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Background: Optimized clinical trial design remains a major challenge in drug development for Alzheimer's Disease AD). To advance our understanding of the underlying pathophysiology of disease and help optimize clinical trial design, CPAD developed a clinical trial simulation (CTS) tool for the pre‐dementia stages of Alzheimer's disease, using CDR‐SB as the primary endpoint. The tool is based on a disease progression model integrating baseline hippocampal volume, the effect of patient drop‐out and relevant sources of variability. The CTS was enhanced using data from contemporary clinical trials, adding fluid and imaging biomarkers of amyloid (Aβ) and tau pathology. Also included are dropout models, a placebo effect model, as well as drug‐effect models. Positron Emission Tomography (PET) measures of tau deposition, leveraging ongoing collaborative efforts to develop a standardized quantification scale across different tau tracers, will also be incorporated, to develop the first disease progression models utilizing a harmonized tau‐PET scale. Method: Data were harmonized and both linear and non‐linear mixed effects models were developed for multiple endpoints, including demographics, time from and to diagnosis, genetic status (APOE4). Monte Carlo simulations were performed to compare the statistical power by sample size in trials with and without enrichment using relevant covariates. A longitudinal mixed‐effects model was fit with longitudinal tau and neurodegeneration outcomes. Cognition and biomarkers were explored in a multivariate model. Result: As of October 2022, CPAD's clinical trial repository contains over 7,000 unique participants that have baseline CDR‐SB measurement, 4,000+ participants with baseline Aβ measures (fluid or PET), and 2,000+ participants with baseline measures of amyloid, tau (e.g., cerebrospinal fluid p‐tau and/or tau‐PET), and neurodegeneration (e.g., MRI or neurofilament light). With these data, we modeled longitudinal trajectories of cognitive decline using baseline levels of amyloid, tau, and neurodegeneration. Future work will add models of longitudinal biomarker trajectories. Conclusion: The disease progression models will serve as the basis for a clinical trial simulation tool to facilitate design of contemporary clinical trials based on a quantitative understanding of the biological progression of key pathological features, amyloid and tau deposition, of AD. [ABSTRACT FROM AUTHOR]

Background: Imaging tau burden in vivo using Positron Emission Tomography (PET) has tremendous potential for advancing our biological understanding of Alzheimer's disease. Towards this direction, there is a need for post‐acquisition imaging data 'harmonization' tools and analysis methods that reduce variance across studies, sites, and scanners, while retaining the power to study complex disease related effects. The Global Alzheimer's Association Interactive Network (GAAIN), the Laboratory of Neuro Imaging (LONI), and the Critical Path for Alzheimer's Disease (CPAD) Consortium have undertaken a collaborative effort to provide a harmonized approach to the analysis of tau PET images. Method: The GAAIN/LONI Pipeline is a software suite that allows the design and execution of neuroimaging workflows through flexible integration of different image preprocessing software and parallel processing via a computer grid. Each workflow has a distinct fingerprint that can be executed locally on different platforms or remotely via GAAIN, thus increasing interoperability and allowing the use of "harmonized" workflows across different datasets. Tau PET workflows were designed and deployed on the GAAIN/LONI Pipeline based on existing literature. In turn, these tau PET workflows were executed on CPAD tau PET to derive tau PET burden. Clinical validation was performed by quantifying tau PET burden on different clinical groups. Analytical validation was performed by correlating the LONI Pipeline results with independently developed pipelines. Result: We first show feasibility results as the tau PET workflows are deployed and executed on CPAD data. For clinical validation, we tested whether derived metrics can distinguish between clinical groups. For analytical validation we tested correlations of results with existing methods that have been independently developed. These results provide a unified analysis model that can be utilized from GAAIN/LONI and CPAD to conduct harmonized analyses on multiple datasets. The full capabilities of this model will be explored in subsequent work. Conclusion: The proposed pipeline, which is going to be part of the publicly available GAAIN/LONI platform, should provide researchers with an optimized roadmap that describes how tau PET imaging data should be processed and analyzed for reliability and reproducibility in the context of drug development and regulatory decision making. [ABSTRACT FROM AUTHOR]

