New drug for schizophrenia could meet desperate need for better treatments
Schizophrenia is a debilitating mental health condition that affects around 24 million people worldwide. Patients experience hallucinations and delusions when they develop schizophrenia, with experts referring to these new thoughts and behaviors as positive symptoms. They also suffer from negative symptoms in which they lose important functions, suffering from dulled emotions, lack of purpose and social withdrawal.
Currently available drugs can control only a portion of these symptoms but, on August 8th, Karuna Therapeutics announced its completion of a phase 3 clinical trial that found a new drug called KarXT could treat both positive and negative symptoms of schizophrenia. It could mean substantial progress against a problem that has stymied scientists for decades.
A long-standing problem
Since the 1950s, antipsychotics have been used to treat schizophrenia. People who suffer from it are thought to have too much of a brain chemical called dopamine, and antipsychotics work by blocking dopamine receptors in the brain. They can be effective in treating positive symptoms but have little impact on the negative ones, which can be devastating for a patient’s quality of life, making it difficult to maintain employment and have successful relationships. About 30 percent of schizophrenia patients don't actually respond to antipsychotics at all. Current drugs can also have adverse side effects including elevated cholesterol, high blood pressure, diabetes and movements that patients cannot control.
The recent clinical trial heralds a new treatment approach. “We believe it marks an important advancement for patients given its new and completely different mechanism of action from current therapies,” says Andrew Miller, COO of Karuna.
Scientists have been looking to develop alternatives. However, “the field of drug treatment of schizophrenia is currently in the doldrums,” says Peter McKenna, a senior researcher at FIDMAG Research Foundation in Spain which specialises in mental health.
In the 2000s there was a major push to target a brain receptor for a chemical called glutamate. Evidence suggested that this receptor is abnormal in the brains of schizophrenia patients, but attempts to try glutamate failed in clinical trials.
After that, many pharmaceutical companies dropped out of the race for a more useful treatment. But some companies continued to search, such as Karuna Therapeutics, led by founder and Chief Operating Officer Andrew Miller and CEO Steve Paul. The recent clinical trial suggests their persistence has led to an important breakthrough with their drug, KarXT. “We believe it marks an important advancement for patients given its new and completely different mechanism of action from current therapies,” Miller says.
How it works
Neurotransmitters are chemical messengers that pass signals between neurons. To work effectively, neurotransmitters need a receptor to bind to. A neurotransmitter called acetylcholine seems to be especially important in schizophrenia. It interacts with sites called muscarinic receptors, which are involved in the network of nerves that calm your body after a stressful event. Post mortem studies in people with schizophrenia have shown that two muscarinic receptors in the brain, the M1 and M4 receptors, are activated at unusually low levels because they don’t receive enough signals from acetylcholine.
The M4 receptor appears to play a role in psychosis. The M1 receptor is also associated with psychosis but is primarily thought to be involved in cognition. KarXT, taken orally, works by activating both of these receptors to signal properly. It is this twofold action that seems to explain its effectiveness. “[The drug’s] design enables the preferential stimulation of these muscarinic receptors in the brain,” Miller says.
How it developed
It all started in the early 1990s when Paul was at pharmaceutical company Eli Lilly. He discovered that Xanomeline, the drug they were testing on Alzheimer's patients, had antipsychotic effects. It worked by stimulating M1 and M4 receptors, so he and his colleagues decided to test Xanomeline on schizophrenia patients, supported by research on the connection between muscarinic receptors and psychosis. They found that Xanomeline reduced both positive and negative symptoms.
Unfortunately, it also caused significant side effects. The problem was that stimulating the M1 and M4 receptors in the brain also stimulated muscarinic receptors in the body that led to severe vomiting, diarrhea and even the temporary loss of consciousness.
In the end, Eli Lilly discontinued the clinical trials for the drug, but Miller set up Karuna Therapeutics to develop a solution. “I was determined to find a way to harness the therapeutic benefit demonstrated in studies of Xanomeline, while eliminating side effects that limited its development,” Miller says.
