving this goal protect public health and confidence in marketed therapeutics.

14. OVERVIEW OF CDM CDM consists of trial data acquistion validation integration database administration backup and quality assurance data is statistically analysed submitted to regulatory authorities for approval

15. IMPORTANCE OF CDM CDM is a vital vehicle in clinical trials to ensure integrity and quality of data being transferred from trial subjects to a database system.it helps: to provide consistent, accurate and valid clinical data to support accuracy of final conclusions and report

16. CDM ensures: that the collected data is complete and accurate so that results are correct that trial database is complete, accurate and a true representation of what took place in trial that trial database is sufficiently clean, to support stastistical analysis, its subsequent presentation and interpretation.

17. CDM ROLE IN CLINICAL RESEARCH CDM provide all data Collection and data validation for clinical trial program it is essential to the overall clinical reseach funtion, as its key delieverable is the data to support the submission assuring the overall accuracy and integrity of the clinical trial data

18. it provides data and database in a usable format in a timely manner. it ensures clean data and a 'ready to lock' database at the study level, data management ends when the database is locked and clinical study report is final at the compound level, data magement ends when the submssion package is assembled and complete.

19. SOURCE DATA RECORDS ORIGINAL RECORDS OF CLINICAL FINDINGS OBSERVATIONS IN CLINCAL TRIALS RECONSRTUCTION AND EVALUATION OF THE TRIAL SOURCE DATA ARE CONTAINED IN SOURCE DOCUMENTS

20. SOURCE DOCUMENTS ORIGINAL DOCUMENTS,DATA,RECORDS HOSPITAL RECORDS,CLINICAL & OFFICE CHARTS LABORATORY NOTES & FINDINGS,MEMORANDA SUBJECT’S DIARIES OR EVALUATION CHECKLISTS PHARMACY DISPENSING RECORDS RECORDED DATA FROM AUTOMATEDINSTRUMENTS COPIES OR TRANSCRIPTIONS CERTIFIED AFTER VERIFICATION AS BEING ACCURATE COPIES

21. SOURCE DOCUMENTS MICROFICHES,PHOTOGRAPHIC NEGATIVES MICROFILM OR MAGNETIC MEDIA X-RAYS SUBJECT FILES RECORD KEPT AT PHARMACY RECORDS AT THE LABORATORIES MEDICOTECHNICAL DEPARTMENTS INVOLVED IN THE CLINICAL TRIAL

22. Source Document: The electronic record to used to keep together a collection of eSource data items for capture, transmission, storage, and/or display; and serving as a source document for a clinical investigation. Raw Data: Data as originally collected. Distinct from derived. Raw Data includes original observations, measurements and activities

23. INTRODUCTION CRO’s DATA GENERATION & PRESENTATION ACCURACY OF TRAILS & REGULATORS INFORMATION TECHNOLOGY (IT) COMPUTERIZED SYSTEM (REMOVAL OF TRADITIONAL SYSTEM PAPER WASTAGE ) GROWTH & REQUIREMENTS OF GOOD DATA MANAGEMENT SYSTEMS THAT COMPANIES WHICH ARE OTHERWISE IT-BASED

24. HAVE FULL FLEGED CLINICAL TRIAL DATA MANAGEMNET SYSTEMS WHICH BRING THEM A GOOD AMOUNT OF BUSINESS AND REVENUE CDM is a fundamental process which controls data accuracy of each trial besides helping the timelessness to be achieved. It helps in linking clinical research co- ordinator = who monitor all the sites & collects the data Link with biostatisticians = who analyze, interpret and report data in clinically meaningful way.

