Researchers advance drugs that treat pain without addiction
Opioids are one of the most common ways to treat pain. They can be effective but are also highly addictive, an issue that has fueled the ongoing opioid crisis. In 2020, an estimated 2.3 million Americans were dependent on prescription opioids.
Opioids bind to receptors at the end of nerve cells in the brain and body to prevent pain signals. In the process, they trigger endorphins, so the brain constantly craves more. There is a huge risk of addiction in patients using opioids for chronic long-term pain. Even patients using the drugs for acute short-term pain can become dependent on them.
Scientists have been looking for non-addictive drugs to target pain for over 30 years, but their attempts have been largely ineffective. “We desperately need alternatives for pain management,” says Stephen E. Nadeau, a professor of neurology at the University of Florida.
A “dimmer switch” for pain
Paul Blum is a professor of biological sciences at the University of Nebraska. He and his team at Neurocarrus have created a drug called N-001 for acute short-term pain. N-001 is made up of specially engineered bacterial proteins that target the body’s sensory neurons, which send pain signals to the brain. The proteins in N-001 turn down pain signals, but they’re too large to cross the blood-brain barrier, so they don’t trigger the release of endorphins. There is no chance of addiction.
When sensory neurons detect pain, they become overactive and send pain signals to the brain. “We wanted a way to tone down sensory neurons but not turn them off completely,” Blum reveals. The proteins in N-001 act “like a dimmer switch, and that's key because pain is sensation overstimulated.”
Blum spent six years developing the drug. He finally managed to identify two proteins that form what’s called a C2C complex that changes the structure of a subunit of axons, the parts of neurons that transmit electrical signals of pain. Changing the structure reduces pain signaling.
“It will be a long path to get to a successful clinical trial in humans," says Stephen E. Nadeau, professor of neurology at the University of Florida. "But it presents a very novel approach to pain reduction.”
Blum is currently focusing on pain after knee and ankle surgery. Typically, patients are treated with anesthetics for a short time after surgery. But anesthetics usually only last for 4 to 6 hours, and long-term use is toxic. For some, the pain subsides. Others continue to suffer after the anesthetics have worn off and start taking opioids.
N-001 numbs sensation. It lasts for up to 7 days, much longer than any anesthetic. “Our goal is to prolong the time before patients have to start opioids,” Blum says. “The hope is that they can switch from an anesthetic to our drug and thereby decrease the likelihood they're going to take the opioid in the first place.”
Their latest animal trial showed promising results. In mice, N-001 reduced pain-like behaviour by 90 percent compared to the control group. One dose became effective in two hours and lasted a week. A high dose had pain-relieving effects similar to an opioid.
Professor Stephen P. Cohen, director of pain operations at John Hopkins, believes the Neurocarrus approach has potential but highlights the need to go beyond animal testing. “While I think it's promising, it's an uphill battle,” he says. “They have shown some efficacy comparable to opioids, but animal studies don't translate well to people.”
Nadeau, the University of Florida neurologist, agrees. “It will be a long path to get to a successful clinical trial in humans. But it presents a very novel approach to pain reduction.”
Blum is now awaiting approval for phase I clinical trials for acute pain. He also hopes to start testing the drug's effect on chronic pain.
Learning from people who feel no pain
Like Blum, a pharmaceutical company called Vertex is focusing on treating acute pain after surgery. But they’re doing this in a different way, by targeting a sodium channel that plays a critical role in transmitting pain signals.
In 2004, Stephen Waxman, a neurology professor at Yale, led a search for genetic pain anomalies and found that biologically related people who felt no pain despite fractures, burns and even childbirth had mutations in the Nav1.7 sodium channel. Further studies in other families who experienced no pain showed similar mutations in the Nav1.8 sodium channel.
Scientists set out to modify these channels. Many unsuccessful efforts followed, but Vertex has now developed VX-548, a medicine to inhibit Nav1.8. Typically, sodium ions flow through sodium channels to generate rapid changes in voltage which create electrical pulses. When pain is detected, these pulses in the Nav1.8 channel transmit pain signals. VX-548 uses small molecules to inhibit the channel from opening. This blocks the flow of sodium ions and the pain signal. Because Nav1.8 operates only in peripheral nerves, located outside the brain, VX-548 can relieve pain without any risk of addiction.
