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Archive for the ‘Personal Health Applications: Tech Innovations serves HealhCare’ 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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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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The Accelerating COVID-19 Therapeutic Interventions and Vaccines (ACTIV) Partnership on May 18, 2020: Leadership of AbbVie, Amgen, AstraZeneca, Bristol Myers Squibb, Eisai, Eli Lilly, Evotec, Gilead, GlaxoSmithKline, Johnson & Johnson, KSQ Therapeutics, Merck, Novartis, Pfizer, Roche, Sanofi, Takeda, and Vir. We also thank multiple NIH institutes (especially NIAID), the FDA, BARDA, CDC, the European Medicines Agency, the Department of Defense, the VA, and the Foundation for NIH

Reporter: Aviva Lev-Ari, PhD, RN

May 18, 2020

Accelerating COVID-19 Therapeutic Interventions and Vaccines (ACTIV) An Unprecedented Partnership for Unprecedented Times

JAMA. Published online May 18, 2020. doi:10.1001/jama.2020.8920

First reported in Wuhan, China, in December 2019, COVID-19 is caused by a highly transmissible novel coronavirus, SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2). By March 2020, as COVID-19 moved rapidly throughout Europe and the US, most researchers and regulators from around the world agreed that it would be necessary to go beyond “business as usual” to contain this formidable infectious agent. The biomedical research enterprise was more than willing to respond to the challenge of COVID-19, but it soon became apparent that much-needed coordination among important constituencies was lacking.

Clinical trials of investigational vaccines began as early as January, but with the earliest possible distribution predicted to be 12 to 18 months away. Clinical trials of experimental therapies had also been initiated, but most, except for a trial testing the antiviral drug remdesivir,2 were small and not randomized. In the US, there was no true overarching national process in either the public or private sector to prioritize candidate therapeutic agents or vaccines, and no efforts were underway to develop a clear inventory of clinical trial capacity that could be brought to bear on this public health emergency. Many key factors had to change if COVID-19 was to be addressed effectively in a relatively short time frame.

On April 3, leaders of the National Institutes of Health (NIH), with coordination by the Foundation for the National Institutes of Health (FNIH), met with multiple leaders of research and development from biopharmaceutical firms, along with leaders of the US Food and Drug Administration (FDA), the Biomedical Advanced Research and Development Authority (BARDA), the European Medicines Agency (EMA), and academic experts. Participants sought urgently to identify research gaps and to discuss opportunities to collaborate in an accelerated fashion to address the complex challenges of COVID-19.

These critical discussions culminated in a decision to form a public-private partnership to focus on speeding the development and deployment of therapeutics and vaccines for COVID-19. The group assembled 4 working groups to focus on preclinical therapeutics, clinical therapeutics, clinical trial capacity, and vaccines (Figure). In addition to the founding members, the working groups’ membership consisted of senior scientists from each company or agency, the Centers for Disease Control and Prevention (CDC), the Department of Veterans Affairs (VA), and the Department of Defense.

Figure.

Accelerating COVID-19 Therapeutic Interventions and Vaccines

ACTIV’s 4 working groups, each with one cochair from NIH and one from industry, have made rapid progress in establishing goals, setting timetables, and forming subgroups focused on specific issues (Figure). The goals of the working group, along with a few examples of their accomplishments to date, include the following.

 

The Preclinical Working Group was charged to standardize and share preclinical evaluation resources and methods and accelerate testing of candidate therapies and vaccines to support entry into clinical trials. The aim is to increase access to validated animal models and to enhance comparison of approaches to identify informative assays. For example, through the ACTIV partnership, this group aims to extend preclinical researchers’ access to high-throughput screening systems, especially those located in the Biosafety Level 3 (BSL3) facilities currently required for many SARS-CoV-2 studies. This group also is defining a prioritization approach for animal use, assay selection and staging of testing, as well as completing an inventory of animal models, assays, and BSL 3/4 facilities.

