Featuring Industry Experts!

Dr. Arjun Roy Keynote Speaker		Dr. Goutam Chakraborty Lunchtime Speaker		Dr. Fred Wood Invited Speaker

On behalf of everyone at PharmaSUG India, we would like to express our sincere gratitude for your speaker sharing and operations activities support.

Presentations

Presentation (click for abstract)	Presenter (click for bio)
Automation Driven Submission	Chandrakala Shivalingaiah, Vita Data Sciences
Exploring Submissions in Medical Device Trials	Shilpakala Vasudevan, Ephicacy Lifescience Analytics
Common Pinnacle 21 Report Issues: Shall We Document or Fix?	Ajay Gupta, PPD
CDISC 360	Arjun Roy, Cliantha Research
Trial Sets in Human Clinical Trials	Fred Wood, Data Standards Consulting Group
Electronic Submission Through Programmer’s Perspective	Aditya Tembe, Accenture Services
Clinical Outcome Assessments and CDISC Questionnaires, Ratings and Scales (QRS): Patient-Focused Drug Development	Shrishaila Patil, NAVITAS LIfesciences
Healthcare Interoperability World: HL7 and FHIR	Ashish Grover, Syneos Health
Automation of Study Build and Data Transformations with Text Analytics	Prasoon Sangwan, TCS
Converting Legacy Study Data to Submission Ready – Challenges and Approach	Ajay Yalwar, Covance
Innovation in Submission – AI/ML/RPA!! What’s New?	Ajay Sinha, Novartis
How Much Do We Know About Indian Regulatory Filing??	Shivani Dharwadkar, Bayer
Overview of Electronic Submissions Gateway	Soma Sekhar. K, Quartesian
Delivering Industry-focused Business Analytics and Data Science Skills	Goutam Chakraborty, Oklahoma State University

Saturday, March 14 Single-Day Event Schedule (in progress)

Time	Agenda
8:15 AM - 9:15 AM	Registration and Breakfast
9:15 AM - 1:00 PM	Conference Program (TBA)
1:00 PM - 2:00 PM	Lunch (included)
2:00 PM - 3:30 PM	Conference Program (TBA)
3:30 PM - 3:45 PM	Afternoon Break
3:45 PM - 5:30 PM	Conference Program (TBA)

Presentation Abstracts

Automation Driven Submission
Chandrakala Shivalingaiah, Vita Data Sciences

The pharmaceutical industry faces numerous challenges during the submission process due to lack of standards and consistency for data collection or regulatory submission. Especially when it is integrated eSubmission; which includes Legacy and SDTM-like data, it is even more challenging and time consuming. This paper will explore the challenges and highlight few solutions to supplement the eSubmission process. Let us dive in to see where and how efficiency can be built. What kind of automation tool can be developed? How do we best organize and expedite when handling multiple eSubmissions? How do we tackle studies which are start with no end in mind?


Exploring Submissions in Medical Device Trials
Shilpakala Vasudevan, Ephicacy Lifescience Analytics Pvt. Ltd.

Guidelines for medical devices in clinical trials have been set by regulatory authorities, including the FDA. The nature of medical device trials is different from conventional pharmaceutical trials, with difference in study design, data collection and even data standards. Medical devices can range from a simple device that can be monitored at home to a very complicated machine that can be handled only by someone with technical expertise. To accommodate this wide range of devices, a set of study data domains have been developed that are currently used for submissions worldwide. In addition, there are guidelines that are being developed to define analysis data set structures. Data standards for medical devices are relatively new and in progress, and there are challenges that exist when dealing with submissions. In this presentation, we will explore how different are medical devices from drugs, what data standards exist for submission, challenges and potential improvements in the future.


Common Pinnacle 21 Report Issues: Shall We Document or Fix?
Ajay Gupta, PPD

Pinnacle 21, also previously known as OpenCDISC Validator, provides great compliance checks against CDISC outputs like SDTM, ADaM, SEND and Define.xml. This validation tool provides a report in Excel or CSV format which contains information as errors, warnings, and notices. At the initial stage of clinical programming when the data is not very clean, this report can sometimes be very large and tedious to review. If the programmer is fairly new to this report s/he might not be aware of some common issues and will have to fully depend on an experienced programmer to pave the road for them. Indirectly, this will add more review time in the budget and might distract the programmer from real issues which affect the data quality. In this presentation, I will discuss some common issues with the Pinnacle 21 report messages created from running against SDTM datasets and propose some solutions based on my experience. Also, I will discuss some scenarios when it is better to document the issue in reviewer’s guide than doing workaround programming. While the author totally agrees that there is no one fit for all solution, my intention is to provide programmers a direction which might help them to find the right solutions for their situation.


