Posts Tagged ‘pharma’

Companies Actively Engaging Artificial Intellegence for Drug Design: 2026

Posted in Artificial Intelligence Applications in Health Care, Artificial Intelligence in CANCER, Artificial Intelligence in Medicine - Applications in Therapeutics, Deep Learning, Drug Development Process, Machine Learning, Pharmaceutical Drug Discovery, Pharmaceutical Industry Competitive Intelligence, tagged Artificial intelligence, artificial intelligence in drug design, biotech, drug design, In Silico Medicine, pharma, recursion pharmaceuticals on July 31, 2026| Leave a Comment »

Companies Actively Engaging Artificial Intellegence for Drug Design: 2026

Curator: Stephen J. Williams, Ph.D.

Work-in-Progress

This is not an exhaustive list of all AI companies involved in the drug discovery process.  Some like Chai Discovery will be featured in separate posts and this will be constantly updated as companies are acquired and formed. 

UPDATED 8/31/2026

First Bankruptcy Casualty for AI-Drug Discovery Firm: BioXcel Files for Chapter 11 Bankruptcy and sells Alzheimers Pipeline to Teva Pharmaceuticals

Aug 27 (Reuters) – AI-driven biopharma firm BioXcel Therapeutics filed for Chapter 11 bankruptcy protection in the U.S. on Thursday.

Here are more details:

• BioXcel listed estimated liabilities in the range of $100 million to $500 million and assets of $10 million to $50 million in its petition filed with U.S. Bankruptcy Court for the District of Delaware.

• The filing comes after BioXcel entered into an amended credit agreement on Monday, under which creditors would provide an additional $1.25 million in loans to the company.

• BioXcel Therapeutics reported a quarterly adjusted loss of 49 cents​​ per share for the three months ended June 30.

• The firm, whose shares have fallen about 55% so far this year, said in May it was exploring strategic options including a sale, merger or licensing agreement of its assets, but did not provide a timeline.

• BioXcel is focused on developing neurological medication. Its experimental drug BXCL501 is marketed as an acute treatment for agitation associated with Alzheimer’s dementia.

(Reporting by Anusha Shah in Bengaluru; Editing by Eileen Soreng)

Source: https://srnnews.com/biopharma-firm-bioxcel-therapeutics-files-for-bankruptcy-in-us/ 

About BioXcel: Source https://www.bioxceltherapeutics.com/ai-based-drug-re-innovation/

The traditional paradigm in the pharmaceutical industry of selecting new chemical entities that are put through lead identification and selection in drug discovery and development is marred by decade-long timelines, high costs that increase every year, and low success rates. Consequently, many diseases and medical conditions remain without treatments. Yet, an underutilized and expansive universe of potential drug candidates exists that consists of approved products that could be effective for additional indications as well as compounds that have already demonstrated safety in prior clinical trials but may have been discontinued by their original developers for various reasons.

BioXcel Therapeutics is focused on AI-based drug re-innovation in neuroscience. We expedite the discovery and development of new indications for existing late-stage drug candidates and/or approved drugs by leveraging NovareAI: a composite of AI tools and approaches. NovareAI enables us to reduce therapeutic development costs, potentially accelerate timelines, while improving the success rate of bringing new treatment options to patients.

Original Article Starts Here

I want to start with a Great interview and white paper by McKinsey where they interview key opinion leaders on the business intelligence on AI firms in the drug discovery world and what to look for in companies that are in this space.

Source: https://www.mckinsey.com/industries/life-sciences/our-insights/how-ai-could-revolutionize-drug-discovery

How AI could revolutionize drug discovery

November 16, 2022 | Video

By Alex Devereson, Christoph Sandler, and Lydia The

Watch VIDEO

https://www.mckinsey.com/Videos/video?vid=6313209549112&plyrid=IzQolWCsY

Artificial intelligence could help scientists develop better medicines faster—and thus improve millions of people’s lives. But for that to happen, companies will need to change the way they work.

