Translational Research Informatics Center

SHANGHAI, Nov. 20, 2023 /PRNewswire/ -- CARsgen Therapeutics Holdings Limited (Stock Code: 2171.HK), a company focused on innovative CAR T-cell therapies for the treatment of hematologic malignancies and solid tumors, announces the formation of its Clinical Advisory Board. This highly regarded panel of experts will provide invaluable guidance and insights for the global clinical development of company's innovative product candidates. "As we embark on this exciting journey to advance our differentiated CAR T-cell therapies, I am delighted to welcome our esteemed Clinical Advisory Board members. Their collective expertise and dedication to advancing cancer treatment align perfectly with CARsgen's vision and clinical development activities. We are confident that their knowledge and experience will contribute to the strategic planning and execution of our clinical development endeavors, ultimately helping us to make a meaningful difference for cancer patients worldwide. Together, we are committed to making cancer curable." Raffaele Baffa, MD, PhD, Chief Medical Officer of CARsgen Therapeutics, remarked. The newly appointed members of the Clinical Advisory Board, listed below in alphabetical order by last name, include: Dr. David S. Hong is the Douglas E Johnson Endowed Professor in and Deputy Chair of the Department of Investigational Cancer Therapeutics at the University of Texas MD Anderson Cancer Center, and Clinical Medical Director of its Clinical Translational Research Center. Dr. Hong was a pioneering driver in forming one of the largest and most innovative Phase I clinical trial units in the world, with over 1,200 patients enrolled in clinical trials in 2022 and over 280 active ongoing clinical trials. Dr. Hong has been the Principal Investigator of ~110 research protocols and published over 400 articles in peer-reviewed journals such as New England Journal of Medicine, Lancet Oncology, etc. Dr. Hong was heavily involved in the early development of many oncology drugs including cabozantinib, siltuximab, dabrafenib, trametinib, regorafenib, lenvatinib, larotrectinib, tisotumab vendotin, tepotinib, and sotorasib. Dr. C. Ola Landgren is Professor of Medicine, Chief of the Myeloma Division, Director of Sylvester Myeloma Institute, Paul J. DiMare Endowed Chair in Immunotherapy, Leader of the Experimental Therapeutics Program, and Co-Leader of the Translational and Clinical Oncology (TCO) Program at NCI-designated Sylvester Comprehensive Cancer Center, University of Miami. Prior to joining University of Miami, he served as Chief of the Myeloma Service at Memorial Sloan Kettering Cancer Center in New York City, and prior to that he was the Chief of the Multiple Myeloma Section at the National Cancer Institute in Bethesda, Maryland. Dr. Landgren is a board-certified hematologist-oncologist and he has published more than 500 papers and chapter books with a primary focus on multiple myeloma, MGUS (monoclonal gammopathy of undetermined significance), and smoldering myeloma. Dr. Landgren's research focuses the mechanisms underlying progression from precursor disease to frank malignancy, underlying mechanisms of disease response/resistance, role of minimal residual disease (MRD) negativity, biological mechanisms associated with sustained MRD negativity, identification of novel treatment targets, early drug development, and identification of novel biomarkers. Dr. Sattva S. Neelapu is a tenured Professor and Deputy Chair in the Department of Lymphoma and Myeloma at The University of Texas MD Anderson Cancer Center. His research is focused on clinical and translational development of novel immunotherapies for B-cell malignancies. His work on the pivotal clinical trial of axicabtagene ciloleucel, CD19 targeted CAR T-cell therapy in aggressive B cell lymphomas, led to its FDA approval as the first CAR T-cell therapy for lymphoma. His laboratory is focused on developing CAR T-cell therapies against novel targets, understanding mechanisms of resistance to CAR T-cell therapy, and developing allogeneic cell therapy approaches. He has authored or co-authored over 275 publications. Dr. Noopur Raje is a Professor of Medicine at Harvard Medical School, the Director of the Center for Multiple Myeloma, and the Rita Kelley Chair in Oncology at Massachusetts General Hospital Cancer Center, Harvard Medical School. Dr. Raje's research focuses primarily on developing novel therapeutic strategies for the treatment of multiple myeloma and related plasma cell disorders. Her laboratory efforts are aimed at identifying cellular signaling pathways that contribute to the survival and proliferation of myeloma cells in the bone environment. Her expertise in cellular therapies has led to FDA approval of the first in class CAR T-cell product, idecabtagene vicleucel in myeloma. She has received several awards and has published widely on multiple myeloma. Dr. Paul G. Richardson is the RJ Corman Professor of Medicine at Harvard Medical School and the clinical program leader and director of clinical research at the Jerome Lipper Multiple Myeloma Center at Dana-Farber Cancer Institute. Dr. Richardson has led the development of several first-generation novel drugs used in multiple myeloma for over 20 years, including the combination of lenalidomide (Revlimid) and bortezomib (Velcade), with the development of the combination of lenalidomide and bortezomib with dexamethasone as frontline therapy in myeloma being one of his important contributions; the subsequent development of this combination with other agents such as daratumumab