
Leveraging Big Data and Predictive Knowledge to Fight Disease
Tuesday, July 28, 2015
Presented By
Drug development is entering an era of precision medicine that is centered on the analysis of massive amounts of data. The ability to integrate, interrogate, model and interpret biological, chemical, pharmacological, genomic and clinical data holistically is key to making more effective and truly personalized medicines to fight disease. Researchers are using innovative technologies and computational techniques to develop predictive knowledge for the identification of promising new treatments, new therapeutic uses for existing molecules, patients who are good candidates for particular clinical trials or treatment protocols, and population signals of adverse drug reactions. This symposium explores the many uses of big data and predictive knowledge to guide drug development and clinical trials.
This event will also be broadcast as a webinar.
Please note: Transmission of presentations via the webinar is subject to individual consent by the speakers. Therefore, we cannot guarantee that every speaker's presentation will be broadcast in full via the webinar. To access all speakers' presentations in full, we invite you to attend the live event in New York City when possible.
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The Biochemical Pharmacology Discussion Group is proudly supported by
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Agenda
* Presentation titles and times are subject to change.
July 28, 2015 | |
8:30 AM | Registration and Continental Breakfast |
9:00 AM | Introductory Remarks |
9:15 AM | Big Data for Drug Discovery and Drug Safety |
9:45 AM | Harnessing the Power of Healthcare Data with Advanced Data Analytics to Fight Disease |
10:15 AM | Artificial Intelligence Strategies for the Analysis of Biomedical Big Data |
10:45 AM | Networking Coffee Break |
11:15 AM | Use of Electronic Health Records for Surveillance and Predictive Analytics |
11:45 AM | Networking Lunch Break and Poster Session |
1:15 PM | Data-Driven Study Population Definition for Clinical and Translational Research |
1:45 PM | Keynote Address |
2:30 PM | Networking Coffee Break |
3:00 PM | Drug Repositioning in the Era of Precision Medicine |
3:30 PM | Use of "Big Data" in the Development of Crizotinib (Xalkori) for ALK+ Metastatic Non-Small Cell Lung Cancer (NSCLC) |
4:15 PM | Predicting Posttraumatic Stress from Multi-modular Data |
4:30 PM | Understanding Multicellular Function and Disease with Human Tissue-Specific Networks |
4:55 PM | Closing Remarks |
5:00 PM | Networking Reception |
6:00 PM | Adjourn |
Speakers
Organizers
Walter Jessen, PhD
Covance Inc.
Robert Martone
St. Jude Children's Research Hospital
Sonya Dougal, PhD
The New York Academy of Sciences
Keynote Speaker
Niven R. Narain, MD
Berg Pharma
Niven R. Narain is Co-Founder, President & CTO of Berg, a Boston-based biopharma company housing fully integrated discovery, clinical, analytics, and diagnostics divisions. Narain is keenly focused on making the healthcare industry more efficient by employing the flagship Interrogative Biology® platform he created which leverages leading-edge biological and clinical insight from patients. The platform merges biology with technology to truly represent a true Precision Medicine approach to understating patient populations with use of artificial intelligence to derive actionable drug targets, biomarkers, and health analytic information. Niven has overseen the development of a robust pipeline at Berg led by BPM 31510, an anticancer technology he discovered that targets the cancer metabolism being developed for solid tumors and skin cancer. In addition to multiple pre-IND assets in diabetes and CNS diseases, Narain collaborated with the US Department of Defense to develop novel biomarkers for the diagnoses and prognosis of prostate cancer currently in CLIA-based clinical trials for product launch. He is inventor of the Interrogative Biology™ platform that has produced and guided clinical development of lead molecules in cancer and diabetes. His technologies and scientific expertise is the subject of key collaborations within the US Department of Defense, NASA, Walter Reed National Military Medical Center, NIH/NCI, in addition to leading academic medical centers such as Harvard Medical School, MD Anderson Cancer Center, Weill Cornell Medical College, among others. Narain has over 400 issued and pending US and international patents, covering novel biological platform technologies and multiple disease indications. Narain was previously Director of Cutaneous Oncology & Therapeutics Research at the Miller School of Medicine and serves as Sr. Biopharma Advisor to Ocean Tomo in Chicago and serves on the Steering Committee for NASA on the Gene Lab/Mars Initiative. A graduate of St. John’s University, NY in Biochemistry/Philosophy, Narain received his PhD training in cancer biology and clinical dermatology research at the Miller School of Medicine.
