Cleveland Clinic Lerner Research Institute

Amarna Therapeutics announces formation of Scientific Advisory Board to support advancement of groundbreaking Nimvec™ AM510 gene therapy against Type 1 Diabetes Members: Prof. Dr. Colin Dayan, Prof. Dr. Desmond Schatz, Prof. Dr. Didac Mauricio, Dr. Luiza Caramori, Dr. Kei Kishimoto, Prof. Dr. Roberto Mallone, Dr. Sylvaine You. Leiden, The Netherlands, 17 June 2024 – Amarna Therapeutics, (“Amarna” or “the Company”) a privately-held biotechnology company focused on developing transformative gene therapies for a range of rare and prevalent genetic diseases, including Type 1 Diabetes Mellitus (T1DM), is pleased to announce the formation of its new Scientific Advisory Board (SAB). The newly established SAB consists of seven distinguished international scientific opinion leaders. These experts bring extensive experience and knowledge in the fields of pre-clinical and clinical development, gene therapy, immunology, endocrinology, and regulatory affairs. The SAB will leverage its extensive expertise to ensure that Amarna’s groundbreaking Nimvec™ AM510 program meets the highest scientific and regulatory standards. This initiative underscores the company's commitment to pioneering treatments for T1DM and enhancing patient outcomes. The SAB's insights will be pivotal in navigating the complex landscape of clinical trials and regulatory approvals, ultimately accelerating the path to market for this groundbreaking therapy. “The formation of this Scientific Advisory Board marks a major development step for Amarna as we continue our evolution in becoming a clinical stage gene therapy development company,” said Henk Streefkerk, CEO of Amarna. “We are proud and excited to bring together leading clinicians and scientists from across the world who are dedicated to, and have decades of experience in, the clinical development of novel therapeutics for genetic diseases. We believe their collective expertise will be instrumental in guiding Amarna advancing its lead drug candidate, Nimvec™ AM510, as we work to optimize its potential to transform the treatment of Type 1 Diabetes Mellitus and a range of other prevalent debilitating genetic disorders.” The members of the Scientific Advisory Board (SAB) are as follows: Colin Dayan, MA MBBS, FRCP, PhD, is Professor of Clinical Diabetes and Metabolism at Cardiff University School of Medicine, in Cardiff (UK), Senior Clinical Researcher at the University of Oxford and Director at the Cardiff Joint Research Office. His focus lies mainly on translational research in immunopathology of Type 1 Diabetes Mellitus, clinical trials on peptide immunotherapy and nanoparticles in T1DM. Desmond Schatz, MD, PhD, is Professor of Pediatrics, Medical Director of the Diabetes Institute, and Medical Director of the Clinical Research Center, at the University of Florida, and served as President of Science and Medicine of the American Diabetes Association (2016). Dr. Schatz has been involved in Type 1 diabetes research since the mid 1980’s and has published over 400 manuscripts, the majority related to the prediction, natural history, genetics, immunopathogenesis and prevention of the disease, as well as the management of children and adolescents with Type 1 diabetes. Didac Mauricio, MD, PhD, is Director of the Department of Endocrinology & Nutrition, Hospital de la Santa Creu i Sant Pau in Barcelona, and Professor with the Faculty of Medicine, University of Vic & Central University of Catalonia (UVic/UCC). He is leading the Diabetes Research Group at his current institution and has recently been appointed Scientific Director of the CIBER of Diabetes and Associated Metabolic diseases (CIBERDEM), Instituto de Salud Carlos III (Ministry of Science and Innovation), in Spain. Luiza Caramori, MD, MSc, PhD, is a Staff Physician at the Institute of Endocrinology and Metabolism at Cleveland Clinic Foundation and at the Department of Cardiovascular and Metabolic Sciences, Lerner Research Institute, Cleveland, Ohio (USA) and an Adjunct Associate Professor at the University of Minnesota, Minneapolis, MN (USA). Dr. Caramori’s is a physician-scientist with 25+ years research experience, whose main research interests include studies on the relationships between kidney structure and function in type 1 diabetes, early molecular and structural predictors of diabetic kidney disease (DKD) in patients with type 1 diabetes, and clinical trials studying repurposed and new drugs for the prevention and treatment of DKD both in patients with type 1 and type 2 diabetes. Kei Kishimoto, PhD, is a highly accomplished immunologist and drug developer with over 30 years of experience in biopharma. He was most recently Chief Scientific Officer of Selecta Biosciences, where he led development of an immune tolerance technology that was successfully validated in Phase 3 clinical