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

#TUBiol5227: Biomarkers & Biotargets: Genetic Testing and Bioethics

Curator: Stephen J. Williams, Ph.D.

The advent of direct to consumer (DTC) genetic testing and the resultant rapid increase in its popularity as well as companies offering such services has created some urgent and unique bioethical challenges surrounding this niche in the marketplace. At first, most DTC companies like 23andMe and Ancestry.com offered non-clinical or non-FDA approved genetic testing as a way for consumers to draw casual inferences from their DNA sequence and existence of known genes that are linked to disease risk, or to get a glimpse of their familial background. However, many issues arose, including legal, privacy, medical, and bioethical issues. Below are some articles which will explain and discuss many of these problems associated with the DTC genetic testing market as well as some alternatives which may exist.

‘Direct-to-Consumer (DTC) Genetic Testing Market to hit USD 2.5 Bn by 2024’ by Global Market Insights

This post has the following link to the market analysis of the DTC market (https://www.gminsights.com/pressrelease/direct-to-consumer-dtc-genetic-testing-market). Below is the highlights of the report.

As you can see,this market segment appears to want to expand into the nutritional consulting business as well as targeted biomarkers for specific diseases.

Rising incidence of genetic disorders across the globe will augment the market growth

Increasing prevalence of genetic disorders will propel the demand for direct-to-consumer genetic testing and will augment industry growth over the projected timeline. Increasing cases of genetic diseases such as breast cancer, achondroplasia, colorectal cancer and other diseases have elevated the need for cost-effective and efficient genetic testing avenues in the healthcare market.
 

For instance, according to the World Cancer Research Fund (WCRF), in 2018, over 2 million new cases of cancer were diagnosed across the globe. Also, breast cancer is stated as the second most commonly occurring cancer. Availability of superior quality and advanced direct-to-consumer genetic testing has drastically reduced the mortality rates in people suffering from cancer by providing vigilant surveillance data even before the onset of the disease. Hence, the aforementioned factors will propel the direct-to-consumer genetic testing market overt the forecast timeline.
 

DTC Genetic Testing Market By Technology

Get more details on this report – Request Free Sample PDF
 

Nutrigenomic Testing will provide robust market growth

The nutrigenomic testing segment was valued over USD 220 million market value in 2019 and its market will witness a tremendous growth over 2020-2028. The growth of the market segment is attributed to increasing research activities related to nutritional aspects. Moreover, obesity is another major factor that will boost the demand for direct-to-consumer genetic testing market.
 

Nutrigenomics testing enables professionals to recommend nutritional guidance and personalized diet to obese people and help them to keep their weight under control while maintaining a healthy lifestyle. Hence, above mentioned factors are anticipated to augment the demand and adoption rate of direct-to-consumer genetic testing through 2028.
 

Browse key industry insights spread across 161 pages with 126 market data tables & 10 figures & charts from the report, “Direct-To-Consumer Genetic Testing Market Size By Test Type (Carrier Testing, Predictive Testing, Ancestry & Relationship Testing, Nutrigenomics Testing), By Distribution Channel (Online Platforms, Over-the-Counter), By Technology (Targeted Analysis, Single Nucleotide Polymorphism (SNP) Chips, Whole Genome Sequencing (WGS)), Industry Analysis Report, Regional Outlook, Application Potential, Price Trends, Competitive Market Share & Forecast, 2020 – 2028” in detail along with the table of contents:
https://www.gminsights.com/industry-analysis/direct-to-consumer-dtc-genetic-testing-market
 

Targeted analysis techniques will drive the market growth over the foreseeable future

Based on technology, the DTC genetic testing market is segmented into whole genome sequencing (WGS), targeted analysis, and single nucleotide polymorphism (SNP) chips. The targeted analysis market segment is projected to witness around 12% CAGR over the forecast period. The segmental growth is attributed to the recent advancements in genetic testing methods that has revolutionized the detection and characterization of genetic codes.
 

