Building on the success of last year's inaugural event, PharmaSUG will be returning to Tokyo this year. Please plan to join us October 24, 2019, at the SAS offices in Tokyo for a full day of presentations and networking. Note that all presentations will be given in Japanese. Please check back soon for more information!
Presentation AbstractsElectronic Data Submission and Utilization in Japan- in Preparation for the End of Transitional Period
Hiromi Sugano, Principal Reviewer for Biostatistics, Office of New Drug II / Office of Advanced Evaluation with Electronic Data, Pharmaceuticals and Medical Devices Agency (PMDA)
Pharmaceuticals and Medical Devices Agency (PMDA) started to accept electronic clinical study data with New Drug Applications on October 1st, 2016. The study data have been successfully received and the new drug reviewers, mainly biostatistics reviewers and medical reviewers in PMDA, use submitted data for their new drug review. PMDA have issued several guidance and FAQs so far, and since the transitional period will be ended on March 31st, 2020, PMDA is now preparing for the next phase. In this presentation, current status and future perspective of electronic data submission will be shown. Additionally, examples of utilization of submitted data in review process and examples of reviewer-friendly style for submitted data or documents will be presented.
(Abstract coming soon)
Efficient Preparation of eData Submission to Both PMDA and FDA
CJUG ADaM Team (Takashi Kitahara, Novartis Pharma K.K.)
Electronic study data submission (eData submission) to Pharmaceuticals and Medical Devices Agency began in 1st October 2016 with a 3.5-year transitional period, and will be mandatory starting in 1st April 2020. On the other hand, eData submission to Food and Drug Administration became effective as of 17th December 2016 for all studies that start after this date for New Drug Application. Although both Health Authorities (HAs) require to submit eData, there are some differences in their requirements. Under the circumstance, industries would like to file NDA to both HAs as simultaneously as possible to maximize value of its product. Thus, it is important for us to know and manage these differences.
Therefore, CDISC Japan User Group ADaM team has been creating a document to summarize differences in the requirements between both HAs and suggestion on streamlined process to achieve simultaneous submission.
In this presentation, major important differences in the requirements and the timelines, tips of streamlined process and the internal team organization to prepare eData submission will be provided.
Central Metadata Repository for Automation in SDTM Dataset Generation
Naoko Izumi, Senior Statistical Programer, Novartis Pharma K.K.
In a world of continual improvement in processes need for automated tool has become the next tag line. In context of data submitted to Health Authorities quality and consistency of data is of prime importance. The traditional method of development needs to give way to automation to speed up the process for dataset generation so that for review of primary and secondary endpoints output enough time is subsided.
In a properly managed setup, standards and metadata can be used to drive automation. This can result in start of programming even before the actual data for that study is collected. To achieve this one simple and efficient way is to have centrally managed metadata repository that can accelerate the implementation of standards and facilitate regulatory compliance. Since most of the structure in SDTM is fixed (provided by SDTMIG) it is easier to generate SDTM datasets through metadata-driven approach.
This presentation describes metadata–driven approach that can be followed for generation of SDTM datasets. The basic advantage of this approach is that all the metadata can be managed, validated and governed centrally, while facilitating faster, more consistent dataset generation.
Let's Join the SAS Global Forum: Build Your Bravery Muscles
Yutaka Morioka, EPS
SAS Global Forum (SASGF) is a premier worldwide event for SAS professionals. I submitted my paper to SASGF2019 held in Dallas and made a presentation on stage with my co-presenter (Jun Hasegawa of EPS Corporation), and also I was selected as a winner of the International Professional Award. This time, I want to share the process leading up to the participation in SASGF and my valuable experiences over there. What I felt deeply in Dallas is that Japanese statistical analysts need to be more actively committed to global networking and show their presence. Besides, they should be more curious and at the same time have a future-oriented vision with room in their mind. After listening to my story, I hope you feel that you can easily do it yourself.
Possibility of Process Improvement by Blockchain Technology in Pharmaceutical Industry
Kentaro Arai, Senior Statistical Programmer, Novartis Pharma K.K.
Blockchain Technology is known as distributed ledger technology and it was developed as the core technology of Bitcoin. Blockchain is a growing list of records called blocks that are linked using cryptography. The characteristic of this technology is decentralized and immutable system. In addition, it is possible to incorporate program called smart contract in Blockchain and it enables automation of pre-defined transaction. In recent years, Blockchain technology is expected to be applied to the processes in various industries. Similarly, in pharma industry, many ideas to make use of Blockchain are being considered.
