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Posts Tagged ‘FDA’

#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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New resource for finding FDA-approved medical devices that incorporate AI

Reporter: Satwik Sunnam, Research Assistant 3, One year Internship in Medical Text Analysis with Deep Learning NLP

This article reports List of FDA approved medical devices that are employing Artificial Intelligence and Machine Learning (AI/ML)

The FDA is providing this initial list of AI/ML-enabled medical devices marketed in the United States as a resource to the public about these devices and the FDA’s work in this area.

Contents of this list: This initial list contains publicly available information on AI/ML-enabled devices. The FDA assembled this list by searching FDA’s publicly-facing information, as well as by reviewing information in the publicly available resources cited below (*) and in other publicly available materials published by the specific manufacturers.

This list is not meant to be an exhaustive or comprehensive resource of AI/ML-enabled medical devices. Rather, it is a list of AI/ML-enabled devices across medical disciplines, based on publicly available information.

Updates to this list: The FDA plans to update this list on a periodic basis based on publicly available information. Send questions or feedback on this list to digitalhealth@fda.hhs.gov.

AI/ML-Enabled Medical Devices

Devices are listed in reverse chronological order by Date of Final Decision. To change the sort order, click the arrows in the column headings.

Use the Submission Number link to display the approval, authorization, or clearance information for the device in the appropriate FDA database. The database page will include a link to the FDA’s publicly available information.

Devices are listed in reverse chronological order by Date of Final Decision. To change the sort order, click the arrows in the column headings.

Use the Submission Number link to display the approval, authorization, or clearance information for the device in the appropriate FDA database. The database page will include a link to the FDA’s publicly available information.

FDA Final Decision in 2021:

List of AI/ML-enabled medical devices marketed in the United States

Date of Final Decision Submission NumberDeviceCompanyPanel (Lead)
06/17/2021K203514Precise PositionPhilips Healthcare (Suzhou) Co., Ltd.Radiology
06/16/2021K202718Qmenta Care Platform FamilyMint Labs, Inc., D/B/A. QMENTARadiology
06/11/2021K210484LINQ II Insertable Cardiac Monitor, Zelda AI ECG Classification SystemMedtronic, Inc.Cardiovascular
06/10/2021K203629IDx-DRDigital Diagnostics Inc.Ophthalmic
06/02/2021DEN200069Cognoa Asd Diagnosis AidCognoa, Inc.Neurology
05/19/2021K210237CINA CHESTAvicenna.AIRadiology
04/30/2021K210001HYPER AiRShanghai United Imaging Healthcare Co.,Ltd.Radiology
04/23/2021K203314Cartesion Prime (PCD-1000A/3) V10.8Canon Medical Systems CorporationRadiology
04/23/2021K203502MEDO-ThyroidMEDO DX Pte. Ltd.Radiology
04/21/2021K210556Preview ShoulderGenesis Software InnovationsRadiology
04/20/2021K203610Automatic Anatomy Recognition (AAR)Quantitative Radiology Solutions, LLCRadiology
04/19/2021K203469AI SegmentationVarian Medical SystemsRadiology
04/16/2021K203517Saige-QDeepHealth, Inc.Radiology
04/14/2021K202992BriefCase, RIB Fractures Triage (RibFx)Aidoc Medical, Ltd.Radiology
04/09/2021DEN200055GI GeniusCosmo Artificial Intelligence – AI, Ltd.Gastroenterology-Urology
04/02/2021K202441Eclipse II with Smart Noise CancellationCarestream Health, Inc.Radiology
04/01/2021DEN200038Gili Pro Biosensor (Also Known as Gili Biosensor System)Continuse Biometrics Ltd.Cardiovascular
03/31/2021K203258syngo.CT Lung CAD (Version VD20)Siemens Healthcare GmbHRadiology
03/31/2021K203443MAGNETOM Vida, MAGNETOM Sola, MAGNETOM Lumina, MAGNETOM Altea with syngo MR XA31ASiemens Medical Solutions USA, Inc.Radiology
03/31/2021K210071SIS System (Version 5.1.0)Surgical Information Sciences, Inc.Radiology
03/26/2021DEN200019Oxehealth Vital SignsOxehealth LimitedCardiovascular
03/24/2021K203225Aquilion ONE (TSX‐306A/3) V10.4 with Spectral Imaging SystemCanon Medical Systems CorporationRadiology
03/23/2021K210209Viz ICHViz.Ai, Inc.Radiology
03/19/2021K203235VBrainVysioneer Inc.Radiology
03/09/2021K203256Imbio RV/LV SoftwareImbio, LLCRadiology
03/05/2021K202300Optellum Virtual Nodule Clinic, Optellum Software, Optellum PlatformOptellum LtdRadiology
03/01/2021DEN200022Analytic for Hemodynamic Instability (AHI)Fifth Eye Inc.Cardiovascular
02/25/2021K202990NinesMeasureNines, Inc.Radiology
02/25/2021K203578OTIS 2.1 Optical Coherence Tomography System, THiA Optical Coherence Tomography SystemPerimeter Medical Imaging AI, Inc.General And Plastic Surgery
02/19/2021K202212TruplanCircle Cardiovascular Imaging Inc.Radiology
02/09/2021K203103Synapse 3D, Synapse 3D Base Tools v6.1Fujifilm CorporationRadiology
02/05/2021K210053LVivo Software ApplicationDiA Imaging Analysis Ltd.Radiology
01/29/2021K201411Visage Breast DensityVisage Imaging GmbHRadiology
01/15/2021K193271UAI Easytriage-RibShanghai United Imaging Intelligence Co., Ltd.Radiology
01/14/2021K202700ART-PlanTheraPanaceaRadiology
01/12/2021K201836Aquilion Lightning (TSX-036A/7) V10.2 With AiCE-ICanon Medical Systems CorporationRadiology
01/09/2021K200717CLEWICU System (ClewICUserver and ClewICUnitor)Clew Medical Ltd.Cardiovascular
01/07/2021K202414BrainInsightHyperfine Research, Inc.Radiology

