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Process Analytical Technology Solution Presentation

Process Analytical Technology Solution Presentation. for Actionable Information. Center for Business Intelligence and Analytics (C-BIA). for Actionable Information. Mission.

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Process Analytical Technology Solution Presentation

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  1. Process Analytical Technology Solution Presentation for Actionable Information

  2. Center for Business Intelligence and Analytics (C-BIA) for Actionable Information

  3. Mission “Create business value for clients by enabling superior performance through unleashing hidden wealth in operational and external data sources combined with innovative Analytics.”

  4. History Cyrus Mehta, Ph.D. Founder and President Cytel Inc. Nitin Patel, Ph.D. Founder and Chairman Cytel Inc. www.cytel.com C-BIA, a division of TechKnit was founded in 2004

  5. FoundersCyrus Mehta, Ph.D. - Founder and President , Cytel Inc. • An influential thought leader in the area of biostatistics • Dr. Mehta has concentrated his research activities on developing software and innovative methods for flexible clinical trial designs and non-parametric exact statistics. • Dr. Mehta has published over 65 papers in journals like JASA, Biometrika and Biometrics. • He and his co-authors, Dr. Nitin Patel and Dr. Karim Hirji received the 1987 George W. Snedecor Award from the American Statistical Association. • In 1995 Dr. Mehta was elected a Fellow of the American Statistical Association. • In 2000, Dr. Mehta was named the Mosteller Statistician of the Year by the Massachusetts Chapter of the American Statistical Association. • In addition to his activities as President of Cytel Inc, Dr. Mehta has been a member of the faculty in the Department of Biostatistics, Harvard School of Public Health since 1979.

  6. FoundersNitin Patel, Ph.D. - Founder, Chairman and Chief Technology Officer • Dr. Patel is a leading expert on the development of fast and accurate computer algorithms to implement computationally intensive statistical methods. • He has published over sixty-five refereed papers in the areas of statistics, operations research and computing. • He and his co-authors, Dr. Cyrus Mehta and Dr. Karim Hirji, received the 1987 George W. Snedecor Award from the American Statistical Association. In 2003, • Dr. Patel was elected a Fellow of the American Statistical Association. • Dr. Patel has been a member of the faculty at MIT's Sloan School and the Operations Research Center since 1995. • Previously, he was a Professor at the Indian Institute of Management, Ahmedabad, and held visiting positions at Harvard, the University of Michigan, the University of Montreal and the University of Pittsburgh.

  7. SAS www.sas.com • SAS is the worlds largest Business Intelligence and analytics software co. • Based out of Cary , NC, USA – SAS has world wide presence across continents . • In India SAS has a marketing office located in Mumbai. • SAS has a R/D center in Pune with a strength of about 250. • SAS tools provide End to End solution across Enterprise

  8. SAS Technology Layer and Products The main technology platform provides the following components – • Data Quality • Data Integration • Data storage • OLAP Server • Friendly Interface

  9. Focus on Pharma Companies SAS has many years of experience in pharma reporting and analytics. • Clinical trial research reporting is done in SAS formats. • Base SAS is used by the lead pharma companies . • SAS STAT is a tool used by leading pharma companies. • SAS Graphs and STAT are industry acknowledged leaders in the area of statistical analysis. • SAS has developed a special focus on regulatory reporting and Pharmacovigilance reporting. • SAS compliments the stringent requirements of Pharma industries in terms of Production processes and testing and trials.

  10. Business Subjects Field Force Incentive Sales Force Effectiveness Forecasting Production Dashboard Sales Dashboard Inventory Dashboard Pharma Subjects Production Quality (PAT) Clinical Data Management (PheedIT) Pharma Company Vigilance CDISC - Clinical Data Inter-Change Standards Consortium SAS Pharma Focus

  11. Uniqueness and Expertise for Actionable Information

  12. People • Team of 50 people in Pune, consisting of: • Statisticians (Ph.D. and Masters in Statistics) • Statistical software developers (Masters in Statistics) • Microsoft • SAS • Data Analysts and Business Intelligence solution designers and developers (MBAs and Masters in Statistics) • Data Managers (MCAs) • Information Technology managers (Engineers and MCAs)

  13. Spirit of Research, Innovation and Experimentation • Cytel is built on research work of the Founders • Imbibed from the founders “mind-set” • Collection of people built it further • Vast repository of methodologies and software library • Witnessed in several products, key amongst them are:

