Citations

Abstract Background: In 2022, Medicare alone spent $42.1 billion on prescription drugs, with antineoplastic and classical hematology drugs accounting for 44% and 8% of the total, respectively (2024 MedPAC Data Book). While antineoplastic drugs have been extensively studied, classical hematology drugs have received comparatively little attention. We analyzed US Food and Drug Administration (FDA) approvals of classical hematology drugs over the past 30 years and examined their per-patient costs and annual total US sales. Methods: We identified all classical hematology drugs (those prescribed by hematologist/oncologists for non-malignant indications) approved by the FDA from January 1, 1995 to December 31, 2024, including novel pharmaceuticals (synthesized from chemical compounds), biologics (derived from living organisms), and biosimilars (highly similar, but not identical, to biologics previously approved). Cost estimates for 2024 were based on wholesale acquisition costs (WAC), using annual WAC for continuous-duration treatments and typical course WAC for fixed-duration treatments. WAC and US gross sales data for 2024 were obtained from IPD Analytics (Aventura, FL). Results: A total of 98 classical hematology drugs were approved over the past 30 years—11% in 1995–2004, 26% in 2005–2014, and 63% in 2015–2024. These included pharmaceuticals (36%), biologics (54%), and biosimilars (10%). Six gene therapies received approval: 3 for hemophilia and 1 each for sickle disease, beta thalassemia, and sickle cell disease/beta thalassemia (dual indication). Common categories of indications included bleeding (31%), anemia (30%), neutropenia (9%), and thrombocytopenia (9%). Leading diagnoses were hemophilia (25%), chemotherapy-induced neutropenia (9%), thromboembolism (8%), paroxysmal nocturnal hemoglobinuria (PNH; 7%), anemia of chronic kidney disease (6%), iron deficiency anemia (6%), and immune thrombocytopenia (5%). The median cost for gene therapies was $2.9 million per treatment (range: $2.2 million–$3.5 million). Among non-gene therapies, median costs were $3,000 per course for fixed-duration treatments (range: $64–$378,000) and $467,000 annually for continuous-duration treatments (range: $2,400–$1.8 million). The most expensive fixed-duration treatments targeted hemophagocytic lymphohistiocytosis ($378,000/8-week course), while the most expensive continuous-duration treatments were for hemophilia (median: $630,000/year; range: $270,000–$853,000). Estimated 2024 US gross sales across all drugs exceeded $70 billion. Top-selling indications included thromboembolism ($43.5 billion), hemophilia ($6.9 billion), immune thrombocytopenia ($4.9 billion), PNH ($4.7 billion), chemotherapy-induced neutropenia ($2.8 billion), anemia of chronic kidney disease ($2.0 billion), and iron deficiency anemia ($1.8 billion). Total sales for all gene therapies combined were $143 million. Six drugs have been discontinued (year approved/year discontinued/reason): betrixaban (2017/2020/business), daprodustat (2023/2024/business), fidanacogene elaparvovec (2024/2025/business), oprelvekin (1997/2011/business), peginesatide (2012/2013/safety), and voxelotor (2019/2024/safety). There are limitations to our study. Some drugs have non-classical hematology indications (anticoagulants in atrial fibrillation; luspatercept in myelodysplastic neoplasm) and/or may also be prescribed by non-classical hematologists (erythropoiesis stimulating agents by nephrologists and medical oncologists). We were unable to disaggregate the sales data in these situations. Therefore, the annual sales for certain indications may be overestimated. Conclusions: Classical hematology drugs contribute substantially to US prescription drug spending, with over $70 billion in annual sales and medications for thromboembolism accounting for over half of the costs. Approvals have accelerated over the past three decades, doubling each decade. Biologics and biosimilars now represent two-thirds of approvals. Continuous-duration treatments, particularly for rare hereditary diseases like hemophilia, carry significant financial burden, with annual costs per patient often exceeding $0.5 million. In these conditions, if gene therapies demonstrate durable efficacy, they may prove to be cost-effective alternatives. As more gene therapies are developed, evaluating their long-term value relative to chronic high-cost treatments will be essential for shaping future reimbursement and access strategies.

This study leverages an experiment of nature framework to examine sex-based differences in ultraendurance swimming using English Channel performance data. Males were faster than females, with the greatest differences observed among older swimmers and top performers. Performance was optimal in moderately cool water temperatures (17–18°C). These findings highlight how naturally occurring endurance events provide unique opportunities to study physiological limits and the combined influences of sex, age, and environment on human performance.

