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Archive for the ‘Pharmaceutical Discovery’ Category


Postmarketing Safety or Effectiveness Data Needed: The 2013 paper was funded by the firm Sarepta Therapeutics, sellers of eteplirsen, a surge in its shares seen after the approval. Eteplirsen will cost patients around $300,000 a year.

 

Curator: Aviva Lev-Ari, PhD, RN

 

On September 19, the FDA okayed eteplirsen to treat Duchenne muscular dystrophy (DMD), a rare genetic disorder that results in muscle degeneration and premature death. Several of its top officials disagreed with the drug’s approval, questioning how beneficial it will be for patients, as ForbesMedPage Today and others reported.

http://retractionwatch.com/2016/09/21/amid-controversial-sarepta-approval-decision-fda-head-calls-for-key-study-retraction/

Factors at play for FDA Approval of eteplirsen

  1. the help of the families of young boys with Duchenne muscular dystrophy, emotional scenes from these families who have campaigned for so long
  2. an executive team from Sarepta who wouldn’t give up,

Ed Kaye, Sarepta, CEO – EK: It’s all about resilience. One of the things we’ve had is a group of people of like minds and anytime one of us gets down, somebody else is there to pick you up. One of the things we’ve always done is: Every time we’ve felt sorry for ourselves, we just need to think about those patients and what they go through. Our struggles in comparison very quickly become meaningless. You end up saying to yourself: What am I complaining about? Quit whining; get up and do your job.

and

3. an emerging new philosophy from some within the FDA, eteplirsen, now Exondys 51, was approved in patients with a confirmed mutation of the dystrophin gene amenable to exon 51 skipping.

http://www.fiercebiotech.com/biotech/sarepta-ceo-ed-kaye-fda-courage-nice-and-resilience?utm_medium=nl&utm_source=internal&mrkid=993697&mkt_tok=eyJpIjoiTXpBeU56aGpNREV3T1RZMiIsInQiOiJIM2poTkVOQ0N6YmxaenVHZDM1RlVvbTFmRkdwZGdxQ0pmYXNVOG5PKzRyenFXTkRMV0dcL3l0bVBPNkJ2NFV3Rnc3bWVFVnUwMCs3YVhWeVhvRkkrUU5FMFJ1RndSQTlHWFRnQmFTbUo3ODg9In0%3D

9/19/2016

FDA grants accelerated approval to first drug for Duchenne muscular dystrophy

The accelerated approval of Exondys 51 is based on the surrogate endpoint of dystrophin increase in skeletal muscle observed in some Exondys 51-treated patients. The FDA has concluded that the data submitted by the applicant demonstrated an increase in dystrophin production that is reasonably likely to predict clinical benefit in some patients with DMD who have a confirmed mutation of the dystrophin gene amenable to exon 51 skipping. A clinical benefit of Exondys 51, including improved motor function, has not been established. In making this decision, the FDA considered the potential risks associated with the drug, the life-threatening and debilitating nature of the disease for these children and the lack of available therapy.

The FDA granted Exondys 51 fast track designation, which is a designation to facilitate the development and expedite the review of drugs that are intended to treat serious conditions and that demonstrate the potential to address an unmet medical need. It was also granted priority review and orphan drug designationPriority review status is granted to applications for drugs that, if approved, would be a significant improvement in safety or effectiveness in the treatment of a serious condition. Orphan drug designation provides incentives such as clinical trial tax credits, user fee waiver and eligibility for orphan drug exclusivity to assist and encourage the development of drugs for rare diseases.

SOURCE

http://www.fda.gov/NewsEvents/Newsroom/PressAnnouncements/ucm521263.htm

The viability of this drug approval depends  on “to be gathered” Postmarketing safety or effectiveness data, aka follow-up confirmatory trials.

Sarepta CEO Ed Kaye on FDA courage, NICE and resilience

BA: When it comes to flexibility, however, the FDA will likely not be flexible if your drug doesn’t prove the desired efficacy in your longer term postmarketing studies. If at the end of this period your drug doesn’t come through, how easy will it be for you to take this off the market? I don’t think anyone, including the FDA, wants a repeat of what happened in 2011 when Roche saw its breast cancer license for Avastin, which had been approved under an accelerated review, pulled after not being safe or effective enough in the follow-up confirmatory trials. But you face this as a possible scenario.

EK: That’s true, but one of the things we’re trying to do to mitigate that is to obviously, with our ongoing studies, prove the efficacy that the FDA wants to see. And you know, if there is a problem with one study then we’d hope to have other data that are supportive. The other thing we’re doing of course is developing that next-generation chemistry in DMD that could prove more effective, so we could certainly consider using that next-gen chemistry to take our work forward and try and make it better.

We have a lot of shots on goal to make sure we can continue to supply a product for these boys, but there is always a risk. If we can’t show efficacy in the way the FDA wants, then yes they have the option to take it off the market.

http://www.fiercebiotech.com/biotech/sarepta-ceo-ed-kaye-fda-courage-nice-and-resilience?utm_medium=nl&utm_source=internal&mrkid=993697&mkt_tok=eyJpIjoiTXpBeU56aGpNREV3T1RZMiIsInQiOiJIM2poTkVOQ0N6YmxaenVHZDM1RlVvbTFmRkdwZGdxQ0pmYXNVOG5PKzRyenFXTkRMV0dcL3l0bVBPNkJ2NFV3Rnc3bWVFVnUwMCs3YVhWeVhvRkkrUU5FMFJ1RndSQTlHWFRnQmFTbUo3ODg9In0%3D

Need for follow-up confirmatory trials remains outstanding

FDA’s Postmarketing Surveillance Programs

http://www.fda.gov/Drugs/GuidanceComplianceRegulatoryInformation/Surveillance/ucm090385.htm

FDA’s Regulations and Policies and Procedures for Postmarketing Surveillance Programs

http://www.fda.gov/Drugs/GuidanceComplianceRegulatoryInformation/Surveillance/ucm090394.htm

 

Positions on Sarepta’s eteplirsen Scientific Approach

Gene Editing for Exon 51: Why CRISPR Snipping might be better than Exon Skipping for DMD

Reporter: Aviva Lev-Ari, PhD, RN

https://pharmaceuticalintelligence.com/2016/01/23/gene-editing-for-exon-51-why-crispr-snipping-might-be-better-than-exon-skipping-for-dmd/

 

QUOTE START

Retraction Watch

Tracking retractions as a window into the scientific process

Amid controversial Sarepta approval decision, FDA head calls for key study retraction

with one comment

FDAThe head of the U.S. Food and Drug Administration (FDA) has called for the retraction of a study about a drug that the agency itself approved earlier this week, despite senior staff opposing the approval.

On September 19, the FDA okayed eteplirsen to treat Duchenne muscular dystrophy (DMD), a rare genetic disorder that results in muscle degeneration and premature death. Several of its top officials disagreed with the drug’s approval, questioning how beneficial it will be for patients, as ForbesMedPage Today and others reported.

In a lengthy report Commissioner Robert Califf sent to senior FDA officials on September 16 — that was made public on September 19 — he called for the retraction of a 2013 study published in Annals of Neurologyfunded by the seller of eteplirsen, which showed beneficial effects of the drug in DMD patients. Califf writes inthe report:

The publication, now known to be misleading, should probably be retracted by its authors.

In a footnote in the report, Califf adds:

In view of the scientific deficiencies identified in this analysis, I believe it would be appropriate to initiate a dialogue that would lead to a formal correction or retraction (as appropriate) of the published report.

The study was not the key factor in the agency’s decision to approve the drug, according to Steve Usdin, Washington editor of the publication BioCentury; still, Usdin told Retraction Watch he is “really surprised” at the call for retraction from top FDA staff, the first he has come across in the last two decades.

The 2013 paper was funded by the firm Sarepta Therapeutics, sellers of eteplirsen, which has seen a surge in its shares after the approval. Eteplirsen will cost patients around $300,000 a year.

DMD affects around 1 in 3,600 boys due to a mutation in the gene that codes for the protein dystrophin, which is important for structural stability of muscles. Eteplirsen is the first drug to treat DMD, and was initially given a green light by Janet Woodcock, director of Center for Drug Evaluation and Research, after a split vote from the FDA’s advisory committee. Despite Califf’s issues with the literature supporting the drug’s use in DMD, he did not overturn Woodcock’s decision, and the agency approved the drug this week.

In 2014, an inspection team visited the Nationwide Children’s Hospital in Columbus, Ohio, where the research was conducted, according to the report. In the report, Ellis Unger, director of the Office of Drug Evaluation I in FDA’s Center for Drug Evaluation, notes:

We found the analytical procedures to be typical of an academic research center, seemingly appropriate for what was simply an exploratory phase 1/2 study, but not suitable for an adequate and well controlled study aimed to serve as the basis for a regulatory action. The procedures and controls that one would expect to see in support of a phase 3 registrational trial were not in evidence.

Specifically, Unger describes concerns about blinding during the experiments, and notes:

The immunohistochemistry images were only faintly stained, and had been read by a single technician using an older liquid crystal display (LCD) computer monitor in a windowed room where lighting was not controlled. (The technician had to suspend reading around mid-day, when brighter light began to fill the room and reading became impossible.)

