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Posts Tagged ‘@bioitworld’


Recents Thoughts of Biotech Innovation: 2015 2016

From WorldofDTCMarketing

Can’t innovate ? Buy small biotech companies that can

cloud-innovationOn a week where a lot of people are taking their final summer vacations the news is that Amgen is buying Onyx and AstraZeneca Plc took a further step to bolster its pipeline of new cancer drugs on Monday by agreeing to acquire privately held U.S. biotech company Amplimmune for up to $500 million.  On paper it’s a good business move but as big pharma companies gobble up small biotech companies they bring with then antiquated processes and business people who are thinking about the bottom line rather than patients.  The results ?  Innovation that led these smaller biotech companies to develop new drugs will be stymied by a bureaucratic business model.

There is a reason why, after being acquired, that so many employees of smaller biotech companies leave.  Either they don’t want to work for big a big pharma bureaucracy or the acquiring company determines that these people are not needed and shows them the door.  Behind all this are people who provided the start-up funding and want to cash in without awaiting the lengthy process of developing new drugs.  In the end however it’s patients who loose.

bureaucracy

Last week Steve Ballmer, the CEO of Microsoft, announced his resignation.  There is a correlation between what happened at Microsoft and the challenges for big pharma.  Steve was forced out because Microsoft became a huge bureaucracy and could not innovate fast enough.  Those of us who have worked in pharma know of the endless 9-5 meetings to move even small projects forward.  Amgen’s culture revolves around back-to-back meetings with executives from other big pharma companies who are trying to put their power on display.  It’s only a matter of time before people from Onyx leave because of Amgen’s prohibitive culture.

Unknown

Until the costs of developing and launching new drugs is lower more and more innovative biotech firms are going to have a for sale sign hanging in the window hoping big pharma can help investors cash in.

And in a Commentary on CNBC

This is biotech’s real problem

Robert J. Mulroy, president and CEO of Merrimack

Thursday, 1 Oct 2015 | 9:38 AM ET

1

COMMENTJoin the Discussion

Here’s a challenge — name a biotech that’s not a small company with one potential blockbuster in the works or an industry giant that’s acquiring the hottest new technologies. Got one? Great! Now try to name four more.

Biotech

Jian Wan | Vetta | Getty Images

The fact is, midsize biotechs (Ironwood Pharmaceuticals andMedivation are couple of examples) are a rarity these days, and that’s a problem for patients, doctors and investors. Start-ups that are in the process of developing and drawing from a foundation of knowledge are often acquired once they have a promising candidate in the pipeline. If the associated research teams aren’t immediately jettisoned (just when their potential for broader breakthroughs is surging), the top innovators go off to launch another venture that doesn’t build on their current research.

There’s also enormous pressure to focus on that “next big thing” that can crowd out other innovations for patients, while blocking valuable, in-depth examination of existing treatments. In oncology, drug combinations (like Genentech’s combination of Herceptin, pertuzumab and docetaxel to treat HER-2 breast cancer) are making huge strides in prolonging patients’ lives. Such combinations require understanding how specific tumors grow, and designing diagnostics that tell doctors whether a patient’s tumor fits that profile. The problem? Not enough small biotechs have the luxury of developing that understanding before they’re acquired so that big biotechs can gain another drug candidate.

As the CEO of a cancer-focused biotech that’s spent the last 15 years building a diverse product pipeline — the lead candidate is under FDA review with a decision expected next month — my view is that pursuing individual drug targets will bring limited success. Cancer is the ultimate engineering challenge, and effective treatments need to address more than a single facet of the problem.

The real winners in the industry will be the companies that understand how their therapies work in combinations with their own and competing therapies, and help physicians make sense of the explosion of new treatments via companion diagnostics. In fact, regulators could potentially require a more integrated approach to manage the ever-increasing influx of new drugs and data. In August, the American Society of Clinical Oncology issued guidelines for doctors on interpreting multi-gene tests for cancer susceptibility, acknowledging the need for more education and regulation.

Most oncology biotech start-ups dream of developing such an integrated approach. But it takes time and money, and an environment that prioritizes in-depth scientific research.

Doing well by patients, doctors and investors means pursuing sustainable innovation, not just one-offs or single-use purchases. Innovation drives value and can build on itself to address complex challenges. And while innovation takes time and entails risk, it mitigates that risk in the long term.

For example, if you have a deep understanding of how your drug works — say, the tumor-growth mechanisms it disrupts — you can determine whether there are signs that the mechanism it targets is present in a particular patient and then enroll only those patients in clinical trials. That allows for smaller, less expensive trials — and a higher chance of success.

An integrated approach across the industry would allow drug developers to identify responders, and then eliminate the non-responders from clinical trials and from the target population post-approval, ensuring patients only receive treatments likely to benefit them and don’t waste their time enrolling in irrelevant trials.

The current cycle of big pharma acquiring start-ups and dismantling the research teams while divesting in their own R&D appears self-perpetuating, but cracks are showing in the high cost — now in the billions — of bringing a single drug to market.

