PharmaSUG Single Day Event
Tokyo, Japan - SAS Institute offices
September 4, 2018

PharmaSUG is excited to announce our very first event in Japan! Mark your calendars for a PharmaSUG Single Day Event in Tokyo on September 4, 2018. The event will be held at SAS Institute at 6-10-1 Roppongi, Minato-ku in Tokyo. Please send your feedback and ideas via email to This email address is being protected from spambots. You need JavaScript enabled to view it..

Registration is open - register now!

The registration fee is $75 (USD), which can be applied as a credit towards your PharmaSUG 2019 registration. Registration deadline is August 20, 2018.

Presentations

Presentation (click for abstract)Presenter (click for bio)
Electronic Data Submission and Utilization in JapanHiromi Sugano, Biostatistics Reviewer, Office of New Drug II / Office of Advanced Evaluation with Electronic Data, Pharmaceuticals and Medical Devices Agency (PMDA)
New CFDA Requirements in NDA and Its Implementation in ProcessEason Yang, Senior Principal Statistical Programmer, Novartis
Best Practice for e-Study Data Submission to PMDACJUG ADaM Team
Data Mapping Using Machine LearningToru Tsunoda, Information Platform Innovation Group, Platform Solution Division, Solution Division
Automated Generation of PowerPoint Presentations Using R in Clinical StudiesNobuo Funao, Biostatistics, Takeda Development Center Japan, Takeda Pharmaceutical Company, Ltd.
Using R-Shiny as a Data Reviewing and Validation Tool in Clinical TrialsMarkus Niederstrasser, Senior Statistical Programmer, Novartis Pharma K.K
Past and Future of Our AI Making Use of Data Governance: How to Make Process/Product InnovationsRyo Kiguchi and Shogo Miyazawa, Data Science, Biostatistics Center, Global Development Division, Shionogi & Co., Ltd
Technology Overview About Artificial Intelligence for Clinical Data ScienceIppei Akiya, Founder and CEO, DataDriven, Inc.
Mapping Reported Term for the Adverse Event into MedDRA Using Deep LearningYoshihiro Nakashima. Manager, Standardization and Management Group, Data Science, Development, Astellas Pharma Inc.

Sponsors

Presentation Abstracts

Electronic Data Submission and Utilization in Japan
Hiromi Sugano, Biostatistics Reviewer, Office of New Drug II / Office of Advanced Evaluation with Electronic Data, Pharmaceuticals and Medical Devices Agency (PMDA)

Abstract coming soon.


New CFDA Requirements in NDA and Its Implementation in Process
Eason Yang, Senior Principal Statistical Programmer, Novartis

This presentation focuses on:
  1. Background of CFDA reform. Why CFDA reform has been happening and what are the objectives and actions for the reform.
  2. New regulations/guidelines/requirements has been released since 2015 including:
    1. Guidelines for MRCT, General Considerations to Clinical Trials, Biostatistics Principles, Communications for Drug Development and Technical Evaluation, Electronic Data Capture, Data Management Planning and Reporting of Statistical Analysis, eCTD Implementation, Post Approval Safety Surveillance
    2. On-site inspection requirements
    3. Priority Review & Approval Procedure
    4. New Chemical Drug Registration Classification
    5. Data Protection Regime
    6. Adjustment of Imported Drug Registration
  3. Case study of breakthrough heart failure treatment Entresto® (21% reduction in CV mortality or HF hospitalization). What is the benefit from the new regulations makes this drug got won CFDA approval merely two years after its launch in Europe and the US.
  4. Summary of the trend and landscape of the future environment of new drugs development in China



Best Practice for e-Study Data Submission to PMDA
CJUG ADaM Team

Abstract coming soon.


Data Mapping Using Machine Learning
Toru Tsunoda, Information Platform Innovation Group, Platform Solution Division, Solution Division

Abstract coming soon.


