Data Integrity in pharmaceutical laboratories is a must, the attached ppt shall help the QC members to understand and develop an integral analytical culture
The document discusses data integrity and provides guidance on ensuring data integrity through the ALCOA principles of Attributable, Legible, Contemporaneous, Original, and Accurate. It defines each of the ALCOA principles and provides examples of their application. Common data integrity issues found by the FDA during inspections are also summarized, including data manipulation, multiple sample runs, backdated documentation, unauthorized data access, logbook recording issues, and others.
Presentation on data integrity in Pharmaceutical IndustrySathish Vemula
Presentation on data integrity in Pharmaceutical Industry
Contents:
- Definition & Basics
- Criteria for integrity of laboratory data
- Regulatory Requirements
- Barriers to Complete Data
- Possible data integrity problems
- Previous observations
- FDA Warning Letters – 2013
- FDA Warning Letters – 2014
- FDA 483’s related to data integrity
- EU – Non compliance Reports
- WHO - Notice of Concern
- Summary of Data Integrity issues
- Consequences- Rebuilding Trust
- Conclusion
The document discusses data integrity and recent regulatory approaches. It defines data integrity as the completeness, consistency, and accuracy of data throughout its lifecycle. Regulatory agencies are increasingly focused on data integrity due to its importance in ensuring product quality and safety. Common data integrity issues found by agencies include fabricated, falsified, or missing records. Ensuring data integrity requires effective quality management systems, risk management, technology solutions, and governance.
CCK Discussion Forum held at ICCBS, University of Karachi, attended by over hundred of registered experienced pharmaceutical professionals participants belonging from dozen of pharmaceutical manufacturing facilities
This document discusses regulatory requirements and previous observations related to data integrity issues. It outlines criteria for integrity of laboratory data according to regulations. It also provides examples of possible data integrity problems that have been observed by regulators, such as altering raw data, manipulating test procedures to obtain passing results, and recording lab activities before they occur. FDA warning letters from 2013 are referenced that identified specific failures to record quality activities at the time they were performed.
FDA Data Integrity Issues - DMS hot fixesVidyasagar P
The document discusses data integrity, including popular causes of integrity issues, consequences, and fixes related to document management systems. It provides definitions of data integrity and discusses regulatory requirements around integrity from agencies like the FDA. Specifically, it summarizes the FDA's 21 CFR Part 11 regulation, which considers electronic records equivalent to paper if certain controls are in place. It also discusses application integrity policies and concludes that ensuring data integrity is important to rebuild regulatory trust if issues are found.
- The document discusses data integrity, which refers to maintaining accurate and consistent data over its entire lifecycle. This is important for the regulated healthcare industry as quality decisions are based on data.
- The FDA uses the ALCOA criteria (Attributable, Legible, Contemporaneous, Original, Accurate) to define expectations for electronic data. Regulatory agencies now focus heavily on data integrity due to instances of fabricated documents and errors.
- Common data integrity issues found by agencies include non-contemporaneous recording, backdating records, re-running samples until desired results are obtained, and data fabrication. Ensuring data integrity helps prevent regulatory actions like warning letters or import bans against companies.
Data integrity refers to the completeness, consistency, and accuracy of data. The data should follow the ALCOA principles: Attributable, Legible, Contemporaneous, Original, and Accurate. A lack of data integrity can lead to warning letters from regulators, import alerts, and no further product approvals. Typical contents of warning letters include failing to investigate complaints, falsifying documentation, and improperly recording batch information. Maintaining data integrity is important to avoid regulatory consequences.
The document discusses data integrity and provides guidance on ensuring data integrity through the ALCOA principles of Attributable, Legible, Contemporaneous, Original, and Accurate. It defines each of the ALCOA principles and provides examples of their application. Common data integrity issues found by the FDA during inspections are also summarized, including data manipulation, multiple sample runs, backdated documentation, unauthorized data access, logbook recording issues, and others.
Presentation on data integrity in Pharmaceutical IndustrySathish Vemula
Presentation on data integrity in Pharmaceutical Industry
Contents:
- Definition & Basics
- Criteria for integrity of laboratory data
- Regulatory Requirements
- Barriers to Complete Data
- Possible data integrity problems
- Previous observations
- FDA Warning Letters – 2013
- FDA Warning Letters – 2014
- FDA 483’s related to data integrity
- EU – Non compliance Reports
- WHO - Notice of Concern
- Summary of Data Integrity issues
- Consequences- Rebuilding Trust
- Conclusion
The document discusses data integrity and recent regulatory approaches. It defines data integrity as the completeness, consistency, and accuracy of data throughout its lifecycle. Regulatory agencies are increasingly focused on data integrity due to its importance in ensuring product quality and safety. Common data integrity issues found by agencies include fabricated, falsified, or missing records. Ensuring data integrity requires effective quality management systems, risk management, technology solutions, and governance.
CCK Discussion Forum held at ICCBS, University of Karachi, attended by over hundred of registered experienced pharmaceutical professionals participants belonging from dozen of pharmaceutical manufacturing facilities
This document discusses regulatory requirements and previous observations related to data integrity issues. It outlines criteria for integrity of laboratory data according to regulations. It also provides examples of possible data integrity problems that have been observed by regulators, such as altering raw data, manipulating test procedures to obtain passing results, and recording lab activities before they occur. FDA warning letters from 2013 are referenced that identified specific failures to record quality activities at the time they were performed.
FDA Data Integrity Issues - DMS hot fixesVidyasagar P
The document discusses data integrity, including popular causes of integrity issues, consequences, and fixes related to document management systems. It provides definitions of data integrity and discusses regulatory requirements around integrity from agencies like the FDA. Specifically, it summarizes the FDA's 21 CFR Part 11 regulation, which considers electronic records equivalent to paper if certain controls are in place. It also discusses application integrity policies and concludes that ensuring data integrity is important to rebuild regulatory trust if issues are found.
- The document discusses data integrity, which refers to maintaining accurate and consistent data over its entire lifecycle. This is important for the regulated healthcare industry as quality decisions are based on data.