These companies are dealing with outside pressures that stymie progress. Less than 10 percent of experimental oncology drugs ever get approved. A tactical approach to the pipeline makes sense from a risk-aversion perspective. But sustainable growth requires strategy and investments in the fundamental science work that drives innovation.

Commentary by Robert J. Mulroy, president and CEO of Merrimack, a biotech company focused on cancer treatments. Prior to joining Merrimack, Mr. Mulroy worked as a management consultant in the pharmaceutical and health-care industries. He has served as an advisor to multiple start-up companies in the biotechnology industry.

The New Biotechnology Innovation Organization

Jim's CornerAt BIO, new discoveries in research and development are constantly being made by our members. We take pride in the contributions they have made across a diverse range of biotechnology industries, including: healthcare, agricultural, industrial and environmental.

As one of the world’s strongest catalysts for innovation, our role within the biotechnology community requires us to reflect on who we are, what we do and how we can better serve our members in future.

Biotechnology scientists and entrepreneurs are not just industrious – they are revolutionary, imaginative, inspired, creative, ingenious and inventive. It is these traits that produce innovation.

BIO Logo Vertical RGBAs you may already know, starting today, the Biotechnology Industry Organization will become the Biotechnology Innovation Organization. It’s a one-word name change – from industry to innovation – but the implications are substantial.

Today is a time of tremendous innovation. So much so that our current name no longer best describes our members and our role as one of the world’s leading innovators.

BIO’s members are on the cutting-edge of science and we believe our new name will allow us to build upon our relationships, create new ones and provide our members with better educational and research opportunities.

Our members are discovering scientific breakthroughs and bringing new and innovative therapies to the marketplace. With the help of biotechnology, people are living longer and healthier lives. Our industry embodies innovation and made the world a better place for people everywhere.

Our meaningful innovations also provide the tools to help feed more people, develop new sustainable fuels and products to help protect the planet and devise unique clean technologies to make our environment safer.

In the more than 22 years since its founding, BIO has united scientists, policymakers and the public in a partnership to drive our remarkable progress even further.

It’s important to note that we are not becoming a different organization. We are not altering our mission or the value we deliver to our members.

We will, however, continue to blaze the trail to accelerate cures – connecting thought leaders, building a stronger, more advanced economy and creating jobs to raise the world’s standard of living.

In the coming years, BIO’s diverse membership – from promising startups to global companies in a wide array of biotechnology and related fields – will drive health, life expectancy and improve quality of life for millions of people.

The Biotechnology Innovation Organization will be there to support our members in their tireless effort to make the world a better place to live.

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Track 9 Pharmaceutical R&D Informatics: Collaboration, Data Science and Biologics @ BioIT World, April 29 – May 1, 2014 Seaport World Trade Center, Boston, MA

Aviva Lev-Ari, PhD, RN

 

April 30, 2014

 

Big Data and Data Science in R&D and Translational Research

10:50 Chairperson’s Remarks

Ralph Haffner, Local Area Head, Research Informatics, F. Hoffmann-La Roche AG

11:00 Can Data Science Save Pharmaceutical R&D?

Jason M. Johnson, Ph.D., Associate Vice President,

Scientific Informatics & Early Development and Discovery Sciences IT, Merck

Although both premises – that the viability of pharmaceutical R&D is mortally threatened and that modern “data science” is a relevant superhero – are

suspect, it is clear that R&D productivity is progressively declining and many areas of R&D suboptimally use data in decision-making. We will discuss

some barriers to our overdue information revolution, and our strategy for overcoming them.

11:30 Enabling Data Science in Externalized Pharmaceutical R&D

Sándor Szalma, Ph.D., Head, External Innovation, R&D IT,

Janssen Research & Development, LLC

Pharmaceutical companies have historically been involved in many external partnerships. With recent proliferation of hosted solutions and the availability

of cost-effective, massive high-performance computing resources there is an opportunity and a requirement now to enable collaborative data science. We

discuss our experience in implementing robust solutions and pre-competitive approaches to further these goals.

12:00 pm Co-Presentation: Sponsored by

Collaborative Waveform Analytics: How New Approaches in Machine Learning and Enterprise Analytics will Extend Expert Knowledge and Improve Safety Assessment

  • Tim Carruthers, CEO, Neural ID
  • Scott Weiss, Director, Product Strategy, IDBS

Neural ID’s Intelligent Waveform Service (IWS) delivers the only enterprise biosignal analysis solution combining machine learning with human expertise. A collaborative platform supporting all phases of research and development, IWS addresses a significant unmet need, delivering scalable analytics and a single interoperable data format to transform productivity in life sciences. By enabling analysis from BioBook (IDBS) to original biosignals, IWS enables users of BioBook to evaluate cardio safety assessment across the R&D lifecycle.

12:15 Building a Life Sciences Data

Sponsored by

Lake: A Useful Approach to Big Data

Ben Szekely, Director & Founding Engineer,

Cambridge Semantics

The promise of Big Data is in its ability to give us technology that can cope with overwhelming volume and variety of information that pervades R&D informatics. But the challenges are in practical use of disconnected and poorly described data. We will discuss: Linking Big Data from diverse sources for easy understanding and reuse; Building R&D informatics applications on top of a Life Sciences Data Lake; and Applications of a Data Lake in Pharma.