Background and purpose: The safety of early initiation of anticoagulant therapy in patients with ischaemic stroke related to atrial fibrillation (AF) is unknown. We investigated the safety of early initiation of direct oral anticoagulants (DOACs), vitamin K antagonists (VKAs) or no anticoagulation. Methods: This observational, retrospective, single‐centre study included consecutive patients with recent (<4 weeks) ischaemic stroke and AF. The primary outcome was the rate of major (intracranial and extracranial) bleeding in patients on different treatment schemes, i.e. DOACs, VKAs and not anticoagulated. We also investigated the rate of ischaemic cerebrovascular events and mortality. Results: We included 959 consecutive patients with AF and ischaemic stroke followed up for an average of 16.1 days after the index event. A total of 559 out of 959 patients (58.3%) were anticoagulated with either VKAs (n = 259) or DOACs (n = 300). Anticoagulation was started after a mean of 7 ± 9.4 days in the DOAC group and 11.9 ± 19.7 days in the VKA group. Early initiation of any anticoagulant was not associated with an increased risk of any major bleeding [odds ratio (OR), 0.49; 95% confidence intervals (CI), 0.21–1.16] and in particular of intracranial bleeding (OR, 0.47; 95% CI, 0.17–1.29; P = 0.143) compared with no anticoagulation. In contrast to VKAs (OR, 0.78; 95% CI, 0.28–2.13), treatment with DOACs (OR, 0.32; 95% CI, 0.10–0.96) reduced the rate of major bleeding compared with no anticoagulation. Early recurrences of ischaemic stroke did not differ significantly among the three groups. Conclusions: Starting DOACs within a mean of 7 days after stroke appeared to be safe. Randomized controlled studies are needed to establish the added efficacy of starting anticoagulation early after stroke. [ABSTRACT FROM AUTHOR]

Critical Path Institute (C‐Path) is a nonprofit organization that brings together patient groups, academic institutions, the pharmaceutical industry and regulatory agencies from around the world to improve public health. C‐Path provides the legal, scientific, and regulatory infrastructure to generate a unique neutral environment for medical product development, supported by more than a decade of experience in data standards development, platform development and hosting, patient‐level data privacy stewardship, data platform security, and controlled access methodology. The Critical Path for Alzheimer's Disease (CPAD) is a pre‐competitive consortium that aims to promote, support, and manage a diverse source and format of data and facilitate knowledge sharing from the Alzheimer disease (AD) drug development community. The CPAD database currently contains 65 clinical trials and observational studies, with over 93,000 individual anonymized patient‐level records, with global access to many datasets through the C‐Path Online Data Repository (CODR). The unparalleled breadth, depth and wealth of data will enable us to discover yet‐to‐be identified unmet needs and unanswered questions. Further, CODR is interoperable with the Global Alzheimer's Association Interactive Network (GAAIN), Vivli and other data platforms. The secure and neutral data sharing framework of CPAD has resulted in the development of several regulatory‐grade tools that help optimize drug development and accelerate bringing new therapies to patients in need. Sufficient case examples of successful data sharing are now available and should inform and encourage future data sharing initiatives. [ABSTRACT FROM AUTHOR]

Background: The Global Alzheimer's Association Interactive Network (GAAIN) and the Critical Path for Alzheimer's Disease (CPAD) Consortium have undertaken a collaborative effort to establish a mechanism that can help evaluate the relationship between fluid‐based, morphometric imaging biomarkers and clinical trajectories of disease progression, across datasets and analyses pipelines housed by CPAD and GAAIN. Additionally, this collaboration will demonstrate the feasibility of bidirectional integration of data management models and will guide development of a federated image analysis pipeline model across these two platforms. Method: To enable interoperability, GAAIN's original architecture is expanded to accommodate the LONI brain structural imaging pipeline. Under this new framework, GAAIN researchers can initiate brain imaging‐related analyses on their servers directly from GAAIN's graphical interface with the results integrating back into GAAIN for further analyses. Feasibility was demonstrated for the determination of correlations for CPAD's biomarker and imaging volumetric data. GAAIN's brain imaging pipeline was executed on CPAD data, allowing for quantification of volumetric images. In turn, blood and volumetric data were inserted in GAAIN allowing further evaluation and analyses. Result: Based on the results, we provide guidelines for a future framework for a federated analysis model that can be utilized from GAAIN and CPAD to establish relationships between blood and brain markers. In this model, for a cohort of interest, analyses of relevant brain marker data can be remotely done in CPAD servers via GAAINs' federated platform after which results can be inserted back to GAAIN alongside CPAD's blood marker data. The full capabilities of this federated model will be explored in subsequent work. Conclusion: As data sharing becomes increasingly prominent and mature, the partnership between GAAIN and CPAD will yield ever greater benefits and knowledge and will continue to grow and evolve in response to the changing Alzheimer's disease landscape. As such, it represents a non‐exclusive door for researchers to make use of the data sets and advanced analytical tools and is intended to leverage the unique competencies of these organizations and lead to the continued evolution of bringing meaningful and high‐impact advances in Alzheimer's disease. [ABSTRACT FROM AUTHOR]