25. Good Clinical Data Management Practice (GCDMP) The objective of GCDMP is to generate high quality database devoid of errors and omissions ICH GUIDELINES US FDA REGULATIONS DRUG AND DEVICE DEVELOPMENT PROCESS The Society of Clinical Data Management (SCDM) has created a comprehensive document- Good Clinical Data Management Practices (GCDMP) (Version 4.0 is the most recently updated version published in May 2007)- that provides guidance on accepted practices of Clinical Data Management (CDM)

26. SYSTEMIC APPROACH FOR CDM INITIAL PLANNING SPONSOR or INVSTIGATOR or CRO Standardized database management system CRF CASE RECORD FORMAT CRF as per database need, setting realistic dates for receipt, verification, query resolution, corrections, Final editing and release of data and finally resource mobilization

27. Preparing for Incoming Data Data management study master file SOP’s should be established to ensure operational documentation for computers, system reliability, Validation and accuracy. System security for hardware software and data from theft and sabogate . Adequate access code and back up of the data. Indexes & Checklists for CRF’s Designing data entry screens

28. Establishing systems for tracking of CRF;s like Barcodes, deciding which CRF copy to be working copy (usually second copy) Validating CRF and other data transfer procedures. Data Transfer may be on Paper or Electronic

29. INCOMING DATA Data received continuously and in a timely manner Helps in data testing methodology, validates data base management system (DBMS), helps in checking accuracy and completeness of CRF Timely clarification of errors and omissions with the investigators. It is also important to decide on unambiguous Codes for subject identification that allow identification of all the data of any subject.

30. INITIAL DATA REVIEW AND VERIFICATION DATA REVIEW COMMITTEE MEMBERS MAINTAINING BLINDING DURING REVIEW AND ENTRY OF THE DATA ERROR DETECTION IS AN IMPORTANT STEP TO BE DONE BEFORE AND DURING DATA REVIEW AND VERIFICATION THE VARIOUS ERRORS THAT ONE CAN EXPECT DURING THIS STAGE CAN RANGE FROM MISSING DATA, FAULTY COMPLETION OF FORMS,QUESTIONABLE VALUES (E.G. HEIGHT 20 FEETS), TREND TESTS TO GROSS PROTOCOL VIOLATIONS

31. SUBSEQUENT ERRORS CAN ALSO BE DETECTED AT VARIOUS STAGES LIKE DURING COMPUTER ENTRY, ERRONEOUS CODING OR INVESTIGATOR’S CORRECTIONS NOT BEING TAKEN INTO ACCOUNT. DATA MONITORING COMMITTEE HELPS IN ASSESSING THE PROGRESS OF TRIALS AT INTERVALS TO RECOMMEND WHETHER TO CONTINUE, MODIFY OR STOP THE TRIALS. IT ALSO EVALUATES SAFETY DATA AND CRITICAL EFFICIACY END POINTS. THERE SHOULD BE WRITTEN OPERATION PROCEDURES AND MAINTENANACE OF ALL MEETING RECORDS

32. DATA ENTRY, VERIFICATION AND VALIDATION The Data entry person should be defined for the specific trial & specified in a data management plan. For transcription from paper CRF to electronic CRF different procedures are used: Double Data Entry form (one person) Double Data Entry form (two persons) Single entry with second look Single data entry with reading aloud Single data entry with source verification

33. Double data entry is not required by regulation by good practice. Data entry process should be chosen based on the skills of the personnel, this will give good impact on to the resources in the project and the reflected evaluation of key variables. Only authorized persons should be entitled to do entry and corrections on the data entry screens. Verification and Validation is done by Data Reviewers, automated computer checks (an error message like when a value is outside the acceptable norms) and during audit It has been that errors in entry is 1 % by good operator. This Decreases to 0.1% by double entry of data by two different operators.

34. CODING FOR Adverse Events COSTART (Coding Symbols for Thesaurus of Adverse Reaction Terms) WHO-ART (Adverse Reaction Terminology) SNOMED (Systematized Nomenclature of Medicine) MedDRA (Medical Dictionary for Regulatory Activities) In House Codes

35. FOR concomitant diseases: international classification of diseases version 10 (ICD-10) FOR concomitant medications: WHO Drug Dictionary Medical Term ----- Preferred term(s)----- Code ERRORS IN CODING Misunderstanding about medical terms, misinterpretation of hand writing, defective translation, foreign Language of CRF, wrong choice of preferred terms and difficulties in transcoding . This errors leads to inconsistencies in final report, decreased credibility of report, delay in report writing and represent evidence of negligence