 

The Therapeutics Clinical Working Group has been charged to prioritize and accelerate clinical evaluation of a long list of therapeutic candidates for COVID-19 with near-term potential. The goals have been to prioritize and test potential therapeutic agents for COVID-19 that have already been in human clinical trials. These may include agents with either direct-acting or host-directed antiviral activity, including immunomodulators, severe symptom modulators, neutralizing antibodies, or vaccines. To help achieve these goals, the group has established a steering committee with relevant expertise and objectivity to set criteria for evaluating and ranking potential candidate therapies submitted by industry partners. Following a rigorous scientific review, the prioritization subgroup has developed a complete inventory of approximately 170 already identified therapeutic candidates that have acceptable safety profiles and different mechanisms of action. On May 6, the group presented its first list of repurposed agents recommended for inclusion in ACTIV’s master protocol for adaptive clinical trials. Of the 39 agents that underwent final prioritization review, the group identified 6 agents—including immunomodulators and supportive therapies—that it proposes to move forward into the master protocol clinical trial(s) expected to begin later in May.

 

The Clinical Trial Capacity Working Group is charged with assembling and coordinating existing networks of clinical trials to increase efficiency and build capacity. This will include developing an inventory of clinical trial networks supported by NIH and other funders in the public and private sectors, including contract research organizations. For each network, the working group seeks to identify their specialization in different populations and disease stages to leverage infrastructure and expertise from across multiple networks, and establish a coordination mechanism across networks to expedite trials, track incidence across sites, and project future capacity. The clinical trials inventory subgroup has already identified 44 networks, with access to adult populations and within domestic reach, for potential inclusion in COVID-19 trials. Meanwhile, the survey subgroup has developed 2 survey instruments to assess the capabilities and capacities of those networks, and its innovation subgroup has developed a matrix to guide deployment of innovative solutions throughout the trial life cycle.

 

The Vaccines Working Group has been charged to accelerate evaluation of vaccine candidates to enable rapid authorization or approval.4 This includes development of a harmonized master protocol for adaptive trials of multiple vaccines, as well as development of a trial network that could enroll as many as 100 000 volunteers in areas where COVID-19 is actively circulating. The group also aims to identify biomarkers to speed authorization or approval and to provide evidence to address cross-cutting safety concerns, such as immune enhancement. Multiple vaccine candidates will be evaluated, and the most promising will move to a phase 2/3 adaptive trial platform utilizing large geographic networks in the US and globally.5 Because time is of the essence, ACTIV will aim to have the next vaccine candidates ready to enter clinical trials by July 1, 2020.

References

1.

Desai  A .  Twentieth-century lessons for a modern coronavirus pandemic.   JAMA. Published online April 27, 2020. doi:10.1001/jama.2020.4165
ArticlePubMedGoogle Scholar

2.

NIH clinical trial shows remdesivir accelerates recovery from advanced COVID-19. National Institutes of Health. Published April 29, 2020. Accessed May 7, 2020. https://www.nih.gov/news-events/news-releases/nih-clinical-trial-shows-remdesivir-accelerates-recovery-advanced-covid-19

3.

NIH to launch public-private partnership to speed COVID-19 vaccine and treatment options. National Institutes of Health. Published April 17, 2020. Accessed May 7, 2020. https://www.nih.gov/news-events/news-releases/nih-launch-public-private-partnership-speed-covid-19-vaccine-treatment-options

4.

Corey  L , Mascola  JR , Fauci  AS , Collins  FS .  A strategic approach to COVID-19 vaccine R&D.   Science. Published online May 11, 2020. doi:10.1126/science.abc5312PubMedGoogle Scholar

5.

Angus  DC .  Optimizing the trade-off between learning and doing in a pandemic.   JAMA. Published online March 30, 2020. doi:10.1001/jama.2020.4984
ArticlePubMedGoogle Scholar

6.

Accelerating COVID-19 Therapeutic Interventions and Vaccines (ACTIV) portal. National Institutes of Health. Accessed May 15, 2020. https://www.nih.gov/ACTIV

7.

Accelerating Medicines Partnership (AMP). National Institutes of Health. Published February 4, 2014. Accessed May 7, 2020. https://www.nih.gov/research-training/accelerating-medicines-partnership-amp
SOURCE

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Reporter and Curator: Dr. Sudipta Saha, Ph.D.

 

Parkinson’s Disease (PD), characterized by both motor and non-motor system pathology, is a common neurodegenerative disorder affecting about 1% of the population over age 60. Its prevalence presents an increasing social burden as the population ages. Since its introduction in the 1960’s, dopamine (DA)-replacement therapy (e.g., L-DOPA) has remained the gold standard treatment. While improving PD patients’ quality of life, the effects of treatment fade with disease progression and prolonged usage of these medications often (>80%) results in side effects including dyskinesias and motor fluctuations. Since the selective degeneration of A9 mDA neurons (mDANs) in the substantia nigra (SN) is a key pathological feature of the disease and is directly associated with the cardinal motor symptoms, dopaminergic cell transplantation has been proposed as a therapeutic strategy.

