Category: Health

In 2000, when FDA led the U.S. to become the first country in the world to regulate the use of reprocessed single-use devices, the agency responded to concerns from manufacturers and physicians that un-regulated re-use of single-use devices could compromise patient safety. Regulation meant that unsafe reprocessing in hospital sterile processing departments ceased—and that third-party reprocessing companies got a regulatory path for reprocessing so that hospitals could gain financial benefit from device re-use without compromising patient safety.

The regulation of reprocessing also—unintentionally—helped U.S. healthcare become more prepared to address climate change concerns. After all, while regulated reprocessing saves hospitals hundreds of thousands of dollars every year, it also dramatically reduces the hospital’s CO2 emissions. However, if the U.S. doesn’t pay attention, that leadership position could soon be in question.

The environmental benefits of reprocessing

The average EP lab can reduce its CO2 emission by more than 2,200 pounds CO2 equivalent per year through reprocessing. Life-cycle analyses (LCAs) comparing the carbon footprint of new electrophysiology (EP) devices with the carbon footprint of new EP devices have demonstrated that the carbon footprint of a reprocessed device is half the footprint of a new device. This environmental savings impact is significantly greater than that of a device recycling program, which implies that a new catheter will be needed. In other words: Every time a catheter is recycled rather than reprocessed, the EP lab increases CO2 emissions.

Reprocessing has emerged as a prime solution for addressing the need for healthcare to reduce its carbon footprint, and the U.S. FDA became an (unknowing) early champion for reducing healthcare’s carbon emissions footprint. The U.S. has, if you will, been ahead of the rest of the world. This may be changing, however, and there is great risk that the U.S. will be left behind other countries, particularly in Europe, in its efforts to make healthcare more “climate friendly.”

In the UK, for example, the National Health Services (NHS) has aggressively engaged in healthcare sustainability efforts with mandates to implement and prefer more-sustainable solutions—including single-use device reprocessing. In France, the Working Group of Cardiac Pacing and Electrophysiology of the French Society of Cardiology recently published a position paper on sustainability in electrophysiology that presents a solution that represents a more deliberate and far-reaching model than anything we have seen in the U.S.

An ambitious vision for reprocessing

The French electrophysiology position paper notes why looking at sustainability in EP is particularly important in the broader context of healthcare environmental impact: EP mainly uses high-tech, single-use medical devices, as opposed to other procedure areas, where reusable, low-complexity devices dominate. In EP, rare metals and rare-earth elements are crucial for manufacturing devices, and the materials are sourced from a complex network of suppliers and locations around the world. The fragile nature of this materials supply network can have an impact on EP procedures when, for example, chip shortages lead to cancelled procedures.

Moreover, atrial fibrillation procedures have a carbon footprint of 170 pounds of carbon, with material production and manufacturing contributing 71 percent of this. This means that reprocessing, which re-uses these parts, has a significant impact on the carbon footprint. Additionally, EP procedures grow by 10-15 percent every year in the U.S. and Europe, and due to the use of scarce materials, the supply of these devices might eventually itself become a problem—a problem that could partially be addressed through reprocessing.

The authors of the positioning paper declare that “reprocessing of certain single-use medical devices appears to be a solution for reducing supply tensions, limiting the environmental impact of medical devices (carbon footprint and reduction of raw material extraction) and lowering procedural cost.” This is a compelling statement by a society of cardiologists. Reprocessing is still prohibited in France, but it is noteworthy that the clinical community, which has traditionally been the most hesitant to adopt reprocessing, is now pushing for the practice to be established. That means they are more progressive than the French authorities.

The key risks associated with reprocessing—loss of functionality and contamination—are also addressed in the paper, but the authors reference studies to conclude that “neither the material properties nor the functional characteristics of the devices were altered as a result of reprocessing. Moreover, the reprocessed catheters did not disadvantage patients or medical doctors […] and reuse of electrophysiology catheters was considered safe for patients.”

Interestingly, the authors warn that “some electrophysiological devices are equipped with lumens (sheaths, irrigated catheters, needles), and precautions should be taken because some areas can be difficult to reach. Cleaning and sterilizing the lumens as well as checking that there is no obstruction or leak are essential. Thus, medical devices with lumens should probably only be reprocessed by facilities with dedicated expertise.” Innovative Health holds several patents on micro-lumen cleaning and inspection, as well as several FDA clearances for devices with micro-lumens. Since more and more devices are designed with lumens, the evolution of reprocessing technology that can clean and inspect these is becoming more and more important every day.

In the U.S., substantial numbers of diagnostic catheters, sheaths, cables, and other devices are being reprocessed and re-used. The use of reprocessed mapping catheters is more rare (due to manufacturer interference), and no ablation catheters have been cleared for reprocessing. From both an environmental and fiscal perspective, this means that the potential for reprocessing is far from fully realized. In fact, in an atrial fibrillation procedure, those two catheters typically represent more than 50 percent of the device costs.

In contrast, among French physicians, “The reuse of mapping and ablation catheters emerged as the solution mentioned most frequently by physicians.” If this interest can be activated in Europe in the form of approved reprocessing of mapping and ablation catheters at a high rate, EP reprocessing environmental impact in the country could quickly leap ahead of that in the U.S.

A multifaceted path to sustainability via reprocessing

The position paper is far-reaching in its recommendations for reprocessing EP devices, and it is also realistic about barriers to increasing sustainability in EP. The authors present a framework for how the multiple stakeholders involved (countries, manufacturers, medical societies, hospitals, and physicians) need to take action to create meaningful change:

• National authorities must mandate LCA analyses for devices and establish carbon emission programs such as the NHS program.
• Manufacturers should start reprocessing their own devices (a particularly bold notion).
• Medical societies should integrate carbon emission footprint discussions into scientific meetings.
• Hospitals should integrate carbon emissions into the process of selecting which devices to use.

FDA’s regulation of single-use device reprocessing effectively made the U.S. the leader in EP sustainability efforts early on. However, very little has been done to build on this early leadership. As a result, we will likely have to look abroad to witness improvements in the sustainability of EP procedures—and the charge is led by an unlikely group. Should the collective opinion of French electrophysiologists shape future re-use practices in France, they will be decades ahead of the U.S. in terms of EP sustainability and financial responsibility.

