C. Michael White, University of Connecticut

In just a few years, brand-name injectable drugs such as Ozempic, Wegovy, Mounjaro and Zepbound have rocketed to fame as billion-dollar annual sellers for weight loss as well as to control blood sugar levels and reduce the risk of heart disease.

But the price of these injections is steep: They cost about US$800-$1,000 per month, and if used for weight loss alone, they are not covered by most insurance policies. Both drugs mimic the naturally occurring hormone GLP-1 to help regulate blood sugar and reduce cravings. They can be taken only with a prescription.

The Food and Drug Administration announced an official shortage of the active ingredients in these drugs in 2022, but on Oct. 2, 2024, the agency announced that the shortage has been resolved for the medicine tirzepatide, the active ingredient in Mounjaro and Zepbound.

Despite the soaring demand and limited supply of these drugs, there are no generic versions available. This is because the patents for semaglutide – the active ingredient in Ozempic and Wegovy, which is still in shortage – and tirzepatide don’t expire until 2033 and 2036, respectively.

As a result, nonbrand alternatives that can be purchased with or without a prescription are flooding the market. Yet these products come with real risks to consumers.

I am a pharmacist who studies weaknesses in federal oversight of prescription and over-the-counter drugs and dietary supplements in the U.S. My research group recently has investigated loopholes that are allowing alternative weight loss products to enter the market.

High demand is driving GLP-1 wannabes

The dietary supplement market has sought to cash in on the GLP-1 demand with pills, teas, extracts and all manner of other products that claim to produce similar effects as the brand names at a much lower price.

Products containing the herb berberine offer only a few pounds of weight loss, while many dietary supplement weight loss products contain stimulants such as sibutramine and laxatives such as phenolphthalein, which increase the risk of heart attacks, strokes and cancer.

The role of compounding pharmacies

Unlike the dietary supplements that are masquerading as GLP-1 weight loss products, compounding pharmacies can create custom versions of products that contain the same active ingredients as the real thing for patients who cannot use either brand or generic products for some reason.

These pharmacies can also produce alternative versions of brand-name drugs when official drug shortages exist.

Since the demand for GLP-1 medications has far outpaced the supply, compounding pharmacies are legally producing a variety of different semaglutide and tirzepatide products.

These products may come in versions that differ from the brand-name companies, such as vials of powder that must be dissolved in liquid, or as tablets or nasal sprays.

Just like the brand-name drugs, you must have a valid prescription to receive them. The prices range from $250-$400 a month – still a steep price for many consumers.

Compounding pharmacies must adhere to the FDA’s sterility and quality production methods, but these rules are not as rigorous for compounding pharmacies as those for commercial manufacturers of generic drugs.

In addition, the products compounding pharmacies create do not have to be tested in humans for safety or effectiveness like brand-name products do.

Proper dosing can also be challenging with compounded forms of the drugs.

Companies that work the system

For people who cannot afford a compounding pharmacy product, or cannot get a valid prescription for semaglutide or tirzepatide, opportunistic companies are stepping in to fill the void. These include “peptide companies,” manufacturers that create non-FDA approved knockoff versions of the drugs.

From November 2023 to March 2024, my team carried out a study to assess which of these peptide companies are selling semaglutide or tirzepatide products. We scoured the internet looking for these peptide companies and collected information about what they were selling and their sales practices.

We found that peptide sellers use a loophole to sell these drugs. On their websites, the companies state that their drugs are for “research purposes only” or “not for human consumption,” but they do nothing to verify that the buyers are researchers or that the product is going to a research facility.

By reading the comments sections of the company websites and the targeted ads on social media, it becomes clear that both buyers and sellers understand the charade. Unlike compounding pharmacies, these peptide sellers do not provide the supplies you need to dissolve and inject the drug, provide no instructions, and will usually not answer questions.

Peptide sellers, since they allegedly are not selling to consumers, do not require a valid prescription and will sell consumers whatever quantity of drug they wish to purchase. Even if a person has an eating disorder such as anorexia nervosa, the companies will happily sell them a semaglutide or tirzepatide product without a prescription. The average prices of these peptide products range from $181-$203 per month.

Skirting regulations

Peptide sellers do not have to adhere to the rules or regulations that drug manufacturers or compounding pharmacies do. Many companies state that their products are 99% pure, but an independent investigation of three companies’ products from August 2023 to March 2024 found that the purity of the products were far less than promised.

