Process Analytical Technology Solution Presentation

for Actionable Information

Center for Business Intelligence and Analytics (C-BIA)

for Actionable Information

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Mission

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

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History

Cyrus Mehta, Ph.D. Founder and PresidentCytel Inc.

Nitin Patel, Ph.D.Founder and ChairmanCytel Inc.

www.cytel.com

C-BIA, a division of TechKnit was founded in 2004

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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.

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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.

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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

SAS www.sas.com

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SAS Technology Layer and ProductsThe main technology platform provides the

following components – Data Quality Data Integration Data storage OLAP Server Friendly Interface

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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.

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SAS Pharma Focus

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

Uniqueness and Expertise

for Actionable Information

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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)

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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:

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C-BIA Management Mayank Shah Chartered 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 Sathe PGDM, IIM, AhmedabadAjay 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 AccountantVanaja 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 EconomicsDan 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.

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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 EngineeringChandran 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.

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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…

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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

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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

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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

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Selected Customers

ProfitLogic, US Bharat Petroleum, India

Mastek, India

Dainik Jagran, India

Trumac, India

TAM Media Research, India

Savita Chemicals, India

KPIT Cummins Infosystems Ltd., India

Tata Motors Ltd., India

Process Analytics Platform

for Actionable Information

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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

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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

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The SAS Pragmatic-PAT Solution:Data Integration, Modelling and Control for Operations

1: Data

2: Integrate

3: Cleanse

4: Maintain

6: Effectiveness Modelling

5: Efficiency Modelling

7: Simulate 8: Improve

10: Deploy and Control

9: Verify

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SAS Pragmatic-PAT Solution Elements

Model Deployer

Model Builder

Data Integrator

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Modelling Cycle: Drives Increased Process Understanding and Operational Improvement

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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)..

Clarify TheObjectives

Extract AnalysisReady Data

VisualModelling

Statistical Modeling

Assess TheFindings

Deploy

Various Users, with different skills

and capabilities

Enabling Technology (respects wide range of Users and Data Types)

NewInformation

. . . Delivered in a way that respects wide range of user skills and capabilities.

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Mapping of data analysis technology to process capability and dependence on

extent and relevance of measured inputs:

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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.

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Mature Manufacturing with Few

Measured Inputs Key processes and inputs associated with excessive variation in 60-minute dissolution

Recursive Partitioning Decision Tree

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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%.

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New Production Facility with Many Measured Inputs

Recursive Partitioning decision tree identifies inputs most strongly associated with variation in % at stage 3-4

Tree Map of PLS Model Coefficients

DOE Summary Analysis

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Project Approach

Capability Presentation

WorkshopPilot

Assessment

Continuous Improvement

Multiple Processes

Pilot

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)

Describe Capability

Credentials

Give examples of potential benefits

AD Assess and Define

AE Analyze andEvaluate

DE Design

CD Create Data MiningMart

SE SEMMA

CO Construct

FT Final Test

DP Deploy Platform

RV Review

PROJECTDEFINITION

PROJECTEXECUTION

LO Load

AQ Analyze DataQuality

RQ Resolve DataQuality Issues

DT Define Target

PROJECTPLANNING

Project Closure

IM Implement Model

PROJECTSUMMATION

PR

OJE

CT

MA

NA

GE

ME

NT

ME

TH

OD

OL

OG

Y

Ongoing Maintenanceand Operation

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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

Thank You

Any Questions?