Photo: ChrisGorgio, Getty Images

Patients often don’t know who the right doctor is for their specific medical needs, leading to them sometimes booking appointments with the incorrect provider. To fix this issue, healthcare marketplace Zocdoc launched a new tool last week to make sure patients are connected with the right providers.

New York City-based Zocdoc helps patients find and book appointments with in-network providers for both in-person and virtual visits. It connects patients to a variety of providers, including primary care physicians, psychiatrists, OB-GYNs and dermatologists. When it initially launched in 2007, it required patients to choose a medical specialty from an alphabetized list when booking an appointment. Now with Zocdoc’s new tool called Guided Search, patients are asked a series of questions about their symptoms. According to a news release, these questions include:

• “What type of care are you looking for?”
• “What best describes the issue you want to address?”

After answering these questions, patients can review providers recommended to them by Zocdoc and book an appointment.

“Most patients aren’t doctors,” said Oliver Kharraz, CEO and founder of Zocdoc, in an email. “When they have a medical issue, they think about their healthcare needs or problems on their own terms. Additionally, many patients know their symptoms but don’t know what type of doctor they need. If a patient has an eye floater, for example, they might not know whether they need to see an optometrist or an ophthalmologist. Guided Search simplifies this.”

The new feature also helps providers, Kharraz said. It saves them time by helping “vet” patients to make sure they’re the right fit for the provider’s specialty. It also helps better prepare clinicians for appointments by providing them with information about why the patient is coming in.

Zocdoc is already seeing results from the Guided Search feature. In its Beta phase, the tool increased search-to-booking conversion rates by up to 14%, according to the news release. In addition, there has been a 100% increase in patients booking appointments for pregnancy care, a 190% increase in patients booking dermatology appointments for cosmetic care and a 200% increase in patients booking dermatologists for hair loss. This shows that Guided Search is “helping patients find doctors who are best suited to address specific care needs or concerns,” the news release said.

Recent research from Zocdoc shows that there is a need for Guided Search. The company surveyed 1,004 adults and 971 providers in January. The survey found that about 57% of patients have gone to a doctor’s appointment to discover that the provider was not the right one for their condition. On the flip side, 24% of providers report that they often see new patients that don’t fit their specialty.

“We want to empower patients to make confident decisions about their care, helping them find their medical match and build a long-lasting relationship,” Kharraz said. “Healthcare is complex, and all too often patients and providers are not properly paired because there is critical context missing. This hinders care for the patient and takes valuable time away from providers. With Guided Search, both parties can be confident that they can find a strong fit for their respective needs.”

Photo: elenabs, Getty Images

Join MedCity News and more than 300 healthcare investors, startups, and innovative-minded executives in Chicago at the Ritz Carlton on May 21-22 for MedCity INVEST 2024. The conference is the premier boutique healthcare investment event in the U.S.  Equal parts networking and curated panels covering the latest trends in healthcare investment, MedCity INVEST 2024 is the ideal event to discuss healthcare funding with investors looking for investment opportunities.

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Amyotrophic lateral sclerosis develops through multiple pathways, so drug research in this neuromuscular disorder has pursued multiple targets. One of those targets faces some doubts after an ALS drug candidate from partners Sanofi and Denali Therapeutics failed to meet the goal of a mid-stage clinical trial.

Denali disclosed the Phase 2 clinical trial failure in a Friday regulatory filing. The South San Francisco-based biotech said Sanofi informed it that the brain-penetrating drug, known at Denali as DNL788 and renamed SAR443820 by the pharmaceutical giant, did not meet the main endpoint of showing a change in the ALS Functional Rating Scale-Revised, a scoring assessment for evaluating symptoms in ALS patients. No other information was disclosed. Sanofi plans to present detailed efficacy and safety results at a future scientific meeting, according to the filing.

The Denali drug is a small molecule designed to block RIPK1, a signaling protein that regulates inflammation and cell death in tissues throughout the body. In 2018, Sanofi and Denali struck up a multi-drug research alliance in neurological and inflammatory diseases. The partnership kicked off with the pharmaceutical giant paying its new partner $125 million up front. The pact put Denali in line for $1 billion in milestone payments.

The partnership’s ALS research initially focused on DNL747, a Denali molecule that reached Phase 1b testing in Alzheimer’s and ALS. Preliminary clinical trial results in 2020 showed this molecule hit its target and was safe and well tolerated. Parallel to this clinical trial, a toxicity study was conducted in monkeys. Results from this research indicated challenges for increasing the dose to achieve higher levels of target inhibition—which the companies concluded may be necessary for maximizing efficacy. Sanofi and Denali decided to pause research with this molecule, shifting focus to others in the partnership, including DNL788/SAR443820.

In 2021, the companies reported Phase 1 results showing that DNL788/SAR443820 was safe at all doses tested in healthy volunteers. Furthermore, the results showed the molecule engaged its target. The collaboration agreement called for Sanofi to lead Phase 2 development in ALS and multiple sclerosis. The ALS study enrolled 305 participants randomly assigned to receive the study drug or a placebo twice daily for 24 weeks.

In a note sent to investors, Leerink Partners analyst Marc Goodman wrote that the trial’s failure in ALS was disappointing, given that the study was well powered and the drug had shown strong engagement with its target in Phase 1 testing. The Phase 2 failure suggests that inhibiting RIPK1 may not be the right approach to treat ALS, he said. But Goodman added that more could be learned from the trial’s 52-week open-label extension study to further assess the drug’s safety and efficacy. Every participant who elects to continue in this extension study will receive the experimental drug. The main goal of the extension study is a combination assessment of function and survival. Meanwhile, the Phase 2 study in MS has completed its enrollment of 174 patients. Sanofi is continuing this study, Denali said in the regulatory filing.

If blocking RIPK1 proves to be ineffective in treat ALS, the implications could extend beyond Sanofi and Denali. In 2021, Eli Lilly paid Rigel Pharmaceuticals $125 million up front to begin a partnership on that biotech’s RIPK1-blocking small molecules. The lead program in this partnership has reached Phase 2 testing in rheumatoid arthritis. A molecule with the capability of penetrating the central nervous system is still in preclinical development. In its financial reports, Rigel said this molecule could address neurodegenerative diseases such as Alzheimer’s disease and ALS.

