BEGIN:VCALENDAR VERSION:2.0 PRODID:icalendar-ruby CALSCALE:GREGORIAN BEGIN:VEVENT DTSTAMP:20240329T055155Z UID:c90ebd5b-d754-49d1-af61-791e7079358a DTSTART:20210427T083000 DTEND:20210428T083000 CLASS:PRIVATE DESCRIPTION:
Data Integrity is a major concern of regulator y agencies worldwide as evidenced by the increasing number of Warning Lett ers issued in that area. Some managements have proceeded to implement data integrity programs on the lines of those implemented in &ldquo\;big data& rdquo\;. This has resulted in the escalation of costs and it is disproport ionate to the benefits gained. Some even wonder why they continue to recei ve Warning Letters in spite of spending the dollars to implement programs such as Data Governance etc. etc.
\n\nThis training focuses on imple menting Data Integrity programs using &ldquo\;the least burdensome&rdquo\; approach\, a technique that regulators themselves employ to conduct their audits. The training also addresses the evolving concepts and guidance fr om regulatory agencies such as the recently issued industry guidance on Pa rt 11 for Clinical Investigations among many others.
\n\nAddressed w ill be case studies\, inspection approaches\, and trends in the issuance o f data integrity 483s and warning letters in the recent past. Take back to your work\, samples of Data Integrity related directives and SOPs such as Data Integrity Policy\, Maintenance of Electronic Records directive and m any more that are required to establish a data integrity infrastructure in your company.
\n\nThis workshop is for novices as well as experienc ed personnel from QA\, IT\, manufacturing\, regulatory and validation grou ps. It addresses data integrity issues in all life science industry sector s where data is required to fulfill regulatory requirements. These sectors include medical devices\, biologics manufacturing\, quality control labor atories\, clinical trials\, blood establishments\, compounding pharmacies etc.
\n\nWhat is Data integrity
\n\nData Life Cycle design and controls
\n\nElements of a Data Integrity Assurance program
\n\nRoles and responsibilities of different group s in ensuring data integrity
\n\nWhat data integrity SOPs do auditor s expect to see during audits
\n\nValidating Data Integrity
\n\n< p> \;\n\n \;
\n\nSome advan ced Data Integrity topics include:
\n\nData Integrity triad
\n\n< p>Data Integrity Maturity Model\n\nDeveloping critical thinking ski lls
\n\nData Integrity Audit trends
\n\n \;
\n\nModule 1
\n\nData and Data Integrity: c oncepts\, meaning of integrity\, data dimensions
\n\n&bull\; Pr actically speaking\, what is data\, raw data\, metadata
\n\n&bull\; Meaning and principles of DI
\n\n&bull\; Data types and their releva nce to DI
\n\n&bull\; DI dimensions with examples of 483 and Warning letters
\n\n&bull\; Why is DI not considered to be a new requiremen t
\n\n \;
\n\nModule 2
\n\nPrim er on 21 CFR Part 11
\n\n&bull\; 21 CFR Part 11 (P11) and Annex 11 (A11) fundamental concepts
\n\n&bull\; P11 Scope and Application guide
\n\n&bull\; Why is Data integrity not the same as x11 (P11 an d A11)
\n\n \;
\n\nBreakout group exercise: Mappin g DI to Part 11
\n\n \;
\n\nModule 3
\n\nData Integrity Guidance from USFDA/MHRA/EMA/WHO/PCS
\n\n&bull\; What are similarities and differences between the guid ance
\n\n \;
\n\nModule 4:
\n\n"\;Implementing a DI assurance plan using the &ldquo\;Least burdensome approac&rdquo\;
\n\n&bull\; PQLI and its relevance to Data Int egrity
\n\n&bull\; What is the &ldquo\;Least Burdensome Approach&rdq uo\;
\n\n&bull\; Why DI issues occur and how to avoid them proactive ly
\n\n&bull\; DI implementation plan: the 5p model and the Controls Triad
\n\n&bull\; What DI SOPs do auditors want to see and what sho uld their contents be
\n\n \;
\n\n \;
\n\nModule 5:
Data Integrity in IT and Manufactur ing IT systems
\n\n&bull\; Data Integrity impact due to the arc hitecture of IT system
\n\n&bull\; Implementing Active Directory ser vice\, Group policy etc. to attain DI
\n\n&bull\; DI susceptibilitie s of hybrid systems commonly found in manufacturing IT systems
\n\n& bull\; DI risks when generating electronic records which are true copies o f paper records
\n\n&bull\; What data integrity items to review for during a Electronic Batch review
\n\n \;
\n\nModul e 6
\n\nData Integrity in the Laboratory
\n\n&bull\; Why is laboratory Data Integrity the key focus of all regulatory audits
\n\n&bull\; Laboratory Data Integrity audit trend and what is needed to avoid citations
\n\n&bull\; Conducting DI risk assessment \, trainee participation required
\n\n&bull\; Core documentation tha t you must have to demonstrate laboratory Data Integrity
\n\n&bull\; What should be the contents of the documents
\n\n&bull\; What is th e role of the laboratory manager in fulfilling DI
\n\n \;
\n\ nBreakout group exercise: Develop an Audit Trail review SOP
\n\n \;
\n\nModule 7
\n\nData Integrity considerations in Clinical Trial Systems (CTS)
\n\n&bull\; Mobile computing issues
\n\n&bull\; Latest US FDA&rsquo\; s Part 11 guidance for CTS
\n\n&bull\; US FDA&rsquo\;s latest Cybers ecurity guidance for CTS
\n\n \;
\n\nModule 8
\n\nHow is Data Integrity audited
\n\n&bull\; De veloping a Data Integrity audit checklist
\n\n&bull\; Critical think ing skills for Internal Auditors
\n\n&bull\; How can you effectively use your Data Integrity Maturity Model during audits
\n\n&bull\; FD A&rsquo\;s new approaches to data integrity audits
\n\n \;
\n\n< strong>Subject Matter Expert: Data Integrity\, GAMP\, CSV\, CFR 21 Part 11 \, Annex 11\, Quality Risk Management\, Manufacturing Process Automation a nd IT systems
\n\nChinmoy Roy has 37+ years of expe rience. He is an internationally recognized subject matter expert in CSV\, CFR 21 Part 11\, Annex 11\, Data Integrity and manufacturing pro cess automation systems. He has been invited to speak and conduct training workshops at several international conferences such as ISPE\, WBF\, Shima dzu&rsquo\;s annual conference for Asia Pacific\, etc.
\n\nHis exper tise stems from his experience in implementing and obtaining &ldquo\;fit f or use&rdquo\; certification for over 200 IT systems. He has worked at and consulted with leading US based companies such as Roche\, Genentech\, Bay er\, Novartis\, Johnson and Johnson etc. His pioneering efforts in impleme nting CFR 21 Part 11 compliant manufacturing IT systems in 1999 while empl oyed by Genentech\, was a precursor to FDA&rsquo\;s issuance of Part 11&rs quo\;s Scope and Application guidance in 2003. His workshops are unique in that he blends his field experience to provide case studies to explain th e intricacies of implementing regulations. Chinmoy is an Electrical Engine er and a Computer Science post graduate.
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\n SUMMARY:Current issues in assuring data integrity in life sciences : 2 Days Seminar BEGIN:VALARM ACTION:DISPLAY TRIGGER:-PT1H SUMMARY:Current issues in assuring data integrity in life sciences : 2 Days Seminar END:VALARM END:VEVENT END:VCALENDAR