has been key for improving therapy in newly diagnosed multiple myeloma. He has also had a leadership role in the development of ixazomib, elotuzumab, daratumumab, isatuximab and pomalidomide, as well as more recently melflufen, and belantamab mafodotin. His current research efforts have been in the development of the potent and orally bioavailable CelMods, including mezigdomide. He has published extensively, having authored, or co-authored, over 460 original articles and 340 reviews, chapters, and editorials in peer-reviewed journals. His work has been recognized with multiple awards, including the Robert A. Kyle lifetime achievement award in 2017 and the Warren Alpert Prize in 2012, as well as the Ernest Beutler award in 2016. Dr. Josep Tabernero, Head of the Medical Oncology Department at the Vall d'Hebron University Hospital, Director of the Vall d'Hebron Institute of Oncology (VHIO) and Professor of Medicine at UVic-UCC. Dr. Tabernero has been Principal Investigator of several Phase I pharmacodynamic studies and translational projects with tumor-directed targeted therapies and immune-based therapies. Dr. Tabernero's research aims at potentiating molecular therapies targeting specific oncoproteins and accelerating more effective personalized cancer medicines for patients displaying genetic lesions or pathway disregulation. Dr. Tabernero's extensive publication record and leadership roles in prestigious organizations like ESMO highlight his contributions to the field. The formation of this Clinical Advisory Board marks a significant step forward for CARsgen as it continues to pioneer CAR T-cell therapies with the potential to transform cancer treatment. These accomplished experts will play a pivotal role in advising the company's efforts to develop effective therapies for patients worldwide. About CARsgen Therapeutics Holdings Limited CARsgen is a biopharmaceutical company with operations in China and the U.S. and is focused on innovative CAR T-cell therapies for the treatment of hematologic malignancies and solid tumors. CARsgen has established a comprehensive CAR T-cell research and development platform, encompassing target discovery, innovative CAR T-cell development, clinical trials, and commercial-scale production. CARsgen has internally developed novel technologies and a product pipeline with global rights to address major challenges of CAR T-cell therapies, such as improving the safety profile, enhancing the efficacy in treating solid tumors, and reducing treatment costs. CARsgen's vision is to become a global biopharmaceutical leader that brings innovative and differentiated cell therapies to cancer patients worldwide and makes cancer curable. Forward-looking Statements All statements in this press release that are not historical fact or that do not relate to present facts or current conditions are forward-looking statements. Such forward-looking statements express the Group's current views, projections, beliefs and expectations with respect to future events as of the date of this press release. Such forward-looking statements are based on a number of assumptions and factors beyond the Group's control. As a result, they are subject to significant risks and uncertainties, and actual events or results may differ materially from these forward-looking statements and the forward-looking events discussed in this press release might not occur. Such risks and uncertainties include, but are not limited to, those detailed under the heading "Principal Risks and Uncertainties" in our most recent annual report and interim report and other announcements and reports made available on our corporate website, . No representation or warranty is given as to the achievement or reasonableness of, and no reliance should be placed on, any projections, targets, estimates or forecasts contained in this press release. Contact CARsgen For more information, please visit Public Relations: PR@carsgen.com Investor Relations: IR@carsgen.com View original content to download multimedia: SOURCE CARsgen Therapeutics Company Codes: HongKong:2171

Researchers have created and preliminarily tested what they believe may be one of the first models for predicting who has the highest probability of being resistant to COVID-19 in spite of exposure to SARS-CoV-2, the virus that causes it. Researchers from Johns Hopkins Medicine and The Johns Hopkins University have created and preliminarily tested what they believe may be one of the first models for predicting who has the highest probability of being resistant to COVID-19 in spite of exposure to SARS-CoV-2, the virus that causes it. The study is reported online today in the journal PLOS ONE. "If we can identify which people are naturally able to avoid infection by SARS-CoV-2, we may be able to learn -- in addition to societal and behavioral factors -- which genetic and environmental differences influence their defense against the virus," says lead study author Karen (Kai-Wen) Yang, a biomedical engineering graduate student in the Translational Informatics Research and Innovation Lab at The Johns Hopkins University. "That insight could lead to new preventive measures and more highly targeted treatments." For its study, the research team set out to determine if a machine-learning statistical model could use health characteristics stored in electronic health records -- providing patient data such as comorbidities (other medical conditions) and prescribed medications -- as a means to pinpoint people with a natural ability to avoid SARS-CoV-2 infection. Those persons, says Yang, could then be studied to better understand