Quality Risk Management Approach for Drug Development and Its Future Prospectives

These days, finding new marketing authorizations, guaranteeing regulatory compliance, and keeping labour costs competitive are extremely tough. Many pharmaceutical companies also struggle to deal with local regulatory issues and stay up with changes in key pharmaceutical markets. Regulations are thoroughly reviewed before being given to the RA department. This team compiles the most critical prescription information for global approval and marketing. This category accepts both new and revised product submissions. This is mostly handled by the RA department. RA\u27s job is to provide feedback on proposed or disputed legislation. This is a proactive measure. The ICH framework allows for more early intervention. Regulators have a wide range of responsibilities. In the US, the FDA must register and clear the goods with the export company\u27s regulatory professional. &nbsp

Federated learning enables big data for rare cancer boundary detection.

Although machine learning (ML) has shown promise across disciplines, out-of-sample generalizability is concerning. This is currently addressed by sharing multi-site data, but such centralization is challenging/infeasible to scale due to various limitations. Federated ML (FL) provides an alternative paradigm for accurate and generalizable ML, by only sharing numerical model updates. Here we present the largest FL study to-date, involving data from 71 sites across 6 continents, to generate an automatic tumor boundary detector for the rare disease of glioblastoma, reporting the largest such dataset in the literature (n = 6, 314). We demonstrate a 33% delineation improvement for the surgically targetable tumor, and 23% for the complete tumor extent, over a publicly trained model. We anticipate our study to: 1) enable more healthcare studies informed by large diverse data, ensuring meaningful results for rare diseases and underrepresented populations, 2) facilitate further analyses for glioblastoma by releasing our consensus model, and 3) demonstrate the FL effectiveness at such scale and task-complexity as a paradigm shift for multi-site collaborations, alleviating the need for data-sharing

Author Correction: Federated learning enables big data for rare cancer boundary detection.

10.1038/s41467-023-36188-7NATURE COMMUNICATIONS14

Federated Learning Enables Big Data for Rare Cancer Boundary Detection

Although machine learning (ML) has shown promise across disciplines, out-of-sample generalizability is concerning. This is currently addressed by sharing multi-site data, but such centralization is challenging/infeasible to scale due to various limitations. Federated ML (FL) provides an alternative paradigm for accurate and generalizable ML, by only sharing numerical model updates. Here we present the largest FL study to-date, involving data from 71 sites across 6 continents, to generate an automatic tumor boundary detector for the rare disease of glioblastoma, reporting the largest such dataset in the literature (n = 6, 314). We demonstrate a 33% delineation improvement for the surgically targetable tumor, and 23% for the complete tumor extent, over a publicly trained model. We anticipate our study to: 1) enable more healthcare studies informed by large diverse data, ensuring meaningful results for rare diseases and underrepresented populations, 2) facilitate further analyses for glioblastoma by releasing our consensus model, and 3) demonstrate the FL effectiveness at such scale and task-complexity as a paradigm shift for multi-site collaborations, alleviating the need for data-sharing

Omecamtiv mecarbil in chronic heart failure with reduced ejection fraction, GALACTIC‐HF: baseline characteristics and comparison with contemporary clinical trials

Aims: The safety and efficacy of the novel selective cardiac myosin activator, omecamtiv mecarbil, in patients with heart failure with reduced ejection fraction (HFrEF) is tested in the Global Approach to Lowering Adverse Cardiac outcomes Through Improving Contractility in Heart Failure (GALACTIC‐HF) trial. Here we describe the baseline characteristics of participants in GALACTIC‐HF and how these compare with other contemporary trials. Methods and Results: Adults with established HFrEF, New York Heart Association functional class (NYHA) ≥ II, EF ≤35%, elevated natriuretic peptides and either current hospitalization for HF or history of hospitalization/ emergency department visit for HF within a year were randomized to either placebo or omecamtiv mecarbil (pharmacokinetic‐guided dosing: 25, 37.5 or 50 mg bid). 8256 patients [male (79%), non‐white (22%), mean age 65 years] were enrolled with a mean EF 27%, ischemic etiology in 54%, NYHA II 53% and III/IV 47%, and median NT‐proBNP 1971 pg/mL. HF therapies at baseline were among the most effectively employed in contemporary HF trials. GALACTIC‐HF randomized patients representative of recent HF registries and trials with substantial numbers of patients also having characteristics understudied in previous trials including more from North America (n = 1386), enrolled as inpatients (n = 2084), systolic blood pressure < 100 mmHg (n = 1127), estimated glomerular filtration rate < 30 mL/min/1.73 m2 (n = 528), and treated with sacubitril‐valsartan at baseline (n = 1594). Conclusions: GALACTIC‐HF enrolled a well‐treated, high‐risk population from both inpatient and outpatient settings, which will provide a definitive evaluation of the efficacy and safety of this novel therapy, as well as informing its potential future implementation

Association between HDL levels and stroke outcomes in the Arab population

Abstract Low HDL levels are associated with an increased stroke incidence and worsened long-term outcomes. The aim of this study was to assess the relationship between HDL levels and long-term stroke outcomes in the Arab population. Patients admitted to the Qatar Stroke Database between 2014 and 2022 were included in the study and stratified into sex-specific HDL quartiles. Long-term outcomes included 90-Day modified Rankin Score (mRS), stroke recurrence, and post-stroke cardiovascular complications within 1 year of discharge. Multivariate binary logistic regression analyses were performed to identify the independent effect of HDL levels on short- and long-term outcomes. On multivariate binary logistic regression analyses, 1-year stroke recurrence was 2.24 times higher (p = 0.034) and MACE was 1.99 times higher (p = 0.009) in the low-HDL compared to the high-HDL group. Mortality at 1 year was 2.27-fold in the low-normal HDL group compared to the reference group (p = 0.049). Lower sex-specific HDL levels were independently associated with higher adjusted odds of 1-year post-stroke mortality, stroke recurrence, and MACE (p < 0.05). In patients who suffer a stroke, low HDL levels are associated with a higher risk of subsequent vascular complication

Comparative performance of image fusion methodologies in eddy current testing

Abstract: Image fusion methodologies have been studied for improving the detectability of eddy current Nondestructive Testing (NDT). Pixel level image fusion has been performed on C-scan eddy current images of a sub-surface defect at two different frequencies. Multi-resolution analysis based Laplacian pyramid and wavelet fusion methodologies, statistical inference based Bayesian fusion and Principal Component Analysis (PCA) based fusion methodologies have been studied towards improving the detectability of defects. The performance of the fusion methodologies has been compared using image metrics such as SNR and entropy. Bayesian based fusion methodology has shown better performance as compared to other methodologies with 33.75 dB improvement in the SNR and an improvement of 3.22 in the entropy