He analysed over 7,000 possible ways of mixing Xanomeline with other agents before settling on KarXT. It combines Xanomeline with a drug called Trospium Chloride, which blocks muscarinic receptors in the body – taking care of the side effects such as vomiting – but leaves them unblocked in the brain. Paul was so excited by Miller’s progress that he joined Karuna after leaving Eli Lilly and founding two previous startups.
“It's a very important approach,” says Rick Adams, Future Leaders Fellow in the Institute of Cognitive Neuroscience and Centre for Medical Image Computing at University College London. “We are in desperate need of alternative drug targets and this target is one of the best. There are other alternative targets, but not many are as close to being successful as the muscarinic receptor drug.”
Clinical Trial
Following a successful phase 2 clinical trial in 2019, the most recent trial involved 126 patients who were given KarXT, and 126 who were given a placebo. Compared to the placebo, patients taking KarXT had a significant 9.6 point reduction in the positive and negative syndrome scale (PANSS), the standard for rating schizophrenic symptoms.
KarXT also led to statistically significant declines in positive and negative symptoms compared to the placebo. “The results suggest that KarXT could be a potentially game-changing option in the management of both positive and negative symptoms of schizophrenia,” Miller says.
Robert McCutcheon, a psychiatrist and neuroscientist at Oxford University, is optimistic about the side effects but highlights the need for more safety trials.
McKenna, the researcher at FIDMAG Foundation, agrees about the drug’s potential. “The new [phase 3] study is positive,” he says. “It is reassuring that one is not dealing with a drug that works in one trial and then inexplicably fails in the next one.”
Robert McCutcheon, a psychiatrist and neuroscientist at Oxford University, said the drug is an unprecedented step forward. “KarXT is one of the first drugs with a novel mechanism of action to show promise in clinical trials.”
Even though the drug blocks muscarine receptors in the body, some patients still suffered from adverse side effects like vomiting, dizziness and diarrhea. But in general, these effects were mild to moderate, especially compared to dopamine-blocking antipsychotics or Xanomeline on its own.
McCutcheon is optimistic about the side effects but highlights the need for more safety trials. “The trial results suggest that gastrointestinal side effects appear to be manageable,” he says. “We know, however, from previous antipsychotic drugs that the full picture regarding the extent of side effects can sometimes take longer to become apparent to clinicians and patients. Careful ongoing assessment during a longer period of treatment will therefore be important.”
The Future
The team is currently conducting three other trials to evaluate the efficacy and long-term safety of KarXT. Their goal is to receive FDA approval next year.
Karuna is also conducting trials to evaluate the effectiveness of KarXT in treating psychosis in patients suffering from Alzheimer’s.
The big hope is that they will soon be able to provide a radically different drug to help many patients with schizophrenia. “We are another step closer to potentially providing the first new class of medicine in more than 50 years to the millions of people worldwide living with schizophrenia,” says Miller.
Two years, six million deaths and still counting, scientists are searching for answers to prevent another COVID-19-like tragedy from ever occurring again. And it’s a gargantuan task.
Our disturbed ecosystems are creating more favorable conditions for the spread of infectious disease. Global warming, deforestation, rising sea levels and flooding have contributed to a rise in mosquito-borne infections and longer tick seasons. Disease-carrying animals are in closer range to other species and humans as they migrate to escape the heat. Bats are thought to have carried the SARS-CoV-2 virus to Wuhan, either directly or through another host animal, but thousands of novel viruses are lurking within other wild creatures.
Understanding how climate change contributes to the spread of disease is critical in predicting and thwarting future calamities. But the problem is that predictive models aren’t yet where they need to be for forecasting with certainty beyond the next year, as we could for weather, for instance.
The association between climate and infectious disease is poorly understood, says Irina Tezaur, a computational scientist at Sandia National Laboratories. “Correlations have been observed but it’s not known if these correlations translate to causal relationships.”