- The FDA uses the ALCOA criteria (Attributable, Legible, Contemporaneous, Original, Accurate) to define expectations for electronic data. Regulatory agencies now focus heavily on data integrity due to instances of fabricated documents and errors.
- Common data integrity issues found by agencies include non-contemporaneous recording, backdating records, re-running samples until desired results are obtained, and data fabrication. Ensuring data integrity helps prevent regulatory actions like warning letters or import bans against companies.
Data integrity refers to the completeness, consistency, and accuracy of data. The data should follow the ALCOA principles: Attributable, Legible, Contemporaneous, Original, and Accurate. A lack of data integrity can lead to warning letters from regulators, import alerts, and no further product approvals. Typical contents of warning letters include failing to investigate complaints, falsifying documentation, and improperly recording batch information. Maintaining data integrity is important to avoid regulatory consequences.
This document discusses data integrity and good documentation practices. It begins with an introduction to the ALCOA principles for data quality, which stand for Attributable, Legible, Contemporaneous, Original, and Accurate. It then defines data integrity and lists some common data integrity issues seen by regulators. The rest of the document outlines good documentation practices, including the importance of conforming to standards, maintaining document versions and revisions, and avoiding issues like undocumented changes. It emphasizes that poor documentation can undermine drug safety and efficacy and break regulatory trust.
Waters: Reviewing Audit Trail Information in Empower Chromatography Data Soft...Waters Corporation
This presentation provides an overview of how to review audit trail information within Waters Empower Chromatography Data Software. (From Inform 2016, our annual software users meeting)
Data integrity is assuming greater importance in current Good Manufacturing Practices in FDA regulated industry with increased emphasis by other regulatory agencies like the EMA. Data integrity and security infractions are not only 21 Code of Federal Regulations (CFR) Part 11 issues but also severe CGMP violations. As FDA increases its focus on data integrity and reliability, inspectors are examining data based on multiple regulations and standards including CGMP, Good Laboratory Practices (GLP), Good Clinical Practices (GCP) and the Application Integrity Policy (AIP) in addition to FDA-recognized consensus standards.
This presentation discusses data integrity regulations and enforcement trends that have led to increased scrutiny of pharmaceutical laboratories by inspectors.
This document discusses data integrity in pharmaceutical quality systems. It defines data and explains its importance in decision making and continual improvement. It notes that the FDA and EMA have issued warning letters and non-compliance reports for data integrity issues. Examples are provided of warning letters issued to two companies for violations like unauthorized data manipulation, lack of audit trails, and insufficient investigations. The causes of data integrity breaches are discussed. It emphasizes establishing a culture of integrity, security protocols, and complying with cGMP guidelines. It provides details on how to properly manage system access, data storage, backups, and recording data according to ALCOA principles. The importance of audit trail software is also covered.
Data Integrity Training by Dr. A. AmsavelDr. Amsavel A
This document provides an overview of a training presentation on data integrity. The presentation covers the objectives of data integrity in ensuring patient safety and quality. It defines key terms like data, data integrity, data governance and data lifecycle. It discusses regulatory requirements for data integrity and principles like ALCOA+. It also describes examples of data integrity breaches found by regulators like falsification and backdating. The presentation notes the consequences of data integrity issues can be severe including regulatory warnings, fines, and loss of trust. It emphasizes the importance of a quality culture and management awareness to ensure data integrity.
www.3-14.com
Source Data expectations for the life sciences industry. Data integrity refers to the completeness, consistency, and accuracy of data. Complete, consistent, and accurate data should be attributable, legible, contemporaneously recorded, original or a true copy, and accurate.
ENSURING DATA INTEGRTY THROUGH "ALCOA" : BASIC DATA INTEGRITY PRINCIPLES APPL...Abhijeet Waghare
Data Integrity refers to the completeness, consistency and accuracy of the data. Complete, consistent and accurate data should be attributable, legible, contemporaneously recorded, original or true copy and accurate across. The acronym ALCOA has been around since the 1990’s, is used by regulated industries as a framework for ensuring data integrity, and is a key to Good Documentation Practice (GDP).
This document discusses data integrity and its importance in the pharmaceutical industry. It defines data integrity as the extent to which all data is complete, consistent and accurate throughout its lifecycle. Regulatory agencies like the FDA and MHRA require data integrity to ensure patient safety. Lack of data integrity can result in warning letters or consent decrees from regulators. The document outlines principles like ALCOA+ to ensure data integrity. It discusses examples of data integrity issues found in warning letters and gives recommendations for proper implementation of data integrity controls and quality culture.
Data integrity, Pharmaceutical industry, Good Manufacturing Practice, GMP, Guidelines, Data management, DI and GMP Compliance, paper and electronic data, Archive and back up
Webinar slides on Data Integrity 101
Organized by: One Quality Solutions Ltd.
Date: 19 May 2023
Time: 09:00-10:00 pm (BST)
Data Integrity in Pharmaceutical Industry is a hot topic now. Due to emerging technology, maintaining integrity of data is a very big challenge. If the challenges are not managed appropriately, there is high potential to receive regulatory enforcement actions including FDA 483s, warning letter or shut down of the pharmaceutical plant. So, it is very important to understand the basic requirements of Data Integrity.
Topics:
1. Introduction to Data Integrity
What is Data Integrity?
Why it is important?
Key Definitions
2. ALCOA+ Principles
History of ALCOA+
ALCOA+ and ALCOA++
3. Document Control
Example observation from Warning Letter
How to implement document control?
4. User Access Management
Example observation from Warning Letter
How to implement user access management?
5. Audit Trail Review
Example observation from Warning Letter
How to implement audit trail review?
Speakers:
Mohammed Raihan Chowdhury
Head of Quality Systems and Services
One Quality Solutions Ltd.
Ex-Data Integrity Lead of Novartis Bangladesh
and
Sharmin Afroz
Sr. Asst. Manager, Product Development-ARD
Data Integrity Expert of Laboratory Instrument
Radiant Pharmaceuticals Ltd.
Bangladesh
Host:
Najmun Nahar
Marketing Executive
One Quality Solutions Ltd.
This slide is related to Good documentation Practice in Pharmaceutical Industries. It was presented in the pharmaceutical industry (Chemidrug Industry Private Ltd.) during the training session.