Targeted analysis is mainly utilized to determine any defects in genes that are responsible for a disorder or a disease. Also, growing demand for personalized medicine amongst the population suffering from genetic diseases will boost the demand for targeted analysis technology. As the technology is relatively cheaper, it is highly preferred method used in direct-to-consumer genetic testing procedures. These advantages of targeted analysis are expected to enhance the market growth over the foreseeable future.
 

Over-the-counter segment will experience a notable growth over the forecast period

The over-the-counter distribution channel is projected to witness around 11% CAGR through 2028. The segmental growth is attributed to the ease in purchasing a test kit for the consumers living in rural areas of developing countries. Consumers prefer over-the-counter distribution channel as they are directly examined by regulatory agencies making it safer to use, thereby driving the market growth over the forecast timeline.
 

Favorable regulations provide lucrative growth opportunities for direct-to-consumer genetic testing

Europe direct-to-consumer genetic testing market held around 26% share in 2019 and was valued at around USD 290 million. The regional growth is due to elevated government spending on healthcare to provide easy access to genetic testing avenues. Furthermore, European regulatory bodies are working on improving the regulations set on the direct-to-consumer genetic testing methods. Hence, the above-mentioned factors will play significant role in the market growth.
 

Focus of market players on introducing innovative direct-to-consumer genetic testing devices will offer several growth opportunities

Few of the eminent players operating in direct-to-consumer genetic testing market share include Ancestry, Color Genomics, Living DNA, Mapmygenome, Easy DNA, FamilytreeDNA (Gene By Gene), Full Genome Corporation, Helix OpCo LLC, Identigene, Karmagenes, MyHeritage, Pathway genomics, Genesis Healthcare, and 23andMe. These market players have undertaken various business strategies to enhance their financial stability and help them evolve as leading companies in the direct-to-consumer genetic testing industry.
 

For example, in November 2018, Helix launched a new genetic testing product, DNA discovery kit, that allows customer to delve into their ancestry. This development expanded the firm’s product portfolio, thereby propelling industry growth in the market.

The following posts discuss bioethical issues related to genetic testing and personalized medicine from a clinicians and scientisit’s perspective

Question: Each of these articles discusses certain bioethical issues although focuses on personalized medicine and treatment. Given your understanding of the robust process involved in validating clinical biomarkers and the current state of the DTC market, how could DTC testing results misinform patients and create mistrust in the physician-patient relationship?

Personalized Medicine, Omics, and Health Disparities in Cancer:  Can Personalized Medicine Help Reduce the Disparity Problem?

Diversity and Health Disparity Issues Need to be Addressed for GWAS and Precision Medicine Studies

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

The following posts discuss the bioethical concerns of genetic testing from a patient’s perspective:

Ethics Behind Genetic Testing in Breast Cancer: A Webinar by Laura Carfang of survivingbreastcancer.org

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

23andMe Product can be obtained for Free from a new app called Genes for Good: UMich’s Facebook-based Genomics Project

Question: If you are developing a targeted treatment with a companion diagnostic, what bioethical concerns would you address during the drug development process to ensure fair, equitable and ethical treatment of all patients, in trials as well as post market?

Articles on Genetic Testing, Companion Diagnostics and Regulatory Mechanisms

Centers for Medicare & Medicaid Services announced that the federal healthcare program will cover the costs of cancer gene tests that have been approved by the Food and Drug Administration

Real Time Coverage @BIOConvention #BIO2019: Genome Editing and Regulatory Harmonization: Progress and Challenges

New York Times vs. Personalized Medicine? PMC President: Times’ Critique of Streamlined Regulatory Approval for Personalized Treatments ‘Ignores Promising Implications’ of Field

Live Conference Coverage @Medcitynews Converge 2018 Philadelphia: Early Diagnosis Through Predictive Biomarkers, NonInvasive Testing

Protecting Your Biotech IP and Market Strategy: Notes from Life Sciences Collaborative 2015 Meeting

Question: What type of regulatory concerns should one have during the drug development process in regards to use of biomarker testing? From the last article on Protecting Your IP how important is it, as a drug developer, to involve all payers during the drug development process?

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Science Policy Forum: Should we trust healthcare explanations from AI predictive systems?