To make accurate longer-term predictions, scientists need more empirical data, multiple datasets specific to locations and diseases, and the ability to calculate risks that depend on unpredictable nature and human behavior. Another obstacle is that climate scientists and epidemiologists are not collaborating effectively, so some researchers are calling for a multidisciplinary approach, a new field called Outbreak Science.
Climate scientists are far ahead of epidemiologists in gathering essential data.
Earth System Models—combining the interactions of atmosphere, ocean, land, ice and biosphere—have been in place for two decades to monitor the effects of global climate change. These models must be combined with epidemiological and human model research, areas that are easily skewed by unpredictable elements, from extreme weather events to public environmental policy shifts.
“There is never just one driver in tracking the impact of climate on infectious disease,” says Joacim Rocklöv, a professor at the Heidelberg Institute of Global Health & Heidelberg Interdisciplinary Centre for Scientific Computing in Germany. Rocklöv has studied how climate affects vector-borne diseases—those transmitted to humans by mosquitoes, ticks or fleas. “You need to disentangle the variables to find out how much difference climate makes to the outcome and how much is other factors.” Determinants from deforestation to population density to lack of healthcare access influence the spread of disease.
Even though climate change is not the primary driver of infectious disease today, it poses a major threat to public health in the future, says Rocklöv.
The promise of predictive modeling
“Models are simplifications of a system we’re trying to understand,” says Jeremy Hess, who directs the Center for Health and the Global Environment at University of Washington in Seattle. “They’re tools for learning that improve over time with new observations.”
Accurate predictions depend on high-quality, long-term observational data but models must start with assumptions. “It’s not possible to apply an evidence-based approach for the next 40 years,” says Rocklöv. “Using models to experiment and learn is the only way to figure out what climate means for infectious disease. We collect data and analyze what already happened. What we do today will not make a difference for several decades.”
To improve accuracy, scientists develop and draw on thousands of models to cover as many scenarios as possible. One model may capture the dynamics of disease transmission while another focuses on immunity data or ocean influences or seasonal components of a virus. Further, each model needs to be disease-specific and often location-specific to be useful.
“All models have biases so it’s important to use a suite of models,” Tezaur stresses.
The modeling scientist chooses the drivers of change and parameters based on the question explored. The drivers could be increased precipitation, poverty or mosquito prevalence, for instance. Later, the scientist may need to isolate the effect of one driver so that will require another model.
There have been some related successes, such as the latest models for mosquito-borne diseases like Dengue, Zika and malaria as well as those for flu and tick-borne diseases, says Hess.
Rocklöv was part of a research team that used test data from 2018 and 2019 to identify regions at risk for West Nile virus outbreaks. Using AI, scientists were able to forecast outbreaks of the virus for the entire transmission season in Europe. “In the end, we want data-driven models; that’s what AI can accomplish,” says Rocklöv. Other researchers are making an important headway in creating a framework to predict novel host–parasite interactions.
Modeling studies can run months, years or decades. “The scientist is working with layers of data. The challenge is how to transform and couple different models together on a planetary scale,” says Jeanne Fair, a scientist at Los Alamos National Laboratory, Biosecurity and Public Health, in New Mexico.
Disease forecasting will require a significant investment into the infrastructure needed to collect data about the environment, vectors, and hosts a tall spatial and temporal resolutions.
And it’s a constantly changing picture. A modeling study in an April 2022 issue of Nature predicted that thousands of animals will migrate to cooler locales as temperatures rise. This means that various species will come into closer contact with people and other mammals for the first time. This is likely to increase the risk of emerging infectious disease transmitted from animals to humans, especially in Africa and Asia.
Other things can happen too. Global warming could precipitate viral mutations or new infectious diseases that don’t respond to antimicrobial treatments. Insecticide-resistant mosquitoes could evolve. Weather-related food insecurity could increase malnutrition and weaken people’s immune systems. And the impact of an epidemic will be worse if it co-occurs during a heatwave, flood, or drought, says Hess.