Unger adds:

Having uncovered numerous technical and operational shortcomings in Columbus, our team worked collaboratively with the applicant to develop improved methods for a reassessment of the stored images…This re-analysis, along with the study published in 2013, provides an instructive example of an investigation with extraordinary results that could not be verified.

Luciana Borio, acting chief scientist at the FDA, is cited in the report saying:

I would be remiss if I did not note that the sponsor has exhibited serious irresponsibility by playing a role in publishing and promoting selective data during the development of this product. Not only was there a misleading published article with respect to the results of Study–which has never been retracted—but Sarepta also issued a press release relying on the misleading article and its findings…As determined by the review team, and as acknowledged by Dr. Woodcock, the article’s scientific findings—with respect to the demonstrated effect of eteplirsen on both surrogate and clinical endpoints—do not withstand proper and objective analyses of the data. Sarepta’s misleading communications led to unrealistic expectations and hope for DMD patients and their families.

Here’s how Sarepta describes the study’s findings in the press release Borio refers to:

Published study results showed that once-weekly treatment with eteplirsen resulted in a statistically significant increase from baseline in novel dystrophin, the protein that is lacking in patients with DMD. In addition, eteplirsen-treated patients evaluable on the 6-minute walk test (6MWT) demonstrated stabilization in walking ability compared to a placebo/delayed-treatment cohort. Eteplirsen was well tolerated in the study with no clinically significant treatment-related adverse events. These data will form the basis of a New Drug Application (NDA) to the U.S. Food and Drug Administration (FDA) for eteplirsen planned for the first half of 2014.

However, Usdin noted that the drug’s approval and the study are two independent events, adding that the 2013 study just “got the ball rolling” for eteplirsen, and the FDA conducted many of its own experiments analyses, as detailed in the newly released report.

Jerry Mendell, the corresponding author of the study (which has so far been cited 118 times, according to Thomson Reuters Web of Science) from Ohio State University in Columbus, told us the allegations were “unfounded” and said the data are “valid.” Therefore, he added, he will not be approaching the journal for a retraction, noting that the FDA asked him hundreds of questions about the paper and audited the trials.

Clifford Saper, the editor-in-chief of Annals of Neurology from the Beth Israel Deaconess Medical Center (which is part of Harvard Medical School), said in an email:

It takes more than a call by a politician for retraction of a paper. It takes actual evidence.

He added:

If the FDA commissioner has, or knows of someone who has, evidence for an error in a paper published in Annals of Neurology, I encourage him to send that evidence to me and a copy to the authors of the article, for their reply. At that point we will engage in a scientific review of the evidence and make appropriate responses.

Linda Lowes, sixth author of the present study, is the last author of a 2016 study in Physical Therapy that was retracted months after publication. Its notice reads:

This article has been retracted by the author due to unintentional deviations in the use of the described modified technique to assess plagiocephaly in the study participants, such that the use of the modified technique cannot be defended for the stated purpose in this population at this time.

Califf was a cardiologist at Duke University during the high-profile scandal of researcher Anil Potti at Duke, which led to more than 10 retractions, settled lawsuits, and medical board reprimands. In 2015, he told TheTriangle Business Journal:

I wish I had gotten myself more involved earlier…There were systems that were not adequate, as we stated. … That was a tough one, I think, for the whole institution.

We’ve contacted the FDA for comment, and will update the post with anything else we learn.

END QUOTE

Correction 9/21/16 10:44 p.m. eastern: When originally published, this post incorrectly reported that Califf was part of an inspection team that visited the Nationwide Children’s Hospital in Ohio, and attributed quotes from Ellis Unger to Califf. We have made appropriate corrections, and apologize for the error.

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SOURCE

http://retractionwatch.com/2016/09/21/amid-controversial-sarepta-approval-decision-fda-head-calls-for-key-study-retraction/

Related Resources on FDA’s Policies on Drugs:

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Milestones in Physiology & Discoveries in Medicine and Genomics: Request for Book Review Writing on Amazon.com


physiology-cover-seriese-vol-3individualsaddlebrown-page2

Milestones in Physiology

Discoveries in Medicine, Genomics and Therapeutics

Patient-centric Perspective 

http://www.amazon.com/dp/B019VH97LU 

2015

 

 

Author, Curator and Editor

Larry H Bernstein, MD, FCAP

Chief Scientific Officer

Leaders in Pharmaceutical Business Intelligence

Larry.bernstein@gmail.com

Preface

Introduction 

Chapter 1: Evolution of the Foundation for Diagnostics and Pharmaceuticals Industries

1.1  Outline of Medical Discoveries between 1880 and 1980

1.2 The History of Infectious Diseases and Epidemiology in the late 19th and 20th Century

1.3 The Classification of Microbiota

1.4 Selected Contributions to Chemistry from 1880 to 1980

1.5 The Evolution of Clinical Chemistry in the 20th Century

1.6 Milestones in the Evolution of Diagnostics in the US HealthCare System: 1920s to Pre-Genomics

 

Chapter 2. The search for the evolution of function of proteins, enzymes and metal catalysts in life processes

2.1 The life and work of Allan Wilson
2.2  The  evolution of myoglobin and hemoglobin
2.3  More complexity in proteins evolution
2.4  Life on earth is traced to oxygen binding
2.5  The colors of life function
2.6  The colors of respiration and electron transport
2.7  Highlights of a green evolution

 

Chapter 3. Evolution of New Relationships in Neuroendocrine States
3.1 Pituitary endocrine axis
3.2 Thyroid function
3.3 Sex hormones
3.4 Adrenal Cortex
3.5 Pancreatic Islets
3.6 Parathyroids
3.7 Gastointestinal hormones
3.8 Endocrine action on midbrain
3.9 Neural activity regulating endocrine response

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

 

Chapter 4.  Problems of the Circulation, Altitude, and Immunity

4.1 Innervation of Heart and Heart Rate
4.2 Action of hormones on the circulation
4.3 Allogeneic Transfusion Reactions
4.4 Graft-versus Host reaction
4.5 Unique problems of perinatal period
4.6. High altitude sickness
4.7 Deep water adaptation
4.8 Heart-Lung-and Kidney
4.9 Acute Lung Injury

4.10 Reconstruction of Life Processes requires both Genomics and Metabolomics to explain Phenotypes and Phylogenetics

 

Chapter 5. Problems of Diets and Lifestyle Changes

5.1 Anorexia nervosa
5.2 Voluntary and Involuntary S-insufficiency
5.3 Diarrheas – bacterial and nonbacterial
5.4 Gluten-free diets
5.5 Diet and cholesterol
5.6 Diet and Type 2 diabetes mellitus
5.7 Diet and exercise
5.8 Anxiety and quality of Life
5.9 Nutritional Supplements

 

Chapter 6. Advances in Genomics, Therapeutics and Pharmacogenomics

6.1 Natural Products Chemistry

6.2 The Challenge of Antimicrobial Resistance

6.3 Viruses, Vaccines and immunotherapy

6.4 Genomics and Metabolomics Advances in Cancer

6.5 Proteomics – Protein Interaction

6.6 Pharmacogenomics

6.7 Biomarker Guided Therapy

6.8 The Emergence of a Pharmaceutical Industry in the 20th Century: Diagnostics Industry and Drug Development in the Genomics Era: Mid 80s to Present

6.09 The Union of Biomarkers and Drug Development

6.10 Proteomics and Biomarker Discovery

6.11 Epigenomics and Companion Diagnostics

 

Chapter  7

Integration of Physiology, Genomics and Pharmacotherapy

7.1 Richard Lifton, MD, PhD of Yale University and Howard Hughes Medical Institute: Recipient of 2014 Breakthrough Prizes Awarded in Life Sciences for the Discovery of Genes and Biochemical Mechanisms that cause Hypertension

7.2 Calcium Cycling (ATPase Pump) in Cardiac Gene Therapy: Inhalable Gene Therapy for Pulmonary Arterial Hypertension and Percutaneous Intra-coronary Artery Infusion for Heart Failure: Contributions by Roger J. Hajjar, MD

7.3 Diagnostics and Biomarkers: Novel Genomics Industry Trends vs Present Market Conditions and Historical Scientific Leaders Memoirs

7.4 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

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

7.6 Imaging Biomarker for Arterial Stiffness: Pathways in Pharmacotherapy for Hypertension and Hypercholesterolemia Management

7.7 Neuroprotective Therapies: Pharmacogenomics vs Psychotropic drugs and Cholinesterase Inhibitors

7.8 Metabolite Identification Combining Genetic and Metabolic Information: Genetic association links unknown metabolites to functionally related genes

7.9 Preserved vs Reduced Ejection Fraction: Available and Needed Therapies

7.10 Biosimilars: Intellectual Property Creation and Protection by Pioneer and by

7.11 Demonstrate Biosimilarity: New FDA Biosimilar Guidelines

 