Automated Generation of PowerPoint Presentations Using R in Clinical Studies
Nobuo Funao, Biostatistics, Takeda Development Center Japan, Takeda Pharmaceutical Company, Ltd.

In closing stage of a clinical study, we should provide a statistical analysis result (SAR) with tables and figures, and then create a clinical study report (CSR) based on the result. In the meantime, we should also create a PowerPoint slide deck (say, topline report) including a brief summary of the study to report our managers or directors. When we create the slide deck, we usually copy contents from the SAR and paste them into the slides. Due to the manual labor, however, the slides would have several errors (e.g., mispostings or writing errors). I would like to introduce an efficient way to automatically generate a PowerPoint slide deck using R in order to reduce work time and avoid errors. This presentation will also include an introduction of R packages of "officer" and "flextable", and an application for a virtual clinical study with CDISC/ADaM data.


Using R-Shiny as a Data Reviewing and Validation Tool in Clinical Trials
Markus Niederstrasser, Senior Statistical Programmer, Novartis Pharma K.K

Abstract coming soon.


Past and Future of Our AI Making Use of Data Governance: How to Make Process/Product Innovations
Ryo Kiguchi and Shogo Miyazawa, Data Science, Biostatistics Center, Global Development Division, Shionogi & Co., Ltd

The Artificial Intelligence (AI) that we define is the system with the series of processes of “Recognition”, “Learning” and “Action”, which assists people's activities. There are various types of data used in AI, and so that, the methods of recognition, learning, and action are different depending on the data format. However, "Data Governance" which collects, manages, and archives any data (including information) for the purpose of innovation is extremely important regardless of the data formats. Based on Data Governance using Python and SAS, we have been using AI for “Process innovation” in the past. Specifically, our "AI SAS programmer" system is that semi-automatically creates SAS programs to analyze clinical data. Using this system, we realized 33% reduction in analysis work. Currently, we are considering cross-cutting use of data governance technology acquired by AI system. In the future, we will make use of this technology in “Product innovation” of new drug development, and we will introduce a part of the idea.


Technology Overview About Artificial Intelligence for Clinical Data Science
Ippei Akiya, Founder and CEO, DataDriven, Inc.

Abstract coming soon.


Mapping Reported Term for the Adverse Event into MedDRA Using Deep Learning
Yoshihiro Nakashima. Manager, Standardization and Management Group, Data Science, Development, Astellas Pharma Inc.

Abstract coming soon.


Presenter Biographies

Hiromi Sugano

Bio coming soon.


Eason Yang

Yi (Eason) Yang is currently Senior Principal Statistical Programmer, global lead of programming team for Entresto® (LCZ696) which is the new foundation of care for reduced heart failure. He joined Novartis in 2010 and has been leading multiple Phase III global trials and pooling activities in Cardiovascular Metabolism, Immunology & Dermatology therapeutic areas. He is also actively participating in the organization and operation of PharmaSUG China conference as member of conference committee.


Toru Tsunoda

Bio coming soon.


Nobuo Funao

Mr. Nobuo Funao has worked for over 14 years at Takeda Pharmaceutical Company Limited as a biostatistician of clinical studies. Mr. Funao has made several presentations at external conferences and given some lectures at universities. Mr. Funao has also published several books about R and SAS.


Markus Niederstrasser

Bio coming soon.


Toru Tsunoda

Bio coming soon.


Ryo Kiguchi

Mr. Ryo Kiguchi has worked for over 4 years at Shionogi & Co., Ltd as a Data Scientist. Mr. Kiguchi has made researches about AI for Bigdata analysis with SAS and Python.


Shogo Miyazawa

Mr. Shogo Miyazawa has worked for over 1 year at Shionogi & Co., Ltd as a Data Scientist. Mr. Miyazawa has made business innovation with SAS and Python.


Ippei Akiya

Bio coming soon.


Yoshihiro Nakashima

Bio coming soon.