CDISC 360
Arjun Roy, Cliantha Research

Benefits of data standardization is yet to be realized. One of the bottleneck is how to link the metadata for automation. CDISC 360 project has been initiated to fill this gap. Workbench is being built on the standard biomedical concepts and metadata which will make it machine readable. Once completed, this will be template for standard data submission and visualization such as Electronic Case Report Forms (CRFs), Tables, Figures and Listings (TFLs) and other ongoing HL7 standards such.


Trial Sets in Human Clinical Trials
Fred Wood, Data Standards Consulting Group

The Trial Sets table has been included in the SDTM since Version 1.3, published in 2012. The only implementation guide in which it appears, however, is the SEND Implementation Guide (SENDIG). The Trial Sets dataset (TX) allows for the subsetting of subjects within an Arm (treatment path) and facilitates the “grouping” multiple Arms together. A Trial Set represents the most granular subdivision of all the experimental factors, treatment factors, inherent characteristics, and distinct sponsor designations as specified in the design of the study.

Within a nonclinical trial, each animal is assigned to a Set in addition to an Arm. The Set Code (SETCD) variable is Required in the SEND DM dataset. While there is no such requirement in the SDTMIG DM dataset, Trial Sets has potential uses in human clinical trials, particularly when the randomization or the study design is based on factors other than treatment (e.g., subjects who have undergone previous heart surgery vs. those who have not). This presentation will provide an introduction to Trial Sets as it’s used in nonclinical studies as well as examples of how this dataset could be used in human clinical trials.


Electronic Submission Through Programmer’s Perspective
Aditya Tembe, Accenture Services Pvt. Ltd.

The final critical endpoint of a clinical trial is a regulatory submission. For regulatory e-submission, thorough understanding of regulatory authority demands, subtle understanding of the process, various component involved, required data, documents and adherence to the guidelines are basic requirements. This paper will cover general assistance for programmers for CDISC implementation, eCTD (Electronic Common Technical Document) along with electronic file structure in it and its fifth module. Also, how programmers can prepare material to be submitted? i.e. naming conventions and file formats of electronic files. This paper will also discuss and answer FAQs about programs, such as:

1. Are sponsors required to submit executable programs?
2. Should sponsors submit standard macros?
3. Is the process different for non-SAS languages, such as R?

It highlights concerns and issues that FDA expressed about e-submissions, such as size of the datasets, length of the character variables in SAS datasets, CDISC compliance checks etc.


Clinical Outcome Assessments and CDISC Questionnaires, Ratings and Scales (QRS): Patient-Focused Drug Development
Shrishaila Patil, NAVITAS Lifesciences

Patients are true experts in their disease and have insights that are impossible to determine without their direct input. It is clear one has to start with an understanding of the impact of the disease on the people who have it, and what they value most in terms of alleviation before you setup a measurement and go forward with truly patient-focused drug development. Patient experience data plays a critical part in medical product development by helping to ensure investigations of the effect of treatments assess outcomes that are meaningful to patients. Patient input can be included in not only the selection of clinical outcomes but also to ensure the appropriateness of instruments used to collect trial data. FDA uses Clinical outcome assessments (COAs) to determine whether a medical product has been shown to provide clinical benefit to patients. CDISC QRS Supplements assist in structuring COA data so that it is collected and reported in a standardized format.

This Paper is an effort to understand types of FDA Clinical Outcome Assessment (COA)'s (Clinician-reported outcome (ClinRO), Observer-reported outcome (ObsRO), Patient-reported outcome (PRO) and Performance outcome (PerfO)), process of selection & development of COAs. This paper also focuses on how CDISC QRS Supplements assist in structuring Clinical Outcome Assessment (COA) data so that it is collected and reported in a standardized format.


