Decentralized Clinical Trial with Integration in Mind (TRPMA)

EffPha’s President and CEO, Dr. Frank Shen was invited by Taiwan Research-based Biopharmaceutical Manufacturers Association (TRPMA) to give a special speech on Dec 2, 2022 with the topic of Decentralized Clinical Trial (DCT) with Integration in Mind, which is a method of conducting clinical trials that Western countries continually investigate in order to address the challenges in clinical trials.

This speech has attracted many TFDA drug reviewers, members of Division of New Drugs in Center for Drug Evaluation (CDE), and the experts who are concerned about the development of new drugs, clinical trials, and the regulations of the development of digital clinical trials to join and discuss.

The COVID-19 pandemic has accelerated global industrial digital transformation, and also significantly impacted clinical application and drug development at the same time. In the post-pandemic era, how to use digital health technologies to perform telehealth and decentralized clinical trials using remote data collection has become a hot global issue. The health authorities, including the US FDA and EMA, take the initiative in developing relevant guidance. TFDA and CDA officials also explained the policy at BioTaiwan Committee (BTC) of the Executive Yuan and Joint Conference of Taiwan and Japan on Medical Products Regulation in 2022, and planned to announce relevant draft guidance in 2023.

In order to let the audience know the development of decentralized clinical trials, Dr. Frank Shen illustrated the points of implementation for decentralized clinical trials based on the changes of clinical trial planning and strategy, and shared his experience by observing the development from remote trials to decentralized clinical trials in the US. In addition, Dr. Frank Shen shared the opportunities and challenges for decentralized clinical trials, and emphasized clinical trials require clear and precise objectives and results in this speech. That is, the implementation could be decentralized, but it could not further lead to the errors and bias of study results.