Data Integrity challenges in the Lab

Transcription

1 Data Integrity challenges in the Lab Peter Boogaard CEO Industrial Lab Automation 20 April 2016

2 What makes the lab process special? Cost center perception Scientific oriented Lack of standardization Complex technologies Paper based Conservative approach

3 Data Integrity OPTION 1 Loss of intellectual property Potential business impact OPTION 2 Business shutdown Not in accordance with regulations

4 Data integrity refers to maintaining and assuring the accuracy and consistency of data over its entire life-cycle

5 ICHQ10 Pharmaceutical Lifecycle Quality System Research Pharmaceutical Development Technology Transfer Commercial Manufacturing Discontinuation Prior Knowledge GxP Management Responsibilities Process Performance & product Quality Monitoring System Preventive Action / Corrective Action (PACA/CAPA) system Change Management System Management Review 4 PQS elements Enablers Knowledge Management Quality Risk Management

6 Data Integrity is cited in 50% more warning letters in 2013! Falsified batch records Testing into compliance Discarding raw data Source FDA 2013 data

7 US FDA Observations Summary (2013) CFR Part 211 Observations CURRENT GOOD MANUFACTURING PRACTICE FOR FINISHED PHARMACEUTICALS Production & Process Controls (Subpart f) Organisational & Personal (Subpart B) Records & Reports (Subpart J) Equipment (Subpart D) Laboratory Controls (Subpart I) Inadequate investigations of Batch Failures Inadequate raw material testing Deficiencies associated with Lab controls

8 Laboratory data integrity observations Alteration of raw, original data and records Multiple analyses of assay with the same sample without adequate justification Manipulation of a poorly defined analytical procedure and associated data analysis in order to obtain passing results Backdating stability test results to meet the required commitments Creating acceptable test results without performing the test Using test results from previous batches to substitute testing for another batch Source: FDA

9 Data Integrity challenges - Devil is in the Details Laboratory processes may seem similar, but lot of details requires in-depth research to enable consistent and complete findings People Master DATA Common data dictionary Unified specification processes Reporting processes Common workflow processes United naming conventions Validation processes IT integration

10 Single point of truth for (meta) data Fragmented Applications Landscape Source Gartner Inc. /AMR

11 Single point of truth for (meta) data Data integrity Challenges Source Gartner Inc. /AMR

12 Scientific Data Sources

13 DATA INTEGRITY

14 Data Integrity nightmare Spreadsheet COPY/PASTE Madness Project Receive Request Lab LIMS EXCEL Receive UNILAB Lab INSTRUMENT Receive Lab FILE Cut & Paste Cut & Paste Cut & Paste Other tests Receive Lab Excel input ELN WEB Receive Lab EXPORT Other tests Excel input Receive WORDLab Cut & Paste Statistical Receive Lab Analysis Cut & Paste Top Line Receive Lab Creation Receive PDF Lab Receive Customer Lab

15 Simplify And Automate Workflows Learn from other industries Data Integrity Challenges in the Labs- PLA 2016

16 New Alternatives to decrease DI Courtesy of Waters Corp.

17 Self-documenting processes Reducing DI at the source

18 Adopt and use data industry standards & processes Yes, we have enough power for your equipment

19 GMP Regulatory Requirements for Data Integrity Instruments must be qualified and fit for purpose [ (b), ] Software must be validated [ ] Any calculations used must be verified [ (b)] Data generated in an analysis must be backed up [ (b)] Reagents and reference solutions are prepared correctly with appropriate records [ (c)] Methods used must be documented and approved [ (a)] Methods must be verified under actual conditions of use [ (a)(2)] Data generated and transformed must meet the criterion of scientific soundness [ (a)] Test data must be accurate and complete and follow procedures [ (a)] Data and the reportable value must be checked by a second individual to ensure accuracy, completeness and conformance with procedures [ (a)(8)] Derived from the laboratory data integrity definition and the applicable 21 CFR 211 GMP regulations FDA s Focus on lab Data integrity Bob McDowall Part 1

20 Methods to increase integrity in the laboratory Conclusion Implement Risk based processes Define a single point of truth for (meta) data Reduce, automate & simplify workflow complexities Stop spreadsheet madness Implement self-documenting processes at the source Utilize best practice analysis protocols Adopt and use data industry standards & processes Avoid custom software extensions

21 Data Integrity Challenges in the Labs- PLA 2016

22 Data Integrity challenges in the Lab Peter Boogaard CEO Industrial Lab Automation 20 April

23 Laboratory Life Cycle model Meta data capture Analytical data Process data Predicted information Statistical data Laboratory knowledge Regulatory External data consumers