One product contained endotoxin – a toxic substance produced by bacteria – suggesting that it was contaminated with microbes. In addition, the products’ promised dosages were off by up 29% to 39%. Poor purity can cause patients to experience fever, chills, nausea, skin irritation, infections and low blood pressure.

In this study, some companies never even shipped the drug, telling the buyers they needed to pay an additional fee to have the product clear customs.

If a consumer is harmed by a poor-quality product, it would be difficult to sue the seller, since the products specifically say they are “not for human consumption.” Ultimately, consumers are being led to spend money on products that may never arrive, could cause an infection, might not have the correct dose, and contain no instructions on how to safely use or store the product.

Will prices for brand-name products come down?

To combat these alternative sellers, pharmaceutical company Eli Lilly began offering an alternative version of its brand-name Zepbound product for weight loss in September 2024.

Instead of its traditional injection pen products that cost more than $1,000 for a month’s supply, this product comes in vials that patients draw up and inject themselves. For patients who take 5 milligrams of Zepbound each week, the vial products would cost them $549 a month if patients buy it through the company’s online pharmacy and can show that they do not have insurance coverage for the drug.

After a grilling on Capitol Hill in September 2024, pharmaceutical company Novo Nordisk came under intense pressure to offer patients without prescription coverage a lower-priced product for its brand-name Wegovy as well.

In the next few years, additional brand-name GLP-1 agonist drugs will likely make it to market. As of October 2024, a handful of these products are in late-phase clinical trials, with active ingredients such as retatrutide, survodutide and ecnoglutide, and more than 18 other drug candidates are in earlier stages of development.

When new pharmaceutical companies enter this market, they will have to offer patients lower prices than Eli Lilly and Novo Nordisk in order to gain market share. This is the most likely medium-term solution to drive down the costs of GLP-1 drugs and eliminate the drug shortages in the marketplace.

C. Michael White, Distinguished Professor of Pharmacy Practice, University of Connecticut

This article is republished from The Conversation under a Creative Commons license. Read the original article.

Veera Sundararaghavan, University of Michigan

John J. Hopfield and Geoffrey E. Hinton received the Nobel Prize in physics on Oct. 8, 2024, for their research on machine learning algorithms and neural networks that help computers learn. Their work has been fundamental in developing neural network theories that underpin generative artificial intelligence.

A neural network is a computational model consisting of layers of interconnected neurons. Like the neurons in your brain, these neurons process and send along a piece of information. Each neural layer receives a piece of data, processes it and passes the result to the next layer. By the end of the sequence, the network has processed and refined the data into something more useful.

While it might seem surprising that Hopfield and Hinton received the physics prize for their contributions to neural networks, used in computer science, their work is deeply rooted in the principles of physics, particularly a subfield called statistical mechanics.

As a computational materials scientist, I was excited to see this area of research recognized with the prize. Hopfield and Hinton’s work has allowed my colleagues and me to study a process called generative learning for materials sciences, a method that is behind many popular technologies like ChatGPT.

What is statistical mechanics?

Statistical mechanics is a branch of physics that uses statistical methods to explain the behavior of systems made up of a large number of particles.

Instead of focusing on individual particles, researchers using statistical mechanics look at the collective behavior of many particles. Seeing how they all act together helps researchers understand the system’s large-scale macroscopic properties like temperature, pressure and magnetization.

For example, physicist Ernst Ising developed a statistical mechanics model for magnetism in the 1920s. Ising imagined magnetism as the collective behavior of atomic spins interacting with their neighbors.

In Ising’s model, there are higher and lower energy states for the system, and the material is more likely to exist in the lowest energy state.

One key idea in statistical mechanics is the Boltzmann distribution, which quantifies how likely a given state is. This distribution describes the probability of a system being in a particular state – like solid, liquid or gas – based on its energy and temperature.

Ising exactly predicted the phase transition of a magnet using the Boltzmann distribution. He figured out the temperature at which the material changed from being magnetic to nonmagnetic.

Phase changes happen at predictable temperatures. Ice melts to water at a specific temperature because the Boltzmann distribution predicts that when it gets warm, the water molecules are more likely to take on a disordered – or liquid – state.

In materials, atoms arrange themselves into specific crystal structures that use the lowest amount of energy. When it’s cold, water molecules freeze into ice crystals with low energy states.

Similarly, in biology, proteins fold into low energy shapes, which allow them to function as specific antibodies – like a lock and key – targeting a virus.

Neural networks and statistical mechanics

Fundamentally, all neural networks work on a similar principle – to minimize energy. Neural networks use this principle to solve computing problems.

