AI in Pharma: Clinical Development 2023 Agenda

8:00 AM   Registration

9:00 AM   Chair’s Opening Remarks

Nishtha Jain, Head of Innovation and Digital Technology, Takeda

9:10 AM   Fireside Chat- AI in Clinical Development: The Wider Opportunities and Obstacles

Delays in clinical development can add between $600,00 and $8 million USD per day to research expenses. The implementation of AI and ML has increasingly become more prominent within these stages of drug development to decrease these delay-causing risks but where do the true wider opportunities and obstacles lie?

• How mature is the level of AI and ML integration within clinical development currently?
• How will AI assist in clinical development in the future to aid the formation of a safer, more streamlined research environment?
• What actions need to be taken to permit the wider use of AI and ML in clinical development?

9:40 AM   Presentation- Utilising AI to Predict Trial Progression and Risks

• Discuss how data science, modelling and simulation can help predict the overall outcomes of clinical development and trials
• Learn how data science and predictions aid efficiency and avoid delays by preemptively selecting the most promising treatments and patient populations prior to trials 
• Deep-dive into the future of AI use for pre-trial risk assessment procedures

Zhaoling Meng, AVP, Global Head Clinical Modeling & Evidence Integration, Digital and Data Sciences, Sanofi

10:05 AM   Presentation- How Generative AI and Chat GPT together with Intelligent Document Review Help Pharma Accelerate Efficiency and Quality Through Development

• Learn How Intelligent Document Review can help bring a document to “digital life” for supporting Generative AI
• How the digital data lake can be used in downstream applications by AI
• How AI and ChatGPT are used in practice to provide efficiency and quality gains

Gary Shorter, Head of Research and Development of AI Products into Products, IQVIA

10:30 AM   Morning Refreshments and Networking

11:20 AM   Presentation- AGI-empowered Clinical Trial Management

• A close-loop end-to-end clinical development platform fully enables the best use of AGI
• AGI’s application on clinical development’s data and documents to enable trial automation
• AI powered clinical logic engine to contribute to the ecosystem of intelligent clinical trials

Sharon Chen, CEO, AlphaLife Sciences

11:45 AM   Panel Discussion with Open Q&A- How Can Patient Selection and Enrollment be Enhanced in Clinical Trials?

As clinical studies begin to target more and more specific populations, recruitment goals are becoming increasingly difficult to fulfil, thus increasing the risk of the study failing. The use of AI has developed into a critical need to enhance patient selection and enrollment.

• How can the use of AI, ML and NLP be used for advanced predictive models to identify appropriate patient groups for trials?
• What is the potential of AI technology to optimise the recruitment process and reduce the volume of unnecessary screenings?
• What are the main challenges in integrating AI technology into the patient selection and enrolment process?
• What has to be done to address diversity issues within clinical trial cohorts and bias in algorithms?

Youssef Idelcaid, Senior Director, Head of Data- Commercial, Medical Affairs & Government Affairs (CMG), Genentech

Ronak Kadakia, Senior Director JRD DSDH Portfolio Management, Johnson & Johnson

Qinghua Song, Head of Data and Statistical Science, Kite Pharma

Moderator: Tomasz Adamusiak, Chief Scientist, Clinical Quality and Data Science, MITRE

12:45 PM   Lunch and Networking Opportunity

2:20 PM   Presentation- AI-Enabled Clinical Trial Endpoints

• Learn how AI tools can enable measurements that are impractical or impossible for humans
• Learn how AI-supported measurements can improve decision-making to avoid costly late-phase failures
• Discuss the requirements and challenges around developing and deploying AI tools in clinical trial

Greg Goldmacher, AVP, Clinical Research, Head of Clinical Imaging and Pathology, Merck

2:45 PM   Presentation- Going Digital: Redefining Clinical Trials

• Discuss how the Scripps Research Digital Trials Center is building clinical trials around the participant and not the clinic
• Explore how maximizing AI-based and other digital health technologies can aid in the collection of real-world data
• Provide examples of how AI plays a role in multi-modal analyses for describing insights into health outcomes

Ed Ramos, Co-Founder Digital Trials Center, Scripps Research

3:10 PM   Afternoon Refreshments and Networking

3:40 PM   Panel Discussion with Open Q&A- How Can the Ongoing Data Obstacles be Addressed?

Interoperability and access to data are integral to the development of AI and ML algorithms but current data challenges are slowing the development of this technology. What can be done to overcome these obstacles?

• What are the main data challenges and how are these stifling the growth of AI and ML based technology within clinical development?
• How should, both structured and unstructured, data from multiple sources (from clinical trials and real-world evidence) be collated and interpreted with AI? 
• How can the inherent bias in currently available data used to train algorithms be overcome? 
• What are the main challenges regarding privacy regulations in clinical development?
• How can the implementation of AI enhance the patient-voice and enable interpretation and analysis of patient-reported data?

Nirmal Keshava, VP Data Science & Innovation, Cerevel Therapeutics

Amin Yakubu, Director, R&D Data Science & RWE Leader, Neuroscience , Janssen Pharmaceuticals

Tomasz Adamusiak, Chief Scientist, Clinical Quality and Data Science, MITRE 

Moderator: Meghan Dierks, Chief Data Officer, Komodo Health

4:40 PM   Chair’s Closing Remarks