Chapter 7.  Biopharma Today

8.1 A Great University engaged in Drug Discovery: University of Pittsburgh

8.2 Introduction – The Evolution of Cancer Therapy and Cancer Research: How We Got Here?

8.3 Predicting Tumor Response, Progression, and Time to Recurrence

8.4 Targeting Untargetable Proto-Oncogenes

8.5 Innovation: Drug Discovery, Medical Devices and Digital Health

8.6 Cardiotoxicity and Cardiomyopathy Related to Drugs Adverse Effects

8.7 Nanotechnology and Ocular Drug Delivery: Part I

8.8 Transdermal drug delivery (TDD) system and nanotechnology: Part II

8.9 The Delicate Connection: IDO (Indolamine 2, 3 dehydrogenase) and Cancer Immunology

8.10 Natural Drug Target Discovery and Translational Medicine in Human Microbiome

8.11 From Genomics of Microorganisms to Translational Medicine

8.12 Confined Indolamine 2, 3 dioxygenase (IDO) Controls the Homeostasis of Immune Responses for Good and Bad

 

Chapter 9. BioPharma – Future Trends

9.1 Artificial Intelligence Versus the Scientist: Who Will Win?

9.2 The Vibrant Philly Biotech Scene: Focus on KannaLife Sciences and the Discipline and Potential of Pharmacognosy

9.3 The Vibrant Philly Biotech Scene: Focus on Computer-Aided Drug Design and Gfree Bio, LLC

9.4 Heroes in Medical Research: The Postdoctoral Fellow

9.5 NIH Considers Guidelines for CAR-T therapy: Report from Recombinant DNA Advisory Committee

9.6 1st Pitch Life Science- Philadelphia- What VCs Really Think of your Pitch

9.7 Multiple Lung Cancer Genomic Projects Suggest New Targets, Research Directions for Non-Small Cell Lung Cancer

9.8 Heroes in Medical Research: Green Fluorescent Protein and the Rough Road in Science

9.9 Issues in Personalized Medicine in Cancer: Intratumor Heterogeneity and Branched Evolution Revealed by Multiregion Sequencing

9.10 The SCID Pig II: Researchers Develop Another SCID Pig, And Another Great Model For Cancer Research

Epilogue

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Agenda @Biotech Week Boston: WHERE THE HEART, TECHNOLOGY AND BUSINESS OF SCIENCE CONVERGE, Conference: October 4 – 7, 2016 | Exhibition: October 5-7, 2016 Boston Convention and Exhibition Center

Reporter: Aviva Lev-Ari, PhD, RN

Conference: October 4 – 7, 2016 | Exhibition: October 5-7, 2016

Boston Convention and Exhibition Center,
Boston, MA

WHERE THE HEART, TECHNOLOGY AND BUSINESS OF SCIENCE CONVERGE

#BIOTECHWEEKBOSTON

https://lifesciences.knect365.com/biotech-week-boston

October 6, 2016 – Key Sessions

Toni Hoover, Ph.D.

Harnessing Science, Technology and Innovation to Improve Global Health

Bill & Melinda Gates Foundation

Rick Berke

STAT Panel Discussion – President Clinton or President Trump: What Our Next President Will Mean for Biotech and Pharma

STAT (STATnews.com)

October 6, 2016

7:30 am 30 mins

Single-use XCell™ ATF Systems for Continuous Processing: 100% Cell Retention, 8x Faster Set-up, No autoclave

12:35 pm 30 mins

cGMP Biologics Production Using Corynex ® : A Highly-Productive Gram-Positive Microbial Protein Secretion System

12:35 pm 30 mins

Advanced Materials for Single Use Systems

12:35 pm 30 mins

Fast Trak Your Molecule to Market: When, Why and How to Outsource Biomanufacturing

12:35 pm 30 mins

An Integrated BalanCD ® CHO Media Solution for Early Therapeutic Antibody Development, Scale-Up and Commercial Supply

12:35 pm 30 mins

Reveal Information that Gives Insights – New Approaches to Sub-Visible Particle Characterization

9:15 am 525 mins

BWB Exhibit Hall Open

9:30 am 45 mins

Harnessing Science, Technology and Innovation to Improve Global Health

  • Toni Hoover, Ph.D., Bill & Melinda Gates Foundation

10:30 am 10 mins

Asahi Kasei Product Presentation

10:40 am 10 mins

How to Reduce Costs, Make Informed Decisions and Gain Insight for Innovation Through BioSolve

10:50 am 10 mins

Increasing Protein Production with Novel Cell-Ess Supplement without Affecting Metabolic Profile

12 pm 60 mins

Oral Poster Presentations

 1:10 pm
10 mins

Lonza Presentation

1:20 pm 15 mins

Distek Presentation

1:35 pm 10 mins

PendoTECH Presentation

2:15 pm 90 mins

Town Hall Forum: An Update on Single-Use Standardization and Alignment

4 pm 10 mins

Sartorius Presentation

4:10 pm 20 mins

Catalent Presentation

4:30 pm 10 mins

Asahi Kasei Presentation

4:40 pm 10 mins

Meissner Filtration Products Presentation

5 pm 60 mins

STAT Panel Discussion – President Clinton or President Trump: What Our Next President Will Mean for Biotech and Pharma

  • Rick Berke, STAT (STATnews.com)
  • Mason Tenaglia, IMS Institute for Healthcare Informatics, Payer & Managed Care Insights
  • Damien Garde, STAT (STATnews.com)
  • Dylan Scott, STAT (STATnews.com)

October 7, 2016

Key Sessions

Steve Wozniak

Innovation & Customer Centricity – Sponsored by Pall Life Sciences

Apple Computer Inc

7:15 am 30 mins

Accelerating Mammalian and Microbial Culture with Single-Use Technology

12:35 pm 30 mins

Unlocking Downstream Efficiency

9:10 am 330 mins

BWB Exhibit Hall Open

9:15 am 60 mins

Innovation & Customer Centricity – Sponsored by Pall Life Sciences

Pall Life Sciences
  • Steve Wozniak, Apple Computer Inc

10:15 am 10 mins

Steve Wozniak Meet & Greet at Pall Lounge

12:30 pm
60 mins

Panel Discussion: Immuno-oncology: What’s Next?

1:30 pm 30 mins

Passport Prize Drawing

10:50 am 20 mins

Innovations in Live Banking of Bio-Specimens: Prospective Advantages to the Retrospective Clinical Failures

11:10 am 20 mins

Innovations in Cell & Gene Therapy

11:30 am 60 mins

PANEL DISCUSSION: Innovations and Technology to Drive Improvements in Healthcare Delivery

SOURCE

https://lifesciences.knect365.com/biotech-week-boston

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Value for Patients – Turning Advances in Science: A Case Study of a Leading Global Pharmaceutical Company – Astellas Pharma Inc.

Astellas Pharma Inc. (https://www.astellas.com/en/) and Astellas Pharma U.S., Inc. (https://www.astellas.us/)

UPDATED on 4/3/2017

Astellas Pharma Inc. and Ogeda SA announced today that Astellas and Ogeda shareholders have entered into a definitive agreement under which Astellas has agreed to acquire Ogeda a privately owned drug discovery company. Ogeda is a clinical-stage drug discovery company that discovers and develops small molecule drugs targeting G-protein coupled receptors (GPCRs). The lead investigational candidate, fezolinetant, is a selective NK3 receptor antagonist, and the positive data from a Phase 2a study result for the non-hormonal treatment of menopause-related vasomotor symptoms (“MR-VMS”) was announced in January 2017. This transaction expands Astellas’ late stage pipeline and is expected to contribute to its mid-to-long term growth.

SOURCE

http://www.prnewswire.com/news-releases/astellas-to-acquire-ogeda-sa-300433141.html

https://endpts.com/astellas-swoops-in-on-a-mid-stage-drug-for-hot-flashes-in-860m-biotech-buyout-deal/?utm_medium=email&utm_campaign=Monday%20%20April%203%202017&utm_content=Monday%20%20April%203%202017+CID_4adac18d4a997566831a3ca0829b655e&utm_source=ENDPOINTS%20emails&utm_term=Astellas%20swoops%20in%20on%20a%20mid-stage%20drug%20for%20hot%20flashes%20in%20860M%20biotech%20buyout%20deal

UPDATED on 8/24/2016

Some analysts suggested Pfizer paid too much, particularly since it will split profits from Xtandi with Japan-based Astellas Pharma, which helps market the drug. Pfizer defended the deal, saying it would add 5 cents to its earnings per share in the first full year.

“The proposed acquisition of Medivation is expected to immediately accelerate revenue growth and drive overall earnings growth potential for Pfizer,” Ian Read, chairman and chief executive of Pfizer, said in the statement on Monday.

SOURCE

http://www.nytimes.com/2016/08/23/business/dealbook/medivation-pfizer-14-billion-deal.html?_r=0

Author: Gail S. Thornton, M.A.

Co-Editor: The VOICES of Patients, HealthCare Providers, Caregivers and Families: Personal Experience with Critical Care and Invasive Medical Procedures  https://pharmaceuticalintelligence.com/biomed-e-books/series-e-titles-in-the-strategic-plan-for-2014-1015/2014-the-patients-voice-personal-experience-with-invasive-medical-procedures/

 

Tokyo-based Astellas Pharma Inc., a top 20 global pharmaceutical research company, has a strong, global company legacy, precision focus and patient-centric vision in creating innovative pharmaceuticals in areas of unmet medical need.

2012-05-10 003_Astellas building

Image SOURCE: Photograph of the Astellas Pharma U.S. building. Courtesy of Astellas Pharma U.S., 5/10/2012.   

The company’s commitment to science is based on development of medicines that address high unmet medical needs in therapeutic areas that include:

  • oncology,
  • urology,
  • immunology,
  • nephrology, and
  • neuroscience.