The Product Quality Review (PQR) is a regular review of all licensed medicinal products conducted to verify consistency of manufacturing processes and the appropriateness of specifications. The objectives of the PQR include determining the need for process, specification or validation changes; verifying compliance; identifying trends; and determining corrective actions. The EU requires annual PQRs that review areas like starting materials, process and product testing results, failed batches, deviations, changes made, and stability monitoring results. The PQR is intended to enhance quality and identify improvements.
Data integrity is critical throughout the CGMP data life cycle, including in the creation, modification, processing, maintenance, archival, retrieval, transmission, and disposition of data after the record’s retention period ends. It would be helpful for data management.
Trends changed from Non compliance to RR --> Gap to RR --> Data Integrity --> DIB --> Smart Audit & Smart Data.
RR = Regulatory Requirements
DIB = Data Integrity Breach
Take a serious Note for Data Integrity whether you are small or big organization. Your Data is the Heart of your business. Regulatory bodies are highly conscious about such issues. For beginners in this path, my small note can help you a lot.
Data Integrity app Link: https://play.google.com/store/apps/details?id=com.innovativeapps.dataintegrity&hl=en
One Step Ahead in Pharma Compliance
Across the internet, there are millions of resources are available which provide information about Computer System Validation.
Refer above Data Integrity app which helps you to understand current regulatory agencies thinking on Data Integrity.
This presentation is compiled by Drug Regulations, a nonprofit organization that provides online pharmaceutical resources. It discusses FDA guidance on data integrity and compliance with cGMP regulations. The guidance clarifies FDA's expectations around the creation and handling of data to ensure its reliability and accuracy according to cGMP standards.
The document discusses good documentation practices for manufacturing units that supply the food and pharmaceutical industries. It defines good documentation practice as standards for creating and maintaining documentation in the pharmaceutical industry. Maintaining good documentation is important for regulatory compliance, customer requirements, and to avoid issues like product safety concerns, litigation, and regulatory action. The document outlines requirements for documentation including being permanent, legible, accurate, prompt, clear, consistent, complete, direct, truthful, current, and traceable. It provides examples and guidelines for meeting each of these requirements.
This document provides information about PHP VARNA #5, including thanks to the hosts, the agenda for the event which includes a talk on PHP 7.x and lightning talks, and details for the next monthly meetup in April. Contact and location information is also listed to find and spread the word about PHP VARNA.
This document summarizes a school management software system called Vidyapith that is proposed by SRRK IT Limited. The key points are:
1) Vidyapith is an all-in-one school management software that can save time, reduce costs, and improve efficiency for functions like student admission, attendance, marks keeping, accounting, etc.
2) It offers modules for students, teachers, academics and accounts that automate tasks like ID card generation, report cards, attendance tracking, fee collection etc.
3) SRRK provides training, support, software updates and claims complaints will be addressed within 2.5 hours for customers adopting Vidyapith.
This document discusses data integrity and good documentation practices. It begins with an introduction to the ALCOA principles for data quality, which stand for Attributable, Legible, Contemporaneous, Original, and Accurate. It then defines data integrity and lists some common data integrity issues seen by regulators. The rest of the document outlines good documentation practices, including the importance of conforming to standards, maintaining document versions and revisions, and avoiding issues like undocumented changes. It emphasizes that poor documentation can undermine drug safety and efficacy and break regulatory trust.
Waters: Reviewing Audit Trail Information in Empower Chromatography Data Soft...Waters Corporation
This presentation provides an overview of how to review audit trail information within Waters Empower Chromatography Data Software. (From Inform 2016, our annual software users meeting)
Data integrity is assuming greater importance in current Good Manufacturing Practices in FDA regulated industry with increased emphasis by other regulatory agencies like the EMA. Data integrity and security infractions are not only 21 Code of Federal Regulations (CFR) Part 11 issues but also severe CGMP violations. As FDA increases its focus on data integrity and reliability, inspectors are examining data based on multiple regulations and standards including CGMP, Good Laboratory Practices (GLP), Good Clinical Practices (GCP) and the Application Integrity Policy (AIP) in addition to FDA-recognized consensus standards.
This presentation discusses data integrity regulations and enforcement trends that have led to increased scrutiny of pharmaceutical laboratories by inspectors.
This document discusses data integrity in pharmaceutical quality systems. It defines data and explains its importance in decision making and continual improvement. It notes that the FDA and EMA have issued warning letters and non-compliance reports for data integrity issues. Examples are provided of warning letters issued to two companies for violations like unauthorized data manipulation, lack of audit trails, and insufficient investigations. The causes of data integrity breaches are discussed. It emphasizes establishing a culture of integrity, security protocols, and complying with cGMP guidelines. It provides details on how to properly manage system access, data storage, backups, and recording data according to ALCOA principles. The importance of audit trail software is also covered.
Data Integrity Training by Dr. A. AmsavelDr. Amsavel A
This document provides an overview of a training presentation on data integrity. The presentation covers the objectives of data integrity in ensuring patient safety and quality. It defines key terms like data, data integrity, data governance and data lifecycle. It discusses regulatory requirements for data integrity and principles like ALCOA+. It also describes examples of data integrity breaches found by regulators like falsification and backdating. The presentation notes the consequences of data integrity issues can be severe including regulatory warnings, fines, and loss of trust. It emphasizes the importance of a quality culture and management awareness to ensure data integrity.
www.3-14.com
Source Data expectations for the life sciences industry. Data integrity refers to the completeness, consistency, and accuracy of data. Complete, consistent, and accurate data should be attributable, legible, contemporaneously recorded, original or a true copy, and accurate.
ENSURING DATA INTEGRTY THROUGH "ALCOA" : BASIC DATA INTEGRITY PRINCIPLES APPL...Abhijeet Waghare
Data Integrity refers to the completeness, consistency and accuracy of the data. Complete, consistent and accurate data should be attributable, legible, contemporaneously recorded, original or true copy and accurate across. The acronym ALCOA has been around since the 1990’s, is used by regulated industries as a framework for ensuring data integrity, and is a key to Good Documentation Practice (GDP).