Healthcare Interoperability World: HL7 and FHIR
Ashish Grover, Syneos Health

Health Level Seven International (HL7) is a not-for-profit, ANSI-accredited standards developing organization dedicated to providing a comprehensive framework and related standards for the exchange, integration, sharing, and retrieval of electronic health information that supports clinical practice and the management, delivery and evaluation of health services. HL7’s prime objective is to simplify the implementation of interfaces between healthcare software applications and various vendors so as to reduce the pain and cost involved in custom interface programming.

The name Health Level 7 symbolizes the seven-layer International Standards Organization (ISO) Communications Model:

1. Physical: Connects the entity to the transmission media
2. Data Link: Provides error control between adjacent nodes
3. Network: Routes the information in the network
4. Transport: Provides end-to-end communication control
5. Session: Handles problems that are not communication issues
6. Presentation: Converts the information
7. Application: Provides different services to the applications

FHIR: Fast Health Interoperability Resource

• FHIR stands for Fast Healthcare Interoperable Resource.
• FHIR combines the best features of HL7 V2, HL7 V3, and CDA, while leveraging the latest web service technologies.
• The design of FHIR is based on RESTful web services. This is in contrast to the majority of IHE profiles which are based on SOAP web services. With RESTful web services, the basic HTTP operations are incorporated including Create, Read, Update and Delete.
• FHIR is based on modular components called “resources,” and these resources can be combined together to solve clinical and administrative problems in a practical way. The resources can be extended and adapted to provide a more manageable solution to the healthcare demand for optionality and customization. Systems can easily read the extensions using the same framework as other resources.

Automation of Study Build and Data Transformations with Text Analytics
Prasoon Sangwan, TCS

Though Clinical Development process has improved a lot in the past few years but Clinical trials are still taking an average of more than two months for Study Build. One of the major challenge is, each sponsor has its own set of standards which is incompliant to the CDISC standards. Additionally, in many of the cases the standard library is not actively in use and the CRFs are designed and built by the study teams as per the convenience of the trail design. This not only increases the time to study build but also impacts the submission process.

This paper explains how ontology based Text Analytics enables to not only automate the study build process for the new studies through build and update of standard metadata library from active and closed studies reducing the time by 50%. But, also to map a sponsor’s existing Metadata library to CDISC standards like CDASH facilitating better re-usability and efficiencies to speed up the critical submission elements like SDTM, ADaM as per the regulatory norms.


Converting Legacy Study Data to Submission Ready – Challenges and Approach
Ajay Yalwar, Covance

With evolving standards by regulatory, the guidance for clinical research states that the studies initiated on or after December 17, 2016 must utilize data standards for collection and analysis as detailed in the Data Standards Catalog. PMDA requires that study data be submitted in CDISC standards for all submissions after April 1, 2020. This has made the sponsors and research organizations to use electronic form of data for collection and analysis, incorporating the standard guidelines provided by CDISC. This guidance often necessitates non-standard data from a legacy study to be converted to CDISC standard format data to make it submission ready. This paper focuses on a case study of converting legacy studies data to PMDA and FDA submission ready and provides some insights on challenges faced and provides some of the useful approaches to overcome such challenges.


Innovation in Submission – AI/ML/RPA!! What’s New?
Ajay Sinha, Novartis

Data is the next fuel and Data representation is engine! Most of the regulatory bodies are trying to see data in refined manner. Sponsors are trying to innovate new ways to streamline the activities and be future ready. With regulatory bodies going in the direction of real time analysis, looking into the data on ongoing trials (for review/discussion) it becomes really necessary for sponsors to have sound planning and ward off the old ways of doing things, be lean and smart.

Not only the processes needs to be reviewed but also the applications for these processes need to be cost-effective. With increased usage of R and Python in statistical programing, deliverables gate for new era has opened and we need to be ready to embrace the change. We will be going through some of the latest updates in terms of submission, usage of new applications (through Artificial Intelligence, Machine Learning, Robotics Process Automation etc), key points to be considered during submission to different regulatory bodies along with few innovative ways to streamline the activities.