Background: Critical Path Institute (C‐Path) coordinates the Critical Path for Alzheimer's Disease (CPAD) Consortium. To accelerate DRUG DEVELOPMENT for Alzheimer's Disease (AD), CPAD developed two regulatory‐endorsed clinical trial simulation (CTS) that sponsors can use to help optimize clinical trial design in 1) the pre‐dementia stages of AD, using CDR‐SB as the primary endpoint, and 2) mild‐to‐moderate AD, using ADAS‐cog as the primary cognitive endpoint. Recently, CPAD members have shared data from several contemporary Phase III clinical trials, offering an opportunity to enrich and expand these model‐based tools with additional fluid and imaging biomarker information, data from predementia subjects and treatment arms. Method: By integrating patient‐level data from high quality Alzheimer's disease (AD) clinical trials, models will be developed for multiple endpoints, and including demographics, time from and to diagnosis, genetic status (APOE4), and will be assessed using non‐linear mixed effects methods. Monte Carlo simulations will be performed to compare the statistical power by sample size in trials with and without enrichment using relevant covariates. Result: As of January 2022, CPAD's clinical trial repository contains 55 studies with 36,344 individual anonymized patient records. Analysis subsets generated from 6 key datasets, rich in fluid and imaging biomarkers, were used to develop of a preliminary set of disease progression models that characterize the time course of clinically relevant measures (ADAS‐Cog, CDR‐SB, other clinical assessment scales and biomarkers). A two‐level regression model was applied to predict baseline CDR‐SB using baseline covariates (demography, ApoE4 status, fluid and imaging biomarkers) and identify significant variables. Base models using parametric mixed‐effects approaches were developed with selection based on goodness‐of‐fit plots. Conclusion: The disease progression models will serve as the basis for clinical trial simulation tools to facilitate informed decision making in the DRUG DEVELOPMENT process and optimize patient and endpoint selection, as well as design of efficacy studies. CPAD will advance the tools through the formal FDA Fit for Purpose pathway, to achieve regulatory endorsement and provide confidence to sponsors for the adoption of the tools. [ABSTRACT FROM AUTHOR]

The article presents information on a study which examined a method for quantifying the impact of soil carbon management on biophysical soil properties, using two apple orchard systems in New Zealand. The soil carbon management is a significant factor for soil quality. The study analyzed the impact of the soil carbon management on biological soil properties, such as microbial biomass carbon, basal respiration, dehydrogenase activity, respiratory quotient, and physical soil properties such as aggregate stability, amount of plant-available water, conductive mean pore diameter near water saturation.

Background: The Critical Path for Alzheimer's Disease (CPAD) consortium's primary objective is to promote, support, and manage pre‐competitive data sharing from Alzheimer disease (AD) clinical trials to allow quantification of Alzheimer's Disease progression across the AD continuum. A quantitative understanding of disease dynamics will drive scientific discovery provided by aggregated and standardized primary clinical trial data. This will provide solutions to optimize the design of clinical trials of AD drugs intended for regulatory review in support of marketing approval. CPAD is a nonprofit, pre‐competitive consortium (https://c‐path.org/programs/cpad/) of the Critical Path Institute (C‐Path) and convenes diverse stakeholders from academia, advocacy groups, industry, and regulators. Method: As of January 2020, CPAD's database (www.codr.c‐path.org) contains data contains 38 studies, representing 14,853 individual records and more than 420,000 data points, standardized to the AD CDISC (Clinical Data Interchange Standards Consortium) standards. CPAD's objective is to collaborate with industry and regulators to leverage their wealth of drug development knowledge by enabling pre‐competitive data sharing from clinical trials in AD. Analysis of datasets (at meta‐data and patient levels) enables generation of tools involving comprehensive disease progression models across the AD continuum, to support successful drug development. Result: CPAD continues ongoing efforts towards expansion of the CPAD database with contemporary datasets containing patient‐level data from both control and active arms of clinical trials in AD. By aggregating such datasets into our rich clinical trial repository, CPAD will fill existing gaps in our models and generate of quantitative solutions for drug development in AD, in collaboration with the Quantitative Medicine team. Key focus of CPAD in 2020 includes completion of the pre‐dementia model (which has received a Letter of Support from the EMA) and generation of the comprehensive disease progression model. This effort will be based on an evolving workflow initiating with dataset exploration at the level of meta‐data and patient level data and determination of model specifications, resulting in model validation, simulations and regulatory submissions. Conclusion: Pre‐competitive sharing of contemporary clinical trial data will allow us to develop a comprehensive understanding of the disease continuum in AD, enable a fully informed trial design and advance effective drug development in AD. [ABSTRACT FROM AUTHOR]