More ALS clinical trial news is coming soon. Amylyx Pharmaceuticals won FDA approval for Relyvrio, a drug that addresses two pathways key to neuron survival. The 2022 regulatory decision was based on results from a placebo-controlled Phase 2 test. A longer and larger Phase 3 clinical trial is ongoing. The company expects topline data will become available in the second quarter of this year.

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Intellectual property can be a thorny issue in health tech and medtech. The intersection of healthcare data and AI is setting up some complex patent showdowns and interesting ethical discussions. What are the implications for how we think about personal health data and innovation based on that data? Munck Wilson Mandala Partner Greg Howison shared how attorneys are thinking about AI, IP, connected devices and the data they generate in response to emailed questions.

You have said that most people neither understand the importance of data nor the role of data derived from connected medical devices  when it comes to IP. What do you mean?

Any medical device with any type of sensor collects data. That data can be ephemeral—meaning it exists only for local evaluation by the device—it can be stored for later retrieval, or it can be offloaded via a wireless transmitter. Implanted devices have historically used a near field communication link for this purpose. However, FDA approval of Bluetooth transmitters for usage within the body has allowed for the advent of Bluetooth-equipped implanted devices that can communicate with smart phones. This ability to communicate between a smart phone and an implanted device will be at the center of all new medical device technology going forward, as any implanted device will be capable of use for real time monitoring and data collection. This data is valuable.

Data from simple medical devices in clinical trials is being collected and stored by medical device companies for the purpose of training large diagnostic models. This collected data is actually a valuable asset for these companies and the question is, who owns the data when a person is involved and associated with the data?

What kinds of legal questions are raised by the way medtech companies, big and small, are sourcing data from their connected devices?

The legal issues relate to HIPAA and privacy. Is it possible to provide a document that transfers all rights in the personal data to a company and if so, what is a company allowed to do with this data? The issue there is the HIPAA umbrella. Even though one has possession of data, protection of that data may still come about under HIPAA. Does the sale or distribution of that data for the purpose of training a model violate HIPAA?  Does the sale or distribution of the trained model violate HIPAA?

There is currently a large copyright case out there dealing with what can be gleaned from a trained model, in which ChatGPT is being sued by litigants claiming that the mere use of their copyrighted creative work to train a model results in the model being a derivative work of their “original work”. 

Such an approach requires there to be close association with what ChatGPT outputs and the author’s original work. 

This line of thought can be extended to models used in the health sector for training and the such. For example, could a well-trained model somehow be used to back into the medical information of a person? Suppose there was an individual with some rare disease that uniquely identifies them, and a query is made to the model as to what other maladies are associated with that disease. In a litigation environment, the litigants would argue that the results of this query might uniquely identify that individual, but the model gurus do not agree with that. It is difficult to argue that a model is a derivative work of or closely associated with a personal profile. There will no doubt be litigation up the road on that. But a well-crafted release should be able to address this issue.   

Are there competing legal theories regarding data ownership when a person/patient is involved?

You have to first think about data in and of itself.  Data per se is like picking up a handful of sand—it is just a bunch of numbers, but the person who gathers it (or uses a device to gather it) “creates” it (or uses a device to create it). One can then store all of this data and keep it under wraps and thus own it. 

Now, suppose the data is created using a machine interfaced with a patient. The creator (the one operating/controlling the machine) still owns the data, but there is now a question as to the patient’s rights. This is the old issue of whether one owns their medical records—collecting data from a machine is no different than writing information in a file, but this does not necessarily revoke a patient’s right to access the information. Thus, one might own the data they created from a patient, but the patient should still have the right to access the data. 

Furthermore, data ownership can have restrictions placed thereupon by privacy concerns. There exists a restriction on the use of clinical information in that it cannot be identifiably associated with a particular patient. The exact same thing applies for collected medical device data. Suppose one were to collect blood pressure data from a class of patients aged 65 – 70 over the course of a year in a very particular locale. This could be used to create a chart of trends and likewise train a model. Since HIPAA concerns are always there, one usually is required to release their data for use by other professionals.

That is a long answer to the question, and the short answer is simply that privacy rules. The creator or collector of the data owns the data because they collected and assembled it into a structured database, but if it is collected from a patient there are some potential restrictions on the use of it. 

What are you seeing as categories of IP use cases among your clients?

IP for these clients can be classified as patent, trademark (branding), and data.  The value of any one of those depends on what business one is in. For example, any business that requires diagnostics in their business plan will likely use AI in their development efforts as a tool, such as for gene therapy research. Almost all drug research will make use of AI models trained on large (possibly proprietary) data sets as a tool in the research and development of their drugs. These are businesses that will “use” their IP, in the form of data, as a tool in their efforts to develop products as a source of value.  

Then there are businesses that will create IP as a source of value. A business developing a new medical device will have patents at the heart of their IP portfolio, of course. When the device is sold, the person or company using the device will create data, and this creation/collection of data will become an important aspect of their IP. Any company invested in clinical trials will have data collection as a central part of their IP portfolio. One expects, however, that any use of a medical device will more than likely have a separate license as to who owns the data. Even though one owns a device, there is usually a software component to the device that is only licensed. For example, when an individual puts on a wearable with a communication link, the data created by the device from the individual’s measurements usually goes to a cloud service, and the user license agreement likely stipulates that all of the data thus collected is owned by the service provider/licensor – look at the fine print!

What kind of nuances are you seeing across your healthcare client categories in terms of legal and regulatory challenges for class 1-3 medical devices? Diagnostic development?

From a pure investment strategy perspective, the time to proof of concept is key. For Class 1 devices, the time required to prove effectiveness is comparatively fast. So, an investor will know if that dog will hunt within a short period of time. Getting over the FDA hurdle is also not that onerous or expensive for Class 1 devices – it may take a couple of years. The time to ROI on these Class 1 devices is short and more in line with the expectations of most investors.  

As one shifts to Class 3 devices the investment perspective changes dramatically. FDA approval for Class 3 devices is expensive and time consuming. Even if the device proves effective and viable for the market, it still has to go through the FDA safety evaluation process, and even if the product gets through the FDA, there remain the hurdles of getting a medical (CPT) code for reimbursement and then gaining acceptance in the marketplace. The investment risk primarily lies in whether the device will even gain traction in the market after clearing the lengthy regulatory processes.