The company is also exploring advancements in new therapeutic areas and related diseases such as,

  • ophthalmology—retinitis pigmentosa (RP), age-related macular degeneration (AMD), diabetic macular edema (DME) and Stargardt’s macular degeneration (SMD) and
  • muscle diseases.

And they are investing in new technologies and modalities, such as,

  • regenerative medicine and cell therapy, and
  • next-generation vaccines.

The company is committed to improving the lives of patients through innovative science and with the highest sense of ethics and integrity. This commitment is reflected in the Astellas Group Code of Conduct, which applies to all employees across the globe and can be accessed through the link below.

Astellas Group Code of Conduct

Boosting research and development productivity remains an important issue for Astellas Pharma Inc., because innovation is vital for the company’s success in developing new therapeutic areas, technologies and modalities of treatment.

Dr. Bernhardt Zeiher, President, Development, is responsible for the more than 800-person development organization that is involved in developing these innovative therapies through cutting-edge clinical research. Dr. Zeiher’s team conducts clinical investigations of novel biological targets and new chemical entities with unique mechanisms of action and looks to determine whether the findings in preclinical testing will translate to benefit for patients.  Clinical studies are conducted globally with operational hubs in the United States, Netherlands and Japan. Astellas relocated their Development headquarters from Japan to the United States in 2008.

Building on its 120-year heritage, Astellas uses creativity and innovation to bring patients new medicines through the more than 17,000 global employees who work to improve the lives of patients and their families. Astellas was formed through the merger of Japan’s third and fifth largest pharmaceutical companies, Yamanouchi, founded in 1923, and Fujisawa, founded in 1894. Yamanouchi brought a record of developing blockbuster drugs, a pipeline full of promising new compounds and a sales and marketing culture of deeply grounded, data-driven expertise. Fujisawa brought dominance in transplantation, a soaring reputation for in-depth understanding of the disease states and treatments within its market niches, and a track record for developing high-profile, market-leading products that become new standards of care.

The company has made steady progress; they reported annual global sales of 1,372,706 million yen (approx. $13.2 billion) through the end of fiscal year 2015, with an annual research and development investment of 225,665 million yen (approx. $2.2 billion) through the end of fiscal year 2015.

Below is my interview with Astellas Dr. Bernhardt Zeiher, President, Development, which occurred in June, 2016.

What is your overall Research & Development (R&D) strategy?

Dr. Zeiher: We are focused on turning innovative science into value for patients in areas of high unmet need where we have, or can quickly acquire, expertise and where Astellas believes new scientific understanding is poised to drive significant innovation. Our commitment to R&D is based on the development of medicines that address high unmet medical needs in our main therapeutic areas of focus: oncology, urology and immunology.  We also have increased efforts to explore advancements in new therapeutic areas such as ophthalmology, nephrology, neuroscience and muscle diseases where there is a high level of unmet medical need. Building on our patient-centric vision, Astellas has been actively investing in new technologies and modalities, such as regenerative medicine and next-generation vaccines.

What are your R&D strengths?

Dr. Zeiher: Astellas is building on its legacy of bringing transformative medications to patients by investing in some of today’s most dynamic areas of scientific exploration. Innovations delivered by Astellas have helped to address and largely solve some of the most significant scientific challenges in urology and transplant. We also have built a strong presence in oncology with treatments for difficult-to-treat cancers, such as prostate and non-small cell lung cancer.

Moving forward in oncology, Astellas has made a deliberate effort to build leadership through organic efforts with a pipeline exemplifying the “follow the biology” approach that includes treatments for prostate, non-small cell lung and pancreatic cancer, and continued research in therapies for breast cancer and acute myeloid leukemia, among others. We also have forged strategic acquisitions and collaborated with industry and academic leaders to further build our portfolio.

In addition, we are leveraging what we know across conditions with similar biologies or mechanisms, building on our expertise to expand into adjacent diseases and proactively seek new opportunities. For example, leveraging our expertise in transplantation and infectious diseases, Astellas is developing the world’s first DNA vaccine for cytomegalovirus (CMV) infections. Currently in clinical trials, ASP0113 is a potential first-in-class agent for immunocompromised individuals undergoing solid organ or hematopoietic stem cell transplant who are at high risk of viral reactivation.

Describe your near-term R&D projects and pipeline activities?

Dr. Zeiher: Currently, the company is working on 35 investigational programs in Phase II and Phase III/registration development, of which half involve new molecular entities. We have a diverse pipeline with a balance of early- and later-stage assets. Later-stage programs include novel therapies/vaccines for cancer, anemia and infectious diseases.

  • Our two most advanced novel oncology agents, ASP2215 and ASP8273, continue to progress through the pipeline. ASP2215 shows promise in the treatment of relapsed or refractory acute myeloid leukemia, and ASP8273 is being evaluated as a treatment for a type of non-small cell lung cancer.
  • Leveraging our expertise in kidney disease, we are developing a first-in-class oral treatment for anemia associated with chronic kidney disease through our licensing agreement with FibroGen.
  • Astellas is developing the world’s first DNA vaccine for cytomegalovirus (CMV) infections. Currently in clinical trials, ASP0113 is a potential first-in-class agent for immunocompromised individuals undergoing solid organ or hematopoietic stem cell transplant who are at risk of viral reactivation. We are also working on a therapeutic vaccine, ASP4070, for Japanese red cedar pollen allergy.

We are building expertise in two new therapeutic areas—ophthalmology and muscle diseases—where there is significant unmet need. Through the Astellas Institute for Regenerative Medicine (AIRM) and external collaborations, we are addressing ophthalmologic diseases with a higher risk of blindness, including age-related and Stargardt’s macular degeneration, retinitis pigmentosa (RP), and diabetic macular edema (DME). In the muscle disease area, we are collaborating with our partner, Cytokinetics, on a skeletal muscle troponin activator which is being investigated in Spinal Muscular Atrophy (SMA). In addition, Astellas and Cytokinetics have agreed to amend their collaboration agreement to enable the development of CK-2127107 for the potential treatment of ALS and to extend their joint research focused on the discovery of additional next-generation skeletal muscle activators through 2017.

The pharmaceutical industry is intensely competitive and it requires an extensive search for technological innovations. How are you positioned to be a leader in developing new medicines that address unmet medical needs in critical therapeutic areas?

Dr. Zeiher: Astellas is focused on accelerating scientific discovery with an open innovation model. The Astellas open innovation model combines in-house R&D with strategic merger and acquisition approaches to advance research in untouched and complex disease states, allowing the company to maintain steady productivity and maximize its return on R&D investment.

With open innovation, Astellas undertakes research activities in the best possible environment. In some cases, the best environment is within the Astellas research laboratories. In many other cases, we look to collaborate with top biotech and academic leaders.  By building partnerships with top researchers and companies that complement our existing expertise, Astellas is able to quickly advance into new technologies and therapeutic areas of research where there is significant unmet medical need.

This approach has helped Astellas credibly enter into, compete and lead in some segments of the most competitive therapeutic areas in the pharmaceutical industry – oncology – and is accelerating the company’s efforts to develop treatments for important emerging therapeutic categories, such as ophthalmology and musculoskeletal disease, as well as leading technologies, such as regenerative medicine and vaccines.

For example, LAMP-vax is a next-generation DNA vaccine that utilizes the body’s natural cellular processing of Lysosomal Associated Membrane Protein (LAMP) to develop a more complete immune response to a target antigen.  The ability to activate a more complete immune response gives the LAMP-vax technology potential across a number of diseases, including allergic disease and cancer immunotherapy.  In 2015, Astellas established a licensing agreement with Immunomic Therapeutics, Inc. for the LAMP-vax products for the treatment or prevention of any and all allergic diseases in humans, including ARA-LAMP-vax for peanut allergy and other research-stage programs for food or environmental allergies.

Earlier this year, Astellas acquired Ocata Therapeutics, Inc., and established the Astellas Institute for Regenerative Medicine (AIRM) to serve as the global hub for Astellas regenerative medicine and cell therapy research. Our most advanced cellular therapy programs are in ophthalmology, but we are exploring other therapeutic areas. We are working on treatments for ophthalmologic diseases that leave patients at risk for blindness, which include retinitis pigmentosa (RP), age-related macular degeneration (AMD), and Stargardt’s macular degeneration (SMD).

Zeiher_Bernie

Image SOURCE: Photograph of Dr. Bernhardt Zeiher, President of Development, at Astellas. Courtesy of Todd Rosenberg, 11/17/2014. 

Dr. Bernhardt Zeiher serves as President, Development, at Astellas. In this role, he is responsible for all phases of drug development.

Prior to his current role, Dr. Zeiher was executive vice president and Therapeutic Area head, Immunology, Infectious Diseases and Transplantation at Astellas. Of note, he led the development of CRESEMBA® (isavuconazonium sulfate), which received Qualified Infectious Disease Product (QIDP) designation from the U.S. Food and Drug Administration and was approved in 2015 for the treatment of two rare invasive fungal infections. Prior to joining Astellas, he served as vice president of the Inflammation/Immunology therapeutic area at Pfizer.

