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The most pressing unmet medical needs correspond to complex diseases caused by a combination of genetic and environmental factors. Traditional drug discovery strategies ignore the complexity of biological systems, screening compounds on individual targets rather than focusing on biomolecular networks. Despite growing evidence that the conditions we aim to treat are complex and require the development of treatments that exhibit polypharmacological properties, current drug discovery programs still rely on simplistic approaches during compound selection. Complexity is then considered during the development phase, where the costs and risks are much higher than in the discovery phase. This symposium aims to challenge the "one-target, one-disease" tradition and to discuss design and implementation of biological assays featuring multiple target strategies during the primary discovery steps.
Networking reception to follow event
Registration Pricing
| Member |
$30 |
| Student / Postdoc Member |
$15 |
| Nonmember (Academia) |
$65 |
| Nonmember (Corporate) |
$85 |
| Nonmember (Non-profit) |
$65 |
| Nonmember (Student / Postdoc / Resident / Fellow) |
$45 |
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Agenda
* Presentation times are subject to change.
Tuesday, December 11, 2012
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8:30 AM
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Registration and Continental Breakfast
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9:00 AM
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Welcome and Introduction
Jennifer Henry, PhD, The New York Academy of Sciences
Manuel Duval, PhD, Enumeral Biomedical Corp.
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9:10 AM
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How Small Molecule Drugs were Initially Discovered: the Unexpected Result of Data Mining the US FDA Database
Manuel Duval, PhD, Enumeral Biomedical Corp.
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9:55 AM
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Phenotypic Profiling of Compounds in BioMAP® Systems Enables Early Evaluation of Efficacy, Selectivity and Safety
Alison O'Mahony, PhD, BioSeek, a division of DiscoveRx
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10:40 AM
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Coffee break
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11:15 AM
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Single Cell Network Profiling (SCNP) Applications to Drug Development: Mapping Drug and Target Interactions at the Single Cell Level
Alessandra Cesano, MD, PhD, Nodality
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12:00 PM
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Understanding the Role of Molecular Networks in Cell Fate Determination
Greg Findlay, PhD, Samuel Lunenfeld Research Institute
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12:45 PM
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Lunch break
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1:45 PM
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Moving from Big Data to Predictive Models of Disease and Drug Response
Joel Dudley, PhD, Mt Sinai School of Medicine
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2:30 PM
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Network Medicine: Rethinking Diseases from a Network Perspective
Albert-László Barabási, PhD, Northeastern University
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3:15 PM
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Coffee break
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3:45 PM
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Panel Discussion
All speakers, moderated by Thomas B. Freeman, MS, Boehringer-Ingelheim
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4:30 PM
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Networking reception
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5:30 PM
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Close
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Speakers
Organizers
Mercedes Beyna, MS
Pfizer Global Research and Development
Mercedes Beyna is a research scientist at Pfizer, where she is using biochemical and imaging approaches in the quest to understand the biology underlying various psychiatric disorders. She also performs molecular and cellular biology-based target identification and assay development functions. Captivated by neuroscience, she has worked in the field for over 10 years, in both academic and industrial laboratory settings. Before joining pharmaceutical R&D, Mercedes held lab manager and senior lab technician positions at New York University (NYU). Her experience includes molecular neurobiology, synapse formation and plasticity, neurotrophin signaling, and developmental neurobiology areas. Mercedes attended Binghamton University, earning her undergraduate degree in Biology, and subsequently received her Master's Degree in Biology from NYU. As the Pfizer lead in the Biochemical Pharmacology Discussion Group at the New York Academy of Sciences, she enjoys developing interesting and educational symposia.
Manuel Duval, PhD
Enumeral Biomedical Corp.
Manuel Duval is a bioinformatics scientist doing computational biology research for Drug Discovery and Development. His interest is in characterizing the mode of action of candidate therapeutics agents to cellular systems and to identify the source of inter-individual variability in drug response. Graduated from the Université Joseph Fourier, Grenoble France, Manuel is working in the Drug R&D industry since 2001 following genomics and bioinformatics research at Texas A&M University in the fields of agro-genomics. Manuel Duval is currently leading the bioinformatics and computational biology activity at Enumeral Biomedical in Cambridge MA.
Thomas B. Freeman, MS
Boehringer-Ingelheim Pharmaceuticals, Inc.