One example of this is neurostimulators for pain treatment. Medtronic is the leader in this area. The new products on the market address specific pain issues with a lower price point and arguably better safety than their predecessors. I have worked on a migraine neurostimulator, which was an interesting area.  This is a head-implanted neurostimulator – a Class 3 device. I think it will be on the market next year. Once you identify a large market (such as that for chronic migraines) that has a need because what is out there may not provide an adequate solution, this presents a possibility for a new device to successfully be introduced into the market. A lot of the existing migraine devices are external and are thus not Class 3 devices, so they are able to be introduced into the market faster than implantable devices. To go the implant route requires one to be sure that it is the correct route. The device I worked on was based upon the well-known minimally invasive Reed procedure developed by Dr. Kenneth Reed, the inventor of this device. Thus, the effectiveness was already well proven for the stimulation process, and it was just the safety issue that needed to clear the FDA due to the device being an implant with a rechargeable battery. This was considered the path to take, as external leads are not as effective in applying precise stimulation to a target nerve as implanted leads.

What are some of the nuances of IP when it comes to the importance of the data derived from connected medical devices that AI/machine learning algorithm models are trained with?

I do not think there are any nuances as to data. It is just a by-product of the device. If a device is developed for an application, such as real time monitoring of blood glucose, the main purpose is to allow a patient to sit there with their phone and monitor their glucose levels in real time. This could be used to, for example, set off an alarm or use the information via an AI engine to predict what levels of dosage are required for an insulin pump. But there is a lot of data that is collected and, when combined with other information on the individual, value exists. There is no sense in throwing that away. It is used on one hand to make the device useful in real time and on the other hand to augment these large training databases.

What’s your outlook for 2024 /what are some of your predictions/what will you be watching/looking out for regarding IP in the area of machine learning algorithms derived from/trained on connected device data? 

I think there will be an increase in use of such things as ChatGPT for user interfaces in the medical industry across the board. The main use that may be most disruptive is in self-diagnosis. One will be able to take a picture of a mole, for example, or a rash associated with shingles, and send it to an AI engine that will provide a surprisingly accurate diagnosis. The use of AI with all these new test strips out there will change telehealth quite a bit. For example, a test strip for a UTI or Strep coupled with a patient’s medical history will be used to trigger a prescription event in a cost-effective manner.  

Then there will be the analysis of a patient in a physician’s office prior to the physician even seeing the patient based on data collected at check-in that will provide a preliminary diagnosis and a recommendation for drugs and such. Think of a patient in a dermatologist’s office walking into a booth and doing a complete body scan—total body photography—prior to meeting with the dermatologist. The scan can be run through an AI engine and the dermatologist provided the results in a fraction of the time normally required and with arguably higher accuracy. Hospitals will also use AI for matching patients to health care providers to make patient check-in more efficient. Click here to view an example of this. 

Most of these uses of AI do not require FDA approval. There will be some ethics issues raised by physicians using this for assisting in diagnoses, but that will be at a different level. For devices, the trained AI model will usually be a fixed model that just has to be shown to be effective as to that medical device, as is the case for a programmed CPU used in current medical devices. As long as the program is fixed in the CPU it is FDA approved, but once changed it has to go back to the FDA, as would be the case with an AI model that has its training altered. There is a concern with adaptive models that retrain themselves as they are used, and that will raise FDA issues. 

Again, IP in the form of the data in a trained AI model used for the operation of a device is valuable in the sense that AI is important in realizing such a device. But the data collected from the operation of the device is a separately valuable byproduct that can be used to create large training databases that are in turn used in creating new AI models. All wearables that collect data can provide a lot of benefits when data collected therefrom is disposed in a patient’s stored profile and used for making any decision regarding that patient, not to mention that their data can be used for making decisions as to other patients in the collective. Wearables will be the primary area to impact the medical device arena in the near future. They are Class 1 devices at best, and most can be processed through an FDA 510(k) procedure.    

Have you been encouraged or discouraged by work done by the FDA and other organizations in attempting to create standards in machine learning regulation and related areas and why?

For the most part, the FDA does not (or should not) have to be all that concerned with AI. In the area of therapeutics, if AI is used to develop a drug, that should not be an issue. However, if AI is used to “evaluate” the effectiveness of the drug, that is where the FDA has an issue to address. Any time AI is used to analyze data to predict some result that would in turn be utilized to generate the result or provide predicted information needed to approve the drug, there will always be a validation issue associated with the AI engine.

For a medical device, on the other hand, the incorporation of a trained AI model into the device is a one-time event that results in a fixed and programmed device that is able to receive inputs from sensors and the such and “predict” some result. The device utilizes a specific, fixed AI engine that has been trained on some databases. If one were to use a wearable with sensors that sense various biological inputs and then use these biological inputs to predict a result such as, for example, blood pressure, the question is whether that prediction is valid. There was a standard for measurement validation put forth back in 2018 for devices that are used to provide blood pressure information on a patient.

Although we now look at purpose-built wrist blood pressure monitors as being accurate, they had to be validated at some point. I am sure there exists some accuracy issue with respect thereto, but the designers do utilize some type of algorithm to account for errors in sensors and the like. As the power of AI engines increases, one can imagine it will be possible to take smart watches and utilize their various sensor inputs to somehow approximate the wearer’s blood pressure with a trained machine learning system. The result, however, will still be nothing more than a prediction. The question remains whether that predicted result is a valid result. 

Therefore, any time you use an AI engine there will be a question as to the validity of the results—an issue that will be of concern to the FDA. As another example, there are systems out there that purport to provide real-time blood glucose measurements. They provide this based upon a measurement of parameters associated with such things as sweat. If these measured parameters are run through an AI engine that provides the blood glucose measurement, it is important to know how valid these results are. There are just a lot of variables that go into the prediction provided by any AI engine, such that any AI-generated prediction presents a concern to the FDA.

The FDA may require some more proof that the model has no issues, but I do not think that will be a big issue. Updating a model will require a bit more work though. When a model used in the operation of a medical device is adaptive and changes based upon feedback, that will have to be addressed separately. In general, the FDA is just beginning to get its arms around all of this. It is just recently that these trained models have been implemented in chips that are capable of being incorporated into a medical device.   

