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Archive for the ‘Big Data’ Category

Vyasa Analytics Demos Deep Learning Software for Life Sciences at Bio-IT World 2018 – Vyasa’s booth (#632)

Posted in Artificial Intelligence, BioIT: BioInformatics, Intelligent Information Systems on May 10, 2018| Leave a Comment »


Vyasa Analytics Demos Deep Learning Software for Life Sciences at Bio-IT World 2018 – Vyasa’s booth (#632)

 

Reporter: Aviva Lev-Ari, PhD, RN

 

BOSTON – May 10, 2018 – Vyasa Analytics, a provider of deep learning software and analytics for life sciences and healthcare organizations, today announces three pre-built deep learning analytics modules for its Cortex software at Bio-IT World Conference & Expo. Cortex enables the secure, scalable application of deep learning-based artificial intelligence (AI) analytics to enterprise data, identifying patterns, relationships and concepts across disparate data sources.

 

The new Neural Concept Recognition, Image Analytics and ChemVector analytics modules in Cortex enable life sciences organizations to quickly and easily apply deep learning analytics to large data streams of text, images and chemical structures. Like all deep learning analytical modules in Cortex’s library, these new modules allow users to ask complex questions of their data and use the answers to gain critical insights.

 

“Life sciences and healthcare organizations are using AI tools to advance research and development and deliver better patient care. Deep learning algorithms provide a set of powerful approaches that help us apply analytics more effectively and comprehensively across large scale data sources,” said Dr. Christopher Bouton, founder and CEO of Vyasa. “The idea of AI has been around for decades, but we are now experiencing a perfect storm of GPU-based computing power, deep learning algorithm advances and highly scalable data sources that enables paradigm-shifting machine learning and analytics capabilities.”

 

Vyasa will be demoing three deep learning analytics modules for Cortex at Bio-IT World 2018 in Boston from May 15 to 17, including:

 

  • Neural Concept Recognition. This module can be trained on text concepts (e.g. drugs, diseases, pathways, conditions, side effects, genes) in structured and unstructured data. Users can ask Cortex complex questions across large scale data sets, and discover unexpected relationships between concept types. Concept recognition analytics is applicable to a wide range of use cases from competitive intelligence, to drug repurposing and EHR analytics.

 

  • Life Sciences R&D Specialized Image Analytics. Deep learning enables novel, powerful forms of image analytics, capable of being trained to detect patterns and objects in large scale image data sources. With just a few clicks in Cortex, the user can connect large streams of image data and apply analytics to those sources. Vyasa has finely-tuned this analysis for life sciences images, and it is ideal for cell assay screening, drug manufacturing and post-market screening for counterfeit packaging and tablets.
  • ChemVector de novo Compound Design. This proprietary Cortex module applies deep learning to chemical structures. Users can drag and drop one or more SDF files containing SMILES strings into Cortex, and Cortex can identify and generate novel compounds that optimize critical variables such as log-p, molecular weight and synthetic viability. ChemVector can be used with a range of other chemistry-specific analytical modules also available in Cortex.

 

 

Dr. Bouton, Vyasa’s founder and CEO, received his BA in Neuroscience (Magna Cum Laude) from Amherst College in 1996 and his Ph.D. in Molecular Neurobiology from Johns Hopkins University in 2001. Previously Dr. Bouton was the CEO of Entagen a software company founded in 2008 that provided innovative Big Data products including Extera and TripleMap. Entagen’s technologies won numerous awards including the “Innovative Technology of the Year Award for Big Data” from the Massachusetts Technology Leadership Council in 2012 and Entagen was recognized as a Gartner “Cool Vendor” in the Life Sciences in 2013. Entagen was acquired by Thomson Reuters in 2013. Dr. Bouton is an author on over a dozen scientific papers and book chapters and his work has been covered in a number of industry news articles.

 

Visit Vyasa and demo Cortex at booth #632, and watch the explainer video at www.vyasa.com.