the factors enabling their resistance. A machine-learning model is a computer program or system that uses mathematical algorithms to find statistical patterns, and then apply the patterns moving forward. This gives such systems the ability to imitate human thinking and reasoning, and similar to the brain, learn over time. "Using a machine-learning system to recognize complex patterns in large numbers of people with COVID-19 enabled another team of Johns Hopkins Medicine researchers in 2021 to predict the course of an individual patient's case and determine the likelihood that it would become severe," says co-senior study author Stuart Ray, M.D., vice chair of medicine for data integrity and analytics, and professor of medicine at the Johns Hopkins University School of Medicine. "Based on their success, our team wondered if the same approach also might be applied to predicting who could be exposed to SARS-CoV-2 in close quarters and still not get infected." To demonstrate the model's ability to predict COVID-19 resistance, the researchers first acquired data from a clinical registry called the Johns Hopkins COVID-19 Precision Medicine Analytics Platform Registry (JH-CROWN). The registry contains information for patients seen within the Johns Hopkins Health System who have been suspected of, or confirmed as, having a SARS-CoV-2 infection. For their resistance study, the researchers only included individuals who received a COVID-19 test between June 10, 2020, and Dec. 15, 2020, and who reported "potential exposure to the virus" as the reason for testing. The ending date was the point at which large-scale COVID-19 vaccination efforts started in the United States. Choosing this date, the researchers say, enabled them to avoid the effects on their findings of vaccines preventing infection rather than natural resistance. The 8,536 study participants who reported exposure as their reason for getting COVID tested were divided into two groups: those who did not share a residence (called a "household" in this study) with any COVID-19 patients or their residence had 10 or more patients; and those who shared a residence with 10 or fewer people, with at least one being a COVID-19 patient. The first group, with 8,476 of the participants, was designated as the Training and Testing Set, while the second group, called the Household Index (HHI) Set, had 60 members, and was used as a separate testing set. Keeping the household number to 10 or fewer, the researchers say, excluded people living in apartment complexes, dormitories and other higher-density, multi-unit living areas where exposure to a particular person positive for SARS-CoV-2 would be less intense. To identify patterns and cluster participants so that those naturally resistant to SARS-CoV-2 stand out, both study sets were analyzed using the Maximal-frequent All-confident pattern Selection Pattern-based Clustering (MASPC) algorithm. MASPC is specifically designed for electronic health record data analysis that combines patient demographic information (age, sex and race), the International Statistical Classification of Diseases and Related Health Problems (ICD) medical diagnostic codes relevant to each case, outpatient medication orders and the number of comorbidities (other diseases) present. "We hypothesized that MASPC would enable us to cluster patients with similar patterns in their data to define them as resistant and non-resistant to SARS-CoV-2, and with the hope that the algorithm would learn with each analysis how to improve the accuracy and reliability of future assignments," says Ray. "This initial study using JH-CROWN data was conducted to give life to that hypothesis, a proof-of-concept trial of our statistical model to show that resistance to COVID-19 might be predictable based a patient's clinical and demographic profile." "In the Training and Testing Set, we identified 56 patterns of ICD codes split into two groups: associated with resistance or not associated," Yang says. "Statistical analyses of how well these patterns differentiated between resistance and non-resistance yielded five patterns that did the best job for our small and localized [Baltimore-Washington, D.C., metroplex] study population to define who was most likely exposed to SARS-CoV-2." "Looking for these patterns in HHI Set -- the individuals most likely to have been exposed to SARS-CoV-2 in close quarters -- and then statistically analyzing the results, our model's best performance was 0.61," says Ray. "Since a score of 0.5 shows only chance association between the prediction and reality, and 1 is 100% association, this shows the model has promise as a tool for identifying people with COVID-19 resistance who can be further studied," says Ray. Limitations to the study, says Ray, include potential bias from self-reporting of COVID-19 exposure by participants, the small number of participants in the HHI group, the possibility that participants tested for SARS-CoV-2 using home kits or at facilities outside the Johns Hopkins system (and therefore, the tests were not recorded in the JH-CROWN database), and the short timeframe of the study itself. He adds that future trails using national patient data are needed to validate the model's ability. Along with Yang and Ray, the members of the study team from Johns Hopkins Medicine and Johns Hopkins University are graduate and undergraduate students Yijia Chen, Jacob Desman, Kevin Gorman, Chloé Paris, Ilia Rattsev, Tony Wei and Rebecca Yoo; and faculty co-senior authors Joseph Greenstein and Casey Overby Taylor. The study authors report no conflicts of interest.