This document discusses data integrity and its importance in the pharmaceutical industry. It defines data integrity as the extent to which all data is complete, consistent and accurate throughout its lifecycle. Regulatory agencies like the FDA and MHRA require data integrity to ensure patient safety. Lack of data integrity can result in warning letters or consent decrees from regulators. The document outlines principles like ALCOA+ to ensure data integrity. It discusses examples of data integrity issues found in warning letters and gives recommendations for proper implementation of data integrity controls and quality culture.
Data integrity, Pharmaceutical industry, Good Manufacturing Practice, GMP, Guidelines, Data management, DI and GMP Compliance, paper and electronic data, Archive and back up
Webinar slides on Data Integrity 101
Organized by: One Quality Solutions Ltd.
Date: 19 May 2023
Time: 09:00-10:00 pm (BST)
Data Integrity in Pharmaceutical Industry is a hot topic now. Due to emerging technology, maintaining integrity of data is a very big challenge. If the challenges are not managed appropriately, there is high potential to receive regulatory enforcement actions including FDA 483s, warning letter or shut down of the pharmaceutical plant. So, it is very important to understand the basic requirements of Data Integrity.
Topics:
1. Introduction to Data Integrity
What is Data Integrity?
Why it is important?
Key Definitions
2. ALCOA+ Principles
History of ALCOA+
ALCOA+ and ALCOA++
3. Document Control
Example observation from Warning Letter
How to implement document control?
4. User Access Management
Example observation from Warning Letter
How to implement user access management?
5. Audit Trail Review
Example observation from Warning Letter
How to implement audit trail review?
Speakers:
Mohammed Raihan Chowdhury
Head of Quality Systems and Services
One Quality Solutions Ltd.
Ex-Data Integrity Lead of Novartis Bangladesh
and
Sharmin Afroz
Sr. Asst. Manager, Product Development-ARD
Data Integrity Expert of Laboratory Instrument
Radiant Pharmaceuticals Ltd.
Bangladesh
Host:
Najmun Nahar
Marketing Executive
One Quality Solutions Ltd.
This slide is related to Good documentation Practice in Pharmaceutical Industries. It was presented in the pharmaceutical industry (Chemidrug Industry Private Ltd.) during the training session.
The Product Quality Review (PQR) is a regular review of all licensed medicinal products conducted to verify consistency of manufacturing processes and the appropriateness of specifications. The objectives of the PQR include determining the need for process, specification or validation changes; verifying compliance; identifying trends; and determining corrective actions. The EU requires annual PQRs that review areas like starting materials, process and product testing results, failed batches, deviations, changes made, and stability monitoring results. The PQR is intended to enhance quality and identify improvements.
Data integrity is critical throughout the CGMP data life cycle, including in the creation, modification, processing, maintenance, archival, retrieval, transmission, and disposition of data after the record’s retention period ends. It would be helpful for data management.
Trends changed from Non compliance to RR --> Gap to RR --> Data Integrity --> DIB --> Smart Audit & Smart Data.
RR = Regulatory Requirements
DIB = Data Integrity Breach
Take a serious Note for Data Integrity whether you are small or big organization. Your Data is the Heart of your business. Regulatory bodies are highly conscious about such issues. For beginners in this path, my small note can help you a lot.
Data Integrity app Link: https://play.google.com/store/apps/details?id=com.innovativeapps.dataintegrity&hl=en
One Step Ahead in Pharma Compliance
Across the internet, there are millions of resources are available which provide information about Computer System Validation.
Refer above Data Integrity app which helps you to understand current regulatory agencies thinking on Data Integrity.
This presentation is compiled by Drug Regulations, a nonprofit organization that provides online pharmaceutical resources. It discusses FDA guidance on data integrity and compliance with cGMP regulations. The guidance clarifies FDA's expectations around the creation and handling of data to ensure its reliability and accuracy according to cGMP standards.
The document discusses good documentation practices for manufacturing units that supply the food and pharmaceutical industries. It defines good documentation practice as standards for creating and maintaining documentation in the pharmaceutical industry. Maintaining good documentation is important for regulatory compliance, customer requirements, and to avoid issues like product safety concerns, litigation, and regulatory action. The document outlines requirements for documentation including being permanent, legible, accurate, prompt, clear, consistent, complete, direct, truthful, current, and traceable. It provides examples and guidelines for meeting each of these requirements.
This document provides information about PHP VARNA #5, including thanks to the hosts, the agenda for the event which includes a talk on PHP 7.x and lightning talks, and details for the next monthly meetup in April. Contact and location information is also listed to find and spread the word about PHP VARNA.
This document summarizes a school management software system called Vidyapith that is proposed by SRRK IT Limited. The key points are:
1) Vidyapith is an all-in-one school management software that can save time, reduce costs, and improve efficiency for functions like student admission, attendance, marks keeping, accounting, etc.
2) It offers modules for students, teachers, academics and accounts that automate tasks like ID card generation, report cards, attendance tracking, fee collection etc.
3) SRRK provides training, support, software updates and claims complaints will be addressed within 2.5 hours for customers adopting Vidyapith.
Jesus Otero was a Spanish sculptor born in 1908 in Santillana del Mar, Cantabria. As a child, he enjoyed watching and caring for animals, and as an adult sculpted them in stone. Some of his sculptures include a bison in Santillana del Mar, a bear near San Glorio pass, a roe deer in Palombera pass, and a salmon in Hermida gorge. He established a museum in Santillana a few months before his death in 1994, which features some of his sculptures of animals like mutton and horses.
The document discusses the memory hierarchy in computers. It explains that memory is organized in a hierarchy with different levels providing varying degrees of speed and capacity. The levels from fastest to slowest are: registers, cache, main memory, and auxiliary memory such as magnetic disks and tapes. Cache memory sits between the CPU and main memory to bridge the speed gap. It exploits locality of reference to improve memory access speed. The document provides details on the working of each memory level and how they interact with each other.
In Pharma and Biotech, Weightage of the Documentation is around 70 % because as per FDA "If you do not have Document, You dint have do it."
So Good Documentation Practice is of tremendous importance for the Industry to comply any regulation like FDA, GMP or ISO.