About Vyasa Analytics

Vyasa Analytics provides deep learning software and analytics for life sciences and healthcare organizations. Cortex is Vyasa’s secure, highly scalable software platform for collaborative knowledge discovery and data analytics. Using Vyasa’s proprietary Neural Concept Recognition technology, Cortex identifies trends and patterns across disparate data sources, empowering project teams to gain insights and drive better decision making. Learn more at www.vyasa.com.

 

 

Angela Zmyslinski
Account Executive
azmyslinski@matternow.com
Office – 401-330-2800

     

SOURCE

From: Angela Zmyslinski <azmyslinski@matternow.com>

Date: Thursday, May 10, 2018 at 2:39 PM

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

Subject: RE: Demo deep learning software for life sciences at Bio-IT World 2018

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Linguamatics announces the official launch of its AI self-service text-mining solution for researchers.

Posted in Artificial Intelligence, BioIT: BioInformatics, Intelligent Information Systems on May 10, 2018| Leave a Comment »


Linguamatics announces the official launch of its AI self-service text-mining solution for researchers.

Reporter: Aviva Lev-Ari, PhD, RN

 

 

 

Linguamatics Introduces Breakthrough Scientific Search Solution

iScite provides end-users with direct access to powerful AI-driven insights from text

Boston — May 9, 2018 — Linguamatics, the leading NLP-based text analytics provider for biomedical applications, today announced the launch of Linguamatics iScite, a breakthrough innovation in scientific search that puts the precision and power of Linguamatics artificial intelligence (AI) technology directly into the hands of scientists, researchers and other knowledge workers. iScite offers a modern, easy-to-use scientific search engine that provides intuitive access to AI-powered searches across key biomedical data sources and delivers insightful answers to search questions.

iScite is designed as a next-generation search experience that empowers non-technical users to conduct their own NLP-based scientific searches to extract data insights. Rather than rely on time- and/or resource-crunched technical experts to create and perform searches, iScite enables users to quickly and independently find precise answers to their high-value questions.

“Traditional search methods are often time-consuming, expensive and ineffective, and the results are imprecise and difficult to sift through,” said Jane Reed, head of life science strategy for Linguamatics. “With iScite, users can take advantage of the power of NLP without the traditional complexities. Our patent-pending Answer-Routing Engine interprets users’ search terms and guides them to the best possible answers to their questions. Searches are seamless across multiple content sources, and users are quickly pointed to the exact content relevant to their search without having to laboriously read through every word of the source documents.”

iScite uses Linguamatics’ award-winning technology stack to handle the nuances of language and the variety of ways people express the same information, ensuring searches are comprehensive and accurate. Using advanced NLP relationship and pattern matching, iScite rapidly guides users directly to the relevant insights extracted from cloud-hosted scientific content. Results are presented in a structured, semantically-meaningful way, with options for dynamic filtering and faceting, and multiple collaboration features to allow easier sharing of insights with co-workers and key stakeholders. Behind the scenes Linguamatics uses a powerful blend of NLP and machine learning-based methods to achieve the best precision and recall.

“By empowering end-user scientists and clinicians with an easy-to-use search engine, we are speeding their access to the right knowledge for decision-making to advance the discovery, development and delivery of therapeutics,” said Linguamatics Executive Chairman John Brimacombe. “iScite has the potential to revolutionize the search process for the biomedical industry by providing everyone with rapid access to the knowledge they need, while freeing data scientists and informaticians to focus on the most challenging, in-depth search projects. iScite is a breakthrough in scientific research, filling an industry demand for a self-service alternative that delivers deep insights in a single search.”

Linguamatics will demonstrate iScite at Bio-IT World 2018 in Boston May 15-17. Visit us at booth #549, or go to our website, http://www.linguamatics.com/iscite, for more information.

 

About Linguamatics
Linguamatics transforms unstructured big data into big insights to advance human health and wellbeing. A world leader in deploying innovative text analytics for high-value knowledge discovery and decision support, Linguamatics’ solutions are used by top commercial, academic and government organizations, including 18 of the top 20 global pharmaceutical companies, the US Food and Drug Administration (FDA) and leading US healthcare organizations.

