Effect of Pimobendan in Dogs with Preclinical Myxomatous Mitral Valve Disease and Cardiomegaly: The EPIC Study - A Randomized Clinical Trial

Background: Pimobendan is effective in treatment of dogs with congestive heart failure (CHF) secondary to myxomatous mitral valve disease (MMVD). Its effect on dogs before the onset of CHF is unknown. Hypothesis/Objectives: Administration of pimobendan (0.4-0.6 mg/kg/d in divided doses) to dogs with increased heart size secondary to preclinical MMVD, not receiving other cardiovascular medications, will delay the onset of signs of CHF, cardiac-related death, or euthanasia. Animals: 360 client-owned dogs with MMVD with left atrial-to-aortic ratio >= 1.6, normalized left ventricular internal diameter in diastole >= 1.7, and vertebral heart sum >10.5. Methods: Prospective, randomized, placebo-controlled, blinded, multicenter clinical trial. Primary outcome variable was time to a composite of the onset of CHF, cardiac-related death, or euthanasia. Results: Median time to primary endpoint was 1228 days (95% CI: 856-NA) in the pimobendan group and 766 days (95% CI: 667-875) in the placebo group (P = .0038). Hazard ratio for the pimobendan group was 0.64 (95% CI: 0.47-0.87) compared with the placebo group. The benefit persisted after adjustment for other variables. Adverse events were not different between treatment groups. Dogs in the pimobendan group lived longer (median survival time was 1059 days (95% CI: 952-NA) in the pimobendan group and 902 days (95% CI: 747-1061) in the placebo group) (P = .012). Conclusions and Clinical Importance: Administration of pimobendan to dogs with MMVD and echocardiographic and radiographic evidence of cardiomegaly results in prolongation of preclinical period and is safe and well tolerated. Prolongation of preclinical period by approximately 15 months represents substantial clinical benefit

Technical and Comparative Aspects of Brain Glycogen Metabolism.

It has been known for over 50 years that brain has significant glycogen stores, but the physiological function of this energy reserve remains uncertain. This uncertainty stems in part from several technical challenges inherent in the study of brain glycogen metabolism, and may also stem from some conceptual limitations. Factors presenting technical challenges include low glycogen content in brain, non-homogenous labeling of glycogen by radiotracers, rapid glycogenolysis during postmortem tissue handling, and effects of the stress response on brain glycogen turnover. Here, we briefly review aspects of glycogen structure and metabolism that bear on these technical challenges, and discuss ways these can be overcome. We also highlight physiological aspects of glycogen metabolism that limit the conditions under which glycogen metabolism can be useful or advantageous over glucose metabolism. Comparisons with glycogen metabolism in skeletal muscle provide an additional perspective on potential functions of glycogen in brain

Case study: ENVRI science demonstrators with D4Science

Whenever a community of practice starts developing an IT solution for its use case(s) it has to face the issue of carefully selecting “the platform” to use. Such a platform should match the requirements and the overall settings resulting from the specific application context (including legacy technologies and solutions to be integrated and reused, costs of adoption and operation, easiness in acquir- ing skills and competencies). There is no one-size-fits-all solution that is suitable for all application context, and this is particularly true for scientific communities and their cases because of the wide heterogeneity characterising them. However, there is a large consensus that solutions from scratch are inefficient and services that facilitate the development and maintenance of scientific community-specific solutions do exist. This chapter describes how a set of diverse communities of practice efficiently developed their science demonstrators (on analysing and pro- ducing user-defined atmosphere data products, greenhouse gases fluxes, particle formation, mosquito diseases) by leveraging the services offered by the D4Science infrastructure. It shows that the D4Science design decisions aiming at streamlin- ing implementations are effective. The chapter discusses the added value injected in the science demonstrators and resulting from the reuse of D4Science services, especially regarding Open Science practices and overall quality of service

764P How long is long enough? An international survey exploring practice variations on the recommended duration of maintenance therapy with PARP inhibitors in platinum sensitive recurrent ovarian cancer and long-term outcomes