I am currently a scientist in the Computational Biology section of the Scientific Knowledge Discovery Department at Boehringer-ingelheim. As such, I have been providing computational analysis in support of exploratory drug discovery programs and particularly focused on the biology of human disease for the Immunology & Inflammation and Cardio-Metabolic therapeutic areas, recently focusing on chronic kidney disease. I began my scientific career by earning a BS in Biology at Franklin & Marshall College in Pennsylvania in 1984. I continued my studies at the Biology Department at Va Tech studying peripheral thyroid hormone metabolism in development, earning an MS. I broadened my experience by working in Molecular Virology (bovine parvovirus replication) and Plant Molecular Biology (ubiquitin-dependent proteolysis and apoptosis) labs also at Va Tech. In 1998 I moved to industry working to engineering insect resistance traits in corn. In 1999, I moved into pharma investing several years in the clinical biomarker group at Pfizer in Groton, Connecticut. We developed biomarkers and assays to support phase I and phase II clinical trials for CNS and Cardiovascular development programs. In 2006, I helped establish a regulated bioanalysis group in support of preclinical biotherapeutics programs at Pfizer. Finally in 2008 I moved into the Computational Biology group at Pfizer supporting exploratory and advanced cardiovascular medicine programs. In recent years I have had the opportunity to participate in the Sage Bionetwork Congresses and have Genetic Alliance meetings. We are working to exploit the technical and conceptual advances to begin to understand the complexity of human physiology and pathohysiology and apply this to drug discovery and development.
Jennifer Henry, PhD
The New York Academy of Sciences
Speakers
Albert-László Barabási, PhD
Northeastern University
Albert-László Barabási is a Distinguished University Professor at Northeastern University, where he directs the Center for Complex Network Research, and holds appointments in the Departments of Physics, Computer Science and Biology, as well as in the Department of Medicine, Harvard Medical School and Brigham and Women Hospital, and is a member of the Center for Cancer Systems Biology at Dana Farber Cancer Institute. A Hungarian born native of Transylvania, Romania, he received his Masters in Theoretical Physics at the Eötvös University in Budapest, Hungary and was awarded a PhD three years later at Boston University. Barabási latest book is "Bursts: The Hidden Pattern Behind Everything We Do" (Dutton, 2010) available in five languages. He has also authored "Linked: The New Science of Networks" (Perseus, 2002), currently available in eleven languages, and is the co-editor of "The Structure and Dynamics of Networks" (Princeton, 2005). His work lead to the discovery of scale-free networks in 1999, and proposed the Barabasi-Albert model to explain their widespread emergence in natural, technological and social systems, from the cellular telephone to the WWW or online communities.
Barabási is a Fellow of the American Physical Society. Among may award his most recent in 2011 was the Lagrange Prize-CRT Foundation for his contributions to complex systems, awarded Doctor Honoris Causa from Universidad Politécnica de Madrid and became an elected fellow in AAAS (Physics).
Alessandra Cesano, MD, PhD
Nodality
Alessandra Cesano joined Nodality in March 2008 as Chief Medical Officer. She is a board certified medical oncologist and holds a PhD in tumor immunology. From 2006 until joining Nodality she was at Biogen Idec as Vice President and Medical Officer, Oncology Medical Research. Alessandra has over 22 year experience in the academic/biotech and pharmaceutical industry focusing in oncology pre-clinical research at The Wistar Institute (Philadelphia, PA), clinical development at both SmithKline Beecham Pharmaceuticals (Collegeville, PA) and Amgen Inc. Along her career path Alessandra has authored over 95 publications and is co-inventor in five patents.
Joel Dudley, PhD
Mt Sinai School of Medicine
Dr. Joel Dudley is Assistant Professor of Genetics and Genomic Sciences and Director of Biomedical Informatics at Mount Sinai School of Medicine. His current research is focused towards solving key problems in genomic and systems medicine through the development and application of translational and biomedical informatics methodologies. Dr. Dudley's published research covers topics in bioinformatics, genomic medicine, personal and clinical genomics, as well as drug and biomarker discovery. His recent work with co-authors describing a novel systems based approach for computational drug repositioning, was featured in the Wall Street Journal, and earned designation as the NHGRI Director's Genome Advance of the Month. He is also co-author (with Konrad Karczewski) of the forthcoming book, Exploring Personal Genomics. Dr. Dudley received a BS in Microbiology from Arizona State University and an MS and PhD in Biomedical Informatics from Stanford University School of Medicine.
Manuel Duval, PhD
Enumeral Biomedical Corp.