Dr. Zeiher earned his Doctor of Medicine at the Case Western Reserve University School of Medicine, and completed an internal medicine residency at University Hospitals of Cleveland as well as a fellowship in Pulmonary and Critical Care Medicine at University of Iowa Hospitals and Clinics. Dr. Zeiher has received several awards, including being named a Fellow by American College of Physicians in 2004, awarded to those who demonstrate excellence and contributions to both medicine and the broader community of internists.

Editor’s note:

We would like to thank Jeff Winton, Andrew Lewis and Julie Monzo from the Astellas communications team for the tremendous help and support they provided during this interview.

 

REFERENCE/SOURCE

Astellas Pharma Inc. (https://www.astellas.com/en/) and Astellas Pharma U.S., Inc. (https://www.astellas.us/)

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

2016

LIVE 4:50 pm – 5:55 pm 4/25/2016 Early Detection and Prevention of Cancer & Innovation Break: Announcing the C³ Prize from Astellas Oncology and the World Medical Innovation Forum @2016 World Medical Innovation Forum: CANCER, April 25-27, 2016, Westin Hotel, Boston

https://pharmaceuticalintelligence.com/2016/04/25/live-450-pm-555-pm-4252016-early-detection-and-prevention-of-cancer-innovation-break-announcing-the-c%C2%B3-prize-from-astellas-oncology-and-the-world-medical-innovation-forum-2016-world/

Top Seven Big Pharma in Thomson Reuters 2015 Top 100 Global Innovators

https://pharmaceuticalintelligence.com/2016/01/04/top-seven-big-pharma-in-thomson-reuters-2015-top-100-global-innovators/

Eye Lens Regenerated

https://pharmaceuticalintelligence.com/2016/03/19/eye-lens-regenerated/

 

2012

Picturing US-Trained PhDs’ Paths and Pharmaceutical Industry’s Crisis of Productivity: Partnerships between Industry and Academia

https://pharmaceuticalintelligence.com/2012/06/27/picturing-us-trained-phds-paths-pharmaceutical-industrys-crisis-of-productivity-partnerships-between-industry-and-academia/

Medicines in Development for Cancer in 2012: An Excellent Response from America’s Biopharmaceutical Research Companies

https://pharmaceuticalintelligence.com/2012/07/31/medicines-in-development-for-cancer-in-2012-an-excellent-response-from-americas-biopharmaceutical-research-companies/

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Targeting amyloidopathy

Larry H. Bernstein, MD, FCAP

LPBI

 

Targeting a rare amyloidotic disease through rationally designed polymer conjugates

Inmaculada Conejos–Sánchez, Isabel Cardoso, Maria J. Saraiva, María J.Vicent
Journal of Controlled Release 178 (2014), 95–100
Saraiva et al. discovered in 2006 a RAGE-based peptide sequence capable of preventing transthyretin (TTR) aggregate-induced cytotoxicity, hallmark of initial stages of an inherited rare amyloidosis known as Familial Amyloidotic Polyneuropathy (FAP). To allow clinical progression of this peptidic sequence as FAP treatment, a family of polymer conjugates has been designed, synthesised and fully characterised. This approach fulfills the strategies defined in the Polymer Therapeutics area as an exhaustive physico-chemical characterisation fitting activity output towards a novel molecular target that is described here. RAGE peptide acts extracellularly, therefore, nointracellular drug delivery was necessary. PEG was selected as carrier and polymer–drug linker optimisation was then carried out by means of biodegradable (disulphide) and non-biodegradable (amide) covalent bonds. Conjugate size in solution, stability under invitro and in vivo scenarios and TTR binding affinity through surface plasmon resonance (SPR) was also performed with all synthesised conjugates. In their in vitro evaluation by monitoring the activation of caspase-3 in Schwann cells, peptide derivatives demonstrated retention of peptide activity reducing TTR aggregates (TTRagg) cytotoxicity upon conjugation and a greater plasma stability than the parent free peptide. The results also confirmed that a more stable polymer–peptide linker (amide) is required to secure therapeutic efficiency.

Polymer therapeutics are well established as successful first generation nanomedicines for treatment of infectious diseases and cancer[1]. Polymer–protein, drug and aptamer conjugates are innovative chemical entities capable of improving bioactive compound properties and thus increasing efficacy and decreasing toxicity[2,3]. Design of second generation of conjugates is now focussing on improved polymer structures, polymer–based combination therapy and novel molecular targets with great potential to further progress the clinical importance of these unique technologies [4]. Novel conjugates for the treatment of neuropathological disorders are proposed in this study. Amyloidosis is well known in the form of Alzheimer’s and Parkinson’s disease, but the target disease here is a rarer pathological disorder named familial amyloid polyneuropathy (FAP). FAPs constitute an important group of inherited amyloidosis diseases, and one of the most commonFAPs is caused by a mutated protein called transthyretin (TTR), which forms amyloid deposits, mainly in the peripheral nervous system [5]. The aggregation cascade of this mutated protein, produces a TTR aggregate (TTRagg) able to trigger neurodegeneration through engagement with the receptor-for-advanced-glycation-end-products (RAGE) which is present on peripheral neurons. RAGE signalling has been defined to be involved in many human pathologies such as Alzhehimer’s disease, diabetes and ageing, among others. This receptor is also up-regulated in tissues fromFAP patients [6]. The secreted RAGE form, named soluble RAGE (sRAGE), acts as a decoy to trap ligands and prevent interaction with cell surface receptors. sRAGE was shown to have important inhibitory effects in several cell cultures and transgenic mouse models, in which it prevented or reversed full-length RAGE signalling.

Saraiva et al. [7] discovered a specific peptidic sequence (named RAGE peptide) that is able to suppress TTRagg-induced cytotoxicity in cell culture. A reduced version of that peptide was proved to maintain the activity and the affinity of the initial peptide. The final peptide (compound A) contains 6 amino acids and responds to the sequence (from N to C terminus): YVRVRY. Although this provides an opportunity to design novel therapeutics for FAP treatment, peptide therapeutics themselves display well known challenges for in vivo use, e.g. low stability, poor pharmacokinetics and potential immunogenicity. Moreover the RAGE peptide demonstrates low solubility in plasma limiting its potential for i.v.administration.

……

Herein, novel specific nanoconjugates for the treatment of amyloidosis, and in particular familial amyloidotic polyneuropathy are reported. Apart from the research reported by Prof Arima et al. [22] using a hepatocyte-targeted FAP siRNA complex with lactosylated dendrimer (G3)/α-cyclodextrin(Lac-α-CDE(G3)), no other type of polymer therapeutic has been reported up to now for the treatment of this chronic degenerative family of diseases. Our rational design started from an active biomolecule of peptidic nature (RAGE peptide) that recognises the TTR prefibrillar aggregates responsible to promote cell death in FAPpatients [7]. The clinical progress of this promising inhibitor was masked by the well-known limitations of peptides, such as low solubility, low stability and possible immunogenicity. PEGylation through various linking strategies was successfully accomplished here as a solution for the named drawbacks, using a systematic approach to maintain peptide activity and receptor binding specificity. The data relating toTTR binding affinity, conjugate linker stability and the conjugate size distribution in solution of PEG– RAGE peptide conjugates indicate that the conjugates containing amide linkers have the greatest potential for further development as FAP inhibitors. Moreover, this novel conjugate has promising possibilities as a FAP therapeutic to be used alone in the early stages of the disease or as part of rationally designed combination therapy [23,24]. Preliminary in vivo studies (biodistribution) are shown in the supporting information demonstrating the enhanced plasma stability of the peptide upon conjugation (Fig.5S) , showing nospecific accumulation in any organ and renal excretion. More exhaustive in vivo experiments are currently ongoing with selected conjugates.

 

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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

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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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Imaging of Cancer Cells

Larry H. Bernstein, MD, FCAP, Curator

LPBI

 

Microscope uses nanosecond-speed laser and deep learning to detect cancer cells more efficiently

April 13, 2016

Scientists at the California NanoSystems Institute at UCLA have developed a new technique for identifying cancer cells in blood samples faster and more accurately than the current standard methods.

In one common approach to testing for cancer, doctors add biochemicals to blood samples. Those biochemicals attach biological “labels” to the cancer cells, and those labels enable instruments to detect and identify them. However, the biochemicals can damage the cells and render the samples unusable for future analyses. There are other current techniques that don’t use labeling but can be inaccurate because they identify cancer cells based only on one physical characteristic.

Time-stretch quantitative phase imaging (TS-QPI) and analytics system

The new technique images cells without destroying them and can identify 16 physical characteristics — including size, granularity and biomass — instead of just one.

The new technique combines two components that were invented at UCLA:

A “photonic time stretch” microscope, which is capable of quickly imaging cells in blood samples. Invented by Barham Jalali, professor and Northrop-Grumman Optoelectronics Chair in electrical engineering, it works by taking pictures of flowing blood cells using laser bursts (similar to how a camera uses a flash). Each flash only lasts nanoseconds (billionths of a second) to avoid damage to cells, but that normally means the images are both too weak to be detected and too fast to be digitized by normal instrumentation. The new microscope overcomes those challenges by using specially designed optics that amplify and boost the clarity of the images, and simultaneously slow them down enough to be detected and digitized at a rate of 36 million images per second.

A deep learning computer program, which identifies cancer cells with more than 95 percent accuracy. Deep learning is a form of artificial intelligence that uses complex algorithms to extract patterns and knowledge from rich multidimenstional datasets, with the goal of achieving accurate decision making.