The devil is in the climate variables
Solid predictions about the future of climate and disease are not possible with so many uncertainties. Difficult-to-measure drivers must be added to the empirical model mix, such as land and water use, ecosystem changes or the public’s willingness to accept a vaccine or practice social distancing. Nor is there any precedent for calculating the effect of climate changes that are accelerating at a faster speed than ever before.
The most critical climate variables thought to influence disease spread are temperature, precipitation, humidity, sunshine and wind, according to Tezaur’s research. And then there are variables within variables. Influenza scientists, for example, found that warm winters were predictors of the most severe flu seasons in the following year.
The human factor may be the most challenging determinant. To what degree will people curtail greenhouse gas emissions, if at all? The swift development of effective COVID-19 vaccines was a game-changer, but will scientists be able to repeat it during the next pandemic? Plus, no model could predict the amount of internet-fueled COVID-19 misinformation, Fair noted. To tackle this issue, infectious disease teams are looking to include more sociologists and political scientists in their modeling.
Addressing the gaps
Currently, researchers are focusing on the near future, predicting for next year, says Fair. “When it comes to long-term, that’s where we have the most work to do.” While scientists cannot foresee how political influences and misinformation spread will affect models, they are positioned to make headway in collecting and assessing new data streams that have never been merged.
Disease forecasting will require a significant investment into the infrastructure needed to collect data about the environment, vectors, and hosts at all spatial and temporal resolutions, Fair and her co-authors stated in their recent study. For example real-time data on mosquito prevalence and diversity in various settings and times is limited or non-existent. Fair also would like to see standards set in mosquito data collection in every country. “Standardizing across the US would be a huge accomplishment,” she says.
Understanding how climate change contributes to the spread of disease is critical for thwarting future calamities.
Jeanne Fair
Hess points to a dearth of data in local and regional datasets about how extreme weather events play out in different geographic locations. His research indicates that Africa and the Middle East experienced substantial climate shifts, for example, but are unrepresented in the evidentiary database, which limits conclusions. “A model for dengue may be good in Singapore but not necessarily in Port-au-Prince,” Hess explains. And, he adds, scientists need a way of evaluating models for how effective they are.
The hope, Rocklöv says, is that in the future we will have data-driven models rather than theoretical ones. In turn, sharper statistical analyses can inform resource allocation and intervention strategies to prevent outbreaks.
Most of all, experts emphasize that epidemiologists and climate scientists must stop working in silos. If scientists can successfully merge epidemiological data with climatic, biological, environmental, ecological and demographic data, they will make better predictions about complex disease patterns. Modeling “cross talk” and among disciplines and, in some cases, refusal to release data between countries is hindering discovery and advances.
It’s time for bold transdisciplinary action, says Hess. He points to initiatives that need funding in disease surveillance and control; developing and testing interventions; community education and social mobilization; decision-support analytics to predict when and where infections will emerge; advanced methodologies to improve modeling; training scientists in data management and integrated surveillance.
Establishing a new field of Outbreak Science to coordinate collaboration would accelerate progress. Investment in decision-support modeling tools for public health teams, policy makers, and other long-term planning stakeholders is imperative, too. We need to invest in programs that encourage people from climate modeling and epidemiology to work together in a cohesive fashion, says Tezaur. Joining forces is the only way to solve the formidable challenges ahead.
This article originally appeared in One Health/One Planet, a single-issue magazine that explores how climate change and other environmental shifts are increasing vulnerabilities to infectious diseases by land and by sea. The magazine probes how scientists are making progress with leaders in other fields toward solutions that embrace diverse perspectives and the interconnectedness of all lifeforms and the planet.
Scientists use AI to predict how hospital stays will go
The Friday Five covers five stories in 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.
Here are the promising studies covered in this week's Friday Five:
- The problem with bedtime munching
- Scientists use AI to predict how stays in hospitals will go
- How to armor the shields of our livers against cancer
- One big step to save the world: turn one kind of plastic into another
- The perfect recipe for tiny brains
And an honorable mention this week: Bigger is better when it comes to super neurons in super agers