"Frankly we need drugs for chronic pain more than acute pain," says Waxman.
The team just finished phase II clinical trials for patients following abdominoplasty surgery and bunionectomy surgery.
After abdominoplasty surgery, 76 patients were treated with a high dose of VX-548. Researchers then measured its effectiveness in reducing pain over 48 hours, using the SPID48 scale, in which higher scores are desirable. The score for Vertex’s drug was 110.5 compared to 72.7 in the placebo group, whereas the score for patients taking an opioid was 85.2. The study involving bunionectomy surgery showed positive results as well.
Waxman, who has been at the forefront of studies into Nav1.7 and Nav1.8, believes that Vertex's results are promising, though he highlights the need for further clinical trials.
“Blocking Nav1.8 is an attractive target,” he says. “[Vertex is] studying pain that is relatively simple and uniform, and that's key to having a drug trial that is informative. But the study needs to be replicated and frankly we need drugs for chronic pain more than acute pain. If this is borne out by additional studies, it's one important step in a journey.”
Vertex will be launching phase III trials later this year.
Finding just the right amount of Nerve Growth Factor
Whereas Neurocarrus and Vertex are targeting short-term pain, a company called Levicept is concentrating on relieving chronic osteoarthritis pain. Around 32.5 million Americans suffer from osteoarthritis. Patients commonly take NSAIDs, or non-steroidal anti-inflammatory drugs, but they cannot be taken long-term. Some take opioids but they aren't very effective.
Levicept’s drug, Levi-04, is designed to modify a signaling pathway associated with pain. Nerve Growth Factor (NGF) is a neurotrophin: it’s involved in nerve growth and function. NGF signals by attaching to receptors. In pain there are excess neurotrophins attaching to receptors and activating pain signals.
“What Levi-04 does is it returns the natural equilibrium of neurotrophins,” says Simon Westbrook, the CEO and founder of Levicept. It stabilizes excess neurotrophins so that the NGF pathway does not signal pain. Levi-04 isn't addictive since it works within joints and in nerves outside the brain.
Westbrook was initially involved in creating an anti-NGF molecule for Pfizer called Tanezumab. At first, Tanezumab seemed effective in clinical trials and other companies even started developing their own versions. However, a problem emerged. Tanezumab caused rapidly progressive osteoarthritis, or RPOA, in some patients because it completely removed NGF from the system. NGF is not just involved in pain signalling, it’s also involved in bone growth and maintenance.
Levicept has found a way to modify the NGF pathway without completely removing NGF. They have now finished a small-scale phase I trial mainly designed to test safety rather than efficacy. “We demonstrated that Levi-04 is safe and that it bound to its target, NGF,” says Westbrook. It has not caused RPOA.
Professor Philip Conaghan, director of the Leeds Institute of Rheumatic and Musculoskeletal Medicine, believes that Levi-04 has potential but urges the need for caution. “At this early stage of development, their molecule looks promising for osteoarthritis pain,” he says. “They will have to watch out for RPOA which is a potential problem.”
Westbrook starts phase II trials with 500 patients this summer to check for potential side effects and test the drug’s efficacy.
There is a real push to find an effective alternative to opioids. “We have a lot of work to do,” says Professor Waxman. “But I am confident that we will be able to develop new, much more effective pain therapies.”
Podcast: The Friday Five weekly roundup in health research
The Friday Five covers five stories in health research that you may have missed this week. There are plenty of controversies and troubling ethical issues in science – and we get into many of them in our online magazine – but this news roundup focuses on scientific creativity and progress to give you a therapeutic dose of inspiration headed into the weekend.