Linguamatics Media contact:
Michelle Ronan Noteboom, Sr. Account Director
Amendola Communications
+ 1 512.426.2870
mnoteboom@acmarketingpr.com

 

SOURCE

From: Chad Van Alstin <cvanalstin@acmarketingpr.com>

Date: Thursday, May 10, 2018 at 11:30 AM

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

Subject: RE: Big News from NLP-Leader Linguamatics at Bio IT World – Can I arrange a meeting?

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Synopsis Days 1,2,3: 2018 Annual World Medical Innovation Forum Artificial Intelligence April 23–25, 2018 Boston, Massachusetts  | Westin Copley Place

Posted in Artificial Intelligence, Big Data, Conference Coverage with Social Media, Intelligent Information Systems on April 26, 2018| Leave a Comment »


Synopsis Days 1,2,3: 2018 Annual World Medical Innovation Forum Artificial Intelligence April 23–25, 2018 Boston, Massachusetts  | Westin Copley Place

Curator: Aviva Lev-Ari, PhD, RN

 

Synopsis Day 1: 2018 Annual World Medical Innovation Forum Artificial Intelligence April 23–25, 2018 Boston, Massachusetts  | Westin Copley Place

https://pharmaceuticalintelligence.com/2018/04/23/synopsis-day-1-2018-annual-world-medical-innovation-forum-artificial-intelligence-april-23-25-2018-boston-massachusetts-westin-copley-place/

 

Synopsis Day 2: 2018 Annual World Medical Innovation Forum Artificial Intelligence April 23–25, 2018 Boston, Massachusetts  | Westin Copley Place

https://pharmaceuticalintelligence.com/2018/04/24/https-pharmaceuticalintelligence-wordpress-com-p47489previewtruesynopsis-day-2-2018-annual-world-medical-innovation-forum-artificial-intelligence-april-23-25-2018-boston-massachus/

 

Synopsis Day 3: 2018 Annual World Medical Innovation Forum Artificial Intelligence April 23–25, 2018 Boston, Massachusetts  | Westin Copley Place

https://pharmaceuticalintelligence.com/2018/04/25/synopsis-day-3-2018-annual-world-medical-innovation-forum-artificial-intelligence-april-23-25-2018-boston-massachusetts-westin-copley-place/

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The BioPharma Industry’s Unrealized Wealth of Data, by Ben Szekely, Vice President, Cambridge Semantics

Posted in Big Data, BioIT: BioInformatics, BioIT: BioInformatics, NGS, Clinical & Translational, Pharmaceuticall R&D Informatics, Clinical Genomics, Cancer Informatics, Biomedical Measurement Science, Computational Biology/Systems and Bioinformatics, HealthCare IT, Intelligent Information Systems, Meta-analysis of transcriptome data, tagged on June 21, 2017| Leave a Comment »


The BioPharma Industry’s Unrealized Wealth of Data, by Ben Szekely, Vice President, Cambridge Semantics

Reporter: Aviva Lev-Ari, PhD, RN

 

 

The BioPharma Industry’s Unrealized Wealth of Data

by Ben Szekely, Vice President of Solutions and Pre-sales, Cambridge Semantics

 

Solving the great medical challenges of our time reside within patient data. Clinical trial data, real-world evidence, patient feedback, genetic data, wearables data and adverse event reports contain signals to target medicines at the right patient populations, improve overall safety, and uncover the next blockbuster therapy for unmet medical needs.

However, data sources are large, diverse, multi-structured, messy and highly regulated presenting numerous challenges. As result, extracting value from data are slow to come and require manual work or long-poll dependencies on IT and Data Science teams.

Fortunately, there are new ways being adopted to take better advantage of the ever-growing volumes of patient data.  Called ‘Smart’ Patient Data Lakes (SPDL), these tools create an Enterprise Knowledge Graph built upon foundational and open Semantic Web technology standards, providing rich descriptions of data and flexibility end-to-end.  With the SPDL, biopharma researchers can:

  • Quickly on-board new data without requiring up-front modeling or mapping, ingesting data from any source versus months or weeks of preparation
  • Dynamically map and prepare data at analytics time
  • Horizontally scale in cloud or on-prem infrastructure to 100’s of nodes – allowing billions of facts to be analyzed, queried and explored in real-time   

The world’s BioPharma and research institutions are sitting on a wealth of highly differentiating and life-saving data and should begin to realize its value via Smart Patient Data Lakes (SPDL).