(Based on policy questions from WIPO)

Should IP policy create new rights in data?

This goes to the question of there being some intangible rights in the data. A very complicated issue. Data is something that is collected from a device or source that creates it. This is what we refer to as a product of the device or process. We can protect the device and the process in most cases, but the issue is whether the product of that device or process is protectable. Right now, the data generated is not protectable as an intangible property. Sometimes one can argue that the final data structure (these large training datasets are structured datasets) is protectible, but not the data itself. These large training datasets will probably remain proprietary at best.   

Should AI algorithms be patentable?

An algorithm per se is unpatentable. This all goes to the premise that abstract ideas are not patentable. If the algorithm changes the operation of the machine in a significant way, that can rise to the level of patentability. But the algorithm by itself is currently not patentable subject matter. This is something Congress would have to deal with.

Photo: metamorworks, Getty Images

The U.S. National Cybersecurity Strategy, announced in March 2023, was joined by several other regulatory and legislative initiatives throughout the year that will have a major impact on the security of the Internet of Medical Things (IoMT) in 2024 and beyond. As these initiatives progress there is also a proven roadmap for meeting their new and evolving compliance requirements so that medical devices are not only safe but also secure.

Threat surface grows

The World Health Organization (WHO) estimates there are 2 million kinds of medical devices that, increasingly, use software for signal processing, data visualization and other functions, as well as wireless connections to transmit data and allow device control. For example, an unprotected infusion pump, might divulge sensitive information to a hacker and some insulin pumps may even allow remote attackers to change take over control of dose delivery.

A November 2023 study published in Nature magazine found that medical devices purchased by national health services worldwide have nearly 700 vulnerabilities, more than half defined as “critical” or “high-severity.” It takes so long to discover these vulnerabilities that, even if patches were applied immediately after the vulnerability was found and announced, it has been estimated that there would still have been roughly 3.2 years of system exposure between when the device was purchased and the patch applied.

This applies to all classes of devices including high-risk IIB and III devices. The study also compared connected medical devices’ weaknesses to those of IoT products in the broader market, and concluded they are as vulnerable as smart bulbs and speakers.

A cascade of initiatives

The U.S. National Cybersecurity Strategy emphasized two main fixes in the overall war against cyber threats: take some of the risk-management burden off end-users, and better incentivize decision-making so that cyberspace is resilient and defensible over the long term. The July announcement of the National Cybersecurity Strategy Implementation Plan (NCSIP) followed late 2022’s new FDA cybersecurity requirements (finalized in September 2023), and the April publication of the ANSI/AAMI SW96:2023 standard for medical device security. With these developments, the FDA now had statutory authority to require that satisfactory cybersecurity measures be incorporated into medical devices before entering the market. The agency also fully endorsed the new ANSI/AAMI standard in November.

Next up was the NIST Cybersecurity Framework (NCF) 2.0 in August 2023 focusing on improvements in authentication, identity management, cybersecurity risk management, supply chain risk management, and vulnerability disclosure – all highly relevant to vulnerable connected medical devices. In its NCF concept paper, NIST also referenced a National Cybersecurity Center of Excellence (NCCoE) project entitled “Trusted IoT Device Network-Layer Onboarding and Lifecycle Management” that will explore credential provisioning for secure network connection. This requires trusted network-layer onboarding, “in combination with additional device security capabilities such as device attestation, application-layer onboarding, secure lifecycle management, and device intent enforcement could improve the security of networks and IoT devices.”

Also in August, the Biden-Harris Administration announced a cybersecurity labeling program for Internet of Things (IoT) devices to help consumers make informed purchases with security in mind. And finally, December saw the U.S. Department of Health and Human Services’ strategy for healthcare sector cybersecurity, which reiterates elements of the new FDA authority over medical-device security requirements.

Recurring themes

Among these initiatives’ most relevant recurring themes for medical devices are standardization, IoT security, and multi-layered “security by design”.

The push for standards is one of NCSIP’s top priorities, and a key element of the FDA’s new authority to establish medical device security requirements for manufacturers. The FDA’s endorsement of  ANSI/AAMI SW96:2023 adds momentum to the first consensus standard that provides specific requirements for managing security across a medical device’s entire lifecycle.

IoT security is a key element of these initiatives, as well, starting with a National Cybersecurity Strategy’s stipulation that “consumers will be able to compare the cybersecurity protections offered by different IoT products, thus creating a market incentive for greater security across the entire IoT ecosystem.” The NIST NCF 2.0 framework’s IoT device security project is another initiative to watch, and healthcare industry observers are already anticipating that the federal IoT labeling program could be expanded and applied to IoMT devices.

Also noteworthy is the recurring emphasis on multi-layered security by design, with examples in both the NCSIP and the ANSI/AAMI standard. The NCSIP focuses on defending critical infrastructure by, among other means, ensuring software and hardware is “secure-by-design” which the US Cybersecurity and Infrastructure Security Agency (CISA) defines as “conceptualized with the security of customers as a core business goal, not just a technical feature.” Reinforcing this concept, the ANSI/AAMI standard mandates the use of more than one method of ensuring devices and systems are protected.

A proven roadmap

Solutions that embody these themes have already been implemented. One of the best examples is the first FDA-cleared Automated Insulin Delivery (AID) systems that require Insulin pumps to be always connected to a Continuous Glucose Monitor (CGM) in compliance with IEEE 2621 certification requirements. Software development kits (SDKs) are now available that embed IEEE 2621-compliant security assurance directly into market-leading AID systems, proving the value of a standards-based approach to protecting wireless connections against cybersecurity threats. They also offer a roadmap for applying a multi-layered security-by-design approach to connecting and protecting other medical devices under control of a user’s smartphone.

This approach typically spans three key security layers. The first is application-layer security to protect the entire communication channel between the smartphone app, medical device, and cloud from many types of malware and wireless channel cybersecurity attacks. Today’s Bluetooth, Wi-Fi and other communication protocols mitigate some, but not all, threats that are inherent to these communication links. Additional measures are required to fully protect all communications channels so that hackers cannot access data or take control.