SOURCE

https://www.fda.gov/medical-devices/software-medical-device-samd/artificial-intelligence-and-machine-learning-aiml-enabled-medical-devices?utm_medium=email

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

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

Reporter: Aviva Lev-Ari, PhD, RN

Al is on the way to lead critical ED decisions on CT

Curator and Reporter: Dr. Premalata Pati, Ph.D., Postdoc

Applying AI to Improve Interpretation of Medical Imaging

Author and Curator: Dror Nir, PhD

Developing Deep Learning Models (DL) for the Instant Prediction of Patients with Epilepsy

Reporter: Srinivas Sriram, Research Assistant I

Science Policy Forum: Should we trust healthcare explanations from AI predictive systems – Some in industry voice their concerns

Curator: Stephen J. Williams, PhD

Al System Used to Detect Lung Cancer

Reporter: Irina Robu, Ph.D.

The Future of Speech-Based Human-Computer Interaction
Reporter: Ethan Coomber

Deep Medicine: How Artificial Intelligence Can Make Health Care Human Again
Reporter: Aviva Lev-Ari, PhD, RN

Supporting the elderly: A caring robot with ‘emotions’ and memory
Reporter: Aviva Lev-Ari, PhD, RN

Developing Deep Learning Models (DL) for Classifying Emotions through Brainwaves
Reporter: Abhisar Anand, Research Assistant I

Read Full Post »

NCCN Shares Latest Expert Recommendations for Prostate Cancer in Spanish and Portuguese

Reporter: Stephen J. Williams, Ph.D.

Currently many biomedical texts and US government agency guidelines are only offered in English or only offered in different languages upon request. However Spanish is spoken in a majority of countries worldwide and medical text in that language would serve as an under-served need. In addition, Portuguese is the main language in the largest country in South America, Brazil.

The LPBI Group and others have noticed this need for medical translation to other languages. Currently LPBI Group is translating their medical e-book offerings into Spanish (for more details see https://pharmaceuticalintelligence.com/vision/)

Below is an article on The National Comprehensive Cancer Network’s decision to offer their cancer treatment guidelines in Spanish and Portuguese.

Source: https://www.nccn.org/home/news/newsdetails?NewsId=2871

PLYMOUTH MEETING, PA [8 September, 2021] — The National Comprehensive Cancer Network® (NCCN®)—a nonprofit alliance of leading cancer centers in the United States—announces recently-updated versions of evidence- and expert consensus-based guidelines for treating prostate cancer, translated into Spanish and Portuguese. NCCN Clinical Practice Guidelines in Oncology (NCCN Guidelines®) feature frequently updated cancer treatment recommendations from multidisciplinary panels of experts across NCCN Member Institutions. Independent studies have repeatedly found that following these recommendations correlates with better outcomes and longer survival.

“Everyone with prostate cancer should have access to care that is based on current and reliable evidence,” said Robert W. Carlson, MD, Chief Executive Officer, NCCN. “These updated translations—along with all of our other translated and adapted resources—help us to define and advance high-quality, high-value, patient-centered cancer care globally, so patients everywhere can live better lives.”

Prostate cancer is the second most commonly occurring cancer in men, impacting more than a million people worldwide every year.[1] In 2020, the NCCN Guidelines® for Prostate Cancer were downloaded more than 200,000 times by people outside of the United States. Approximately 47 percent of registered users for NCCN.org are located outside the U.S., with Brazil, Spain, and Mexico among the top ten countries represented.

“NCCN Guidelines are incredibly helpful resources in the work we do to ensure cancer care across Latin America meets the highest standards,” said Diogo Bastos, MD, and Andrey Soares, MD, Chair and Scientific Director of the Genitourinary Group of The Latin American Cooperative Oncology Group (LACOG). The organization has worked with NCCN in the past to develop Latin American editions of the NCCN Guidelines for Breast Cancer, Colon Cancer, Non-Small Cell Lung Cancer, Prostate Cancer, Multiple Myeloma, and Rectal Cancer, and co-hosted a webinar on “Management of Prostate Cancer for Latin America” earlier this year. “We appreciate all of NCCN’s efforts to make sure these gold-standard recommendations are accessible to non-English speakers and applicable for varying circumstances.”

NCCN also publishes NCCN Guidelines for Patients®, containing the same treatment information in non-medical terms, intended for patients and caregivers. The NCCN Guidelines for Patients: Prostate Cancer were found to be among the most trustworthy sources of information online according to a recent international study. These patient guidelines have been divided into two books, covering early and advanced prostate cancer; both have been translated into Spanish and Portuguese as well.

NCCN collaborates with organizations across the globe on resources based on the NCCN Guidelines that account for local accessibility, consideration of metabolic differences in populations, and regional regulatory variation. They can be downloaded free-of-charge for non-commercial use at NCCN.org/global or via the Virtual Library of NCCN Guidelines App. Learn more and join the conversation with the hashtag #NCCNGlobal.


[1] Bray F, Ferlay J, Soerjomataram I, Siegel RL, Torre LA, Jemal A. Global Cancer Statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin, in press. The online GLOBOCAN 2018 database is accessible at http://gco.iarc.fr/, as part of IARC’s Global Cancer Observatory.

About the National Comprehensive Cancer Network

The National Comprehensive Cancer Network® (NCCN®) is a not-for-profit alliance of leading cancer centers devoted to patient care, research, and education. NCCN is dedicated to improving and facilitating quality, effective, efficient, and accessible cancer care so patients can live better lives. The NCCN Clinical Practice Guidelines in Oncology (NCCN Guidelines®) provide transparent, evidence-based, expert consensus recommendations for cancer treatment, prevention, and supportive services; they are the recognized standard for clinical direction and policy in cancer management and the most thorough and frequently-updated clinical practice guidelines available in any area of medicine. The NCCN Guidelines for Patients® provide expert cancer treatment information to inform and empower patients and caregivers, through support from the NCCN Foundation®. NCCN also advances continuing educationglobal initiativespolicy, and research collaboration and publication in oncology. Visit NCCN.org for more information and follow NCCN on Facebook @NCCNorg, Instagram @NCCNorg, and Twitter @NCCN.

