Posts Tagged ‘#BIOIT’

10:15AM 11/13/2014 – 10th Annual Personalized Medicine Conference at the Harvard Medical School, Boston

Reporter: Aviva Lev-Ari, PhD, RN


REAL TIME Coverage of this Conference by Dr. Aviva Lev-Ari, PhD, RN – Director and Founder of LEADERS in PHARMACEUTICAL BUSINESS INTELLIGENCE, Boston http://pharmaceuticalintelligence.com

10:15 a.m. Panel Discussion — IT/Big Data

IT/Big Data

The human genome is composed of 6 billion nucleotides (using the genetic alphabet of T, C, G and A). As the cost of sequencing the human genome is decreasing at a rapid rate, it might not be too far into the future that every human being will be sequenced at least once in their lifetime. The sequence data together with the clinical data are going to be used more and more frequently to make clinical decisions. If that is true, we need to have secure methods of storing, retrieving and analyzing all of these data.  Some people argue that this is a tsunami of data that we are not ready to handle. The panel will discuss the types and volumes of data that are being generated and how to deal with it.

IT/Big Data


Amy Abernethy, M.D.
Chief Medical Officer, Flatiron

Role of Informatics, SW and HW in PM. Big data and Healthcare

How Lab and Clinics can be connected. Oncologist, Hematologist use labs in clinical setting, Role of IT and Technology in the environment of the Clinicians

Compare Stanford Medical Center and Harvard Medical Center and Duke Medical Center — THREE different models in Healthcare data management

Create novel solutions: Capture the voice of the patient for integration of component: Volume, Veracity, Value

Decisions need to be made in short time frame, documentation added after the fact

No system can be perfect in all aspects

Understanding clinical record for conversion into data bases – keeping quality of data collected

Key Topics


Stephen Eck, M.D., Ph.D.
Vice President, Global Head of Oncology Medical Sciences,
Astellas, Inc.

Small data expert, great advantage to small data. Populations data allows for longitudinal studies,

Big Mac Big Data – Big is Good — Is data been collected suitable for what is it used, is it robust, limitations, of what the data analysis mean

Data analysis in Chemical Libraries – now annotated

Diversity data in NOTED by MDs, nuances are very great, Using Medical Records for building Billing Systems

Cases when the data needed is not known or not available — use data that is available — limits the scope of what Valuable solution can be arrived at

In Clinical Trial: needs of researchers, billing clinicians — in one system

Translation of data on disease to data object

Signal to Noise Problem — Thus Big data provided validity and power


J. Michael Gaziano, M.D., M.P.H., F.R.C.P.
Scientific Director, Massachusetts Veterans Epidemiology Research
and Information Center (MAVERIC), VA Boston Healthcare System;
Chief Division of Aging, Brigham and Women’s Hospital;
Professor of Medicine, Harvard Medical School

at BWH since 1987 at 75% – push forward the Genomics Agenda, VA system 25% – VA is horizontally data integrated embed research and knowledge — baseline questionnaire 200,000 phenotypes – questionnaire and Genomics data to be integrated, Data hierarchical way to be curated, Simple phenotypes, validate phenotypes, Probability to have susceptibility for actual disease, Genomics Medicine will benefit Clinicians

Data must be of visible quality, collect data via Telephone VA – on Med compliance study, on Ability to tolerate medication

–>>Annotation assisted in building a tool for Neurologist on Alzheimer’s Disease (AlzSWAN knowledge base) (see also Genotator , a Disease-Agnostic Tool for Annotation)

–>>Curation of data is very different than statistical analysis of Clinical Trial Data

–>>Integration of data at VA and at BWH are tow different models of SUCCESSFUL data integration models, accessing the data is also using a different model

–>>Data extraction from the Big data — an issue

–>>Where the answers are in the data, build algorithms that will pick up causes of disease: Alzheimer’s – very difficult to do

–>>system around all stakeholders: investment in connectivity, moving data, individual silo, HR, FIN, Clinical Research

–>>Biobank data and data quality


Krishna Yeshwant, M.D.
General Partner, Google Ventures;
Physician, Brigham and Women’s Hospital

Computer Scientist and Medical Student. Were the technology is going?

