New Hope for Organ Transplantation: Life Without Anti-Rejection Drugs
Rob Waddell dreaded getting a kidney transplant. He suffers from a genetic condition called polycystic kidney disease that causes the uncontrolled growth of cysts that gradually choke off kidney function. The inherited defect has haunted his family for generations, killing his great grandmother, grandmother, and numerous cousins, aunts and uncles.
But he saw how difficult it was for his mother and sister, who also suffer from this condition, to live with the side effects of the drugs they needed to take to prevent organ rejection, which can cause diabetes, high blood pressure and cancer, and even kidney failure because of their toxicity. Many of his relatives followed the same course, says Waddell: "They were all on dialysis, then a transplant and ended up usually dying from cancers caused by the medications."
When the Louisville native and father of four hit 40, his kidneys barely functioned and the only alternative was either a transplant or the slow death of dialysis. But in 2009, when Waddell heard about an experimental procedure that could eliminate the need for taking antirejection drugs, he jumped at the chance to be their first patient. Devised by scientists at the University of Louisville and Northwestern University, the innovative approach entails mixing stem cells from the live kidney donor with that of the recipient to create a hybrid immune system, known as a chimera, that would trick the immune system and prevent it from attacking the implanted kidney.
The procedure itself was done at Northwestern Memorial Hospital in Chicago, using a live kidney donated by a neighbor of Waddell's, who camped out in Chicago during his recovery. Prior to surgery, Waddell underwent a conditioning treatment that consisted of low dose radiation and chemotherapy to weaken his own immune system and make room for the infusion of stem cells.
"The low intensity chemo and radiation conditioning regimen create just enough space for the donor stem cells to gain a foothold in the bone marrow and the donor's immune system takes over," says Dr. Joseph Levanthal, the transplant surgeon who performed the operation and director of kidney and pancreas transplantation at Northwestern University Feinberg School of Medicine. "That way the recipient develops an immune system that doesn't see the donor organ as foreign."
"As a surgeon, I saw what my patients had to go through—taking 25 pills a day, dying at an early age from heart disease, or having a 35% chance of dying every year on dialysis."
A week later, Waddell had the kidney transplant. The following day, he was infused with a complex cellular cocktail that included blood-forming stem cells derived from his donor's bone marrow mixed what are called tolerance inducing facilitator cells (FCs); these cells help the foreign stem cells get established in the recipient's bone marrow.
Over the course of the following year, he was slowly weaned off of antirejection medications—a precaution in case the procedure didn't work—and remarkably, hasn't needed them since. "I felt better than I had in decades because my kidneys [had been] degrading," recalls Waddell, now 54 and a CPA for a global beverage company. And what's even better is that this new approach offers hope for one of his sons who has also inherited the disorder.
Kidney transplants are the most frequent organ transplants in the world and more than 23,000 of these procedures were done in the United States in 2019, according to the United Network for Organ Sharing. Of this, about 7,000 operations are done annually using live organ donors; the remainder use organs from people who are deceased. Right now, this revolutionary new approach—as well as a similar strategy formulated by Stanford University scientists--is in the final phase of clinical trials. Ultimately, this research may pave the way towards realizing the holy grail of organ transplantation: preventing organ rejection by creating a tolerant state in which the recipient's immune system is compatible with the donor, which would eliminate the need for a lifetime of medications.
"As a surgeon, I saw what my patients had to go through—taking 25 pills a day, dying at an early age from heart disease, or having a 35% chance of dying every year on dialysis," says Dr. Suzanne Ildstad, a transplant surgeon and director of the Institute for Cellular Therapeutics at the University of Louisville, whose discovery of facilitator cells were the basis for this therapeutic platform. Ildstad, who has spent more than two decades searching for a better way, says, "This is something I have worked for my entire life."
The Louisville group uses a combination of chemo and radiation to replace the recipient's immune and blood forming cells with that of the donor. In contrast, the Stanford protocol involves harvesting the donor's blood stem cells and T-cells, which are the foot soldiers of the immune system that fight off infections and would normally orchestrate the rejection of the transplanted organ. Their transplant recipients undergo a milder form of "conditioning" that only radiates discrete parts of the body and selectively targets the recipient's T-cells, creating room for both sets of T-cells, a strategy these researchers believe has a better safety profile and less of a chance of rejection.
"We try to achieve immune tolerance by a true chimerism," says Dr. Samuel Strober, a professor of medicine for immunology and rheumatology at Stanford University and a leader of this research team. "The recipients immune system cells are maintained but mixed in the blood with that of the donor."
Studies suggest both approaches work. In a 2018 clinical trial conducted by Talaris Therapeutics, a Louisville-based biotech founded by Ildstad, 26 of 37 (70%) of the live donor kidney transplant recipients no longer need immunosuppressants. Last fall, Talaris began the final phase of clinical tests that will eventually encompass more than 120 such patients.
The Stanford group's cell-based immunotherapy, which is called MDR-101 and is sponsored by the South San Francisco biotech, Medeor Therapeutics, has had similar results in patients who received organs from live donors who were either well matched, such as one from siblings, meaning they were immunologically identical, or partially matched; Talaris uses unrelated donors where there is only a partial match.
In their 2020 clinical trial of 51 patients, 29 were fully matched and 22 were a partial match; 22 of the fully matched recipients didn't need antirejection drugs and ten of the partial matches were able to stop taking some of these medications without rejection. "With our fully matched, roughly 80% have been completely off drugs up to 14 years later," says Strober, "and reducing the number of drugs from three to one [in the partial matches] means you have far fewer side effects. The goal is to get them off of all drugs."
But these protocols are limited to a small number of patients—living donor kidney recipients. As a consequence, both teams are experimenting with ways to broaden their approach so they can use cadaver organs from deceased donors, with human tests planned in the coming year. Here's how that would work: after the other organs are removed from a deceased donor, stem cells are harvested from the donor's vertebrae in the spinal column and then frozen for storage.
"We do the transplant and give the patient a chance to recover and maintain them on drugs," says Ildstad. "Then we do the tolerance conditioning at a later stage."
If this strategy is successful, it would be a genuine game changer, and open the door to using these protocols for transplanting other cadaver organs, including the heart, lungs and liver. While the overall procedure is complex and costly, in the long run it's less expensive than repeated transplant surgeries, the cost of medications and hospitalizations for complications caused by the drugs, or thrice weekly dialysis treatments, says Ildstad.
And she adds, you can't put a price tag on the vast improvement in quality of life.
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