For example, imagine an image made up of pixels where you only can see a part of the picture. Some pixels are visible, while the rest are hidden. To determine what the image is, you consider all possible ways the hidden pixels could fit together with the visible pieces. From there, you would choose from among what statistical mechanics would say are the most likely states out of all the possible options.

Hopfield and Hinton developed a theory for neural networks based on the idea of statistical mechanics. Just like Ising before them, who modeled the collective interaction of atomic spins to solve the photo problem with a neural network, Hopfield and Hinton imagined collective interactions of pixels. They represented these pixels as neurons.

Just as in statistical physics, the energy of an image refers to how likely a particular configuration of pixels is. A Hopfield network would solve this problem by finding the lowest energy arrangements of hidden pixels.

However, unlike in statistical mechanics – where the energy is determined by known atomic interactions – neural networks learn these energies from data.

Hinton popularized the development of a technique called backpropagation. This technique helps the model figure out the interaction energies between these neurons, and this algorithm underpins much of modern AI learning.

The Boltzmann machine

Building upon Hopfield’s work, Hinton imagined another neural network, called the Boltzmann machine. It consists of visible neurons, which we can observe, and hidden neurons, which help the network learn complex patterns.

In a Boltzmann machine, you can determine the probability that the picture looks a certain way. To figure out this probability, you can sum up all the possible states the hidden pixels could be in. This gives you the total probability of the visible pixels being in a specific arrangement.

My group has worked on implementing Boltzmann machines in quantum computers for generative learning.

In generative learning, the network learns to generate new data samples that resemble the data the researchers fed the network to train it. For example, it might generate new images of handwritten numbers after being trained on similar images. The network can generate these by sampling from the learned probability distribution.

Generative learning underpins modern AI – it’s what allows the generation of AI art, videos and text.

Hopfield and Hinton have significantly influenced AI research by leveraging tools from statistical physics. Their work draws parallels between how nature determines the physical states of a material and how neural networks predict the likelihood of solutions to complex computer science problems.

Veera Sundararaghavan, Professor of Aerospace Engineering, University of Michigan

This article is republished from The Conversation under a Creative Commons license. Read the original article.

Chris Meyers, George Washington University

The Drug Enforcement Administration announced in early 2024 that it would act on President Joe Biden’s call to reclassify marijuana, moving it from the tightly controlled Schedule I category that it has been in since 1970 to the less restrictive Schedule III status of the Controlled Substances Act. That triggered a long process of hearings and reviews that will not be completed until after the presidential election in November.

The news drew strong reactions from critics: 25 Republican lawmakers sent a letter to Attorney General Merrick Garland protesting any changes to federal marijuana laws. They argued that the decision “was not properly researched … and is merely responding to the popularity of marijuana and not the actual science.”

As a philosopher and drug policy expert, I focus on assessing arguments and evidence rather than politics or rhetoric. So, what are the arguments for and against rescheduling cannabis?

Scheduling under the Controlled Substances Act

The Controlled Substances Act places each prohibited drug into one of five schedules based on known medical use, addictive potential and safety. Schedule I drugs – which, along with marijuana, also includes heroin, LSD, psilocybin, ecstasy (MDMA) and quaaludes – is the most restrictive category.

Schedule I substances cannot be legally used for any purpose, including medical use or research, though an exception for research can be made with special permission from the DEA. The criteria for inclusion in the Schedule I category is that the substance has a high potential for abuse, is extremely addictive and has “no currently accepted medical use.”

Schedule II, which is slightly less restrictive than Schedule I, includes drugs that are addictive and potentially unsafe but also have some accepted medical use. These include strong opioids such as fentanyl, as well as cocaine, PCP and methamphetamine. Though they are still tightly regulated, Schedule II drugs can be used medically with a prescription or administered by a licensed physician.

Schedule III is much less restrictive and is intended for substances with legitimate medical use and only moderate risk of abuse or dependency. This category includes low-dose morphine, anabolic steroids and ketamine.

Schedule IV – which includes the sedative valium, the weak opioid tramadol and sleep medicines such as Ambien – is even less restrictive.

The least restrictive category is Schedule V, which includes cough syrups with codeine and calcium channel blockers such as gabapentin and pregabalin. All scheduled drugs require a doctor’s prescription and can be distributed only by licensed pharmacies.

What rescheduling would mean for marijuana

The push to reschedule is largely to make federal laws consistent with state medical marijuana programs that – as of October 2024 – are legal in 38 states plus the District of Columbia.

