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Tweets for AI and Machine Learning in Clinical Trials April 12th, 2018 hosted at Pfizer’s Innovation Research Lab in Cambridge, MA @AVIVA1950 @pharma_BI

 

 

 

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Synopsis for AI & Machine Learning in Clinical Trials, APRIL 12, 2018 PFIZER INNOVATION RESEARCH LAB – CAMBRIDGE, MA

 

Recap Book

http://viewer.zmags.com/publication/9d58c338#/9d58c338/30

 

Aviva Lev-Ari, PhD, RN, Director and Founder of  LPBI Group

will attend and cover in Real Time the Conference 

@pharma_BI

@AVIVA1950

  • Tweets for AI and Machine Learning in Clinical Trials April 12th, 2018 hosted at Pfizer’s Innovation Research Lab in Cambridge, MA @AVIVA1950 @pharma_BI

https://pharmaceuticalintelligence.com/2018/04/12/tweets-for-ai-and-machine-learning-in-clinical-trials-april-12th-2018-hosted-at-pfizers-innovation-research-lab-in-cambridge-ma-aviva1950-pharma_bi/

About Aviva Lev-Ari, PhD, RN and LPBI Group

 

 

AI and Machine Learning in Clinical Trials

April 12th, 2018 hosted at Pfizer’s Innovation Research Lab in Cambridge, MA

1 Portland St, Cambridge, MA 02139

With case studies from Pfizer, Novartis, Merck, AstraZeneca, MIT, Takeda, Sanofi & more, you will not
want to miss the latest in leveraging AI and Machine Learning in Clinical Trials.

#Pfizer #Merck #Sanofi #AstraZeneca #Novartis #Takeda #BMS #Biogen #GSK #MIT #Medable #Saama #RapidMiner

100+ innovators, data scientists, informatics, senior clinical trials execs & tech experts will convene to
discuss advances in artificial intelligence, machine learning, & clinical study data analytics.

Faculty of Advisors and Speakers:

Dan Karlin, Head of Digital Medical, Informatics, Regulatory Strategy, Pfizer
Joseph Lehar, Exec. Dir, Computational Biology, Merck
David Tester, Head, Data Sciences & Engineering, Chief Data Office, Sanofi
Bhaskar Dutta, Principal Scientist, Advanced Analytics Center, AstraZeneca
Jonas Dorn, Project Manager, Digital Health, Novartis
Jyoti Shah, Assoc. Dir, Data Development, Merck
Raj Bandaru, Sr. Director, Data Sciences Strategy, Sanofi
Ronald Dorenbos, Assoc. Dir, Materials Innovation, Takeda
Zeshan Farooqui, Sr. Clinical Site Manager, BMS
Shwen Gwee, Head, Digital Strategy, Global Clinical Ops, Biogen
Munther Baara, Head, New Clinical Paradigm, Pfizer
Shyamal Patel, Sr. Manager, PfIRe Lab, Pfizer
Bill Tobia, Lead Clinical Research Instructor, GSK
Regina Barzilay, Delta Electronics Professor, MIT
Amir Lahav, Digital Innovation Lead, Pfizer
Michelle Longmire, CEO, Medable
Karim Damji, SVP Product and Marketing, Saama
Malai Sankarasubbu, VP, AI Innovation, Saama
Ingo Mierswa, Founder/President, RapidMiner

You can take a look at the latest agenda here: http://panagorapharma.com/ai/schedule/

You can register at the following link using the promo code BOSTONBIOTECH25 for 25% off
registrations: https://panagorapharma.com/ai/registration/

If you have any other questions, you can reach out to the organizer:

Doug Lavender
CoFounder
PanAgora Pharma
Doug@panagoraconferences.com
Phone: 203-253- 6401

 

CORE THEMES:

1. An Exploration of Machine Learning for Clinical Study Data
2. Natural Language Processing (NLP) for Patient Voice Analysis via Social Channels
3. Machine Learning and Artificial Intelligence for Recruitment
4. The Potential of Machine Learning and AI for Adverse Event Identification
5. Real-time Patient Data Analysis

AGENDA for Thursday, April 12th, 2018

8:00 – 9:00 am Conference Registration Open in Pfizer Lobby – 1 Portland Street, Cambridge, MA

9:00 – 9:10 am Opening Remarks from Conference Chairman

Robert “Joe” Mather, Executive Director, Head of Digital Collaborations, Pfizer

9:10 – 9:50 am KEYNOTE PANEL: AI & ML to Support Clinical Trials – Where do we begin?
The internet of things, mHealth, wearable and sensor-enabled devices present an
unprecedented opportunity for accelerated data collection. What does it mean for life
sciences – are we prepared to handle the influx of data, and create valuable visibility to
accelerate trials? Where should we start? What are the best current applications? How can
we leverage AI and Machine Learning for Adverse Event Identification?

