Posts Tagged ‘FDA’

#TUBiol5227: Biomarkers & Biotargets: Genetic Testing and Bioethics

Posted in and Bioethics, Bio-Ethics, BioBanking, BioIT: BioInformatics, BioIT: BioInformatics, NGS, Clinical & Translational, Pharmaceutical R&D Informatics, Clinical Genomics, Cancer Informatics, Biological Networks, Biomarkers & Medical Diagnostics, BioSimilars, Biotechnology, BioTechnology - Venture Creation, Cancer and Current Therapeutics, Cancer Genomics, Clinical Diagnostics, Clinical Genomics, Conference Coverage with Social Media, Ethics and Leadership, FDA, FDA Regulatory Affairs, Federal Budget Appropriations, Health Economics and Outcomes Research, Health Law Policy, Healthcare costs and reimbursement, Intellectual Property, Innovations, Commercialization, Investment in technological breakthrough, interventional oncology, Next Generation Sequencing (NGS), Patent Law in Biotech, Patents, Patient Experience, Personal Health Applications: Tech Innovations serves HealhCare, Personalized and Precision Medicine & Genomic Research, Personalized Medicine Coalition, Precision Cancer Medicine, Regulated Clinical Trials: Design, Methods, Components and IRB related issues, Voices of Patients and Healthcare Providers, tagged #TUBiol5227, bioethics, biomarkers, Drug development, FDA, healthcare, insurers, legal, Patient, payers, physician-patient, Regulatory on September 28, 2021| Leave a Comment »

#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.
 

Get more details on this report – Request Free Sample PDF
 

Nutrigenomic Testing will provide robust market growth

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Articles on Genetic Testing, Companion Diagnostics and Regulatory Mechanisms

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

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

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

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

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

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

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

Posted in AI Models in Healthcare, AI-assisted Cardiac MRI, Artificial Intelligence - Breakthroughs in Theories and Technologies, Artificial Intelligence Applications in Health Care, Artificial Intelligence in Health Care - Tools & Innovations, Artificial Intelligence in Medicine - Applications in Therapeutics, Bio-Ethics, BioIT: BioInformatics, Biological Networks, FDA, FDA Regulatory Affairs, Health Economics and Outcomes Research, Health Law Policy, Image Processing/Computing, Machine Learning, Personal Health Applications: Tech Innovations serves HealhCare, Regulated Clinical Trials: Design, Methods, Components and IRB related issues, tagged AAAS, AI medical imaging, AI/ML, Artificial intelligence, explainability, explanatory AI, FDA, healthcare, image-interpretation training, interpretable AI, Machine Learning models, medical decision-making, medical decisionmaking, Medical imaging, Safety on July 31, 2021| Leave a Comment »

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

Posted in BioSimilars, Drug Development Process, FDA, FDA Regulatory Affairs, FDA, CE Mark & Global Regulatory Affairs: process management and strategic planning - GCP, GLP, ISO 14155, Patient Experience: Personal Memories of Invasive Medical Intervantion, Value-based Drug Pricing, tagged Biopharmaceutical, FDA, FDA commisioner, Food and Drug Administration, health costs, New Drug Application, Personalized and Precision Medicine & Genomic Research, pharma, Pharmaceutical industry, Scott Gottlieb on March 8, 2019| Leave a Comment »

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

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

Not quite two years into his stewardship of the FDA, Scott Gottlieb, M.D., is stepping down. His sudden departure will likely send shock waves through biopharma, because the commissioner was popular for his market-based policies to lower drug costs and work to speed new brands to approval.

Gottlieb won Senate confirmation as FDA commissioner back in May 2017, an uncertain time for the drug industry. During the selection process, his name had been favored by a majority of biopharma executives polled by an analyst. At the time, one anonymous executive said he’s the “least likely to rail for the obliteration of the current efficacy, safety, risk-benefit model that is a foundation for advancement of new treatments in the United States”—unlike at least one other candidate for the job.

That preference proved out after Gottlieb took over the agency, as his policies favored speed and competition. And when the news broke Tuesday afternoon, the industry trade group PhRMA praised his “exemplary leadership.”

“During his tenure, he focused on innovation in drug development and review, increased competition, and advanced the regulatory framework for approving novel technologies, including gene therapies,” the organization said in a statement. “His efforts have made a meaningful impact for patients in need of innovative medicines.”

Gottlieb tweeted Tuesday that he’s “immensely grateful” for the opportunity to lead the FDA. In a letter to colleagues, he wrote that “there’s nothing that could pull me away from this role other than the challenge of being apart from my family for these past two years and missing my wife and two young children.”

President Donald Trump tweeted that he’s done an “absolutely terrific job” and will be missed.

In his resignation letter, tweeted by STAT reporter Matthew Herper, Gottlieb reviewed his efforts at the agency over the past two years. It’s a long list, but here are a few examples:

• The FDA worked to avoid drug shortages after a hurricane struck Puerto Rico,
• led a global investigation into drug impurities,
• advanced reviews of cell and gene therapies, and
• bolstered approvals of complex generic drugs.

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

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

During the campaign and since the U.S. presidential election, President Donald Trump has pledged to bring down drug costs and, in some cases, railed against the industry for its pricing. Now, his new FDA commissioner is laying out some approaches the agency will take to fight high prices.

Importantly, the FDA can’t regulate drug prices, but it can implement measures aimed at deterring the types of price hikes that have made so many headlines over more than a year.

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At a U.S. House of Representatives budget hearing on Thursday, new FDA commissioner Scott Gottlieb said his agency will publish and regularly update a list of medications that are off patent and have no competition, work to improve generic review times and seek to “curtail gaming” of regulations by the industry that allows companies to extend patent monopolies.

RELATED: Trump to pharma: You’re ‘getting away with murder,’ and I’m the one to stop it

On the first point, Gottlieb said a regularly updated list of off-patent meds could “entice competitors into the market” and ultimately lower costs. One such old, off-patent med with no generic competition is infamous to industry watchers: Daraprim.

Back in 2015, after picking up the medication from Marathon Pharma, Martin Shkreli’s Turing Pharma raised Daraprim’s price exponentially, triggering intense backlash.

In his opening remarks at the budget hearing, Gottlieb said he’s working on a “drug competition action plan” that he will unveil soon. However, the new commissioner did offer a few strategies to lower costs.

The FDA will work to “improve processes that enable generic versions of complex drugs to be approved for marketing,” and simplify the overall generic review process, Gottlieb said. He wants the agency to achieve those goals while “completely eliminating” a backlog of generic medications waiting for a review, he told members of Congress.

Further, the FDA will seek to stop misuse of Risk Evaluation Mitigation Strategies that have hurt generic drug developers’ ability to get samples of branded medications needed to develop copycats.

The proposals will likely be welcomed by those who’ve asked for drug pricing reform but have seen little action from Congress despite more than a year of calls from its members to rein in drug prices.

In a Thursday statement, the pharmacy benefit manager industry group PCMA hailed the proposals.

“As the Trump administration seeks to combat high drug costs, we applaud FDA Commissioner Gottlieb’s decision to publish a ‘watch list’ of off-patent drugs,” PCMA CEO Mark Merritt said in a statement. “This move, which PCMA and others have long advocated, will help deter manufacturers from buying the rights to drugs like Daraprim for the explicit purpose of raising their price.”

UPDATED 3/19/2019

National Cancer Chief, Ned Sharpless, Named F.D.A.’s Acting Commissioner

From the NYTimes

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

By Sheila Kaplan

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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