Speakers
Marc D. Chioda, PharmD
Pfizer Inc,
Marc Chioda is an Associate Medical Director on the Lung Cancer team at Pfizer Oncology. He is the US Medical Affairs Lead for crizotinib (Xalkori) and also supports pipeline compounds in development such as PF- 06463922; Pfizer’s next-generation ALK/ROS inhibitor. With over a decade of pharmaceutical industry experience, Marc has worked on a variety of teams spanning pre-clinical research, clinical research and health economics/outcomes research. He has practiced in hospital, community and specialty pharmacy settings. Marc is a co-inventor on five pharmaceutical patents and joined Pfizer in 2013 after serving as adjunct clinical faculty at Rutgers University where he also earned his Doctor of Pharmacy (PharmD) degree.
Leonard James, MD, PhD
Pfizer Inc.
Lee James is a senior director in clinical development at Pfizer and is the Global Clinical Lead for PF-06463922, Pfizer’s next generation ALK/ROS1 inhibitor. Prior to this role, he worked with multiple teams in clinical development and medical affairs involving both Xalkori (crizotinib) and Sutent (sunitinib).
Lee received his Bachelor of Science from Cornell University, and both MD and a PhD in Molecular and Cellular Biology from the University of Washington in Seattle. His PhD thesis on Myc target genes took place at the Fred Hutchinson Cancer Research Center. He completed his residency in Internal Medicine at the University of Chicago Hospitals and his post-graduate training through the Oncology/Hematology fellowship program at Memorial Sloan Kettering Cancer Center, with a focus on lung cancer. Prior to Pfizer, he was a Medical Oncologist in private practice, where he led the clinical implementation of a new EMR system and served as PI on industry-sponsored clinical trials.
Iya Khalil, PhD
GNS Healthcare
Iya Khalil is responsible for initiating and developing GNS Healthcare’s partnerships with pharma, biotech, and academia. In addition, she oversees the execution of projects in these areas.
She has extensive experience in creating and applying computational approaches that leverage large-scale genomic, clinical, and molecular data for healthcare innovation. Prior to joining GNS Healthcare, she worked at Cornell University, the University of Washington, and Abbott Labs.
A frequent speaker at industry events and conferences, Iya is an inventor on a number of pending patents and has published multiple articles on in silico technologies applied to drug discovery and development. She is also a co-founder and board member of the New Libya Foundation, a non-profit organization dedicated to nurturing the development of civil society organizations in Libya. Iya holds a BS in physics from the University of Washington and a Ph.D. in physics from Cornell University.
Arjun Krishnan, PhD
Princeton University
Arjun Krishnan is a Senior Researcher at the Lewis-Sigler Institute for Integrative Genomics at Princeton University. He has a B.Tech in Biotechnology and a PhD in Genetics, Bioinformatics & Computational Biology. Arjun’s research interests lie in understanding various aspects of multicellular biology: (1) tissue/cell-type specific gene expression, function and interaction, (2) functional and evolutionary relationships between cell-types/tissues, and (3) role of tissue-specificity in disease manifestation and drug response. One of the principal approaches he takes is to integrate large-scale functional genomics data to build computational models of gene interactions in specific biological contexts. These models are designed to capture scarce expert biomedical knowledge and make systematic genome-wide predictions of gene function, disease-gene association and effects of genetic perturbation.
Sisi Ma, PhD
New York University School of Medicine
Dr. Sisi Ma is a research scientist at the Center for Health Informatics and Bioinformatics, New York University Langone Medical Center. Dr. Ma’s primary research interest is the application of statistical modeling, machine learning, and causal analysis methods in the field of biology and medicine. Specifically, her approaches include (1) devising and implementing new causal discovery methods that are specifically tailored to the characteristics of biomedical data, (2) benchmarking novel and existing causal discovery and predictive modeling methods in order to evaluate their efficacy on biomedical data, (3) designing analytical experiments to discover critical contributing factors to pathologies and diseases from multimodality high dimensional high volume data to aid the development of diagnostic technologies and identification of potential treatment targets. Dr. Ma received her PhD in Psychology in 2014, her MS in Computer Science in 2013 from Rutgers University, and her BSc in medical research in 2008 from Peking University Health Science Center.
Michael Matheny, MD, MS, MPH
Vanderbilt University
Michael E. Matheny, MD, MS, MPH, is Director of the Vanderbilt Center for Population Health Informatics, Associate Director of the TVHS Veterans Affairs Biomedical Informatics Fellowship, and Assistant Professor of Bioinformatics, Medicine, and Biostatistics at Vanderbilt University. He received an MD from the University of Kentucky, an MS in Biomedical Informatics from Massachusetts Institute of Technology, an MPH from Harvard University, and is a Fellow of American College of Physicians with board certifications in Internal Medicine and Clinical Informatics. He has expertise in developing and adapting methods for post-marketing medical device surveillance as well as the development and evaluation of NLP tools, predictive analytics, and automated surveillance applications within large observational cohorts. He currently has funding from Veterans Affairs HSR&D, PCORI, NHGRI, FDA, and Astra Zeneca.
