One Day, There Might Be a Drug for a Broken Heart
For Tony Y., 37, healing from heartbreak is slow and incomplete. Each of several exes is associated with a cluster of sore memories. Although he loves the Blue Ridge Mountains, he can't visit because they remind him of a romantic holiday years ago.
If a new drug made rejections less painful, one expert argues, it could relieve or even prevent major depression.
Like some 30 to 40 percent of depressed patients, Tony hasn't had success with current anti-depressants. One day, psychiatrists may be able to offer him a new kind of opioid, an anti-depressant for people suffering from the cruel pain of rejection.
A Surprising Discovery
As we move through life, rejections -- bullying in school, romantic breakups, and divorces -- are powerful triggers to depressive episodes, observes David Hsu, a neuroscientist at Stony Brook University School of Medicine in Long Island, New York. If a new drug made them less painful, he argues, it could relieve or even prevent major depression.
Our bodies naturally produce opioids to soothe physical pain, and opioid drugs like morphine and oxycodone work by plugging into the same receptors in our brains. The same natural opioids may also respond to emotional hurts, and painkillers can dramatically affect mood. Today's epidemic of opioid abuse raises the question: How many lives might have been saved if we had a safe, non-addictive option for medicating emotional pain?
Already one anti-depressant, tianeptine, locks into the mu opioid receptor, the target of morphine and oxycodone. Scientists knew that tianeptine, prescribed in some countries in Europe, Asia, and Latin America, acted differently than the most common anti-depressants in use today, which affect the levels of other brain chemicals, serotonin and norepinephrine. But the discovery in 2014 that tianeptine tapped the mu receptor was a "huge surprise," says co-author Jonathan Javitch, chief of the Division of Molecular Therapeutics at Columbia University.
The news arrived when scientists' basic understanding of depression is in flux; viewed biologically, it may cover several disorders. One of them could hinge on opioids. It's possible that some people release fewer opioids naturally or that the receptors for it are less effective.
Javitch has launched a startup, Kures, to make tianeptine more effective and convenient and to find other opioid-modulators. That may seem quixotic in the midst of an opioid epidemic, but tianeptine doesn't create dependency in low, prescription doses and has been used safely around the world for decades. To identify likely patients, cofounder Andrew Kruegel is looking for ways to "segment the depressed population by measures that have to do with opioid release," he says.
Is Emotional Pain Actually "Pain"?
No one imagines that the pain from rejection or loss is the same as pain from a broken leg. Physical pain is two perceptions—a sensory perception and an "affective" one, which makes pain unpleasant.
Exploration of an overlap between physical and what research psychologists call "social pain" has heated up since the mid-2000s.
The sensory perception, processed by regions of the brain called the primary and secondary somatosensory cortices and the posterior insula, tells us whether the pain is in your arm or your leg, how strong it is and whether it is a sting, ache, or has some other quality. The affective perception, in another part of the brain called the dorsal anterior cingulate cortex and the anterior insula, tells us that we want the pain to stop, fast! When people with lesions in the latter areas experience a stimulus that ordinarily would be painful, they don't mind it.
Science now suggests that emotional pain arises in the affective brain circuits. Exploration of an overlap between physical and what research psychologists call "social pain" has heated up since the mid-2000s. Animal evidence goes back to the 1970s: babies separated from their mothers showed less distress when given morphine, and more if dosed with naloxone, the opioid antagonist.
Parents, of course, face the question of whether Baby feels alone or wet whenever she howls. And the answer is: both hurt. Being abandoned is the ultimate threat in our early life, and it makes sense that a brain system to monitor social threats would piggyback upon an existing system for pain. Piggybacking is a feature of evolution. An ancestor who felt "hurt" when threatened by rejection might learn adaptive behavior: to cooperate or run.
In 2010, a large multi-university team led by Nathan DeWall at the University of Kentucky, reported that acetaminophen (Tylenol) reduced social pain. Undergraduates took 500 mg of acetaminophen upon awakening and at bedtime every day for three weeks and reported nightly about their day using a previously-tested "Hurt Feelings Scale," rating how strongly they agreed with questions like, "Today, being teased hurt my feelings."
Over the weeks, their reports of hurt feelings steadily declined, while remaining flat in a control group that took placebos. In a second experiment, the research group showed that, compared to controls, people who had taken acetaminophen for three weeks showed less brain activity in the affective brain circuits while they experienced rejection during a virtual ball-tossing game. Later, Hsu's brain scan research supported the idea that rejection triggers the mu opioid receptor system, which normally provides pain-dampening opioids.