How Much Do We Know About Indian Regulatory Filing??
Shivani Dharwadkar, Bayer Pharmaceuticals

The Clinical Trials (CTs) are approved by Central Drugs Standard Control Organization (CDSCO), headed by the Drugs Controller General of India (DCGI) in India. The investigator also needs to obtain the ethics committee (EC) approval from DCGI registered EC prior to initiating a study. In US, the review of clinical trial may be conducted in parallel with the US regulatory board; Food and Drug Administrations (FDA) and the EC or Institutional Review Board (IRB) approval process. But EC approval must be obtained prior to the sponsor being permitted to initiate the clinical trial.

The approval time in India varies depending upon the drug where it is manufactured. Suppose the drug is developed outside India it takes 90 days for the approval; where as if it is discovered, researched and manufactured in India it will take only 30 days for approval. The purpose of this topic is to highlight the comparative aspects between the US FDA and India CDSCO submission process. This is based on different key points such as the application format, approval time, trial authorization, insurance, safety reporting, compensation, regulations and documents requirement. The participation of volunteers as a “subject” in USA is more than in India. This may be the main key point for more CTs approval in US than in India.


Overview of Electronic Submissions Gateway
Soma Sekhar. K, Quartesian

The Food and Drug Administration (FDA) Electronic Submissions Gateway (ESG) is an Agency-wide solution for accepting electronic regulatory submissions. The FDA ESG is a highly scalable, easily available, high performance and secure exchange point for FDA and its partners to transact a variety of documents and submissions over industry-standard protocols. The FDA ESG enables the secure submission of premarket and postmarket regulatory information for review. The FDA ESG enables the FDA to process regulatory information automatically, functioning as a single point of entry for receiving and processing all electronic submissions in a highly secure environment. The FDA ESG complies with secure Hypertext Transfer Protocol (HTTP) messaging standards and uses digital certificates for secure communication. The electronic submission process encompasses the receipt, acknowledgment of receipt (to the sender), routing and notification (to a receiving Center or Office) of the delivery of an electronic submission.

The FDA ESG is the central transmission point for sending information electronically to the FDA. Within that context, the FDA ESG is a conduit along which submissions travel to reach the proper FDA Center or Office.FDA ESG provides two methods, WebTrader (WT) and AS2, for making submissions to FDA. FDA ESG has been in production since 2006 and is used by 100s of users to send 1,000s of submissions every day.

• WT: A web portal designed for low volume submitters. WT allows users to login, digitally sign and submissions, and view responses through a simple web interface.
• AS2: A system-to-system connection to exchange submissions with FDA. AS2 requires a Gateway software implementation on submitters end.

In this paper Overview of Electronic Submissions Gateway will be explained.


Delivering Industry-Focused Business Analytics and Data Science Skills
Goutam Chakraborty, Director of MS in Business Analytics and Data Science, Oklahoma State University

The modern business environment requires skilled graduates from a multi-platform post-graduate academic curriculum that includes a diverse range of topics, such as Data Analytics, Machine Learning, Deep Learning,  Marketing, Statistics, Business, MIS and Industrial Engineering. These need to be focused and refined through a continuous consultative process with industry, in order to ensure validity and relevance. This talk addresses the critical strategic and tactical decisioning in the design, execution and sustainment of such a program.

