7/28/2019 PAT v1.0

1/35

Process Analytical Technology

Solution Presentation

for Actionable Information

7/28/2019 PAT v1.0

2/35

Center for Business Intelligence and

Analytics (C-BIA)

for Actionable Information

7/28/2019 PAT v1.0

3/35

23-Jan-07 3

MissionCreate business value for clients by enabling

superior performance through unleashing

hidden wealth in operational and external

data sources combined with innovativeAnalytics.

7/28/2019 PAT v1.0

4/35

23-Jan-07 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

7/28/2019 PAT v1.0

5/35

23-Jan-07 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 andBiometrics.

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.

7/28/2019 PAT v1.0

6/35

23-Jan-07 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.

7/28/2019 PAT v1.0

7/35

23-Jan-07 7

SAS is the worlds largest Business Intelligence andanalytics software co.

Based out of Cary , NC, USASAS has world widepresence across continents .

In India SAS has a marketing office located in Mumbai.

SAS has a R/D center in Pune with a strength of about250.

SAS tools provide End to End solution across Enterprise

SAS www.sas.com

7/28/2019 PAT v1.0

8/35

23-Jan-07 8

SAS Technology Layer and Products

The main technology platform provides thefollowing components

Data Quality

Data Integration Data storage

OLAP Server

Friendly Interface

7/28/2019 PAT v1.0

9/35

23-Jan-07 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 areaof statistical analysis.

SAS has developed a special focus on regulatory reporting andPharmacovigilance reporting.

SAS compliments the stringent requirements of Pharma industries interms of Production processes and testing and trials.

7/28/2019 PAT v1.0

10/35

23-Jan-07 10

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 CompanyVigilance

CDISC - Clinical Data Inter-Change StandardsConsortium

7/28/2019 PAT v1.0

11/35

Uniqueness and Expertise

for Actionable Information

7/28/2019 PAT v1.0

12/35

23-Jan-07 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)

7/28/2019 PAT v1.0

13/35

23-Jan-07 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 itfurther

Vast repository of

methodologies and

software library Witnessed in several

products, key amongst

them are:

http://www.xlminer.com/

7/28/2019 PAT v1.0

14/35

23-Jan-07 14

C-BIA Management Mayank ShahChartered Accountant

Mayank has over 27 years' experience as Consultant, Executive and Academician inthe field of MIS and BI applications for business. Mayank is Consultant andExecutive Director of TechKnit and leads C-BIA.

Ajay SathePGDM, IIM, AhmedabadAjay has over 17 years' experience in IT industry specializing in Software

Development and Technology Management. Ajay is Director of TechKnit andCEO of Cytel India.

Shrikant Athavale, Industrial EngineerShrikant has nearly 36 years' experience in Industrial Engineering and QualityManagement. 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 SatyamComputer 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 coordinateactivities in the field in South Asia. He first worked in South Asia in 1999 when hewas a Fulbright Scholar in India.

7/28/2019 PAT v1.0

15/35

23-Jan-07 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. MITSloan School of Management.

Suresh Ankolekar, Ph.D.

Dr. Ankolekar has over 23 years of academic and consulting experience at Indian Institute ofManagement,Ahmedabad (IIMA) and Maastricht School of Management, Netherlands (MSM).Prof. Ankolekar has developed commercial software to solve large-scale optimizationproblems in transportation and has provided consulting in analytical software projects to CytelInc., 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 themonetary authority of the country, is a well-known expert in the banking and financialanalytics, data warehousing and data mining.

Sunil Lakdawala, Ph. D.

Dr. Lakdawala has over 20 years' of consulting experience in IT applications for businessincluding Data Warehousing and Data Mining. Dr. Lakdawala is a consultant in BI applicationsand 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 incompanies with sizeable software teams. Chandran is Vice President with Cytel Indiaheading Technology Management function besides managing consulting assignments.

