Highlights of 2023 SAPA Scientific Symposium
On May 6th, 2023, Sino-American Pharmaceutical Professionals Association (SAPA) held the annual Scientific Symposium, themed “The Power of AI, Data Science and Cutting-Edge Technologies in Pharmaceutical Industry” at the Rutgers Robert Wood Johnson Medical School in New Jersey. This event was attended by almost 300 experts and scholars from industry, academia, and regulatory agencies. The attendees had a lively discussion of the recent development of artificial intelligence (“AI”), data science, cutting-edge technologies, and their impact on the innovation in the biopharma industry.
The event chairs, Dr. Wei Ding (Head of Bioinformatics and Data Science at Alexion AstraZeneca) and Dr. Xiaojiao Xue (Principal Scientist in Clinical Pharmacology and DMPK at PTC Therapeutics), delivered the opening remarks. Dr. Wei Ding started the event by introducing the current hot topics in the field. Dr. Xue introduced the program agenda and expressed great appreciation for the event’s organizing committee and all volunteers. She also gave special thanks to Dr. Janet Alder, Associate Professor in Neuroscience and Cell Biology, Assistant Dean for Graduate Academic and Student Affairs at Rutgers School of Graduate Studies, and Director of Rutgers iJOBS for her generous support for this event. The president of SAPA, Dr. Yongmei Li (CEO, The EmerTher Company), welcomed all attendees and provided an overview of SAPA’s flagship events and mission.
I. Morning Session
Group photo of SAPA 2023 Scientific Symposium Organizing Committee
The first keynote speaker is Dr. Venkat Sethuraman, who is a SVP and Head of Global Biometrics & Data Sciences at Bristol-Myers Squibb (“BMS”). His talk focused on the use of AI algorithms and machine learning (“ML”) in the data analysis involved in drug target and biomarker discovery, radiomics, clinical data interpretation, and digital health. Dr. Sethuraman believes that AI/ML are useful tools to improve the efficiency and productivity of innovation, which we should not feel threatened by. Instead, we should plan to work with AI/ML strategically in the long run.
First keynote speaker, Dr. Venkat Sethuraman
As a leader in drug discovery chemistry, Dr. Zhicai Shi, as the second plenary speaker, shared his insight on how AI could serve as an enabling technology in drug chemistry. Dr. Shi is the Global Head of Discovery Chemistry Capabilities at Janssen Pharmaceuticals, a Johnson & Johnson Company. He discussed his team’s work on multiple novel applications of AI/ML in drug discovery, such as direct-to-biology high-throughput chemistry and DNA-encoded chemical library. In particular, his team used AI-based modeling and automation to predict synthesis route and facilitate hit finding, thereby significantly improving the yield, efficiency, and probability of success of drug synthesis and discovery.
The second plenary speaker, Dr. Zhicai Shi
Dr. Henry Wei is an Executive Director in the Development Innovation team at Regeneron. He discussed a number of pioneering work in AI/ML and their applications to the optimization of various aspects of a clinical trial, such as site selection, patient recruitment, and contract negotiation. Dr. Wei emphasized the co-development and mutual facilitation of machine learning and human learning. Ultimately, the development of effective AI/ML tools relies on humans’ ability to identify the right data.
The third plenary speaker, Dr. Henry Wei
II. Lunch Break
The lunch break provided not only a taste of delicious food, but also an opportunity for the speakers, the sponsors, and the audience to freely interact with each other at the lunch tables and exhibition booths.
Lunch Break and Vendor Booths
In addition, BeiGene held a sponsor presentation during the lunch break. Dr. Jingjing Ye and Ms. Shiying Zhang, from BeiGene’s Global Statistics and Data Sciences (“GSDS”) group, gave a comprehensive introduction of BeiGene’s global network, pipelines, and competitive advantage. In particular, they focused on the self-service analytical platform developed by GSDS, which combines AI with human judgment and is well suited to clinical study site feasibility assessment.
Sponsor presentation by Beigene
III. Afternoon Session
- Session A
Dr. Jerry Li (Director, Cluster Head of Global Biometrics and Data Science at BMS) and Dr. Jiangchao Chen (Director, WuXi AppTec, Inc.) hosted the parallel Session A which focused on “Advancements and Applications of AI and Data Science.”
Dr. Xiaoyan Wang, Chief Scientific Officer from Melax Tech, gave a speech on how large language models (“LLM”) advance the use of real-world data (“RWD”) in drug innovation and patient care. However, challenges remain in fully harnessing the power of the related AI and natural language processing (“NLP”) technologies. As Dr. Wang pointed out, one challenge is the lack of standardization in data collection and sharing. Another challenge is protecting patient privacy and data security. As more patient data is collected and analyzed, the risk of data breaches and unauthorized access increases.
Session A Speaker, Dr. Xiaoyan Wang
Dr. Ruihao Huang, AI & ML reviewer from the U.S. FDA, discussed the use of AI/ML in drug development and precision medicine. With the help of AI/ML technologies, drug discovery and development can be more efficient, cost-effective, and personalized. Dr. Huang pointed out that the FDA is now using these technologies to analyze clinical trial data and identify potential safety issues, which has facilitated its decision-making process regarding drug approval.
Session A Speaker, Dr. Ruihao Huang
The third presentation was given by Dr. Huanmei Wu, who is a professor and Assistant Dean for Global Engagement at Temple University. Using the COVID-19 vaccines as an example, she introduced how ML and NLP technologies managed to identify adverse events of drugs effectively and in real-time. By analyzing large amounts of data from various sources, including social media, electronic health records, and surveillance systems, ML and NLP algorithms can identify potential adverse events and specific risk factors that may not otherwise be discovered.
