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Archive for the ‘Pharmaceuticall R&D Informatics’ Category


genomicsinpersonalizedmedicinecovervolumeone

Content Consultant: Larry H Bernstein, MD, FCAP

Genomics Orientations for Personalized Medicine

Volume One

http://www.amazon.com/dp/B018DHBUO6

electronic Table of Contents

Chapter 1

1.1 Advances in the Understanding of the Human Genome The Initiation and Growth of Molecular Biology and Genomics – Part I

1.2 CRACKING THE CODE OF HUMAN LIFE: Milestones along the Way – Part IIA

1.3 DNA – The Next-Generation Storage Media for Digital Information

1.4 CRACKING THE CODE OF HUMAN LIFE: Recent Advances in Genomic Analysis and Disease – Part IIC

1.5 Advances in Separations Technology for the “OMICs” and Clarification of Therapeutic Targets

1.6 Genomic Analysis: FLUIDIGM Technology in the Life Science and Agricultural Biotechnology

Chapter 2

2.1 2013 Genomics: The Era Beyond the Sequencing of the Human Genome: Francis Collins, Craig Venter, Eric Lander, et al.

2.2 DNA structure and Oligonucleotides

2.3 Genome-Wide Detection of Single-Nucleotide and Copy-Number Variation of a Single Human Cell 

2.4 Genomics and Evolution

2.5 Protein-folding Simulation: Stanford’s Framework for Testing and Predicting Evolutionary Outcomes in Living Organisms – Work by Marcus Feldman

2.6 The Binding of Oligonucleotides in DNA and 3-D Lattice Structures

2.7 Finding the Genetic Links in Common Disease: Caveats of Whole Genome Sequencing Studies

Chapter 3

3.1 Big Data in Genomic Medicine

3.2 CRACKING THE CODE OF HUMAN LIFE: The Birth of Bioinformatics & Computational Genomics – Part IIB 

3.3 Expanding the Genetic Alphabet and linking the Genome to the Metabolome

3.4 Metabolite Identification Combining Genetic and Metabolic Information: Genetic Association Links Unknown Metabolites to Functionally Related Genes

3.5 MIT Scientists on Proteomics: All the Proteins in the Mitochondrial Matrix identified

3.6 Identification of Biomarkers that are Related to the Actin Cytoskeleton

3.7 Genetic basis of Complex Human Diseases: Dan Koboldt’s Advice to Next-Generation Sequencing Neophytes

3.8 MIT Team Researches Regulatory Motifs and Gene Expression of Erythroleukemia (K562) and Liver Carcinoma (HepG2) Cell Lines

Chapter 4

4.1 ENCODE Findings as Consortium

4.2 ENCODE: The Key to Unlocking the Secrets of Complex Genetic Diseases

4.3 Reveals from ENCODE Project will Invite High Synergistic Collaborations to Discover Specific Targets  

4.4 Human Variome Project: encyclopedic catalog of sequence variants indexed to the human genome sequence

4.5 Human Genome Project – 10th Anniversary: Interview with Kevin Davies, PhD – The $1000 Genome

4.6 Quantum Biology And Computational Medicine

4.7 The Underappreciated EpiGenome

4.8 Unraveling Retrograde Signaling Pathways

4.9  “The SILENCE of the Lambs” Introducing The Power of Uncoded RNA

4.10  DNA: One man’s trash is another man’s treasure, but there is no JUNK after all

Chapter 5

5.1 Paradigm Shift in Human Genomics – Predictive Biomarkers and Personalized Medicine – Part 1 

5.2 Computational Genomics Center: New Unification of Computational Technologies at Stanford

5.3 Personalized Medicine: An Institute Profile – Coriell Institute for Medical Research: Part 3

5.4 Cancer Genomics – Leading the Way by Cancer Genomics Program at UC Santa Cruz

5.5 Genome and Genetics: Resources @Stanford, @MIT, @NIH’s NCBCS

5.6 NGS Market: Trends and Development for Genotype-Phenotype Associations Research

5.7 Speeding Up Genome Analysis: MIT Algorithms for Direct Computation on Compressed Genomic Datasets

5.8  Modeling Targeted Therapy

5.9 Transphosphorylation of E-coli Proteins and Kinase Specificity

5.10 Genomics of Bacterial and Archaeal Viruses

Chapter 6

6.1  Directions for Genomics in Personalized Medicine

6.2 Ubiquinin-Proteosome pathway, Autophagy, the Mitochondrion, Proteolysis and Cell Apoptosis: Part III

6.3 Mitochondrial Damage and Repair under Oxidative Stress

6.4 Mitochondria: More than just the “Powerhouse of the Cell”

6.5 Mechanism of Variegation in Immutans

6.6 Impact of Evolutionary Selection on Functional Regions: The imprint of Evolutionary Selection on ENCODE Regulatory Elements is Manifested between Species and within Human Populations