Background: There is no consensus on the optimal duration of PARP inhibitor (PARPi) maintenance therapy for exceptional responders with platinum sensitive recurrent ovarian cancer (PS-ROC), defined as being progression-free for 5 years (yr) or longer. The current label is to continue PARPi until progression or toxicity, however practice varies internationally. The risk of late recurrence and of late onset myelodysplastic syndrome (MDS) and acute myeloid leukaemia (AML) are unknown.// Methods: An online international survey of experts from June 2023 to April 2024, disseminated at Gynaecologic Cancer Intergroup meetings and by Chairs of Cooperative Groups to select experienced members.// Results: 178 responses were received from 25 countries, including Australia (n=27), the United Kingdom (21), The Netherlands (16), France (13), Canada (12), Italy (11), Japan (11), Spain (11) and others (53). Most clinicians (136, 76%) lacked guidelines regarding PARPi duration. For those with guidelines, recommendations varied: 1 yr (1), 2 yr (10), 3 yr (3), or indefinite (17). Recommendations also varied for those without guidelines: most (101, 56.7%) recommended ≥5 yr, and 33 (18.3%) recommended 5 yr. Surveillance practices including the frequency of clinical reviews, blood tests and imaging varied substantially.// Conclusions: This survey highlights the diverse practice variations and disparate views on the optimal duration of maintenance therapy with PARPi in PS-ROC. The survey suggests a notable risk of late progression and MDS/AML among exceptional responders. Detailed individual patient data is needed to draw more reliable conclusions. A large international retrospective cohort study is underway to investigate long term outcomes of exceptional responders with PS-ROC. The results will help inform development of uniform practice guidelines

Use of Quantitative Pharmacology in the Development of HAE1, a High-Affinity Anti-IgE Monoclonal Antibody

HAE1, a high-affinity anti-IgE monoclonal antibody, is discussed here as a case study in the use of quantitative pharmacology in the development of a second-generation molecule. In vitro, preclinical, and clinical data from the first-generation molecule, omalizumab, were heavily leveraged in the HAE1 program. A preliminary mechanism-based pharmacokinetic/pharmacodynamic (PK/PD) model for HAE1 was developed using an existing model for omalizumab, together with in vitro binding data for HAE1 and omalizumab. When phase I data were available, the model was refined by simultaneously modeling PK/PD data from omalizumab studies with the available HAE1 phase I data. The HAE1 clinical program was based on knowledge of the quantitative relationship between a pharmacodynamic biomarker, suppression of free IgE, and clinical response (e.g., lower exacerbation rates) obtained in pivotal studies with omalizumab. A clinical trial simulation platform was developed to predict free IgE levels and clinical responses following attainment of a target free IgE level (≤10 IU/ml). The simulation platform enabled selection of four doses for the phase II dose-ranging trial by two independent methods: dose-response non-linear fitting and linear mixed modeling. Agreement between the two methods provided confidence in the doses selected. Modeling and simulation played a large role in supporting acceleration of the HAE1 program by enabling data-driven decision-making, often based on confirmation of projections and/or learning from incoming new data

Characterising paediatric mortality during and after acute illness in Sub-Saharan Africa and South Asia: a secondary analysis of the CHAIN cohort using a machine learning approach

Background A better understanding of which children are likely to die during acute illness will help clinicians and policy makers target resources at the most vulnerable children. We used machine learning to characterise mortality in the 30-days following admission and the 180-days after discharge from nine hospitals in low and middle-income countries (LMIC). Methods A cohort of 3101 children aged 2–24 months were recruited at admission to hospital for any acute illness in Bangladesh (Dhaka and Matlab Hospitals), Pakistan (Civil Hospital Karachi), Kenya (Kilifi, Mbagathi, and Migori Hospitals), Uganda (Mulago Hospital), Malawi (Queen Elizabeth Central Hospital), and Burkina Faso (Banfora Hospital) from November 2016 to January 2019. To record mortality, children were observed during their hospitalisation and for 180 days post-discharge. Extreme gradient boosted models of death within 30 days of admission and mortality in the 180 days following discharge were built. Clusters of mortality sharing similar characteristics were identified from the models using Shapley additive values with spectral clustering. Findings Anthropometric and laboratory parameters were the most influential predictors of both 30-day and post-discharge mortality. No WHO/IMCI syndromes were among the 25 most influential mortality predictors of mortality. For 30-day mortality, two lower-risk clusters (N = 1915, 61%) included children with higher-than-average anthropometry (1% died, 95% CI: 0–2), and children without signs of severe illness (3% died, 95% CI: 2–4%). The two highest risk 30-day mortality clusters (N = 118, 4%) were characterised by high urea and creatinine (70% died, 95% CI: 62–82%); and nutritional oedema with low platelets and reduced consciousness (97% died, 95% CI: 92–100%). For post-discharge mortality risk, two low-risk clusters (N = 1753, 61%) were defined by higher-than-average anthropometry (0% died, 95% CI: 0–1%), and gastroenteritis with lower-than-average anthropometry and without major laboratory abnormalities (0% died, 95% CI: 0–1%). Two highest risk post-discharge clusters (N = 267, 9%) included children leaving against medical advice (30% died, 95% CI: 25–37%), and severely-low anthropometry with signs of illness at discharge (46% died, 95% CI: 34–62%). Interpretation WHO clinical syndromes are not sufficient at predicting risk. Integrating basic laboratory features such as urea, creatinine, red blood cell, lymphocyte and platelet counts into guidelines may strengthen efforts to identify high-risk children during paediatric hospitalisations. Funding Bill & Melinda Gates Foundation OPP1131320