trials. Prior to joining Selecta, he was Vice President of Research at Momenta Pharmaceuticals and Senior Director Inflammation at Millennium Pharmaceuticals. His expertise is focused on immunology, immune tolerance, autoimmune diseases, vaccines, immunogenicity and gene therapy. KeiRoberto Mallone, MD, PhD, is Professor and Hospital Physician in Clinical Immunology and Diabetology at Paris Cité University - Cochin Hospital and Research Director at INSERM U1016 Cochin Institute in Paris, France and at the Indiana Biosciences Research Institute (IBRI) in Indianapolis (USA). His research spans from preclinical studies with human samples and mouse models to clinical trials. It focuses on autoimmune T cells and beta-cell vulnerability and the understanding of type 1 diabetes mechanisms to develop T-cell-based biomarkers and therapeutics aimed at blunting T-cell aggressiveness and enhancing beta-cell resilience. Sylvaine You, MD, PhD, is a distinguished researcher specializing in the immune mechanisms driving type 1 diabetes (T1DM) and the development of therapeutic strategies. She is Research Director at Cochin Institute (INSERM) in Paris, France, focuses on T-cell Tolerance, Biomarkers and Therapies in type 1 Diabetes. Additionally, she co-leads a research team at the Indiana Biosciences Research Institute (IBRI), exploring the mechanisms that fail to keep autoreactive T lymphocytes inoffensive and make the beta cells more visible and vulnerable to an autoimmune attack. === E N D S === About Type 1 Diabetes Mellitus Type 1 Diabetes is a debilitating disease occurring in millions of patients globally, with rising incidences each year, where despite advancements in therapy the life expectancy remains lower than the general population. Diabetes is an autoimmune disease where self-reactive T lymphocytes selectively attack and destroy insulin-producing β cells lodged within the pancreas, leaving the patient unable to maintain glucose homeostasis. Proinsulin (PI) is the primary self-antigen involved in the autoimmune β cell destruction. To date, Type 1 Diabetes cannot be cured, and the glucose homeostasis can be more or less maintained in patients by daily insulin injections. Although Diabetes is seen as a manageable disease nowadays, secondary complications of the current therapy are considerable and lead to significant morbidity and mortality. Using Nimvec™ AM510 we intend to restore the immune tolerance to proinsulin and potentially cure the patients. About Nimvec™ AM510 The development of AM510 is based on our proprietary NimvecTM platform, which has demonstrated exceptional promise in preclinical studies. Unlike other gene therapies that induce a strong immune response, limiting the possibility for repeat dosing and efficacy, NimvecTM does not trigger such responses. Instead, it moderates the immune system to induce tolerance, making it an ideal vehicle for our therapeutic approach. Our preclinical data with Nimvec™ AM510 showcases its protective effects in delaying the onset of hyperglycemia and preventing the development of T1DM in relevant animal models. About Amarna Amarna Therapeutics has developed a groundbreaking non-immunogenic viral platform NimvecTM to deliver any transgene of choice into humans. This platform shows exceptional promise in preclinical studies for delivering treatments, potential cures, and disease prevention globally. Amarna is advancing a pipeline of transformative gene therapies for a range of rare and prevalent diseases, including monogenetic indications, autoimmune diseases and chronic inflammation. The lead program Nimvec™ AM510 is being developed for the treatment of patients with Type 1 Diabetes Mellitus. Follow up programs include Multiple Sclerosis, Age-related Macular Degeneration, and Hemophilia. More information on Follow us on LinkedIn . For further inquiries please contact: Amarna Therapeutics Henk Streefkerk, CEO E-mail: info@amarnatherapeutics.com LifeSpring Life Sciences Communication, Amsterdam Léon Melens Tel: +31 6 538 16 427 E-mail: lmelens@lifespring.nl

A new study shows that genetics play a larger role in melanoma risk than recognized. Researchers report that up to 15% (1 in 7) of patients who received melanoma diagnoses from Cleveland Clinic physicians between 2017 and 2020 carried mutations in cancer susceptibility genes. When it comes to skin cancer, most people think of warnings about sunburn and tanning beds. Thoughts of "cancer genes" or inherited risks are reserved for diseases like breast cancer or colon cancer. A new study challenges this status quo by showing that genetics play a larger role in melanoma risk than recognized. Physicians rarely order genetic screens to assess risk factors for patients with a family history of melanoma because, according to the previous, limited studies, only 2-2.5% of all cases are genetic. For the same