Bio IT World Expo 2016  
Bio IT World Expo 2016
Archived Content

Track 6: Systems Pharmacology 

Track 6 focuses on how compounds (drugs) work in the body. How are they influenced by various ‘omics’? How do they vary by tissue? The practical implications of such a compound-centric approach are exciting: new targets, new screens, new markers, new understanding of drug failure mechanisms. The systems computational tool sets including multi-scale modeling, simulation, web-based platforms, etc. will be emphasized.

Final Agenda

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7:00 am Workshop Registration and Morning Coffee

8:00 Pre-Conference Workshops*

*Separate Registration Required

2:00 - 7:00 pm Main Conference Registration

4:00 Event Chairperson’s Opening Remarks

Cindy Crowninshield, RD, LDN, Conference Director, Cambridge Healthtech Institute

4:05 Keynote Introduction

Kevin Brode, Senior Director, Health & Life Sciences, Americas Hitachi Data Systems


Do Network Pharmacologists Need Robot Chemists?

Andrew HopkinsAndrew L. Hopkins, DPhil, FRSC, FSB, Division of Biological Chemistry and Drug Design, College of Life Sciences, University of Dundee


OKTA10 Minute Welcome to the Reception!

Mike Nolte, Regional Sales Manager – East, Okta

Cycle Computing logo
5:00 Welcome Reception in the Exhibit Hall with Poster Viewing

Drop off a business card at the CHI Sales booth for a chance to win 1 of 2 iPads® or 1 of 2 Kindle Fires®!*

*Apple® is not a sponsor or participant in this program


7:00 am Registration and Morning Coffee

8:00 Chairperson’s Opening Remarks

Phillips Kuhl, Co-Founder and President, Cambridge Healthtech Institute

8:05 Keynote Introduction

Sanjay Joshi, CTO, Life Sciences, EMC Isilon


Atul ButteAtul Butte, M.D., Ph.D., Division Chief and Associate Professor, Stanford University School of Medicine; Director, Center for Pediatric Bioinformatics, Lucile Packard Children's Hospital; Co-founder, Personalis and Numedii


8:55 Benjamin Franklin Award & Laureate Presentation

9:15 Best Practices Award Program

9:45 Coffee Break in the Exhibit Hall with Poster Viewing

Pharmacodynamic Models 

11:00 Chairperson’s Remarks

Eugene Myshkin, Senior Research Scientist, Thomson Reuters, IP & Science

» Featured Speaker 

11:10 Systems Pharmacology in a Post-Genomic Era

Peter Sorger, Ph.D., Professor, Systems Biology, Harvard Medical School; Co-Chair, Harvard Initiative in Systems Pharmacology

I will describe the emergence of “systems pharmacology” as a means to guide the creation of new molecular matter, study cellular networks and their perturbation by drugs, understand pharmaco-kinetics and pharmaco-dynamics in mouse and man and design and analyze clinical trial data. The approach combines mathematical modeling with empirical measurement as a means to tackle basic and clinical problems in pharmacology. Ultimately we aim for models that describe drug responses at multiple temporal and physical scales from molecular mechanism to whole-organism physiology.

Thomson Reuters logo 12:00 pm Systems Pharmacology Approaches to Drug Repositioning

Eugene Myshkin, Senior Research Scientist, Thomson Reuters, IP & Science

Drug repositioning requires advanced computational approaches and comprehensive knowledgebase information to reach success. Thomson Reuters will present on recent advances in drug repositioning approaches, their validation and performance, best practices in using systems biology content, and successful case studies.