David Tester, Head, Data Science & Engineering, Chief Data Office, Sanofi

  • Do exploratory AI & ML outside the context of Clinical Trials 1st

Joseph Lehar, Executive Director, Computational Biology, Merck

  • Oncology – images of response to treatment are complex, Pathology is assisted by AI
  • AI can assist in cell classification
  • Biggest opportunity of AI %& ML in Immunology, use non invasive medium even behavioral indicators
  • Informed Consent in Clinical Trials
  • Development of AI models to avoid bias
  • Monitoring the Trials identify signals

Bhaskar Dutta, Principal Scientist, Advanced Analytics Center, AstraZeneca

  • Structure exploration in first study, signals used in second study
  • Even in Informatics groups there can be and there is resistance to acceptance of AI and ML
  • 80%-90% clean the data holistic data view integration and Privacy
  • pooling data sets across companies for benefits of sampling: Parkinson Disease case
  • Patients Voice in a Biomarker study as partners vs Patients as Customers

Moderator: Robert “Joe” Mather, Exec. Dir, Head of Digital Collaborations, Pfizer

  • Data sharing across the organization
  • How the audience feel about sharing code not only data

 

9:50 – 10:20 am CASE STUDY: Making Sense of Sensor Data: A Case Study in Data Quality Evaluation

Bhaskar Dutta, Principal Scientist, Advanced Analytics Center, AstraZeneca

  • Making sense of sensor data – 40 clinical data scientists and expanding
  • Tactical impact, Strategic build, Horizon Scanning &evaluaiton capabilities, Quantitative Solutions
  • % of Healthcare spending of GDP: LOWER THE % BY DIGITAL TECHNOLOGIES
  • Improve adherence no need of new drugs
  • 70% of Patients are interested in Monitoring their Health digitally
  • wearable sensors – will increase the quality of monitoring
  • Burden of Chronic disease: i.e., Asthma (23Millions), Diabetes (29Million)
  • COst direct and Indirect
  • Patient Needs
  • Challenging in using digital solutions: Lack of integration,
  • Values: to Patients, to HCP, Pharma: Drug discovery, Drug Cost
  • Digital-solutions Lifecycle: Pharma perspective: Need characterization, device sensor characterization,
  • at AstraZeneca: Project – iPREDICT – individualize PREdiction of DIsease Control using digital sensor Technology
  • Device Brands and their Price to Consumer: ZephyrBioPatch, Garmin Vivosmart, MS Band 2, GoBe, HealthPatch MD, BodyGardian, BioPatch
  • Usability Survey: Ease of setting up, Ease of use, 1st impression, comfort, likely to recommend
  • Data capturing: Missing, quality of recording – data quality evaluation: signal to noise ratio
  • poor compliance
  • Data Privacy – GPS data is the most PRIVATE: de-identification of IDs, GPS can generate identifiable data
  • Integration with other data streams
  • Six different Groups: Patient cnetrality, Applications Usability,
  • They are hiring in the MD area

 

10:20 – 10:50 am Using AI and Machine Learning to Improve Clinical Trials

• Clinical trial dedicated mobile apps can improve patient experience in clinical trials and
increase data collection and yield,
• Advanced analytics on patient data
§ HIPAA compliance, data collection & analysis

Michelle Longmire, CEO, Medable

  • Enabling Direct personalized medicine
  • current process: 1-5 drugs >$2Bil, 12 years
  • Apply AI in a Case study on mild cognitive impairment:
  1. Recruitment,
  2. Trial (drug efficacy)
  3. Endpoint (crude assessment)
  • AI – From Engagement to Insight:
  1. Trial Process, – identify Patients in populations before onset of disease
  2. Discovery, – Adaptive Trials
  3. Transformation – Digitome, Digital Biomarkers
  • Input: Patient reported data – to measure daily progress
  • Probabilistic condition for algorithm development
  • Input: Smartphone sensors: 6-minute walk
  • Input: Contextual data – Location, air quality, weather, disease & crime
  • Input: VOICE: Google Home, Amazon Alexa, Apple: Siri
  • Input: Devices: fitbit, Tomtom, biovation – Swiss company – 6 paramenters per second: Cognition applications
  • Bayesian Nets: Conditional probabilities
  • Deep learning: Pathern in data : Problem/data
  • Partnering with other Medical Centers

MEDABLE INSIGHT: Signature of Digitome

  • AI platform
  • Choose form anumber of Neural Networks (NN) ‘pattern’ to allow
  • Train Multiple NN, Time series Data, Visualization: View Data
  • Cerebrum Demo: Correlate patterns

10:50 – 11:10 am NETWORKING COFFEE AND REFRESHMENT BREAK

11:10 – 11:40 am CASE STUDY: Machine Learning for Clinical Study Data

Shyamal Patel, Sr. Manager, PfIRe Lab, Pfizer

SEE Digital Biomarkers Journal

  • DIGITAL biomarkers: from algorithms to Endpoints
  • Algorithms (gait speed, HR)–>> Biomarkers (Change is stat is it change in Disease stage?)–>>> Endpoints (relevant for target)
  • Wearable devices are tight coupled on body for continuous monitoring
  • smartphone: Sensor
  • connect devices
  • iPhone – Sensor packed powerhouse: Movement, Location, Context, Emotion (Camera, microphone)
  • 70% of data is unstructured: Text, image, video  – SOURCE: IBM
  • Why use AI for building digital biomarkers: AI: Data _ Answers =Rules vs classic Programming: Data + rules = Answers
  • AI enables:
  1. Learn efficintly large data sets
  2. make updates when more data becomes available
  3. Deploy at scale across platforms

DEEP Learning: automated driving, Object recognition, robotics, speech recognition

Case Study 1: Implement Heuristic algorithms (published in literature) Evaluate Performance (agreement with clinical ratings under controlled conditions) Train Machine Learning Models (Annotation as ground truth) to AI models

  • detect hand tremor – Quantify Tremor

Outcomes: 

  1. achieve significant reduction in false positive rate
  2. strong agreement with ratings provided by trained clinical raters

Case Study 2: Mining the sound signal for biomarkers

Outcomes:

  1. 85% accuracy in hackaton

Evaluating AI driven Digital Biomarkers:

Accuracy – Problem: Over fitting

Speed

Explainability – How does the model works? – understand the trade offfs

Scalability – do not be a hammer looking for a nail

 

11:40 – 12:10 pm Accelerating Clinical Trials using Natural Language Understanding

Pharma has a big text problem. Lots of useful information buried in unstructured data
formats that is difficult to use. Natural Language Understanding will help to turn what was
once unusable data into meaningful insights that can be applied to the clinical trial
development continuum. NLU engines also open up the possibility for users to have a more
interactive relationship with their vast data stores using speech or chat messaging in a
conversational experience
Come and see how we are using Natural Language Understanding to solve problems:
• Adverse events in the real world and clinical trials
• Better matched patients for on-going clinical trials
• Hidden associations from interactions between physiology, therapies, and clinical
outcomes

Karim Damji, SVP Product, Saama
Malai Sankarasubbu, VP of AI Research, Saama

  • Too many variations
  • ADE – Adverse Drug Event extraction from Biomedical Text

Data Manager: Delivers Clinical Data Analytics as a Service using Saama platform 

Implementation of dashboard: Smart Assistant for Clinical Operations:

  • Initiate a conversation over multiple natural channels of engagement
  • Identify intent and entity Need for NLU engine !!!!!
  1. Intent extractor
  2. Entity Extractor
  3. Conversation Experience (CX): One question per one answer – not a good CX

Saama: ChatBot Voice interaction

  • Rank studies on Pancreatic Cancer in ClinicalTrials.gov by Inclusion vs Exclusion Criteria
  • Entity extraction and Patinet matching for EHR Data
  1. Protein
  2. Chemical compound
  3. Organism
  4. Environment
  5. Tissue
  6. Disease/phenotype
  7. Gene Ontology Term

12:10 – 12:40 pm CASE STUDY: Bringing Digital Health and Artificial Intelligence to Merck

Merck is building up digital health capabilities to increase patient engagement, improve trial
performance, and develop clearer disease phenotypes. I will describe some efforts across
the organization in this area & provide examples of smart trials / AI collaborations underway.

Joseph Lehar, Executive Director, Computational Biology, Merck

  • Digital health innovations at Merck
  1. quantitative phyenotypes – clearer disease signals
  2. trial performance – more effective and more efficient
  3. Patient outcomes – Better ones
  4. Data analytics & Infrastructure – enabling 1,2,3
  • Smart trials: pacient-centric studies
  • Pilot studies: Smart dosing, sampling and analytics
  • at home vs at clinic
  • smart pill packs daily blood spot for PK/DNA, e-Diary
  • less expensive sampling
  • Key findings: More trials should have smart monitoring
  • Future expansion: Better, more relevant, wider: Less invasive , Apply to active clinical trials , scale up to larger populations
  • Collaborate with big Technology companies on AI
  • Flexible, scientific partnerships
  • Projects with like success sooner
  • Projects underway or being actively planned
  • Value-based models on Trials outcomes
  • Cross functional collaborations: Organizations, Projects: i.e., Oncology, Objectives

 

12:40 – 1:00 pm SINEQUA PRESENTATION

Jeff Evernham, Sinequa

evernham@sinequa.com

  • Content of the data: Expand, Link, Enrich, Improve
  • Data set Index
  • Row IndexStructured and Unstructured (Textual)
  • DIscovery: Common variables across all data sets
  • Cognitive Analytics: SEARCH, NLP, Integrated ML
  • Single study –>> Multiple Studies –> numerical variables –>> Enriched categorical variables Unstructured data

1:00 – 1:50 pm EXECUTIVE NETWORKING LUNCHEON

1:50 – 2:15 pm CASE STUDY: We want to teach a machine to think like a physician, but how do we tell how

a physician thinks?
Inter- and intra-rater variability can severely impact the data quality of our clinical trials. If we
could teach machine learning algorithms to assess patients like experienced physicians, we
would have every patient assessed the exact same way across all the sites in a clinical trial.
As a bonus, we could make these medical assessments available in underserved areas of the
world. However, how can we train a machine learning algorithm on data annotated by
humans, if we know that those human annotations are unreliable? We will present a
framework, and the journey that led us to it, that allows combining the judgments of
multiple human raters into one consensus scale and thus provide high quality ground truth,
an aspect of machine learning that doesn’t always get the attention it deserves.

Jonas Dorn, Digital Solutions Director, Novartis

  • Rater consistency is limited given by n-Raters to K-Patients – Human consistency is limited: Disease severity score assigned
  • ML –>> Scores are generated
  • What is ground truth to be considered GOOD?
  • Comparative video rating
  • Converting ranking into scores, “true Score”
  • True score + uncertainty + rater consistency – compare realization – compare realization to threshold, comes with uncertainty
  • Combine all rating by all doctores = continuous consensus score (with uncertainties) vs Coarse ratings (raw/consensus)
  • Create consistent score through comparisons
  • Conclusion: Humans are bad at absolute ratings but good at comparison
  • Comparison-based enable virtual rating

2:15 – 2:45 pm PANEL DISCUSSION: Hearing the Voice of the Patient – How Ambient Listening Devices and Artificial Intelligence Can Improve the Clinical Trial Experience

The healthcare industry, and in particular, the clinical research sector, has recently focused
its attention on achieving “patient-centricity”. Driven by the desire to better engage clinical
trial volunteers, coupled by the need to demonstrate value-added medical products, this
has become much more than the latest buzz word. However, once the trial begins, the
patient oftentimes may feel isolated in the process – quite simply, they need to ask
questions and receive answers that they can understand. Is this an opportunity to effectively & efficiently use ambient listening devices?