The second layer brings trust to all system elements through authentication. Hackers must be prevented from gaining “root access” to privileges that enable them to cause harm. Authentication validates the integrity of the user, smartphone app, cloud, consumables, and any associated devices connected to the solution’s communication system. It can be implemented with software or hardware. Hardware Security Modules (HSMs) may also be provisioned to medical devices at the factory to give both the medical device and the consumable the cryptographic keys and digital certificates they need to behave like secure elements (SE) in the system.

Finally, it is essential that there be secure, always-on connectivity between a medical device’s smartphone apps, IoT devices, and the cloud. Without this assurance layer, a communications lapse – always a risk with handheld devices or smartphones — could prevent the system from receiving the most recent data so it can immediately change device operation to meet patients’ care requirements. One solution is a software app running in the smartphone’s background that harvests IoT device data whenever the device is near the smartphone. A second approach is to use additional “bridge” hardware that communicates with the wearable device and the cloud and can be configured either for continuous operation or for use only when the primary IoT-to-cloud path is unavailable.

2023 was a busy year for healthcare industry security, and especially for initiatives focused on connected medical devices. There is growing and coordinated momentum behind the goal of ensuring these devices improve people’s lives without introducing them to cybersecurity threats and associated safety risks. There also is a proven playbook for implementing the type of multi-layered, security-by-design strategies these initiatives advocate.

Photo: Traitov, Getty Images

In the ever-evolving landscape where technology intersects with healthcare, Brain-Computer Interfaces (BCIs) are emerging as a transformative force, turning the once-fantastic imaginings of science fiction into tangible realities.

BCI technology points towards a future of more seamless integration between the human brain and external devices, unlocking unprecedented possibilities in healthcare applications. From diagnostics to treatment methodologies, BCI technology can unravel new dimensions of understanding and intervention, fundamentally altering the landscape of patient care.

The evolution of brain-computer interfaces

The inception of BCI technology marked a pivotal moment for individuals with disabilities, offering new avenues for communication and control. Initially, BCIs catered primarily to those with severe motor impairments or communication challenges, providing rudimentary but life-changing means to interact with their environment. For instance, early BCIs enabled individuals with paralysis to communicate using simple binary choices or control basic computer cursors with their thoughts.

Fast forward to today, and we find ourselves immersed in a sea of sophistication from the world of implants to non-invasive (wearable) products. BCIs have evolved into intricate systems with the ability to interpret complex neural patterns, unlocking the potential for more nuanced and sophisticated interactions. It’s not just about issuing commands. Today, it’s about deciphering the very language of the brain.

The impact extends even further, with applications from communicating and monitoring astronauts in space to controlling AR/VR devices. These technologies are reshaping how users navigate and interact with devices in boundless ways, introducing a new era of human-machine collaboration.

BCI technology and transforming patient care: What’s being developed now 

There are a large array of applications for brain-computer interface technology across the entire medical field that are already being tested and utilized today. Below are some examples across multiple functions of healthcare:

• Assistive technology and devices BCI devices currently translate brain and bio-signals into device commands. A good example: head word devices like a headband or glasses, that act as a ‘head mouse’ for controlling devices through head and eye movements, which is beneficial for those with motor disabilities.
• Epilepsy EEG is widely used to detect abnormal brain activity associated with epilepsy, helping clinicians confirm the diagnosis and determine appropriate treatment strategies.
• Patient monitoring EEG monitoring is employed during certain surgeries, such as those involving the brain or major blood vessels, to assess and ensure the patient’s brain function remains stable throughout the procedure. It is also used to assess brain activity in patients in a coma, helping healthcare providers understand the extent of neurological damage and guide treatment decisions.
• Sleep disorders Brain wave patterns are monitored during sleep, aiding in the diagnosis and management of various sleep disorders, including sleep apnea and insomnia.
• Traumatic brain injury (TBI) EEG is valuable in assessing brain function following TBI and related conditions, providing information on the severity of damage and aiding in treatment decisions.

These diverse applications of BCI technology showcase its capacity to fundamentally transform healthcare practices, enhancing the quality of life for many patients and healthcare practitioners.

BCI technology in healthcare: What’s next?

As we gaze into the future of BCIs in healthcare, the landscape appears incredibly promising, signaling a transformative era in patient care. While current advancements focus on aiding those with physical limitations, the scope of BCIs, as we just saw, is expanding to revolutionize various aspects of healthcare, from diagnostics to treatment modalities.It’s evident that we are only beginning to uncover the full capabilities of these interfaces.

There is still much research and development needed to improve signal quality and advanced AI and machine learning will also be vital in analyzing and applying this complex data effectively. Moreover, addressing ethical considerations and privacy concerns is paramount, and researchers and scientists will need to develop guidelines and frameworks to ensure that BCIs are used responsibly. With continued interdisciplinary research and collaboration, the journey of BCIs in healthcare promises to be as transformative as it is pioneering, with the full scope of their impact still to be discovered.

Below are some ways BCIs could advance patient care, if these goals are met:

• Hearing impairments and devices BCI sensors in hearable devices will blend environmental, brain, and body data so the device will focus on the sounds the user is paying attention to, solving the “Cocktail Party Problem.”

• Cognitive function and treatments The future wave of BCI applications will offer new strategies in mental health treatment. This includes neurofeedback therapy for conditions like ADHD, anxiety, and depression, where patients will be able to receive real-time feedback on brain activity, aiding in symptom reduction. Neurostimulation is expected to show promising results, particularly in depression treatment. Combined with BCI, a “closed-loop” treatment could be created. For autism disorders, BCI technology will be instrumental in developing personalized interventions. BCI-based cognitive therapy programs will also be beneficial for the aging population, enhancing memory, attention, and executive functions. Additionally, BCIs will be invaluable in the early detection of cognitive impairments, enabling more timely interventions for neurodegenerative disorders.

• Chronic pain management BCIs are set to venture into chronic pain management, utilizing closed-loop neurostimulation to modulate pain receptors in the brain.

• Neuroprosthetics In the world of neuroprosthetics, BCI technology will enable the control of prosthetic devices through wearables where users might be able to control an artificial hand using a writing band or BCI-controlled headband.