 

Researchers showed that mammalian fibroblasts can be converted into embryonic stem cell (ESC)-like induced pluripotent stem cells (iPSCs) by introducing four transcription factors i.e., Oct4, Sox2, Klf4, and c-Myc. This was then accomplished with human somatic cells, reprogramming them into human iPSCs (hiPSCs), offering the possibility of generating patient-specific stem cells. There are several major barriers to implementation of hiPSC-based cell therapy for PD. First, probably due to the limited understanding of the reprogramming process, wide variability exists between the differentiation potential of individual hiPSC lines. Second, the safety of hiPSC-based cell therapy has yet to be fully established. In particular, since any hiPSCs that remain undifferentiated or bear sub-clonal tumorigenic mutations have neoplastic potential, it is critical to eliminate completely such cells from a therapeutic product.

 

In the present study the researchers established human induced pluripotent stem cell (hiPSC)-based autologous cell therapy. Researchers reported a platform of core techniques for the production of mDA progenitors as a safe and effective therapeutic product. First, by combining metabolism-regulating microRNAs with reprogramming factors, a method was developed to more efficiently generate clinical grade iPSCs, as evidenced by genomic integrity and unbiased pluripotent potential. Second, a “spotting”-based in vitro differentiation methodology was established to generate functional and healthy mDA cells in a scalable manner. Third, a chemical method was developed that safely eliminates undifferentiated cells from the final product. Dopaminergic cells thus produced can express high levels of characteristic mDA markers, produce and secrete dopamine, and exhibit electrophysiological features typical of mDA cells. Transplantation of these cells into rodent models of PD robustly restored motor dysfunction and reinnervated host brain, while showing no evidence of tumor formation or redistribution of the implanted cells.

 

Together these results supported the promise of these techniques to provide clinically applicable personalized autologous cell therapy for PD. It was recognized by researchers that this methodology is likely to be more costly in dollars and manpower than techniques using off-the-shelf methods and allogenic cell lines. Nevertheless, the cost for autologous cell therapy may be expected to decrease steadily with technological refinement and automation. Given the significant advantages inherent in a cell source free of ethical concerns and with the potential to obviate the need for immunosuppression, with its attendant costs and dangers, it was proposed that this platform is suitable for the successful implementation of human personalized autologous cell therapy for PD.

 

References:

 

https://www.jci.org/articles/view/130767/pdf?elqTrackId=2fd7d0edee744f9cb6d70a686d7b273b

 

https://www.ncbi.nlm.nih.gov/pubmed/31714896

 

https://www.ncbi.nlm.nih.gov/pubmed/23666606

 

https://www.ncbi.nlm.nih.gov/pubmed/27343168

 

https://www.ncbi.nlm.nih.gov/pubmed/21495962

 

https://www.ncbi.nlm.nih.gov/pubmed/28083784

 

https://www.ncbi.nlm.nih.gov/pubmed/20336395

 

https://www.ncbi.nlm.nih.gov/pubmed/28585381

 

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The Journey of Antibiotic Discovery

Reporter and Curator: Dr. Sudipta Saha, Ph.D.

 

The term ‘antibiotic’ was introduced by Selman Waksman as any small molecule, produced by a microbe, with antagonistic properties on the growth of other microbes. An antibiotic interferes with bacterial survival via a specific mode of action but more importantly, at therapeutic concentrations, it is sufficiently potent to be effective against infection and simultaneously presents minimal toxicity. Infectious diseases have been a challenge throughout the ages. From 1347 to 1350, approximately one-third of Europe’s population perished to Bubonic plague. Advances in sanitary and hygienic conditions sufficed to control further plague outbreaks. However, these persisted as a recurrent public health issue. Likewise, infectious diseases in general remained the leading cause of death up to the early 1900s. The mortality rate shrunk after the commercialization of antibiotics, which given their impact on the fate of mankind, were regarded as a ‘medical miracle’. Moreover, the non-therapeutic application of antibiotics has also greatly affected humanity, for instance those used as livestock growth promoters to increase food production after World War II.