Messy situation, interaction IT and HC, Boston and Silicon Valley are focusing on Consumers, Google Engineers interested in developing Medical and HC applications — HUGE interest. Application or Wearable – new companies in this space, from Computer Science world to Medicine – Enterprise level – EMR or Consumer level – Wearable — both areas are very active in Silicon Valley

IT stuff in the hospital HARDER that IT in any other environment, great progress in last 5 years, security of data, privacy. Sequencing data cost of big data management with highest security

Constrained data vs non-constrained data

Opportunities for Government cooperation as a Lead needed for standardization of data objects


Questions from the Podium:

  • Where is the Truth: do we have all the tools or we don’t for Genomic data usage
  • Question on Interoperability
  • Big Valuable data — vs Big data
  • quality, uniform, large cohort, comprehensive Cancer Centers
  • Volume of data can compensate quality of data
  • Data from Imaging – Quality and interpretation – THREE radiologist will read cancer screening




– See more at: http://personalizedmedicine.partners.org/Education/Personalized-Medicine-Conference/Program.aspx#sthash.qGbGZXXf.dpuf











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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

Reporter: 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


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


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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.


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,


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


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.


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)


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




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

Reporter: 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



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.


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


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


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.


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


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


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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PLENARY KEYNOTE PRESENTATIONS: TUESDAY, APRIL 29 | 4:00 – 5:00 PM @ BioIT World, April 29 – May 1, 2014 Seaport World Trade Center, Boston, MA


Reporter: Aviva Lev-Ari, PhD, RN



TUESDAY, APRIL 29 | 4:00 – 5:00 PM

Keynote Introduction: Sponsored by Dave Wilson, Senior Director, Business Development Manager, Global Channels, Hitachi Data Systems

John Quackenbush, Ph.D.

CEO, GenoSpace; Professor, Dana-Farber

Cancer Institute and Harvard School of Public Health

John Quackenbush received his Ph.D. in 1990 in theoretical physics from UCLA working on string theory models. Following two years as a postdoctoral fellow in physics, Dr. Quackenbush applied for and received a Special Emphasis Research Career Award from the National Center for Human Genome Research to work on the Human Genome Project. He spent two years at the Salk Institute and two years at Stanford University working at the interface of genomics and computational biology. In 1997 he joined the faculty of The Institute for Genomic Research (TIGR) where his focus began to shift to understanding what was encoded within the human genome. Since joining the faculties of the Dana-Farber Cancer Institute and the Harvard School of Public Health in 2005, his work has focused on decoding and modeling the networks of interacting genes that drive disease. In 2011 he and partner Mick Correll launched GenoSpace to facilitate genomic data analysis and interpretation, focused on accelerating research and delivering relevant and actionable solutions for personalized medicine.




2900 attendees 140 exhibitor, 250 Speakers, Best of Show Awart, Best Practices Award, Franklin Award, Memorial to Pat McGovern ex-CEO and Chairman of IDG and launcher of BioIT, McGovern Institute for Brain Research @MIT his gift $350 million, [Broad’s gift to MIT was $650million]

Hitachi Data Perspective

Cloud and Aanlytics

John Quackenbush about Precision Medicine

Desire to use an information ecosystem for mediicine

The DRIVER is DATA – access t data Data that drives innovations in BioMedical


  • Cloud Computing data, information and STORAGE of Data, data access, integration,
  • iPhone – applications for needs,

Bio – anniversary of DNA discovery structure in 1953

Genome Sequence – Transforming Medicine: Big Data: Volume, Velocity, Variety

Genomic Medicine – data for interpretation of Symptoms: diet, exercise

Cost of generation of data drops clinical relevance of data – sequencing now $1000 pay with credit card

Cost of the Analysis – $100,000 – Research number the genes translational, identify biomarkers to better achieve efficacy in segments of the population.

Diagnosis – Clinical Medicine

Reimbursement – few $ to identify VARIANCE relevant to treat disease

Cloud – secure the infrastructure – same dat looked by different parties to answer different questions.

GenoSpace for Research – N= many patients

GenoSpace for Clinical Care – N=1

GenoSpace for Patient Community – N=many individual patients


  • Secure storage data
  • analytics and visualization
  • diverse data
  • share dat securely

data in transit to be secure,  consumption of data

R&D Context

1000 Patients

50 Clinical site

large complex data

MMRF’s COMMPASS Study @Dana Farber – Multiple Myeloma Research Foundation

PORTAL design – to make data analysis of Cohort of Patioets, attribute analyzer, tools to find properties of cohort, compare across cohorts

Data analysis made easy – Precision Medicine based on Prediction

Population level data

end stage treatment

clincal trial

Translational Research – Pharma targets patients 

MMRF – gateway to the Community, interface for Patients to provide information during the course of Treatment, PATIENTS share, 1000 patients signed up to share data

  • Patient Reported outcomes
  • data integration
  • clinical trial recruitment
  • biomarker discovery