6.7 Cardiac Ca2+ Signaling: Transcriptional Control

6.8 Unraveling Retrograde Signaling Pathways

6.9 Reprogramming Cell Fate

6.10 How Genes Function

6.11 TALENs and ZFNs

6.12 Zebrafish—Susceptible to Cancer

6.13 RNA Virus Genome as Bacterial Chromosome

6.14 Cloning the Vaccinia Virus Genome as a Bacterial Artificial Chromosome 

6.15 Telling NO to Cardiac Risk- DDAH Says NO to ADMA(1); The DDAH/ADMA/NOS Pathway(2)

6.16  Transphosphorylation of E-coli proteins and kinase specificity

6.17 Genomics of Bacterial and Archaeal Viruses

6.18  Diagnosing Diseases & Gene Therapy: Precision Genome Editing and Cost-effective microRNA Profiling

Chapter 7

7.1 Harnessing Personalized Medicine for Cancer Management, Prospects of Prevention and Cure: Opinions of Cancer Scientific Leaders @ http://pharmaceuticalintelligence.com

7.2 Consumer Market for Personal DNA Sequencing: Part 4

7.3 GSK for Personalized Medicine using Cancer Drugs Needs Alacris Systems Biology Model to Determine the In Silico Effect of the Inhibitor in its “Virtual Clinical Trial”

7.4 Drugging the Epigenome

7.5 Nation’s Biobanks: Academic institutions, Research institutes and Hospitals – vary by Collections Size, Types of Specimens and Applications: Regulations are Needed

7.6 Personalized Medicine: Clinical Aspiration of Microarrays

Chapter 8

8.1 Personalized Medicine as Key Area for Future Pharmaceutical Growth

8.2 Inaugural Genomics in Medicine – The Conference Program, 2/11-12/2013, San Francisco, CA

8.3 The Way With Personalized Medicine: Reporters’ Voice at the 8th Annual Personalized Medicine Conference, 11/28-29, 2012, Harvard Medical School, Boston, MA

8.4 Nanotechnology, Personalized Medicine and DNA Sequencing

8.5 Targeted Nucleases

8.6 Transcript Dynamics of Proinflammatory Genes

8.7 Helping Physicians identify Gene-Drug Interactions for Treatment Decisions: New ‘CLIPMERGE’ program – Personalized Medicine @ The Mount Sinai Medical Center

8.8 Intratumor Heterogeneity and Branched Evolution Revealed by Multiregion Sequencing[1]

8.9 Diagnosing Diseases & Gene Therapy: Precision Genome Editing and Cost-effective microRNA Profiling

Chapter 9

9.1 Personal Tale of JL’s Whole Genome Sequencing

9.2 Inspiration From Dr. Maureen Cronin’s Achievements in Applying Genomic Sequencing to Cancer Diagnostics

9.3 Inform Genomics Developing SNP Test to Predict Side Effects, Help MDs Choose among Chemo Regimens

9.4 SNAP: Predict Effect of Non-synonymous Polymorphisms: How Well Genome Interpretation Tools could Translate to the Clinic

9.5  LEADERS in Genome Sequencing of Genetic Mutations for Therapeutic Drug Selection in Cancer Personalized Treatment: Part 2

