1. DATA DRIVEN TRIALS FOR ONCOLOGY STUDIES
Prasida Dinesh
Associate Director, CDM
Covance India Pharmaceutical Services Private Limited
Santhosh Kumar
Study Build Team Lead , GCDS SME
Accenture Solutions Private Limited

2. Team Covance India Pharmaceutical Services Private Limited
Prasida Dinesh
Associate Director CDM
Dr Shobha Muniyappa
Sr Manager CDM
Sampreetha Hebli
Sr Project Manager
Accenture Solutions Pvt. Ltd
Santhosh Kumar
Study Setup programmer
Shilpa Srinivas
Study Setup programmer
Apurva Kumar
STDM programmer
Sharada Rani Harsur
Reports programmer

3. Agenda Oncology trials – Some facts Challenges of Oncology
Drivers for efficiency and effectiveness improvement
Keys to build data driven strategy
Key considerations for Oncology trials
Data driven approach – Why?
Implementation of Data driven approach
Evolution in Oncology trials
Case study and Exercise

4. Oncology trials – Some facts
Improving cancer treatments is a priority
million new diagnoses
million deaths from cancer
2030 – 23.6 million new cases (expected to increase by almost 70%)

5. Challenges of Oncology
General
Inclusion /Exclusion – Eligibility criteria
Patient motivation and retention
Outcome – Death
Dosage adjustment
Response evaluation
Logistics –IP, Sample
Highly complex early phase study designs
Change control
Cohort expansion
Advances in immuno - oncology
Data protection laws
Site Related
Site Experience
Number of patients and time for recruitment
Targeted therapies – Niche genetic subpopulation
Availability of complete source data
Availability of Tumor Sample/Biopsy
Availability of Local Lab/Radiology/Pharmacy
Experience in IP and Sample logistics
Documentation – Outpatient/Inpatient
SAE reporting – Hospitalization
Logistics of assessments
Additional time for review and approval in case of additional indications

6. Drivers for efficiency and effectiveness improvement
E2E knowledge
Enhanced Data Validation tools
Better Imaging data reconciliation and Complex data review
Increased protocol complexity and New oncology indications
Aggressive enrolment rates
High visit volume
Increased training requirement under heavy time pressure
Large volume of unique patient population
Multiple interim analysis during the course of trials
Frequent data base locks
High rate of Interim analysis

7. Keys to build data driven strategy
Maximize use of the standards
Source Data – Choose the right data
Tools that can predict and optimize business outcomes
Map the analyzed outcomes to the goal for decision making

8. Key considerations for Oncology trials
Patient Recruitment and Retention
Defining a baseline forecast
Forecasting enrollment probability
Previously failing treatment
Duration of Recruitment
Write protocol focusing on data that is absolutely necessary resulting in:
better quality,
quicker recruitment
faster timelines
Database Design
Safety and Efficacy considerations
Multiple eCRF designs for multiple treatment arms
Complex structure
IVRS/IWRS systems are strongly recommended
Effective Usage of study and comparator medication
Dynamic/adaptive CRF designing leading to effective data collection reducing the manual review
Using standard library for edit check
Study setup and Conduct
More Resources at setup and monitoring
Multiple cycles resulting in high data volume
Tool driven data review
High volume of Serious Adverse Events and Adverse events
Consistent entry and review of tumor assessment data
Foresee the problem areas of data entry and creating a comprehensive completion guideline

9. Data driven approach – Why?
Strategic decisions based on data analysis and interpretation
Enables accurate forecast of planning and risk based leveraging of resources
Limit risk and increase the likelihood of getting your desired outcome
Quick decisions on real time analysis saving trial conduct cost and increases speed 9

10. Implementation of Data driven approach
Reduce number of trials without compromising data quality
Faster first patient
Faster enrolment
Enable greater predictability and mitigate risks
Accelerated data delivery
Innovative approach to trial design
Real time access to operations- critical information
Target-based information related to a drug
Establish chemical properties to predict toxic clinical trial outcomes
Data-driven approach combining optimized processes with advanced technology incorporates
Understanding patient pathway and ecosystem dynamics
Utilization of patient-centered healthcare data to optimize recruitment
Using electronic patient reported outcomes – PRO - CTCAE
Site Specific Strategies
Designing clinical trials using adaptive designs – Adaptations could be implemented through study protocol, protocol amendments or statistical analysis changes

11. Evolution in Oncology trials
Virtual Trials
Application of molecular medicine
Flexible Operating model – driven by advancement in wearable and remote technology
Predictive analysis will be common
Data will drive the compliance and drive a efficient approval process
Clinical trial – an integrated healthcare option

12. Intervention/treatment
Exercise - 1
Brief Summary:
This phase II trial studies how well pembrolizumab works in treating participants with cancer that has spread to other places in the body, has come back or has spread to nearby tissues or lymph nodes. Monoclonal antibodies such as, pembrolizumab, may interfere with the ability of tumor cells to grow and spread.
Condition or disease
Intervention/treatment
Phase
BRCA1 Gene Mutation BRCA2 Gene Mutation, Locally Advanced Solid Neoplasm Metastatic Malignant Solid Neoplasm POLD1 Gene Mutation POLE Gene Mutation Recurrent Malignant Solid Neoplasm Recurrent Ovarian Carcinoma Stage III Breast Cancer AJCC v7 Stage III Ovarian Cancer AJCC v8Stage IIIA Breast Cancer AJCC v7Stage IIIA Ovarian Cancer AJCC v8Stage IIIB Breast Cancer AJCC v7Stage IIIB Ovarian Cancer AJCC v8Stage IIIC Breast Cancer AJCC v7Stage IIIC Ovarian Cancer AJCC v8Stage IV Breast Cancer AJCC v6 and v7Stage IV Ovarian Cancer AJCC v8Stage IVA Ovarian Cancer AJCC v8Stage IVB Ovarian Cancer AJCC v8
Other: Laboratory Biomarker Analysis
Biological: Pembrolizumab
Phase 2

13. Case study material -Types of trial designs in Precision Oncology
Basket trials (or studies) test the effect of one drug on a single mutation in a variety of tumor types, at the same time.
Basket Trial
Umbrella trials (or studies) have many different treatment arms within one trial. People are assigned to a particular treatment arm of the trial based on their type of cancer and the specific molecular makeup of their cancer.
Umbrella Trial
Targeted therapy is a type of treatment that uses drugs or other substances to identify and attack specific types of cancer cells and limiting harm to normal cells.
Targeted Therapy
Molecular profiling is a method of testing genetic characteristics as well as any unique biomarkers of a cancerous tumor. The results are used to identify and create targeted therapies, which are designed to work most effectively for specific cancer tumor profiles.
Molecular Profiling
Genomic profiling is laboratory method use to learn more about the genetic makeup of a person or cell type and the way those genes interact with each other and the environment.
Genomic Profiling
 The PMI is a $215 million proposed investment in President Obama’s 2016 Budget to accelerate biomedical research and provide clinicians with new tools to select the therapies that will work best in individual patients
Precision Medicine Initiative

14. Identify the type of trial design
Basket trial
Umbrella trial