reason, insurance companies rarely cover these tests outside of the most extreme situations. In the medical field, genetic testing is generally not offered for cancers that don't meet a threshold of 5%. A study from researchers and clinicians led by Cleveland Clinic's Joshua Arbesman, MD, and Stanford Medicine's Pauline Funchain, MD (formerly Cleveland Clinic), suggests that melanoma more than meets that threshold. Their results, published in the Journal of the American Academy of Dermatology, report that up to 15% (1 in 7) of patients who received melanoma diagnoses from Cleveland Clinic physicians between 2017 and 2020 carried mutations in cancer susceptibility genes. The research team, which includes Cleveland Clinic Center for Immunotherapy and Precision Oncology's Ying Ni, PhD, and Claudia Marcela Diaz, PhD, analyzed international patient databases and found similar results. "Hereditary cancers can wreak havoc through families and leave devastation in their wake. Genetic testing lets us proactively identify, screen and even treat these families to equip them with the tools they need to get the best healthcare possible," says Dr. Arbesman. "I would recommend physicians and insurance companies expand their criteria when it comes to offering genetic testing to individuals with family histories of melanoma, because inherited predisposition to it isn't nearly as rare as we think it is." Dr. Arbesman, who runs a lab in Cleveland Clinic Lerner Research Institute's Cancer Biology, also says his findings support an increasingly popular opinion amongst cancer biologists: there are risk factors beyond sun exposure that can influence an individual's chances of developing melanoma. "Not all of my patients had inherited mutations that made them more susceptible to the sun," he says. "There's clearly something more going on here and more research is needed." Dr. Arbesman and his team are studying many of the genes that came up in his patients' genetic tests to learn more about how melanoma develops and how it can be treated. For example, he is working to determine if some of his patients and their families who show inherited mutations may benefit more from immunotherapy compared to those who don't carry inherited mutations. His lab is also working to determine how other patients' genes contributed to the development and the severity of their melanoma.

Neurotypical humans readily optimize performance in 'reversal learning' games, but while mice learn the winning strategy, they refuse to commit to it, a new study shows. The research provides a mathematical way to track the rodents' more mixed tactics. Neuroscience discoveries ranging from the nature of memory to treatments for disease have depended on reading the minds of mice, so researchers need to truly understand what the rodents' behavior is telling them during experiments. In a new study that examines learning from reward, MIT researchers deciphered some initially mystifying mouse behavior, yielding new ideas about how mice think and a mathematical tool to aid future research. The task the mice were supposed to master is simple: Turn a wheel left or right to get a reward and then recognize when the reward direction switches. When neurotypical people play such "reversal learning" games they quickly infer the optimal approach: stick with the direction that works until it doesn't and then switch right away. Notably, people with schizophrenia struggle with the task. In the new study in PLOS Computational Biology, mice surprised scientists by showing that while they were capable of learning the "win-stay, lose-shift" strategy, they nonetheless refused to fully adopt it. "It is not that mice cannot form an inference-based model of this environment -- they can," said corresponding author Mriganka Sur, Newton Professor in The Picower Institute for Learning and Memory and MIT's Department of Brain and Cognitive Sciences (BCS). "The surprising thing is that they don't persist with it. Even in a single block of the game where you know the reward is 100 percent on one side, every so often they will try the other side." While the mouse motif of departing from the optimal strategy could be due to a failure to hold it in memory, said lead author and Sur Lab graduate student Nhat Le, another possibility is that mice don't commit to the "win-stay, lose-shift" approach because they don't trust that their circumstances will remain stable or predictable. Instead, they might deviate from the optimal regime to test whether the rules have changed. Natural settings, after all, are rarely stable or predictable. "I'd like to think mice are smarter than we give them credit for," Le said. But regardless of which reason may cause the mice to mix strategies, added co-senior author Mehrdad Jazayeri, Associate Professor in BCS and the McGovern Institute for Brain Research, it is