 

Currently, more than 2 million North Americans acquire infections associated with antibiotic resistance every year, resulting in 23,000 deaths. In Europe, nearly 700 thousand cases of antibiotic-resistant infections directly develop into over 33,000 deaths yearly, with an estimated cost over €1.5 billion. Despite a 36% increase in human use of antibiotics from 2000 to 2010, approximately 20% of deaths worldwide are related to infectious diseases today. Future perspectives are no brighter, for instance, a government commissioned study in the United Kingdom estimated 10 million deaths per year from antibiotic resistant infections by 2050.

 

The increase in antibiotic-resistant bacteria, alongside the alarmingly low rate of newly approved antibiotics for clinical usage, we are on the verge of not having effective treatments for many common infectious diseases. Historically, antibiotic discovery has been crucial in outpacing resistance and success is closely related to systematic procedures – platforms – that have catalyzed the antibiotic golden age, namely the Waksman platform, followed by the platforms of semi-synthesis and fully synthetic antibiotics. Said platforms resulted in the major antibiotic classes: aminoglycosides, amphenicols, ansamycins, beta-lactams, lipopeptides, diaminopyrimidines, fosfomycins, imidazoles, macrolides, oxazolidinones, streptogramins, polymyxins, sulphonamides, glycopeptides, quinolones and tetracyclines.

 

The increase in drug-resistant pathogens is a consequence of multiple factors, including but not limited to high rates of antimicrobial prescriptions, antibiotic mismanagement in the form of self-medication or interruption of therapy, and large-scale antibiotic use as growth promotors in livestock farming. For example, 60% of the antibiotics sold to the USA food industry are also used as therapeutics in humans. To further complicate matters, it is estimated that $200 million is required for a molecule to reach commercialization, with the risk of antimicrobial resistance rapidly developing, crippling its clinical application, or on the opposing end, a new antibiotic might be so effective it is only used as a last resort therapeutic, thus not widely commercialized.

 

Besides a more efficient management of antibiotic use, there is a pressing need for new platforms capable of consistently and efficiently delivering new lead substances, which should attend their precursors impressively low rates of success, in today’s increasing drug resistance scenario. Antibiotic Discovery Platforms are aiming to screen large libraries, for instance the reservoir of untapped natural products, which is likely the next antibiotic ‘gold mine’. There is a void between phenotanypic screening (high-throughput) and omics-centered assays (high-information), where some mechanistic and molecular information complements antimicrobial activity, without the laborious and extensive application of various omics assays. The increasing need for antibiotics drives the relentless and continuous research on the foreground of antibiotic discovery. This is likely to expand our knowledge on the biological events underlying infectious diseases and, hopefully, result in better therapeutics that can swing the war on infectious diseases back in our favor.

 

During the genomics era came the target-based platform, mostly considered a failure due to limitations in translating drugs to the clinic. Therefore, cell-based platforms were re-instituted, and are still of the utmost importance in the fight against infectious diseases. Although the antibiotic pipeline is still lackluster, especially of new classes and novel mechanisms of action, in the post-genomic era, there is an increasingly large set of information available on microbial metabolism. The translation of such knowledge into novel platforms will hopefully result in the discovery of new and better therapeutics, which can sway the war on infectious diseases back in our favor.

 

References:

 

https://www.mdpi.com/2079-6382/8/2/45/htm

 

https://www.ncbi.nlm.nih.gov/pubmed/19515346

 

https://www.ajicjournal.org/article/S0196-6553(11)00184-2/fulltext

 

https://www.ncbi.nlm.nih.gov/pubmed/21700626

 

http://www.med.or.jp/english/journal/pdf/2009_02/103_108.pdf

 

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Digital Therapeutics: A Threat or Opportunity to Pharmaceuticals

Digital Therapeutics: A Threat or Opportunity to Pharmaceuticals

Reporter and Curator: Dr. Sudipta Saha, Ph.D.