Abstract Introduction This is the first International Consultation on Sexual Medicine (ICSM) position paper on sexual health in older adults. Physical, cognitive, and psychosocial factors create a unique set of challenges related to sexuality in older adults. Objectives This study explores the impact of the physiological aspects of aging, state of health, psychological, relationship-related, and sociocultural factors, on sexual health in older adults. The goal of this review is to provide recommendations for the comprehensive evaluation and treatment of this population. Methods A narrative review was conducted under the auspices of the ICSM, as part of its ongoing efforts to develop expert, evidence-based guidance in the field of sexual health. A multidisciplinary panel of international experts composed of specialists in urology, gynecology, psychiatry, sexual medicine, social work, and sex therapy was convened. A comprehensive literature review was conducted based on a search of psychological sources (PsycInfo), medical databases (PubMed, Medline), and Google Scholar. All retrieved articles were reviewed and critically assessed. Recommendations were then formulated using a modified Delphi method. Results Although there is extensive literature on the prevalence and risk of sexual challenges of adults over the age of 65, there is limited evidence regarding the quality of treatment modalities. In particular, there is scant data on women in this age group. This population faces specific and multifactorial challenges, which point to the need for a comprehensive approach to their care. Conclusion Any approach to sexual health in older adults needs to take all the factors that affect sexual health into account, while endorsing the full spectrum of sexual expression in a non-judgmental way. Individuals in a relationship should be offered a couple-centered approach. Special attention is needed for individuals in cognitive decline. Healthcare providers must be aware of ageist biases, which may prevent them from providing appropriate care.

Abstract Description Sequencing antigen-specific T cell receptors (TCR) remains a major technologic challenge, requiring labor- and cost-intensive single-cell approaches. We have developed a rapid and economical method for sequencing TCR mRNA from antigen stimulated peripheral blood mononuclear cells using targeted long-read nanopore sequencing with Oxford Nanopore Technologies’ (ONT) Minion sequencer. Targeted PCR amplification of TCR alpha and beta cDNA was performed with a template switching oligo (TSO). Dual unique molecular identifiers (UMI) were used for single cell, oligoclonal, or bulk cell sample specific sequence identification followed by nanopore sequencing. The reads were demultiplexed by UMIs and aligned to germline V,D, and J gene sequences from the IMGT database for identification. Error corrected sequences were then used to determine the non-templated complementarity determining region 3 (CDR3) sequence for the TCRs. From bulk PBMCs, TCR Beta Chain had 796 unique TRBV-TRBJ combination and TCR Alpha Chain had 2053 unique TRAV-TRAJ combinations. Human PBMC samples were spiked with 10% Jurkat cells and sequenced using this method for 12 hours resulting in 200K reads. After read demultiplexing, filtering (Q > 25), and alignment, it was found that 8.8% of TCR beta and 7.0% TCR alpha reads matched the Jurkat TCR sequence in the 10% spiked sample. This method provides a fast and cost-effective approach to generating antigen specific TCR sequences for downstream functional studies. Funding Sources Supported by NIH/NCI 5U01CA281660 Topic Categories Technological Innovations in Immunology (TECH)

BACKGROUND: MODULAR antitachycardia pacing (ATP), a multicenter, international trial, assesses a modular cardiac rhythm management system: a subcutaneous implantable cardioverter defibrillator in wireless communication with a leadless pacemaker (LP) capable of pace-terminating ventricular tachycardia. METHODS: Enrolees had one or more clinical risk factors for ventricular tachycardia and did not require chronic pacing. Complications included prespecified major LP system- and procedure-related complications, and any complication related to the LP, subcutaneous implantable cardioverter defibrillator, implantation, or study protocol. Survival analysis was performed to identify complication-free rates, therapy delivery, and all-cause mortality. RESULTS: The 297 patients enrolled had an ejection fraction of 35±13%, 43% secondary prevention indications, and 59% with prior ventricular arrhythmias. Of 286 patients undergoing LP implantation (100% success), 251 patients completed 12-month follow-up. Mortality rate was 6%, with none related to the implant procedure. Median follow-up duration was 23.4 months (interquartile range, 17.9–28.1). The LP major complication-free rate was 97.2%, exceeding the performance goal. The overall LP+ subcutaneous implantable cardioverter defibrillator system-related complication-free rate was 88.5%. Appropriate tachyarrhythmia-therapy (ATP+shock) rates were 14.4%, and appropriate shock rates were 8.5%. Inappropriate total tachyarrhythmia therapy was 9.5% of which 8.5% were shocks. ATP was 67.3% successful in terminating ventricular arrhythmia episodes and accelerated ventricular arrhythmias in 10.1% of episodes. Overall therapy burden (ATP+shock) was 96/100 patient-years, of which 44/100 patient-years was for shock delivery. CONCLUSIONS: One-year outcomes of the first modular pacing-defibrillator system reveal low system and LP complication rates and good ATP efficacy rates, suggesting that the modular cardiac rhythm management is a viable alternative to single-chamber implantable cardioverter defibrillators using low-energy pacing capability without the need for transvenous leads. CLINICAL TRIAL REGISTRATION: URL: https://clinicaltrials.gov/ ; Unique identifier: NCT04798768.