Please see LPBI Group’s efforts in medical text translation and Natural Language Processing of Medical Text at

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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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Did FDA Reverse Course on Convalescent Plasma Therapy for COVID-19?

Reporter: Stephen J. Williams, PhD

 

Starting with a timeline of recent announcements by the FDA on convalescent plasma therapy

April 16, 2020

FDA STATEMENT

Coronavirus (COVID-19) Update: FDA Encourages Recovered Patients to Donate Plasma for Development of Blood-Related Therapies

 

As part of the all-of-America approach to fighting the COVID-19 pandemic, the U.S. Food and Drug Administration has been working with partners across the U.S. government, academia and industry to expedite the development and availability of critical medical products to treat this novel virus. Today, we are providing an update on one potential treatment called convalescent plasma and encouraging those who have recovered from COVID-19 to donate plasma to help others fight this disease.

Convalescent plasma is an antibody-rich product made from blood donated by people who have recovered from the disease caused by the virus. Prior experience with respiratory viruses and limited data that have emerged from China suggest that convalescent plasma has the potential to lessen the severity or shorten the length of illness caused by COVID-19. It is important that we evaluate this potential therapy in the context of clinical trials, through expanded access, as well as facilitate emergency access for individual patients, as appropriate.

The response to the agency’s recently announced national efforts to facilitate the development of and access to convalescent plasma has been tremendous. More than 1,040 sites and 950 physician investigators nationwide have signed on to participate in the Mayo Clinic-led expanded access protocol. A number of clinical trials are also taking place to evaluate the safety and efficacy of convalescent plasma and the FDA has granted numerous single patient emergency investigational new drug (eIND) applications as well.

Source: https://www.fda.gov/news-events/press-announcements/coronavirus-covid-19-update-fda-encourages-recovered-patients-donate-plasma-development-blood

August 23, 2020

 

Recommendations for Investigational COVID-19 Convalescent Plasma

 

  • FDA issues guidelines on clinical trials and obtaining emergency enrollment concerning convalescent plasma

FDA has issued guidance to provide recommendations to health care providers and investigators on the administration and study of investigational convalescent plasma collected from individuals who have recovered from COVID-19 (COVID-19 convalescent plasma) during the public health emergency.

The guidance provides recommendations on the following:

Because COVID-19 convalescent plasma has not yet been approved for use by FDA, it is regulated as an investigational product.  A health care provider must participate in one of the pathways described below.  FDA does not collect COVID-19 convalescent plasma or provide COVID-19 convalescent plasma.  Health care providers or acute care facilities should instead obtain COVID-19 convalescent plasma from an FDA-registered blood establishment.

Excerpts from the guidance document are provided below.

Background

The Food and Drug Administration (FDA or Agency) plays a critical role in protecting the United States (U.S.) from threats including emerging infectious diseases, such as the Coronavirus Disease 2019 (COVID-19) pandemic.  FDA is committed to providing timely guidance to support response efforts to this pandemic.

One investigational treatment being explored for COVID-19 is the use of convalescent plasma collected from individuals who have recovered from COVID-19.  Convalescent plasma that contains antibodies to severe acute respiratory syndrome coronavirus 2 or SARS-CoV-2 (the virus that causes COVID-19) is being studied for administration to patients with COVID-19. Use of convalescent plasma has been studied in outbreaks of other respiratory infections, including the 2003 SARS-CoV-1 epidemic, the 2009-2010 H1N1 influenza virus pandemic, and the 2012 MERS-CoV epidemic.

Although promising, convalescent plasma has not yet been shown to be safe and effective as a treatment for COVID-19. Therefore, it is important to study the safety and efficacy of COVID-19 convalescent plasma in clinical trials.

Pathways for Use of Investigational COVID-19 Convalescent Plasma

The following pathways are available for administering or studying the use of COVID-19 convalescent plasma:

  1. Clinical Trials

Investigators wishing to study the use of convalescent plasma in a clinical trial should submit requests to FDA for investigational use under the traditional IND regulatory pathway (21 CFR Part 312). CBER’s Office of Blood Research and Review is committed to engaging with sponsors and reviewing such requests expeditiously. During the COVID-19 pandemic, INDs may be submitted via email to CBERDCC_eMailSub@fda.hhs.gov.

  1. Expanded Access

An IND application for expanded access is an alternative for use of COVID-19 convalescent plasma for patients with serious or immediately life-threatening COVID-19 disease who are not eligible or who are unable to participate in randomized clinical trials (21 CFR 312.305). FDA has worked with multiple federal partners and academia to open an expanded access protocol to facilitate access to COVID-19 convalescent plasma across the nation. Access to this investigational product may be available through participation of acute care facilities in an investigational expanded access protocol under an IND that is already in place.

Currently, the following protocol is in place: National Expanded Access Treatment Protocol

  1. Single Patient Emergency IND

Although participation in clinical trials or an expanded access program are ways for patients to obtain access to convalescent plasma, for various reasons these may not be readily available to all patients in potential need. Therefore, given the public health emergency that the COVID-19 pandemic presents, and while clinical trials are being conducted and a national expanded access protocol is available, FDA also is facilitating access to COVID-19 convalescent plasma for use in patients with serious or immediately life-threatening COVID-19 infections through the process of the patient’s physician requesting a single patient emergency IND (eIND) for the individual patient under 21 CFR 312.310. This process allows the use of an investigational drug for the treatment of an individual patient by a licensed physician upon FDA authorization, if the applicable regulatory criteria are met.  Note, in such case, a licensed physician seeking to administer COVID-19 convalescent plasma to an individual patient must request the eIND (see 21 CFR 312.310(b)).

To Obtain a Single Patient Emergency IND  

The requesting physician may contact FDA by completing Form FDA 3926 (https://www.fda.gov/media/98616/download) and submitting the form by email to CBER_eIND_Covid-19@FDA.HHS.gov.