7/28/2019 PAT v1.0

16/35

23-Jan-07 16

C-BIA Partnerships

SAS: Silver Alliance partner

Mastek: BI Solutions

Cytel-Cognizant: Pharmaceuticals- clinical

trials

Statistics.comXLMiner marketing in USA

Syscon Infotech: BI solutions

Intech SystemsBI Solutions

7/28/2019 PAT v1.0

17/35

23-Jan-07 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

7/28/2019 PAT v1.0

18/35

23-Jan-07 18

Technology and Infrastructure in Pune

6000 sq. feet of office space in Pune

Secured Network with high bandwidthConnectivity

Windows and SAS Platforms with more thansix servers

Methodologies and SOPs for, BI solutions,Analytics and Clinical Trials

Well established software developmentpractices

7/28/2019 PAT v1.0

19/35

23-Jan-07 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

7/28/2019 PAT v1.0

20/35

23-Jan-07 20

Selected Customers

ProfitLogic, US Bharat 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

7/28/2019 PAT v1.0

21/35

Process Analytics Platform

for Actionable Information

7/28/2019 PAT v1.0

22/35

23-Jan-07 22

Guidance for Industry

PATA Framework for Innovative

Pharmaceutical Development,Manufacturing, and Quality Assurance

Encourages the right approach measurement, dataintegration, statistical modelling & process understanding basedon data.

Companies able to demonstrate process understanding will betreated differently, e.g. be allowed to change processes withoutrevalidation 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 (Chemistryand Manufacturing Controls).

http://www.fda.gov/cder/guidance/6419fnl.pdf

http://www.fda.gov/cder/guidance/6419fnl.pdfhttp://www.fda.gov/cder/guidance/6419fnl.pdf

7/28/2019 PAT v1.0

23/35

23-Jan-07 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

7/28/2019 PAT v1.0

24/35

23-Jan-07 24

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

7/28/2019 PAT v1.0

25/35

23-Jan-07 25

SAS Pragmatic-PAT Solution Elements

Model Deployer

Model Builder

Data Integrator

7/28/2019 PAT v1.0

26/35

23-Jan-07 26

Modelling Cycle: Drives Increased Process

Understanding and Operational Improvement

7/28/2019 PAT v1.0

27/35

23-Jan-07 27

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:

Eff iciency 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 The

Objectives

Extract Analysis

Ready Data

Visual

Modelling

Statistical

Modeling

Assess The

FindingsDeploy

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.

7/28/2019 PAT v1.0

28/35

23-Jan-07 28

Mapping of data analysis technology to process capability

and dependence on extent and relevance of measured

inputs:

7/28/2019 PAT v1.0

29/35

23-Jan-07 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 theperformance specification.

Historically, 16% of production batches fail to meet the 60-minutedissolution requirement of NLT 70%. QA investigations into lot failuresrarely 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 increaseprocess 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 from2.3 sigma to 3.1 sigma with a predicted defect rate of 5%.

Ongoing DOE investigations at reduced scale focused on generatingunderstanding required to gain further reductions in defects.

7/28/2019 PAT v1.0

30/35

23-Jan-07 30

Mature Manufacturing with Few Measured Inputs

Key processes and inputs associated with excessive variation in

60-minute dissolution

Recursive Partitioning Decision Tree

7/28/2019 PAT v1.0

31/35

23-Jan-07 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 cascadeimpactor 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%.

7/28/2019 PAT v1.0

32/35

23-Jan-07 32

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

7/28/2019 PAT v1.0

33/35

23-Jan-07 33

Project Approach

Capability

PresentationWorkshop

Pilot

Assessment

Continuous

Improvement

Multiple

Processes

Pilot

Discover Vision

Where does the

client want to be?

What are theclients

information

needs?

Build a business

case

Calculate the ROI

Present findingsto project sponsor

Data Source

Review including

Quality

Assessment

Project Scoping

Time to

Information (as-is

and to-be)

Describe

Capability

Credentials

Give examples ofpotential benefits

ADAssessand Define

AEAnalyzeandEvaluate

DEDesign

CDCreateDataMiningMart

SESEMMA

COConstruct

FTFinalTest

DPDeployPlatform

RVReview

PROJECTDEFINITION

PROJECT

EXECUTIONLOLoad

AQAnalyzeData

Quality

RQResolveDataQualityIssues

DTDefineTarget

PROJECT

PLANNING

ProjectClosure

IMImplementModel

PROJECTSUMMATION

PROJECTMA

NAGEMENTMETHODOLOGY

OngoingMaintenanceandOperation

7/28/2019 PAT v1.0

34/35

23-Jan-07 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

7/28/2019 PAT v1.0

35/35

Thank You

Any Questions?