Abstract INTRODUCTION Glioblastoma is the most common malignant adult brain tumor with poor prognosis, largely due to its heterogeneous landscape. The AI-RANO and RANO-RGP groups organized the BraTS-Pathology 2024 challenge, providing a publicly-available dataset and a benchmarking environment for AI models to automatically identify six clinically-relevant histopathologic sub-regions in H&E-stained whole slide images (WSIs). METHODS We reclassified the TCGA-GBM and TCGA-LGG data according to the WHO 2021 criteria and identified diagnostic WSIs of 188 glioblastoma (IDH-wt, CNS WHO Gr.4) patients. A well-defined annotation protocol was communicated to eight neuropathologists, across eight institutions, who created “ground truth” annotations for Cellular Tumor (CT), Geographic Necrosis, Cortical Infiltration, Pseudopalisading Necrosis (PN), Microvascular Proliferation, and Penetration into White Matter (PWM). Comprehensive WSI curation for elimination of artifactual content and follow up partitioning resulted in approximately 300,000 data samples/patches. FINDINGS Inter-observer variability assessment highlighted difficulties in establishing a “gold standard”. The least annotated sub-regions (PWM and PN) were the most challenging to the participating methods, whereas all methods achieved their highest accuracy in the most annotated sub-region (CT). The highest average performance across sub-regions was obtained by a foundation model-based method. However, this top-ranked solution achieved an F1 score of 0.51 (Accuracy=0.65, Sensitivity=0.56, Specificity=0.93). CONCLUSIONS Our findings suggest that achieving state-of-the-art performance, in automatically identifying clinically-relevant glioblastoma sub-regions, remains an unsolved research problem. Analysis of the ground-truth annotations underscores the critical need for more precisely defined and neuropathologically standardized annotation protocol. Current AI algorithms are facing difficulties in addressing glioblastoma’s heterogeneous histologic landscape in the absence of large, comprehensively, and consistently annotated data. Building upon these findings, and towards developing more accurate AI algorithms, the 2025 iteration of the BraTS-Path challenge is currently on-going by leveraging two million data samples, aiming to enhance our disease understanding and improve diagnostic accuracy, ultimately contributing to improved patient outcomes.

Abstract Computer vision now enables deep learning models to link histopathologic features to molecular features in a quantitative manner not previously achievable by human pathologists. However, human interpretability is often limited. We trained a deep neural network to predict gene expression from digital whole slide images (WSIs) using 848 central nervous system (CNS) tumors with paired WSI and RNA-sequencing data. We used inferred RNA expression levels as a surrogate for protein expression of the CNS tumor lineage markers GFAP, OLIG2, and SSTR2, as well as the proliferation marker MKI67 (Ki67). We established thresholds for categorizing the inferred expression levels as positive or negative based on levels observed in cross-validation testing in select tumor types to approximate routine clinical interpretation of immunohistochemical (IHC) staining. We tested the sensitivity and specificity of our ‘virtual’ IHC on an independent, multi-institutional cohort of over 2,000 CNS tumors with objective diagnostic labels derived from DNA methylation-based tumor classification. As a dichotomous variable, inferred GFAP expression showed a sensitivity of 74% and a specificity of 99% as a glial marker in a cohort of gliomas and meningiomas. In the same cohort, OLIG2 showed a sensitivity of 75% and a specificity of 97% as a glial marker, while SSTR2 showed a sensitivity of 83% and a specificity of 97% as a marker of meningioma. In a cohort of ependymomas and gliomas, virtual IHC for OLIG2 showed a sensitivity of 75% and a specificity of 82% in distinguishing ependymomas from gliomas. Finally, in a cohort of gliomas, inferred expression of MKI67 demonstrated a trend consistent with what would be expected by actual IHC, with increasing MKI67 expression with increasing tumor grade. Our model provides the basis for a human interpretable and clinically applicable deep neural network to aid human pathologists in the diagnosis and grading of CNS tumors.

Abstract Molecular testing has become critical components in the diagnostic classification of the central nervous system (CNS) tumors. However, these methods require substantial resources, limiting accessibility for many patients. Recent advances in artificial intelligence (AI) and computer vision empower deep learning models to infer molecular features from histopathology images, which may often be more readily available than molecular testing, to classify CNS tumors. We trained pan-CNS tumor models to predict DNA methylation and gene expression from whole-slide images (WSIs). These molecular predictions were then used in a hierarchical machine learning framework, termed Neuropath-AI, to predict nine broad tumor families and 52 tumor types using a large diverse cohort of 5,715 histopathology samples, of which 2,988 had paired DNA methylation profiling and 848 had paired RNA-sequencing. We evaluated Neuropath-AI on another large independent multi-institutional cohort of 5,516 CNS tumors, for which Neuropath-AI predicted tumor class with an associated confidence score. Neuropath-AI designated 46% of test samples as predictable with high-confidence, which it then accuracy classified in 97% of samples. It made moderate-confidence (and above) predictions for 87% of test samples, for which it achieved a diagnostic accuracy of 80% for the top-1 prediction and 86% accuracy for a top-2 predictions. A comparison with human neuropathologists showed that Neuropath-AI is comparable at classifying tumors. A second test showed that human neuropathologists were more accurate when the Neuropath-AI classifier results were provided to them, testifying to the potential translational benefit of integrating AI classification tools into the pathologist’s workflow. Our model provides the basis for a clinically applicable deep learning assistant to improve human efficiency and accuracy of CNS tumor diagnosis. The model will be made publicly available and can be readily implemented to assist human pathologists in clinical workflows, in further expanded prospective studies.