Capabilities and Quality Delivery System for Statistical, Data Management, and Medical Writing Services June, 2008

1. Capabilities and Quality Delivery System for Statistical, Data Management, and Medical Writing Services June, 2008

2. Presentation Outline • A. Everest Clinical Research Services Inc. • Organization • History: The legacy Pharmacia Statistical Operations Group • Strong Performance Metrics and Testimonials • B. Advantages • Execution Excellence (Quality and Timeliness) • Enabling Tools and Processes • Expertise and Experience • Cost Effectiveness and Benefits

3. Everest Clinical Research Services Inc. Snapshot • Previously a well-established and high performing unit within Pharmacia Corporation, with 12 years history of providing statistical and data management services. • Became independent Contract Research Organization (CRO) in January 2004 after the Pfizer acquisition of Pharmacia. • Had a successful 4.5 year operation as an independent CRO. Built partnership or preferred vendor relationship with a number of major pharmaceutical and biotechnology companies. • Provide statistical, data management, medical writing, and Web-based patient randomization (IWRS) services. • The organization currently has a resource level of 56 full time FTEs and approximately 15 part-time consultants and freelance medical writers (75% vs. 25% ratio of full-time personnel vs. part-time consultants). • Corporate Headquarters is in Toronto (Markham), Canada with a subsidiary operation in Little Falls, New Jersey, USA.

4. Everest Clinical Research Services Inc. Functions and Structure Board of Directors Finance/Accounting and Legal Services President/CEO Scientific and Operational Advisory HR, Training, Quality Assurance & Compliance Strategic Planning & Business Development, Contract Negotiation Clinical Data Management Biostatistical Sciences Medical Coding, Safety Monitoring, Medical Writing Network & Security, System Administration, Web Technologies Statistical Programming & Database Administration Regulatory Submission Support (eCTD) Project Management Quality Assurance and Compliance

5. Key Corporate Executives Irene Zhang, M.Sc., President/CEO Irene Zhang has 25 years experience in statistics, programming, and data management. Of these 25 years, 17 have been in the pharmaceutical clinical trial environment. Irene joined Johnson & Johnson as a Statistical Programmer and started her career in the pharmaceutical industry in 1989. She then joined The Upjohn Company in 1992 as the first biostatistician in the statistical operations unit when the operation was starting up. She became the Manager of Clinical Data Management in 1996, and was the Director of the unit from 1997 to 2003 (The company went through two mergers during this period - The Upjohn Company, Pharmacia & Upjohn, and Pharmacia). Over the years, Irene has led many efforts in evaluating and implementing new technologies and redesigning workflow processes to improve the effectiveness and efficiency of clinical trial data management and statistical operations. After the Statistical Operations unit was closed due to the Pfizer acquisition of Pharmacia, Irene recruited a group of the existing personnel from the unit and formed the initial structure of Everest in January 2004.Irene obtained her M.Sc. Degree in Statistics from the Queen’s University of Ontario, Canada, in 1989. Prior to her M.Sc. studies at Queen’s University, she had 6 years teaching experience with the last 3 years teaching applied mathematics and statistics in the Guangdong Provincial Economics and Business Management Institute in Guang Zhou, China.

6. Key Corporate Executives T. Y. Lee, Sc. D. VP of Strategic Planning and Business Development Has 36 years experience in statistics, pharmaceutical new drug development, medical and clinical research, Contract Research Organization (CRO) business development and management, consulting and mentoring of young business professionals. Worked as a Biostatistician at Merck (1973-1976), Assistant Director at Hoechst-Roussel Inc. (1976-1978), Director/VP of Biostatistics and Programming at Ayerst Laboratories, a division of American Home Products (1978-1987). Created his own CRO (ACER/EXCEL) in 1988 after the Ayerst operation was closed as a result of the acquisition of Ayerst by Wyeth. Managed the CRO business, developed its operations in different continents, and sold the ACER/EXCEL to Kendle International Inc. in 1998. Dr. T. Y. Lee has since semi-retired and committed his energy and financial resources to support selected non-profit organizations and Christian mission organizations, to do volunteer work and to be available as a mentor to young people. He remains in contact with the pharmaceutical industry by doing consulting work and following up on the trends and contacts in the industry. Dr. T. Y. Lee received his Doctorial degree in Biostatistics from University of Pittsburgh, Pennsylvania, US, 1973, and associate MBA from Wharton Business School, University of Pennsylvania, 1977.