• Neurological disorder diagnosis Moreover, as signal quality improves, BCIs will be able to offer non-invasive diagnostic capabilities for neurological disorders such as Alzheimer’s and Parkinson’s, marking a significant advancement in the field. The convergence of cutting-edge technology and neuroscience paints a picture of a future where BCIs play a pivotal role in not just addressing physical constraints but also in optimizing mental well-being and advancing the forefront of neurological healthcare. As research and development in this field continue to accelerate, the full spectrum of possibilities for BCIs in shaping the future of healthcare is yet to be fully explored.

This journey is not just about connecting the human brain to computers. It is about redefining how we understand, approach and experience healthcare. The symphony of technological innovation and human neuroscience is orchestrating a future where BCIs play an integral role in shaping a more inclusive, advanced, and compassionate healthcare ecosystem.

Photo credit: DrAfter123, Getty Images

Engineering a patient’s immune cells into cancer treatments works for treating blood cancers, but has fallen short in solid tumors—until now. An Iovance Biotherapeutics treatment employing a different type of cell has won accelerated FDA approval for advanced melanoma, marking the first product approval of any cell therapy for a solid tumor.

The regulatory decision announced Friday covers melanoma in adults whose cancer cannot be surgically removed or has spread following treatment with an immunotherapy or a targeted cancer therapy. The cell therapy of San Carlos, California-based Iovance, known in development as lifileucel, will be marketed under the brand name Amtagvi.

Amtagvi, a one-time treatment, is made from a type of cell called a tumor-infiltrating lymphocyte, or TIL. The body produces TILs to fight cancer, and they are found in the tumor. The Iovance cell therapy is made by surgically removing a small piece of a patient’s tumor and isolating TILs from that sample. Those TILs are multiplied in a lab, then shipped back to the hospital for infusion into the patient.

The TIL manufacturing process is similar to that of CAR T-therapies, which are made by harvesting and multiplying a patient’s T cells. One key difference is what happens in the lab. CAR T-treatments are engineered to go after a target on the surface of a cancer cell. TILs don’t need this engineering step because they already have the ability to recognize a patient’s cancer cells.

Amtagvi’s Clinical Trial Results

The FDA based its approval on the results of an open-label, single-arm Phase 2 clinical trial. Participants had advanced melanoma that was previously treated with at least one systemic therapy, including a type of immunotherapy called a checkpoint inhibitor, and if positive for a BRAF mutation, a BRAF inhibitor. The main goal was to measure the objective response rate and the duration of response. Of the 73 patients who received Amtagvi within the recommended dosing range, the overall response rate was 31.5%. The median duration of response was not reached. Results were published in in 2022 in the Journal for ImmunoTherapy of Cancer.

The most common adverse reactions reported from the clinical trial included chills, fever, fatigue, faster than normal heart rate, and diarrhea. Amtagvi’s label carries a black box warning that the treatment can lead to death. In the clinical trial, treatment-related mortality was 7.6%. Adverse reactions associated with these deaths included severe infections, internal bleeding, kidney failure, respiratory failure, and abnormal heart rhythm. Patients may experience prolonged and severe cytopenia, which is a low levels of certain blood cells. The risk of heart and kidney injury are also flagged in the boxed warning.

CAR T-therapies also carry black box warnings, with the main complications risks being an excessive immune response called cytokine release syndrome and neurotoxicity. Fred Vogt, interim Iovance CEO and the company’s president and general counsel, noted that Amtagvi was not associated with either of these complications risks. FDA approval of CAR T-therapies came with Risk Evaluation Mitigation Strategies (REMS), programs to manage the risks associated with treatment.

“The [Amtagvi] black box is pretty good, we think it’s much better than what the CAR T’s have,” Vogt said during a Friday conference call. “It doesn’t have a REMs, first and foremost, like the CAR T’s do. It’s got boxed warnings essentially for known risks, from the lymphodepletion and the IL-2 therapies that we provide.”

Lymphodepletion, a key step in all cell therapies, involves using chemotherapy to reduce levels of a patient’s immune cells to improve the survival chances of the cell therapy. IL-2 refers an engineered version of a protein that stimulates an immune response. In the clinical trial, treatment with Amtagvi was followed by aldesleukin, an IL-2 therapy whose uses include the treatment of metastatic melanoma. In the Amtagvi treatment regimen, aldesleukin’s role is to activate the TILs’ anti-tumor activity. Aldesleukin also has risks: Its label includes a black box warning of a potential complication called capillary leak syndrome, leading to low blood pressure and low levels of the protein albumin in the blood. This complication can lead to life-threatening organ damage.

Because aldesleukin is a key part of the Amtagvi treatment regimen, Iovance last year acquired global rights to Proleukin, an IL-2 product from Clinigen. Iovance paid £166.9 million (about $207.2 million) up front for the product’s rights. The acquisition agreement puts Clinigen in line for milestone payments and royalties from sales.

Premium Pricing for the First TIL Cell Therapy

Iovance set a $515,000 wholesale price for Amtagvi, which is higher than the $373,000 to $475,000 price range for currently available CAR T-therapies. The company will make the TIL therapy at its Philadelphia manufacturing facility. Together with a nearby contract manufacturer, the company says it has the capacity to produce Amtagvi for “several thousand patients annually.” The current manufacturing process is 34 days, which is a comparable to the lengthy, multi-step manufacturing process for CAR T-therapies. Vogt said Iovance believes it can improve Amtagvi’s manufacturing timeframe.

Jim Ziegler, executive vice president, commercial, said about 30 authorized treatment centers are now ready to collect and ship tumor tissue for Amtagvi manufacturing. He expects that number to grow to 50 centers within the next three months. Based on interactions with payers, Ziegler said Iovance expects coverage of Amtagvi will be similar to insurance coverage of approved CAR T-therapies.

TIL therapy competition is coming. The Achilles Therapeutics pipeline includes a TIL therapeutic candidate currently in a Phase 1/2 clinical trial evaluating it in combination with a checkpoint inhibitor as a potential treatment for melanoma. Lyell Immunopharma has a TIL therapy in early clinical development in melanoma, colorectal cancer, non-small cell lung cancer (NSCLC), and other solid tumors. Instil Bio’s pipeline includes a TIL in Phase 1 development in NSCLC, ovarian, and kidney cancers. Instil’s former lead program had reached mid-stage clinical development in advanced melanoma, but that therapy was discontinued in late 2022 due to a pipeline reprioritization.