9.6 The Initiation and Growth of Molecular Biology and Genomics – Part I

9.7 Personalized Medicine-based Cure for Cancer Might Not Be Far Away

9.8 Personalized Medicine: Cancer Cell Biology and Minimally Invasive Surgery (MIS)

 Chapter 10

10.1 Pfizer’s Kidney Cancer Drug Sutent Effectively caused REMISSION to Adult Acute Lymphoblastic Leukemia (ALL)

10.2 Imatinib (Gleevec) May Help Treat Aggressive Lymphoma: Chronic Lymphocytic Leukemia (CLL)

10.3 Winning Over Cancer Progression: New Oncology Drugs to Suppress Passengers Mutations vs. Driver Mutations

10.4 Treatment for Metastatic HER2 Breast Cancer

10.5 Personalized Medicine in NSCLC

10.6 Gene Sequencing – to the Bedside

10.7 DNA Sequencing Technology

10.8 Nobel Laureate Jack Szostak Previews his Plenary Keynote for Drug Discovery Chemistry

Chapter 11

11.1 mRNA Interference with Cancer Expression

11.2 Angiogenic Disease Research Utilizing microRNA Technology: UCSD and Regulus Therapeutics

11.3 Sunitinib brings Adult acute lymphoblastic leukemia (ALL) to Remission – RNA Sequencing – FLT3 Receptor Blockade

11.4 A microRNA Prognostic Marker Identified in Acute Leukemia 

11.5 MIT Team: Microfluidic-based approach – A Vectorless delivery of Functional siRNAs into Cells.

11.6 Targeted Tumor-Penetrating siRNA Nanocomplexes for Credentialing the Ovarian Cancer Oncogene ID4

11.7 When Clinical Application of miRNAs?

11.8 How mobile elements in “Junk” DNA promote cancer. Part 1: Transposon-mediated tumorigenesis,

11.9 Potential Drug Target: Glycolysis Regulation – Oxidative Stress-responsive microRNA-320

11.10  MicroRNA Molecule May Serve as Biomarker

11.11 What about Circular RNAs?

Chapter 12

12.1 The “Cancer Establishments” Examined by James Watson, Co-discoverer of DNA w/Crick, 4/1953

12.2 Otto Warburg, A Giant of Modern Cellular Biology

12.3 Is the Warburg Effect the Cause or the Effect of Cancer: A 21st Century View?

12.4 Hypothesis – Following on James Watson

12.5 AMPK Is a Negative Regulator of the Warburg Effect and Suppresses Tumor Growth In Vivo

12.6 AKT signaling variable effects

12.7 Rewriting the Mathematics of Tumor Growth; Teams Use Math Models to Sort Drivers from Passengers

12.8 Phosphatidyl-5-Inositol signaling by Pin1

Chapter 13

13.1 Nanotech Therapy for Breast Cancer

13.2 BRCA1 a tumour suppressor in breast and ovarian cancer – functions in transcription, ubiquitination and DNA repair

13.3 Exome sequencing of serous endometrial tumors shows recurrent somatic mutations in chromatin-remodeling and ubiquitin ligase complex genes

13.4 Recurrent somatic mutations in chromatin-remodeling and ubiquitin ligase complex genes in serous endometrial tumors

13.5 Prostate Cancer: Androgen-driven “Pathomechanism” in Early onset Forms of the Disease

13.6 In focus: Melanoma Genetics

13.7 Head and Neck Cancer Studies Suggest Alternative Markers More Prognostically Useful than HPV DNA Testing

13.8 Breast Cancer and Mitochondrial Mutations

13.9  Long noncoding RNA network regulates PTEN transcription

Chapter 14

14.1 HBV and HCV-associated Liver Cancer: Important Insights from the Genome

14.2 Nanotechnology and HIV/AIDS treatment

14.3 IRF-1 Deficiency Skews the Differentiation of Dendritic Cells

14.4 Sepsis, Multi-organ Dysfunction Syndrome, and Septic Shock: A Conundrum of Signaling Pathways Cascading Out of Control

14.5  Five Malaria Genomes Sequenced

14.6 Rheumatoid Arthritis Risk

14.7 Approach to Controlling Pathogenic Inflammation in Arthritis

14.8 RNA Virus Genome as Bacterial Chromosome

14.9 Cloning the Vaccinia Virus Genome as a Bacterial Artificial Chromosome

Chapter 15

15.1 Personalized Cardiovascular Genetic Medicine at Partners HealthCare and Harvard Medical School

15.2 Congestive Heart Failure & Personalized Medicine: Two-gene Test predicts response to Beta Blocker Bucindolol

15.3 DDAH Says NO to ADMA(1); The DDAH/ADMA/NOS Pathway(2)

15.4 Peroxisome Proliferator-Activated Receptor (PPAR-gamma) Receptors Activation: PPARγ Transrepression for Angiogenesis in Cardiovascular Disease and PPARγ Transactivation for Treatment of Diabetes