Some in industry voice their concerns

Curator: Stephen J. Williams, PhD

Post on AI healthcare and explainable AI

   In a Policy Forum article in ScienceBeware explanations from AI in health care”, Boris Babic, Sara Gerke, Theodoros Evgeniou, and Glenn Cohen discuss the caveats on relying on explainable versus interpretable artificial intelligence (AI) and Machine Learning (ML) algorithms to make complex health decisions.  The FDA has already approved some AI/ML algorithms for analysis of medical images for diagnostic purposes.  These have been discussed in prior posts on this site, as well as issues arising from multi-center trials.  The authors of this perspective article argue that choice of type of algorithm (explainable versus interpretable) algorithms may have far reaching consequences in health care.

Summary

Artificial intelligence and machine learning (AI/ML) algorithms are increasingly developed in health care for diagnosis and treatment of a variety of medical conditions (1). However, despite the technical prowess of such systems, their adoption has been challenging, and whether and how much they will actually improve health care remains to be seen. A central reason for this is that the effectiveness of AI/ML-based medical devices depends largely on the behavioral characteristics of its users, who, for example, are often vulnerable to well-documented biases or algorithmic aversion (2). Many stakeholders increasingly identify the so-called black-box nature of predictive algorithms as the core source of users’ skepticism, lack of trust, and slow uptake (3, 4). As a result, lawmakers have been moving in the direction of requiring the availability of explanations for black-box algorithmic decisions (5). Indeed, a near-consensus is emerging in favor of explainable AI/ML among academics, governments, and civil society groups. Many are drawn to this approach to harness the accuracy benefits of noninterpretable AI/ML such as deep learning or neural nets while also supporting transparency, trust, and adoption. We argue that this consensus, at least as applied to health care, both overstates the benefits and undercounts the drawbacks of requiring black-box algorithms to be explainable.

Source: https://science.sciencemag.org/content/373/6552/284?_ga=2.166262518.995809660.1627762475-1953442883.1627762475

Types of AI/ML Algorithms: Explainable and Interpretable algorithms

  1.  Interpretable AI: A typical AI/ML task requires constructing algorithms from vector inputs and generating an output related to an outcome (like diagnosing a cardiac event from an image).  Generally the algorithm has to be trained on past data with known parameters.  When an algorithm is called interpretable, this means that the algorithm uses a transparent or “white box” function which is easily understandable. Such example might be a linear function to determine relationships where parameters are simple and not complex.  Although they may not be as accurate as the more complex explainable AI/ML algorithms, they are open, transparent, and easily understood by the operators.
  2. Explainable AI/ML:  This type of algorithm depends upon multiple complex parameters and takes a first round of predictions from a “black box” model then uses a second algorithm from an interpretable function to better approximate outputs of the first model.  The first algorithm is trained not with original data but based on predictions resembling multiple iterations of computing.  Therefore this method is more accurate or deemed more reliable in prediction however is very complex and is not easily understandable.  Many medical devices that use an AI/ML algorithm use this type.  An example is deep learning and neural networks.

The purpose of both these methodologies is to deal with problems of opacity, or that AI predictions based from a black box undermines trust in the AI.

For a deeper understanding of these two types of algorithms see here:

https://www.kdnuggets.com/2018/12/machine-learning-explainability-interpretability-ai.html

or https://www.bmc.com/blogs/machine-learning-interpretability-vs-explainability/

(a longer read but great explanation)

From the above blog post of Jonathan Johnson

  • How interpretability is different from explainability
  • Why a model might need to be interpretable and/or explainable
  • Who is working to solve the black box problem—and how

What is interpretability?

Does Chipotle make your stomach hurt? Does loud noise accelerate hearing loss? Are women less aggressive than men? If a machine learning model can create a definition around these relationships, it is interpretable.

All models must start with a hypothesis. Human curiosity propels a being to intuit that one thing relates to another. “Hmm…multiple black people shot by policemen…seemingly out of proportion to other races…something might be systemic?” Explore.

People create internal models to interpret their surroundings. In the field of machine learning, these models can be tested and verified as either accurate or inaccurate representations of the world.