HOW to deliver data to POINT of CARE: Cancer more data Clinical (Pathology/Lab)

BioPoetry: Story what the data analysis MEANS

CURATION OF DATA – GenoSpace – for Clinical Labs

  • Pathology Group: Sequencing
  • Application development for REPORTS: FullView – meta data GEnoSpace 
  • Look at the assay for standard of Care
  • PDF format to scan and place in EMR, language suggestive,
  • MD’s Portal, giving access to Patients to add data


Thomson Reuter – Annotate

An OS for Precision Medicin

Genomics and integration with Clinical data

how to create system for all parties involved. Use of data for multiple needs that overlap

Information management – patient at the center

Precision Medicine is the FUTURE – Digital Architects for Precision Mediicne



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LPBI Repository of HashTags for Scientific Conferences

Reporter: Aviva Lev-Ari, PhD, RN

BioIT World, April 29 – May 1, 2014, Seaport World Trade Center, Boston, MA



Searches by Dr. Stephen  J Williams

Hashtags that get more than 3000 views and @sites that have at least 3000 followers


2014 Bio-IT World Twitter feed @bioitworld

#Boston   —-NOTE that #city has alot of appeal now

Twitter Feeds

@Biotech News
@science 2_0
Brian Dolan@mobilehealth    —- has 4000 followers


14th ANNUAL BIOTECH IN EUROPE FORUM For Global Partnering & Investment

30th September – 1st October 2014 • Congress Center Basel

SACHS Associates, London





We need to establish for our business, I.e.,


Open Access OnLine Scientific Journal
BioMed-MedTech Venture
Scientific Conference Press Coverage

25 characters for each #______

How about the following

#pharma_ ScientificConferencePress 


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April 2014: Tsunami in the Global Pharmaceutical Industry & Consumer Health Care Sector – New Organizational Structure Emerging

Commentator: Aviva Lev- Ari, PhD, RN



UPDATED on 2/19/2015

2014 – The Year of Pharma Very Expensive M&A





UPDATED on 7/21/2014

Allergan Reports Second Quarter 2014 Operating Results


FierceBiotech reported t

 Allergan aims ax at R&D, eliminating 1,500 jobs in bitter takeover fight

By John Carroll

Struggling to escape Valeant’s ($VRX) unwanted $53 billion takeover attempt, Allergan came up with plans to chop back its budget–axing 1,500 workers and eliminating another 250 vacant positions. Allergan’s release Monday morning is light on details, but the company clearly plans to cut back on early discovery work in what had been a rapidly growing R&D division.

Altogether, Allergan ($AGN) says its cost-cutting regimen–which will eliminate 13% of its workforce–will reduce its 2015 budget by $475 million. Reductions in spending will hit across the board, affecting its commercial organization, general and administrative functions, manufacturing and research and development. The emphasis at the company now is preserving “customer-facing” staff as well as all the key development programs now in the pipeline.

But there was a clear hint of where the ax will fall. The company noted that while it will continue all programs in the clinic, “any reductions in discovery programs will not impact approvals within the strategic plan period.”

Allergan CEO David Pyott

Allergan execs had earlier promised some deep cuts as they continue to resist the increasingly bitter charges being leveled against the company and its executive staff by Valeant and its allies. But the company just lost a key ally. The Wall Street Journal reported this morning that one of its biggest investors, Capital Research & Management, sold its stake in the company after meeting with CEO David Pyott.

In a call with analysts Monday morning Pyott emphasized that the company is in the hunt for new acquisitions, both large and small. He steered clear of mentioning any possible buyout targets, but offered that the perfect profile would be a “specialist in nature” with a good growth profile, good margins and a new therapeutic “pillar” that they could use to develop new products and grow sales more.

Bill Ackman and Valeant have been working to scrape together a 25% stake in the company, which they say will trigger a shareholders’ meeting to vote on its slate of proposed directors.

Just a few weeks ago Allergan was forced to acknowledge that the FDA had rejected–for the third time–migraine drug Semprana. Two of those rejections came after Pyott bought the therapy. Allergan said today that the next FDA action on Semprana is expected by the end of the second quarter in 2015.

R&D cutbacks were definitely not on Pyott’s agenda when he began the year. In an interview with FierceBiotech at the J.P. Morgan conference in January, Pyott bullishly outlined plans to beef up its growing R&D wing, which at that time had a staff of about 2,500. Pyott outlined plans to add hundreds more investigators as it looked to boost its total research allocation from $1 billion to $1.5 billion over the next 5 years. And a confident Pyott added that he was ready and willing to spend billions more to cover the cost of new acquisitions and pacts aimed at expanding the company’s core research focuses–while pondering the addition of a new drug category to the list of 5 core focuses if the opportunity looks right.