15.5 BARI 2D Trial Outcomes

15.6 Gene Therapy Into Healthy Heart Muscle: Reprogramming Scar Tissue In Damaged Hearts

15.7 Obstructive coronary artery disease diagnosed by RNA levels of 23 genes – CardioDx, a Pioneer in the Field of Cardiovascular Genomic  Diagnostics

15.8 Ca2+ signaling: transcriptional control

15.9 Lp(a) Gene Variant Association

15.9.1 Two Mutations, in the PCSK9 Gene: Eliminates a Protein involved in Controlling LDL Cholesterol

15.9.2. Genomics & Genetics of Cardiovascular Disease Diagnoses: A Literature Survey of AHA’s Circulation Cardiovascular Genetics, 3/2010 – 3/2013

15.9.3 Synthetic Biology: On Advanced Genome Interpretation for Gene Variants and Pathways: What is the Genetic Base of Atherosclerosis and Loss of Arterial Elasticity with Aging

15.9.4 The Implications of a Newly Discovered CYP2J2 Gene Polymorphism Associated with Coronary Vascular Disease in the Uygur Chinese Population

15.9.5  Gene, Meis1, Regulates the Heart’s Ability to Regenerate after Injuries.

15.10 Genetics of Conduction Disease: Atrioventricular (AV) Conduction Disease (block): Gene Mutations – Transcription, Excitability, and Energy Homeostasis

15.11 How Might Sleep Apnea Lead to Serious Health Concerns like Cardiac and Cancers?

Chapter 16

16.1 Can Resolvins Suppress Acute Lung Injury?

16.2 Lipoxin A4 Regulates Natural Killer Cell in Asthma

16.3 Biological Therapeutics for Asthma

16.4 Genomics of Bronchial Epithelial Dysplasia

16.5 Progression in Bronchial Dysplasia

Chapter 17

17.1 Breakthrough Digestive Disorders Research: Conditions Affecting the Gastrointestinal Tract.

17.2 Liver Endoplasmic Reticulum Stress and Hepatosteatosis

17.3 Biomarkers-identified-for-recurrence-in-hbv-related-hcc-patients-post-surgery

17.4  Usp9x: Promising Therapeutic Target for Pancreatic Cancer

17.5 Battle of Steve Jobs and Ralph Steinman with Pancreatic cancer: How We Lost

Chapter 18

18.1 Ubiquitin Pathway Involved in Neurodegenerative Disease

18.2 Genomic Promise for Neurodegenerative Diseases, Dementias, Autism Spectrum, Schizophrenia, and Serious Depression

18.3 Neuroprotective Therapies: Pharmacogenomics vs Psychotropic Drugs and Cholinesterase Inhibitors

18.4 Ustekinumab New Drug Therapy for Cognitive Decline Resulting from Neuroinflammatory Cytokine Signaling and Alzheimer’s Disease

18.5 Cell Transplantation in Brain Repair

18.6 Alzheimer’s Disease Conundrum – Are We Near the End of the Puzzle?

Chapter 19

19.1 Genetics and Male Endocrinology

19.2 Genomic Endocrinology and its Future

19.3 Commentary on Dr. Baker’s post “Junk DNA Codes for Valuable miRNAs: Non-coding DNA Controls Diabetes”

19.4 Therapeutic Targets for Diabetes and Related Metabolic Disorders

19.5 Secondary Hypertension caused by Aldosterone-producing Adenomas caused by Somatic Mutations in ATP1A1 and ATP2B3 (adrenal cortical; medullary or Organ of Zuckerkandl is pheochromocytoma)

19.6 Personal Recombination Map from Individual’s Sperm Cell and its Importance

19.7 Gene Trap Mutagenesis in Reproductive Research

19.8 Pregnancy with a Leptin-Receptor Mutation

19.9 Whole-genome Sequencing in Probing the Meiotic Recombination and Aneuploidy of Single Sperm Cells

19.10 Reproductive Genetic Testing

Chapter 20

20.1 Genomics & Ethics: DNA Fragments are Products of Nature or Patentable Genes?

20.2 Understanding the Role of Personalized Medicine

20.3 Attitudes of Patients about Personalized Medicine

20.4  Genome Sequencing of the Healthy

20.5   Genomics in Medicine – Tomorrow’s Promise

20.6  The Promise of Personalized Medicine

20.7 Ethical Concerns in Personalized Medicine: BRCA1/2 Testing in Minors and Communication of Breast Cancer Risk

 20.8 Genomic Liberty of Ownership, Genome Medicine and Patenting the Human Genome

Chapter 21

Recent Advances in Gene Editing Technology Adds New Therapeutic Potential for the Genomic Era:  Medical Interpretation of the Genomics Frontier – CRISPR – Cas9