Covered in this week's Friday Five:
- Sex differences in cancer
- Promising research on a vaccine for Lyme disease
- Using a super material for brain-like devices
- Measuring your immunity to Covid
- Reducing dementia risk with leisure activities
One day in recent past, scientists at Columbia University’s Creative Machines Lab set up a robotic arm inside a circle of five streaming video cameras and let the robot watch itself move, turn and twist. For about three hours the robot did exactly that—it looked at itself this way and that, like toddlers exploring themselves in a room full of mirrors. By the time the robot stopped, its internal neural network finished learning the relationship between the robot’s motor actions and the volume it occupied in its environment. In other words, the robot built a spatial self-awareness, just like humans do. “We trained its deep neural network to understand how it moved in space,” says Boyuan Chen, one of the scientists who worked on it.
For decades robots have been doing helpful tasks that are too hard, too dangerous, or physically impossible for humans to carry out themselves. Robots are ultimately superior to humans in complex calculations, following rules to a tee and repeating the same steps perfectly. But even the biggest successes for human-robot collaborations—those in manufacturing and automotive industries—still require separating the two for safety reasons. Hardwired for a limited set of tasks, industrial robots don't have the intelligence to know where their robo-parts are in space, how fast they’re moving and when they can endanger a human.
Over the past decade or so, humans have begun to expect more from robots. Engineers have been building smarter versions that can avoid obstacles, follow voice commands, respond to human speech and make simple decisions. Some of them proved invaluable in many natural and man-made disasters like earthquakes, forest fires, nuclear accidents and chemical spills. These disaster recovery robots helped clean up dangerous chemicals, looked for survivors in crumbled buildings, and ventured into radioactive areas to assess damage.
Now roboticists are going a step further, training their creations to do even better: understand their own image in space and interact with humans like humans do. Today, there are already robot-teachers like KeeKo, robot-pets like Moffin, robot-babysitters like iPal, and robotic companions for the elderly like Pepper.
But even these reasonably intelligent creations still have huge limitations, some scientists think. “There are niche applications for the current generations of robots,” says professor Anthony Zador at Cold Spring Harbor Laboratory—but they are not “generalists” who can do varied tasks all on their own, as they mostly lack the abilities to improvise, make decisions based on a multitude of facts or emotions, and adjust to rapidly changing circumstances. “We don’t have general purpose robots that can interact with the world. We’re ages away from that.”
Robotic spatial self-awareness – the achievement by the team at Columbia – is an important step toward creating more intelligent machines. Hod Lipson, professor of mechanical engineering who runs the Columbia lab, says that future robots will need this ability to assist humans better. Knowing how you look and where in space your parts are, decreases the need for human oversight. It also helps the robot to detect and compensate for damage and keep up with its own wear-and-tear. And it allows robots to realize when something is wrong with them or their parts. “We want our robots to learn and continue to grow their minds and bodies on their own,” Chen says. That’s what Zador wants too—and on a much grander level. “I want a robot who can drive my car, take my dog for a walk and have a conversation with me.”
Columbia scientists have trained a robot to become aware of its own "body," so it can map the right path to touch a ball without running into an obstacle, in this case a square.
Jane Nisselson and Yinuo Qin/ Columbia Engineering
Today’s technological advances are making some of these leaps of progress possible. One of them is the so-called Deep Learning—a method that trains artificial intelligence systems to learn and use information similar to how humans do it. Described as a machine learning method based on neural network architectures with multiple layers of processing units, Deep Learning has been used to successfully teach machines to recognize images, understand speech and even write text.
Trained by Google, one of these language machine learning geniuses, BERT, can finish sentences. Another one called GPT3, designed by San Francisco-based company OpenAI, can write little stories. Yet, both of them still make funny mistakes in their linguistic exercises that even a child wouldn’t. According to a paper published by Stanford’s Center for Research on Foundational Models, BERT seems to not understand the word “not.” When asked to fill in the word after “A robin is a __” it correctly answers “bird.” But try inserting the word “not” into that sentence (“A robin is not a __”) and BERT still completes it the same way. Similarly, in one of its stories, GPT3 wrote that if you mix a spoonful of grape juice into your cranberry juice and drink the concoction, you die. It seems that robots, and artificial intelligence systems in general, are still missing some rudimentary facts of life that humans and animals grasp naturally and effortlessly.