Iovance is also testing Amtagvi in combination with the Merck checkpoint inhibitor Keytruda as a frontline treatment for melanoma. This Phase 3 study will serve as the confirmatory clinical trial required of the therapy’s accelerated approval. The Iovance pipeline also includes tests of Amtagvi—by itself and in combination with checkpoint inhibitors—in cervical cancer, NSCLC, and head and neck squamous cell carcinoma.

Another Iovance TIL therapy, LN-145, has reached pivotal testing in NSCLC. This study was placed under an FDA clinical hold in late December due to a fatality potentially related to the therapy’s preconditioning regimen. Vogt said Iovance is working to resolve the clinical hold and hopes to have an update in a few weeks.

Photo by Iovance Biotherapeutics

Welcome back to another episode of MedCity FemFwd, a podcast dedicated to discussing the breakthroughs and challenges in women’s health. In this episode, we’re joined by Régine Sitruk-Ware, a distinguished scientist at the Population Council’s Center for Biomedical Research (CBR), and Chelsea Polis, senior scientist of epidemiology at CBR.

Medical device startup Contraline recently released promising data for its male birth control product called Adam. Sitruk-Ware and Polis discuss what the development of a male birth control product could mean for women’s health.

To hear the full episode and learn more, click here:

This month, the U.S. Department of Health and Human Services (HHS) and the Substance Abuse and Mental Health Services Administration (SAMHSA) took steps to expand access to opioid use disorder treatment through its new final rule, which includes a provision that permanently allows for the initiation of treatment (methadone and buprenorphine) via telehealth. However, the final rule narrowly applies to only practitioners working in Opioid Treatment Programs (OTPs) and does not include other providers like primary care physicians and addiction medicine specialists.

Telehealth advocates applaud HHS’ final rule but say further action needs to be taken by the Drug Enforcement Administration (DEA) to more broadly expand access to treatment via telehealth. While HHS and SAMHSA have jurisdiction over OTPs, the prescribing of controlled substances is under the DEA’s jurisdiction.

OTPs are clinics licensed by a state’s health department and require federal accreditation to dispense medication-assisted treatment (MAT) services. The telehealth capabilities for OTPs began during the Covid-19 pandemic when it was more difficult to seek treatment in person. Under the final rule, practitioners in OTPs can start patients on methadone via an audio-visual telehealth appointment and start patients on buprenorphine via an audio-only telehealth appointment. The final rule doesn’t allow for an audio-only appointment for methadone because, compared to buprenorphine, it “holds a higher risk of sedation, especially if taken by someone who already is experiencing some drowsiness,” according to the SAMHSA.

In addition to the telehealth changes, the final rule also allows patients to receive take-home doses of methadone and expands provider eligibility to allow nurse practitioners and physician assistants to order medications in OTPs. It also removed criteria that required patients to have a history of addiction for a full year before being eligible for treatment.

“The easier we make it for people to access the treatments they need, the more lives we can save,” said HHS Deputy Secretary Andrea Palm in a statement. “With these announcements, we are dramatically expanding access to life-saving medications and continuing our efforts to meet people where they are in their recovery journeys.”

What the final rule does not include is the virtual prescribing of a broader range of controlled substances in addition to buprenorphine, such as Adderall, Percocet and Xanax. The rule also does not apply to a broader range of providers, such as primary care providers, psychiatrists and virtual providers. The DEA released a proposed rule last year that would roll back Covid-19 flexibilities that allowed a range of practitioners to prescribe controlled substances virtually. However, after receiving a record number of comments, the agency has extended the flexibilities through December of this year. And now with HHS issuing this final rule on OTPs, some are hoping the DEA will expand who is eligible to provide care through telehealth and make these flexibilities permanent.

While the final rule is “really important,” one telehealth advocate is left wanting more. 

“We hope that this forward-looking access to care that’s been achieved through this final rule will be something that is taken up by the DEA, working in conjunction with SAMHSA on this broader overarching policy that we still don’t have a permanent foundation for. … SAMHSA has applied lessons learned to ensure appropriate levels of access for those patients who will go to treatment centers,” said Kyle Zebley, senior vice president of public policy at the American Telemedicine Association, in an interview. He added that he hopes the DEA “exceeds” what SAMHSA has done when it comes to the virtual prescribing of controlled substances. 

One provider who is not affected by HHS’ rule and is awaiting direction from the DEA is Bicycle Health. The Boston-based telehealth company currently offers access to buprenorphine and treats patients across 32 states. 

Dr. Brian Clear, chief medical officer of the company, said “it’s about time” that the HHS final rule was passed, but noted that there are limitations. In March of 2021, there were just 1,816 OTPs in the U.S., according to Pew Charitable Trusts. That just barely scratches the surface of the need when it comes to opioid use disorder.

Clear argued that HHS and SAMHSA’s rulemaking is based on evidence from primary care providers and programs like Bicycle Health that shows virtual prescribing of buprenorphine is safe and effective. Therefore, it would be “absurd” if the DEA decides providers like Bicycle Health can’t also prescribe treatment virtually. 

Another virtual opioid use disorder provider also cheered the final rule while echoing Clear and Zebley on its limitations.

“There are significant ways in which this final rule can help expand access to OTP services, but challenges also remain for patients who want or need low-barrier access to medication-based treatment for SUD,” said Ben Maclean, general counsel at Portland, Oregon-based Boulder Care.

What specifically should the DEA do to expand access?

Zebley of the American Telemedicine Association said the agency should create a special registration process that allows medical professionals to register with the agency in order to virtually prescribe controlled substances. Congress mandated the DEA to create this process back in 2008, but the agency has yet to do so.

Zebley said it’s “dangerous to make too many predictions” about whether the DEA will follow HHS’ footsteps. However, he noted that the virtual prescribing of controlled substances has now been allowed for four years. By the time the extension ends in December, it’ll be just shy of five years. 

“Why would we throw the door back up, gates back up, build that wall back up and leave a lot of vulnerable patients out in the cold? I do think it is a life or death circumstance for some,” he said. “Some Americans receiving care now will have their continuity of care severed and some Americans in the future will never have this opportunity to have that level of access to care that they need when and where they need it.”

Photo: sorbetto, Getty Images