 

 

CONTACT: Nadia Haidar

Global Results Communications ∙ 949-278-7328 ∙ nhaidar@globalresultspr.com

 

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Dr. Doudna: RNA synthesis capabilities of Synthego’s team represent a significant leap forward for Synthetic Biology

Posted in Big Data, BioIT: BioInformatics, BioIT: BioInformatics, NGS, Clinical & Translational, Pharmaceuticall R&D Informatics, Clinical Genomics, Cancer Informatics, Computational Biology/Systems and Bioinformatics, CRISPR/Cas9 & Gene Editing, Genome Biology, HealthCare IT, Intelligent Information Systems, mRNA Therapeutics on January 4, 2017| Leave a Comment »


Dr. Doudna: RNA synthesis capabilities of Synthego’s team represent a significant leap forward for Synthetic Biology

Reporter: Aviva Lev-Ari, PhD, RN

 

Synthego Raises $41 Million From Investors, Including a Top Biochemist

By MICHAEL J. de la MERCED JAN. 4, 2017

Jennifer Doudna, a professor at the University of California, Berkeley who helped develop advanced gene-editing techniques, is among investors in Synthego.

Synthego also drew in Dr. Doudna, who had crossed paths with the company’s head of synthetic biology at various industry conferences. According to Mr. Dabrowski, the money from her trust represents the single-biggest check from a non-institutional investor that the start-up has raised.

Synthego’s new funds will help the company take its products to a more global customer base, as well as broaden its offerings. The longer-term goal, Mr. Dabrowski said, is to help fully automate biotech research and take care of much of the laboratory work that scientists currently handle themselves.

The model is cloud technology, where companies rent out powerful remote server farms to handle their computing needs rather than rely on their own hardware.

“We’ll be able to do their full research workflow,” he said. “If you look at how cloud computing developed, it used to be that every company handled their server farm. Now it’s all handled in the cloud.”

SOURCE

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

UPDATED – Status “Interference — Initial memorandum” – CRISPR/Cas9 – The Biotech Patent Fight of the Century: UC, Berkeley and Broad Institute @MIT

Reporter: Aviva Lev-Ari, PhD, RN

https://pharmaceuticalintelligence.com/2016/01/06/status-interference-initial-memorandum-crisprcas9-the-biotech-patent-fight-of-the-century/

 

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Translation of whole human genome sequencing to clinical practice: The Joint Initiative for Metrology in Biology (JIMB) is a collaboration between NIST and Stanford University

Posted in Big Data, Bio Instrumentation in Experimental Life Sciences Research, BioIT: BioInformatics, BioIT: BioInformatics, NGS, Clinical & Translational, Pharmaceuticall R&D Informatics, Clinical Genomics, Cancer Informatics, Biological Networks, Biological Networks, Gene Regulation and Evolution, Biomarkers & Medical Diagnostics, Biomedical Measurement Science, Cell Biology, Cell Biology, Signaling & Cell Circuits, Chemical Genetics, Clinical Genomics, DNA repair, Gene Regulation, Gene Regulation and Evolution, Gene Therapy & Gene Editing Development, Genetics & Innovations in Treatment, Genome Biology, Genomic Testing: Methodology for Diagnosis, Variation in human protein-coding regions on December 18, 2016| Leave a Comment »


Translation of whole human genome sequencing to clinical practice: The Joint Initiative for Metrology in Biology (JIMB) is a collaboration between the National Institute of Standards & Technology (NIST) and Stanford University.

Reporter: Aviva Lev-Ari, PhD, RN

 

JIMB’s mission is to advance the science of measuring biology (biometrology). JIMB is pursuing fundamental research, standards development, and the translation of products that support confidence in biological measurements and reliable reuse of materials and results. JIMB is particularly focused on measurements and technologies that impact, are related to, or enabled by ongoing advances in and associated with the reading and writing of DNA.