How can we leverage AI and Machine Learning for the detection of adverse events, using NLP and other strategies for analysis?
Amir Lahav, Digital Innovation Lead, Rare Disease Research Unit, Pfizer

  • speech technology – voice activated mechanism
  • voice recording for Ataxia Patients – for interaction with Patients
  • Accustic pattern recognition analysis of Human voice detects Asthman or CVD in Patient : voice for detection of disease: Stroke Patient,

Zeshan Farooqui, Sr. Clinical Site Manager, Bristol-Myers Squibb

Malai Sankarasubbu, VP of AI Research, Saama

  • Multiple Indexes

Moderated by: Bill Tobia, Lead Clinical Research Instructor, GSK

Voice of patient on audio technology

2:45 – 3:15 pm CASE STUDY: Clinical Data Integration from Translational Modeling Using Machine

Learning

Raj Bandaru, Sr. Director, Sr. Director, Translational Informatics, Sanofi

  • Clinical Data Integration for Translational Modeling
  • Challenges of Data Discovery Integration of Clinical Data
  • Automated Data Cataloging
  • Data DIscovery – 80% effort
  • Crawler – Bayesian machine learning – >> data Catalog (Index) –>>  Meta Data (Information) –>>> Elastic Data– >> synonyms and hierarchhical search –>. Ontologies and Access Management
  • Probabilistic model –>> no need for complete ontologies
  • self learning, self maintaining, meta data management, Data on demand, LOW of no IT support, cost a fraction of dat integration projects
  • GOAL: develop a classifier that predicts data class and relevnce to the question being asked
  • Metadata driven Risk-based De-Identification Strategy: Internal Use, External Use
  • Data Analytics Ask a question using Amazon Alexa
  • Data science and knowledge management Team

2:50 – 3:10PM Moving beyond Actigraphy: Using AI to make sense of multi-parameter wearable sensor data

Chris Economos, VP of Business Development, PhysIQ – AI for Personalized Anomaly Detection

  • Contnuous Biosensor Data +Deep Learning to Potentially DIagnose Heart Hailure Likelihood of Heart FAilure derived from Activity Alone: Heart Failure vs Normal Vs Cancer Treatment vs COPD
  • Activity + HR: Heart Failure vs Normal Vs Cancer Treatment vs COPD
  • “baseline” vs “estimates”
  • the difference is “Residuals”
  • Actual, RR, HR, Higher than Expected: Deterioration vs Improvement
  • Chris Economos, VP of Business Development, PhysIQ Case Study: Phase 3 Cardiovascular Clinical Trial: 600 patients, 97 sites, 14 countries, 9 languages 2 CROs
  • All Causes Hospitalization vs Worsening HF Hospitalization
  • Application of AI to data detection of exacerbation

3:15 – 3:35 pm NETWORKING COFFEE AND REFRESHMENT BREAK

3:35 – 4:05 pm Learning Disease Progression and Patient Stratification Models from Images and Text

 

Regina Barzilay, Delta Electronics Professor, MIT EECS, MIT Koch Institute for
Integrative Cancer Research

  • Predict recurrences, sensitivity to Treatment, LCIS – Lobar Carcinoma In-Situ
  • Enabling New Science – NLP Atypia – 7000 cases
  • Reducing Over-treatment – 87% excision are of benign tissue
  • 31% cancers were visible a year prior to cancer
  • Interpretable Neural Models
  • Multi-Task Representation Learning: Small sample size: Task “N” Tumor Size change GOALS: Correlate similar tasks

 

4:05 – 4:25 pm How AI will transform Clinical Trials

Ronald Dorenbos, Associate Director, Materials & Innovation, Takeda

  • Patient’s Perspective: AI can help patients to get better faster, present the disease
  • Future of clinical Trials: Personalization, Patients becoming the point-of-care, Adherence, Healthier Life Style
  • patient acceptance and adoption of digital health and AI are growing
  • In Pharma: SImulation Modeling, Predicting reaction to therapies Virtual Clinical Trials

 

4:25 – 5:00 pm PANEL DISCUSSION: How to make all the Data Machine Learnable?

Raj Bandaru, Sr. Director, Data Sciences Strategy, Sanofi

  • advises to use models that will signal noise vs clean the data upfront with endless effort

Jonas Dorn, Digital Solutions Director, Novartis

  • Cleaning data MUST be done before modeling
  • At present AI will not change the WOrld as fast, future of AI will move slowly

Ingo Mierswa, Founder and President, RapidMiner

  • missing data is not an excuse, it worth a chance
  • Data Engineering and Data modeling is separate in hands of two groups, optimal modeling requires one group, cooperation and validation both groups need be involved along the entire cycle
  • Support the RIGHT to own the data

Jyoti Shah, Associate Director, Data Development, Merck

  • A lot of data and high quality of Data
  • Digital technology – data collected by machine becomes part of the process
  • Patients Centers will disctate the pace of AI adoption, they want to own data

Moderated by: Munther Baara, Head, New Clinical Paradigm, Pfizer

5:00 – 6:30 pm Networking Drinks Reception / END OF CONFERENCE

SOURCE

http://panagorapharma.com/ai/schedule/

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“I’m expecting a flood of trials to get registered,” FDA Commissioner Robert Califf

Reporter: Aviva Lev-Ari, PhD, RN

 

WASHINGTON — Researchers will have to publicly report the results of many more clinical trials, including some for drugs and devices that never reach the market, under new government rules announced Friday.