3.3.7

3.3.7   Digital Therapeutics: A Threat or Opportunity to Pharmaceuticals, 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

Digital Therapeutics (DTx) have been defined by the Digital Therapeutics Alliance (DTA) as “delivering evidence based therapeutic interventions to patients, that are driven by software to prevent, manage or treat a medical disorder or disease”. They might come in the form of a smart phone or computer tablet app, or some form of a cloud-based service connected to a wearable device. DTx tend to fall into three groups. Firstly, developers and mental health researchers have built digital solutions which typically provide a form of software delivered Cognitive-Behaviour Therapies (CBT) that help patients change behaviours and develop coping strategies around their condition. Secondly there are the group of Digital Therapeutics which target lifestyle issues, such as diet, exercise and stress, that are associated with chronic conditions, and work by offering personalized support for goal setting and target achievement. Lastly, DTx can be designed to work in combination with existing medication or treatments, helping patients manage their therapies and focus on ensuring the therapy delivers the best outcomes possible.

Pharmaceutical companies are clearly trying to understand what DTx will mean for them. They want to analyze whether it will be a threat or opportunity to their business. For a long time, they have been providing additional support services to patients who take relatively expensive drugs for chronic conditions. A nurse-led service might provide visits and telephone support to diabetics for example who self-inject insulin therapies. But DTx will help broaden the scope of support services because they can be delivered cost-effectively, and importantly have the ability to capture real-world evidence on patient outcomes. They will no-longer be reserved for the most expensive drugs or therapies but could apply to a whole range of common treatments to boost their efficacy. Faced with the arrival of Digital Therapeutics either replacing drugs, or playing an important role alongside therapies, pharmaceutical firms have three options. They can either ignore DTx and focus on developing drug therapies as they have done; they can partner with a growing number of DTx companies to develop software and services complimenting their drugs; or they can start to build their own Digital Therapeutics to work with their products.

Digital Therapeutics will have knock-on effects in health industries, which may be as great as the introduction of therapeutic apps and services themselves. Together with connected health monitoring devices, DTx will offer a near constant stream of data about an individuals’ behavior, real world context around factors affecting their treatment in their everyday lives and emotional and physiological data such as blood pressure and blood sugar levels. Analysis of the resulting data will help create support services tailored to each patient. But who stores and analyses this data is an important question. Strong data governance will be paramount to maintaining trust, and the highly regulated pharmaceutical industry may not be best-placed to handle individual patient data. Meanwhile, the health sector (payers and healthcare providers) is becoming more focused on patient outcomes, and payment for value not volume. The future will say whether pharmaceutical firms enhance the effectiveness of drugs with DTx, or in some cases replace drugs with DTx.

Digital Therapeutics have the potential to change what the pharmaceutical industry sells: rather than a drug it will sell a package of drugs and digital services. But they will also alter who the industry sells to. Pharmaceutical firms have traditionally marketed drugs to doctors, pharmacists and other health professionals, based on the efficacy of a specific product. Soon it could be paid on the outcome of a bundle of digital therapies, medicines and services with a closer connection to both providers and patients. Apart from a notable few, most pharmaceutical firms have taken a cautious approach towards Digital Therapeutics. Now, it is to be observed that how the pharmaceutical companies use DTx to their benefit as well as for the benefit of the general population.

References:

https://eloqua.eyeforpharma.com/LP=23674?utm_campaign=EFP%2007MAR19%20EFP%20Database&utm_medium=email&utm_source=Eloqua&elqTrackId=73e21ae550de49ccabbf65fce72faea0&elq=818d76a54d894491b031fa8d1cc8d05c&elqaid=43259&elqat=1&elqCampaignId=24564

https://www.s3connectedhealth.com/resources/white-papers/digital-therapeutics-pharmas-threat-or-opportunity/

http://www.pharmatimes.com/web_exclusives/digital_therapeutics_will_transform_pharma_and_healthcare_industries_in_2019._heres_how._1273671

https://www.mckinsey.com/industries/pharmaceuticals-and-medical-products/our-insights/exploring-the-potential-of-digital-therapeutics

https://player.fm/series/digital-health-today-2404448/s9-081-scaling-digital-therapeutics-the-opportunities-and-challenges

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Curator: Gail S. Thornton, M.A.