More evidence comes from nonhuman primates with lesions in the affective circuits: They cry less when separated from caregivers or social groups.
Heartbreak seems to lie in those regions: women with major depression are more hurt by romantic rejection than normal controls are and show more activity in those areas in brain scans, Hsu found. Also, factors that make us more vulnerable to rejection -- like low self-esteem -- are linked to more activity in the key areas, studies show.
The trait "high rejection sensitivity" increases your risk of depression more than "global neuroticism" does, Hsu observes, and predicts a poor recovery from depression. Pain sensitivity is another clue: People with a gene linked to it seem to be more hurt by social exclusion. Once you're depressed, you become more rejection-sensitive and prone to pain—a classic bad feedback loop.
"Ideally, we'd have biomarkers to distinguish when loss becomes complicated grief and then depression, and we might prevent the transition with a drug."
Helen Mayberg, a neurologist renowned for her study of brain circuits in depression, sees, as Hsu does, the possibility of preventing depressions. "Nobody would suggest we treat routine bad social pain with drugs. But it is true that in susceptible people, losing a partner, for example, can lead to a full-blown depression," says Mayberg, who is the founding director of The Center for Advanced Circuit Therapeutics at Mount Sinai's Icahn School of Medicine in New York City. "Ideally, we'd have biomarkers to distinguish when loss becomes complicated grief and then depression, and we might prevent the transition with a drug. It would be like taking medication when you feel the warning symptoms of a headache to prevent a full-blown migraine."
A Way Out of the Opioid Crisis?
The exploration of social pain should lead us to a deeper understanding of pain, beyond the sharp distinctions between "physical" and "psychological." Finding our way out of the current crisis may require that deeper understanding. About half of the people with opioid prescriptions have mental health disorders. "I expect there are a lot of people using street opioids—heroin or prescriptions purchased from others--to self-medicate psychological pain," Kreugel says.
What we may need, he suggests, is "a new paradigm for using opioids in psychiatry: low, sub-analgesic, sub-euphoric dosing." But so far it hasn't been easy. Investors don't flock to fund psychiatric drugs and in 2018, the word opioid is poison.
As for Tony Y., he's struggled for three years to recover from his most serious relationship. "Driving around highways looking at exit signs toward places we visited together sometimes fills me with unbearable anguish," he admits. "And because we used to do so much bird watching together, sometimes a mere glimpse of a random bird sets me off." He perks up at the idea of a heartbreak drug. "If the side effects didn't seem bad, I would consider it, absolutely."
A new type of cancer therapy is shrinking deadly brain tumors with just one treatment
Few cancers are deadlier than glioblastomas—aggressive and lethal tumors that originate in the brain or spinal cord. Five years after diagnosis, less than five percent of glioblastoma patients are still alive—and more often, glioblastoma patients live just 14 months on average after receiving a diagnosis.
But an ongoing clinical trial at Mass General Cancer Center is giving new hope to glioblastoma patients and their families. The trial, called INCIPIENT, is meant to evaluate the effects of a special type of immune cell, called CAR-T cells, on patients with recurrent glioblastoma.
How CAR-T cell therapy works
CAR-T cell therapy is a type of cancer treatment called immunotherapy, where doctors modify a patient’s own immune system specifically to find and destroy cancer cells. In CAR-T cell therapy, doctors extract the patient’s T-cells, which are immune system cells that help fight off disease—particularly cancer. These T-cells are harvested from the patient and then genetically modified in a lab to produce proteins on their surface called chimeric antigen receptors (thus becoming CAR-T cells), which makes them able to bind to a specific protein on the patient’s cancer cells. Once modified, these CAR-T cells are grown in the lab for several weeks so that they can multiply into an army of millions. When enough cells have been grown, these super-charged T-cells are infused back into the patient where they can then seek out cancer cells, bind to them, and destroy them. CAR-T cell therapies have been approved by the US Food and Drug Administration (FDA) to treat certain types of lymphomas and leukemias, as well as multiple myeloma, but haven’t been approved to treat glioblastomas—yet.
CAR-T cell therapies don’t always work against solid tumors, such as glioblastomas. Because solid tumors contain different kinds of cancer cells, some cells can evade the immune system’s detection even after CAR-T cell therapy, according to a press release from Massachusetts General Hospital. For the INCIPIENT trial, researchers modified the CAR-T cells even further in hopes of making them more effective against solid tumors. These second-generation CAR-T cells (called CARv3-TEAM-E T cells) contain special antibodies that attack EFGR, a protein expressed in the majority of glioblastoma tumors. Unlike other CAR-T cell therapies, these particular CAR-T cells were designed to be directly injected into the patient’s brain.