12:40 Luncheon Presentation I:

Sponsored by

Chemical Data Visualization in Spotfire

Matthew Stahl, Ph.D., Senior Vice President,

OpenEye Scientific Software

Spotfire deftly facilitates the analysis and interrogation of data sets. Domain specific data, such as chemistry, presents a set of challenges that general data analysis tools have difficulty addressing directly. Fortunately, Spotfire is an extensible platform that can be augmented with domain specific abilities. Spotfire has been augmented to naturally handle cheminformatics and chemical data visualization through the integration of OpenEye toolkits. The OpenEye chemistry extensions for Spotfire will be presented.

1:10 Luncheon Presentation II 

1:50 Chairperson’s Remarks

Yuriy Gankin, Ph.D., Co. Founder and CSO, GGA Software Services

1:55 Enable Translational Science by Integrating Data across the R&D Organization

Christian Gossens, Ph.D., Global Head, pRED Development Informatics Team,

pRED Informatics, F. Hoffmann-La Roche Ltd.

Multi-national pharmaceutical companies face an amazingly complex information management environment. The presentation will show that

a systematic system landscaping approach is an effective tool to build a sustainable integrated data environment. Data integration is not mainly about

technology, but the use and implementation of it.

2:25 The Role of Collaboration in Enabling Great Science in the Digital Age: The BARD Data Science Case Study

Andrea DeSouza, Director, Informatics & Data Analysis,

Broad Institute

BARD (BioAssay Research Database) is a new, public web portal that uses a standard representation and common language for organizing chemical biology data. In this talk, I describe how data professionals and scientists collaborated to develop BARD, organize the NIH Molecular Libraries Program data, and create a new standard for bioassay data exchange.

May 1. 2014

BIG DATA AND DATA SCIENCE IN R&D AND TRANSLATIONAL RESEARCH

10:30 Chairperson’s Opening Remarks

John Koch, Director, Scientific Information Architecture & Search, Merck

10:35 The Role of a Data Scientist in Drug Discovery and Development

Anastasia (Khoury) Christianson, Ph.D., Head, Translational R&D IT, Bristol-

Myers Squibb

A major challenge in drug discovery and development is finding all the relevant data, information, and knowledge to ensure informed, evidencebased

decisions in drug projects, including meaningful correlations between preclinical observations and clinical outcomes. This presentation will describe

where and how data scientists can support pharma R&D.

11:05 Designing and Building a Data Sciences Capability to Support R&D and Corporate Big Data Needs

Shoibal Datta, Ph.D., Director, Data Sciences, Biogen Idec

To achieve Biogen Idec’s strategic goals, we have built a cross-disciplinary team to focus on key areas of interest and the required capabilities. To provide

a reusable set of IT services we have broken down our platform to focus on the Ingestion, Digestion, Extraction and Analysis of data. In this presentation, we will outline how we brought focus and prioritization to our data sciences needs, our data sciences architecture, lessons learned and our future direction.

11:35 Data Experts: Improving Sponsored by

Translational Drug-Development Efficiency

Jamie MacPherson, Ph.D., Consultant, Tessella

We report on a novel approach to translational informatics support: embedding Data Experts’ within drug-project teams. Data experts combine first-line

informatics support and Business Analysis. They help teams exploit data sources that are diverse in type, scale and quality; analyse user-requirements and prototype potential software solutions. We then explore scaling this approach from a specific drug development team to all.

 

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PLENARY KEYNOTE PRESENTATIONS: THURSDAY, MAY 1 | 8:00 – 10:00 AM @ BioIT World, April 29 – May 1, 2014 Seaport World Trade Center, Boston, MA

 

Reporter: Aviva Lev-Ari, PhD, RN

 

Keynote Introduction: Sponsored by Fred Lee, M.D., MPH, Director, Healthcare Strategy and Business Development, Oracle Health Sciences

Heather Dewey-Hagborg

Artist, Ph.D. Student, Rensselaer Polytechnic Institute

Heather Dewey-Hagborg is an interdisciplinary artist, programmer and educator who explores art as research and public inquiry. She recreates identity from strands of human hair in an entirely different way. Collecting hairs she finds in random public places – bathrooms, libraries, and subway seats – she uses a battery of newly developing technologies to create physical, life-sized portraits of the owners of these hairs. Her fixation with a single hair leads her to controversial art projects and the study of genetics. Traversing media ranging from algorithms to DNA, her work seeks to question fundamental assumptions underpinning perceptions of human nature, technology and the environment. Examining culture through the lens of information, Heather creates situations and objects embodying concepts, probes for reflection and discussion. Her work has been featured in print, television, radio, and online. Heather has a BA in Information Arts from Bennington College and a Masters degree from the Interactive Telecommunications Program at Tisch School of the Arts, New York University. She is currently a Ph.D. student in Electronic Arts at Rensselaer Polytechnic Institute.

 

Yaniv Erlich, Ph.D.