12:30 Luncheon Presentation (Sponsorship Opportunity Available) or Lunch on Your Own

High Content Analysis: Cancer Cell Lines 

1:40 Chairperson’s Remarks

William Reinhold, Manager, Genomics and Bioinformatics Group, Laboratory of Molecular Pharmacology (LMP), National Cancer Institute (NCI)

1:45 Systems Pharmacology Using CellMiner and the NCI-60 Cancerous Cell Lines

William Reinhold, Manager, Genomics and Bioinformatics Group, Laboratory of Molecular Pharmacology (LMP), National Cancer Institute (NCI)

CellMiner is a web-based application that allows rapid access to and comparison between 20,503 compound activities and the expression levels of 26,065 genes and 360 microRNAs. Included are 102 FDA-approved drugs as well as 53 in clinical trials. The tool is designed for the non-informatisist, and allows the user wide latitude in defining the question of interest. This opens the door to systems pharmacological studies for physicians, molecular biologists and others without bioinformatics expertise.

2:15 Oncology Drug Combinations at Novartis

Joseph Lehár, Ph.D., Director of Bioinformatics OTR, Oncology Translational Medicine, Novartis Institutes for Biomedical Research

Novartis is undertaking a large-scale effort to comprehensively describe cancer through the lens of cell cultures and tissue samples.  In collaboration with academic and industrial partners, we have generated mutation status, gene copy number, and gene expression data for a library of 1,000 cancer cell lines, representing most cancer lineages and common genetic backgrounds.  Most of these cell lines have been tested for chemosensitivity against ~1,200 cancer-relevant compounds, and we are systematically exploring drug combinations for synergy against ~100 prioritized CCLE lines.  We expect this large-scale campaign to enable efficient patient selection for clinical trials on existing cancer drugs, reveal many therapeutically promising drug synergies or anti-resistance combinations, and provide unprecedented detail on functional interactions between cancer signaling pathways.   I will discuss early highlights of this work and describe our plans to make use of this resource. 

2:45 Selected Oral Poster Presentation: Genotype-Based Analysis for Cancer Therapy Using Large-Scale Data Modeling

Nayoung Kim, Ph.D. Candidate, Biological Sciences, Sookmyung Women’s University

An integrative approach of large-scale omics and drug response data on various cell lines enables us to identify the cellular signaling and drug sensitivity in cancer. Signatures in different levels of biological process such as gene expression, protein expression and protein activation have applications in finding novel diagnostic or prognostic biomarkers. They are also key components in accelerating mechanism-based drug discovery or genotype-specific repositioning. Here we present a system-level analysis of cell line data for predicting the sensitivity and mechanisms of targeted drug response based on major genotypes of cancers. Association study with the genotypic classification was performed on drug data and omics data such as transcriptome, proteome and phosphateome on human cancer cell lines. This approach reproduced the known patterns of mechanism-based drug response in cancers. Also, gene and protein signatures significantly associated with genotype were identified and integrated into a drug-oriented network. Furthermore, we process the optimization of public gene sets to draw an advanced pathway-based interpretation using omics data and develop RNAi screening systems for analysis of cancer-regulation markers and anticancer effects perturbed by mutation. This study provides an integrated approach for omics, drug response data and cancer mutation types in cancers.

3:15 Refreshment Break in the Exhibit Hall with Poster Viewing


Pharmacodynamic Models for Oncology 

3:45 Systems Biology in Cancer Immunotherapy: Applications in the Understanding of Mechanism of Action and Therapeutic Response

Debraj GuhaThakurta, Ph.D., Associate Director, Systems Biology, Dendreon Corporation

We are using high-content platforms (DNA and protein microarrays, RNA-seq) in various stages of the development of cellular immunotherapies for cancer. We will provide examples of genomic applications that can aid in the mechanistic understanding and the discovery of molecular markers associated with the efficacy of a cancer immunotherapy..

4:15 Use of Systems Pharmacology to Aid Cancer Clinical Development

Anna Georgieva Kondic, Ph.D., MBA, Senior Principal Scientist, Modeling and Simulation, Merck Research Labs

The last few years have seen an increased use of physiologically-based pharmacokinetics and pharmacodynamics models in Oncology drug development. This is partially due to an improved mechanistic understanding of disease drivers and the collection of better patient-level quantitative data that lends itself to modeling. In this talk, a suite of studies where systems modeling was successfully used to inform either preclinical to clinical transition or clinical study design will be presented. The talk will complete with a potential systems pharmacology framework that can be used systematically in drug development.