Interpretability means that the cause and effect can be determined.

What is explainability?

ML models are often called black-box models because they allow a pre-set number of empty parameters, or nodes, to be assigned values by the machine learning algorithm. Specifically, the back-propagation step is responsible for updating the weights based on its error function.

To predict when a person might die—the fun gamble one might play when calculating a life insurance premium, and the strange bet a person makes against their own life when purchasing a life insurance package—a model will take in its inputs, and output a percent chance the given person has at living to age 80.

Below is an image of a neural network. The inputs are the yellow; the outputs are the orange. Like a rubric to an overall grade, explainability shows how significant each of the parameters, all the blue nodes, contribute to the final decision.

In this neural network, the hidden layers (the two columns of blue dots) would be the black box.

For example, we have these data inputs:

  • Age
  • BMI score
  • Number of years spent smoking
  • Career category

If this model had high explainability, we’d be able to say, for instance:

  • The career category is about 40% important
  • The number of years spent smoking weighs in at 35% important
  • The age is 15% important
  • The BMI score is 10% important

Explainability: important, not always necessary

Explainability becomes significant in the field of machine learning because, often, it is not apparent. Explainability is often unnecessary. A machine learning engineer can build a model without ever having considered the model’s explainability. It is an extra step in the building process—like wearing a seat belt while driving a car. It is unnecessary for the car to perform, but offers insurance when things crash.

The benefit a deep neural net offers to engineers is it creates a black box of parameters, like fake additional data points, that allow a model to base its decisions against. These fake data points go unknown to the engineer. The black box, or hidden layers, allow a model to make associations among the given data points to predict better results. For example, if we are deciding how long someone might have to live, and we use career data as an input, it is possible the model sorts the careers into high- and low-risk career options all on its own.

Perhaps we inspect a node and see it relates oil rig workers, underwater welders, and boat cooks to each other. It is possible the neural net makes connections between the lifespan of these individuals and puts a placeholder in the deep net to associate these. If we were to examine the individual nodes in the black box, we could note this clustering interprets water careers to be a high-risk job.

In the previous chart, each one of the lines connecting from the yellow dot to the blue dot can represent a signal, weighing the importance of that node in determining the overall score of the output.

  • If that signal is high, that node is significant to the model’s overall performance.
  • If that signal is low, the node is insignificant.

With this understanding, we can define explainability as:

Knowledge of what one node represents and how important it is to the model’s performance.

So how does choice of these two different algorithms make a difference with respect to health care and medical decision making?

The authors argue: 

“Regulators like the FDA should focus on those aspects of the AI/ML system that directly bear on its safety and effectiveness – in particular, how does it perform in the hands of its intended users?”

A suggestion for

  • Enhanced more involved clinical trials
  • Provide individuals added flexibility when interacting with a model, for example inputting their own test data
  • More interaction between user and model generators
  • Determining in which situations call for interpretable AI versus explainable (for instance predicting which patients will require dialysis after kidney damage)

Other articles on AI/ML in medicine and healthcare on this Open Access Journal include

Applying AI to Improve Interpretation of Medical Imaging

Real Time Coverage @BIOConvention #BIO2019: Machine Learning and Artificial Intelligence #AI: Realizing Precision Medicine One Patient at a Time

LIVE Day Three – World Medical Innovation Forum ARTIFICIAL INTELLIGENCE, Boston, MA USA, Monday, April 10, 2019

Cardiac MRI Imaging Breakthrough: The First AI-assisted Cardiac MRI Scan Solution, HeartVista Receives FDA 510(k) Clearance for One Click™ Cardiac MRI Package

 

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The Regulatory challenge in adopting AI

Author and Curator: Dror Nir, PhD

3.4.3

3.4.3   The Regulatory challenge in adopting AI, Volume 2 (Volume Two: Latest in Genomics Methodologies for Therapeutics: Gene Editing, NGS and BioInformatics, Simulations and the Genome Ontology), Part 2: CRISPR for Gene Editing and DNA Repair

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/

Read Full Post »

“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

Read Full Post »

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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