Stanford innovators and industry entrepreneurs have joined forces with the measurement experts from NIST to create a new engine powering the bioeconomy. It’s called JIMB — “Jim Bee” — the Joint Initiative for Metrology in Biology. JIMB unites people, platforms, and projects to underpin standards-based research and innovation in biometrology.

Genome in a Bottle
Authoritative Characterization of
Benchmark Human Genomes

The Genome in a Bottle Consortium is a public-private-academic consortium hosted by NIST to develop the technical infrastructure (reference standards, reference methods, and reference data) to enable translation of whole human genome sequencing to clinical practice. The priority of GIAB is authoritative characterization of human genomes for use in analytical validation and technology development, optimization, and demonstration. In 2015, NIST released the pilot genome Reference Material 8398, which is genomic DNA (NA12878) derived from a large batch of the Coriell cell line GM12878, characterized for high-confidence SNPs, indel, and homozygous reference regions (Zook, et al., Nature Biotechnology 2014).

There are four new GIAB reference materials available.  With the addition of these new reference materials (RMs) to a growing collection of “measuring sticks” for gene sequencing, we can now provide laboratories with even more capability to accurately “map” DNA for genetic testing, medical diagnoses and future customized drug therapies. The new tools feature sequenced genes from individuals in two genetically diverse groups, Asians and Ashkenazic Jews; a father-mother-child trio set from Ashkenazic Jews; and four microbes commonly used in research. For more information click here.  To purchase them, visit:

Data and analyses are publicly available (GIAB GitHub). A description of data generated by GIAB is published here. To standardize best practices for using GIAB genomes for benchmarking, we are working with the Global Alliance for Genomics and Health Benchmarking Team (benchmarking tools).

High-confidence small variant and homozygous reference calls are available for NA12878, the Ashkenazim trio, and the Chinese son with respect to GRCh37.  Preliminary high-confidence calls with respect to GRCh38 are also available for NA12878.   The latest version of these calls is under the latest directory for each genome on the GIAB FTP.

The consortium was initiated in a set of meetings in 2011 and 2012, and the consortium holds open, public workshops in January at Stanford University in Palo Alto, CA and in August/September at NIST in Gaithersburg, MD. Slides from workshops and conferences are available online. The consortium is open and welcomes new participants.

SOURCE
http://jimb.stanford.edu/giab

Stanford innovators and industry entrepreneurs have joined forces with the measurement experts from NIST to create a new engine powering the bioeconomy. It’s called JIMB — “Jim Bee” — the Joint Initiative for Metrology in Biology. JIMB unites people, platforms, and projects to underpin standards-based research and innovation in biometrology.

JIMB World Metrology Day Symposium
May 22, 2017
JIMB WORLD METROLOGY DAY SYMPOSIUM

Stanford University

JIMB’s mission is to motivate standards-based measurement innovation to facilitate translation of basic science and technology development breakthroughs in genomics and synthetic biology.

By advancing biometrology, JIMB will push the boundaries of discovery science, accelerate technology development and dissemination, and generate reusable resources.

 SOURCE

http://jimb.stanford.edu/

VIEW VIDEO

https://player.vimeo.com/video/184956195?wmode=opaque&api=1&#8243;,”url”:”https://vimeo.com/184956195&#8243;,”width”:640,”height”:360,”providerName”:”Vimeo”,”thumbnailUrl”:”https://i.vimeocdn.com/video/594555038_640.jpg&#8221;,”resolvedBy”:”vimeo”}” data-block-type=”32″>

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

“Genome in a Bottle”: NIST’s new metrics for Clinical Human Genome Sequencing

Reporter: Aviva Lev-Ari, PhD, RN

https://pharmaceuticalintelligence.com/2012/09/06/genome-in-a-bottle-nists-new-metrics-for-clinical-human-genome-sequencing/