36. To minimize errors only qualified and trained staffs should be employed in the process the data entry operators should insist on legible filling of CRFs. It can also be minimized by keeping a log book of difficult coding cases, doing translation-retranslation and centralizing of the final coding

37. DATA QUERIES Problems faced by data entry operators Subject has to go back to investigator Operators are failure to check the inclusion and exclusion criteria Inconsistent Calendar Dates Illegible entries Unfamiliar Drugs Names Text in unfamiliar Language Entries in incorrect place at CRFs Failure to specify indication for concomitant medication Lack of reason for change in medication Inconsistencies in physical examination at start and finish Incomplete information on Adverse Events Varying Units & Normal ranges in case of Laboratory Data.

38. Query Tracking and Resolution A proper SOP has to be made in place of query tracking and solving Operator should draw a list of QUERIES This List should be sent to investigators who verifies, corrects, signs and corrects the dates the query Three copies should be send to the same format then To the data entry operator who operates the same

39. At the end a validation program is done and run to follow the program and check the editing done. Any change or correction must be readily spottable and is called as AUDIT TRIAL. This Trial may be given in the computers where computers saves the date and time of correction, new value along with old value and access code used to make changes or on paper

40. DATA OUTPUT, REVIEW & FREEZING As the data comes the manager and stastician finalizes the data and queries are resolved. Thereafter a final audit is performed, data is frozen and sent to the statistician. Goal of perfectly accurate database is usually unrealistic. It is preferable to set acceptable limits of error that do not alter the validity of statistical analysis and results and conclusions drawn from the study

41. ARCHIVING Data mangers and statistician are responsible for archiving the electronic database, associated computer programs, Data monitoring conventions, audit trials and final report. They also maintain also all sponsor-specific essential documents as per regulatory requirements.

42. PATIENT DATA BUDGET/BILLING PHARMACEUTICAL PROTOCOL CLINICAL RESEARCH ASSOCIATE TRAINING TRIAL MONITOR REGULATORY FORMS CONTRACT RESEARCH RESEARCH SITE MANAGER / INVESTIGATOR CLINCAL DATA MANAGEMENT SOLUTION GATHERS AND CENTRALIZES DATA

43. REQUIREMENTS FOR ACQUIRING/ CAPTURING/ COPYING SOURCE DATA In general data & documents containing source data must first be specified in the trial protocol. Source Data are the original data, the recordings and all information regarding Clinical Investigations, Laboratory findings, anamnesis, interviews, patient diaries and other sources. The original documents have to be archived. Copies have to be dated and signed by a responsible person (Certified copies) If the original data is stored electronically, a printout has to be made or a list of dates and versions of stored documents signed/dated by Principal Investigator.

44. In the case of eSource data, of course, this is not possible. A copy of eSource data shall be accepted in place of eSource data, if the copy hass been produced and verified against the eSource data based on procedures defined in a SOP for acquiring data duplication and verification. Appropriate handling is also required for scanning source documents. The Scanning process has to be validated prior to implementation in a trial to ensure the integrity of the generated record

45. In the CRF is the source document (e.g., in psychiatric instruments like psychometric scales ) this has to be defined in the protocol. If work has been used as a transcription instrument (e.g., Transitional documentation prior to electronic data entry), these are to be considered as informal source data sheets and have to be filed and quality checked appropriately. In general, source data must be accessible and verifiable and the quality of digitisation must be carefully controlled using appropriately defined SOPs.

46. pCRF to eCRF Transfer In this scenario, clinical data are at first collected with a pCRF . Investigator has less time or has to move between locations (e.g. emergency ward, operation theatre) In a remote data entry scenario, it is often not the investigator, but special assistance personnel who enters data from the pCRF into the eCRF This transcription step must be quality assured.

47. Type of personnel needed (i.e., for data entry, for data review, etc.) Criteria chosen to qualify them must be clearly defined. For using eCRF , specific training programs for investigators and assistance personnel must be included. Appropriate quality control steps have to be implemented and double data entry may be performed. pCRF transfer as well as status (arrived, re-viewed, non-correct, requested queries, correct, closed ) must be clearly tracked.