FACT SHEET FOR PATIENTS AND PARENTS/CAREGIVERS EMERGENCY USE AUTHORIZATION (EUA) OF COVID-19 CONVALESCENT PLASMA FOR TREATMENT OF COVID-19 IN HOSPITALIZED PATIENTS

  • FDA issues fact sheet for patients on donating plasma

August 23, 2020

 

FDA Issues Emergency Use Authorization for Convalescent Plasma as Potential Promising COVID–19 Treatment, Another Achievement in Administration’s Fight Against Pandemic

 

Today, the U.S. Food and Drug Administration issued an emergency use authorization (EUA) for investigational convalescent plasma for the treatment of COVID-19 in hospitalized patients as part of the agency’s ongoing efforts to fight COVID-19. Based on scientific evidence available, the FDA concluded, as outlined in its decision memorandum, this product may be effective in treating COVID-19 and that the known and potential benefits of the product outweigh the known and potential risks of the product.

Today’s action follows the FDA’s extensive review of the science and data generated over the past several months stemming from efforts to facilitate emergency access to convalescent plasma for patients as clinical trials to definitively demonstrate safety and efficacy remain ongoing.

The EUA authorizes the distribution of COVID-19 convalescent plasma in the U.S. and its administration by health care providers, as appropriate, to treat suspected or laboratory-confirmed COVID-19 in hospitalized patients with COVID-19.

Alex Azar, Health and Human Services Secretary:
“The FDA’s emergency authorization for convalescent plasma is a milestone achievement in President Trump’s efforts to save lives from COVID-19,” said Secretary Azar. “The Trump Administration recognized the potential of convalescent plasma early on. Months ago, the FDA, BARDA, and private partners began work on making this product available across the country while continuing to evaluate data through clinical trials. Our work on convalescent plasma has delivered broader access to the product than is available in any other country and reached more than 70,000 American patients so far. We are deeply grateful to Americans who have already donated and encourage individuals who have recovered from COVID-19 to consider donating convalescent plasma.”

Stephen M. Hahn, M.D., FDA Commissioner:
“I am committed to releasing safe and potentially helpful treatments for COVID-19 as quickly as possible in order to save lives. We’re encouraged by the early promising data that we’ve seen about convalescent plasma. The data from studies conducted this year shows that plasma from patients who’ve recovered from COVID-19 has the potential to help treat those who are suffering from the effects of getting this terrible virus,” said Dr. Hahn. “At the same time, we will continue to work with researchers to continue randomized clinical trials to study the safety and effectiveness of convalescent plasma in treating patients infected with the novel coronavirus.”

Scientific Evidence on Convalescent Plasma

Based on an evaluation of the EUA criteria and the totality of the available scientific evidence, the FDA’s Center for Biologics Evaluation and Research determined that the statutory criteria for issuing an EUA criteria were met.

The FDA determined that it is reasonable to believe that COVID-19 convalescent plasma may be effective in lessening the severity or shortening the length of COVID-19 illness in some hospitalized patients. The agency also determined that the known and potential benefits of the product, when used to treat COVID-19, outweigh the known and potential risks of the product and that that there are no adequate, approved, and available alternative treatments.

 

August 24, 2020

Donate COVID-19 Plasma

 

  • FDA posts video and blog about how to donate plasms if you had been infected with COVID

 

https://youtu.be/PlX15rWdBbY

 

 

Please go to https://www.fda.gov/emergency-preparedness-and-response/coronavirus-disease-2019-covid-19/donate-covid-19-plasma

to read more from FDA

 

 

August 25, 2020

 

CLINICAL MEMORANDUM From: , OBRR/DBCD/CRS To: , OBRR Through: , OBRR/DBCD , OBRR/DBCD , OBRR/DBCD/CRS Re: EUA 26382: Emergency Use Authorization (EUA) Request (original request 8/12/20; amended request 8/23/20) Product: COVID-19 Convalescent Plasma Items reviewed: EUA request Fact Sheet for Health Care Providers Fact Sheet for Recipients Sponsor: Robert Kadlec, M.D. Assistant Secretary for Preparedness and Response (ASPR) Office of Assistant Secretary for Preparedness and Response (ASPR) U.S. Department of Health and Human Services (HHS) EXECUTIVE SUMMARY COVID-19 Convalescent Plasma (CCP), an unapproved biological product, is proposed for use under an Emergency Use Authorization (EUA) under section 564 of the Federal Food, Drug, and Cosmetic Act (the Act),(21 USC 360bbb-3) as a passive immune therapy for the treatment of hospitalized patients with COVID-19, a serious or life-threatening disease. There currently is no adequate, approved, and available alternative to CCP for treating COVID-19. The sponsor has pointed to four lines of evidence to support that CCP may be effective in the treatment of hospitalized patients with COVID-19: 1) History of convalescent plasma for respiratory coronaviruses; 2) Evidence of preclinical safety and efficacy in animal models; 3) Published studies of the safety and efficacy of CCP; and 4) Data on safety and efficacy from the National Expanded Access Treatment Protocol (EAP) sponsored by the Mayo Clinic. Considering the totality of the scientific evidence presented in the EUA, I conclude that current data for the use of CCP in adult hospitalized patients with COVID-19 supports the conclusion that CCP meets the “may be effective” criterion for issuance of an EUA from section 564(c)(2)(A) of the Act. It is reasonable to conclude that the known and potential benefits of CCP outweigh the known and potential risks of CCP for the proposed EUA. Current data suggest the largest clinical benefit is associated with high-titer units of CCP administered early course of the disease.