How does one give robots a genome? Zador has an idea. We can’t really equip machines with real biological nucleotide-based genes, but we can mimic the neuronal blueprint those genes create.
It's not exactly the robots’ fault. Compared to humans, and all other organisms that have been around for thousands or millions of years, robots are very new. They are missing out on eons of evolutionary data-building. Animals and humans are born with the ability to do certain things because they are pre-wired in them. Flies know how to fly, fish knows how to swim, cats know how to meow, and babies know how to cry. Yet, flies don’t really learn to fly, fish doesn’t learn to swim, cats don’t learn to meow, and babies don’t learn to cry—they are born able to execute such behaviors because they’re preprogrammed to do so. All that happens thanks to the millions of years of evolutions wired into their respective genomes, which give rise to the brain’s neural networks responsible for these behaviors. Robots are the newbies, missing out on that trove of information, Zador argues.
A neuroscience professor who studies how brain circuitry generates various behaviors, Zador has a different approach to developing the robotic mind. Until their creators figure out a way to imbue the bots with that information, robots will remain quite limited in their abilities. Each model will only be able to do certain things it was programmed to do, but it will never go above and beyond its original code. So Zador argues that we have to start giving robots a genome.
How does one do that? Zador has an idea. We can’t really equip machines with real biological nucleotide-based genes, but we can mimic the neuronal blueprint those genes create. Genomes lay out rules for brain development. Specifically, the genome encodes blueprints for wiring up our nervous system—the details of which neurons are connected, the strength of those connections and other specs that will later hold the information learned throughout life. “Our genomes serve as blueprints for building our nervous system and these blueprints give rise to a human brain, which contains about 100 billion neurons,” Zador says.
If you think what a genome is, he explains, it is essentially a very compact and compressed form of information storage. Conceptually, genomes are similar to CliffsNotes and other study guides. When students read these short summaries, they know about what happened in a book, without actually reading that book. And that’s how we should be designing the next generation of robots if we ever want them to act like humans, Zador says. “We should give them a set of behavioral CliffsNotes, which they can then unwrap into brain-like structures.” Robots that have such brain-like structures will acquire a set of basic rules to generate basic behaviors and use them to learn more complex ones.
Currently Zador is in the process of developing algorithms that function like simple rules that generate such behaviors. “My algorithms would write these CliffsNotes, outlining how to solve a particular problem,” he explains. “And then, the neural networks will use these CliffsNotes to figure out which ones are useful and use them in their behaviors.” That’s how all living beings operate. They use the pre-programmed info from their genetics to adapt to their changing environments and learn what’s necessary to survive and thrive in these settings.
For example, a robot’s neural network could draw from CliffsNotes with “genetic” instructions for how to be aware of its own body or learn to adjust its movements. And other, different sets of CliffsNotes may imbue it with the basics of physical safety or the fundamentals of speech.
At the moment, Zador is working on algorithms that are trying to mimic neuronal blueprints for very simple organisms—such as earthworms, which have only 302 neurons and about 7000 synapses compared to the millions we have. That’s how evolution worked, too—expanding the brains from simple creatures to more complex to the Homo Sapiens. But if it took millions of years to arrive at modern humans, how long would it take scientists to forge a robot with human intelligence? That’s a billion-dollar question. Yet, Zador is optimistic. “My hypotheses is that if you can build simple organisms that can interact with the world, then the higher level functions will not be nearly as challenging as they currently are.”
Lina Zeldovich has written about science, medicine and technology for Popular Science, Smithsonian, National Geographic, Scientific American, Reader’s Digest, the New York Times and other major national and international publications. A Columbia J-School alumna, she has won several awards for her stories, including the ASJA Crisis Coverage Award for Covid reporting, and has been a contributing editor at Nautilus Magazine. In 2021, Zeldovich released her first book, The Other Dark Matter, published by the University of Chicago Press, about the science and business of turning waste into wealth and health. You can find her on http://linazeldovich.com/ and @linazeldovich.