Moving marijuana to Schedule III would not change its legal status in states where it is banned. It would make marijuana legal at the federal level but only for medical use. Recreational use would still be federally prohibited, even though it is currently legal in 24 states plus Washington.

Rescheduling, however, might not make medical marijuana any easier for patients to access and could even make it much harder for some. Currently, getting a medical marijuana card is quite easy in most states. In Washington D.C., where I live, patients can self-certify.

If marijuana is reclassified as Schedule III, medical marijuana programs will have to start requiring a doctor’s prescription, just like with all other scheduled substances. And it could be distributed only by licensed pharmacies, which would put medical dispensaries that are now selling it without a license from the Food and Drug Administration out of business.

Rescheduling, however, would give medical marijuana legitimacy as a bona fide medicine. And the intent of the move is to increase access, even if it is unclear how rescheduling would achieve that.

So, assuming that rescheduling would have the intended effect of expanding access to medical marijuana, should it be rescheduled?

Medical uses of marijuana

Though there are three criteria for Schedule I in the Controlled Substances Act, the DEA in fact relies on only the medical use criterion. This was the basis of the DEA’s proposal to reschedule marijuana. The fact that almost 75% of Americans live in a state with a medical marijuana program suggests that marijuana has an accepted medical use.

More importantly, Schedule III of the Controlled Substances Act already includes dronabinol, which is delta-9 THC, the active ingredient in marijuana. Although dronabinol is synthesized in the lab rather than extracted from the cannabis plant, it is the exact same molecule. The FDA approved THC in the form of dronabinol in 1985 for treating anorexia caused by HIV/AIDS as well as nausea and vomiting due to chemotherapy. Placing marijuana in the same schedule as its primary active ingredient makes a lot of sense.

Another argument in favor of rescheduling is that it would open up new opportunities for medical research into marijuana’s effects, research that is currently hampered by its Schedule I status. This work is critical because the system of cannabinoid receptors through which marijuana causes its therapeutic and psychoactive effects is crucial for almost every aspect of human functioning.

Research has shown that cannabis is effective not only in treating nausea and AIDS but also chronic pain and some symptoms of multiple sclerosis.

There is also good evidence that marijuana can help treat other conditions, including Lou Gehrig’s disease (amyotrophic lateral sclerosis, or ALS), glaucoma, irritable bowel syndrome, insomnia, migraine, post-traumatic stress disorder and Tourette syndrome. Keeping marijuana in the Schedule I category severely hampers research that might establish more effective treatments for these conditions.

Balancing risks and benefits

Those opposed to rescheduling cite possible health risks associated with marijuana consumption. Heavy use is linked to an increased risk of developing schizophrenia. However, the increased risk of schizophrenia from cannabis use is comparable to that caused by watching excessive television, eating junk food or smoking cigarettes.

Long-term marijuana use can also lead to sleep problems and diminished visuospatial memory. It can also cause gastrointestinal trouble, such as cannabis hyperemesis syndrome, which is characterized by nausea, vomiting and abdominal pain. The symptoms, while extremely unpleasant, are temporary and occur only after consuming marijuana. The condition disappears in people who stop using.

Marijuana use can also be addictive. According to the Centers for Disease Control and Prevention, about three out of every 10 regular marijuana users meet the diagnostic criteria for cannabis use disorder.

All of the concerns above are legitimate, though it is worth noting that virtually no effective medicine is free from undesirable side effects. And although marijuana can be habit-forming, it is not as addictive as alcohol, tobacco, oxycodone, cocaine, methamphetamine or benzodiazepines. None of those other drugs are categorized as Schedule I, and alcohol and tobacco are not scheduled at all.

Unlike most other prescription medications, marijuana use is associated with many benefits. For example, in states where marijuana has been legalized, worker’s compensation payments have fallen by an average of 21% among people over 40. Researchers think that this is because marijuana helps workers better manage chronic pain. The use of marijuana for pain management also helps to reduce dependency on opioids. One study found that U.S. counties with one or two marijuana dispensaries had an average of 17% fewer opioid-related fatalities compared with counties with no dispensaries.

Research also shows that marijuana use can help to prevent Alzheimer’s by blocking the enzymes that produce amyloid plaques. It also shows promise for reducing a person’s risk of developing Type 2 diabetes by helping the body regulate insulin and glucose levels.

All of these benefits add up to marijuana users having an overall lower rate of premature death than nonusers.

Chris Meyers, Adjunct Professor of Philosophy, George Washington University

This article is republished from The Conversation under a Creative Commons license. Read the original article.