The federal rules, which also require more complete reporting of deaths, clarify and strengthen a 2007 law that requires researchers to report results of many human studies of experimental treatments for ailments such as diabetes, cancer, and heart disease.

Government officials said the new rules are meant to improve compliance with requirements for public registration of trials and posting of data on the ClinicalTrials.gov website. But advocates for transparency in clinical research cautioned that the success of the new rules, which take effect Jan. 18, 2017, will depend on the vigor of government enforcement.

A recent analysis in the journal BMJ found that GlaxoSmithKline, Paxil’s manufacturer, failed to disclose 2001 data showing the drug to be no more effective than a placebo, and was linked to increased suicide attempts by teens.

SEE SOURCE

https://s3.amazonaws.com/public-inspection.federalregister.gov/2016-22129.pdf

New federal rules target woeful public reporting of clinical trial results

Biden threatens funding cuts for researchers who fail to report clinical trial results

“Under the law, it says you must report. If you don’t report, the law says you shouldn’t get funding,” Biden said, citing a STAT investigation that found widespread reporting lapses.

SOURCE

Biden threatens funding cuts for researchers who fail to report clinical trial results

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Final Opportunity to Save up to €300 – CHI’s World Preclinical Congress Europe event taking place 14-16 November 2016 in Lisbon, Portugal

Reporter: Bethany Gray, CHI

UPDATED on 9/16/2016

REGISTER TODAY 

http://www.worldpreclinicaleurope.com/

 

Today is Your Final Opportunity to Save up to €300 to Attend World Preclinical Congress Europe 2016

Today Friday, 16 September is the final day for the early savings deadline for CHI’s World Preclinical Congress Europe event taking place 14-16 November 2016 in Lisbon, Portugal. Don’t miss this opportunity to take advantage of reduced registration rates and save up to €300.

This year’s event is comprised of:

  • 4 Conferences covering very interesting and relevant topics in preclinical research
  • Short Courses* on specialized topics offering interactive discussions with experts
  • Interactive Breakout Discussions on key issues organized in an informal setting
  • Exhibit Hall offering a glimpse at the latest tools and reagents
  • Poster sessions featuring cutting-edge, ongoing research
  • Networking opportunities to meet a global gathering of scientists from academia and industry
  • Exclusive focus on ideas, technologies and interests driving preclinical decision-making
  • Sponsored talks by leading technology and service providers showcasing new offerings

Don’t miss your opportunity to network with chemists, biologists, pharmacologists, toxicologists, scientists from screening groups and other areas of preclinical research, at this year’s event. Save up to €300 off your registration through Friday, 9 September.

We look forward to seeing you this November in Lisbon.

Sincerely,

Bethany Gray

Senior Marketing Manager, World Preclinical Congress Europe

Cambridge Healthtech Institute

For Sponsorship and Exhibit opportunities, including podium presentations and 1-2-1 meetings, please contact:

Companies A-K:

Ilana Quigley, Sr Business Development Manager, 781-972-5457 | iquigley@healthtech.com

Companies L-Z

Joseph Vacca, M.Sc., Associate Director, Business Development, 781-972-5431 | jvacca@healthtech.com

Who you will meet with at WPC Europe? View prospectus End Main Content Start Footer

Cambridge Healthtech Institute

250 First Avenue, Suite 300 | Needham, MA 02494 | P: 781.972.5400 | E: chi@healthtech.com

www.healthtech.com

SOURCE

From: Bethany Gray <bethanyg@healthtech.com>

Date: Friday, September 9, 2016 at 4:00 AM

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

Subject: Today is Your Final Opportunity to Save up to €300 to Attend World Preclinical Congress Europe

2016

From: Bethany Gray <bethanyg@healthtech.com>

Date: Friday, September 16, 2016 at 4:00 AM

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

Subject: Today is Your Final Opportunity to Save up to €300 to Attend World Preclinical Congress Europe 2016

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The presence of any Valvular Heart Disease (VHD) did not influence the comparison of Dabigatran [Pradaxa, Boehringer Ingelheim] with Warfarin

Reporter: Aviva Lev-Ari, PhD, RN

 

UPDATED on 10/22/2018

Dabigatran (Pradaxa) was no better than aspirin for prevention of recurrent stroke among patients with an embolic stroke of undetermined source in the RE-SPECT ESUS trial reported at the World Stroke Congress.

 

Pradaxa® (dabigatran etexilate)
Clinical experience of Pradaxa® equates to over 9 million patient-years in all licensed indications worldwide. Pradaxa® has been in the market for more than ten years and is approved in over 100 countries.15
Currently approved indications for Pradaxa® are:16,17
  • Prevention of stroke and systemic embolism in patients with non-valvular atrial fibrillation and a risk factor for stroke
  • Primary prevention of venous thromboembolic events in patients undergoing elective total hip replacement surgery or total knee replacement surgery
  • Treatment of deep vein thrombosis (DVT) and pulmonary embolism (PE) and the prevention of recurrent DVT and recurrent PE in adults
Dabigatran, a direct thrombin inhibitor (DTI), was the first widely approved drug in a new generation of direct oral anticoagulants, available to target a high unmet medical need in the prevention and treatment of acute and chronic thromboembolic diseases.18,19,20
REFERENCES