Manuel Duval is a bioinformatics scientist doing computational biology research for Drug Discovery and Development. His interest is in characterizing the mode of action of candidate therapeutics agents to cellular systems and to identify the source of inter-individual variability in drug response. Graduated from the Université Joseph Fourier, Grenoble France, Manuel is working in the Drug R&D industry since 2001 following genomics and bioinformatics research at Texas A&M University in the fields of agro-genomics. Manuel Duval is currently leading the bioinformatics and computational biology activity at Enumeral Biomedical in Cambridge MA.
Greg Findlay, PhD
Samuel Lunenfeld Research Institute
Greg Findlay studied undergraduate Biochemistry at the University of Dundee in Scotland, before receiving his PhD from the Institute of Cancer Research in London, UK, under the tutelage of Dr. Richard Lamb. Whilst working on the mTOR pathway during his graduate studies, he developed an interest in integrative network biology. He is currently a postdoctoral fellow in Dr. Tony Pawson’s laboratory at the Samuel Lunenfeld Research Institute, Toronto, Canada, where he focuses on understanding how network information induces cell fate changes that are relevant to development and disease.
Alison O'Mahony, PhD
Bioseek, a division of DiscoveRx
Dr. O'Mahony is currently the Director of Inflammation Biology at BioSeek, a division of DiscoveRx. As part of the senior management team at BioSeek, Dr. O'Mahony helps lead the scientific application of their proprietary BioMAP® platform technology. BioMAP employs predictive primary human cell-based disease models to generate uniquely informative biological activity profiles for compounds in therapeutic development. Dr. O'Mahony serves as the senior scientific lead on both client-based profiling projects and business development opportunities. Dr. O'Mahony also directs the development of new BioMAP® Systems covering new therapeutic areas aimed at expanding the biological coverage the BioMAP platform to enable the selection and development of promising compounds fordrug development. Prior to joining BioSeek, Dr. O'Mahony was an Investigator at the Gladstone Institute at UCSF working on inflammatory-associated NF-?B signaling in the CNS. Dr. O'Mahony has over 20 years specializing in cell biology, signal transduction and biomarker expression in the fields of inflammation, neuroscience and cell biology, including phenotypic screening in primary human cells. Dr. O'Mahony published multiple peer-reviewed papers and invited book chapters and has received a number of international and national awards for her work.
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Abstracts
How Small Molecule Drugs were Initially Discovered: the Unexpected Result of Data Mining the US FDA Database
Manuel Duval, PhD, Enumeral Biomedical Corp.
For more than 20 years, drug discovery relies mainly on the following two assumptions (i) one can trigger a therapeutic response by modulating the activity of a single gene product; (ii) a synthetic compound that has been uncovered by its in vitro activity on a recombinant protein can perform its activity in a physiological environment. These two assumptions determine how drug discovery operates. They drive the concepts of target (i.e. the gene product that is supposed to be the point of therapeutic intervention) and pipeline (once lead compounds have been found via in vitro screening procedures, they follow a sequential series of downstream development, including pre-clinical safety assays run in animal model systems, up to clinical trials). These reductionist approaches have been considerably scaled up thanks to technology developments in combinatorial chemistry, robotic (including High Throughput Screening), molecular biology, and the completion of the human genome DNA sequence. The expectation during the nineties is that the rate of drug discovery will dramatically increase while its associated cost will decrease. Twenty years later, it is a fact that not only has the discovery of new drugs decreased but its cost has also concomitantly surged. We performed a retrospective study of how successful programs lead to actual marketed drugs. We concluded and asserted that the notions of targets and pipeline not only have limitations but also cause the industry to collectively fail to deliver on critical medical needs.
Phenotypic Profiling of Compounds in BioMAP® Systems Enables Early Evaluation of Efficacy, Selectivity and Safety
Alison O'Mahony, PhD, Bioseek, a division of DiscoveRx
Traditional target-based drug discovery often relies on a predefined, sometimes poorly validated target biology typically assessed under simple biochemical or single-cell assays. In contrast, profiling compounds in primary human cell-based BioMAP® systems, designed to recapitulate complex signals and phenotypic responses of diseased tissues, simultaneously addresses target selectivity, efficacy and safety-related activities in an early, efficient and unbiased manner. Here, we present the analysis of several anti-inflammatory kinase inhibitors in development, including inhibitors of Jak kinase (tofacitinib), syk kinase (fostamatinib), and others (p38 MAPK and MEK kinase). Specific activities of these compounds, when tested in a panel of 12 BioMAP® systems led to mechanistic hypotheses relating to dose-dependent selectivity, efficacy and safety of these drugs in patients. We observe a correlation between dose-dependent selectivity, efficacy and adverse effects that include gastrointestinal perforations (Jak), hypertension (syk) and skin rash (p38 and MEK). Earlier detection and better understanding of these effects can help guide future clinical development. Our results support the use of a systems biology approach for phenotypic screening compounds to help prioritize drug candidates prior to testing in humans.