Jason H. Moore, PhD, MS
University of Pennsylvania
Jason Moore has an MS in Applied Statistics and a PhD in Human Genetics from the University of Michigan. We was then an Assistant and Associate Professor of Molecular Physiology and Biophysics at Vanderbilt University where held an Ingram Chair in Cancer Research and served as Director of the Advanced Computing Center for Research and Education. He then moved to the Geisel School of Medicine at Dartmouth where he was the Frank Lane Research Scholar in Computational Genetics and later the Third Century Professor of Genetics and founding Director of the Institute for Quantitative Biomedical Sciences. Dr. Moore is now the Edward Rose Professor of Informatics and Chief of the Division of Informatics in the Department of Biostatistics and Epidemiology at the Perelman School of Medicine of the University of Pennsylvania. He serves as founding Director of the Penn Institute for Biomedical Informatics and Senior Associate Dean for Informatics. Dr. Moore has been recognized for his work in human genetics and translational bioinformatics as an elected fellow of the American Association for the Advancement of Science and as a Kavli Fellow of the National Academy of Sciences.
Nicholas Tatonetti, PhD
Columbia University Medical Center
Dr. Nicholas Tatonetti is assistant professor of biomedical informatics in the Departments of Biomedical Informatics, Systems Biology, and Medicine and is Director of Clinical Informatics at the Herbert Irving Comprehensive Cancer Center at Columbia University. He received his PhD from Stanford University where he focused on the development of novel statistical and computational methods for observational data mining. He applied these methods to drug safety surveillance where he discovered and validated new drug effects and interactions. His lab at Columbia is focused on expanding upon his previous work in detecting, explaining, and validating drug effects and drug interactions from large-scale observational data. Widely published in both clinical and bioinformatics, Dr. Tatonetti is passionate about the integration of hospital data (stored in the electronic health records) and high-dimensional biological data (captured using next-generation sequencing, high-throughput screening, and other "omics" technologies). Dr. Tatonetti has been featured by the New York Times, Genome Web, and Science Careers. His work has been picked up by the mainstream and scientific media and generated hundreds of news articles.
Craig P. Webb, PhD
NuMedii, Inc.
Dr. Webb is the Chief Scientific Officer at NuMedii, where he has primary responsibility for leading the company’s technology development, and translating new drug indication hypotheses into the clinic. Prior to this role, he spent 13 years at the Van Andel Research Institute (VARI) in Michigan, where he directed the community’s efforts in translational science and precision oncology. Under his leadership, VARI formed four new companies in the areas of translational informatics (TransMed Systems and Intervention Insights), molecular diagnostics (The Center for Molecular Medicine) and clinical trial operations (ClinXus). He has a BSc in Biochemistry, a PhD in cell biology, and a post-doctoral fellowship in molecular oncology. He has published more than 60 peer reviewed publications and book chapters and has presented over 80 invited lectures on precision medicine and drug repositioning.
Chunhua Weng, PhD
Columbia University
Dr. Chunhua Weng is an Associate Professor of Biomedical Informatics and co-Director for the Biomedical Informatics Core of the CTSA at Columbia University. Before arriving at Columbia, she obtained an undergraduate degree in computer science from Nankai University, P. R. China, a master’s degree in Information and Computer Science from University of California at Irvine, and a Ph.D. in Biomedical and Health Informatics from University of Washington at Seattle. Dr. Weng’s current primary research interests are (1) designing and applying text knowledge engineering methods to improve the computability of clinical research designs and to support knowledge management and reuse for clinical research; (2) designing data-driven methods to improve the efficiency and patient-centeredness of clinical research; and (3) design socio-technical solutions to integrate patient care and clinical research workflows towards the creation of a rapid learning health system.