This document summarizes a school management software system called Vidyapith that is proposed by SRRK IT Limited. The key points are:
1. Vidyapith is an all-in-one school management software that can save time, reduce costs, and improve efficiency for functions like student admission, attendance, marks keeping, accounting, etc.
2. It offers modules for students, teachers, academics and accounts that automate manual school processes and make them paperless and easily accessible.
3. SRRK IT Limited provides training, support, updates and hosting for Vidyapith to ensure clients remain satisfied, and aims to solve any complaints within 2.5 hours.
Data integrity refers to the correctness and completeness of data in a database. It is preserved through constraints that restrict what values can be inserted or updated. The main types of constraints are required data, validity checking, entity integrity, and referential integrity constraints. Deferred constraint checking allows constraints to be checked at transaction commit rather than for each statement, which is useful when multiple updates are needed to maintain consistency.
The document summarizes guidelines from the International Council for Harmonisation of Technical Requirements for Pharmaceuticals for Human Use (ICH) related to pharmaceutical quality. It discusses ICH Q8 on pharmaceutical development, Q9 on quality risk management, and Q10 on pharmaceutical quality systems. Q10 describes a comprehensive approach to an effective quality system across a product's lifecycle. It is based on ISO concepts and complements Q8 and Q9. The document reviews the objectives, scope, key elements, and continual improvement aspects of an integrated pharmaceutical quality system as defined in ICH Q10.
The document provides feedback from various stages of creating a music video. For the initial pitch presentation, classmates suggested doing more research on target audiences and including narration. Feedback on the rough cut was positive about the locations, shots, narrative, and lip syncing, but noted faster edits could be used for the second artist. Feedback on further stages advised improvements like making the artist lighter in an advert or adding effects to digipak pictures, while praising effective elements like shots, editing, and lighting in the final video.
O logotipo do cirurgião Flávio Arruda representa as iniciais de seu nome de forma concreta, simbólica e imaginária, representando "F", "A" e um sorriso. O manual fornece diretrizes sobre o uso correto das cores, fontes, dimensões e aplicações do logotipo para manter a identidade visual.
Este documento resume los servicios y programas de Nova Telecom S.p.A para el año 2014. Detalla los servicios actuales de ingeniería y soporte que ofrece, así como nuevos servicios planeados como un centro de comando y reducción de costos de terceros. También describe los programas de recursos humanos, calidad y continuidad operacional para el año, incluyendo la certificación ISO 9001, capacitación en ITIL y mejoras en el clima laboral.
Pharmaceutical Quality - The Office ofAjaz Hussain
The keynote address at the Fall meeting of the CPPR Industrial Advisory Board and the Site Directors held yesterday (27 October 2014) at Purdue University. The talk provides a perspective on the recent organizational changes announced by FDA CDER - the Office of Pharmaceutical Quality.
Data Integrity in a GxP-regulated Environment - Pauwels Consulting AcademyPauwels Consulting
On Tuesday, December 6, 2016, our colleague Angelo Rossi, Senior Regulatory Compliance Consultant, gave an interesting presentation about “Data Integrity in a GxP-regulated Environment” at the Brussels Office of Pauwels Consulting in Diegem.
In his presentation, Angelo covered definitions and concepts of data integrity, the change in regulatory focus, lessons learned from recent FDA warning letters, importants highlights of regulations and guidelines. Angelo also presented a practical example of data integrity for a computerized system.
Please contact us at contact@pauwelsconsulting.com or +32 9 324 70 80 if you have any further questions regarding our consulting services in this area.
Quick Overview: Pharmaceutical Data IntegrityPeter Dellva
Brief overview of the most important aspects of pharmaceutical data integrity. Slideshare includes pharmaceutical and biopharmaceutical industry key resources.
Data Integrity; Ensuring GMP Six Systems Compliance Pharma TrainingMarcep Inc.
This document outlines a two-day workshop on data integrity compliance to meet regulatory standards. The workshop will cover data integrity requirements from the FDA, EU, and other regulators; challenges in ensuring data integrity across paper, hybrid, and electronic systems; best practices for data generation, recording, transformation and reporting; and conducting risk assessments and monitoring metrics to ensure ongoing data integrity. Attendees will learn how to strengthen internal audits and compliance across their quality systems, facilities, materials management, and other areas to meet data integrity expectations.
Data integrity is becoming increasingly important under cGMP regulations. Without reliable data, the quality of raw materials, in-process materials, and finished products cannot be ensured. Data integrity issues constitute both 21 CFR Part 11 and serious cGMP violations. If laboratory data integrity is compromised, products may not comply with regulatory terms or be released for sale. Regulatory agencies like the FDA have increased their focus on data integrity and reliability in recent years. Inspectors examine data based on multiple standards including cGMP, GLP, GCP, and data application integrity policies using a "guilty until proven innocent" approach.
Data integrity issues are regularly cited by global regulatory agencies in inspection reports. National cultures can influence compliance with data integrity standards due to differences in power distance, individualism, and time orientation. Regulators now specifically target data integrity during inspections and audit raw data to verify submitted information. Companies must consider cultural factors when ensuring global compliance and promote quality cultures through codes of conduct.
This document summarizes a presentation on FDA 483 trending topics and solutions. It discusses FDA definitions and common observations related to data integrity, documentation practices, visual inspection execution and documentation of defects, and container/closure integrity. For each topic, it provides examples of recent FDA inspection citations in biologics, devices, and drugs. It also discusses factors that may influence FDA inspections such as regulatory maturity, communication, investigator expertise, and company culture.
Clinical Data Management (CDM) is a critical phase in clinical research that leads to generating high-quality, reliable data from clinical trials. CDM involves collecting, integrating, and ensuring the availability of appropriate quality and cost data. It encompasses entering, verifying, validating, and quality controlling the data gathered during clinical trials. The goal of CDM is to ensure the data supports conclusions drawn from the research.
This document discusses data governance and integrity in the pharmaceutical industry. It begins with an overview of data integrity, including definitions of data and how to assure data integrity across the data lifecycle. It then discusses data governance, including how to develop a data governance plan. The plan should consider prevention of data integrity issues and detection of any issues. It should include relevant policies, training, and controls. Finally, it provides tips for implementing a data governance plan in phases with oversight and metrics to track progress.