SOURCE

https://www.boehringer-ingelheim.com/press-release/Results-from-two-Pradaxa-trials-to-be-presented-at-WSC

 

 

Event Rate and Outcome Risk, With vs Without Valvular Heart Disease

Outcome Valvular heart disease, event rate/y, % No valvular heart disease, event rate/y, % HR (95% CI)* P
Stroke, systemic embolic event 1.61 1.41 1.09 (0.88–1.33) 0.43
Major bleeding 4.36 2.84 1.32 (1.16–1.33) <0.001
Intracranial hemorrhage 0.51 0.41 1.20 (0.83–1.74) 0.32
All-cause mortality 4.45 3.67 1.09 (0.96–1.23) 0.18
*Adjusted using propensity scores

ORIGINAL RESEARCH ARTICLE

Comparison of Dabigatran versus Warfarin in Patients with Atrial Fibrillation and Valvular Heart Disease: The RE-LY Trial

Michael D. Ezekowitz, Rangadham Nagarakanti, Herbert Noack, Martina Brueckmann, Claire Litherland, Mark Jacobs, Andreas Clemens,Paul A. Reilly, Stuart J. Connolly, Salim Yusuf and Lars Wallentin

 http://dx.doi.org/10.1161/CIRCULATIONAHA.115.020950

 

Results—There were 3950 patients with any VHD:

  • 3101 had mitral regurgitation,
  • 1179 tricuspid regurgitation,
  • 817 aortic regurgitations,
  • 471 aortic stenosis and
  • 193 mild mitral stenosis.

At baseline patients with any VHD had more

  • heart failure,
  • coronary disease,
  • renal impairment and
  • persistent atrial fibrillation.

Patients with any VHD had higher rates of

  • major bleeds (HR 1.32; 95% CI 1.16-1.5)

but similar

  • stroke or systemic embolism (SEE) rates (HR 1.09; 95% CI 0.88-1.33).

For D110 patients, major bleed rates were lower than warfarin (HR 0.73; 95% CI 0.56-0.95 with and HR 0.84; 95% CI 0.71-0.99 without VHD) and

For D150 similar to warfarin in patients with (HR 0.82; 95% CI 0.64-1.06) or without VHD (HR 0.98; 95% CI 0.83-1.15).

For D150 patients stroke/SEE rates were lower versus warfarin with (HR 0.59; 95% CI 0.37-0.93) and without VHD (HR 0.67; 95% CI 0.52-0.86) and similar to warfarin for D110 irrespective of presence of VHD (HR 0.97 CI 0.65-1.45 and 0.85 CI 0.70-1.10).

For intracranial bleeds and death rates for D150 and D110 were lower vs warfarin independent of presence of VHD.

Conclusions—The presence of any VHD did not influence the comparison of dabigatran with warfarin.

Clinical Trial Registration—URL: http://clinicaltrials.gov. Unique Identifier: NCT00262600.

SOURCES

http://circ.ahajournals.org/content/early/2016/08/05/CIRCULATIONAHA.115.020950

http://www.medscape.com/viewarticle/867482?nlid=108872_3866&src=WNL_mdplsfeat_160816_mscpedit_card&uac=93761AJ&spon=2&impID=1179558&faf=1

 

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Roche provides update on phase III study of Gazyva/Gazyvaro in people with previously untreated diffuse large B-cell lymphoma

 

Reporter: Aviva Lev-Ari, PhD, RN

 

Basel, 18 July 2016

Roche provides update on phase III study of Gazyva/Gazyvaro in people with previously untreated diffuse large B-cell lymphoma

  • GOYA study did not meet its primary endpoint of improvement in progression-free survival with Gazyva/Gazyvaro plus CHOP chemotherapy versus MabThera/Rituxan plus CHOP chemotherapy

Roche (SIX: RO, ROG; OTCQX: RHHBY) today announced that the phase III GOYA study evaluating Gazyva®/Gazyvaro® (obinutuzumab) plus CHOP chemotherapy (G-CHOP) in people with previously untreated diffuse large B-cell lymphoma (DLBCL) did not meet its primary endpoint of significantly reducing the risk of disease worsening or death (progression-free survival; PFS) compared to MabThera/Rituxan (rituximab) plus CHOP chemotherapy (R-CHOP). Adverse events with Gazyva/Gazyvaro and MabThera/Rituxan were consistent with those seen in previous clinical trials when each was combined with various chemotherapies. Data from the GOYA study will be presented at an upcoming medical meeting.

“Two previous studies showed Gazyva/Gazyvaro helped people with previously untreated follicular lymphoma or chronic lymphocytic leukaemia live longer without their disease worsening compared to MabThera/Rituxan, when each was combined with chemotherapy. We were hopeful we could show a similar result for people with diffuse large B-cell lymphoma and once again improve on the standard of care,” said Sandra Horning, MD, Chief Medical Officer and Head of Global Product Development. “We will continue to analyse the GOYA data to better understand the results, and to study other investigational treatments in this disease with the goal of further helping these patients.”

About the GOYA study

GOYA (NCT01287741) is a global phase III open-label, multi-centre, randomised two-arm study examining the efficacy and safety of the combination of Gazyva/Gazyvaro plus CHOP chemotherapy (G-CHOP) compared to MabThera/Rituxan plus CHOP chemotherapy (R-CHOP). GOYA included 1,418 previously untreated patients with CD20-positive DLBCL. The primary endpoint of the study is investigator-assessed PFS, with secondary endpoints including PFS assessed by independent review committee (IRC), response rate (overall response, ORR; and complete response, CR), overall survival (OS), disease free survival (DFS) and safety profile. The GOYA study is being conducted in cooperation with the Fondazione Italiana Linfomi (FIL, Italy).