Source: https://www.fda.gov/media/141480/download

 

And Today August 26, 2020

  • A letter, from Senator Warren, to Commissioner Hahn from Senate Committee asking for documentation for any communication between FDA and White House

August 25, 2020 Dr. Stephen M. Hahn, M.D. Commissioner of Food and Drugs U.S. Food and Drug Administration 10903 New Hampshire Avenue Silver Spring, MD 20993 Dear Commissioner Hahn: We write regarding the U.S. Food and Drug Administration’s (FDA) troubling decision earlier this week to issue an Emergency Use Authorization (EUA) for convalescent plasma as a treatment for coronavirus disease 2019 (COVID-19).1 Reports suggests that the FDA granted the EUA amid intense political pressure from President Trump and other Administration officials, despite limited evidence of convalescent plasma’s effectiveness as a COVID-19 treatment.2 To help us better understand whether the issuance of the blood plasma EUA was motivated by politics, we request copies of any and all communications between FDA and White House officials regarding the blood plasma EUA.

Source: https://www.warren.senate.gov/imo/media/doc/2020.08.25%20Letter%20to%20FDA%20re%20Blood%20Plasma%20EUA.pdf

…….. which may have been a response to this article

FDA chief walks back comments on effectiveness of coronavirus plasma treatment

 

From CNBC: https://www.cnbc.com/2020/08/25/fda-chief-walks-back-comments-on-effectiveness-of-coronavirus-plasma-treatment.html

PUBLISHED TUE, AUG 25 202010:45 AM EDTUPDATED TUE, AUG 25 20204:12 PM EDT

Berkeley Lovelace Jr.@BERKELEYJR

Will Feuer@WILLFOIA

KEY POINTS

  • The authorization will allow health-care providers in the U.S. to use the plasma to treat hospitalized patients with Covid-19.
  • The FDA’s emergency use authorization came a day after President Trump accused the agency of delaying enrollment in clinical trials for vaccines or therapeutics.
  • The criticism from Trump and action from the FDA led some scientists to believe the authorization, which came on the eve of the GOP national convention, was politically motivated.

FDA Commissioner Dr. Stephen Hahn is walking back comments on the benefits of convalescent plasma, saying he could have done a better job of explaining the data on its effectiveness against the coronavirus after authorizing it for emergency use over the weekend.

Commisioners responses over Twitter

https://twitter.com/SteveFDA/status/1298071603675373569?s=20

https://twitter.com/SteveFDA/status/1298071619236245504?s=20

August 26, 2020

In an interview with Bloomberg’s , FDA Commissioner Hahn reiterates that his decision was based on hard evidence and scientific fact, not political pressure.  The whole interview is at the link below:

https://www.bloomberg.com/news/articles/2020-08-25/fda-s-hahn-vows-to-stick-to-the-science-amid-vaccine-pressure?sref=yLCixKPR

Some key points:

  • Dr. Hahn corrected his initial statement about 35% of people would be cured by convalescent plasma. In the interview he stated:

I was trying to do what I do with patients, because patients often understand things in absolute terms versus relative terms. And I should’ve been more careful, there’s no question about it. What I was trying to get to is that if you look at a hundred patients who receive high titre, and a hundred patients who received low titre, the difference between those two particular subset of patients who had these specific criteria was a 35% reduction in mortality. So I frankly did not do a good job of explaining that.

  • FDA colleagues had frank discussion after the statement was made.  He is not asking for other people in HHS to retract their statements, only is concerned that FDA has correct information for physicians and patients
  • Hahn is worried that people will not enroll due to chance they may be given placebo
  • He gave no opinion when asked if FDA should be an independent agency

 

For more articles on COVID19 please go to our Coronavirus Portal at

https://pharmaceuticalintelligence.com/coronavirus-portal/

 

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Multiple Barriers Identified Which May Hamper Use of Artificial Intelligence in the Clinical Setting

Reporter: Stephen J. Williams, PhD.

From the Journal Science:Science  21 Jun 2019: Vol. 364, Issue 6446, pp. 1119-1120

By Jennifer Couzin-Frankel

3.3.21

3.3.21   Multiple Barriers Identified Which May Hamper Use of Artificial Intelligence in the Clinical Setting, 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 a commentary article from Jennifer Couzin-Frankel entitled “Medicine contends with how to use artificial intelligence  the barriers to the efficient and reliable adoption of artificial intelligence and machine learning in the hospital setting are discussed.   In summary these barriers result from lack of reproducibility across hospitals. For instance, a major concern among radiologists is the AI software being developed to read images in order to magnify small changes, such as with cardiac images, is developed within one hospital and may not reflect the equipment or standard practices used in other hospital systems.  To address this issue, lust recently, US scientists and government regulators issued guidance describing how to convert research-based AI into improved medical images and published these guidance in the Journal of the American College of Radiology.  The group suggested greater collaboration among relevant parties in developing of AI practices, including software engineers, scientists, clinicians, radiologists etc. 

As thousands of images are fed into AI algorithms, according to neurosurgeon Eric Oermann at Mount Sinai Hospital, the signals they recognize can have less to do with disease than with other patient characteristics, the brand of MRI machine, or even how a scanner is angled.  For example Oermann and Mount Sinai developed an AI algorithm to detect spots on a lung scan indicative of pneumonia and when tested in a group of new patients the algorithm could detect pneumonia with 93% accuracy.  

However when the group from Sinai tested their algorithm from tens of thousands of scans from other hospitals including NIH success rate fell to 73-80%, indicative of bias within the training set: in other words there was something unique about the way Mt. Sinai does their scans relative to other hospitals.  Indeed, many of the patients Mt. Sinai sees are too sick to get out of bed and radiologists would use portable scanners, which generate different images than stand alone scanners.  

The results were published in Plos Medicine as seen below:

PLoS Med. 2018 Nov 6;15(11):e1002683. doi: 10.1371/journal.pmed.1002683. eCollection 2018 Nov.

Variable generalization performance of a deep learning model to detect pneumonia in chest radiographs: A cross-sectional study.

Zech JR1, Badgeley MA2, Liu M2, Costa AB3, Titano JJ4, Oermann EK3.

Abstract

BACKGROUND:

There is interest in using convolutional neural networks (CNNs) to analyze medical imaging to provide computer-aided diagnosis (CAD). Recent work has suggested that image classification CNNs may not generalize to new data as well as previously believed. We assessed how well CNNs generalized across three hospital systems for a simulated pneumonia screening task.