This document provides an overview of clinical data management processes. It discusses the goals of clinical data management which are to provide high-quality, accurate data through processes like case report form design, data entry, validation, and coding. It describes some commonly used clinical data management software and standards/guidelines like 21 CFR Part 11 and SCDM's Good Clinical Data Management Practices. The document is a project report submitted by students to fulfill requirements for a degree at Apollo Hospitals, New Delhi.
Introduction to Data Quality
Data consistency and usefulness should be considered when determining data quality. An essential step in the management of data quality is its evaluation. Data quality metrics are determined by data traits and successful business outcomes from insights. If there is a discrepancy in the data stream, we ought to be able to recognize the specific faulty data. The next step is to locate data inaccuracies that need to be fixed and determine whether the data systems is suitable for the intended use. Many initiatives could be doomed by issues with data quality, which could result in extra costs, lost sales opportunities, or fines for inappropriate financial or regulatory compliance reporting in any industry.
Today's organizations rely on data for all their decisions and view it as a crucial corporate asset. Data quality is becoming more important in company data strategy as business analysts and data scientists seek to find reliable data to power the solutions.
As per EU MDR, Post Marketing Clinical Follow-up (PMCF) is a continuous process where device manufacturers need to proactively collect and evaluate clinical data of the device when it is used as per the intended purpose. EU MDR gives more emphasize on PMCF data to confirm the safety and performance of the device throughout its expected lifetime, ensure continued acceptability of identified risks and detect emerging risks based on factual evidence.
This document discusses data integrity expectations from regulatory agencies like the TGA. It defines data integrity as ensuring data is complete, consistent and accurate throughout its lifecycle. Recent global cases of data integrity issues at drug manufacturers are presented, involving falsification of records, deletion of lab data, and lack of controls around electronic data changes. The ALCOA principles for attributable, legible, contemporaneous, original and accurate data are described. TGA expectations include manufacturers understanding their vulnerabilities, assessing data integrity risks, designing systems to prevent issues, training staff, and having ongoing review systems. Conclusions state existing quality systems should ensure data integrity and traceability, and manufacturers are responsible for preventing and detecting integrity issues.
The Food and Drug Administration (FDA) released Clinical Trial Imaging Endpoint Process Standards guidance for clinical trials industry. Why? To standardize. To automate. To move closer to zero-delay clinical trials. Read AG Mednet's perspective on this FDA guidance.
21CFR regulations & its applicability in the industry and FDA perspective on the same and FDA check points on 21CFR regulations during their inspection.
Machine Learning for Predictive Data Analysis in Clinical ResearchClinosolIndia
Machine learning (ML) techniques have the potential to revolutionize predictive data analysis in clinical research by enabling researchers to uncover insights, make informed decisions, and develop more personalized treatment approaches. Here's how machine learning can be applied to predictive data analysis in clinical research
Data Cleaning and Validation: Best Practices for Data IntegrityClinosolIndia
Data cleaning and validation are critical processes to ensure the integrity, accuracy, and reliability of clinical data. These best practices can help maintain data quality and enhance the validity of research outcomes:
Define Data Cleaning and Validation Procedures Early: Establish clear data cleaning and validation procedures as part of the study protocol or data management plan. Define data validation rules, data range checks, and data cleaning criteria upfront to ensure consistency and adherence to predefined standards.
Use Electronic Data Capture (EDC) Systems: Implement EDC systems that offer built-in data validation checks, range validations, and skip patterns. EDC systems can prevent certain types of errors during data entry and facilitate real-time validation as data is collected.
Develop Data Validation Checks: Create automated validation checks to identify discrepancies, outliers, missing data, and inconsistencies. These checks can include cross-field validations, data range validations, and logical validations based on predefined rules.
Standardize Data Entry: Enforce standardized data entry formats and units to minimize variability and errors. Provide clear instructions to data entry personnel to ensure consistent and accurate data collection.
Implement Double Data Entry and Review: For critical data points, consider implementing a double data entry process where data is entered by two independent personnel. Any discrepancies between the two entries are flagged for resolution. A third reviewer can adjudicate discrepancies if necessary.
Study start up activities in clinical data managementsoumyapottola
Study start-up (SSU) is so much more than a one-time document management exercise. It’s a global, strategic operation that can get new drugs approved faster – and it’s ripe for innovation – from Site Selection to Site Activation and Site Training.
Many SSU tech solutions deployed by sponsors don’t deliver the results promised because they add burden without benefits to clinical research sites. The result? Site staff simply avoid using them.
When that happens, document exchange and tracking falls back to paper, email and Excel formats – with CRAs holding the processes together. The tools that were supposed to solve a problem become part of the problem – and consume preThe implementation and conduct of a study can be a complex process that involves a
team from various disciplines and multiple steps that are dependent on one another. This
document offers guidance for navigating the study start-up processcious clinical trial budget.
A successful clinical study start-up is a crucial first step and an important factor for the overall success of the trial. For this reason, SCRO has experienced study start-up teams, offering customized services depending on your needs, whether it be fuWhile the definition varies across companies, study startup typically includes the process of identifying and qualifying sites, collecting essential documents at the study and site level, and submitting these documents for ethics approval. Successful study startup requires coordination between sites, sponsors, and contract research organizations (CROs) to achieve critical milestones in a compliant manner.ll-service or single activities.
How to achieve better time management in EDC start up
Clinical data management requires strict time management processes, especially in study start up within an electronic data capture (EDC) system. Three steps that clinical data management teams can take to outline the planning and executing of each task that needs to be considered are as follows:
Make a List: Create a daily or weekly task list and schedule when each task will be completed. This strategy will assist you in maintaining focus and staying organized.
Set realist goals: Be realistic about what you can finish in the amount of time you have. When setting unrealistic goals, failure is almost certain to follow.
Explore time-saving techniques: Examples of techniques that could help save time include grouping similar tasks together or using a timer to stay focused.