Co-Editor: The VOICES of Patients, Hospital CEOs, HealthCare Providers, Caregivers and Families: Personal Experience with Critical Care and Invasive Medical Procedures

  •  In a national survey, the Fiber Choice® line of chewable prebiotic fiber tablets and gummies, achieved the #1 share of gastroenterologist (GE) recommendations, more than four times greater than that for the nearest branded competitor
  • Fiber Choice contains a well-studied prebiotic fiber that promotes regularity and supports the growth of beneficial microorganisms for general digestive health
  • The convenience, taste and efficacy of Fiber Choice, makes it a GE-endorsed choice toward helping address the “fiber gap” in American diets

 Boca Raton, Fla. – (June 3, 2018) – IM HealthScience® (IMH), innovators of medical foods and dietary supplements, today announced a high-quality and replicated nationwide survey conducted among a representative and projectible sample of U.S. gastroenterologists, which revealed Fiber Choice® as the #1-recommended chewable prebiotic fiber brand.

The results of a ProVoice survey, fielded in May 2018 by IQVIA, showed Fiber Choice as the leader by far. Its share of gastroenterologist endorsements was more than four times greater than that of its nearest branded competitor.

Less than 3 percent of Americans get the recommended minimum amount of fiber, and 97 percent need to increase their fiber intake[1]. Although the recommended daily fiber intake is 25 to 38 grams[2], most Americans only get about half that amount. This “fiber gap” reflects a diet with relatively few high-fiber foods, such as fruits, vegetables, nuts, legumes and whole-grains, and is large enough for the U.S. government to deem it a public health concern for most of the U.S. population.

To help bridge this gap, gastroenterologists recommend fibers including Fiber Choice chewable tablets and gummies. For doctors, it’s a simple, convenient and tasty way to help their patients get the fiber needed for overall good digestive health.

“Dietary fiber is known for keeping our bodies regular,” said Michael Epstein, M.D., FACG, AGAF, a leading gastroenterologist and Chief Medical Advisor of IM HealthScience. “Most importantly, it’s essential that you get enough fiber in your diet. One way to do that is to supplement your daily intake of dietary fiber with natural, prebiotic fiber supplements.”

Inulin, the 100 percent natural prebiotic soluble fiber in Fiber Choice, has been studied extensively and has been shown to support laxation and overall digestive health as well as glycemic control, lowered cholesterol, improved cardiovascular health, weight control and better calcium absorption.

Fiber Choice can be found in the digestive aisle at Walmart, CVS, Target, Rite Aid and many other drug and food retailers.

About ProVoice Survey
ProVoice has the largest sample size of any professional healthcare survey in the U.S., with nearly 60,000 respondents across physicians, nurse practitioners, physician assistants, optometrists, dentists, and hygienists, measuring recommendations across more than 120 over-the-counter categories. Manufacturers use ProVoice for claim substantiation, promotion measurement, and HCP targeting.

IQVIA fielded replicated surveys in April 2018 and May 2018 respectively among U.S. gastroenterologists for IM HealthScience. The ProVoice survey methodology validated the claim at a 95 percent confidence level that “Fiber Choice® is the #1 gastroenterologist-recommended chewable prebiotic fiber supplement.”

About Fiber Choice®

The Fiber Choice® brand of chewables and gummies is made of inulin [pronounced: in-yoo-lin], a natural fiber found in many fruits and vegetables. Inulin works by helping to build healthy, good bacteria in the colon, while keeping food moving through the digestive system. This action has a beneficial and favorable effect in softening stools and improving bowel function.

Research shows that the digestive system does more than digest food; it plays a central role in the immune system. The healthy bacteria that live in the digestive tract promote immune system function, so prebiotic fiber helps nourish the body. Inulin also has secondary benefits, too, of possibly lowering cholesterol, balancing blood chemistry and regulating appetite, which can help reduce calorie intake and play a supporting role in weight management.

The usual adult dosage with Fiber Choice Chewable tablets is two tablets up to three times a day and for Fiber Choice Fiber Gummies is two gummies up to six per day.

About IM HealthScience®

IM HealthScience® (IMH) is the innovator of IBgard and FDgard for the dietary management of Irritable Bowel Syndrome (IBS) and Functional Dyspepsia (FD), respectively. In 2017, IMH added Fiber Choice®, a line of prebiotic fibers, to its product line via an acquisition. The sister subsidiary of IMH, Physician’s Seal®, also provides REMfresh®, a well-known continuous release and absorption melatonin (CRA-melatonin™) supplement for sleep. IMH is a privately held company based in Boca Raton, Florida. It was founded in 2010 by a team of highly experienced pharmaceutical research and development and management executives. The company is dedicated to developing products to address overall health and wellness, including conditions with a high unmet medical need, such as digestive health. The IM HealthScience advantage comes from developing products based on its patented, targeted-delivery technologies called Site Specific Targeting (SST). For more information, visit www.imhealthscience.com to learn about the company, or www.IBgard.com,  www.FDgard.comwww.FiberChoice.com, and www.Remfresh.com.