METHODS AND FINDINGS:

A cross-sectional design with multiple model training cohorts was used to evaluate model generalizability to external sites using split-sample validation. A total of 158,323 chest radiographs were drawn from three institutions: National Institutes of Health Clinical Center (NIH; 112,120 from 30,805 patients), Mount Sinai Hospital (MSH; 42,396 from 12,904 patients), and Indiana University Network for Patient Care (IU; 3,807 from 3,683 patients). These patient populations had an age mean (SD) of 46.9 years (16.6), 63.2 years (16.5), and 49.6 years (17) with a female percentage of 43.5%, 44.8%, and 57.3%, respectively. We assessed individual models using the area under the receiver operating characteristic curve (AUC) for radiographic findings consistent with pneumonia and compared performance on different test sets with DeLong’s test. The prevalence of pneumonia was high enough at MSH (34.2%) relative to NIH and IU (1.2% and 1.0%) that merely sorting by hospital system achieved an AUC of 0.861 (95% CI 0.855-0.866) on the joint MSH-NIH dataset. Models trained on data from either NIH or MSH had equivalent performance on IU (P values 0.580 and 0.273, respectively) and inferior performance on data from each other relative to an internal test set (i.e., new data from within the hospital system used for training data; P values both <0.001). The highest internal performance was achieved by combining training and test data from MSH and NIH (AUC 0.931, 95% CI 0.927-0.936), but this model demonstrated significantly lower external performance at IU (AUC 0.815, 95% CI 0.745-0.885, P = 0.001). To test the effect of pooling data from sites with disparate pneumonia prevalence, we used stratified subsampling to generate MSH-NIH cohorts that only differed in disease prevalence between training data sites. When both training data sites had the same pneumonia prevalence, the model performed consistently on external IU data (P = 0.88). When a 10-fold difference in pneumonia rate was introduced between sites, internal test performance improved compared to the balanced model (10× MSH risk P < 0.001; 10× NIH P = 0.002), but this outperformance failed to generalize to IU (MSH 10× P < 0.001; NIH 10× P = 0.027). CNNs were able to directly detect hospital system of a radiograph for 99.95% NIH (22,050/22,062) and 99.98% MSH (8,386/8,388) radiographs. The primary limitation of our approach and the available public data is that we cannot fully assess what other factors might be contributing to hospital system-specific biases.

CONCLUSION:

Pneumonia-screening CNNs achieved better internal than external performance in 3 out of 5 natural comparisons. When models were trained on pooled data from sites with different pneumonia prevalence, they performed better on new pooled data from these sites but not on external data. CNNs robustly identified hospital system and department within a hospital, which can have large differences in disease burden and may confound predictions.

PMID: 30399157 PMCID: PMC6219764 DOI: 10.1371/journal.pmed.1002683

[Indexed for MEDLINE] Free PMC Article

Images from this publication.See all images (3)Free text

 

Surprisingly, not many researchers have begun to use data obtained from different hospitals.  The FDA has issued some guidance in the matter but considers “locked” AI software or unchanging software as a medical device.  However they just announced development of a framework for regulating more cutting edge software that continues to learn over time.

Still the key point is that collaboration over multiple health systems in various countries may be necessary for development of AI software which is used in multiple clinical settings.  Otherwise each hospital will need to develop their own software only used on their own system and would provide a regulatory headache for the FDA.

Other articles on Artificial Intelligence in Clinical Medicine on this Open Access Journal include:

Top 12 Artificial Intelligence Innovations Disrupting Healthcare by 2020

The launch of SCAI – Interview with Gérard Biau, director of the Sorbonne Center for Artificial Intelligence (SCAI).

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

50 Contemporary Artificial Intelligence Leading Experts and Researchers

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New Neuromodulation Device to Treat Migraines

Reporter: Irina Robu, PhD

Theranica, Israeli startup is developing a non-invasive medical device that treats migraine pain through smartphone-controlled electric pulses unlike existing pharmaceutical solutions like triptans and ergotamine. The company recently received FDA De-novo clearance on Nerivio Migra, a class II medical device to treat acute migraine pain.

The non-invasive medical device, Nerivio Migra contains a bioelectric patch which is placed on the upper arm and a linked smartphone app which controls the electrical impulses and records data. The device’s electric pulses excite C-fiber nerves, generating an analgesic mechanism in the brain that lightens migraine pain.

In order to diminish the overuse of painkillers, the company developed the non-invasive device and tested it among acute migraine patients both two and 48 hours after treatment. Side effects from the device were mild and resolved within 24 hours.

Theranica’s product is lower in price than the existing alternatives and it is using existing smartphone technology. Their initial focus is on marketing to headache clinics as a start. And hoping to expand the indications for its device to the pediatric migraine population and finally use its platform to treat other idiopathic pain conditions like cluster headaches.

SOURCE

Israeli startup gets FDA nod for neuromodulation device to treat migraines

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

Reporter: Stephen J Williams, PhD @StephenJWillia2

 

Genome editing offers the potential of new and effective treatments for genetic diseases. As companies work to develop these treatments, regulators are focused on ensuring that any such products meet applicable safety and efficacy requirements. This panel will discuss how European Union and United States regulators are approaching therapeutic use of genome editing, issues in harmonization between these two – and other – jurisdictions, challenges faced by industry as regulatory positions evolve, and steps that organizations and companies can take to facilitate approval and continued efforts at harmonization.

 

CBER:  because of the nature of these gene therapies, which are mainly orphan, there is expedited review.  Since they started this division in 2015, they have received over 1500 applications.

Spark: Most of the issues were issues with the primary disease not the gene therapy so they had to make new endpoint tests so had talks with FDA before they entered phase III.   There has been great collaboration with FDA,  now they partnered with Novartis to get approval outside US.  You should be willing to partner with EU pharmas to expedite the regulatory process outside US.  In China the process is new and Brazil is behind on their gene therapy guidance.  However there is the new issue of repeat testing of your manufacturing process, as manufacturing of gene therapies had been small scale before. However he notes that problems with expedited review is tough because you don’t have alot of time to get data together.  They were lucky that they had already done a randomized trial.