Common carp are an important pest species in Australia, yet little is known regarding their age and growth there. We examined otolith sections of common carp to validate their utility for age determination. For the 1999 year-class in Hut Lake near Barmah, we confirmed the absolute age at first annulus formation as age 1 by repeated sampling of a discrete young-of-year cohort. We confirmed the annual periodicity of annulus formation for common carp in a mark-recapture experiment when 19 recaptured adult common carp (from an original stocking of 141 fish marked by injection with oxytetracycline [OTC]) showed visible fluorescent marks on their otoliths. Time at liberty for these fish ranged from 6 to 25 months, and their ages on recapture ranged from 3 to 14 years. Increment counts outside the OTC mark agreed completely with time at liberty. We calculated precision estimates on age determinations as the average percent error (APE) and estimated the coefficient of variation (CV = −0.15 + 1.41APE) between readers and for each of two readers over time. Precision was assessed by rereading subsamples. The APE was less than 5% and CV was less than 8% in all cases. We conclude that examination of thin otolith sections is a suitable method for the determination of annual age estimates for common carp age 0-14. [ABSTRACT FROM AUTHOR]

Background: The primary objective of the Critical Path for Alzheimer's Disease (CPAD) consortium, a pre‐competitive consortium of the Critical Path Institute, is to promote, support, and manage pre‐competitive data and knowledge sharing from Alzheimer disease (AD) drug development stakeholders. Since 2019, CPAD has embarked on a new data sharing initiative to acquire fluid and imaging biomarker‐rich data sources for development of novel tools to quantify Alzheimer's Disease progression across the AD continuum. Method: Patient‐level data from contemporary Phase2/Phase3 AD clinical trials and observational studies is acquired through formal data contribution agreements within CPAD, curated in collaboration with data contributors, standardized to common structure and terminology, and input into a relational data model (RDM) tool to generate analysis subsets. Data standardization is being accomplished using Clinical Data Interchange Standards Consortium (CDISC) standards. The RDM was developed using the open‐source software R and converts CDISC structure to analysis ready files for quantitative analyses through user defined primary keys, i.e. unique identifying variables for extracting information. The RDM includes a front‐end data interrogator that provides users with versatile capabilities to visualize and tabulate the data. Result: As of October 2020, CPAD's database (https://codr.c‐path.org/) contains 41 studies, standardized to the AD CDISC standards, of which three datasets were used to generate analysis subsets. Four additional datasets were recently acquired and are being standardized. Execution of Data Contribution Agreements (DCAs) is in progress for two contemporary datasets rich in key fluid and imaging biomarkers. Nine additional prioritized datasets are targeted for acquisition. The RDM is currently being used to transform the CDISC standardized data into analysis ready subsets containing patient demographics (age, sex), fluid biomarker information (Aβ 40/42), and clinical outcome assessments (Clinical Dementia Rating Scale – Sum of Boxes). Conclusion: The data acquisition and analysis platform developed is stimulating pre‐competitive efforts to generate quantitative tools for advancing AD drug development. Previously developed tools, including a regulatory endorsed clinical trial simulation tool, help optimize trial design and advance drug development in AD. [ABSTRACT FROM AUTHOR]

Background: Model‐informed drug development (MIDD) can improve cost effectiveness of research and development (R&D) decision‐making. The Critical Path for Alzheimer's Disease (CPAD) Consortium has developed a mild‐to‐moderate Alzheimer's disease clinical trial simulator, which is intended to optimize clinical trial design in this target population, when using ADAS‐Cog as the primary endpoint. This tool represented the first‐ever FDA and EMA‐endorsed quantitative drug development tool. The CPAD team has also developed a pre‐dementia disease progression model, which received an EMA letter of support, as a trial enrichment tool for pre‐dementia trials that use CDR‐SB as the primary endpoint. In order to democratize the use of these tools, graphical user interfaces (GUIs) were developed for these two models. Method: The GUIs were developed using R‐Shiny, focusing on the drug‐disease‐trial model aspects of the clinical trial simulator for mild‐to‐moderate AD, with emphasis on the sources of variability (APOE4, sex, baseline MMSE and age); and focusing on the disease progression predictors of the trial enrichment model for pre‐dementia (baseline MMSE, age and hippocampal volume, APOE4 and sex). Result: The GUIs were beta‐tested by pharmacometricians, statisticians, clinical trialists and clinicians. Their feedback was used to further improve the functionality of the GUIs. Focus was put on the ability of the users to intuitively set up the simulation framework for specific studies, the underlying assumptions, and the graphical and tabular output of simulated results. Conclusion: The publicly accessible GUIs will increase the usability of these quantitative drug development tools. Examples of their application include informing stage‐gate decisions; optimization of study design; patient selection; and sample size estimations. [ABSTRACT FROM AUTHOR]

