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Archive for the ‘Regulated Clinical Trials: Design, Methods, Components and IRB related issues’ Category


The Regulatory challenge in adopting AI

Author and Curator: Dror Nir, PhD

In the last couple of years we are witnessing a surge of AI applications in healthcare. It is clear now, that AI and its wide range of health-applications are about to revolutionize diseases’ pathways and the way the variety of stakeholders in this market interact.

Not surprisingly, the developing surge has waken the regulatory watchdogs who are now debating ways to manage the introduction of such applications to healthcare. Attributing measures to known regulatory checkboxes like safety, and efficacy is proving to be a complex exercise. How to align claims made by manufacturers, use cases, users’ expectations and public expectations is unclear. A recent demonstration of that is the so called “failure” of AI in social-network applications like FaceBook and Twitter in handling harmful materials.

‘Advancing AI in the NHS’ – is a report covering the challenges and opportunities of AI in the NHS. It is a modest contribution to the debate in such a timely and fast-moving field!  I bring here the report’s preface and executive summary hoping that whoever is interested in reading the whole 50 pages of it will follow this link: f53ce9_e4e9c4de7f3c446fb1a089615492ba8c

Screenshot 2019-04-07 at 17.18.18

 

Acknowledgements

We and Polygeia as a whole are grateful to Dr Dror Nir, Director, RadBee, whose insights

were valuable throughout the research, conceptualisation, and writing phases of this work; and to Dr Giorgio Quer, Senior Research Scientist, Scripps Research Institute; Dr Matt Willis, Oxford Internet Institute, University of Oxford; Professor Eric T. Meyer, Oxford Internet Institute, University of Oxford; Alexander Hitchcock, Senior Researcher, Reform; Windi Hari, Vice President Clinical, Quality & Regulatory, HeartFlow; Jon Holmes, co-founder and Chief Technology Officer, Vivosight; and Claudia Hartman, School of Anthropology & Museum Ethnography, University of Oxford for their advice and support.

Author affiliations

Lev Tankelevitch, University of Oxford

Alice Ahn, University of Oxford

Rachel Paterson, University of Oxford

Matthew Reid, University of Oxford

Emily Hilbourne, University of Oxford

Bryan Adriaanse, University of Oxford

Giorgio Quer, Scripps Research Institute

Dror Nir, RadBee

Parth Patel, University of Cambridge

All affiliations are at the time of writing.

Polygeia

Polygeia is an independent, non-party, and non-profit think-tank focusing on health and its intersection with technology, politics, and economics. Our aim is to produce high-quality research on global health issues and policies. With branches in Oxford, Cambridge, London and New York, our work has led to policy reports, peer-reviewed publications, and presentations at the House of Commons and the European Parliament. http://www.polygeia.com @Polygeia © Polygeia 2018. All rights reserved.

Foreword

Almost every day, as MP for Cambridge, I am told of new innovations and developments that show that we are on the cusp of a technological revolution across the sectors. This technology is capable of revolutionising the way we work; incredible innovations which could increase our accuracy, productivity and efficiency and improve our capacity for creativity and innovation.

But huge change, particularly through adoption of new technology, can be difficult to  communicate to the public, and if we do not make sure that we explain carefully the real benefits of such technologies we easily risk a backlash. Despite good intentions, the care.data programme failed to win public trust, with widespread worries that the appropriate safeguards weren’t in place, and a failure to properly explain potential benefits to patients. It is vital that the checks and balances we put in place are robust enough to sooth public anxiety, and prevent problems which could lead to steps back, rather than forwards.

Previous attempts to introduce digital innovation into the NHS also teach us that cross-disciplinary and cross-sector collaboration is essential. Realising this technological revolution in healthcare will require industry, academia and the NHS to work together and share their expertise to ensure that technical innovations are developed and adopted in ways that prioritise patient health, rather than innovation for its own sake. Alongside this, we must make sure that the NHS workforce whose practice will be altered by AI are on side. Consultation and education are key, and this report details well the skills that will be vital to NHS adoption of AI. Technology is only as good as those who use it, and for this, we must listen to the medical and healthcare professionals who will rightly know best the concerns both of patients and their colleagues. The new Centre for Data Ethics and Innovation, the ICO and the National Data Guardian will be key in working alongside the NHS to create both a regulatory framework and the communications which win society’s trust. With this, and with real leadership from the sector and from politicians, focused on the rights and concerns of individuals, AI can be advanced in the NHS to help keep us all healthy.

Daniel Zeichner

MP for Cambridge

Chair, All-Party Parliamentary Group on Data Analytics

 

Executive summary

Artificial intelligence (AI) has the potential to transform how the NHS delivers care. From enabling patients to self-care and manage long-term conditions, to advancing triage, diagnostics, treatment, research, and resource management, AI can improve patient outcomes and increase efficiency. Achieving this potential, however, requires addressing a number of ethical, social, legal, and technical challenges. This report describes these challenges within the context of healthcare and offers directions forward.