The study was published in the open-access journal Nature Scientific Reports. The researchers write in the paper that the system could lead to data-driven diagnoses by cells’ physical characteristics, which could allow quicker and earlier diagnoses of cancer, for example, and better understanding of the tumor-specific gene expression in cells, which could facilitate new treatments for disease.

The research was supported by NantWorks, LLC.

 

Abstract of Deep Learning in Label-free Cell Classification

Label-free cell analysis is essential to personalized genomics, cancer diagnostics, and drug development as it avoids adverse effects of staining reagents on cellular viability and cell signaling. However, currently available label-free cell assays mostly rely only on a single feature and lack sufficient differentiation. Also, the sample size analyzed by these assays is limited due to their low throughput. Here, we integrate feature extraction and deep learning with high-throughput quantitative imaging enabled by photonic time stretch, achieving record high accuracy in label-free cell classification. Our system captures quantitative optical phase and intensity images and extracts multiple biophysical features of individual cells. These biophysical measurements form a hyperdimensional feature space in which supervised learning is performed for cell classification. We compare various learning algorithms including artificial neural network, support vector machine, logistic regression, and a novel deep learning pipeline, which adopts global optimization of receiver operating characteristics. As a validation of the enhanced sensitivity and specificity of our system, we show classification of white blood T-cells against colon cancer cells, as well as lipid accumulating algal strains for biofuel production. This system opens up a new path to data-driven phenotypic diagnosis and better understanding of the heterogeneous gene expressions in cells.

references:

Claire Lifan Chen, Ata Mahjoubfar, Li-Chia Tai, Ian K. Blaby, Allen Huang, Kayvan Reza Niazi & Bahram Jalali. Deep Learning in Label-free Cell Classification. Scientific Reports 6, Article number: 21471 (2016); doi:10.1038/srep21471 (open access)

Supplementary Information

 

Deep Learning in Label-free Cell Classification

Claire Lifan Chen, Ata Mahjoubfar, Li-Chia Tai, Ian K. Blaby, Allen Huang,Kayvan Reza Niazi & Bahram Jalali

Scientific Reports 6, Article number: 21471 (2016)    http://dx.doi.org:/10.1038/srep21471

Deep learning extracts patterns and knowledge from rich multidimenstional datasets. While it is extensively used for image recognition and speech processing, its application to label-free classification of cells has not been exploited. Flow cytometry is a powerful tool for large-scale cell analysis due to its ability to measure anisotropic elastic light scattering of millions of individual cells as well as emission of fluorescent labels conjugated to cells1,2. However, each cell is represented with single values per detection channels (forward scatter, side scatter, and emission bands) and often requires labeling with specific biomarkers for acceptable classification accuracy1,3. Imaging flow cytometry4,5 on the other hand captures images of cells, revealing significantly more information about the cells. For example, it can distinguish clusters and debris that would otherwise result in false positive identification in a conventional flow cytometer based on light scattering6.

In addition to classification accuracy, the throughput is another critical specification of a flow cytometer. Indeed high throughput, typically 100,000 cells per second, is needed to screen a large enough cell population to find rare abnormal cells that are indicative of early stage diseases. However there is a fundamental trade-off between throughput and accuracy in any measurement system7,8. For example, imaging flow cytometers face a throughput limit imposed by the speed of the CCD or the CMOS cameras, a number that is approximately 2000 cells/s for present systems9. Higher flow rates lead to blurred cell images due to the finite camera shutter speed. Many applications of flow analyzers such as cancer diagnostics, drug discovery, biofuel development, and emulsion characterization require classification of large sample sizes with a high-degree of statistical accuracy10. This has fueled research into alternative optical diagnostic techniques for characterization of cells and particles in flow.

Recently, our group has developed a label-free imaging flow-cytometry technique based on coherent optical implementation of the photonic time stretch concept11. This instrument overcomes the trade-off between sensitivity and speed by using Amplified Time-stretch Dispersive Fourier Transform12,13,14,15. In time stretched imaging16, the object’s spatial information is encoded in the spectrum of laser pulses within a pulse duration of sub-nanoseconds (Fig. 1). Each pulse representing one frame of the camera is then stretched in time so that it can be digitized in real-time by an electronic analog-to-digital converter (ADC). The ultra-fast pulse illumination freezes the motion of high-speed cells or particles in flow to achieve blur-free imaging. Detection sensitivity is challenged by the low number of photons collected during the ultra-short shutter time (optical pulse width) and the drop in the peak optical power resulting from the time stretch. These issues are solved in time stretch imaging by implementing a low noise-figure Raman amplifier within the dispersive device that performs time stretching8,11,16. Moreover, warped stretch transform17,18can be used in time stretch imaging to achieve optical image compression and nonuniform spatial resolution over the field-of-view19. In the coherent version of the instrument, the time stretch imaging is combined with spectral interferometry to measure quantitative phase and intensity images in real-time and at high throughput20. Integrated with a microfluidic channel, coherent time stretch imaging system in this work measures both quantitative optical phase shift and loss of individual cells as a high-speed imaging flow cytometer, capturing 36 million images per second in flow rates as high as 10 meters per second, reaching up to 100,000 cells per second throughput.

Figure 1: Time stretch quantitative phase imaging (TS-QPI) and analytics system; A mode-locked laser followed by a nonlinear fiber, an erbium doped fiber amplifier (EDFA), and a wavelength-division multiplexing (WDM) filter generate and shape a train of broadband optical pulses. http://www.nature.com/article-assets/npg/srep/2016/160315/srep21471/images_hires/m685/srep21471-f1.jpg

 

Box 1: The pulse train is spatially dispersed into a train of rainbow flashes illuminating the target as line scans. The spatial features of the target are encoded into the spectrum of the broadband optical pulses, each representing a one-dimensional frame. The ultra-short optical pulse illumination freezes the motion of cells during high speed flow to achieve blur-free imaging with a throughput of 100,000 cells/s. The phase shift and intensity loss at each location within the field of view are embedded into the spectral interference patterns using a Michelson interferometer. Box 2: The interferogram pulses were then stretched in time so that spatial information could be mapped into time through time-stretch dispersive Fourier transform (TS-DFT), and then captured by a single pixel photodetector and an analog-to-digital converter (ADC). The loss of sensitivity at high shutter speed is compensated by stimulated Raman amplification during time stretch. Box 3: (a) Pulse synchronization; the time-domain signal carrying serially captured rainbow pulses is transformed into a series of one-dimensional spatial maps, which are used for forming line images. (b) The biomass density of a cell leads to a spatially varying optical phase shift. When a rainbow flash passes through the cells, the changes in refractive index at different locations will cause phase walk-off at interrogation wavelengths. Hilbert transformation and phase unwrapping are used to extract the spatial phase shift. (c) Decoding the phase shift in each pulse at each wavelength and remapping it into a pixel reveals the protein concentration distribution within cells. The optical loss induced by the cells, embedded in the pulse intensity variations, is obtained from the amplitude of the slowly varying envelope of the spectral interferograms. Thus, quantitative optical phase shift and intensity loss images are captured simultaneously. Both images are calibrated based on the regions where the cells are absent. Cell features describing morphology, granularity, biomass, etc are extracted from the images. (d) These biophysical features are used in a machine learning algorithm for high-accuracy label-free classification of the cells.

On another note, surface markers used to label cells, such as EpCAM21, are unavailable in some applications; for example, melanoma or pancreatic circulating tumor cells (CTCs) as well as some cancer stem cells are EpCAM-negative and will escape EpCAM-based detection platforms22. Furthermore, large-population cell sorting opens the doors to downstream operations, where the negative impacts of labels on cellular behavior and viability are often unacceptable23. Cell labels may cause activating/inhibitory signal transduction, altering the behavior of the desired cellular subtypes, potentially leading to errors in downstream analysis, such as DNA sequencing and subpopulation regrowth. In this way, quantitative phase imaging (QPI) methods24,25,26,27 that categorize unlabeled living cells with high accuracy are needed. Coherent time stretch imaging is a method that enables quantitative phase imaging at ultrahigh throughput for non-invasive label-free screening of large number of cells.

In this work, the information of quantitative optical loss and phase images are fused into expert designed features, leading to a record label-free classification accuracy when combined with deep learning. Image mining techniques are applied, for the first time, to time stretch quantitative phase imaging to measure biophysical attributes including protein concentration, optical loss, and morphological features of single cells at an ultrahigh flow rate and in a label-free fashion. These attributes differ widely28,29,30,31 among cells and their variations reflect important information of genotypes and physiological stimuli32. The multiplexed biophysical features thus lead to information-rich hyper-dimensional representation of the cells for label-free classification with high statistical precision.

We further improved the accuracy, repeatability, and the balance between sensitivity and specificity of our label-free cell classification by a novel machine learning pipeline, which harnesses the advantages of multivariate supervised learning, as well as unique training by evolutionary global optimization of receiver operating characteristics (ROC). To demonstrate sensitivity, specificity, and accuracy of multi-feature label-free flow cytometry using our technique, we classified (1) OT-IIhybridoma T-lymphocytes and SW-480 colon cancer epithelial cells, and (2) Chlamydomonas reinhardtii algal cells (herein referred to as Chlamydomonas) based on their lipid content, which is related to the yield in biofuel production. Our preliminary results show that compared to classification by individual biophysical parameters, our label-free hyperdimensional technique improves the detection accuracy from 77.8% to 95.5%, or in other words, reduces the classification inaccuracy by about five times.     ……..