Background: Adequate assessment of trial design optimization is an important factor to inform clinical trial design for complex disorders such as Alzheimer's disease (AD). Critical Path Institute's Critical Path for Alzheimer's Consortium (CPAD) has achieved EMA qualification and FDA Fit‐for‐Purpose endorsement of the first‐ever quantitative drug development tool (the clinical trial simulator for mild‐to‐moderate AD) and is in the process of developing a disease progression model spanning the entire disease continuum, to enable clinical trial design optimization. The objective of this work is to develop a structural model that characterizes disease progression in AD patients using the integrated CPAD database and optimize experimental design using clinical trial simulations. Method: A disease progression model characterizing the time course of clinically relevant measures (ADAS‐Cog, CDR‐SB, other clinical scales and biomarkers), will be developed using an integrated dataset comprised of multiple observational and clinical trial data sources. Covariates including demographics, time from and to diagnosis, genetic status (APOE4), co‐morbidities and medication use will be assessed using non‐linear mixed effects methods. Monte Carlo simulations will be performed to compare the statistical power by sample size in trials with and without enrichment using relevant covariates. Result: Model selection based on the log‐likelihood ratio and goodness‐of‐fit plots will be used to select the model structure that most adequately describes the integrated database. Covariate analyses will be performed to identify variables that constitute relevant predictors of baseline severity and disease progression rates. Trial simulations for a therapeutic effect using standard drug effect models for symptomatic and disease‐modifying effects will be conducted to estimate an optimal sample size when trials are enriched with a clinically relevant covariate. Conclusion: The simulation output from the disease progression model will enable a more efficient design of clinical studies with better informed inclusion criteria and enrichment strategies. The proposed quantitative methodology permits a comprehensive integration of relevant patient characteristics available for decision‐making. [ABSTRACT FROM AUTHOR]

The article presents a case study of a 45-year-old man with rare condition of eosinophilic pleural effusion. The patient was presented with a developing non-productive cough and pleuritic chest pain. His chest radiograph had confirmed bilateral pleural effusion and his computed tomography pulmonary angiogram has excluded pulmonary embolism and other pulmonary parenchymal pathology.

Background: Asthma remission has emerged as a potential treatment goal. This study evaluated the effectiveness of two biologics (mepolizumab/omalizumab) in achieving asthma remission., Methods: This observational study included 453 severe asthma patients (41% male; mean age ± SD 55.7 ± 14.7 years) from two real-world drug registries: the Australian Mepolizumab Registry and the Australian Xolair Registry. The composite outcome clinical remission was defined as zero exacerbations and zero oral corticosteroids during the previous 6 months assessed at 12 months and 5-item Asthma Control Questionnaire (ACQ-5) ≤1 at 12 months. We also assessed clinical remission plus optimization (post-bronchodilator FEV1 ≥80%) or stabilization (post-bronchodilator FEV1 not greater than 5% decline from baseline) of lung function at 12 months. Sensitivity analyses explored various cut-offs of ACQ-5/FEV1 scores. The predictors of clinical remission were identified., Results: 29.3% (73/249) of AMR and 22.8% (37/162) of AXR cohort met the criteria for clinical remission. When lung function criteria were added, the remission rates were reduced to 25.2% and 19.1%, respectively. Sensitivity analyses identified that the remission rate ranged between 18.1% and 34.9% in the AMR cohort and 10.6% and 27.2% in the AXR cohort. Better lung function, lower body mass index, mild disease and absence of comorbidities such as obesity, depression and osteoporosis predicted the odds of achieving clinical remission., Conclusion: Biologic treatment with mepolizumab or omalizumab for severe asthma-induced asthma remission in a subgroup of patients. Remission on treatment may be an achievable treatment target and future studies should consider remission as an outcome measure., (© 2023 The Authors. Allergy published by European Academy of Allergy and Clinical Immunology and John Wiley & Sons Ltd.)