In this presentation, I will introduce the basic technology overview of Blockchain, the use case of Blockchain in pharmaceutical industry and the overview of blockchain system considered by CJUG-SDTM (CDISC Japan User Group SDTM team) Blockchain sub team.
Machine Learning Algorithms / Artificial Intelligence Technology in Clinical Development
TBD, Shionogi & Co., Ltd
(Abstract coming soon)
Catching the Wave of Disruptive Innovations in Real World Evidence.
Yousuke Nishida, Health Service Relations Group, Real World Data Science Dept., Chugai Pharmaceutical Co., Ltd.
The environment of real world evidence (RWE) is now changing rapidly. In particular, new players have entered the healthcare industry. How should we catch the wave of disruptive RWE innovations as a pharmaceutical company? And how can we adapt to the innovations? I would like to suggest the competency model for data scientists. I will also introduce an outline of revised Good Post-Marketing Study Practice, based on a database study which we conducted as a mock study in the Working Team 3 in Federation of Pharmaceutical Manufacturers’ Associations of JAPAN. This session will also include a discussion of Electronic Patient-Reported Outcome and digital solutions. These solutions also have issues that we have to address. Let’s think together for a bright RWE future!
Utilization and Obstacles of Real World Data Within Japan and Future Possibilities
Masaki Nakamura, Director/EBM Division Business Head, Medical Data Vision Co., Ltd.
Various databases are currently pursued with the advancement of utilization of real word data within Japan. Databases which can be explored in Japan and its characteristics, obstacles in utilizing databases, case studies output based by MDV data will be introduced within this session. Moreover, possibilities of data utilization and introduction of current situation of database study conducted post reform of the Good Post-Marketing Study Practice (GPSP) last year will be introduced.
Building Real World Evidence on Cloud in Practice
Naoki Mashiko, Amazon Web Services Japan K.K.
Globally, healthcare policies and drug payments are undergoing change. The pharmaceutical industry is responding with Real World Evidence (RWE) to capture various data types (e.g. claims, payer, EHR, mobiles/wearables, social, genomics) from clinical through post-market activities to prove drug products are efficacious, to maintain formulary preference, and to maximize reimbursement.
Cloud technologies, such as Amazon Web Services (AWS), meet the platform requirements for RWE, helping pharmaceutical organizations quickly establish a cost effective, scalable platform. In this session, we cover the benefits of cloud, how to build the RWE platform on cloud, and how to make the systems secure and compliant. More specifically, we introduce a best practice to deploy SAS Viya on the AWS cloud. By deploying the SAS platform on AWS, you get SAS analytics, data visualization, and machine learning capabilities in an AWS-validated environment. The deployment is automated by an AWS CloudFormation template and takes about one hour.
Presenter BiographiesKentaro Arai
(Bio coming soon)
CJUG ADaM Team
CDISC Japan User Group (CJUG) ADaM Team consists of 68 members (as of April 2018) from Pharma, CRO, Academia and Regulatory agencies. Their objectives are to discuss issues and provide recommendations on the ADaM standards and whole process of eData submission and to provide materials which support the creation of ADaM datasets and other ADaM-related deliverables.
(Bio coming soon)
Naoki Mashiko is a Senior Solution Architect for enterprise healthcare and life sciences (HCLS) customers in Amazon Web Services (AWS) Japan, technically helping customers to build new businesses and services on AWS. He also leads the internal technical community for HCLS in Japan.
(Bio coming soon)
Masaki Nakamura currently is head of managing the large scale data utilization business of MDV.
Yousuke Nishida works at Chugai Pharmaceutical Co., Ltd. His mission is to gather information and network in order to explore and implement a strategy of improving the Chugai brand through behavioral economics, digital solutions and other disciplines. He founded the external digital health team “Digital Health Insight” this year.
Hiromi Sugano is a Principal Reviewer for Biostatistics for Pharmaceuticals and Medical Devices Agency (PMDA), Japan. She is in charge of the biostatistics review and consultation in Office of New Drug II. She has mainly reviewed cardiovascular disease related drug so far. Additionally, she works for Office of Advanced Evaluation with Electronic Data, and she is in charge of supporting utilization of submitted and accumulated electronic data in PMDA through offering training and practical assistance for usage of analysis software to reviewers.