This information is for educational purposes only and is not meant to be a substitute for the advice of a physician or other health care professional. You should not use this information for diagnosing a health problem or disease. The company will strive to keep information current and consistent but may not be able to do so at any specific time. Generally, the most current information can be found on www.fiberchoice.com. Individual results may vary.

SOURCE/REFERENCES

[1] Greger, Michael, M.D., FACLM. (2015, September 29). Where Do You Get Your Fiber? [Blog post]. Retrieved from https://nutritionfacts.org/2015/09/29/where-do-you-get-your-fiber/

[2] Institute of Medicine. 2005. Dietary Reference Intakes for Energy, Carbohydrate, Fiber, Fat, Fatty Acids, Cholesterol, Protein, and Amino Acids. Washington, DC: The National Academies Press. https://doi.org/10.17226/10490.

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

2018

Benefits of fiber in diet

https://pharmaceuticalintelligence.com/2018/03/14/benefits-of-fiber-in-diet/

2016

Nutrition & Aging: Dr. Simin Meydani appointed Vice Provost for Research @Tufts University

https://pharmaceuticalintelligence.com/2016/08/01/nutrition-aging-dr-simin-meydani-appointed-vice-provost-for-research-tufts-university/

2015

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The Future of Hospitals – How Medical Care and Technology Work Together to Advance Patient Care 

Curator: Gail S. Thornton, M.A.

Co-Editor: The VOICES of Patients, Hospital CEOs, HealthCare Providers, Caregivers and Families: Personal Experience with Critical Care and Invasive Medical Procedures

 

Gap Medics (https://www.gapmedics.com/blog/), the world’s leading provider of hospital work experience placements for high school and university students, recently released their “Futuristic Hospitals” infographic. The infographic reviews a collection of top hospitals in the world based on several key factors:

  • overall patient care,
  • innovative medical and technological excellence,
  • efforts toward sustainability,
  • environmental stewardship, and
  • social responsibility, as well as
  • other innovative health care features

to help advance the field of medicine and, ultimately, patient care.

Futuristic Hospitals Infographic

Image SOURCE: Infographic of Futuristic Hospitals courtesy of Evolved Digital and Gap Medics. Reprinted here with Permission from the Source.

 

“Many leading hospital facilities are now rolling out significant improvements and changes that couldn’t have been envisioned 10 years ago,” said Ian McIntosh, Director, Evolved Digital (http://evolveddigital.co.uk/), a U.K.-based digital marketing company specializing in search engine optimization and content marketing, whose team created the infographic for Gap Medics.

Science and innovation are working together to help convey higher expectations for quality medical and health care and advancements in the hospital experience for health care providers, patients and their families.

Particularly, the infographic analyzed prominent hospitals around the world so patients and their families can learn about the latest advances and efforts in patient care and hospital and medical technology.

In this infographic, we investigated the most cutting-edge hospital facilities in the world, where best-in-class technology and innovative medical care are making a difference in providing a quality experience all over the world.

“Gap Medics creates programs offered to thousands of students from Europe, Asia and the United States so they have the opportunity to gain insights into the work of doctors, nurses, physician assistants, midwives and dentists before the students begin their clinical training,” said Dave Brown, Director, Gap Medics, a U.K.-based company that provides hospital work experience between 1-8 weeks to students 16 years of age and older.

This one-in-a-lifetime opportunity helps students better understand their chosen career path, develop as people, and strengthen their university application process.