To help get started, here is a list of EDC considerations for Study Start-Up deadlines:
Protocol finalization and study enrollment
Split go-live considerations
eCRF Specification meetings (this will ensure proper collaboration and minimize any back-and-forth communication)
EDC add-on modules (which will be required and need validation?)
ePRO/eCOA used with licensed questionnaires.
IRB requirements for add-on modules (eConsent/ePRO)
Enhancing Data Quality in Clinical Trials: Best Practices and Quality Control...ClinosolIndia
Ensuring data quality is crucial in clinical trials to generate reliable and valid results. High-quality data allows for accurate analysis, interpretation, and decision-making regarding the safety and efficacy of investigational products. Here are some best practices and quality control measures to enhance data quality in clinical trials:
Standardized Data Collection: Implement standardized data collection procedures, including the use of case report forms (CRFs) or electronic data capture (EDC) systems. Clearly define data elements, variables, and measurement scales to minimize inconsistencies and errors in data entry.
Training and Education: Provide comprehensive training to investigators, site staff, and data entry personnel on the protocol, data collection procedures, and Good Clinical Practice (GCP) guidelines. Training ensures understanding and adherence to the study requirements, leading to accurate and consistent data collection.
Source Data Verification (SDV): Perform source data verification to compare data recorded in the CRFs or EDC systems with the original source documents (e.g., medical records, laboratory reports). This process helps identify discrepancies, errors, or missing data, ensuring data accuracy and integrity.
Data Management Plan: Develop a robust data management plan that outlines procedures for data collection, handling, storage, and analysis. The plan should include data validation checks, query resolution processes, and data reconciliation between different data sources.
Electronic Data Capture (EDC) Systems: Utilize EDC systems to facilitate real-time data capture, improve data accuracy, and streamline data management processes. EDC systems often have built-in data validation checks, range checks, and skip patterns to minimize data entry errors.
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These lecture slides, by Dr Sidra Arshad, offer a simplified look into the mechanisms involved in the regulation of respiration:
Learning objectives:
1. Describe the organisation of respiratory center
2. Describe the nervous control of inspiration and respiratory rhythm
3. Describe the functions of the dorsal and respiratory groups of neurons
4. Describe the influences of the Pneumotaxic and Apneustic centers
5. Explain the role of Hering-Breur inflation reflex in regulation of inspiration
6. Explain the role of central chemoreceptors in regulation of respiration
7. Explain the role of peripheral chemoreceptors in regulation of respiration
8. Explain the regulation of respiration during exercise
9. Integrate the respiratory regulatory mechanisms
10. Describe the Cheyne-Stokes breathing
Study Resources:
1. Chapter 42, Guyton and Hall Textbook of Medical Physiology, 14th edition
2. Chapter 36, Ganong’s Review of Medical Physiology, 26th edition
3. Chapter 13, Human Physiology by Lauralee Sherwood, 9th edition
- Video recording of this lecture in English language: https://youtu.be/Pt1nA32sdHQ
- Video recording of this lecture in Arabic language: https://youtu.be/uFdc9F0rlP0
- Link to download the book free: https://nephrotube.blogspot.com/p/nephrotube-nephrology-books.html
- Link to NephroTube website: www.NephroTube.com
- Link to NephroTube social media accounts: https://nephrotube.blogspot.com/p/join-nephrotube-on-social-media.html
Nano-gold for Cancer Therapy chemistry investigatory projectSIVAVINAYAKPK
chemistry investigatory project
The development of nanogold-based cancer therapy could revolutionize oncology by providing a more targeted, less invasive treatment option. This project contributes to the growing body of research aimed at harnessing nanotechnology for medical applications, paving the way for future clinical trials and potential commercial applications.
Cancer remains one of the leading causes of death worldwide, prompting the need for innovative treatment methods. Nanotechnology offers promising new approaches, including the use of gold nanoparticles (nanogold) for targeted cancer therapy. Nanogold particles possess unique physical and chemical properties that make them suitable for drug delivery, imaging, and photothermal therapy.
The biomechanics of running involves the study of the mechanical principles underlying running movements. It includes the analysis of the running gait cycle, which consists of the stance phase (foot contact to push-off) and the swing phase (foot lift-off to next contact). Key aspects include kinematics (joint angles and movements, stride length and frequency) and kinetics (forces involved in running, including ground reaction and muscle forces). Understanding these factors helps in improving running performance, optimizing technique, and preventing injuries.
STUDIES IN SUPPORT OF SPECIAL POPULATIONS: GERIATRICS E7shruti jagirdar
Unit 4: MRA 103T Regulatory affairs
This guideline is directed principally toward new Molecular Entities that are
likely to have significant use in the elderly, either because the disease intended
to be treated is characteristically a disease of aging ( e.g., Alzheimer's disease) or
because the population to be treated is known to include substantial numbers of
geriatric patients (e.g., hypertension).
Know the difference between Endodontics and Orthodontics.Gokuldas Hospital
Your smile is beautiful.
Let’s be honest. Maintaining that beautiful smile is not an easy task. It is more than brushing and flossing. Sometimes, you might encounter dental issues that need special dental care. These issues can range anywhere from misalignment of the jaw to pain in the root of teeth.
PGx Analysis in VarSeq: A User’s PerspectiveGolden Helix
Since our release of the PGx capabilities in VarSeq, we’ve had a few months to gather some insights from various use cases. Some users approach PGx workflows by means of array genotyping or what seems to be a growing trend of adding the star allele calling to the existing NGS pipeline for whole genome data. Luckily, both approaches are supported with the VarSeq software platform. The genotyping method being used will also dictate what the scope of the tertiary analysis will be. For example, are your PGx reports a standalone pipeline or would your lab’s goal be to handle a dual-purpose workflow and report on PGx + Diagnostic findings.
The purpose of this webcast is to:
Discuss and demonstrate the approaches with array and NGS genotyping methods for star allele calling to prep for downstream analysis.
Following genotyping, explore alternative tertiary workflow concepts in VarSeq to handle PGx reporting.
Moreover, we will include insights users will need to consider when validating their PGx workflow for all possible star alleles and options you have for automating your PGx analysis for large number of samples. Please join us for a session dedicated to the application of star allele genotyping and subsequent PGx workflows in our VarSeq software.