About Gazyva/Gazyvaro (obinutuzumab)

Gazyva/Gazyvaro is an engineered monoclonal antibody designed to attach to CD20, a protein expressed on certain B cells, but not on stem cells or plasma cells. Gazyva/Gazyvaro is designed to attack and destroy targeted B-cells both directly and together with the body’s immune system.

Gazyva/Gazyvaro is currently approved in more than 70 countries in combination with chlorambucil, for people with previously untreated chronic lymphocytic leukaemia. The approvals were based on the CLL11 study, showing significant improvements with Gazyva/Gazyvaro plus chlorambucil across multiple clinical endpoints, including PFS, overall response rate (ORR), complete response rate (CR), and minimal residual disease (MRD) when compared head-to-head with MabThera/Rituxan plus chlorambucil.

In February 2016, Gazyva was approved by the US Food and Drug Administration in combination with bendamustine followed by Gazyva alone for people with follicular lymphoma who did not respond to a Rituxan-containing regimen, or whose follicular lymphoma returned after such treatment. In June 2016, Gazyvaro was approved by the European Commission in combination with bendamustine followed by Gazyvaro maintenance in people with follicular lymphoma who did not respond or who progressed during or up to six months after treatment with MabThera or a MabThera-containing regimen. Both approvals were based on the phase III GADOLIN study, showing a significant improvement in progression-free survival with Gazyva/Gazyvaro-based therapy compared to bendamustine alone. Gazyva is marketed as Gazyvaro in the EU and Switzerland.

In May 2016, the phase III GALLIUM study in people with previously untreated follicular lymphoma met its primary endpoint early. GALLIUM compared the efficacy and safety of Gazyva/Gazyvaro plus chemotherapy (CHOP, CVP or bendamustine) followed by Gazyva/Gazyvaro alone, head-to-head with MabThera/Rituxan plus chemotherapy followed by MabThera/Rituxan alone. Results from a pre-planned interim analysis showed that Gazyva/Gazyvaro-based treatment resulted in superior progression-free survival compared to MabThera/Rituxan-based treatment. Adverse events with either Gazyva/Gazyvaro or MabThera/Rituxan were consistent with those seen in previous clinical trials when each was combined with various chemotherapies. Data from the GALLIUM study will be presented at an upcoming medical meeting and submitted to health authorities for approval consideration.

Additional combination studies investigating Gazyva/Gazyvaro with other approved or investigational medicines, including cancer immunotherapies and small molecule inhibitors, are underway across a range of blood cancers.

About DLBCL

Diffuse large B-cell lymphoma (DLBCL) is the most common subtype of non-Hodgkin lymphoma (NHL), accounting for about one in three cases of NHL1. DLBCL is an aggressive (fast-growing) type of NHL, which is generally responsive to treatment in the frontline2. However, as many as 40% of patients will relapse, at which time salvage therapy options are limited and survival is short2. Approximately 123,000 people worldwide are estimated to be diagnosed with DLBCL each year3.

About Roche in haematology

For more than 20 years, Roche has been developing medicines that redefine treatment in haematology. Today, we are investing more than ever in our effort to bring innovative treatment options to people with diseases of the blood. In addition to approved medicines MabThera/Rituxan (rituximab), Gazyva/Gazyvaro (obinutuzumab), and Venclexta™ (venetoclax) in collaboration with AbbVie, Roche’s pipeline of investigational haematology medicines includes Tecentriq (atezolizumab), an anti-CD79b antibody drug conjugate (polatuzumab vedotin/RG7596) and a small molecule antagonist of MDM2 (idasanutlin/RG7388). Roche’s dedication to developing novel molecules in haematology expands beyond oncology, with the development of the investigational haemophilia A treatment emicizumab (ACE910).

About Roche

Roche is a global pioneer in pharmaceuticals and diagnostics focused on advancing science to improve people’s lives.

Roche is the world’s largest biotech company, with truly differentiated medicines in oncology, immunology, infectious diseases, ophthalmology and diseases of the central nervous system. Roche is also the world leader in in vitro diagnostics and tissue-based cancer diagnostics, and a frontrunner in diabetes management. The combined strengths of pharmaceuticals and diagnostics under one roof have made Roche the leader in personalised healthcare – a strategy that aims to fit the right treatment to each patient in the best way possible.

Founded in 1896, Roche continues to search for better ways to prevent, diagnose and treat diseases and make a sustainable contribution to society. Twenty-nine medicines developed by Roche are included in the World Health Organization Model Lists of Essential Medicines, among them life-saving antibiotics, antimalarials and cancer medicines. Roche has been recognised as the Group Leader in sustainability within the Pharmaceuticals, Biotechnology & Life Sciences Industry seven years in a row by the Dow Jones Sustainability Indices.

The Roche Group, headquartered in Basel, Switzerland, is active in over 100 countries and in 2015 employed more than 91,700 people worldwide. In 2015, Roche invested CHF 9.3 billion in R&D and posted sales of CHF 48.1 billion. Genentech, in the United States, is a wholly owned member of the Roche Group. Roche is the majority shareholder in Chugai Pharmaceutical, Japan. For more information, please visit http://www.roche.com.