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LIVE 9/21 8AM to 10:55 AM Expoloring the Versatility of CRISPR/Cas9 at CHI’s 14th Discovery On Target, 9/19 – 9/22/2016, Westin Boston Waterfront, Boston

Posted in Big Data, Biological Networks, Cell Biology, Cell Biology, Signaling & Cell Circuits, Clinical Genomics, Conference Coverage with Social Media, CRISPR/Cas9 & Gene Editing, Disease Biology, Genome Biology, Mutagenesis, RNA Biology, RNA Biology, Cancer and Therapeutics, Scientific & Biotech Conferences: Press Coverage, Uncategorized, Variation in human protein-coding regions, tagged , , , , , , , on September 21, 2016| Leave a Comment »


LIVE 9/21 8AM to 10:55 AM Expoloring the Versatility of CRISPR/Cas9 at CHI’s 14th Discovery On Target, 9/19 – 9/22/2016, Westin Boston Waterfront, Boston

http://www.discoveryontarget.com/

http://www.discoveryontarget.com/crispr-therapies/

Leaders in Pharmaceutical Business Intelligence (LPBI) Group is a

Media Partner of CHI for CHI’s 14th Annual Discovery on Targettaking place September 19 – 22, 2016 in Boston.

In Attendance, streaming LIVE using Social Media

Aviva Lev-Ari, PhD, RN

Editor-in-Chief

http://pharmaceuticalintelligence.com

#BostonDOT16

@BostonDOT

 

COMMENTS BY Stephen J Williams, PhD

EXPLORING THE VERSATILITY OF CRISPR/Cas9

 

8:00 Chairperson’s Opening Remarks

TJ Cradick , Ph.D., Head of Genome Editing, CRISPR Therapeutics

 

@CRISPRTX

 

8:10 Functional Genomics Using CRISPR-Cas9: Technology and Applications

Neville Sanjana, Ph.D., Core Faculty Member, New York Genome Center and Assistant Professor, Department of Biology & Center for Genomics and Systems Biology, New York University

 

CRISPR Cas9 is easier to target to multiple genomic loci; RNA specifies DNA targeting; with zinc finger nucleases or TALEEN in the protein specifies DNA targeting

 

  • This feature of crisper allows you to make a quick big and cheap array of a GENOME SCALE Crisper Knock out (GeCKO) screening library
  • How do you scale up the sgRNA for whole genome?; for all genes in RefSeq, identify consitutive exons using RNA-sequencing data from 16 primary human tissue (alot of genes end with ‘gg’) changing the bases on 3’ side negates crisper system but changing on 5’ then crisper works fine
  • Rank sequences to be specific for target
  • Cloned array into lentiviral and put in selectable markers
  • GeCKO displays high consistency betweens reagents for the same gene versus siRNA; GeCKO has high screening sensitivity
  • 98% of genome is noncoding so what about making a library for intronic regions (miRNA, promoter regions?)
  • So you design the sgRNA library by taking 100kb of gene-adjacent regions
  • They looked at CUL3; (data will soon be published in Science)
  • Do a transcription CHIP to verify the lack of binding of transcription factor of interest
  • Can also target histone marks on promoter and enhancer elements
  • NYU wants to explore this noncoding screens
  • sanjanalab.org

 

@nyuniversity

 

8:40 Therapeutic Gene Editing With CRISPR/Cas9

TJ Cradick , Ph.D., Head of Genome Editing, CRISPR Therapeutics

 

NEHJ is down and dirty repair of single nonhomologous end but when have two breaks the NEHJ repair can introduce the inversions or deletions

 

    • High-throughput screens are fine but can limit your view of genomic context; genome searches pick unique sites so use bioinformatic programs  to design specific guide Rna
    • Bioinformatic directed, genome wide, functional screens
    • Compared COSMID and CCTOP; 320 COSMID off-target sites, 333 CCtop off target
    • Young lab GUIDESeq program genome wide assay useful to design guides
    • If shorten guide may improve specificity; also sometime better sensitivity if lengthen guide

 

  • Manufacturing of autologous gene corrected product ex vivo gene correction (Vertex, Bayer, are partners in this)