Sidley Austin:  EU regulatory you make application with advance therapy you don’t have a national option, the regulation body assesses a committee to see if has applicability. Then it goes to a safety committee.  EU has been quicker to approve these advance therapies. Twenty five percent of their applications are gene therapies.  Companies having issues with manufacturing.  There can be issues when the final application is formalized after discussions as problems may arise between discussions, preliminary applications, and final applications.

Sarepta: They have a robust gene therapy program.  Their lead is a therapy for DMD (Duchenne’s Muscular Dystrophy) where affected males die by 25. Japan and EU have different regulatory applications and although they are similar and data can be transferred there is more paperwork required by EU.  The US uses an IND for application. Global feedback is very challenging, they have had multiple meetings around the world and takes a long time preparing a briefing package….. putting a strain on the small biotechs.  No company wants to be either just EU centric or US centric they just want to get out to market as fast as possible.

 

Please follow LIVE on TWITTER using the following @ handles and # hashtags:

@Handles

@pharma_BI

@AVIVA1950

@BIOConvention

# Hashtags

#BIO2019 (official meeting hashtag)

 

 

 

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Real Time Coverage @BIOConvention #BIO2019: Chat with @FDA Commissioner, & Challenges in Biotech & Gene Therapy June 4 Philadelphia

Reporter: Stephen J. Williams, PhD @StephenJWillia2

 

  • taking patient concerns and voices from anecdotal to data driven system
  • talked about patient accrual hearing patient voice not only in ease of access but reporting toxicities
  • at FDA he wants to remove barriers to trial access and accrual; also talk earlier to co’s on how they should conduct a trial

Digital tech

  • software as medical device
  • regulatory path is mixed like next gen sequencing
  • wearables are concern for FDA (they need to recruit scientists who know this tech

Opioids

  • must address the crisis but in a way that does not harm cancer pain patients
  • smaller pain packs “blister packs” would be good idea

Clinical trial modernization

  • for Alzheimers disease problem is science
  • for diabetes problem is regulatory
  • different diseases calls for different trial design
  • have regulatory problems with rare diseases as can’t form control or placebo group, inhumane. for example ras tumors trials for MEK inhibitors were narrowly focused on certain ras mutants
Realizing the Promise of Gene Therapies for Patients Around the World

103ABC, Level 100

Speakers
Lots of promise, timeline is progressing faster but we need more education on use of the gene therapy
Regulatory issues: Cell and directly delivered gene based therapies have been now approved. Some challenges will be the ultrarare disease trials and how we address manufacturing issues.  Manufacturing is a big issue at CBER and scalability.  If we want to have global impact of these products we need to address the manufacturing issues
 of scalability.
Pfizer – clinical grade and scale is important.
Aventis – he knew manufacturing of biologics however gene therapy manufacturing has its separate issues and is more complicated especially for regulatory purposes for clinical grade as well as scalability.  Strategic decision: focusing on the QC on manufacturing was so important.  Had a major issue in manufacturing had to shut down and redesign the system.
Albert:  Manufacturing is the most important topic even to the investors.  Investors were really conservative especially seeing early problems but when academic centers figured out good efficacy then they investors felt better and market has exploded.  Now you can see investment into preclinical and startups but still want mature companies to focus on manufacturing.  About $10 billion investment in last 4 years.

How Early is Too Early? Valuing and De-Risking Preclinical Opportunities

109AB, Level 100

Speakers
Valuing early-stage opportunities is challenging. Modeling will often provide a false sense of accuracy but relying on comparable transactions is more art than science. With a long lead time to launch, even the most robust estimates can ultimately prove inaccurate. This interactive panel will feature venture capital investors and senior pharma and biotech executives who lead early-stage transactions as they discuss their approaches to valuing opportunities, and offer key learnings from both successful and not-so-successful experiences.
Dr. Schoenbeck, Pfizer:
  • global network of liaisons who are a dedicated team to research potential global startup partners or investments.  Pfizer has a separate team to evaluate academic laboratories.  In Most cases Pfizer does not initiate contact.  It is important to initiate the first discussion with them in order to get noticed.  Could be just a short chat or discussion on what their needs are for their portfolio.

Question: How early is too early?

Luc Marengere, TVM:  His company has early stage focus, on 1st in class molecules.  The sweet spot for their investment is a candidate selected compound, which should be 12-18 months from IND.  They will want to bring to phase II in less than 4 years for $15-17 million.  Their development model is bad for academic labs.  During this process free to talk to other partners.

Dr. Chaudhary, Biogen:  Never too early to initiate a conversation and sometimes that conversation has lasted 3+ years before a decision.  They like build to buy models, will do convertible note deals, candidate compound selection should be entering in GLP/Tox phase (sweet spot)

Merck: have MRL Venture Fund for pre series A funding.  Also reiterated it is never too early to have that initial discussion.  It will not put you in a throw away bin.  They will have suggestions and never like to throw out good ideas.

Michael Hostetler: Set expectations carefully ; data should be validated by a CRO.  If have a platform, they will look at the team first to see if strong then will look at the platform to see how robust it is.

All noted that you should be completely honest at this phase.  Do not overstate your results or data or overhype your compound(s).  Show them everything and don’t have a bias toward compounds you think are the best in your portfolio.  Sometimes the least developed are the ones they are interested in.  Also one firm may reject you however you may fit in others portfolios better so have a broad range of conversations with multiple players.

 

 

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A Timeline of Dr. Gottlieb’s Tenure at the FDA: 2017-2019

Reporter: Stephen J. Williams, Ph.D.