Introduction

21.1 Introducing CRISPR/Cas9 Gene Editing Technology – Works by Jennifer A. Doudna

21.1.1 Ribozymes and RNA Machines – Work of Jennifer A. Doudna

21.1.2 Evaluate your Cas9 gene editing vectors: CRISPR/Cas Mediated Genome Engineering – Is your CRISPR gRNA optimized for your cell lines?

21.1.3 2:15 – 2:45, 6/13/2014, Jennifer Doudna “The biology of CRISPRs: from genome defense to genetic engineering”

21.1.4  Prediction of the Winner RNA Technology, the FRONTIER of SCIENCE on RNA Biology, Cancer and Therapeutics  & The Start Up Landscape in BostonGene Editing – New Technology The Missing link for Gene Therapy?

21.2 CRISPR in Other Labs

21.2.1 CRISPR @MIT – Genome Surgery

21.2.2 The CRISPR-Cas9 System: A Powerful Tool for Genome Engineering and Regulation

Yongmin Yan and Department of Gastroenterology, Hepatology & Nutrition, University of Texas M.D. Anderson Cancer, Houston, USADaoyan Wei*

21.2.3 New Frontiers in Gene Editing: Transitioning From the Lab to the Clinic, February 19-20, 2015 | The InterContinental San Francisco | San Francisco, CA

21.2.4 Gene Therapy and the Genetic Study of Disease: @Berkeley and @UCSF – New DNA-editing technology spawns bold UC initiative as Crispr Goes Global

21.2.5 CRISPR & MAGE @ George Church’s Lab @ Harvard

21.3 Patents Awarded and Pending for CRISPR

21.3.1 Litigation on the Way: Broad Institute Gets Patent on Revolutionary Gene-Editing Method

21.3.2 The Patents for CRISPR, the DNA editing technology as the Biggest Biotech Discovery of the Century

2.4 CRISPR/Cas9 Applications

21.4.1  Inactivation of the human papillomavirus E6 or E7 gene in cervical carcinoma cells using a bacterial CRISPR/Cas 

21.4.2 CRISPR: Applications for Autoimmune Diseases @UCSF

21.4.3 In vivo validated mRNAs

21.4.6 Level of Comfort with Making Changes to the DNA of an Organism

21.4.7 Who will be the the First to IPO: Novartis bought in to Intellia (UC, Berkeley) as well as Caribou (UC, Berkeley) vs Editas (MIT)??

21.4.8 CRISPR/Cas9 Finds Its Way As an Important Tool For Drug Discovery & Development

Summary

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Announcement from LPBI Group: key code LPBI16 for Exclusive Discount to attend Boston’s Discovery on Target (September 19-22, 2016, CRISPR: Mechanisms to Applications on 9/19/2016)

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Leaders in Pharmaceutical Business Intelligence (LPBI) Group is a Media Partner of CHI for CHI’s 14th Annual Discovery on Target taking place September 19 – 22, 2016 in Boston.

As a proud partner of this event, Leaders in Pharmaceutical Business Intelligence Group has secured a special discounted price for you to attend, resulting in a $200 discount on a commercial registration and $100 discount on an academic registration!

*This offer is valid for new registrants only, does not apply to previously registered attendees or short courses, and cannot be combined with any other offer. You must mention key code LPBI16 to receive this discount.

Don’t miss your opportunity to network with 1,100+ of your peers at this year’s event. Special early registration savings are currently available through Friday, August 12.