Below is a Mckinsey report on How AI will Change the Future of Biotech

https://www.mckinsey.com/featured-insights/the-next-normal/biotech

CHARTING THE FUTURE

AI will be embedded into everyday research

The AI-driven drug discovery industry continues to grow, fueled by new entrants in the market, significant capital investment, and technology maturation. We’ve identified more than 250 companies working in the industry. More than half of them are based in the United States, but key hubs are emerging in Western Europe and Southeast Asia as well. The best of these companies will fully integrate AI into research workflows, as the exhibit shows. By putting AI at the center of the research engine, companies can transform research at scale—and bring about dramatic improvements in patient outcomes. For the full article, see:

Parts of a high-throughput screening (HTS) process embedded with AI technology

1. High-throughput screen commenced with diverse compound sets Scientist selects diverse compound sets (a set of chemical compounds with a wide range of chemical structures) as first high-throughput screen

• In silico/ on-the-chip simulations

• In-vitro/ ‘wet lab’ experiments

2. Automated compound selection and transfer Using HTS machinery, individual compounds are transferred to individual wells of cells under experimental conditions

3. Computer-vision-based hit selection Cell response to each compound is measured using microscope analysis (eg, through computer vision techniques); promising compounds are labeled “hits”

4. Automated machine learning (ML) model training from screen outcomes Information from HTS for first few plates is automatically transferred into an ML pipeline, which “learns” how cells respond to each kind of chemical structure

5. Compound library inferencing and prioritization ML algorithm then scans the remainder of the library compounds and predicts which plates should be prioritized to identify the highest number of hits in the next screen

6. Automated compound selection based on ML recommendations ML recommendations are automatically queued and used in the next round of HTS. The cycle continues, with the algorithm continuously learningfrom “real world” outputs. Recommendations trigger scientists to explore new chemical space and begin downstream screening processes more quickly. These recommendations feed into the selection of chemical compounds in step 1

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Article (3 pages)

Human bodies are incredibly complex. It takes many years to discover even just one new medicine to successfully treat a disease. Could artificial intelligence help speed up that process? McKinsey experts believe so. (The following transcript has been edited for clarity.)

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Lydia The: What excites me about AI and drug discovery is the convergence between technology, drug development, and biology, which is going to lead to better drugs being developed faster—using all of the capabilities that Silicon Valley and the tech ecosystem have developed—to help us have even greater impact on patients.

Christoph Sandler: Today, to discover and develop a drug takes more than ten years.

Alex Devereson: We might be able to have drugs in one-tenth of the time, from being discovered to being able to treat patients. Today, many diseases simply have no treatments whatsoever. I think, and I hope, we’re going to see a world where we can generate therapies that can treat those patients very effectively. Fundamentally, we will have life-changing, game-changing drugs—on a scale and at a pace that we’ve never seen before—getting to the right patient at the right time.

The promise of personalized medicine

Christoph Sandler: In the not-so-distant future, we might collect health data across different inputs: from wearables, from our electronic medical records, or from clinical or academic research. And we will have the opportunity, on a voluntary basis, to upload these data into a central, secure, trusted data storage system.

Lydia The: You could imagine using the data to figure out not just what drug might work for you but exactly what drug would work for you at what time, in what sequence, in what dose—really personalized to you.

Will AI replace scientists?

Lydia The: What we’ve found is that technology doesn’t supplant the people. Rather, it will enable scientists to do things faster and better—and potentially develop insights that humans would not be able to develop at all.

Alex Devereson: Scientists will be able to discover things with machine learning that they could never have thought of by themselves, generate entirely new ideas, and move at a pace at which one person can do what it would have taken 100 to do before.

Lydia The: While in previous generations, scientists would spend a lot of their time—maybe even the majority of their time—on manual efforts such as pipetting from one tray to another tray or manually curating and cleaning data, I think AI will help us do all of those things in a more automated and quick way, and develop hypotheses that can then lead a scientist to think through, “What would the next experiment be? What are the implications of the data?”