 

Feature Extraction

The decomposed components of sequential line scans form pairs of spatial maps, namely, optical phase and loss images as shown in Fig. 2 (see Section Methods: Image Reconstruction). These images are used to obtain biophysical fingerprints of the cells8,36. With domain expertise, raw images are fused and transformed into a suitable set of biophysical features, listed in Table 1, which the deep learning model further converts into learned features for improved classification.

The new technique combines two components that were invented at UCLA:

A “photonic time stretch” microscope, which is capable of quickly imaging cells in blood samples. Invented by Barham Jalali, professor and Northrop-Grumman Optoelectronics Chair in electrical engineering, it works by taking pictures of flowing blood cells using laser bursts (similar to how a camera uses a flash). Each flash only lasts nanoseconds (billionths of a second) to avoid damage to cells, but that normally means the images are both too weak to be detected and too fast to be digitized by normal instrumentation. The new microscope overcomes those challenges by using specially designed optics that amplify and boost the clarity of the images, and simultaneously slow them down enough to be detected and digitized at a rate of 36 million images per second.

A deep learning computer program, which identifies cancer cells with more than 95 percent accuracy. Deep learning is a form of artificial intelligence that uses complex algorithms to extract patterns and knowledge from rich multidimenstional datasets, with the goal of achieving accurate decision making.

The study was published in the open-access journal Nature Scientific Reports. The researchers write in the paper that the system could lead to data-driven diagnoses by cells’ physical characteristics, which could allow quicker and earlier diagnoses of cancer, for example, and better understanding of the tumor-specific gene expression in cells, which could facilitate new treatments for disease.

The research was supported by NantWorks, LLC.

 

http://www.nature.com/article-assets/npg/srep/2016/160315/srep21471/images_hires/m685/srep21471-f2.jpg

The optical loss images of the cells are affected by the attenuation of multiplexed wavelength components passing through the cells. The attenuation itself is governed by the absorption of the light in cells as well as the scattering from the surface of the cells and from the internal cell organelles. The optical loss image is derived from the low frequency component of the pulse interferograms. The optical phase image is extracted from the analytic form of the high frequency component of the pulse interferograms using Hilbert Transformation, followed by a phase unwrapping algorithm. Details of these derivations can be found in Section Methods. Also, supplementary Videos 1 and 2 show measurements of cell-induced optical path length difference by TS-QPI at four different points along the rainbow for OT-II and SW-480, respectively.

Table 1: List of extracted features.

Feature Name    Description         Category

 

Figure 3: Biophysical features formed by image fusion.

(a) Pairwise correlation matrix visualized as a heat map. The map depicts the correlation between all major 16 features extracted from the quantitative images. Diagonal elements of the matrix represent correlation of each parameter with itself, i.e. the autocorrelation. The subsets in box 1, box 2, and box 3 show high correlation because they are mainly related to morphological, optical phase, and optical loss feature categories, respectively. (b) Ranking of biophysical features based on their AUCs in single-feature classification. Blue bars show performance of the morphological parameters, which includes diameter along the interrogation rainbow, diameter along the flow direction, tight cell area, loose cell area, perimeter, circularity, major axis length, orientation, and median radius. As expected, morphology contains most information, but other biophysical features can contribute to improved performance of label-free cell classification. Orange bars show optical phase shift features i.e. optical path length differences and refractive index difference. Green bars show optical loss features representing scattering and absorption by the cell. The best performed feature in these three categories are marked in red.

Figure 4: Machine learning pipeline. Information of quantitative optical phase and loss images are fused to extract multivariate biophysical features of each cell, which are fed into a fully-connected neural network.

The neural network maps input features by a chain of weighted sum and nonlinear activation functions into learned feature space, convenient for classification. This deep neural network is globally trained via area under the curve (AUC) of the receiver operating characteristics (ROC). Each ROC curve corresponds to a set of weights for connections to an output node, generated by scanning the weight of the bias node. The training process maximizes AUC, pushing the ROC curve toward the upper left corner, which means improved sensitivity and specificity in classification.

….   How to cite this article: Chen, C. L. et al. Deep Learning in Label-free Cell Classification.

Sci. Rep. 6, 21471; http://dx.doi.org:/10.1038/srep21471

 

Computer Algorithm Helps Characterize Cancerous Genomic Variations

http://www.genengnews.com/gen-news-highlights/computer-algorithm-helps-characterize-cancerous-genomic-variations/81252626/

To better characterize the functional context of genomic variations in cancer, researchers developed a new computer algorithm called REVEALER. [UC San Diego Health]

Scientists at the University of California San Diego School of Medicine and the Broad Institute say they have developed a new computer algorithm—REVEALER—to better characterize the functional context of genomic variations in cancer. The tool, described in a paper (“Characterizing Genomic Alterations in Cancer by Complementary Functional Associations”) published in Nature Biotechnology, is designed to help researchers identify groups of genetic variations that together associate with a particular way cancer cells get activated, or how they respond to certain treatments.

REVEALER is available for free to the global scientific community via the bioinformatics software portal GenePattern.org.

“This computational analysis method effectively uncovers the functional context of genomic alterations, such as gene mutations, amplifications, or deletions, that drive tumor formation,” said senior author Pablo Tamayo, Ph.D., professor and co-director of the UC San Diego Moores Cancer Center Genomics and Computational Biology Shared Resource.

Dr. Tamayo and team tested REVEALER using The Cancer Genome Atlas (TCGA), the NIH’s database of genomic information from more than 500 human tumors representing many cancer types. REVEALER revealed gene alterations associated with the activation of several cellular processes known to play a role in tumor development and response to certain drugs. Some of these gene mutations were already known, but others were new.

For example, the researchers discovered new activating genomic abnormalities for beta-catenin, a cancer-promoting protein, and for the oxidative stress response that some cancers hijack to increase their viability.

REVEALER requires as input high-quality genomic data and a significant number of cancer samples, which can be a challenge, according to Dr. Tamayo. But REVEALER is more sensitive at detecting similarities between different types of genomic features and less dependent on simplifying statistical assumptions, compared to other methods, he adds.

“This study demonstrates the potential of combining functional profiling of cells with the characterizations of cancer genomes via next-generation sequencing,” said co-senior author Jill P. Mesirov, Ph.D., professor and associate vice chancellor for computational health sciences at UC San Diego School of Medicine.

 

Characterizing genomic alterations in cancer by complementary functional associations

Jong Wook Kim, Olga B Botvinnik, Omar Abudayyeh, Chet Birger, et al.

Nature Biotechnology (2016)              http://dx.doi.org:/10.1038/nbt.3527

Systematic efforts to sequence the cancer genome have identified large numbers of mutations and copy number alterations in human cancers. However, elucidating the functional consequences of these variants, and their interactions to drive or maintain oncogenic states, remains a challenge in cancer research. We developed REVEALER, a computational method that identifies combinations of mutually exclusive genomic alterations correlated with functional phenotypes, such as the activation or gene dependency of oncogenic pathways or sensitivity to a drug treatment. We used REVEALER to uncover complementary genomic alterations associated with the transcriptional activation of β-catenin and NRF2, MEK-inhibitor sensitivity, and KRAS dependency. REVEALER successfully identified both known and new associations, demonstrating the power of combining functional profiles with extensive characterization of genomic alterations in cancer genomes

 

Figure 2: REVEALER results for transcriptional activation of β-catenin in cancer.close

(a) This heatmap illustrates the use of the REVEALER approach to find complementary genomic alterations that match the transcriptional activation of β-catenin in cancer. The target profile is a TCF4 reporter that provides an estimate of…

 

An imaging-based platform for high-content, quantitative evaluation of therapeutic response in 3D tumour models

Jonathan P. Celli, Imran Rizvi, Adam R. Blanden, Iqbal Massodi, Michael D. Glidden, Brian W. Pogue & Tayyaba Hasan

Scientific Reports 4; 3751  (2014)    http://dx.doi.org:/10.1038/srep03751

While it is increasingly recognized that three-dimensional (3D) cell culture models recapitulate drug responses of human cancers with more fidelity than monolayer cultures, a lack of quantitative analysis methods limit their implementation for reliable and routine assessment of emerging therapies. Here, we introduce an approach based on computational analysis of fluorescence image data to provide high-content readouts of dose-dependent cytotoxicity, growth inhibition, treatment-induced architectural changes and size-dependent response in 3D tumour models. We demonstrate this approach in adherent 3D ovarian and pancreatic multiwell extracellular matrix tumour overlays subjected to a panel of clinically relevant cytotoxic modalities and appropriately designed controls for reliable quantification of fluorescence signal. This streamlined methodology reads out the high density of information embedded in 3D culture systems, while maintaining a level of speed and efficiency traditionally achieved with global colorimetric reporters in order to facilitate broader implementation of 3D tumour models in therapeutic screening.