important for researchers to recognize that they do and to be able to tell when and how they are choosing one strategy or another. "This study highlights the fact that, unlike the accepted wisdom, mice doing lab tasks do not necessarily adopt a stationary strategy and it offers a computationally rigorous approach to detect and quantify such non-stationarities," he said. "This ability is important because when researchers record the neural activity, their interpretation of the underlying algorithms and mechanisms may be invalid when they do not take the animals' shifting strategies into account." Tracking thinking The research team, which also includes co-author Murat Yildirim, a former Sur lab postdoc who is now an assistant professor at the Cleveland Clinic Lerner Research Institute, initially expected that the mice might adopt one strategy or the other. They simulated the results they'd expect to see if the mice either adopted the optimal strategy of inferring a rule about the task, or more randomly surveying whether left or right turns were being rewarded. Mouse behavior on the task, even after days, varied widely but it never resembled the results simulated by just one strategy. To differing, individual extents, mouse performance on the task reflected variance along three parameters: how quickly they switched directions after the rule switched, how long it took them to transition to the new direction, and how loyal they remained to the new direction. Across 21 mice, the raw data represented a surprising diversity of outcomes on a task that neurotypical humans uniformly optimize. But the mice clearly weren't helpless. Their average performance significantly improved over time, even though it plateaued below the optimal level. In the task, the rewarded side switched every 15-25 turns. The team realized the mice were using more than one strategy in each such "block" of the game, rather than just inferring the simple rule and optimizing based on that inference. To disentangle when the mice were employing that strategy or another, the team harnessed an analytical framework called a Hidden Markov Model (HMM), which can computationally tease out when one unseen state is producing a result vs. another unseen state. Le likens it to what a judge on a cooking show might do: inferring which chef contestant made which version of a dish based on patterns in each plate of food before them. Before the team could use an HMM to decipher their mouse performance results, however, they had to adapt it. A typical HMM might apply to individual mouse choices, but here the team modified it to explain choice transitions over the course of whole blocks. They dubbed their modified model the blockHMM. Computational simulations of task performance using the blockHMM showed that the algorithm is able to infer the true hidden states of an artificial agent. The authors then used this technique to show the mice were persistently blending multiple strategies, achieving varied levels of performance. "We verified that each animal executes a mixture of behavior from multiple regimes instead of a behavior in a single domain," Le and his co-authors wrote. "Indeed 17/21 mice used a combination of low, medium and high-performance behavior modes." Further analysis revealed that the strategies afoot were indeed the "correct" rule inference strategy and a more exploratory strategy consistent with randomly testing options to get turn-by-turn feedback. Now that the researchers have decoded the peculiar approach mice take to reversal learning, they are planning to look more deeply into the brain to understand which brain regions and circuits are involved. By watching brain cell activity during the task, they hope to discern what underlies the decisions the mice make to switch strategies. By examining reversal learning circuits in detail, Sur said, it's possible the team will gain insights that could help explain why people with schizophrenia show diminished performance on reversal learning tasks. Sur added that some people with autism spectrum disorders also persist with newly unrewarded behaviors longer than neurotypical people, so his lab will also have that phenomenon in mind as they investigate. Yildirim, too, is interested in examining potential clinical connections. "This reversal learning paradigm fascinates me since I want to use it in my lab with various preclinical models of neurological disorders," he said. "The next step for us is to determine the brain mechanisms underlying these differences in behavioral strategies and whether we can manipulate these strategies." Funding for the study came from The National Institutes of Health, the Army Research Office, a Paul and Lilah Newton Brain Science Research Award, the Massachusetts Life Sciences Initiative, The Picower Institute for Learning and Memory and The JPB Foundation.