Principal Investigator and Whitehead Fellow, Whitehead Institute for Biomedical Research

 

Dr. Yaniv Erlich is Andria and Paul Heafy Family Fellow and Principal Investigator at the Whitehead Institute for Biomedical Research. He received a bachelor’s degree from Tel-Aviv University, Israel and a PhD from the Watson School of Biological Sciences at Cold Spring Harbor Laboratory in 2010. Dr. Erlich’s research interests are computational human genetics. Dr. Erlich is the recipient of the Burroughs Wellcome Career Award (2013), Harold M. Weintraub award (2010), the IEEE/ACM-CS HPC award (2008), and he was selected as one of 2010 Tomorrow’s PIs team of Genome Technology.

 

Isaac Samuel Kohane, M.D., Ph.D.

Henderson Professor of Health Sciences and Technology, Children’s Hospital and Harvard Medical School;

Director, Countway Library of Medicine; Director, i2b2 National Center for Biomedical Computing;

Co-Director, HMS Center for Biomedical Informatics

 

Isaac Kohane, MD, PhD, co-directs the Center for Biomedical Informatics at Harvard Medical School. He applies computational techniques, whole genome analysis, and functional genomics to study human diseases through the developmental lens, and particularly through the use of animal model systems. Kohane has led the use of whole healthcare systems, notably in the i2b2 project, as “living laboratories” to drive discovery research in disease genomics (with a focus on autism) and pharmacovigilance

(including providing evidence for the cardiovascular risk of hypoglycemic agents which ultimately contributed to “black box”ing by the FDA) and comparative effectiveness with software and methods adopted in over 84 academic health centers internationally. Dr. Kohane has published over 200 papers in the medical literature and authored a widely used book on Microarrays for an Integrative Genomics. He has been elected to multiple honor societies including the American Society for Clinical Investigation, the American College of Medical Informatics, and the Institute of Medicine. He leads a doctoral program in genomics and bioinformatics within the Division of Medical Science at Harvard University. He is also an occasionally practicing pediatric endocrinologist.

 

#SachsBioinvestchat, #bioinvestchat

#Sachs14thBEF

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Track 5 Next-Gen Sequencing Informatics: Advances in Analysis and Interpretation of NGS Data @ BioIT World, April 29 – May 1, 2014 Seaport World Trade Center, Boston, MA

Reporter: Aviva Lev-Ari, PhD, RN

 

NGS Bioinformatics Marketplace: Emerging Trends and Predictions

10:50 Chairperson’s Remarks

Narges Baniasadi, Ph.D., Founder & CEO, Bina Technologies, Inc.

11:00 Global Next-Generation Sequencing Informatics Markets: Inflated Expectations in an Emerging Market

Greg Caressi, Senior Vice President, Healthcare and Life Sciences, Frost & Sullivan

This presentation evaluates the global next-generation sequencing (NGS) informatics markets from 2012 to 2018. Learn key market drivers and restraints,

key highlights for many of the leading NGS informatics services providers and vendors, revenue forecasts, and the important trends and predictions that

affect market growth.

Organizational Approaches to NGS Informatics

11:30 High-Performance Databases to Manage and Analyze NGS Data

Joseph Szustakowski, Ph.D., Head, Bioinformatics, Biomarker Development,

Novartis Institutes for Biomedical Research

The size, scale, and complexity of NGS data sets call for new data management and analysis strategies. High-performance database systems

combine the advantages of both established and cutting edge technologies. We are using high performance database systems to manage and analyze NGS, clinical, pathway, and phenotypic data with great success. We will describe our approach and concrete success stories that demonstrate its efficiency and effectiveness.

12:00 pm Taming Big Science Data Growth with Converged Infrastructure

Aaron D. Gardner, Senior Scientific Consultant,

BioTeam, Inc.

Many of the largest NGS sites have identified IO bottlenecks as their number one concern in growing their infrastructure to support current and projected

data growth rates. In this talk Aaron D. Gardner, Senior Scientific Consultant, BioTeam, Inc. will share real-world strategies and implementation details

for building converged storage infrastructure to support the performance, scalability and collaborative requirements of today’s NGS workflows.

12:15 Next Generation Sequencing:  Workflow Overview from a High-Performance Computing Point of View

Carlos P. Sosa, Ph.D., Applications Engineer, HPC Lead,

Cray, Inc.

Next Generation Sequencing (NGS) allows for the analysis of genetic material with unprecedented speed and efficiency. NGS increasingly shifts the burden

from chemistry done in a laboratory to a string manipulation problem, well suited to High- Performance Computing. We explore the impact of the NGS

workflow in the design of IT infrastructures. We also present Cray’s most recent solutions for NGS workflow.

SOSA in REAL TIME

Bioinformatics and BIG DATA – NGS @ CRAY i 2014

I/O moving, storage data – UNIFIED solution by Cray

  • Data access
  • Fast Access
  • Storage
  • manage high performance computinf; NGS work flow, multiple human genomes 61 then 240 sequentiallt, with high performance in 51 hours, 140 genomes in simultaneous

Architecture @Cray for Genomics

  • sequensors
  • Galaxy
  • servers for analysis
  •  workstation: Illumina, galaxy, CRAY does the integration of 3rd party SW using a workflow LEVERAGING the network, the fastest in the World, network useding NPI for scaling and i/O
  • Compute blades, reserves formI?O nodes, the Fastest interconnet in the industry
  • scale of capacity and capability, link interconnect in the file System: lustre
  • optimization of bottle neck: capability, capacity, file structure for super fast I/O

12:40 Luncheon Presentation I

Erasing the Data Analysis Bottleneck with BaseSpace

Jordan Stockton, Ph.D., Marketing Director,

Enterprise Informatics, Illumina, Inc.