The attrition rates for preclinical development of oncology therapeutics are particularly dismal due to a complex set of factors which includes 1) the failure of pre-clinical models to recapitulate determinants of in vivo treatment response, and 2) the limited ability of available assays to extract treatment-specific data integral to the complexities of therapeutic responses1,2,3. Three-dimensional (3D) tumour models have been shown to restore crucial stromal interactions which are missing in the more commonly used 2D cell culture and that influence tumour organization and architecture4,5,6,7,8, as well as therapeutic response9,10, multicellular resistance (MCR)11,12, drug penetration13,14, hypoxia15,16, and anti-apoptotic signaling17. However, such sophisticated models can only have an impact on therapeutic guidance if they are accompanied by robust quantitative assays, not only for cell viability but also for providing mechanistic insights related to the outcomes. While numerous assays for drug discovery exist18, they are generally not developed for use in 3D systems and are often inherently unsuitable. For example, colorimetric conversion products have been noted to bind to extracellular matrix (ECM)19 and traditional colorimetric cytotoxicity assays reduce treatment response to a single number reflecting a biochemical event that has been equated to cell viability (e.g. tetrazolium salt conversion20). Such approaches fail to provide insight into the spatial patterns of response within colonies, morphological or structural effects of drug response, or how overall culture viability may be obscuring the status of sub-populations that are resistant or partially responsive. Hence, the full benefit of implementing 3D tumour models in therapeutic development has yet to be realized for lack of analytical methods that describe the very aspects of treatment outcome that these systems restore.

Motivated by these factors, we introduce a new platform for quantitative in situ treatment assessment (qVISTA) in 3D tumour models based on computational analysis of information-dense biological image datasets (bioimage-informatics)21,22. This methodology provides software end-users with multiple levels of complexity in output content, from rapidly-interpreted dose response relationships to higher content quantitative insights into treatment-dependent architectural changes, spatial patterns of cytotoxicity within fields of multicellular structures, and statistical analysis of nodule-by-nodule size-dependent viability. The approach introduced here is cognizant of tradeoffs between optical resolution, data sampling (statistics), depth of field, and widespread usability (instrumentation requirement). Specifically, it is optimized for interpretation of fluorescent signals for disease-specific 3D tumour micronodules that are sufficiently small that thousands can be imaged simultaneously with little or no optical bias from widefield integration of signal along the optical axis of each object. At the core of our methodology is the premise that the copious numerical readouts gleaned from segmentation and interpretation of fluorescence signals in these image datasets can be converted into usable information to classify treatment effects comprehensively, without sacrificing the throughput of traditional screening approaches. It is hoped that this comprehensive treatment-assessment methodology will have significant impact in facilitating more sophisticated implementation of 3D cell culture models in preclinical screening by providing a level of content and biological relevance impossible with existing assays in monolayer cell culture in order to focus therapeutic targets and strategies before costly and tedious testing in animal models.

Using two different cell lines and as depicted in Figure 1, we adopt an ECM overlay method pioneered originally for 3D breast cancer models23, and developed in previous studies by us to model micrometastatic ovarian cancer19,24. This system leads to the formation of adherent multicellular 3D acini in approximately the same focal plane atop a laminin-rich ECM bed, implemented here in glass-bottom multiwell imaging plates for automated microscopy. The 3D nodules resultant from restoration of ECM signaling5,8, are heterogeneous in size24, in contrast to other 3D spheroid methods, such as rotary or hanging drop cultures10, in which cells are driven to aggregate into uniformly sized spheroids due to lack of an appropriate substrate to adhere to. Although the latter processes are also biologically relevant, it is the adherent tumour populations characteristic of advanced metastatic disease that are more likely to be managed with medical oncology, which are the focus of therapeutic evaluation herein. The heterogeneity in 3D structures formed via ECM overlay is validated here by endoscopic imaging ofin vivo tumours in orthotopic xenografts derived from the same cells (OVCAR-5).

 

Figure 1: A simplified schematic flow chart of imaging-based quantitative in situ treatment assessment (qVISTA) in 3D cell culture.

(This figure was prepared in Adobe Illustrator® software by MD Glidden, JP Celli and I Rizvi). A detailed breakdown of the image processing (Step 4) is provided in Supplemental Figure 1.

A critical component of the imaging-based strategy introduced here is the rational tradeoff of image-acquisition parameters for field of view, depth of field and optical resolution, and the development of image processing routines for appropriate removal of background, scaling of fluorescence signals from more than one channel and reliable segmentation of nodules. In order to obtain depth-resolved 3D structures for each nodule at sub-micron lateral resolution using a laser-scanning confocal system, it would require ~ 40 hours (at approximately 100 fields for each well with a 20× objective, times 1 minute/field for a coarse z-stack, times 24 wells) to image a single plate with the same coverage achieved in this study. Even if the resources were available to devote to such time-intensive image acquisition, not to mention the processing, the optical properties of the fluorophores would change during the required time frame for image acquisition, even with environmental controls to maintain culture viability during such extended imaging. The approach developed here, with a mind toward adaptation into high throughput screening, provides a rational balance of speed, requiring less than 30 minutes/plate, and statistical rigour, providing images of thousands of nodules in this time, as required for the high-content analysis developed in this study. These parameters can be further optimized for specific scenarios. For example, we obtain the same number of images in a 96 well plate as for a 24 well plate by acquiring only a single field from each well, rather than 4 stitched fields. This quadruples the number conditions assayed in a single run, at the expense of the number of nodules per condition, and therefore the ability to obtain statistical data sets for size-dependent response, Dfrac and other segmentation-dependent numerical readouts.

 

We envision that the system for high-content interrogation of therapeutic response in 3D cell culture could have widespread impact in multiple arenas from basic research to large scale drug development campaigns. As such, the treatment assessment methodology presented here does not require extraordinary optical instrumentation or computational resources, making it widely accessible to any research laboratory with an inverted fluorescence microscope and modestly equipped personal computer. And although we have focused here on cancer models, the methodology is broadly applicable to quantitative evaluation of other tissue models in regenerative medicine and tissue engineering. While this analysis toolbox could have impact in facilitating the implementation of in vitro 3D models in preclinical treatment evaluation in smaller academic laboratories, it could also be adopted as part of the screening pipeline in large pharma settings. With the implementation of appropriate temperature controls to handle basement membranes in current robotic liquid handling systems, our analyses could be used in ultra high-throughput screening. In addition to removing non-efficacious potential candidate drugs earlier in the pipeline, this approach could also yield the additional economic advantage of minimizing the use of costly time-intensive animal models through better estimates of dose range, sequence and schedule for combination regimens.

 

Microscope Uses AI to Find Cancer Cells More Efficiently

Thu, 04/14/2016 – by Shaun Mason

http://www.mdtmag.com/news/2016/04/microscope-uses-ai-find-cancer-cells-more-efficiently

Scientists at the California NanoSystems Institute at UCLA have developed a new technique for identifying cancer cells in blood samples faster and more accurately than the current standard methods.

In one common approach to testing for cancer, doctors add biochemicals to blood samples. Those biochemicals attach biological “labels” to the cancer cells, and those labels enable instruments to detect and identify them. However, the biochemicals can damage the cells and render the samples unusable for future analyses.

There are other current techniques that don’t use labeling but can be inaccurate because they identify cancer cells based only on one physical characteristic.

The new technique images cells without destroying them and can identify 16 physical characteristics — including size, granularity and biomass — instead of just one. It combines two components that were invented at UCLA: a photonic time stretch microscope, which is capable of quickly imaging cells in blood samples, and a deep learning computer program that identifies cancer cells with over 95 percent accuracy.

Deep learning is a form of artificial intelligence that uses complex algorithms to extract meaning from data with the goal of achieving accurate decision making.

The study, which was published in the journal Nature Scientific Reports, was led by Barham Jalali, professor and Northrop-Grumman Optoelectronics Chair in electrical engineering; Claire Lifan Chen, a UCLA doctoral student; and Ata Mahjoubfar, a UCLA postdoctoral fellow.

Photonic time stretch was invented by Jalali, and he holds a patent for the technology. The new microscope is just one of many possible applications; it works by taking pictures of flowing blood cells using laser bursts in the way that a camera uses a flash. This process happens so quickly — in nanoseconds, or billionths of a second — that the images would be too weak to be detected and too fast to be digitized by normal instrumentation.

The new microscope overcomes those challenges using specially designed optics that boost the clarity of the images and simultaneously slow them enough to be detected and digitized at a rate of 36 million images per second. It then uses deep learning to distinguish cancer cells from healthy white blood cells.

“Each frame is slowed down in time and optically amplified so it can be digitized,” Mahjoubfar said. “This lets us perform fast cell imaging that the artificial intelligence component can distinguish.”

Normally, taking pictures in such minuscule periods of time would require intense illumination, which could destroy live cells. The UCLA approach also eliminates that problem.

“The photonic time stretch technique allows us to identify rogue cells in a short time with low-level illumination,” Chen said.

The researchers write in the paper that the system could lead to data-driven diagnoses by cells’ physical characteristics, which could allow quicker and earlier diagnoses of cancer, for example, and better understanding of the tumor-specific gene expression in cells, which could facilitate new treatments for disease.   …..  see also http://www.nature.com/article-assets/npg/srep/2016/160315/srep21471/images_hires/m685/srep21471-f1.jpg

Chen, C. L. et al. Deep Learning in Label-free Cell Classification.    Sci. Rep. 6, 21471;   http://dx.doi.org:/10.1038/srep21471

 

 

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