W10. Data Science Driving Better Informed Decisions

TUESDAY, APRIL 21 | 12:30 - 3:30 PM

ABOUT THIS WORKSHOP:

This workshop will highlight how data science is succeeding in helping Pharma organizations make data driven decisions to gain efficiencies and let companies grow their research programs effectively.

Attendees will learn how to bridge the worlds of data scientists and bench researchers and see how novel tools and applications can impact their research.

WORKSHOP AGENDA:

12:30 pm Workshop Introduction

12:35 Got Data? Got ML? Raise the Bar for Clinical Trial Feasibility!

Meghan Raman, Head, R&D Data Lake and Analytics, Bristol-Myers Squibb

Lack of consistent process and usage of non-standardized data result in sub-optimal clinical trial feasibility identification and longer study startup timelines. Country and site identification processes for clinical trials are complex and time consuming as they rely on various standalone data sources and disparate datasets. Foundational datasets and a little bit of machine learning capability can provide an innovative Clinical Trial Feasibility solution to improve study startup timelines through efficient site, country selection and recruitment.

1:20 Application of Data Science and AI to Improve the Identification and Development of New Drug Candidates

Nigel Greene, PhD, Director, Head, Data Science and Artificial Intelligence, Imaging and Data Analytics, Clinical Pharmacology and Safety Sciences, AstraZeneca

Drug discovery and development is a multiparameter optimization problem that requires a fine balance between efficacy, ADME and safety. Although improvements have been made in the attrition rate for drug candidates there is still plenty of room for improvement. There are strong economic drivers to reduce the costs of discovering new medicines particularly with the rising costs of development and the concerns over late stage failure. Data science and artificial intelligence is being seen as a potential way to improve the discovery and development of new drugs as well as reduce the costs and time to bring these to the clinic. This talk will highlight some of the current investments in computational methods and highlight some of the key gaps in realizing these benefits.

2:00 Refreshment Break

2:20 Leveraging Data Science for Evidence Generation by Integrating Real-World Data from Research and Practice

Farhan (CJ) Hameed, MD, MS, VP, Global Real World Data – Strategy, Analytics & Insights (GRWD-SAI), Analytics, Informatics & Business IntelIigence (AIBI), Pfizer Digital

Is the current state of the healthcare industry really transformative? There is certainly a paradigm shift in adopting and developing new and robust methodologies to collect and analyze the data. Does it really provide a meaningful feedback to patients, providers, regulators and researchers? We will discuss some of the key challenges such as selection of the right tools for advanced analytics and type of datasets, accessibility issues and lack of data standardization, and hidden challenges to generate regulatory grade evidence. Using a patient-centric approach based on evidence generated medicine (EGM) through utilization of real-world data and novel digital end points to enable a robust learning health system (LHS).

3:00 Q&A with Attendees/Speakers

3:30 Workshop Ends

INSTRUCTORS:

Nigel Greene, PhD, Director, Head, Data Science and Artificial Intelligence, Imaging and Data Analytics, Clinical Pharmacology and Safety Sciences, AstraZeneca

Nigel Greene leads the Data Science and Artificial Intelligence department in Clinical Pharmacology and Safety Sciences at AstraZeneca and is interested in the application of artificial intelligence methods to understand of mechanisms of drug-induced toxicity and their translation to a clinical patient population. Previously Dr. Greene was a head of the Predictive Compound ADME and Safety group at AstraZeneca. Dr Greene also spent 14 years at Pfizer, Inc. where he started in Drug Safety R&D and later transitioned to the Compound Safety Prediction group in Medicinal Chemistry. In his early career, Dr. Greene worked for Lhasa Ltd. where he pioneered computational toxicology, and for Tripos, Inc.

Nigel’s other activities outside of AstraZeneca have included being the Chair of the Board of Trustees for Lhasa Ltd. and he has served on multiple National Research Council committees sponsored by the US Environmental Protection Agency, US Food and Drug Administration, and the National Institutes of Health.

Dr. Greene received his BSc and PhD from the University of Leeds in the UK.

Hameed_FarhanFarhan (CJ) Hameed, MD, MS, VP, Global Real World Data – Strategy, Analytics & Insights (GRWD-SAI), Analytics, Informatics & Business IntelIigence (AIBI), Pfizer Digital

Farhan "CJ" Hameed is a biomedical informatician and real-world data strategist with diverse experience in healthcare, spanning academia, patient care, clinical research and informatics for over 18 years. In his current role at Pfizer, he focuses on development and harvesting strategic alliances for end-to-end utilization of real-world data (RWD) in drug development to generate regulatory grade real-world evidence (RWE). In his earlier work at Pfizer, CJ led Informatics initiative at the Quantitative Medicine and Neuroscience Research Units and steered the development and implementation of semantically driven interoperable drug discovery analytics platforms and knowledge management systems for multiple therapeutic areas. In Digital Medicine group and Pfizer Innovative Research (PfIRe) Lab, his team led the development of analytics-based reporting systems leveraging AI & Machine Learning by incorporating ontologies, clinical and wearables data standards for the real world and clinical trial studies. Prior to joining Pfizer, CJ led several clinical informatics projects, built multi-specialty evidence-based knowledgebase systems in partnership with several international academic institutes and publicly funded organizations and steered development of international drug databases, and clinical decision support systems (CDSS). He also held several academic positions in the past – an Associate Professor at the College of Pharmacy at Chicago State University and Midwestern University and, currently, teaches at Northeastern University, Boston Health Informatics graduate program. CJ holds a master's degree in health informatics from Northeastern University and a medical degree from Dow University of Health Sciences. He is a HIMSS fellow and recently attained American Medical Informatics Association fellowship status.

Raman_MeghanMeghan Raman, Head, R&D Data Lake and Analytics, Bristol-Myers Squibb

Meghan Raman has 20+ years of experience in successfully leading large scale business transformation programs. She has cross industry domain expertise including Life Sciences, Financial, Consulting, Insurance, Telecomm and Resellers. She is experienced in building and leading global, high performance teams. She has also built Analytics frameworks and applications in multiple domains to drive revenue uplift and productivity. Meghan has led process development and implementation activities in Clinical, Regulatory and Pharmacovigilance domains. She has set up Analytics-as-a-Service framework and integration efforts between Product Registration, Safety Reporting and Clinical Trial Management.

She is currently leading the R&D Data Lake and Integration portfolio for multiple domains including Translational Medicine, Clinical, Safety, Regulatory and Medical. Her current work enables data scientist access to clinical, operational, biomarker and real world data in a standard location, R&D Data Lake. Her work also enables scientist/clinicians capability to search biomedical terms. In addition her work provides data scientists a reproducible research environment with a business glossary, data catalog & lineage. Her other responsibilities include providing Study Start-up analytic capabilities and enabling a clinical metadata repository to store and manage clinical data standards.

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