7. Legacy Pharmacia Statistical Operations Group • Customer Focused • Can-Do-Spirit • Advanced Technology • Continuous Improvement • Ethical and Professional 12 Years Legacy, 12 Years Success

8. Legacy Statistical Operations Group >>> Everest CRS 2008 Striving for Best In Class 2004 - 2007 Successful 4 Years as a CRO 2004 Became A CRO: Everest CRS 2000-2003 Pharmacia Biostat & DM 1996-1999 Pharmacia & Upjohn Biostat & DM 1992-1995 Upjohn Int’l Biostat & DM

9. Our Services and Core Competences • Statistical and Data Management Services for Phase I – IV Clinical Trials • Statistical Consultation to Clinical Research and Market Support Professionals • Development and Maintenance of Clinical Trial Portal and Monitoring Tools • Development and Maintenance of Web-Based Randomization Systems (IWRS) • Medical and Technical Writing Services • Regulatory Submission Support Services (eCTD) • Process Redesign for Clinical Trial Statistics and Data Management

10. % of Outstanding Queries at Last Patient Last Visit % of Outstanding Queries at LPLV * Everest Quality Standard: 0-2.0% of Outstanding Queries at Last Patient Out

11. Data Entry Error Rate by Database Acceptance Sampling QA Procedures Data Entry Error Rate * Pharmaceutical and CRO Industry Acceptable Error Rate is <0.5%

12. Number of Working Days from Last CRFs Received to Database Closure Days (median) 22 Days 15 Days 15 Days 14 Days 15 Days • Everest will follow Sponsor’s timeline requirements on database activities. Database can be closed within 5-10 working days after the last batch CRFs received from the clinical sites. Half of the completed clinical trial databases were closed within 15 days after the last batch of CRFs received from the sponsor.

13. Electronic Data Capture Systems:Number of Working Days from Last Data Record Received to Database Closure We use the following EDC systems: InForm, TrialStat EDC, PDS, and iDataFax • Data querying and reconciliation for EDC studies are ongoing. • Outstanding data queries are kept to the minimum on an ongoing basis and before database lock. • Database lock checklist will be run as the study progress, with certain checks against the complete database to be run after the last data records received. • Database acceptance checks (quality assessments) are done prior to the last data records received, ahead of database lock. • Database can be locked within 5 working days of last patient records received.

14. What Our Clients Say About Us “It has been a great pleasure to work with your teams.” “They are highly motivated and always go above and beyond to provide us with quality and timely services.” “You are really a ‘Can-Do’ team! You always try your hardest and find solutions for problems…. your ‘Can-Do’ attitude results in continuous improvements in all of the things you do….” “We are very impressed by everything your team has done, they have contributed a lot to the success of the projects.”

15. Our Major Statistical, Data Management, and Medical Writing Processes • Develop Protocol Summary • Define Resources, Milestones and Timelines, and Manage Ongoing Project • Set Up and Maintain Study Webpage • Develop Protocol • Develop Statistical Analysis Plan (SAP) • Develop Statistical Report Shell • Develop Case Report Forms (CRFs/eCRFs) Mockups • Develop and Maintain Data Management Plan (DMP) • Develop CRFs/eCRFs and Completion Instructions • Set Up Database • Program Data Validation Checks and Study Monitoring Reports

16. Our Major Statistical, Data Management, and Medical Writing Processes • Setup Interactive Web-based Randomization System (IWRS) • Process Data • Program and Validate Statistical Tables, Listings, and Graphics (TLGs) • QC and QA TLGs • Close/Lock Database • Generate Reports/Publications • Archive Study

17. 2. Define Detailed Work Scope, Resources, Milestones, Timelines, and Manage Project 4. Develop Protocol 13. Process Data 16. Close/Lock Database 5. Develop SAP 8. Develop CRFs/eCRFs & CRF Completion Instructions 17. Generate Reports/ Publications 10. Set Up Database 12. Setup IWRS 14. Program TLGs 18. Archive Study 6. Develop Statistical Report Shell 1. Develop Protocol Summary 11. Program Data Validation Checks & Monitoring Reports 15. QC & QA TLGs 7. Develop CRF/ eCRF Mocks 9. Develop and Maintain DMP 3. Set Up and Maintain Study Website Our Major Statistical, Data Management, and Medical WritingProcesses Trial Preparation Trial Conduct Trial Reporting Approved Protocol Summary First Patient Enrolled Database Lock

18. Each of the Process Models Have Been Defined with Detailed Procedures SOPs, Work Instructions, Templates, and Checklists Are Well Defined Personnel Are Trained to Follow These Processes and Procedures An Example of Decomposing A Major Process Model into Sub-Models 6. Develop Statistical Analysis Plan (SAP) 6a. Define Statistical Methodologies 6b. Develop Mockup Tables, Listings, and Graphs (TLGs)

19. An Example of Process Model with Detailed Procedures Process Owner: Study Biostatistician 6a. Define Statistical Methodologies Output Input • Draft or final protocol • Study Reports & SAPs of similar studies in all phases • CRFs if available • Therapeutic area standards/conventions including statistical methodologies, output displays & data standards • SAP template • Standard Output Displays for common data modules • Coding dictionaries used • Medical and Clinical opinions • Other references • Review current protocol and CRFs if available, and those for similar past studies. • Review Study Reports & SAPs of similar studies in all phases. • Review literature/references. • Use SAP template and Standard Output Displays. • Define analysis patient populations. • Use Product Data Dictionary. • Define details of statistical analysis and presentation of results (e.g. patient disposition, demographics & baseline characteristics, efficacy, drug exposure, safety and other variables), including specifications of statistical models, data handling rules, derived variables algorithms, etc. • Seek opinion of Lead or Head if necessary, and send draft SAP for internal review by assigned peer reviewer or Head. • Make necessary revisions resulting from the peer review, and send the draft SAP to medical review. • Discuss review comments with medical and make necessary revisions to the draft. • Obtain medical approval once all comments or issues have been properly addressed. • Sign SAP and post it on the Study Webpage. • Approved Statistical Analysis Plan (SAP) text portion SOPs, Templates, Checklists: Statistical Analysis Plan Template Statistical Analysis and Programming QC/QA Plan Standard Output Displays (Mock Up Tables, Listings, and Graphs) CDISC, Product Data Dictionary