4:45 Two-Edged Sword Role of the Mammalian DNA Methyltransferases: New Implication to Cancer Therapy Targeting the Epigenetic Pathway 

C.-K. James Shen, Ph.D., Distinguished Research Fellow, Institute of Molecular Biology, Academia Sinica

Methylation at the 5-position of cytosine (C) to generate 5-methylcytosine (5-mC) on the vertebrate genomes is an essential epigenetic modification that regulates different biological processes including carcinogenesis. This modification has been known to be accomplished by the combined catalytic actions of three DNA methyltransferases (DNMTs), the de novo enzymes DNMT3A/ DNMT3B and the maintenance enzyme DNMT1. This property of DNMTs and the imbalance of CpG methylation in cancer cells have led to the development of cancer therapeutic drugs/ chemicals targeting the DNA methylation activities of DNMTs. However, we have recently discovered that the mammalian DNMTs could also act as active DNA 5-mC demethylases in a Ca++ion-and redox state-dependent manner. This suggests new directions for re-investigation of the structures of DNMTs and their functions in the genome wide and/or local DNA methylation in the mammalian cells. In particular, the concept and strategies for drug therapy targeting the DNMTs may need to be re-evaluated.

5:15 Best of Show Awards Reception in the Exhibit Hall

6:15 Exhibit Hall Closes

Thursday, April 11

7:00 am Breakfast Presentation (Sponsorship Opportunity Available) or Morning Coffee

Modeling and Mining Targets 

8:45 Chairperson’s Opening Remarks

I-Ming Wang, Ph.D., Associate Scientific Director, Research Solutions and Bioinformatics, Informatics and Analysis, Merck Research Laboratory

8:50 Systems Biology Approach for Identification of New Targets and Biomarkers

I-Ming Wang, Ph.D., Associate Scientific Director, Research Solutions and Bioinformatics, Informatics and Analysis, Merck Research Laboratory

A representative gene signature was identified by an integrated analysis of expression data in twelve rodent inflammatory models/tissues. This "inflammatome" signature is highly enriched in known drug target genes and is significantly overlapped with macrophage-enriched metabolic networks (MEMN) reported previously. A large proportion of genes in this signature are tightly connected in several tissue-specific Bayesian networks built from multiple mouse F2 crosses and human tissue cohorts; furthermore, these tissue networks are very significantly overlapped. This indicates that variable expression in this set of co-regulated genes is the main driver of many disease states. Disease-specific gene sets with the potential of being utilized as biomarkers were also identified with the approach we applied. The identification of this "inflammatome" gene signature extends the coverage of MEMN beyond adipose and liver in the metabolic disease to multiple diseases involving various affected tissues.

9:20 Optimizing Therapeutic Index (TI) by Exploring Co-Dependencies of Target and Therapeutic Properties

Madhu Natarajan, Ph.D., Associate Director, Computational Biology, Discovery Research, Shire HGT

Conventional drug-discovery informatics workflows employ combinations of mechanistic/probabilistic in-silico methods to rank lists of targets; therapeutics are then developed for "optimal" targets. I describe a systems pharmacology approach that instead integrates systematic in-silico therapeutic perturbation with models of target/disease biology to identify conditions for optimal TI; non-intuitively optimal TI is sometimes achieved by pairing sub-optimal targets with therapeutics having appropriate properties.

9:50 Leveraging Mathematical Models to Understand Population Variability in Response to Cardiac Drugs

Eric Sobie, Ph.D., Associate Professor, Pharmacology & Systems Therapeutics, Icahn School of Medicine, Mount Sinai School of Medicine

Mathematical models of heart cells and tissues are sufficiently advanced that the models can predict mechanisms underlying pro-arrhythmic or anti-arrhythmic effects of drugs. At present, however, these models are not adequate for understanding variability across a population, i.e., why a drug may be effective in one patient but ineffective in another patient. I will describe novel computational approaches my laboratory has developed to quantify and predict differences between individuals in response to cardiac drugs.