Preliminary AGENDA and Registration Link

http://www.DiscoveryOnTarget.com

For sponsorship & exhibit information, please contact: Jon Stroup, Sr Business Development Manager,
(+1) 781-972-5483, jstroup@healthtech.com

 

See us in CHI’s Media Partners section online:

http://www.discoveryontarget.com/Discoveryontarget_content.aspx?id=125312

Contact: 617-244-4024, avivalev-ari@alum.berkeley.edu

@pharma_BI

@AVIVA1950

ANNOUNCEMENT

Leaders in Pharmaceutical Business Intelligence (LPBI) Group, Boston

pharma_bi-background0238

will cover in REAL TIME

Cambridge Healthtech Institute’s

Discovery on Target

September 19-22, 2016,

CRISPR: Mechanisms to Applications 

September 19, 2016

Westin Boston Waterfront, Boston, MA

In Attendance, streaming LIVE using Social Media

Aviva Lev-Ari, PhD, RN

Editor-in-Chief

http://pharmaceuticalintelligence.com

and

Stephen J Williams, PhD

Senior Editor

http://pharmaceuticalintelligence.com

flyer2forApril2016BioWorld

 

Leaders in Pharmaceutical Business Intelligence (LPBI) Group is a Media Partner of CHI for CHI’s 14th Annual Discovery on Target taking place September 19 – 22, 2016 in Boston.

 

As a proud partner of this event, Leaders in Pharmaceutical Business Intelligence Group has secured a special discounted price for you to attend, resulting in a $200 discount on a commercial registration and $100 discount on an academic registration!

*This offer is valid for new registrants only, does not apply to previously registered attendees or short courses, and cannot be combined with any other offer. You must mention key code LPBI16 to receive this discount.

Don’t miss your opportunity to network with 1,100+ of your peers at this year’s event. Special early registration savings are currently available through Friday, June 3.

 

Preliminary AGENDA and Registration Link

http://www.DiscoveryOnTarget.com

For sponsorship & exhibit information, please contact: Jon Stroup, Sr Business Development Manager,
(+1) 781-972-5483, jstroup@healthtech.com

 

See us in CHI’s Media Partners section online:

http://www.discoveryontarget.com/Discoveryontarget_content.aspx?id=125312

Contact: 617-244-4024, avivalev-ari@alum.berkeley.edu

@pharma_BI

@AVIVA1950

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Roche/Genentech’s Late-Stage Pipeline beyond Cancer: Ocrelizumab, against primary progressive MS & relapsing/remitting MS – $2.7 billion peak sales forecast

 

Reporter: Aviva Lev-Ari, PhD, RN

 

SOURCE

http://www.fool.com/investing/general/2016/03/19/youll-never-guess-which-pharma-likely-owns-40-of-2.aspx

 

Beyond Cancer

 

1. ocrelizumab, $2.7 billion peak sales forecast


What has the multiple sclerosis market excited about ocrelizumab is its success against primary progressive MS. Until orcrelizumab, no treatment in history has succeeded in a Phase III trial against this extremely debilitating form of MS.

Ocrelizumab is also being positioned for relapsing/remitting MS. Clinical trial data released in October showed that the treatment cut MS relapses by almost half compared with Merck’s competing drug, Rebif.

On a commercial basis, ocrelizumab’s expanded label (to include both forms of MS) should greatly increase its revenue potential. While a conservative estimate of ocrelizumab’s peak sales puts it at $2.7 billion, some see a peak sales potential for ocrelizumab in the neighborhood of $6 billion. That’s certainly a long shot, but not out of the question, since it is based on a MS market that is now worth $19 billion growing at 5% annually, with ocrelizumab eventually reaching a 30% market share.

Roche has stated plans for applying for regulatory approval for ocrelizumab in the first half of 2016. The drug’s accelerated approval status means an expedited review, with the FDA likely to take action on the application within 6 months. While ocrelizumab’s timeline depends on many variables, there is potential for sales to begin by year-end 2016.

 

Cancer Indications

 

2. Atezolizumab: $2.5 billion peak sales projected


Roche’s immuno-oncology drug atezolizumab follows ocrelizumab in blockbuster potential. Drugs such as atezolizumab (atezo) work by turning off cancer’s ability to remain undetected by the immune system, and atezo has put up some impressive data in its clinical trials. For example, in its POPLAR trial against advanced non-small-cell lung cancer, atezo doubled the likelihood of survival in patients taking the drug relative to placebo.

Being first matters, however. The market already has powerful competitors for atezo in Merck’s Keytruda and Bristol-Myers Squibb‘s (NYSE:BMY) Opdivo. On the other hand, both Keytruda and Opdivo are PD-1 treatments, and atezo works through another mechanism, PD-L1.