20. An Example of Process Model with Detailed Procedures Process Owner: Study Biostatistician 6b. Develop Mockup Tables, Listings, and Graphs (TLGs) Input Output • Draft or final protocol • SAP text sections (statistical analysis methodologies) • Medical Director’s and Medical Writer’s opinions, including the plan for in-text tables of the Clinical Study Report • Health Economics input if applicable • CRFs if available • Clinical Study Report of similar studies in all phases • Standard Output Displays • Examples & template of a Statistical Analysis Plan • Product Data Dictionary • Requirements for electronic publishing or computer assisted submission • Review protocol and SAP text sections (statistical methodologies). • Collect & assemble TLGs from previous studies to be used for the current study. • Follow Examples of a Statistical Analysis Plan and Standard Output Displays. • Mock up study specific output displays. • Provide detailed footnote text such as specifications of statistical models or test procedures, special data handling applied to the tests, notations for headings or other parameters used in the output displays mockup, etc. • Refer to Product Data Dictionary if available. • Refer to CRFs if available. • Send draft mockup TLGs for Medical/Clinical review. • Send draft TLGs to Study Programmer for comments. • Discuss comments with reviewers and make necessary revisions. • Obtain Medical approval of mockup TLGs. • Sign SAP and post it on the Study Webpage. • Approved Mockup TLGs of the SAP SOPs, Templates, Checklists: Statistical Analysis Plan Template Statistical Analysis and Programming QC/QA Plan Standard Output Displays CDISC, Product Data Dictionary

21. Main Data Management Systems and Tools • We Use 4 Web-based EDC Applications: • InForm, TrialStat EDC, PDS, and iDataFax. • We Perform Data Management for Paper Flow Studies Using the Paper-based Data Management Approach within iDataFax (This system can handle both EDC and Paper CRFs within the same database. A trial can have some sites using web-based EDC data entry and other sites sending in paper CRFs to the data center for centralized data processing).

22. For EDC Studies • We Follow the Standard Processes and Procedures Recommended by the Developer of the Software Vendors. • We Incorporate Trial Sponsors’ Standard Processes and Procedures into the Everest Processes and Procedures When Required.

23. iDataFax handles paper CRFs by Fax or by 100% manual data entry (scanning CRFs) • Store and manage completed CRF images/data records • Automate workflow management • Retain electronic audit trail information • 21 CFR Part 11 compliant • Extract data from DataFax • Manipulate, analyze and report data • SAS datasets for interim and final data transfer to sponsor companies • Provide real-time access to patient and study information: • Patient enrolment, compliance, progress, completion • CRF status, queries and resolutions, completed CRF images • Safety monitoring information (AE, ConMed listings, etc.) • Site performance reports (% clean CRFs, faxing delay, CRF fields with most queries, time to respond to queries, etc.) • Centralized randomization • Disseminate drug information, publications/abstracts, and marketing materials to trial physicians’ desktops • Serve as study document centre, publish latest version of protocol, amendments, DMP, SAP, QA documents, meeting minutes. Ready for use electronic submission package. IDataFax: SAS: Secure Web Portal: For Paper-Flow Studies Using iDataFax

28. Summary of Advantages • Execution Excellence (Quality and Timeliness) • Enabling Tools and Processes • Expertise and Experience • Cost Effectiveness and Benefits

29. Expertise and Experience • Experienced with Phase I - IV studies covering a wide range of therapeutic areas (currently the split between Phase I-III and Phase IV projects with the organization is 35% : 65%) • Experienced in conducting regulatory submission trials and filing NDA submissions using eCTD applications • Experienced in conducting international trials • Using best working practices in all functional areas of services

30. Therapeutic Area Expertise and Experience Therapeutic area experience and expertise covers a wide range of areas such as, but is not limited to, the following: • Oncology • Hepatology • Virology • Arthritis and Pain • Allergy/Respiratory • Infectious Diseases • Nephrology • Cardiovascular Diseases • Endocrinology • Urology • Ophthalmology • CNS • Women’s Health • Dermatology

31. Cost Effectiveness and Benefits • Due to the low overhead of the organization, quality and timely services can be offered at a competitive rate • The Canadian government’s tax credit incentive for research and development work performed in Canada will provide additional and direct tax benefits to the Sponsors who have operations in Canada

32. Everest Contact Information Please Contact Irene Zhang at Office Phone: +1 (905)752-5201 Office Phone: +1 (973)774-1160 Cell Phone: +1 (416)788-8678 Or at the Corporate Headquarters office address: 675 Cochrane Drive, Suite 408, East Tower Markham, Ontario, Canada L3R 0B8 Everest Corporate Website Address: www.ecrscorp.com