Since the inception of next generation sequencing, great attention has been paid to challenges such as storage, alignment, and variant calling. We believe

that this narrow focus has distracted many biologists from higher-level scientific goals, and that simplifying this process will expedite the discovery

process in the field of applied genomics. In this talk we will show that applications in BaseSpace can empower a new class of researcher to go from

sample to answer quickly, and can allow software developers to make their tools accessible to a vast and receptive audience.

1:10 Luncheon Presentation II: Sponsored by

The Empowered Genome Community: First Insights from Shareable Joint Interpretation of Personal Genomes for Research

Nathan Pearson, Ph.D. Principal Genome Scientist,

QIAGEN

Genome sequencing is becoming prevalent however understanding each genome requires comparing many genomes. We launched the Empowered Genome Community, consisting of people from programs such as the Personal Genome Project (PGP) and Illumina’s Understand Your Genome. Using Ingenuity Variant Analysis, members have identified proof of principle insights on a common complex disease (here,myopia) derived by open collaborative analysis of PGP genomes.

Pearson in REAL TIME

One Genome vs. population of Genomes

IF one Genome:

  1. ancestry
  2. family health
  3. less about drug and mirrors
  4. health is complex

CHallenges

1. mine genome

2. what all genome swill do for Humanity not what my genome can do for me

3. Cohort analysis, rich for variance

4. Ingenuity Variant Analysis – secure environment

5. comparison of genomes, a sequence, reference matching

6. phynogenum, statistical analysis as Population geneticists do

Open, collabrative myopia analysis GENES rare leading to myuopia – 111 genomes

– first-pass finding highlight 12 plausibly myopia-relevant genes: variants in cases vs control

– refine finding and analysis, statistical association, common variance

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Track 4 Bioinformatics: Utilizing Massive Quantities of –omic Information across Research Initiatives @ BioIT World, April 29 – May 1, 2014 Seaport World Trade Center, Boston, MA

Reporter: Aviva Lev-Ari, PhD, RN

 

Bioinformatics for Big Data

10:50 Chairperson’s Remarks

Les Mara, Founder, Databiology, Ltd.

 

11:00 Data Management Best Practices for Genomics Service Providers

Vas Vasiliadis, Director, Products, Computation Institute,

University of Chicago and Argonne National Laboratory

Genomics research teams in academia and industry are increasingly limited at all stages of their work by large and unwieldy datasets, poor integration between the computing facilities they use for analysis, and difficulty in sharing analysis results with their customers and collaborators. We will discuss issues with current approaches and describe emerging best practices for managing genomics data through its lifecycle.

Vas in REAL TIME

Computation Institute @ University of Chicago solutions to non profit entities, scale and make available in an affordable way “I have nothing to say on Big Data”, 57.7% survey by NAS, average time researcher spend on research, it will get worse, research data management morphed into better ways, industrial robust way, commercial start ups are role model. All functions of an enterprise now available as applications for small business.

  • Highly scaleable, invisible
  • high performance
  • In Genomics, tools – shipping hard drive new ways to develop research infrastructure:
  • dropbox, does not scale Amazon’s Webservices is the cloud
  • security in sharing across campuses, InCommon – cross domains sw access constrains are mitigated.
  • identity provision for multiple identity – identity Hub, one time association done, Group Hubs, i.e., ci connect – UChicago, access to systems at other campuses – connecting science to cycles of data, network not utilizied efficiently – tools not design for that, FTP, Firewalls are designed for data not Big data.
  • Science DMZ – carve realestate for Science data transfer, monitoring the transfer
  • Reproducibility, Provenance, Public mandates
  • Data publication Service: VIVO, fisshare, Fedora, duracloud, doi, identification, store, preserve,, curation workflow
  • Search for discovery: Faceted Search. browse distributed, access locally – automation required, outsourcing, delivery throufg SaaS
  • We are all on cloud

11:30 NGS Analysis to Drug Discovery: Impact of High-Performance Computing in Life Sciences

Bhanu Rekepalli, Ph.D., Assistant Professor and Research Scientist, Joint Institute for Computational Sciences, The University of Tennessee, Oak Ridge National Laboratory

We are working with small-cluster-based applications most widely used by the scientific community on the world’s premier supercomputers. We incorporated these parallel applications into science gateways with user-friendly, web-based portals. Learn how the research at UTK-ORNL will help to bridge the gap between the rate of big data generation in life sciences and the speed and ease at which biologists and pharmacists can study this data.