How to Control Your Asthma Tips by gokuldas hospital.Gokuldas Hospital
Respiratory issues like asthma are the most sensitive issue that is affecting millions worldwide. It hampers the daily activities leaving the body tired and breathless.
The key to a good grip on asthma is proper knowledge and management strategies. Understanding the patient-specific symptoms and carving out an effective treatment likewise is the best way to keep asthma under control.
Computer in pharmaceutical research and development-Mpharm(Pharmaceutics)MuskanShingari
Statistics- Statistics is the science of collecting, organizing, presenting, analyzing and interpreting numerical data to assist in making more effective decisions.
A statistics is a measure which is used to estimate the population parameter
Parameters-It is used to describe the properties of an entire population.
Examples-Measures of central tendency Dispersion, Variance, Standard Deviation (SD), Absolute Error, Mean Absolute Error (MAE), Eigen Value
Are you looking for a long-lasting solution to your missing tooth?
Dental implants are the most common type of method for replacing the missing tooth. Unlike dentures or bridges, implants are surgically placed in the jawbone. In layman’s terms, a dental implant is similar to the natural root of the tooth. It offers a stable foundation for the artificial tooth giving it the look, feel, and function similar to the natural tooth.
3. Data: Facts and statistics collected together for reference or
analysis.
Integrity: The qualityof being honest and having strong moral
principles.
Data Integrityin the context of Pharmaceutical Industry within a
GMP environment: Generating, transforming, maintaining and
assuringthe completeness and consistency of data over it’s entire
life cycle in compliance with applicable regulations.
4. Importance of data integrity
Undermines the safety and efficacy and assurance of quality of
the drugs that consumers will take.
5. Importance of data integrity
Any data integrity issues highlighted usually break trust.
6. Importance of data integrity
The regulators trust the companies to do the right thingwhen no
one is seeing.
7. What can be considered as data
integrity concern
Not recording activitiesonline
Backdating
Fabricatingdata
Copyingexistingdata as new
Re-processing without justification
Discardingdata
Releasinga failing product/material
Passing a failingproduct/material
Not saving dataeither electronically or in hardcopy format
8. Criteria for integrity of data
FDA warningletters and announcements of MHRAhave
highlightedthe increasing focus on data integrityin pharma
Industry.
Data integrity is a subject that many pharma industries currently
have significant concerns about.
To add to those concerns, even the term“dataintegrity” can have
widely differing meanings or interpretation, and there are
currently few definitive reference sources availableon the subject.
9. Criteria for integrity of data
The acronymALCOAhas been widely associatedwithdata
integrity by FDA and was first used by Stan Woollen when he
worked for the agency to help himremembercompliance terms
relevant to data quality.
10. Criteria for integrity of data
The goodautomatedmanufacturingpractice (GAMP) guide “A
Risk-BasedApproach to GxP Complaint Laboratory
Computerized Systems” includes an appendix 3 on data integrity.
The terms used in the appendix are sometimes referredto as
“ALCOA+” because they incorporate additional terms basedon
the European Medicines Agency’s concept paper on electronic
data in clinical trials. The terms associated withALCOA+ are
described as:
Attributable, Legible, Contemporaneous, Original, Accurate, com
plete, consistent, enduring, and available.
11. Criteria for integrity of data
ALCOA+
Who performed an action and when?
If a record is changed, whodid it any why?
What is the link to the source data
12. Criteria for integrity of data
ALCOA+
Data must be preserved /recorded permanently in a durable
mediumand shouldbe readable
13. Criteria for integrity of data
ALCOA+
The data should be recorded at the time when the work is
performed and data/time stamps shouldfollowin order
14. Criteria for integrity of data
ALCOA+
The information provided in the data shall be either original
record or certified true copy
15. Criteria for integrity of data
ALCOA+
A: Accurate
No errors or editing shall be performed without approved
documented amendments
16. Criteria for integrity of data
ALCOA+
Complete: All data includingreprocessing performedon the
product shall be available
Consistent: Consistent application of data time stamps shall be
available in expected sequence
Enduring: All data shall be recorded on batch documents directly
Available: All data shall be available / accessible for review/ audit
for the life time of therecord.
17. Common data integrity issues
Common Passwords
Where peopleshare passwords, it is not possible to identify who
creates or changes records.
18. Common data integrity issues
User privileges
The systemconfiguration for the software does not adequately define
or segregate user levels and users have accessto inappropriate
software privileges such as modification of data
19. Common data integrity issues
Computer systemcontrol
Laboratories have failed to implement adequate controls over
data, and unauthorized access to modify, delete, or not save
electronic files is not prevented; the file, therefore, may not be
original, accurate, or complete
20. Common data integrity issues
Processing Methods
Integration parameters are not controlled and there is no
procedure to define integration. Regulators are concerned over re-
integration of chromatograms
21. Common data integrity issues
Incomplete data
The recordis not complete in this case. The definition of complete
data is open to interpretation
22. Common data integrity issues
Audit trails
The Laboratories turn off the audit-trail functionalitywithin the
system. It is, therefore, not clear who has modifieda fileor why
23. GMP regulatory requirements for data
integrity
Derived fromthe data integrity definition and the applicable 21
CFR 211 GMP regulations there are some of the following points:
Equipment must be qualifiedand fit for purpose [§211.160(b),
§211.63]
26. GMP regulatory requirements for data
integrity
Data generatedduring analysis must be backed up [§211.68(b)]
27. GMP regulatory requirements for data
integrity
Reagents and reference solutions are prepared correctly with
appropriaterecords [§211.194(c)]
28. GMP regulatory requirements for data
integrity
Methods used must be documented and approved [§211.160(a)]
Methods must be verified under actual conditions of use
[§211.194(a)(2)]
29. GMP regulatory requirements for data
integrity
Data generatedand transformed must meet the criterion of
scientific soundness [§211.160(a)]
30. GMP regulatory requirements for data
integrity
Test data must be accurate, complete and procedures should
be followed [§211.194(a)]
31. GMP regulatory requirements for data
integrity
Data and the reportable value must be checkedby a second
individual to ensure accuracy, completeness and conformance
with procedures [§211.194(a)(8)]