 

 

They need to use a clones from multiple microarrays before using the GUidESeq but GUIDEseq is better for REMOVING the off targets than actually producing the sgRNA library you want (seems the methods for library development are not fully advanced to do this)

 

The score sometimes for the sgRNA design programs do not always give the best result because some sgRNAs are genome context dependent

9:10 Towards Combinatorial Drug Discovery: Mining Heterogeneous Phenotypes from Large Scale RNAi/Drug Perturbations

Arvind Rao, Ph.D., Assistant Professor, Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center

 

Bioinformatics in CRISPR screens:  they looked at image analysis of light microscopy of breast cancer cells and looked for phenotypic changes

 

  • Then they modeled in a small pilot and then used the algorithm for 20,000 images (made morphometric measurements)
  • Can formulate training statistical algorithms to make a decision tree how you classify data points
  • Although their algorithms worked well there was also human input from scientists

Aggregate ranking of hits programs available on web like LINKS

 

@MDAndersonNews

 

10:25 CRISPR in Stem Cell Models of Eye Disease

Alexander Bassuk, M.D., Ph.D., Associate Professor of Pediatrics, Department of Molecular and Cellular Biology, University of Iowa

 

Blind athlete Michael Stone, biathlete, had eye disease since teenager helped fund and start the clinical trial for Starbardt disease; had one bad copy of ABCA4, heterozygous (inheritable in Ahkenazi Jewish) – a recessive inheritable mutation with juvenile macular degeneration

  • Also had another male in family with disease but he had another mutation in the RPGR gene
  • December 2015 paper Precision Medicine: Genetic Repair of retinitis pigmentosa in patient derived stem cells
  • They were able to correct the iPSCs in the RPGR gene derived from patient however low efficiency of repair, scarless repair, leaves changes in DNA, need clinical grade iPSCs, and need a humanized model of RPGR

@uiowa

10:55 CRISPR in Mouse Models of Eye Disease

Vinit Mahajan, M.D., Ph.D., Assistant Professor of Ophthalmology and Visual Sciences, University of Iowa College of Medicine

  • degeneration of the retina will see brown spots, the macula will often be preserved but retinal cells damaged but with RPGR have problems with peripheral vision, retinitis pigmentosa get tunnel vision with no peripheral vision (a mouse model of PDE6 Knockout recapitulates this phenotype)
  • the PDE6 is linked to the rhodopsin GTP pathway
  • rd1 -/- mouse has something that looks like retinal pigmentosa; has mutant PDE6; is actually a nonsense mutation in rd1 so they tried a crisper to fix in mice
  • with crisper fix of rd1 nonsense mutation the optic nerve looked comparible to normal and the retina structure restored
  • photoreceptors layers- some recovery but not complete
  • sequence results show the DNA is a mosaic so not correcting 100% but only 35% but stil leads to a phenotypic recovery; NHEJ was about 12% to 25% with large deletions
  • histology is restored in crspr repaired mice
  • CRSPR off target effects: WGS and analyze for variants SNV/indels, also looked at on target and off target regions; there were no off target SNVs indels while variants that did not pass quality control screening not a single SNV
  • Rhodopsin mutation accounts for a large % of patients (RhoD190N)
  • injection of gene therapy vectors: AAV vector carrying CRSPR and cas9 repair templates

CAPN mouse models

  • family in Iowa have dominant mutation in CAPN5; retinal degenerates
  • used CRSPR to generate mouse model with mutation in CAPN5 similar to family mutation
  • compared to other transgenic methods CRSPR is faster to produce a mouse model

To Follow LIVE CONFERENCE COVERAGE PLEASE FOLLOW ON TWITTER USING

Meeting #: #BostonDOT16

Meeting @: @BostonDOT

 

Overall good meeting #s:

#personalizedmedicine

#innovation

#cancer

#immunology

#immunooncology

#pharmanews

#CRSPR

#geneediting

#crisper

#biotech

 

AND FOLLOW these @

@pharma_BI

@AVIVA_1950

@BiotechNews

@CHI

@FierceBiotech

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