Genentech researchers believe PD-L1 is a more significant engine in cancer than PD-1. If they are correct, atezo will have a more long-lasting effect on stopping cancer growth, which would make the drug a potential first choice. Roche is driving some 36 studies  toward making a broad case for atezo with the FDA. Encouraging data keeps coming in. But investors should realize that how this drug will perform against competition from Keytruda and Opdivo is still very much an open question.

A more immediate commercial advantage for atezo is that Roche has a powerful in-house diagnostic division providing tools that can tag patients likely to respond to the drug. Many cancer therapies are ineffective with a large percentage of patients, and by specifically identifying those cancer patients who should benefit, Roche can personalize cancer treatment. That’s a big plus with payers, who naturally want to conserve their money for therapies more likely to be effective. As personalized medicine becomes steadily more widespread, full-year sales for Roche’s diagnostic division have grown–increasing 6% in 2015 to $10.7 billion.

Atezo’s breakthrough therapy designation gives it a solid chance of rolling out this year, but some industry watchers are deferring atezo’s projected launch date until 2017. Calculating a launch date is an inexact science, so that’s certainly possible.

3. Venetoclax: $1.4 billion projected for Roche

Roche’s third blockbuster speeding toward FDA approval is AbbVie partnered venetoclax. The drug is targeted to treat a highly virulent form of leukemia (chronic lymphocytic leukemia), specifically in those patients with a mutation that makes the cancer more aggressive and often results in shortened survival. Late-stage trials are also ongoing in non-Hodgkin’s lymphoma, acute myeloid leukemia, and multiple myeloma.

Roche has U.S. marketing rights  to the drug, and FiercePharma estimates Roche’s share of peak sales at $1.4 billion by 2020. The drug, which has already been fast-tracked for approval under the agency’s breakthrough designation last May, scored a priority review from the FDA in January. Roche expects FDA clearance in 2016.

 

SOURCE

http://www.fool.com/investing/general/2016/03/19/youll-never-guess-which-pharma-likely-owns-40-of-2.aspx

 

Other related articles published in this Open Access Online Scientific Journal include the following: 

Immune-Oncology Molecules In Development & Articles on Topic in @pharmaceuticalintelligence.com

Curators: Stephen J Williams, PhD and Aviva Lev-Ari, PhD, RN

https://pharmaceuticalintelligence.com/2016/01/11/articles-on-immune-oncology-molecules-in-development-pharmaceuticalintelligence-com/

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The late Cambridge Mayor Alfred Vellucci welcomed Life Sciences Labs to Cambridge, MA – June 1976

Reporter: Aviva Lev-Ari, PhD, RN

How Cambridge became the Life Sciences Capital

Worth watching is the video below, which captures the initial Cambridge City Council hearing on recombinant DNA research from June 1976. The first speaker is the late Cambridge mayor Alfred Vellucci.

Vellucci hoped to pass a two-year moratorium on gene splicing in Cambridge. Instead, the council passed a three-month moratorium, and created a board of nine Cambridge citizens — including a nun and a nurse — to explore whether the work should be allowed, and if so, what safeguards would be necessary. A few days after the board was created, the pro and con tables showed up at the Kendall Square marketplace.

At the time, says Phillip Sharp, an MIT professor, Cambridge felt like a manufacturing town that had seen better days. He recalls being surrounded by candy, textile, and leather factories. Sharp hosted the citizens review committee at MIT, explaining what the research scientists there planned to do. “I think we built a relationship,” he says.

By early 1977, the citizens committee had proposed a framework to ensure that any DNA-related experiments were done under fairly stringent safety controls, and Cambridge became the first city in the world to regulate research using genetic material.

 

WATCH VIDEO

How Cambridge became the life sciences capital

Scott Kirsner can be reached at kirsner@pobox.com. Follow him on Twitter@ScottKirsner and on betaboston.com.

SOURCE

How Cambridge became the life sciences capital

http://www.betaboston.com/news/2016/03/17/how-cambridge-became-the-life-sciences-capital/

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Robotically Driven System Could Reduce Cost of Discovering Drugs

Reporter: Irina Robu, PhD

However, their approach had only been tested using synthetic or previously acquired data, the team’s current model builds on this by letting the computer choose which experiments to do. The experiments were then carried out using liquid-handling robots and an automated microscope.

A total of 9,216 experiments were done, each consisting of acquiring images for a given cell clone in the presence of a given drug. The challenge for the algorithm was to learn how proteins were affected in each of these experiments, without performing all of them.

