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 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

Sidd Bhattacharya 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 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.

Chengxi Zang 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.


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.).