Presenter Biographies

Goutam Chakraborty

Dr. Goutam Chakraborty is the director of MS in Business Analytics and Data Science, the director of Graduate Certificate in Business Data Mining, the director of Graduate Certificate in Marketing Analytics and SAS® professor of Marketing Analytics at Oklahoma State University. Before joining academics, he has worked for a subsidiary of Union Carbide, USA and with a subsidiary of British American Tobacco, UK. In addition to his academic responsibilities, he provides consulting services on issues related to developing business analytics capabilities, digital business strategy, building and managing customer relationships. Companies such as Aetna, Mercruiser, Thrifty Rent-A-Car, Berendsen Fluid Power, Globe Life Insurance, Vanguard Realtors, Hilti, Love’s Travel Stops and others have used his consulting services. At Oklahoma State University, he teaches descriptive business analytics, predictive business analytics, advanced business analytics, advanced marketing research analytics to masters and Ph.D. students. Goutam is an internationally known expert in the field of analytics and has presented numerous programs and workshops to executives, educators, and research professionals in the U.S., Europe, Asia, Australia, South America and Middle East. He has won numerous teaching awards including SAS® Distinguished Professor Award from SAS Institute, Regents Distinguished Teaching Award at OSU; Outstanding Direct Marketing Educator Award, from the DMEF, New York; Outstanding Marketing Teacher Award, from the Academy of Marketing Science, Coral Gables, Florida.Goutam's research has been published in many scholarly journals such as Journal of Interactive Marketing, Journal of Advertising Research, Journal of Advertising, Journal of Business Research, and Industrial Marketing Management. He coauthored two books: Text Mining and Analysis: Practical Methods, Examples, and Case Studies Using SAS® and Contemporary Database Marketing: Concepts and Applications. In addition, Goutam has served on the editorial review board of Journal of Business Research and Journal of Academy of Marketing Science. He has chaired the national conference for direct marketing educators in 2004 and 2005, co-chaired the M2007 data mining conference and chaired SAS Global Forum 2018 conference. Goutam served as a member of SAS® Customer Analytics Advisory Board, and JMP® Discovery Summit Steering Committee and continues to serve as a member of SAS® Global Users Group Executive board.


Shivani Dharwadkar

Shivani Dharwadkar is currently working as Statistical Analyst at Bayer Oncology Strategic Business Unit, Hyderabad. She holds a master’s degree in Biotechnology. Overall, she has 9 years of diversified experience in Industry and Academics teaching university colleges. Currently she is involved in clinical trial analysis and submission activities, preparing annual reports for regulators.


Ashish Grover

Ashish Grover has a B.Tech in Electronics and Communication in 2012, and has 7 years’ experience in Statistical Programming and Clinical Data Analysis. HeI started his career at TCS and worked for European pharmaceutical clients like Boehringer Ingelheim and Ferring Pharmaceuticals before moving to Syneos Health, where he works as a Senior Statistical Programmer. He has experience of working in multiple therapeutic areas and his current role involves creation and validation of ADaM dataset and TLF.


Ajay Gupta

Ajay is a Programming Technical Manager at PPD. He received his master’s degree in Biomedical Engineering from Louisiana Tech University in 2006. Since 2010, he has been a regular presenter at SAS conferences, especially PharmaSUG. He has also been a member of the PharmaSUG conference committee for the past four years, and is interested in topics related to SDTM, Pinnacle21, Visual Basic for Applications, Spotfire, Risk Based Monitoring, SAS Grid and SAS Application development.


Shrishaila Patil

Shrishaila has done Masters in Biotechnology from Bangalore University & have more than 15 years of experience across Clinical Database programming, SDTM, ADaM & TLF Programming. He is currently working as Vice President at Navitas Data Sciences, heading the India Statistical Programming department. He is also working as “PhUSE India Membership” officer. He is active in most of the conferences in the industry & his hobbies are playing Table tennis, Reading etc. Most recently, he has authored an International book “FDA Clinical Outcome assessments and CDISC QRS supplements” under “Clinical disciplines” category with LAMBERT Academic Publishing group.


Arjun Roy

He leads a team of 100 scientists to provide specialized biostatistical services and software solutions to pharma and consumer health care. He has offered more than 25 years of experience in Pharmaceutical and CRO industry in India, US, and France. He is instrumental to several new drug, 505b2, and Clinical/PK ANDA approvals. Mr. Roy was lastly associated with AB Science, Paris as Director (Head of Biometry) where he has work on targeted therapies in the field of Oncology, Chronic inflammatory and Neurological degenerative diseases. Prior to that he has associated with Sun Pharmaceuticals Industries Limited (erstwhile Ranbaxy lab) at Gurgaon, Cytel Software at Cambridge, Covance Lab at Madison and Fresenius kabi oncology, Delhi. He has earned PG degree in Mathematical Statistics from Delhi, research experience from ISI, Kolkata, and Senior Residency from UCMS & GTB Hospital. He has published more than 10 papers and own patent for antimalarial medicine. He is member of American Statistical Association (ASA), Professional Statistician in UK (PSI) and Indian Society of Medical Statistics (ISMS), India. His interests included evidence based thinking, HL7 data standards and data visualization.


