Researchers Get Closer to Gene Editing Treatment for Cardiovascular Disease
Later this year, Verve Therapeutics of Cambridge, Ma., will initiate Phase 1 clinical trials to test VERVE-101, a new medication that, if successful, will employ gene editing to significantly reduce low-density lipoprotein cholesterol, or LDL.
LDL is sometimes referred to as the “bad” cholesterol because it collects in the walls of blood vessels, and high levels can increase chances of a heart attack, cardiovascular disease or stroke. There are approximately 600,000 heart attacks per year due to blood cholesterol damage in the United States, and heart disease is the number one cause of death in the world. According to the CDC, a 10 percent decrease in total blood cholesterol levels can reduce the incidence of heart disease by as much as 30 percent.
Verve’s Founder and CEO, Sekar Kathiresan, spent two decades studying the genetic basis for heart attacks while serving as a professor of medicine at Harvard Medical School. His research led to two critical insights.
“One is that there are some people that are naturally resistant to heart attack and have lifelong, low levels of LDL,” the cardiologist says. “Second, there are some genes that can be switched off that lead to very low LDL cholesterol, and individuals with those genes switched off are resistant to heart attacks.”
Kathiresan and his team formed a hypothesis in 2016 that if they could develop a medicine that mimics the natural protection that some people enjoy, then they might identify a powerful new way to treat and ultimately prevent heart attacks. They launched Verve in 2018 with the goal of creating a one-time therapy that would permanently lower LDL and eliminate heart attacks caused by high LDL.
"Imagine a future where somebody gets a one-time treatment at the time of their heart attack or before as a preventive measure," says Kathiresan.
The medication is targeted specifically for patients who have a genetic form of high cholesterol known as heterozygous familial hypercholesterolemia, or FH, caused by expression of a gene called PCSK9. Verve also plans to develop a program to silence a gene called ANGPTL3 for patients with FH and possibly those with or at risk of atherosclerotic cardiovascular disease.
FH causes cholesterol to be high from birth, reaching levels of 200 to 300 milligrams per deciliter. Suggested normal levels are around 100 to 129 mg/dl, and anything above 130 mg/dl is considered high. Patients with cardiovascular disease usually are asked to aim for under 70 mg/dl, but many still have unacceptably high LDL despite taking oral medications such as statins. They are more likely to have heart attacks in their 30s, 40s and 50s, and require lifelong LDL control.
The goal for drug treatments for high LDL, Kathiresan says, is to reduce LDL as low as possible for as long as possible. Physicians and researchers also know that a sizeable portion of these patients eventually start to lose their commitment to taking their statins and other LDL-controlling medications regularly.
“If you ask 100 patients one year after their heart attack what fraction are still taking their cholesterol-lowering medications, it’s less than half,” says Kathiresan. “So imagine a future where somebody gets a one-time treatment at the time of their heart attack or before as a preventive measure. It’s right in front of us, and it’s something that Verve is looking to do.”
In late 2020, Verve completed primate testing with monkeys that had genetically high cholesterol, using a one-time intravenous injection of VERVE-101. It reduced the monkeys’ LDL by 60 percent and, 18 months later, remains at that level. Kathiresan expects the LDL to stay low for the rest of their lives.
Verve’s gene editing medication is packaged in a lipid nanoparticle to serve as the delivery mechanism into the liver when infused intravenously. The drug is absorbed and makes its way into the nucleus of the liver cells.
Verve’s program targeting PCSK9 uses precise, single base, pair base editing, Kathiresan says, meaning it doesn't cut DNA like CRISPR gene editing systems do. Instead, it changes one base, or letter, in the genome to a different one without affecting the letters around it. Comparing it to a pencil and eraser, he explains that the medication erases out a letter A and makes it a letter G in the A, C, G and T code in DNA.
“We need to continue to advance our approach and tools to make sure that we have the absolute maximum ability to detect off-target effects,” says Euan Ashley, professor of medicine and genetics at Stanford University.
By making that simple change from A to G, the medication switches off the PCSK9 gene, automatically lowering LDL cholesterol.
“Once the DNA change is made, all the cells in the liver will have that single A to G change made,” Kathiresan says. “Then the liver cells divide and give rise to future liver cells, but every time the cell divides that change, the new G is carried forward.”
Additionally, Verve is pursuing its second gene editing program to eliminate ANGPTL3, a gene that raises both LDL and blood triglycerides. In 2010, Kathiresan's research team learned that people who had that gene completely switched off had LDL and triglyceride levels of about 20 and were very healthy with no heart attacks. The goal of Verve’s medication will be to switch off that gene, too, as an option for additional LDL or triglyceride lowering.