The originality of this work was to identify new phenotypes on its own as part of the learning process. To do this, it clustered the images to form phenotypes. The phenotypes were used to form a predictive model, so the learner could estimate the outcomes of unmeasured experiments. The basis of the model was to identify different sets of proteins that responded similarly to sets of drugs, so that it could predict the trend in the unmeasured experiments. The learner repeated the process for a total of 30 rounds, completing 2,697 out of the 9,216 possible experiments. As it progressively performed the experiments, it identified more phenotypes and more patterns in how sets of proteins were affected by sets of drugs.

Using an assortment of calculations, the team determined that the algorithm was able to learn a 92% accurate model for how the 96 drugs affected the 96 proteins, from only 29% of the experiments conducted.

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More Than 25 Percent of the Novel New Drugs Approved by FDA in 2015 are Personalized Medicines

Reporter: Aviva Lev-Ari, PhD, RN

A new analysis from the Personalized Medicine Coalition (PMC) documents an upward trend in the number of personalized medicine approvals at FDA, with personalized medicines accounting for more than 1 in 4 novel new drugs (NNDs) approved in 2015.

The analysis, titled 2015 Progress Report: Personalized Medicine at FDA, lists the 13 personalized medicines approved as NNDs in 2015, which represent 28 percent of the 45 NNDs the agency approved overall. The new approvals accelerate a trend PMC first noted in 2014, when the Coalition classified 21 percent of the year’s NNDs as personalized medicines.

PMC Science Policy Vice President Daryl Pritchard, Ph.D., said the momentum is driven by scientific validation of personalized medicine’s ability to improve patient outcomes.

“The scientific community has established personalized medicine as a successful approach to treating many diseases,” Pritchard said. “The increasing number of approvals for these drugs reflects that progress.”

SOURCE

http://www.pharmpro.com/news/2016/01/many-novel-drugs-approved-fda-2015-are-personalized-medicines

 

2015 Progress Report Personalized Medicine at FDA

 

More Than 25 Percent of the Novel New Drugs Approved by FDA in 2015 are Personalized Medicines

The transformation of health care from one-size-fits-all, trial-and-error medicine to a targeted approach utilizing an individual patient’s molecular information continues to accelerate as the U.S. Food and Drug Administration (FDA) more regularly and rapidly approves new personalized medicines. FDA’s Center for Drug Evaluation and Research (CDER) approved 45 novel new drugs (NNDs), either

new molecular entities or new therapeutic biologics, in 2015. Of these 45 NNDs, 13 of them — more than 25 percent — were personalized medicines as classified by the Personalized Medicine Coalition (PMC), thus continuing a trend that began last year when nine of 41 NNDs were classified as personalized medicines.

 

SOURCE

http://www.personalizedmedicinecoalition.org/Userfiles/PMC-Corporate/file/2015_Progress_Report_PM_at_FDA.pdf

 

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iShares Nasdaq Biotech Index Fund (IBB -1.8%) is down 21% since December 2015 !!!!!!!

Reporter: Aviva Lev-Ari, PhD, RN

UPDATED ON 2/2/2016

The U.S. biotech bubble is a part of the overallU.S. stock bubble that inflated as a result of global central bank stimulus programs, including the Federal Reserve’s quantitative easing and record low interest rates. Lofty U.S. stock valuations helped to create euphoria in various “hot,” market-leading sectors such as biotech. As with the Dot-com bubble and U.S. housing bubble, malinvestments typically build up during a bubble, and biotech in recent years is no exception. The early phase of the biotech bubble’s popping is why there have been scandals in the sector such as Valeant Pharmaceuticals, Theranos, and the Martin Shkreli ordeal. As the biotech meltdown continues to unfold, investors should expect to see many more of these types of scandals.

SOURCE

The Biotech Sector has been unable to fight off the widespread selling.

The iShares Nasdaq Biotech Index Fund (IBB -1.8%) is down again, albeit on modestly higher volume.

It’s down over 21% since late December.

Representative tickers on Wednesday, January 20, 2016 at 11:37 AM:

23,821 people have IBB in their portfolio
SOURCE

From: <account@seekingalpha.com> on behalf of SA Breaking News Team <account@seekingalpha.com>

Date: Wednesday, January 20, 2016 at 11:37 AM

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

Subject: BIIB: Biotechs succumb to market sell-off; IBB down 2%

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