Diane Wuest, PhD
GNS Healthcare
Sponsors
Promotional Partner
American Society for Clinical Pharmacology and Therapeutics (ASCPT)
New York Local Section of the American Chemical Society
Society for Industrial and Applied Mathematics
The Biochemical Pharmacology Discussion Group is proudly supported by
American Chemical Society
Abstracts
Use of “Big Data” in the Development of Crizotinib (Xalkori) for ALK+ Metastatic Non-Small Cell Lung Cancer
Marc D. Chioda, PharmD and Leonard P. James, MD, PhD
Pfizer Oncology, New York, New York, United States
Harnessing the Power of Healthcare Data with Advanced Data Analytics to Fight Disease
Iya Khalil, PhD, GNS Helathcare, Cambridge, Massachusetts, United States
Artificial Intelligence Strategies for the Analysis of Biomedical Big Data
Jason H. Moore, PhD, Institute for Biomedical Informatics, University of Pennsylvania, Philadelphia, Pennsylvania, United States
Big Data For Drug Discovery and Drug Safety
Nicholas P. Tatonetti, PhD, Assistant Professor, Departments of Biomedical Informatics and Systems Biology, Columbia University, New York, NY, USA
Drug Repositioning in the Era of Precision Medicine
Craig P. Webb, PhD, NuMedii Inc., Palo Alto, California, United States
Data-Driven Study Population Definition for Clinical and Translational Research
Chunhua Weng, PhD, Columbia University, New York City, New York, USA
To address this problem, I will present a novel data-driven methodology framework for study population definition and research eligibility criteria language generation using electronic patient data, especially data from electronic health records. I will use Type 2 diabetes as a proof of concept to show how the value distributions for A1c and age differ between the aggregated target populations from existing Type 2 diabetes trials and the real-world diabetic population. I will discuss how this framework can potentially (1) increase the transparency and contextual assessment of a new clinical trial’s generalizability early on; and (2) facilitate rapid, shared decision making among patients, clinicians, and clinical researchers in determining “studiable” populations and hence to improve the feasibility and efficiency of clinical research recruitment.
Keynote Address
The Future of Developing Drugs: Employing Artificial Intelligence and Biology
Niven R. Narain, PhD1
Coauthors: Michael A. Kiebish, PhD1, Rangaprasad Sarangarjan, PhD1, Viatcheslav Akmaev, PhD1, and Vivek K. Vishnudas, PhD1
1 Berg Health, Framingham, MA, United States
Predicting Posttraumatic Stress from Multi-modular Data
Sisi Ma, PhD1
One goal of the current study is to assess the feasibility of distinguishing the trauma survivors that would display non-remitting stress response from the ones that display remitting stress responses at a time that is early enough, so that early interventions can be administered effectively. Multimodal data including clinical information, patient history, and peripheral neuroendocrine markers were collected in the emergency room, at 1 week, 1 month, and 5 months from survivors of traumatic events. The model constructed with data from the four time points showed better performance progressively. The result also indicates that it is possible to identify individuals that would display non-remitting stress response as early as 1 week.
In addition, to investigate the causal mechanisms of the development and prognosis of PTSD, causal network were constructed among the measured variables. Both known and novel pathways leading to PTSD have been discovered. The identification of novel causal pathways to PTSD could contribute to development of new diagnosis and treatment technologies.
Coauthors: Isaac Galatzer-levy, PhD2, Alexander Statnikov, PhD1
1 Center of Heath Informatics and Bioinformatics, NYU Langone Medical Center, New York, New York
2 Department of Psychiatry, NYU School of Medicine, New York, New York
Understanding Multicellular Function and Disease with Human Tissue-Specific Networks
Arjun Krishnan*,4
Coauthors: Casey S. Greene*,1,2,3, Aaron K. Wong*,5, Emanuela Ricciotti6,7, Rene A. Zelaya1, Daniel S. Himmelstein8, Ran Zhang9, Boris M. Hartmann10, Elena Zaslavsky10, Stuart C. Sealfon10, Daniel I. Chasman11, Garret A. FitzGerald6,7, Kara Dolinski4, Tilo Grosser6,7, Olga G. Troyanskaya4,5,12
1 Department of Genetics, The Geisel School of Medicine at Dartmouth, Hanover, New Hampshire, United States
2 Dartmouth-Hitchcock Norris Cotton Cancer Center, Lebanon, New Hampshire, United States
3 Institute for Quantitative Biomedical Sciences, Dartmouth College, Hanover, New Hampshire, United States
4 Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, New Jersey, United States
5 Department of Computer Science, Princeton University, Princeton, New Jersey, United States
6 Department of Pharmacology, University of Pennsylvania, Philadelphia, Pennsylvania, United States
7 Institute for Translational Medicine and Therapeutics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, United States
8 Biology and Medical Informatics, University of California, San Francisco, United States.
9 Department of Molecular Biology, Princeton University, Princeton, New Jersey, United States
10 Department of Neurology, Icahn School of Medicine at Mount Sinai, New
York, New York, United States
11 Division of Preventive Medicine, Brigham and Women's Hospital and Harvard Medical School
Boston, Massachusetts, United States
12 Simons Center for Data Analysis, Simons Foundation, New York, New York, United States
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