Data governance

AI-assisted healthcare will demand better collection and sharing of health data between NHS, industry and academic stakeholders. This requires a data governance system that ensures ethical management of health data and enables its use for the improvement of healthcare delivery. Data sharing must be supported by patients. The recently launched NHS data opt-out programme is an important starting point, and will require monitoring to ensure that it has the transparency and clarity to avoid exploiting the public’s lack of awareness and understanding. Data sharing must also be streamlined and mutually beneficial. Current NHS data sharing practices are disjointed and difficult to negotiate from both industry and NHS perspectives. This issue is complicated by the increasing integration of ’traditional’ health data with that from commercial apps and wearables. Finding approaches to valuate data, and considering how patients, the NHS and its partners can benefit from data sharing is key to developing a data sharing framework. Finally, data sharing should be underpinned by digital infrastructure that enables cybersecurity and accountability.

Digital infrastructure

Developing and deploying AI-assisted healthcare requires high quantity and quality digital data. This demands effective digitisation of the NHS, especially within secondary care, involving not only the transformation of paper-based records into digital data, but also improvement of quality assurance practices and increased data linkage. Beyond data digitisation, broader IT infrastructure also needs upgrading, including the use of innovations such as wearable technology and interoperability between NHS sectors and institutions. This would not only increase data availability for AI development, but also provide patients with seamless healthcare delivery, putting the NHS at the vanguard of healthcare innovation.

Standards

The recent advances in AI and the surrounding hype has meant that the development of AI-assisted healthcare remains haphazard across the industry, with quality being difficult to determine or varying widely. Without adequate product validation, including in

real-world settings, there is a risk of unexpected or unintended performance, such as sociodemographic biases or errors arising from inappropriate human-AI interaction. There is a need to develop standardised ways to probe training data, to agree upon clinically-relevant performance benchmarks, and to design approaches to enable and evaluate algorithm interpretability for productive human-AI interaction. In all of these areas, standardised does not necessarily mean one-size-fits-all. These issues require addressing the specifics of AI within a healthcare context, with consideration of users’ expertise, their environment, and products’ intended use. This calls for a fundamentally interdisciplinary approach, including experts in AI, medicine, ethics, cognitive science, usability design, and ethnography.

Regulations

Despite the recognition of AI-assisted healthcare products as medical devices, current regulatory efforts by the UK Medicines and Healthcare Products Regulatory Agency and the European Commission have yet to be accompanied by detailed guidelines which address questions concerning AI product classification, validation, and monitoring. This is compounded by the uncertainty surrounding Brexit and the UK’s future relationship with the European Medicines Agency. The absence of regulatory clarity risks compromising patient safety and stalling the development of AI-assisted healthcare. Close working partnerships involving regulators, industry members, healthcare institutions, and independent AI-related bodies (for example, as part of regulatory sandboxes) will be needed to enable innovation while ensuring patient safety.

The workforce

AI will be a tool for the healthcare workforce. Harnessing its utility to improve care requires an expanded workforce with the digital skills necessary for both developing AI capability and for working productively with the technology as it becomes commonplace.

Developing capability for AI will involve finding ways to increase the number of clinician-informaticians who can lead the development, procurement and adoption of AI technology while ensuring that innovation remains tied to the human aspect of healthcare delivery. More broadly, healthcare professionals will need to complement their socio-emotional and cognitive skills with training to appropriately interpret information provided by AI products and communicate it effectively to co-workers and patients.

Although much effort has gone into predicting how many jobs will be affected by AI-driven automation, understanding the impact on the healthcare workforce will require examining how jobs will change, not simply how many will change.

Legal liability

AI-assisted healthcare has implications for the legal liability framework: who should be held responsible in the case of a medical error involving AI? Addressing the question of liability will involve understanding how healthcare professionals’ duty of care will be impacted by use of the technology. This is tied to the lack of training standards for healthcare professionals to safely and effectively work with AI, and to the challenges of algorithm interpretability, with ”black-box” systems forcing healthcare professionals to blindly trust or distrust their output. More broadly, it will be important to examine the legal liability of healthcare professionals, NHS trusts and industry partners, raising questions

