AI in Pharma Summit: Clinical Trials Agenda

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Main Agenda

  • Thursday, October 13th
Thursday, October 13th

8:00 AM ET   Registration, Breakfast, Networking

9:00 AM ET   Chair’s Opening Remarks

John Reynders, Chief Data Sciences Officer, Neumora

9:10 AM ET   Keynote Presentation: Using AI in Clinical Trials: Where Are We Now?

  • A deep dive into where the industry is now and what is currently holding it back
  • Exploring the main breakthroughs in the industry
  • Sharing perspectives on the use of AI in Pharma
Andrea de Souza, Associate VP, Research Information and Digital Solutions, Eli Lilly and Company
Yongming Qu, VP, Statistics, Eli Lilly and Company

9:40 AM ET   Keynote Panel Discussion: Data: Challenges, Issues and Proposed Solutions for Pharmaceutical Companies

Data continues to be the biggest challenge facing AI in Pharma. This panel will discuss the main data challenges which need to be overcome to accelerate widespread adoption of AI in clinical trials. 

  • What are the greatest data challenges facing the adoption of AI in drug discovery?
  • What standards should we be applying to ensure the data collected is high quality?
  • How can we integrate data from different sources and account for variation between laboratories collection methods?  
  • How can we enrich clinical data, for example with data from wearable sensors?
  • How can we overcome these data issues to accelerate the adoption and use of AI in the pharmaceutical industry?

Ryan Copping, Global Head of Data Science Acceleration, Product Development Data Sciences,  Roche & Genentech

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

Gayle Wittenberg, VP Neuroscience Data Science and Digital Health, Janssen R&D

Luis Olmos, Director of Clinical Affairs, Unlearn

 

10:30 AM ET   Morning Break

11:15 AM ET   Presentation: Using AI to Improve Operational Efficiency in Clinical Trials

  • A deep dive into using NLP mine unstructured data and to screen out information to condense complicated data 
  • What structures and processes need to be in place to be able to effectively leverage the power of AI to improve the operational efficiency of clinical trials 
  • Discussing how AI can improve the efficiency of the clinical trial process, to reduce both cost and duration of the clinical trial

Amir Emadzadeh, Director, Data science, Gilead Sciences

11:40 AM ET  Fireside Chat: Leveraging an AI Ecosystem to innovate: Strategies for  AI innovators to scale their science

In this session, we will hear, what the future of AI innovations will look like in the Clinical R&D world, and how leveraging the power of innovation ecosystems can help a startup’s journey in the space

  • Challenges in using AI to accelerate science  – why is AI different than other technologies 
  • Where is the real pickle in AI?
  • What are gaps in business models, tools and talent that inhibit AI companies today 
  • What new solutions are helping the computational scientist, what new solutions are helping the clinical leader?
  • Role of an ecosystem playing to strengths – Pharma data science, academic research and technology companies 
  • Partnering with innovative technology companies – key lessons, methods and best practices

Ganesh Padmanabhan, Founder & Chief Creative Officer, Stories in AI 

Varun Ramdevan, Digital Health and Technology Partnerships, J&J

George Washko, Professor of Medicine, Harvard Medical School

12:30 AM ET   Lunch Break

2:00 PM ET   Presentation: Using AI to Create a Personalized Clinical Trial Experience for the Patient

  • With the rise of telehealth options post covid, patients are looking for a more personalized experience. Explore how AI has the potential to create a personalized trial participation experience 
  • Explore how we can integrate multiple types of data to prescribe appropriate and targeted therapies
  • Discuss how we can harness biomarker testing to fulfill the promise of precision medicine 

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

2:25 PM ET   Case Study: Applying AI and ML Methods to Generate Novel Insights from Clinical Data

  • A deep dive into how we can use AI to improve the efficiency of analyzing large data sets, which has previously slowed the pace of clinical trials 
  • Explore how we can use AI to stratify patients and identify patient subgroups 
  • Discussing how we can harness the power of AI to extract meaningful insights from data

Mohsen Hejrati, Head of AI & Cloud Engineering, Genentech

2:50 PM ET   Presentation: Data-Driven Precision Neuroscience

John Reynders, Chief Data Sciences Officer, Neumora  

3:15 PM ET   Afternoon Break

4:00 PM ET   Presentation: The Application of AI/ML in Clinical Development

Shams Zaman, Director, Global Biometrics & Data Sciences, Bristol Myers Squibb

4:25 PM ET   Panel Discussion: What are the Biggest Opportunities on the Horizon for the Use of AI in Clinical Trials?

Bringing a drug to market is a notoriously long and expensive process, with a low success rate. AI will play a major role in transforming the clinical trial process, and ultimately improve the development of safe and effective life changing therapies. There is established awareness and interest in using AI in Pharma, now it’s time to demonstrate the value of these tools in the clinical trial process.  

  • How do we eliminate data silos?
  • Is collaboration and changing perception between data and clinical scientists key to accelerating AI in clinical trials?
  • Looking to the future, how can we harness the power of AI to answer some of the biggest challenges facing the pharmaceutical industry?

Ariel Dowling, Senior Director, Head of Sensing and Measurement, Takeda

Faisal Khan, Corporate Vice President Advanced Analytics, AI and RWD, Novo Nordisk

Alex Aronov, Executive Director, Head of Data Science, Vertex Pharmaceuticals 

Steve Kundrot, CTO, TriNetX

Real-world data for the life sciences and healthcare | TriNetX

5:15 PM ET   Chair’s Closing Remarks

Close of the AI in Pharma Summit: Clinical Trials

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