“Success with our first drug, VERVE-101, will give us more confidence to move forward with our second drug,” Kathiresan says. “And it opens up this general idea of making [genomic] spelling changes in the liver to treat other diseases.”
The approach is less ethically concerning than other gene editing technologies because it applies somatic editing that affects only the individual patient, whereas germline editing in the patient’s sperm or egg, or in an embryo, gets passed on to children. Additionally, gene editing therapies receive the same comprehensive amount of testing for side effects as any other medicine.
“We need to continue to advance our approach and tools to make sure that we have the absolute maximum ability to detect off-target effects,” says Euan Ashley, professor of medicine and genetics at Stanford University and founding director of its Center for Inherited Cardiovascular Disease. Ashley and his colleagues at Stanford’s Clinical Genomics Program and beyond are increasingly excited about the promise of gene editing.
“We can offer precision diagnostics, so increasingly we’re able to define the disease at a much deeper level using molecular tools and sequencing,” he continues. “We also have this immense power of reading the genome, but we’re really on the verge of taking advantage of the power that we now have to potentially correct some of the variants that we find on a genome that contribute to disease.”
He adds that while the gene editing medicines in development to correct genomes are ahead of the delivery mechanisms needed to get them into the body, particularly the heart and brain, he’s optimistic that those aren’t too far behind.
“It will probably take a few more years before those next generation tools start to get into clinical trials,” says Ashley, whose book, The Genome Odyssey, was published last year. “The medications might be the sexier part of the research, but if you can’t get it into the right place at the right time in the right dose and not get it to the places you don’t want it to go, then that tool is not of much use.”
Medical experts consider knocking out the PCSK9 gene in patients with the fairly common genetic disorder of familial hypercholesterolemia – roughly one in 250 people – a potentially safe approach to gene editing and an effective means of significantly lowering their LDL cholesterol.
Nurse Erin McGlennon has an Implantable Cardioverter Defibrillator and takes medications, but she is also hopeful that a gene editing medication will be developed in the near future.
Erin McGlennon
Mary McGowan, MD, chief medical officer for The Family Heart Foundation in Pasadena, CA, sees the tremendous potential for VERVE-101 and believes patients should be encouraged by the fact that this kind of research is occurring and how much Verve has accomplished in a relatively short time. However, she offers one caveat, since even a 60 percent reduction in LDL won’t completely eliminate the need to reduce the remaining amount of LDL.
“This technology is very exciting,” she said, “but we want to stress to our patients with familial hypercholesterolemia that we know from our published research that most people require several therapies to get their LDL down., whether that be in primary prevention less than 100 mg/dl or secondary prevention less than 70 mg/dl, So Verve’s medication would be an add-on therapy for most patients.”
Dr. Kathiresan concurs: “We expect our medicine to lower LDL cholesterol by about 60 percent and that our patients will be on background oral medications, including statins that lower LDL cholesterol.”
Several leading research centers are investigating gene editing treatments for other types of cardiovascular diseases. Elizabeth McNally, Elizabeth Ward Professor and Director at the Center for Genetic Medicine at Northwestern University’s Feinberg School of Medicine, pursues advanced genetic correction in neuromuscular diseases such as Duchenne muscular dystrophy and spinal muscular atrophy. A cardiologist, she and her colleagues know these diseases frequently have cardiac complications.
“Even though the field is driven by neuromuscular specialists, it’s the first therapies in patients with neuromuscular diseases that are also expected to make genetic corrections in the heart,” she says. “It’s almost like an afterthought that we’re potentially fixing the heart, too.”
Another limitation McGowan sees is that too many healthcare providers are not yet familiar with how to test patients to determine whether or not they carry genetic mutations that need to be corrected. “We need to get more genetic testing done,” she says. “For example, that’s the case with hypertrophic cardiomyopathy, where a lot of the people who probably carry that diagnosis and have never been genetically identified at a time when genetic testing has never been easier.”
One patient who has been diagnosed with hypertrophic cardiomyopathy also happens to be a nurse working in research at Genentech Pharmaceutical, now a member of the Roche Group, in South San Francisco. To treat the disease, Erin McGlennon, RN, has an Implantable Cardioverter Defibrillator and takes medications, but she is also hopeful that a gene editing medication will be developed in the near future.
“With my condition, the septum muscles are just growing thicker, so I’m on medicine to keep my heart from having dangerous rhythms,” says McGlennon of the disease that carries a low risk of sudden cardiac death. “So, the possibility of having a treatment option that can significantly improve my day-to-day functioning would be a major breakthrough.”
McGlennon has some control over cardiovascular destiny through at least one currently available technology: in vitro fertilization. She’s going through it to ensure that her children won't express the gene for hypertrophic cardiomyopathy.
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