48. Personnel responsible for different phases of pCRF entry must be traked as well as all the changes. Because the investigator’s signature is required, he is responsible for the correct transcription of the data. Appropriate workflow support should be implemented in the Electronic Data Capture (EDC) system.

49. ESSENTIAL REQUIREMENTS GOOD CDM SYSTEM System evaluation and provider/vendor selection. System installation, setup and configuration. System configuration management (Configuration of Audit Trial e.g. reason for change optional or not?). System access and profile management.

50. Change Control Risk Assessment of any change in the system. Controlled processes of making changes to the system, consisting of announcement, assessment and approval of the change. System Security Password policy. Firewall configuration. Physical & Logical security, in particular also at the sites (EDC). System controls. Network security for remote access.

51. Database and communication security Encryption of data storage, data Transfer. Electronic signature has to comply also with national regulations [EDC]. Data protection Handling of personally identifiable data (e.g., blinding of additionally submitted identifying data; sites should eliminate personal identifiers from source documents prior to submission). Specification of minimum subject identifiers. Safeguarding that (future) use of data is in accordance with informed consent.

52. 4 Regulation of access to electronic or paper based data storage. 5 Particularly strict standards for genetic data. Secure data handling procedures. Use of pseudonyms/anonyms where appropriate. Secure cross-border data transfer. Data backup and recovery Disaster system recovery Database security

53. Data Archiving Database specification. Data files. Audit Trial . Clinical Data (open standards – vendor independent, e.g., CSV, XML, PDF, ODM, from CDISC) Archiving reports. Scanned paper CRFs. Content and Variable definitions (metadata).

54. Report on data completeness at respondent and variable level. Secure Storage and access control. Business continuity Migration of data/meta-data (in case of system retirement) System Validation. Risk management. All components of the system have to be judged according to their risk to violate GCP. GCP-compliance has to be guaranteed especially for high-risk components. Maintenance of GCP-compliance even after updates or other changes to the system.

55. Periodic review/audits. Safeguard of Blinding. Help Desk. CONCLUSION The importance of CDM can be realized from the fact a lot of pure IT companies are involved in CDM activities and this contributes a big share in their revenue. Some of the advances in CDM are:

56. New hardware's like PCs, Electronic notebooks Remote data entry. Optical mark recognition like bar codes. Optical character recognition like fingerprints. Facsimile. Smart cards for each patient. Computer assisted new drug application [CANDA] by FDA.

57. DATA MANAGEMENT JOB ROLES clinical database programmer to set up and maintain the data management system and associated programs accordg to prject specifications so that the study data base is ready to use.

58. clinical system development: designer, developer, tester (CRF developer) for designing data collection tools-either paper CRF or EDC screens - to collect data that is required by the study protocol, works closely with the database prgrammer to set up the clincial database to accept study data.

59. clinical data coordinator to assist in the development of project data management plans, review CRF data and resolve queries assocted with CRF data.

60. lead data manager for supervising the entire CDM process: prepares DMP, approves CDM procedure and collecting and allocating the database access to team members.

61. Clinical Trial Data

62. DATA MANAGEMENT WORKFLOW Receipt of CRFs (CRF Tracking/Filing) First Pass Entry Second Pass Entry Clinical Data Man ag e m en t Batch Validation Thesaurus Manual Coding Discrepancy Man ag e m en t DCF R esolut i ons SAE Reconciliation Quality Control Plan Databa s e Lock Auto C oding Electronic Archival Data C lar i f i ca ti on Form (DCF)

63. KEY MEMEBERS: The Key members involved in Data Management: Clinical Data Manager Database Administrator Database Programmer Clinical Data Coordinator Clinical Data Associate

64. Clinical Trail Overview

65. 9 Clinical Investigator Site coordinator Pharmacologist Trialist/Methodologist Biostatistician Lab Coordinator Reference lab Project manager Clinical Research Manager/Associate Monitor Regulatory affairs Clinical Data Management Clinical Safety Surveillance Associate (SSA) IT IT/IS personnel Trial pharmacist Clinical supply Auditor/Compliance MULTIDISCIPLINARY TEAMS IN CLINICAL TRIALS

66. Paper CRF e - CRF