Bhanu in REAL TIME

Cost per Genome does down, 2011 from $100,000 to $1,000

  • Solutions:
  • architecture
  • parallel informatics
  • SW modules
  • web-based gateway
  • XSEDE.org sponsured by NSF at all sponsored research by NSF
  • LCF – applications: Astrophysics, Bioinfo, CFD, highly scalable wrappers for the analysis Blast scaling results in Biology
  • Next generation super computers: Xeon/Phi

NICS Informatics Science gateway – PoPLAR Portal for Parallel Scaling Life Sciences Applications & Research

  • automated workflows
  • Smithsonian Institute, generate genomes fro all life entities in the universe: BGI
  • Titan Genomic Data analysis –   Everglade ecosystem, sequenced
  • Univ S. Carolina great computing infrastructure
  • Super computer: KRAKEN
  • 5-10 proteins modeling on supercomputers for novel drug discovery
  • Vascular Tree system for Heart transplant – visualization and modeling

12:00 pm The Future of Biobank Informatics

Bruce Pharr, Vice President, Product Marketing, Laboratory Systems, Remedy Informatics

As biobanks become increasingly essential to basic, translational, and clinical research for genetic studies and personalized medicine, biobank informatics must address areas from biospecimen tracking, privacy protection, and quality management to pre-analytical and clinical collection/identification of study data elements. This presentation will examine specific requirements for third-generation biobanks and how biobank informatics will meet those requirements.

Bruce Pharr in REAL TIME

Flexible Standartization

BioBank use of informatics in the1980s – bio specimens. 1999 RAND research 307 M biospecimens in US biobanks growing at 20M per year.

2nd – Gen Bioband

2005 – 3rd-Gen Biobanks – 15000 studies on Cancer, biospecimen, Consent of donors is a must.

Biobank – PAtion , Procedure, specimen acquistion, storage, processing, distribution, analysis

Building Registries – Mosaic Platform

  • Specimen Track BMS,
  • Mosaic Ontology:  application and Engine

1. standardize specimen requirement

Registries set up the storage: administrator dashboard vs user bashboard

2. Interoperability

3. Quality analysis

4. Informed Consent

 

12:15 Learn How YarcData’s Graph Analytics Appliance Makes It Easy to Use Big Data in Life Sciences

Ted Slater, Senior Solutions Architect, Life Sciences, YarcData, a division of Cray

YarcData, a division of Cray, offers high performance solutions for big data graph analytics at scale, finally giving researchers the power to leverage all the data they need to stratify patients, discover new drug targets, accelerate NGS analysis, predict biomarkers, and better understand diseases and their treatments.

12:40 Luncheon Presentation I

The Role of Portals for Managing Biostatistics Projects at a CRO

Les Jordan, Director, Life Sciences IT Consulting, Quintiles

This session will focus on how portals and other tools are used within Quintiles and at other pharmas to manage projects within the biostatistics department.

1:10 Luncheon Presentation II (Sponsorship Opportunity Available) or Lunch on Your Own

1:50 Chairperson’s Remarks

Michael Liebman, Ph.D., Managing Director, IPQ Analytics, LLC

Sabrina Molinaro, Ph.D., Head of Epidemiology, Institute of ClinicalPhysiology, National Research Council –

CNR Italy

1:55 Integration of Multi-Omic Data Using Linked Data Technologies

Aleksandar Milosavljevic, Ph.D., Professor, Human Genetics; Co-Director,

Program in Structural & Computational Biology and Molecular Biophysics;

Co-Director, Computational and Integrative Biomedical Research Center,

Baylor College of Medicine

By virtue of programmatic interoperability (uniform REST APIs), Genboree servers enable virtual integration of multi-omic data that is distributed across multiple physical locations. Linked Data technologies of the Semantic Web provide an additional “logical” layer of integration by enabling distributed queries across the distributed data and by bringing multi-omic data into the context of pathways and other background knowledge required for data interpretation.

2:25 Building Open Source Semantic Web-Based Biomedical Content Repositories to Facilitate and Speed Up Discovery and Research

Bhanu Bahl, Ph.D., Director, Clinical and Translational Science Centre,

Harvard Medical School

Douglas MacFadden, CIO, Harvard Catalyst at Harvard Medical School

Eagle-i open source network at Harvard provides a state-of-the-art informatics

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AWARDS: Best of Show Awards, Best Practices Awards and 2014 Benjamin Franklin Award  @ BioIT World, April 29 – May 1, 2014 Seaport World Trade Center, Boston, MA

Reorter: Aviva Lev-Ari, PhD, RN

 

Best of Show Awards

The Best of Show Awards offer exhibitors an opportunity to distinguish their products from the competition. Judged by a team of leading industry experts and Bio-IT World editors, this award identifies exceptional innovation in technologies used by life science professionals today. Judging and the announcement of winners is conducted live in the Exhibit Hall. Winners will be announced on Wednesday, April 30 at 5:30pm. The deadline for product submissions is February 21, 2014. To learn more about this program, contact Ryan Kirrane at 781-972-1354 or email rkirrane@healthtech.com.