References
1 –  Lyon, France: IARC Press; 2008. World Health Organization Classification of Tumors of Haematopoietic and Lymphoid Tissues.
2 – Maurer, JM et al. (2014). Event-free survival at 24 months is a robust end point for disease-related outcome in diffuse large B-cell lymphoma treated with immunochemotherapy. J Clin Oncol 32: 1066-73.
3 – Numbers derived from GLOBOCAN 2012: Estimated cancer incidence, mortality and prevalence worldwide in 2012. http://globocan.iarc.fr. Accessed June 2016.

SOURCE

http://www.roche.com/media/store/releases/med-cor-2016-07-18.htm

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Juno Therapeutics to Resume JCAR015 Phase II ROCKET Trial AND Acquires privately held Boston, MA-based RedoxTherapies

Reporter: Aviva Lev-Ari, PhD, RN

UPDATED on 2/5/2018

Anatomy of a $9B buyout: Celgene’s quick turn from Juno’s close collaborator to new owner

 john carroll — on February 5, 2018 05:50 AM EST

https://endpts.com/anatomy-of-a-9b-buyout-celgenes-quick-turn-from-junos-close-collaborator-to-new-owner/?utm_medium=email&utm_campaign=Monday%20February%205%202018&utm_content=Monday%20February%205%202018+CID_aecea465e79bcafc58b92d3615dfacda&utm_source=ENDPOINTS%20emails&utm_term=Anatomy%20of%20a%209B%20buyout%20Celgenes%20quick%20turn%20from%20Junos%20close%20collaborator%20to%20new%20owner

 

PDATED on 11/13/2017

Juno analysis of shuttered study offers clues for CAR-T

https://www.biopharmadive.com/news/juno-analysis-of-shuttered-study-offers-clues-for-car-t/510634/

 

UPDATED on 11/28/2016

Latest deaths in Juno trial underscore the need for greater transparency in clinical trials

 

quote

In recent years, numerous states have passed so-called “right-to-try” laws that encourage patients to seek access to experimental drugs outside of the clinical trial framework. In addition, libertarian activists and even some individuals associated with the incoming Trump administration continue to propose moving new medicines out into widespread use after only scant safety testing. That would increase the number of patients at risk for adverse outcomes, like the ones observed in the Juno trials, before we even know whether the drugs work.

READ MORE

Right-to-try laws could curtail the development of innovative new therapies

 

The best way to identify transformative new medicines, protect patients from unexpectedly dangerous drugs, and avoid wasting health care resources is by subjecting experimental products to well-designed clinical trials that enroll sufficient numbers of patients and test relevant clinical outcomes that can then be independently reviewed by the experts at the FDA. When severe, unanticipated problems arise, the FDA needs a transparent and systematic evaluation process that can provide public insight into what happened and why. That would contribute to the progress of science and the development of the next generation of safer, better therapies.

https://www.statnews.com/2016/11/24/deaths-juno-trial-transparency-fda/

 

 

Juno Therapeutics to Resume JCAR015 Phase II ROCKET Trial

SEATTLE–(BUSINESS WIRE)–Jul. 12, 2016– Juno Therapeutics, Inc. (Nasdaq: JUNO), a biopharmaceutical company focused on re-engaging the body’s immune system to revolutionize the treatment of cancer, today announced that the U.S. Food and Drug Administration has removed the clinical hold on the Phase II clinical trial of JCAR015 (known as the “ROCKET” trial) in adult patients with relapsed or refractory B cell acute lymphoblastic leukemia (r/r ALL).

Under the revised protocol, the ROCKET trial will continue enrollment using JCAR015 with cyclophosphamide pre-conditioning only.

 

SOURCE

http://ir.junotherapeutics.com/phoenix.zhtml?c=253828&p=irol-newsArticle&ID=2184987

 

 

Juno buys early-stage biotech for access to immuno-oncology candidate

Jul 14 2016, 16:32 ET | About: Juno Therapeutics (JUNO) | By: Douglas W. House, SA News Editor

 

Juno Therapeutics (NASDAQ:JUNOacquires privately held Boston, MA-based RedoxTherapies. Juno’s primary aim of the deal was to secure an exclusive license to vipadenant, a small molecule adenosine A2a receptor antagonist that may disrupt key immunosuppressive pathways in the tumor microenvironment in certain cancers.

Redox licensed vipadenant from London-based Vernalis in October 2014. It was under development for the treatment of Parkinson’s disease by Biogen (NASDAQ:BIIB) but safety concerns scuppered the effort in 2010 despite encouraging efficacy in mid-stage studies. Biogen returned the rights to Vernalis in 2011.

Under the terms of the transaction, Juno will pay $10M in upfront cash plus undisclosed milestones.

SOURCE

http://seekingalpha.com/news/3193337-juno-buys-early-stage-biotech-access-immuno-oncology-candidate?source=email_rt_mc_readmore&app=1&uprof=46#email_link

 

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

What does this mean for Immunotherapy? FDA put a temporary hold on Juno’s JCAR015, Three Death of Celebral Edema in CAR-T Clinical Trial and Kite Pharma announced Phase II portion of its CAR-T ZUMA-1 trial

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

https://pharmaceuticalintelligence.com/2016/07/09/what-does-this-mean-for-immunotherapy-fda-put-a-temporary-hold-on-jcar015-three-death-of-celebral-edema-in-car-t-clinical-trial-and-kite-pharma-announced-phase-ii-portion-of-its-car-t-zuma-1-trial/

 

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