Allergan beat out Street estimates for Q2 and raised its earnings estimates for the next two years, a move that analysts say could make Valeant pay more than $53 billion if it plans to complete the acquisition.

“The company raised its guidance to a range of $8.20-$8.40 in 2015 and ~$10 in 2016, versus our $6.70 and $8.23 and consensus of $6.90 and $8.18, respectively,” noted Sterne Agee analyst Shibani Malhotra this morning. “Applying an 18x – 20x multiple to 2016 guidance gives a standalone value of $180-$200 per share. Today’s announcement by Allergan makes it more difficult for Valeant (VRX, $121.97, NR) to demonstrate how a merger can add incremental value and AGN shareholders may now require Valeant to pay a greater premium for Allergan, we believe.”

Related Articles:

‘Unpromising’ Allergan drug projects headed for the chopping block–report

FDA hands out its third rejection for Allergan’s migraine drug Semprana

Hostile Allergan bid is part of Valeant’s war on ‘value-destroying’ R&D


From: FierceBiotech <editors@fiercebiotech.com>

Reply-To: <editors@fiercebiotech.com> Date: Mon, 21 Jul 2014 16:22:21 +0000 (GMT)

To: <avivalev-ari@alum.berkeley.edu>

Subject: | 07.21.14 | Allergan slashes R&D, cuts 1,500 jobs; J&J partner gets a ‘breakthrough’


  • Much higher Concentration ratio in the Global Pharmaceutical Industry & Consumer Health Care Sector following the FIVE Tzunami Waves presented, below.
  • The Consumer is expected to experience an increase in product prices involved

 April 28, 2014 – Wave: Pfizer is willing to pay 58.7 billion pounds, or $98.7 billion for AstraZeneca

UPDATED on 4/28/2014


Updated, 9:06 a.m. | Pfizer publicly announced its interest in acquiring AstraZeneca of Britain on Monday, in what would be one of the biggest in an already swelling series of deal efforts among drug makers.

In a statement, Pfizer said it was willing to pay 58.7 billion pounds, or $98.7 billion. That would make it one of the largest-ever acquisition efforts in the pharmaceutical industry, surpassing Pfizer’s $90 billion takeover of Warner-Lambert 14 years ago.

Pfizer’s prospective bid was valued at £46.61 a share, roughly 30 percent above where AstraZeneca was trading at the beginning of the year.

The move is aimed at putting pressure on AstraZeneca, which has turned down a number of informal takeover approaches from Pfizer.

AstraZeneca shares surged 16.1 percent, to £47.37 in afternoon trading in London on Monday. Shares in Pfizer were up 2.6 percent in premarket trading, at $31.53.

On Monday, AstraZeneca said in a statement that it had agreed to meet in January with Pfizer, which made a preliminary offer of cash and stock representing a value of £46.61 a share – the same amount Pfizer revealed on Monday.

AstraZeneca said its board determined in January that the offer “very significantly undervalued AstraZeneca and its prospects.”


New York Times cites the following link on

Monday, April 28, 2014 – 2:08am EDT







Wave #1: Novartis & GlaxoSmithKline – Swiss and British

The Swiss pharmaceutical giant Novartis announced an overhaul of its operations on Tuesday that included an agreement to

  • buy the cancer drug business of its British rival GlaxoSmithKline for up to $16 billion. The deals announced on Tuesday come on the heels of eye-popping transactions in the drug sector in recent months and speculation about even more to come.

As part of its restructuring, Novartis said it would

  • sell its vaccine business to GlaxoSmithKline for $7.1 billion and combine its over-the-counter pharmaceutical business with Glaxo’s consumer drug business.

That new joint venture would be one of the world’s biggest companies in the consumer health care sector. Its products would

  • include Novartis’s Excedrin pain reliever and Maalox antacid, and
  • Glaxo’s Aquafresh toothpaste and Nicorette chewing gum.

Novartis, based in Basel, Switzerland, also said it had agreed to

  • sell its animal health division to Eli Lilly & Company for $5.4 billion, and that
  • it would put its flu vaccine business up for sale.
Novartis plans several deals with GlaxoSmithKline as part of its restructuring.

The deals grew out of a strategic review begun last year as Novartis faced pressure from investors to exit some of its less profitable businesses.

“This is about getting us into fighting shape for the next 10 years,” Joseph Jimenez, Novartis’s chief executive, said by telephone.