2014 WINNER(s) are announced in Real Time

2014 – Five categories

1. Clinical ad Health IT – Astazeneca with Tessella – Real Time Analytics for Clinical Trial (RTACT) – engine for innovations

2. Research and Drug Discovery: U-bioPRED with the TranSMART Foundation – Open Source  – Emperial College – Biomarkers for Asthma,  hospitals, 340 universities, 34 Pharmas

3. Informatics: Pistoia Alliance – HELM – Pfizer, released data for HELM Project

4. Knowledge Management Finalists: GENENTECH – Genentech Cell Line Resource

5. IT Infrastructure/HPC Winner:

Baylor College of Medicine with DNAnexus –

 

2014 Judges’Prize – UK for Patient Data Intgration

2014 Editors’ Choice Award: Mount Sinai – Rethinking Type 2 Diabetes through Data Informatics

2014 Benjamin Franklin Award

The Benjamin Franklin Award for Open Access in the Life Sciences is a humanitarian/bioethics award presented annually by the Bioinformatics Organization to an individual who has, in his or her practice, promoted free and open access to the materials and methods used in the life sciences. Nominations are now being accepted!

The winner will be announced in the Ampitheater at 9:00am on Wednesday, April 30 during the Plenary Keynote and Awards Program, WEDNESDAY, APRIL 30 | 8:00 – 9:45 AM.

Full details including previous laureates and entry forms are available at www.bioinformatics.org/franklin.

2014 WINNER is:

Helen Berman, Ph.D.

Board of Governors Professor of Chemistry and Chemical Biology, Rutgers University;

Founding Member, Worldwide Protein Data Bank (wwPDB); Director, Research Collaboratory for Structural Bioinformatics PDB (RCSB PDB)

Helen: ACCEPTANCE AWARD SPEECH

Proteins: Synthesis, enzymes, Health & Disease

PDB depositors: 850 new entries / month, 468 Miliions downloads & views, PDB Access

History of sharing the databank on protein

J.D. Bernl – 1944 crystalied Pepsin with Dorothy Hodgkin Oxford, manyWomen Distingushed

1960 – Early structure of proteins: Myoglobin, hemoglobin

1970

1980

1990

2000  Ribosomes

2010s: macromolecule machines

  • Science of protein structure
  • Technology: electromicroscopy,  Structure Genomics – data driven science Hybrid methods at Present for 3D structure identification

COMMUNITY ATTITUDE –  1971 PDB archive established at Cold Spring Harbor, Walter Hamilton, petition to have an Open DB of Protein, Brookhaven Labs, to be shared with UK, Nature New Biology: Seven Structures to the DB

1982 – AIDs epidemic – NIH – requested data to be Open, community set its own rules on data organization Fred Richards, Yale, requested on moral ground, DB to be Open.

1993 – mandatory to sahre dat linked to publication, no Journal will accet  an article id data was not in PDB.

1996 – dictionary put together

2008: experimental data madatory to be put in PDB, Validation

2011: PDBx  definition of X-Ray, NMR, and 3DEM, small-angle Scattering

Collaboration with to enable: self storage, structure based drug design

SCIENCE in ther IMPORTANT to be put there, IT evolved, changes to data

global organization collaboration

Communities to work together

L.D>Bernal – SOcial function of Science, 1939

Elenor Ostrom 2009 Nobel Prize in Economics – Community collaboration by rules

Best Practices Awards

Add value to your Conference & Expo attendance, sponsorship or exhibit package, and further heighten your visibility with the creative positioning offered as a Best Practices participant. Winners will be selected by a peer review expert panel in early 2014.

Bio-IT World will present the Awards in the Amphitheater at 9:30am on Wednesday, April 30 during the Plenary Keynote and Awards Program, WEDNESDAY, APRIL 30 | 8:00 – 9:45 AM

Early bird deadline (no fee) for entry is December 16, 2013 and final deadline (fee) for entry is February 10, 2014. Full details including previous winners and entry forms are available at Bio-ITWorldExpo.com.

2014 WINNER(s) are:

 

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Track 6 Systems Pharmacology: Pathways to Patient Response @ BioIT World, April 29 – May 1, 2014 Seaport World Trade Center, Boston, MA

Aviva Lev-Ari, PhD, RN

 April 30, 2014

Modeling: Novel Tools

10:50 Chairperson’s Remarks

Avi Ma’ayan, Ph.D., Associate Professor, Pharmacology and Systems

Therapeutics, Icahn School of Medicine at Mount Sinai

11:00 The Human Avatar: Quantitative Systems Pharmacology to Support Physician Decision Making in Neurology and Psychiatry

Hugo Geerts, Ph.D., MBA, BA, CSO, In Silico Biosciences;

Adjunct Associate Professor, Perelman School of Medicine, University of Pennsylvania

CNS Quantitative Systems Pharmacology uses computer-based mechanistic modeling integrating brain network neurophysiology, functional imaging of

genetics, pharmacology of drug-receptor interactions and parameterization with clinical data. A patient model (“human avatar”) can be developed

accounting for polypharmacy and life history of traumatic events to help identify optimal treatments.