The INCIPIENT trial results
The INCIPIENT trial involved three patients who were enrolled in the study between March and July 2023. All three patients—a 72-year-old man, a 74-year-old man, and a 57-year-old woman—were treated with chemo and radiation and enrolled in the trial with CAR-T cells after their glioblastoma tumors came back.
The results, which were published earlier this year in the New England Journal of Medicine (NEJM), were called “rapid” and “dramatic” by doctors involved in the trial. After just a single infusion of the CAR-T cells, each patient experienced a significant reduction in their tumor sizes. Just two days after receiving the infusion, the glioblastoma tumor of the 72-year-old man decreased by nearly twenty percent. Just two months later the tumor had shrunk by an astonishing 60 percent, and the change was maintained for more than six months. The most dramatic result was in the 57-year-old female patient, whose tumor shrank nearly completely after just one infusion of the CAR-T cells.
The results of the INCIPIENT trial were unexpected and astonishing—but unfortunately, they were also temporary. For all three patients, the tumors eventually began to grow back regardless of the CAR-T cell infusions. According to the press release from MGH, the medical team is now considering treating each patient with multiple infusions or prefacing each treatment with chemotherapy to prolong the response.
While there is still “more to do,” says co-author of the study neuro-oncologist Dr. Elizabeth Gerstner, the results are still promising. If nothing else, these second-generation CAR-T cell infusions may someday be able to give patients more time than traditional treatments would allow.
“These results are exciting but they are also just the beginning,” says Dr. Marcela Maus, a doctor and professor of medicine at Mass General who was involved in the clinical trial. “They tell us that we are on the right track in pursuing a therapy that has the potential to change the outlook for this intractable disease.”
Since the early 2000s, AI systems have eliminated more than 1.7 million jobs, and that number will only increase as AI improves. Some research estimates that by 2025, AI will eliminate more than 85 million jobs.
But for all the talk about job security, AI is also proving to be a powerful tool in healthcare—specifically, cancer detection. One recently published study has shown that, remarkably, artificial intelligence was able to detect 20 percent more cancers in imaging scans than radiologists alone.
Published in The Lancet Oncology, the study analyzed the scans of 80,000 Swedish women with a moderate hereditary risk of breast cancer who had undergone a mammogram between April 2021 and July 2022. Half of these scans were read by AI and then a radiologist to double-check the findings. The second group of scans was read by two researchers without the help of AI. (Currently, the standard of care across Europe is to have two radiologists analyze a scan before diagnosing a patient with breast cancer.)
The study showed that the AI group detected cancer in 6 out of every 1,000 scans, while the radiologists detected cancer in 5 per 1,000 scans. In other words, AI found 20 percent more cancers than the highly-trained radiologists.
Scientists have been using MRI images (like the ones pictured here) to train artificial intelligence to detect cancers earlier and with more accuracy. Here, MIT's AI system, MIRAI, looks for patterns in a patient's mammograms to detect breast cancer earlier than ever before. news.mit.edu
But even though the AI was better able to pinpoint cancer on an image, it doesn’t mean radiologists will soon be out of a job. Dr. Laura Heacock, a breast radiologist at NYU, said in an interview with CNN that radiologists do much more than simply screening mammograms, and that even well-trained technology can make errors. “These tools work best when paired with highly-trained radiologists who make the final call on your mammogram. Think of it as a tool like a stethoscope for a cardiologist.”
AI is still an emerging technology, but more and more doctors are using them to detect different cancers. For example, researchers at MIT have developed a program called MIRAI, which looks at patterns in patient mammograms across a series of scans and uses an algorithm to model a patient's risk of developing breast cancer over time. The program was "trained" with more than 200,000 breast imaging scans from Massachusetts General Hospital and has been tested on over 100,000 women in different hospitals across the world. According to MIT, MIRAI "has been shown to be more accurate in predicting the risk for developing breast cancer in the short term (over a 3-year period) compared to traditional tools." It has also been able to detect breast cancer up to five years before a patient receives a diagnosis.
The challenges for cancer-detecting AI tools now is not just accuracy. AI tools are also being challenged to perform consistently well across different ages, races, and breast density profiles, particularly given the increased risks that different women face. For example, Black women are 42 percent more likely than white women to die from breast cancer, despite having nearly the same rates of breast cancer as white women. Recently, an FDA-approved AI device for screening breast cancer has come under fire for wrongly detecting cancer in Black patients significantly more often than white patients.
As AI technology improves, radiologists will be able to accurately scan a more diverse set of patients at a larger volume than ever before, potentially saving more lives than ever.