10:20 Coffee Break in the Exhibit Hall and Poster Competition Winners Announced

10:45 Plenary Keynote Panel Chairperson’s Remarks

Kevin Davies, Ph.D., Editor-in-Chief, Bio-IT World

10:50 Plenary Keynote Panel Introduction

Yury Rozenman, Head of BT for Life Sciences, BT Global Services

Niven R. Narain, President & CTO, Berg Pharma


11:05 The Life Sciences CIO Panel

Remy Evard, CIO, Novartis Institutes for BioMedical Research
Martin Leach, Ph.D., Vice President, R&D IT, Biogen Idec
Andrea T. Norris, Director, Center for Information Technology (CIT) and Chief Information Officer, NIH
Gunaretnam (Guna) Rajagopal, Ph.D., VP & CIO - R&D IT, Research, Bioinformatics & External Innovation, Janssen Pharmaceuticals
Cris Ross, Chief Information Officer, Mayo Clinic
Matthew Trunnell, CIO, Broad Institute of MIT and Harvard

12:15 pm Luncheon in the Exhibit Hall with Poster Viewing

Modeling Molecular and Pathophysiological Data 

1:55 Chairperson’s Remarks

Jake Chen, Ph.D., Associate Professor, Indiana University School of Informatics & Purdue University Department of Computer Science; Director, Indiana Center for Systems Biology and Personalized Medicine

2:00 Predicting Adverse Side Effects of Drugs Using Systems Pharmacology

Jake Chen, Ph.D., Associate Professor, Indiana University School of Informatics & Purdue University Department of Computer Science; Director, Indiana Center for Systems Biology and Personalized Medicine

A new way of studying drug toxicity is to incorporate biomolecular annotation and network data with clinical observations of drug targets upon drug perturbations. I will describe the development of a novel computational modeling framework, with which we demonstrated the highest drug toxicity prediction accuracies ever reported by far. Adoption of this framework may have profound practical drug discovery implications.

2:30 Holistic Integration of Molecular and Physiological Data and Its Application in Personalized Healthcare

David de Graaf, Ph.D. President and CEO, Selventa

There are multiple industry-wide challenges in aggregating molecular and pathophysiological data for systems pharmacology to transform the process of drug discovery and development. One of the ways to address these challenges is to utilize a common computable biological expression language (BEL) that can provide a comprehensive knowledge network for new discoveries. An application of BEL and its use in identifying clinically relevant predictive biomarkers for patient stratification will be presented.

3:00 The Role of Informatics in ADME Pharmacogenetics

Boyd SteereBoyd Steere, Ph.D., Senior Research Scientist, Lilly Research Laboraories, IT Research Informatics, Eli Lilly

The leveraging of pharmacogenetics to support decisions in early-phase clinical trial design requires informatics methods to integrate, visualize, and analyze heterogeneous data sets from many different discovery platforms.  This presentation describes challenges and solutions in making sense of diverse sets of genetic, protein, and metabolic data in support of ADME pharmacology projects.


3:30 A Systems Pharmacology Approach to Understand and Optimize Functional Selectivity for Non-Selective Drugs

Joshua Apgar, Principal Scientist, Systems Biology, Dept. of Immunology & Inflammation, Boehringer Ingelheim Pharmaceuticals, Inc.

Most commonly the selectivity of a compound is defined in an in vitro or cellular assay, and it is thought of as principally a function of the binding energy of the drug to its on-target and off-target proteins; however, in vivo functional selectivity is much more complicated, and is affected by systems level effects such as multiple feedback processes within and between the various on- and off-target pathways. These systems level processes are often impossible to reconstruct in vitro as they involve many cell types, tissues, and organs systems throughout the body. We show here that through mathematical modeling we were able to identify, in silico, molecular properties that are critical to driving functional selectivity. The models, although simple, capture the key systems pharmacology needed to understand the on- an off- target effects. Surprisingly, in this case, the key driver of functional selectivity is not the affinity of the drugs but rather the pharmacokinetics, with drugs having a short half-life predicted to be the most functionally selective.

4:00 Conference Adjourns

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