Recommendations

  1. The NHS, the Centre for Data Ethics and Innovation, and industry and academic partners should conduct a review to understand the obstacles that the NHS and external organisations face around data sharing. They should also develop health data valuation protocols which consider the perspectives of patients, the NHS, commercial organisations, and academia. This work should inform the development of a data sharing framework.
  2. The National Data Guardian and the Department of Health should monitor the NHS data opt-out programme and its approach to transparency and communication, evaluating how the public understands commercial and non-commercial data use and the handling of data at different levels of anonymisation.
  3. The NHS, patient advocacy groups, and commercial organisations should expand public engagement strategies around data governance, including discussions about the value of health data for improving healthcare; public and private sector interactions in the development of AI-assisted healthcare; and the NHS’s strategies around data anonymisation, accountability, and commercial partnerships. Findings from this work should inform the development of a data sharing framework.
  4. The NHS Digital Security Operations Centre should ensure that all NHS organisations comply with cybersecurity standards, including having up-to-date technology.
  5. NHS Digital, the Centre for Data Ethics and Innovation, and the Alan Turing Institute should develop technological approaches to data privacy, auditing, and accountability that could be implemented in the NHS. This should include learning from Global Digital Exemplar trusts in the UK and from international examples such as Estonia.
  6. The NHS should continue to increase the quantity, quality, and diversity of digital health data across trusts. It should consider targeted projects, in partnership with professional medical bodies, that quality-assure and curate datasets for more deployment-ready AI technology. It should also continue to develop its broader IT infrastructure, focusing on interoperability between sectors, institutions, and technologies, and including the end users as central stakeholders.
  7. The Alan Turing Institute, the Ada Lovelace Institute, and academic and industry partners in medicine and AI should develop ethical frameworks and technological approaches for the validation of training data in the healthcare sector, including methods to minimise performance biases and validate continuously-learning algorithms.
  8. The Alan Turing Institute, the Ada Lovelace Institute, and academic and industry partners in medicine and AI should develop standardised approaches for evaluating product performance in the healthcare sector, with consideration for existing human performance standards and products’ intended use.
  9. The Alan Turing Institute, the Ada Lovelace Institute, and academic and industry partners in medicine and AI should develop methods of enabling and evaluating algorithm interpretability in the healthcare sector. This work should involve experts in AI, medicine, ethics, usability design, cognitive science, and ethnography, among others.
  10. Developers of AI products and NHS Commissioners should ensure that usability design remains a top priority in their respective development and procurement of AI-assisted healthcare products.
  11. The Medicines and Healthcare Products Regulatory Agency should establish a digital health unit with expertise in AI and digital products that will work together with manufacturers, healthcare bodies, notified bodies, AI-related organisations, and international forums to advance clear regulatory approaches and guidelines around AI product classification, validation, and monitoring. This should address issues including training data and biases, performance evaluation, algorithm interpretability, and usability.
  12. The Medicines and Healthcare Products Regulatory Agency, the Centre for Data Ethics and Innovation, and industry partners should evaluate regulatory approaches, such as regulatory sandboxing, that can foster innovation in AI-assisted healthcare, ensure patient safety, and inform on-going regulatory development.
  13. The NHS should expand innovation acceleration programmes that bridge healthcare and industry partners, with a focus on increasing validation of AI products in real-world contexts and informing the development of a regulatory framework.
  14. The Medicines and Healthcare Products Regulatory Agency and other Government bodies should arrange a post-Brexit agreement ensuring that UK regulations of medical devices, including AI-assisted healthcare, are aligned as closely as possible to the European framework and that the UK can continue to help shape Europe-wide regulations around this technology.
  15. The General Medical Council, the Medical Royal Colleges, Health Education England, and AI-related bodies should partner with industry and academia on comprehensive examinations of the healthcare sector to assess which, when, and how jobs will be impacted by AI, including analyses of the current strengths, limitations, and workflows of healthcare professionals and broader NHS staff. They should also examine how AI-driven workforce changes will impact patient outcomes.
  16. The Federation of Informatics Professionals and the Faculty of Clinical Informatics should continue to lead and expand standards for health informatics competencies, integrating the relevant aspects of AI into their training, accreditation, and professional development programmes for clinician-informaticians and related professions.
  17. Health Education England should expand training programmes to advance digital and AI-related skills among healthcare professionals. Competency standards for working with AI should be identified for each role and established in accordance with professional registration bodies such as the General Medical Council. Training programmes should ensure that ”un-automatable” socio-emotional and cognitive skills remain an important focus.
  18. The NHS Digital Academy should expand recruitment and training efforts to increase the number of Chief Clinical Information Officers across the NHS, and ensure that the latest AI ethics, standards, and innovations are embedded in their training programme.
  19. Legal experts, ethicists, AI-related bodies, professional medical bodies, and industry should review the implications of AI-assisted healthcare for legal liability. This includes understanding how healthcare professionals’ duty of care will be affected, the role of workforce training and product validation standards, and the potential role of NHS Indemnity and no-fault compensation systems.
  20. AI-related bodies such as the Ada Lovelace Institute, patient advocacy groups and other healthcare stakeholders should lead a public engagement and dialogue strategy to understand the public’s views on liability for AI-assisted healthcare.

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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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