 

REFERENCE/SOURCE

http://evolveddigital.co.uk/

https://www.gapmedics.com/blog/2017/03/27/futuristic-hospitals/

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

 

“Sudden Cardiac Death,” SudD is in Ferrer inCode’s Suite of Cardiovascular Genetic Tests to be Commercialized in the US

Curator: Aviva Lev-Ari, PhD, RN

https://pharmaceuticalintelligence.com/2014/02/10/sudden-cardiac-death-sudd-is-in-ferrer-incodes-suite-of-cardiovascular-genetic-tests-to-be-commercialized-in-the-us/

 

Hybrid Cath Lab/OR Suite’s da Vinci Surgical Robot of Intuitive Surgical gets FDA Warning Letter on Robot Track Record

Reporter: Aviva Lev-Ari, PhD, RN

https://pharmaceuticalintelligence.com/2013/07/19/hybrid-cath-labor-suites-da-vinci-surgical-robot-of-intuitive-surgical-gets-fda-warning-letter-on-robot-track-record/

 

3D Cardiovascular Theater – Hybrid Cath Lab/OR Suite, Hybrid Surgery, Complications Post PCI and Repeat Sternotomy

Curator: Aviva Lev-Ari, PhD, RN

https://pharmaceuticalintelligence.com/2013/07/19/3d-cardiovascular-theater-hybrid-cath-labor-suite-hybrid-surgery-complications-post-pci-and-repeat-sternotomy/

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President Carter’s Status

Author: Larry H. Bernstein, MD, FCAP

 

 

Most Experts Not Surprised by Carter’s Status 

But early response does not mean ‘cure’

http://www.medpagetoday.com/HematologyOncology/SkinCancer/55076

 

http://clf1.medpagetoday.com/media/images/55xxx/55076.jpg

by Charles Bankhead
Staff Writer, MedPage Today

 

Former President Jimmy Carter’s announcement that he is free of metastatic melanoma surprised many people but, not most melanoma specialists contacted by MedPage Today.

With the evolution of modern radiation therapy techniques and targeted drugs, more patients with metastatic melanoma achieve complete and partial remissions, including remission of small brain metastases like the ones identified during the evaluation and initial treatment of Carter. However, the experts — none of whom have direct knowledge of Carter’s treatment or medical records — cautioned that early remission offers no assurance that the former president is out of the woods.

“If I had a patient of my own with four small brain mets undergoing [stereotactic radiation therapy], I would tell them that I fully expected the radiation to take care of those four lesions,” said Vernon K. Sondak, MD, of Moffitt Cancer Center in Tampa. “The fact that President Carter reports that it has done just that is not a surprise to me at all.

“I would also tell my patient that the focused radiation only treats the known cancer in the brain, and that if other small areas of cancer are present, they will likely eventually grow large enough to need radiation or other treatment as well, and that periodic brain scans will be required to monitor for this possibility.”

Carter also is being treated with the immune checkpoint inhibitor pembrolizumab (Keytruda), which is known to stimulate immune cells that then migrate to tumor sites to eradicate the lesions, noted Anna Pavlick, DO, of NYU Langone Medical Center in New York City.

“Melanoma is no longer a death sentence, and we are really changing what happens to patients,” said Pavlick. “It really is amazing.”

Carter’s melanoma story began to emerge in early August when he had surgery to remove what was described as “a small mass” from his liver. Following the surgery, Carter announced that his doctors had discovered four small melanoma lesions in his brain, confirming a suspicion the specialists had shared with him at the time of the surgery.

Carter subsequently underwent focused radiation therapy to eradicate the brain lesions and initiated a 12-week course of treatment with pembrolizumab. The radiation therapy-targeted therapy combination was a logical option for Carter, given observations that the PD-L1 inhibitor has synergy with radiation, noted Stergios Moschos, MD, of the University of North Carolina Lineberger Comprehensive Cancer Center at Chapel Hill.

“I have seen this in other patients with metastatic melanoma,” said Gary K. Schwartz, MD, of Columbia University Medical Center in New York City. “It is remarkable but absolutely possible within the realm of immunotherapy today.”

Although Carter’s announcement is undeniably good news, the optimism should be tempered by a long-term perspective, suggested Nagla Abdel Karim, MD, PhD, of the University of Cincinnati Medical Center.

“We do have similar stories; however, we would be careful to call it a ‘complete remission’ and ‘disease control’ and not a ‘cure,’ so far,” said Karim. “We would resume therapy and follow-up any autoimmune side effects. Most important is the quality of life, which he seems to enjoy, and we are very happy with that.”

Darrell S. Rigel, MD, also of NYU Langone Medical Center, represented the lone dissenter among specialists who responded to MedPage Today‘s request for comments.

“I’m happy for him, but it’s very unusual, especially in older men, who usually have a worse prognosis,” said Rigel. “He is on a new drug that may have a little more promise, but there is no definitive cure at this point.”

 

 

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