  14. C-BIA Management • Mayank ShahChartered Accountant Mayank has over 27 years' experience as Consultant, Executive and Academician in the field of MIS and BI applications for business. Mayank is Consultant and Executive Director of TechKnit and leads C-BIA. • Ajay SathePGDM, IIM, Ahmedabad Ajay has over 17 years' experience in IT industry specializing in Software Development and Technology Management. Ajay is Director of TechKnit and CEO of Cytel India. • Shrikant Athavale, Industrial Engineer Shrikant has nearly 36 years' experience in Industrial Engineering and Quality Management. Shrikant is Executive Director of TechKnit and leads C-elt, an e-Learning unit. • Vanaja Vaidyanathan, MBA and Cost and Works Accountant Vanaja has over eight years of experience in Business Intelligence practice, including work experience with A F. Ferguson & Co., Asian Paints, GE Capital and Satyam Computer services and is in charge of delivery at C-BIA. • Dan Crowell, MSc. in Economics, London School of Economics Dan is our associate based in USA, looking after business development and client interaction. Dan worked with IFC, GBI Team during 2004 to 2006 to coordinate activities in the field in South Asia. He first worked in South Asia in 1999 when he was a Fulbright Scholar in India.

  15. Experts Panel • Nitin Patel, Ph. D. Dr. Patel is a leading expert on the development of fast and accurate computer algorithms to implement computationally intensive statistical methods. Dr. Patel is Founder and Co-Chairman of Cytel Software Corporation, Cambridge, MA, USA and Visiting Professor. MIT Sloan School of Management. • Suresh Ankolekar, Ph.D. Dr. Ankolekar has over 23 years of academic and consulting experience at Indian Institute of Management,Ahmedabad (IIMA) and Maastricht School of Management, Netherlands (MSM). Prof. Ankolekar has developed commercial software to solve large-scale optimization problems in transportation and has provided consulting in analytical software projects to Cytel Inc., and others. Prior to his doctoral study in management at IIMA, he worked as industrial engineer at Larsen and Toubro (Bombay). • Ashok Nag, Ph. D. Dr. Nag, a former senior executive of the Reserve Bank of India, the central bank and the monetary authority of the country, is a well-known expert in the banking and financial analytics, data warehousing and data mining. • Sunil Lakdawala, Ph. D. Dr. Lakdawala has over 20 years' of consulting experience in IT applications for business including Data Warehousing and Data Mining. Dr. Lakdawala is a consultant in BI applications and is visiting professor at S. P. Jain Institute of Management & Research. • V. Chandran, Aeronautical Engineering Chandran has over 22 years' experience in technology functions, including CTO positions in companies with sizeable software teams. Chandran is Vice President with Cytel India heading Technology Management function besides managing consulting assignments.

  16. C-BIA Partnerships • SAS: Silver Alliance partner… • Mastek: BI Solutions… • Cytel-Cognizant: Pharmaceuticals- clinical trials… • Statistics.com…XLMiner marketing in USA • Syscon Infotech: BI solutions… • Intech Systems – BI Solutions…

  17. Expertise • Data Warehousing • Manage large volume of data • Building data warehouse and ‘cubes’ • Online Analytical Processes (OLAP) • Studying data patterns by slicing, dicing and drilling • Making inference • Data Mining • Manage large volume of data • Sampling • Building valid models • Making predictions – scoring • Statistical Analysis • Manage large volume of data • Data distribution • Pareto • Outliers • Trends • Correlations • Clinical Trial Reporting and Analytics

  18. Technology and Infrastructure in Pune • 6000 sq. feet of office space in Pune • Secured Network with high bandwidth Connectivity • Windows and SAS Platforms with more than six servers • Methodologies and SOPs for, BI solutions, Analytics and Clinical Trials • Well established software development practices

  19. C-BIA Advantage • Focus and Expertise • BI and Analytics • Multiple levels of expertise in BI • Understand business management issues • Character • Entrepreneurial • Innovative • Quick to respond and deliver • Stickler for on-time-zero-defect delivery • Cost advantage

  20. ProfitLogic, US B harat Petroleum, India Mastek, India Dainik Jagran, India Trumac, India TAM Media Research, India Savita Chemicals, Ind ia KPIT Cummins Infosystems Ltd. , India Tata Motors Ltd., India Selected Customers

  21. Process Analytics Platform for Actionable Information

  22. Guidance for Industry PAT — A Framework for Innovative Pharmaceutical Development, Manufacturing, and Quality Assurance • Encourages the right approach … measurement, data integration, statistical modelling & process understanding based on data. • Companies able to demonstrate process understanding will be treated differently, e.g. be allowed to change processes without revalidation if have data and models to backup decisions. • Essentially companies able to show they are doing the right things will have relaxed regulation regarding CMC (Chemistry and Manufacturing Controls). • http://www.fda.gov/cder/guidance/6419fnl.pdf