Session A Speaker, Dr. Huanmei Wu
The fourth speaker was Dr. Jason Stevens, a Senior Principal Scientist at BMS. His talk focused on how the integration of data science with high-throughput experimentation (“HTE”) accelerated the chemistry research, such as those employing base metal catalysis. According to Dr. Stevens, the first step involves using exploratory data analysis to understand the structure and pattern of experimental data. With that understanding, ML tools are applied to the dataset to build predictive models that can be used for optimization of synthesis routes.
Session A Speaker, Dr. Jason Stevens
Dr. Huafeng Xu is the founder and CEO of Atommap, a computational drug discovery company. Taking the QUAISAR platform as an example, he discussed multi-scale and multi-fidelity molecular modeling for computation-driven drug discovery. Such emerging platform technologies are able to predict the strength of small molecule-protein interactions. It enables and accelerates every aspect of drug discovery, and facilitates the identification of new drug targets, therapeutic approaches, and new chemical matter.
Session A Speaker, Dr. Huafeng Xu
A panel discussion with the above five speakers followed. Dr. Jingjing Ye moderated the panel discussion. The panelists covered various topics such as ethical issues with AI and potential problems with the use of ChatGPT in data analysis. Especially, the panelists believed that AI would never replace humans, as we still need humans with the requisite domain knowledge to examine the validity of data and the capacity to build human connections under many practical contexts.
Session A Panel Discussion
Session A Group Photo
2. Session B
The parallel session B was moderated by Dr. Jiaying Liu, Senior Scientist at Merck & Co. Inc. and Jack Li, Associate Scientist at PTC Therapeutics, discussing New Frontiers: Navigating the Future of Pharmaceuticals.
Session A group photo
The first speaker, Dr. Yizhou Dong is a professor at Icahn School of Medicine at Mount Sinai. His speech covered the recent innovations in lipid nanoparticle (“LNP”) delivery platforms and the application of RNA-LNP technology in cancer immunotherapy. Dr. Dong saw great potential in the LNP delivery technology as a transformative force in future medicine.
Session B speaker, Dr. Yizhou Dong
Dr. Jiaying Shen, a Distinguished Scientist at Merck, gave an insightful talk on combination product development and innovation. She addressed the key differences between medical devices and combination products from several aspects, including concept, clinical trial design, and commercialization. Dr. Shen also summarized the regulatory challenges associated with combination product development, and addressed the importance of early communication among different teams and treating the combination product as a whole unit during its development journey.
Session B speaker, Dr. Jiaying Shen
The third speaker Dr. Danyi Wen is the founder and CEO of LIDE Biotech, a translational medicine service provider. She introduced her company’s application of functional diagnosis and omics platforms in transforming preclinical and clinical studies of cancer. She believed the use of AI would better integrate and analyze different datasets for the development of more personalized cancer therapies in the future.
Session B speaker, Dr. Danyi Wen
Dr. CJ (Chunjuan) Song, Vice President of CNS Disease Research and Pharmacology & Toxicology at Exegenesis Bio, provided a comprehensive overview of the history and current progress of gene therapy. Dr. Song discussed potential solutions to the remaining challenges in gene therapy, such as targeted delivery, reducing vector immunogenicity, standardizing manufacture, and maintaining long-term efficacy. She also addressed the potential of gene therapy in treating rare monogenic diseases and age-related conditions.
Session B speaker, Dr. Chunjuan (CJ) Song
Before the end of the session, during the open forum, Dr. Jiaying Shen and Dr. Chunjuan Song engaged in an interactive discussion on the collaboration between different teams in the development of innovative therapies, including how to integrate a clinical device team with an early drug discovery team. They also explored the challenges faced with gene therapy clinical trials and manufacturing, and factors to consider in selecting clinical devices, such as the cost and the ease of use, particularly in the context of gene therapy.
Session B group photo
3. Session C
Parallel Session C, also called “Coding Boot Camp,” represents a brand-new program first launched at a SAPA event. In this highly interactive training workshop, the speakers conducted on-site demonstrations of various data analysis and online development tools. In addition, they worked with several teaching assistants to provide hands-on training to the audience on the use of these tools, including coding. This session was led by Dr. Brian Jiang, Senior Manager at Pfizer.
Boot Camp
Dr. Huan Jin, Senior Scientist at Regeneron, introduced tools for genetic variant detection and annotations.
Dr. Yunyun Zhou and Dr. Jingye Yang, from the Children’s Hospital of Philadelphia Research Institute, focused on the software tools their team has developed for genetic variant interpretation and deep phenotyping.
Dr. Zhiwei Yin, Senior Scientist at BMS, delivered a crash course on Shiny, an R package that makes it easy to build interactive web apps.
The audience gained a significant improvement in their data science skill set, which would facilitate their future work in the pharmaceutical industry. At the end of the program, the audience was invited to join a WeChat group for further discussions on the new skills they have learned. This program has so far received a lot of positive feedback.
Boot camp group photo
IV. Conclusion
The SAPA Scientific Symposium of 2023 came to a successful conclusion at 5pm. It has so far received numerous positive feedback from the attendees, who consider this event as an effective networking and learning platform for professionals interested in the AI/ML applications and cutting-edge technologies in pharma.