Over the next decade, Mr. Jimenez said, health care systems will be under strain, trying to hold down costs as the number of older people grows rapidly – even as fewer people are actually able to pay for their medications. “It’s a demographic fact,” he said.

Pharmaceutical companies across the globe “are looking at their portfolios,” he said, “and they’re asking, ‘How can I be a winner in this industry?’ The winners will be the ones who can innovate, who have global scale.”

According to data from Thomson Reuters, deals this year in the health care sector – driven primarily by acquisitions by pharmaceutical companies – have resulted in global transactions worth about $64.1 billion through April 10. That is the sector’s strongest start to a year since 2009.

The deals would allow Novartis to focus on higher-margin businesses in which the company already has scale, while staying active in the over-the-counter market.

By acquiring Glaxo’s oncology business, Novartis would expand its cancer drug offerings, including adding Tafinlar and Mekinist, two recently approved drugs used to treat skin cancer. The GlaxoSmithKline cancer drug business had revenue of about $1.6 billion in 2013. For Glaxo, the proposed deals are expected to provide greater scale in two of the company’s core businesses –

  • vaccines and
  • over-the-counter products.

The transactions are expected to increase its annual revenue by £1.3 billion, to about £26.9 billion.

The transactions with GlaxoSmithKline, expected to be completed by the first half of 2015, are subject to regulatory and shareholder approval.

Mr. Jimenez, an American who took over as Novartis C.E.O. in 2010, said he anticipated few regulatory hurdles, as the businesses being combined would be complementary ones, for the most part.

Glaxo’s combined consumer health care business, based on 2013 performance, would have revenue of £6.5 billion, making it the largest provider of over-the-counter drugs.

GlaxoSmithKline would hold a controlling interest of 63.5 percent of the combined company, with the rest held by Novartis.

“Opportunities to build greater scale and combine high quality assets in vaccines and consumer health care are scarce,” Andrew Witty, the GlaxoSmithKline chief executive, said in a statement. “With this transaction we will substantially strengthen two of our core businesses and create significant new options to increase value for shareholders.”

Emma Walmsley, the president of Glaxo’s consumer health care segment, will serve as chief executive of the combined consumer business.

The deal is also expected to expand Glaxo’s vaccine portfolio, including adding Bexsero, a treatment for meningitis.

Novartis, which had revenue of $57.9 billion in 2013, employs about 136,000 people in 150 countries.

GlaxoSmithKline was advised by Lazard, Citigroup, Zaoui & Company and Arkle Associates.

Wave # 2: Mallinckrodt Pharmaceuticals Irish and American partners

Mallinckrodt Pharmaceuticals, the Irish drug maker:

spun off from the medical device company Covidien last year, agreed this year to buy Questcor Pharmaceuticals for $5.6 billion in cash and shares, and acquired Cadence Pharmaceuticals of San Diego for about $1.3 billion in cash.

After a failed bid to gain control of the German pharmaceutical wholesaler Celesio, the health care company McKesson Corporation received enough shareholder support in January to complete the $8.3 billion deal.

Wave #3: Pharmaceutical Industries in India

And Sun Pharmaceutical Industries of India said this month that it would pay about $4 billion in stock for Ranbaxy Laboratories, a smaller Indian rival.

In addition to the Novartis deal, there are potentially tens of billions of dollars in transactions being discussed in the sector.

Wave #4: Allergen under hostile take over

Pershing Square Capital Management, which is led by the activist investor William A. Ackman, and the health care company Valeant are teaming up on a bid to buy Allergan, the maker of Botox, for about $46 billion. The bid by Valeant was announced on Tuesday.

Wave #5: AstraZeneca declines Pfizer

The British drug company AstraZeneca recently spurned several informal takeover approaches by Pfizer, according to a person briefed on the matter. One of those approaches valued AstraZeneca at about 60 billion pounds, or nearly $100 billion, according to The Sunday Times, a British newspaper.

The announcement on Tuesday was positive for Novartis shares, which rose 2.5 percent in midday trading in Zurich.

Pharmaceutical shares also rose elsewhere. In London trading, GlaxoSmithKline added 5.4 percent and AstraZeneca gained 6.6 percent. In Frankfurt, Eli Lilly rose 2.1 percent and Pfizer rose 2.4 percent. Sanofi rose 1.8 percent in Paris, and Roche gained 0.7 percent in Zurich.

Neil Gough, David Gelles, Michael J. de la Merced, Alexandra Stevenson and Andrew Pollack contributed reporting.


Novartis Builds a Major Overhaul on a Flurry of Deals


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