 

From FiercePharma.com

FDA chief Scott Gottlieb steps down, leaving pet projects behind

Scott Gottlieb FDA
FDA Commissioner Scott Gottlieb was appointed by President Trump in 2017. (FDA)

Also under his command, the FDA took quick and decisive action on drug costs. The commissioner worked to boost generic approvals and crack down on regulatory “gaming” that stifles competition. He additionally blamed branded drug companies for an “anemic” U.S. biosimilars market and recently blasted insulin pricing.

His sudden departure will likely leave many agency efforts to lower costs up in the air. After the news broke, many pharma watchers posted on Twitter that Gottlieb’s resignation is a loss for the industry.

During his tenure as FDA commissioner, Gottlieb’s name had been floated for HHS chief when former HHS secretary Tom Price resigned due to a travel scandal, but Gottlieb said he was best suited for the FDA commissioner job. Now, former Eli Lilly executive Alex Azar serves as HHS secretary, and on Tuesday afternoon, Azar praised Gottlieb for his work at the agency.

Also read from FiercePharma:

Gottlieb’s quick goodbye triggers investor panic, biopharma bewilderment and at least one good riddance

AUDIT Podcast

An emergency Scott Gottlieb podcast

 

Why is Scott Gottlieb quitting the FDA? Who will replace him?

 

A Timeline of Dr. Gottlieb’s Tenure at the FDA

From FiercePharma.com

New FDA commissioner Gottlieb unveils price-fighting strategies

Scott Gottlieb
New FDA commissioner Scott Gottlieb laid out some approaches the agency will take to fight high prices.

UPDATED 3/19/2019

Dr. Norman E. Sharpless was named acting commissioner of the Food and Drug Administration on Tuesday. For the last 18 months, he had been director of the National Cancer Institute.CreditTom Williams/CQ Roll Call, via Getty Images
Image
Dr. Norman E. Sharpless was named acting commissioner of the Food and Drug Administration on Tuesday. For the last 18 months, he had been director of the National Cancer Institute.CreditCreditTom Williams/CQ Roll Call, via Getty Images

WASHINGTON — Dr. Norman E. (Ned) Sharpless, director of the National Cancer Institute, will serve as acting commissioner of the Food and Drug Administration, Alex M. Azar III, secretary of health and human services, announced on Tuesday.

Dr. Sharpless temporarily will fill the post being vacated by Dr. Scott Gottlieb, who stunned public health experts, lawmakers and consumer groups last week when he abruptly announced that he was resigningfor personal reasons.

Dr. Sharpless has been director of the cancer center, part of the National Institutes of Health, since October 2017. He is also chief of the aging biology and cancer section in the National Institute on Aging’s Laboratory of Genetics and Genomics. His research focuses on the relationship between aging and cancer, and development of new treatments for melanoma, lung cancer and breast cancer.

“Dr. Sharpless’s deep scientific background and expertise will make him a strong leader for F.D.A.,” said Mr. Azar, in a statement. “There will be no let up in the agency’s focus, from ongoing efforts on drug approvals and combating the opioid crisis to modernizing food safety and addressing the rapid rise in youth use of e-cigarettes.”

Dr. Douglas Lowy, known for seminal research on the link between human papillomavirus and multiple cancer types including cervical, and ultimately leading to development of a vaccine, will be named head of the NCI to replace Dr. Sharpless. Dr. Lowy currently is Deputy Director of the NCI.

Other posts on the Food and Drug Administration and FDA Approvals during Dr. Gotlieb’s Tenure on this Open Access Journal Include:

 

Regulatory Affairs: Publications on FDA-related Issues – Aviva Lev-Ari, PhD, RN

FDA Approves La Jolla’s Angiotensin 2

In 2018, FDA approved an all-time record of 62 new therapeutic drugs (NTDs) [Not including diagnostic imaging agents, included are combination products with at least one new molecular entity as an active ingredient] with average Peak Sales per NTD $1.2Billion.

Alnylam Announces First-Ever FDA Approval of an RNAi Therapeutic, ONPATTRO™ (patisiran) for the Treatment of the Polyneuropathy of Hereditary Transthyretin-Mediated Amyloidosis in Adults

FDA: Rejects NDA filing: “clinical and non-clinical pharmacology sections of the application were not sufficient to complete a review”: Celgene’s Relapsing Multiple Sclerosis Drug – Ozanimod

Expanded Stroke Thrombectomy Guidelines: FDA expands treatment window for use (Up to 24 Hours Post-Stroke) of clot retrieval devices (Stryker’s Trevo Stent) in certain stroke patients

In 2017, FDA approved a record number of 19 personalized medicines — 16 new molecular entities and 3 gene therapies – PMC’s annual analysis, titled Personalized Medicine at FDA: 2017 Progress Report

FDA Approval marks first presentation of bivalirudin in frozen, premixed, ready-to-use formulation

Skin Regeneration Therapy One of First Tissue Engineering Products Evaluated by FDA

FDA approval on 12/1/2017 of Amgen’s evolocumb (Repatha) a PCSK9 inhibitor for the prevention of heart attacks, strokes, and coronary revascularizations in patients with established cardiovascular disease

FDA Approval of Anti-Depression Digital Pill Tracks Use When Swallowed and transmits to MDs Smartphone – A Breakthrough in Medication Remote Compliance Monitoring

Medical Devices Early Feasibility FDA’s Pathway – Accelerated Recruitment for Randomized Clinical Trials: Replacement and Repair of Mitral Valves

Novartis’ Kymriah (tisagenlecleucel), FDA approved genetically engineered immune cells, would charge $475,000 per patient, will use Programs that Payers will pay only for Responding Patients 

FDA has approved the world’s first CAR-T therapy, Novartis for Kymriah (tisagenlecleucel) and Gilead’s $12 billion buy of Kite Pharma, no approved drug and Canakinumab for Lung Cancer (may be?)

FDA: CAR-T therapy outweigh its risks tisagenlecleucel, manufactured by Novartis of Basel – 52 out of 63 participants — 82.5% — experienced overall remissions – young patients with Leukaemia [ALL]

‘Landmark FDA approval bolsters personalized medicine’ by Edward Abrahams, PhD, President, PMC

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