Prasoon Sangwan

Prasoon Sangwan is a Program Manager for life sciences at Tata Consultancy Services . She has led major clinical programs for protocol digitization, Clinical Data warehouse, Risk based monitoring, Modernizing Central Lab processing and Real World evidence with her extensive knowledge and innovative style.


Soma Sekhar. K

Soma Sekhar Koramatla is currently working as a Team Lead- Statistical Programming in Quartesian and he has been associated with Quartesian since 4 years. He has 7.5 years of experience in the clinical research industry and expertise in CDISC- SDTM Standards/Programming and SDTM/ADaM Case Report Tabulation (CRT) packages creation. He holds a bachelor’s degree in Bioinformatics and worked with IQVIA before joining Quartesian.


Chandrakala Shivalingaiah

Kala has done her undergrad in computer science from Bangalore University and MBA in Bioscience from NC State University. She has been in various CRO’s for over 15 years and is currently working as Director of Data operations within VDS. During her experience she has worked with many pharmaceutical companies as Liaison, Acting Head of SP, Program Director and many other roles. Prior to bringing her skills to BioPharma; Kala worked in several different industries such as; CRM; finance and information technology. She brings a breadth of experience in business intelligence; metrics; customer service; quality systems and process improvement; and applies that knowledge to the clinical setting.


Ajay Sinha

Ajay Sinha has over 15 years of experience in Life Science industry. He is Advanced SAS Certified and holds various other professional certifications. He has anchored the setup of large Biostatistics/SAS Programming unit both at India and US locations. He has served various leadership positions in past and has helped start-up to set up and stabilize vital functional units, mid-sized CROs – to accelerate productivity with enhanced quality, IT- deploying new software’s into the system successfully (including computer validation inspections (IQ/OQ/PQ) in compliance with 21 Code of Federal Regulations (21 CFR, Part 11)).

He is currently working at Novartis, as Associate Director (Group Head) in the Statistical Programming Groups, helping streamline various activities in the SP function. He has served as Guest Faculty and Expert Speaker on various topics and has also presented multiple papers at National and International Conferences. He is influential, result oriented and consummate professional leader with excellent people management skill.


Aditya Tembe

Aditya Tembe has 8 years experience as Statistical Programmer. Currently he is serving Accenture Service Pvt. Ltd. as Clinic Trial Analysis Adv Specialist.


Shilpakala Vasudevan

Shilpa holds a Master's degree from Georgia Institute of Technology, Atlanta. She started her career in the US, where she worked for some years before returning back to India to work with Ephicacy. She has close to 14 years of varied experience working for several leading pharmaceutical companies and CROs. She has presented papers in many conferences around the globe, and has won best papers in PharmaSUG US and China recently.


Fred Wood

Fred is Vice President for Consulting Services at TalentMine. He leads the Data Standards Consulting Group, and is an SDTM and SEND Implementation Advisor. He has been active in leading the development of CDISC standards since 1999, and is one of the principal contributors to the CDISC Study Data Tabulation Model (SDTM). Fred is a founding member of the SDS Team (1999), the SEND Team (2002), and the Medical Devices Team (2007), and has led or co-led these for many years; he currently serves on the Leadership Teams of all three. Fred served for more than fifteen years on the CDISC Technical Leadership Committee and five years on the CDISC Standards Review Council. He is currently a member of the CDISC Global Governance Group, which oversees the development and publication of all CDISC standards and documents.

Prior to joining TalentMine, Fred led the Data Standards Consulting Group within Accenture's Accelerated R&D Services for 11 years. This includes time as Vice President, Data Standards Consulting at Octagon Research Solutions, which was acquired by Accenture in 2012. Fred joined Octagon in 2006, coming from Procter & Gamble Pharmaceuticals, where he was the Global Data Standards Manager in the Clinical Data Management Department. This position was preceded by many years as a Senior Toxicologist at P&G, supporting Rx and OTC products. Fred has a Ph.D. and an M.S. from the University of Massachusetts in Amherst, and a B.S. from Springfield College in Springfield, Massachusetts.


Ajay Yalwar

Ajay Yalwar is Associate Director, Statistical Programming, FSPx at Covance and prior to this he was associated with GSK as Associate Manager, Statistical Programming. With close to 14 years in statistical programming he has led several complex studies apart from leading and mentoring large high-performing teams of statistical programmers.

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