 

11:30 VisANT: An Integrative Network Platform to Connect Genes, Drugs, Diseases and Therapies

Zhenjun Hu, Ph.D., Research Associate Professor, Center for Advanced Genomic Technology,

Bioinformatics Program, Boston University

With the rapid accumulation of our knowledge on diseases, disease-related genes and drug targets, network-based analysis plays an increasingly

important role in systems biology, systems pharmacology and translational science. The new release of VisANT aims to provide new functions to facilitate

the convenient network analysis of diseases, therapies, genes and drugs.

12:00 pm Selected Oral Poster Presentation: Individualized PK/PD Biosimulations for Precision Drug Dosing: Diabetes Mellitus

Clyde Phelix, Ph.D., Associate Professor, Biology,

University of Texas San Antonio

Individualized biosimulations offer many advantages to precision medicine. Using one’s transcriptome to determine parameters of kinetic models of metabolism reanimates that individual for in silico testing. The Transcriptome-To-Metabolome™ Model is multiorgan and multicompartmental, including over 30 primary and secondary metabolic pathways and transport processes. Thus pharmacokinetics/pharmacodynamics studies can be performed in silico before treating each patient.

12:40 Luncheon Presentations (Sponsorship Opportunities Available) or Lunch on Your Own

Modeling: Cancer

1:50 Chairperson’s Remarks

Hugo Geerts, Ph.D., MBA, BA, CSO, In Silico Biosciences; Adjunct Associate Professor, Perelman School of Medicine, University of Pennsylvania

In REAL TIME

»»1:55 FEATURED PRESENTATION

Identifying Drug Targets from Drug-Induced Changes in Genome-Wide mRNA Expression

Avi Ma’ayan, Ph.D., Associate Professor, Pharmacology and Systems Therapeutics, Icahn School of Medicine at Mount Sinai

We collected and organized publicly available genome-wide gene expression data where hundreds of drugs were used to treat mammalian cells and changes in expression were compared to a control. We then developed computational methods that try to find the drug targets from the expression changes. We show that different steps in the analysis can contribute to approaching the right answer.

In REAL TIME

System biology and drug related by phynotypes, drugs causes diseasespatient and side effects

Networs,

Gene-set Libraries stored in Gene Matrix Transpose(GMT) files, KEGG Example

Drug-set Libraries

Drug-Drug similarity data, SIDER 2 Side Effect Resource, FDA adverse effect Report data

Connactivity Map: Broad  Institute, L1000 cell lines microarray, different  drug dose, DRUG effect on GENES

  • develop new compondts,
  • measure toxicity

LINC-L1000 data overview, Drug-drug similarity structure, connversion

for Vector side effect

LINCS Canvas Browser

Cell-Line/Drug Browser

New method for clustering patient by outcomes, survival analysis

http://www/maayanlab.net/LINCS/LCB/

Drug interact with target drug vs transcription factors, over expression

Over expression of transcription factors vs knock out for validation

2:25 Infrastructure for Comparison of Systematically Generated Cancer Networks vs. Literature Models

Dexter Pratt, Project Director,

NDEx, Cytoscape Consortium

Cancer subtype genetic networks can be generated by systematic analysis of patient somatic mutation data. Comparison to existing models of cancer

mechanisms is an important step in investigating these data-derived models. Recent work on Network Based Stratification (NBS) at the Ideker Lab will be

described along with tools for network comparison under development in the NDEx project.

In REAL TIME

Network based classification, unsupervised methoods

Ovarian cancer- sparse mutations, no two patients share same mutation, clustering by expression profile – can be cause, gene – gene interaction, smooth knowlede,

Reference networks, Common Entity identification system used, started at UCSD. overlap of curated PATHWAYS, query, neighborhoods in the reference network,

Using mapping tables to mapp identifiers for entity correspondence

Complex Reference Networks N:1 and 1:N

Transcriptionalcontrol motif, extract motifs mapp data to motifs, concordence,  and other metrics to be computed fromreferenced data,

Boundaries of Pathways – Reaction chain,  Differentially expressed genes –>> enzymes –>>> reactions  (differentilly regulated) –>> smaoll molecules

CONCLUTIONS

Cliniccal relevance, hypothesis motifs and interactions.

MAY 1, 2014

Modeling: Drug/Dose Response

1:55 Chairperson’s Remarks

Birgit Schoeberl, Ph.D., Vice President, Research, Merrimack Pharmaceuticals

»»2:00 FEATURED PRESENTATION

Systems Approaches to Risk Assessment

Lawrence J. Lesko, Ph.D., FCP, Clinical Professor and Director, Center for Pharmacometrics and Systems Pharmacology, University of Florida

“Idiosyncratic” adverse drug events (ADEs) are a substantial societal burden in terms of morbidity, mortality and healthcare costs. Predicting who

will suffer ADEs from what medications is extremely difficult with current observational or surveillance approaches. A new mechanistic approach to

drug safety science is sorely needed. Systems approaches may address this unmet medical need.

2:30 Pharmacodynamic Characterization of Compounds in Drug Discovery

Rui-Ru Ji, Ph.D., Principal Scientist, Genomics, Bristol-Myers Squibb

The transcriptome reacts in a dose-dependent manner to compound treatment. We will present methodology and will discuss multiple applications of dose

response profiling of the whole transcriptome.

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