What companies should do today

Alex Devereson: I think the challenge a lot of companies have is that they want to explore new ideas but, for good reason, they are reluctant to commit until they’ve seen some results and some tangible impact. So they explore a lot of pilots.

Lydia The: We have a term for that. We call it “pilot purgatory”: companies focus on one pilot, and they see the returns in a single pilot, but they don’t establish that approach and way of operating across their organization.

Define a ‘North Star’

Lydia The: One of the most important things for companies to escape pilot purgatory is a real mindset shift, from the top end all the way through the rest of the organization.

Christoph Sandler: It is important for the organization to define what a North Star for them should be, so that the data and analytics transformation of the R&D function can be targeted toward that North Star.

Identify—and solve—the biggest pain points

Alex Devereson: Truly understand what your biggest scientific and operational pain points are. For the lab scientists, for the patients who are in your clinical trials, for the patients who might get the drug: What is the biggest unsolved problem today?

Embed analytics into decision making

Christoph Sandler: Bring the organization on board and help them understand the potential of data and analytics.

Alex Devereson: You need to really, truly redesign a process that embeds analytics, where it’s not something on the side; it’s truly a part of the decision making.

Christoph Sandler: And you need to establish trust in the data and in these models. Equally important as the technical part is the human part.

Deliver value quickly

Alex Devereson: Don’t set up a project that delivers only after five years. Have a view on how you can deliver value in three months—and on what it takes in terms of analytics, data, and technology—with a relentless laser focus on value for the patient and for the scientific process.

 

 

Source: https://www.statnews.com/2026/06/03/alnylam-partner-with-inceptive-nucleics-ai-foundation-models/?utm_campaign=the_readout&utm_medium=email&_hsenc=p2ANqtz-_DOWjLTkMjWQ-ewilam07aNlz79w35ktjIKopXav_x3PpkOIfQIqnnMJlxdznIvjv-lGeo3tI4BUwIuxqEclEu4fHA9Q&_hsmi=422199873&utm_content=422199873&utm_source=hs_email

By Meghana Keshavan Biotech Correspondent

The inventor of some of AI’s technical underpinnings is shifting focus to an RNA startup. Also, Rick Pazdur has some ideas on how to determine whether Revolution Medicines’ pancreatic cancer drug can work in a first-line setting. And an oncologist explains why she believes the failed Grail trial matters.

artificial intelligence The AI architect taking aim at RNAi Jakob Uszkoreit helped create the transformer architecture — the “T” in ChatGPT — that sparked the generative AI boom. Now he’s trying to do something just as ambitious in drug development, STAT’s Brittany Trang writes. His startup, Inceptive Nucleics, is building biological foundation models that can be applied across a wide range of sequence-based medicines, from RNA interference therapies to mRNA and antisense drugs. That vision caught the attention of Alnylam, which yesterday announced that it had struck a three-year partnership worth up to $2 billion in potential milestone payments and royalties, along with a $30 million upfront investment. The idea is that AI could perhaps do more than analyze biological data — and instead design the molecules themselves.

 

Top 10 Leading AI Drug Discovery Companies Transforming the Market: Trends, Technologies, Growth Outlook & Competitive Landscape (2026-2034)

Source: https://www.linkedin.com/pulse/top-10-leading-ai-drug-discovery-companies-transforming-fsc5e/ 

IntelMarketResearch

MARKET OVERVIEW:

Artificial Intelligence (AI) is redefining the future of pharmaceutical research, making drug development faster, smarter, and more cost-effective than ever before. What once took more than a decade and billions of dollars can now be significantly accelerated through advanced machine learning algorithms, generative AI, predictive analytics, and computational biology.

AI Drug Discovery Market is projected to grow from USD 3.41 billion in 2026 to USD 10.45 billion by 2034, registering a robust 15.7% CAGR. This remarkable expansion reflects the pharmaceutical industry’s increasing reliance on AI-powered platforms to improve research efficiency, reduce clinical failures, and identify breakthrough therapies for complex diseases.

As healthcare systems worldwide demand faster innovation and precision medicine continues to evolve, AI is becoming an essential component of modern drug discovery rather than an experimental technology.

