Session 3: Clinical Operations
Join us in exploring the realm of clinical operations, where technology and data science converge to streamline trial processes and enhance operational efficiency. Discover how advanced analytics, real-world evidence, and digital solutions are revolutionizing data collection, monitoring, and analysis. Learn from industry experts who will share best practices, case studies, and insights on leveraging data-driven approaches to drive operational excellence and bring life-changing medications to patients faster.
Chair: Nareen Katta, AbbVie
Nareen Katta works as the Head of Data
Science and Analytics at AbbVie. Nareen has over 20 years of
experience in the pharmaceutical industry. In his current
role, Nareen is responsible for building and executing the
advanced analytics strategy, that covers both Scientific and
Business Operations, across Clinical Development Continuum,
Geostrategy and Study start-up, Centralized and Risk Based
Monitoring, Site Engagement, Business Performance, Precision
Medicine, Patient Safety and R&D. In addition, Nareen is
actively engaged in evaluating the opportunities created by
the technology trends like big data, automation, machine
learning and AI, digital health etc. and strategically
instantiating them at AbbVie to drive organizational
transformation. Nareen has an MBA from The University of
Chicago Booth School of Business and a MS in Electrical
Engineering from University of Texas at Arlington.
Integration of AI in Clinical Trials: Benefits, Risks, and Precautions
Sidd Bhattacharya, Partner, Pharma & Life Sciences, PWC
As a Partner in the Healthcare
Analytics and Artificial Intelligence (AI) practice, Sidd
leads PwC’s Life Sciences Gen AI leader. He brings more than
20 years of product engineering experience with
market-leading organizations launching innovative,
cross-industry solutions with a specialization in Cloud
Native & Artificial Intelligence. Sidd has over 10 years
of his role to pharmaceutical, biotechnology, and medical
device industries.
Relevant experience:
•
Sidd has cross-functional expertise focused on the
intersection between R&D strategy, operations, and
technology. He has diverse experience ranging from ERP
system implementation, operating model design, M&A
post-merger integration, innovation, and digital strategy.
• Sidd has conceptualized, designed, and built several
industry first FDA validated Artificial Intelligence
(AI)-enabled product enabling intelligent automation in the
pharmaceutical R&D domain.
• Sidd is a thought
leader at PwC’s life sciences digital/AI transformation
practice and speaker at top tier industry conferences, and
is also a lead advisor to Senior leadership at Fortune 500
companies; advising on AI strategy and implementation. He
has co-lead programs to expand the AI capabilities,
including the knowledge domains.
Abstract: In the rapidly evolving landscape of healthcare, artificial intelligence (AI) is becoming a pivotal tool in enhancing clinical trial operations. This presentation aims to provide a balanced perspective on the integration of AI in clinical trials, particularly focusing on its application within pharmaceutical companies. We'll examine key benefits through real-world case studies, address the risks and necessary precautions in AI application, and explore the future possibilities of AI. This session is aimed at professionals interested in understanding and implementing AI in clinical trials.
Supporting Physicians to Become Clinical Researchers
Natalie Monegro, Director, Diversity & Patient Inclusion, AbbVie
Natalie Monegro is a Director on
the Diversity and Patient Inclusion team. Since joining
AbbVie in December 2021, Natalie has built new capabilities
to help teams across all therapeutic areas in the company to
proactively ensure that AbbVie's trial populations are
representative of the disease populations being studied.
Prior to joining AbbVie, Natalie spent 15 years as a
regulatory and communications consultant coaching and
preparing sponsors – from big pharma to small biotech – for
regulatory interactions and to gain regulatory approval for
their products. She has experience across the continuum of
clinical development and across all therapeutic areas. The
breadth of her experience positions her as a thought leader
on global harmonization of inclusive clinical trial
practices.
Abstract: More investigators are needed to support the future of clinical research. AbbVie will discuss their approach to onboarding new investigators and the support given to upskill them to be ‘research ready’. We will discuss how data is used to identify disparities and the gaps where investigators are needed and how we use this intelligence to support site and patient selection.
High-Throughput Target Trial Emulation with Real-World Data for Alzheimer’s Disease Drug Repurposing and Beyond
Chengxi Zang, Instructor, Department of Population Health Sciences at Weill Medical College of Cornell University.
Dr. Chengxi Zang currently is an
Instructor in the Department of Population Health Sciences,
at Weill Medical College of Cornell University. He is also a
faculty in the WCM Institute of AI for Digital Health
(AIDH). He got his Ph.D. from Tsinghua University in January
2019 with an Excellent Ph.D. Dissertation Award in the
Computer Science Department and an Excellent Ph.D. Award in
Tsinghua University. His long-term research interest is AI
for healthcare (AI4Health). His current focus is using
AI/Machine Learning, and large-scale Real-World health Data
(RWD) to generate Robust, Generalizable, and High-throughput
Real-World Evidence (RWE), aiming to solve top healthcare
challenges including drug repurposing for Alzheimer's
Disease, understanding Long COVID, preventing suicide, and
to accelerate drug discovery and development process. He
also develops advanced deep generative models, causal
inference models, graph neural networks, etc. His research
has been published in the top medical journals such as
Nature Medicine, Nature Communications, Journal of General
Internal Medicine, Scientific Reports, Cell Patterns,
Archives of Pathology & Laboratory Medicine, as well as
top computer science venues including KDD, AAAI, TKDE, ICDM,
etc. His papers have won the ICDM'18 Best Paper Candidate
and the Best Paper Award at AAAI'20 Workshop on Deep
Learning on Graphs. His research/algorithms/codes have been
applied to companies including NAVIDIA, Boehringer
Ingelheim, Tencent, WeChat, etc., and have received wide
media coverage.
Abstract:
Target trial emulation is the process of mimicking target randomized trials using real- world data, where effective confounding control for unbiased treatment effect estimation remains a main challenge. Although various approaches have been proposed for this challenge, a systematic evaluation is still lacking. Here we emulated trials for thousands of medications from two large-scale real-world data warehouses, covering over 10 years of clinical records for over 170 million patients, aiming to identify new indications of approved drugs for Alzheimer’s disease. We assessed different propensity score models under the inverse probability of treatment weighting framework and suggested a model selection strategy for improved baseline covariate balancing. We also found that the deep learning-based propensity score model did not necessarily outperform logistic regression-based methods in covariate balancing. Finally, we highlighted five top- ranked drugs (pantoprazole, gabapentin, atorvastatin, fluticasone, and omeprazole) originally intended for other indications with potential benefits for Alzheimer’s patients. The proposed high-throughput target trial emulation pipeline could inform real-world evidence generation at scale and can potentially accelerate innovations in epidemiology and the drug discovery and development process (e.g., understanding long COVID, drug repurposing for Paxlovid, etc.).