Single Cell Network Profiling (SCNP) Applications to Drug Development: Mapping Drug and Target Interactions at the Single Cell Level
Alessandra Cesano, MD, PhD, Nodality
Single-cell network profiling (SCNP) is a multiparametric flow cytometry-based analysis that can simultaneously measure, at the single cell level, both extracellular surface markers and changes in intracellular signaling proteins in response to extracellular modulators. Measuring changes in signaling proteins following the application of an external stimulus informs on the functional capacity of the signaling network, which cannot be assessed by the measurement of basal signaling alone (2). In addition, the simultaneous analysis of multiple pathways in multiple cell subsets can provide insight into the connectivity of both cell signaling networks and immune cell subtypes (3). SCNP technology is particularly well suited to the investigation of signaling activity within the many interdependent cell types that make up complex tissues such as the immune system or tumors because it allows for the simultaneous interrogation of modulated signaling network responses in multiple cell subtypes within heterogeneous populations without the additional cellular manipulation required for the isolation of specific cell types. Applications of the technology to clinical medicine as well as drug development will be discussed.
Understanding the Role of Molecular Networks in Cell Fate Determination
Greg Findlay, PhD, Samuel Lunenfeld Research Institute
The increasing in protein domain complexity throughout evolution suggests that interaction networks are more highly connected in metazoans, although how this relates to control of biological decisions remains uncertain. During mammalian development, the multi-domain Sos1/Grb2 RasGEF complex integrates a network of protein and lipid interactions to drive embryonic stem cells towards the essential primitive endoderm (PrE) lineage. Using this system, we find that the Sos1/Grb2 interaction network is optimized not for individual ligand affinities, but to cooperatively determine timing and selectivity in cell fate determination. Furthermore, we show that targeting Sos1/Grb2 to specific ligands within this network is essential for lineage specification. Taken together, our data explain why protein domain complexity is critical for multicellular organization, and advocates the use of complex biological models to understand how signaling networks impact upon development and disease.
Moving from Big Data to Predictive Models of Disease and Drug Response
Joel Dudley, PhD, Mt Sinai School of Medicine
Human diseases and drug response are complex traits that involve entire networks of changes at the molecular level driven by genetic and environmental perturbations. Changes at the molecular level can induce changes in biochemical processes or broader molecular networks that affect cell behavior, and changes in cell behavior can affect normal tissue or whole organ function, eventually leading to pathophysiological states at the organism level that we associate with disease. While the vast majority of previous efforts to elucidate disease and drug response traits have focused on single dimensions of the system, achieving a more comprehensive view of common human diseases requires examining living systems in multiple dimensions and at multiple scales. Studies focused on identifying changes in DNA that correlate with changes in disease or drug response traits, changes in gene expression that correlate with disease or drug response traits, or changes in other molecular traits (e.g., metabolite, methylation status, protein phosphorylation status, and so on) that correlate with disease or drug response are fairly routine and have met with great success in many cases. However, to further our understanding of the complex network of molecular and cellular changes that impact disease risk, disease progression, severity, and drug response, we can more formally integrate these different data dimensions. By integrating diverse types of data on a large scale we observe that some forms of common human diseases like diabetes are most likely the result of perturbations to specific gene networks that in turn causes changes in the states of other gene networks both within and between tissues that drive biological processes associated with disease. The notion that some forms of common human disease are the result of complex interactions among networks has significant implications for drug discovery: designing drugs or drug combinations to impact entire network states rather than designing drugs that target specific disease associated genes. This talk will highlight key findings from our work applying multiscale approaches to understand complex human disease and relevant therapeutic strategies.
Network Medicine: Rethinking Diseases from a Network Perspective
Albert-László Barabási, PhD, Northeastern University
A disease is rarely a consequence of an abnormality in a single gene, but reflects the perturbations of the complex intracellular network. The emerging tools of network medicine offer a platform to explore systematically not only the molecular complexity of a particular disease, leading to the identification of disease modules and pathways, but also the molecular relationships between apparently distinct (patho)phenotypes. Advances in this direction are essential to identify new disease genes, to uncover the biological significance of disease-associated mutations identified by genome-wide association studies and full genome sequencing, and to identify drug targets and biomarkers for complex diseases.
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