  23. PAT Tools From FDA Guidance • Multivariate tools for design, data acquisition and analysis • Process Analysers (at-line, on-line, in-line measurement tools) • Process Control Tools • Continuous Improvement and Knowledge Management Tools

  24. 9: Verify 7: Simulate 8: Improve 10: Deploy and Control 6: Effectiveness Modelling 5: Efficiency Modelling 3: Cleanse 2: Integrate 4: Maintain 1: Data The SAS Pragmatic-PAT Solution:Data Integration, Modelling and Control for Operations

  25. SAS Pragmatic-PAT Solution Elements Model Builder Model Deployer Data Integrator

  26. Modelling Cycle: Drives Increased Process Understanding and Operational Improvement

  27. Clarify The Objectives Extract Analysis Ready Data Visual Modelling Statistical Modeling Assess The Findings Deploy SAS Pragmatic-PAT Model Builder Capabilities: Visual Modeling: Literally “see” and interact with the sources of variation to quickly understand the status quo. Statistical Modeling: Easily use a wide repertoire of proven statistical technology to target: • Efficiency Models - Predict failures enabling corrective action and control prior to an adverse event (thereby reducing your rejects and rework). • Effectiveness Models - Understand the root causes and drivers of problems (thereby enabling process and systemic improvement).. Various Users, with different skills and capabilities New Information Enabling Technology (respects wide range of Users and Data Types) . . . Delivered in a way that respects wide range of user skills and capabilities.

  28. Mapping of data analysis technology to process capability and dependence on extent and relevance of measured inputs:

  29. Case Study 1: Mature Manufacturing with Few Measured Inputs • Established tablet product manufactured at several doses. • Prior measurement systems based on storing finished material while offline QA tests performed to assure the finished product meets the performance specification. • Historically, 16% of production batches fail to meet the 60-minute dissolution requirement of NLT 70%. QA investigations into lot failures rarely found an assignable cause. • Team tasked with investigating process and dramatically improving sigma capability. • Data-sparse situation typical of mature manufacturing; focused on the process for tablets at single concentration. • Deployed effectiveness modeling techniques to cost effectively increase process understanding: • Process Mapping to identify key metrics/variables • Retrospective data collection around key variables • Visual Exploratory Data Mining • Decision Trees • Identified and verified interim solution to increase sigma capability from 2.3 sigma to 3.1 sigma with a predicted defect rate of 5%. • Ongoing DOE investigations at reduced scale focused on generating understanding required to gain further reductions in defects.

  30. Key processes and inputs associated with excessive variation in 60-minute dissolution Recursive Partitioning Decision Tree Mature Manufacturing with Few Measured Inputs

  31. Case Study 2: New Production Facility with Many Measured Inputs • Inhaler product been in commercial production for a couple of years. • Extensive inline measurement systems designed into the facility. • Data-rich environment of 520 measured inputs. • V1 to V30 processing parameters of milling, blending and packaging • V31 to V100 properties of material 1 • V101 to V170 properties of material 2 • V171 to V520 properties of material 3. • The key performance metric is percentage of a given dose reaching stage 3-4 of a cascade impactor test, which must be between 15% and 25%. • Prior to application of Pragmatic PAT, 240 commercial batches were manufactured, approximately 14% of which failed to meet the performance requirement of the cascade impactor test. QA investigations rarely found assignable cause. • Deployed effectiveness and efficiency modeling techniques to increase process understanding: • Decision Trees • PLS • DOE • Variation in four key process variables responsible for batch failures. • DOE used to specify new controls on the four process inputs. • Result is increase in capability to 4.8 sigma with a predicted batch reject rate of 0.1%.

  32. Tree Map of PLS Model Coefficients Recursive Partitioning decision tree identifies inputs most strongly associated with variation in % at stage 3-4 DOE Summary Analysis New Production Facility with Many Measured Inputs

  33. Project Approach Capability Presentation Workshop Pilot Pilot Assessment Continuous Improvement Multiple Processes Describe Capability Credentials Give examples of potential benefits Discover Vision Where does the client want to be? What are the client’s information needs? Build a business case Calculate the ROI Present findings to project sponsor Data Source Review including Quality Assessment Project Scoping Time to Information (as-is and to-be)

  34. Actions & Next Steps • Capabilities Presentation • Capabilities, credentials & references • Workshop • Discovery • Where do You want to be? • What are Your business needs? • Value Assessment • Create the business case • Calculate the ROI • Building Application • Multiple Process • Continuous Improvement

  35. Thank You Any Questions?

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