What to do next now we are working paperless

What to do next now we are working paperless Contents  Personal Info  Introduction  Lab Data and Systems Overview  Next Steps  Data Retention ...
Author: Conrad Cummings
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What to do next now we are working paperless

Contents  Personal Info  Introduction  Lab Data and Systems Overview  Next Steps  Data Retention  Business Intelligence  Efficiency Improvement and added value for Customers

Personal Info 

Corné Nous, Netherlands



Bachelor in Biochemistry (HLO)



28 Years at MSD/N.V. Organon, Oss, Netherlands



Started as Pharmaceutical Lab Assistant in 1987



8 Years experience in Pre-Clinical Analysis, Instrument Support and Chromatographic Method Development



25 Years in Lab Automation



Deployment Engineer, Technical&Functional Application Management, Project Management, Account Management, Architecture R&D Platforms



Contributor to Allotrope



Now starting own consultancy on Lab Automation Support



[email protected]

Introduction  3-4 decades of Lab Automation  Lab automation evolved from data processing/calculation support to integrated systems to support product release, NDA filings and other reporting  All data is now available electronically and often electronic only  Focus on Pharma Product Development

Systems  CDS = Chromatographic Data System

 SDMS/ECM = Scientific Data Management System, Enterprise Content Manager  EDMS = Electronic Document Management System  ELN = Electronic Lab Notebook

 LIMS = Laboratory Information Management System  LIS = Logistic Information System  SCADA = Supervisory Control And Data Acquisition

 MES = Manufacturing Execution System  Inventory Systems

Lab Data Storage & Processes Statistic Tools

EDMS

Query Tools

LIS

LIMS

Web Tools

MES

ELN CDS

SCADA

SDMS ECM

The Lab Data Life Cycle

Prepare for Analysis

Set-Up Instrument

Acquire & Process

Review & Approve

Report

Archive

Data Retention  GxP, Legal Hold and other Regulations demand data storage for many years depending on the goal for which the data was generated  Pharma companies apply 20-30 years or longer retention periods  The originating systems have a lifecycle of 5-10 years  Convert data to open standards to keep raw data files readable (AnIML, MzML, JCAMP-DX…)  Data viewers available in SDMS/ECM systems, Open Source software or commercial software  Viewers for Data in Standardized format and for Proprietary data formats  Virtualization of instrument workstations/clients ensures full functionality (reprocess)  Pharma take overs

Virtualization Process  VMWare provides tools to image the workstation and make it available as a virtual machine  This creates an OS independent solution to open the data in the proprietary format  Ensure transfer of license keys to VM image (Microoft and proprietary software)  Store image on media that is readable for many years

 Other vendors? (e.g. Oracle Virtual Box)

Decission & Knowledge support  Search on Structure, Substructure and Spectra to identify new impurities and degradation products  Use chemistry and physics data to predict product stability  Ensure Data is consistent:  Meta Data definitions  Data Stewards guard consequent use of meta data

Restrictions and in-efficiencies in the current environments  Data are spread over several systems

 Search or analytics are performed in a step-by-step approach  Querying is slow  Data is structured (Databases)/ non-structured (ELN/EDMS) Different Query tools apply  Large amounts of data are stored on expensive SAN solutions  Data structures end Meta data need to be re-defined if new applications of the data are required

Big Data approach  Big Data approach can help in improving efficiency in data searches and analytics  Combine company data (structured and non-structured) in one Data Lake and add external data (web)  Search and perform analytics on the combined data

Big Data application on Lab Data Interact, Visualize

Analytics, Statistics, Data Mining

Data Lake

Structured & Non-Structured Data Lab Data Archive

Chemistry Databases

Spectral Libraries

External Scientific Sources

Structure Eludation

Structure Eludation Search Tools

Chemistry Databases

Spectral Libraries

Use data for process optimization  Optimize Resource Planning    

Spread resource utilization Retire surplus of instruments Plan Calibrations and Maintenance Group samples

 Determine run time, costs and earnings per analysis  Provide added value to customers  Determine Trends  Warn for overdue sample delivery dates

Summary  In the 3-4 decades of Lab Automation data use has been transferred from data processing support to fully supported Lab processes.  Data Archival and Retrieval have become mandatory requirements in respect to Regulations  Special attention is required to keep raw-data files viewable  Using Big Data technologies in combining data with other repositories, libraries and external content provide new capabilities and improved R&D productivity  Data can be used to improve Lab processes