Key Technologies Powering AI Drug Discovery

The industry’s rapid growth is supported by several advanced technologies that work together throughout the drug development pipeline.

Machine Learning helps identify disease targets, recognize biological patterns, and predict therapeutic outcomes using historical datasets.

Deep Learning improves molecular analysis by recognizing highly complex biological relationships that conventional computational methods may overlook.

Generative AI creates entirely new molecular structures designed for specific therapeutic applications, enabling scientists to explore millions of virtual compounds before physical synthesis.

Predictive Modeling estimates toxicity, efficacy, pharmacokinetics, and safety profiles early in development, reducing unnecessary experimentation.

Molecular Docking Simulations evaluate how drug molecules interact with proteins, helping researchers prioritize candidates with the highest therapeutic potential.

AI-Driven Clinical Trial Optimization assists pharmaceutical companies in selecting suitable patient populations, improving trial design, and increasing the probability of successful outcomes.

Top 10 Leading AI Drug Discovery Companies Driving Innovation

The competitive landscape is rapidly evolving, with established technology providers and specialized biotechnology companies competing to develop next-generation AI platforms.

1. Insilico Medicine

Insilico Medicine has become one of the industry’s most recognized innovators through its end-to-end AI drug discovery platform. The company combines generative AI with biological research to accelerate target identification and therapeutic development.

2. Exscientia

Exscientia focuses on AI-designed precision medicines and has established multiple collaborations with leading pharmaceutical organizations to improve drug candidate selection and optimization. Exscientia is a pioneering artificial intelligence-driven pharmatech company that was acquired by Recursion Pharmaceuticals in November 2024

3. Atomwise

Known for its deep learning platform, Atomwise uses artificial intelligence to identify promising small-molecule therapies across oncology, infectious diseases, and rare disorders.

4. Recursion Pharmaceuticals

Recursion Pharmaceuticals integrates high-content imaging, automation, and AI to analyze millions of biological experiments, enabling large-scale phenotypic drug discovery.

5. BERG

BERG (now operating or known as BPGbio) specializes in biology-driven AI by combining multi-omics datasets with artificial intelligence to identify biomarkers and novel therapeutic opportunities.

6. Cyclica

Cyclica’s AI platform focuses on polypharmacology, helping researchers understand complex drug-protein interactions while improving drug design efficiency. , focusing on MatchMaker and POEM technology platforms before being acquired by Recursion for $40 million in May 2023.

7. GNS Healthcare

GNS Healthcare applies causal AI and patient-level analytics to support personalized medicine and identify drug repurposing opportunities.

8. DeepCure

DeepCure develops generative chemistry platforms capable of designing optimized molecules while reducing early-stage research complexity.

9. Schrödinger

Schrödinger combines computational chemistry, molecular simulation, and AI technologies to improve molecular modeling and accelerate pharmaceutical innovation.

10. IBM Watson Health & DeepMind Health

These technology pioneers continue expanding AI capabilities across healthcare by leveraging advanced analytics, large-scale biological data processing, and intelligent research platforms that support drug development initiatives. https://www.ibm.com/products/watsonx

There are many other companies that are using AI in Innovative ways to support drug discovery.  Here is another list from

 

11 Innovative Companies Using AI for Drug Discovery

By Sherri Gordon

Published December 15, 2025

Overview

A look at 11 companies using AI-driven platforms to reshape drug discovery, including generative models, computational chemistry, and data-centric biology.

• Recursion Pharmaceuticals
• Insitro
• Generate Biomedicines
• Genesis Therapeutics
• Iambic Therapeutics
• Terray Therapeutics
• Relay Therapeutics
• Absci
• Valo Health
• DeepCure
• A-Alpha Bio

 

For more articles on AI and healthcare on this Open Access Scientific Journals please see our Portals at

Medicine with GPT-4 & Chat GPT

AGI, generativeAI, Grok, DeepSeek & Expert Models in Healthcare

Artificial Intelligence: Genomics & Cancer

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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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