Labeling Changes Research Articles

Summaries of safety labeling changes approved by FDA—Boxed warnings highlights July–September 2024

Summaries of safety labeling changes approved by FDA—Boxed warnings highlights July–September 2024

Tscluster: A python package for the optimal temporal clustering framework

Temporal clustering extends the conventional task of data clustering by grouping time series data according to shared temporal trends across sociospatial units, with diverse applications in the social sciences, especially urban science. The two dominant methods are as follows: Time Series Clustering (TSC), with dynamic cluster centres but static labels for each entity, and Sequence Label Analysis (SLA), with static cluster centres but dynamic labels. To implement the universe of models spanning the design space between TSC and SLA, we present tscluster, an open-source Python framework. tscluster offers: (1) several innovative techniques, such as Bounded Dynamic Clustering (BDC), that are not available in existing libraries, allowing users to set an upper bound on the number of label changes and identify the most dynamically evolving time series; (2) a user-friendly interface for applying and comparing these methods; (3) globally optimal solutions for the clustering objective by employing a mixed-integer linear programming formulation, enhancing the reproducibility and robustness of the results in contrast to existing methods based on initialization-sensitive local optimization; and (4) a suite of visualization tools for interpretability and comparison of clustering results. We present our framework using a case study of neighbourhood change in Toronto, comparing two methods available in tscluster. Supplemental materials provide an additional case study of local business development in Chicago and a detailed mathematical exposition of our framework. tscluster can be installed via PyPI (pypi.org/project/tscluster), and the source code is accessible on Github (github.com/tscluster-project/tscluster). Documentation is available online at the tscluster website (tscluster.readthedocs.io).

Intrusion detection based on concept drift detection and online incremental learning

Purpose The primary purpose of this paper is to introduce the drift detection method-online random forest (DDM-ORF) model for intrusion detection, combining DDM for detecting concept drift and ORF for incremental learning. The paper addresses the challenges of dynamic and nonstationary data, offering a solution that continuously adapts to changes in the data distribution. The goal is to provide effective intrusion detection in real-world scenarios, demonstrated through comprehensive experiments and evaluations using Apache Spark. Design/methodology/approach The paper uses an experimental approach to evaluate the DDM-ORF model. The design involves assessing classification performance metrics, including accuracy, precision, recall and F-measure. The methodology integrates Apache Spark for distributed computing, using metrics such as processed records per second and input rows per second. The evaluation extends to the analysis of IP addresses, ports and taxonomies in the MAWILab data set. This comprehensive design and methodology showcase the model’s effectiveness in detecting intrusions through concept drift detection and online incremental learning on large-scale, heterogeneous data. Findings The paper’s findings reveal that the DDM-ORF model achieves outstanding classification results with 99.96% accuracy, demonstrating its efficacy in intrusion detection. Comparative analysis against a convolutional neural network-based model indicates superior performance in anomalous and suspicious detection rates. The exploration of IP addresses, ports and taxonomies uncovers valuable insights into attack patterns. Apache Spark evaluation attests to the system’s high processing rates. The study emphasizes the scalability, availability and fault tolerance of DDM-ORF, making it suitable for real-world scenarios. Overall, the paper establishes the model’s proficiency in handling dynamic, nonstationary data for intrusion detection. Research limitations/implications The research acknowledges certain limitations, including the potential challenge of DDM detecting only frequency changes in class labels and not complex concept drifts. The incremental random forest’s reliance on memory may pose constraints as the forest size increases, potentially leading to overfitting. Addressing these limitations could involve exploring alternative concept drift detection algorithms and implementing ensemble pruning techniques for memory efficiency. Further research avenues may investigate algorithms balancing accuracy and memory usage, such as compressed random forests, to enhance the model’s effectiveness in evolving data environments. Practical implications The study’s practical implications are noteworthy. The proposed DDM-ORF model, designed for intrusion detection through concept drift detection and online incremental learning, offers a scalable, available and fault-tolerant solution. Leveraging Apache Spark and Microsoft Azure Cloud enhances processing capabilities for large data sets in dynamic, nonstationary scenarios. The model’s applicability to heterogeneous data sets and its achievement of high-accuracy multi-class classification make it suitable for real-world intrusion detection. Moreover, the auto-scaling features of Microsoft Azure Cloud contribute to adaptability, ensuring efficient resource utilization without downtime. These practical implications underscore the model’s relevance and effectiveness in diverse operational contexts. Social implications The DDM-ORF model’s social implications are significant, contributing to enhanced cybersecurity measures. By providing an effective intrusion detection system, it helps safeguard digital ecosystems, preserving user privacy and securing sensitive information. The model’s accuracy in identifying and classifying various intrusion attempts aids in mitigating potential cyber threats, thereby fostering a safer online environment for individuals and organizations. As cybersecurity is paramount in the digital age, the social impact lies in fortifying the resilience of networks, systems and data against malicious activities, ultimately promoting trust and reliability in online interactions. Originality/value The DDM-ORF model introduces a novel approach to intrusion detection by combining drift detection and online incremental learning. This originality lies in its utilization of the DDM-ORF algorithm, offering a dynamic and adaptive system for evolving data. The model’s contribution extends to its scalability, fault-tolerance and suitability for heterogeneous data sets, addressing challenges in dynamic, nonstationary environments. Its application on a large-scale data set and multi-class classification, along with integration with Apache Spark and Microsoft Azure Cloud, enhances the field’s understanding and application of intrusion detection, providing valuable insights for securing digital infrastructures.

Voluntary versus mandatory food labels, Australia.

To compare uptake of the voluntary Health Star Rating front-of-pack nutrition labelling system with uptake of a mostly mandatory country-of-origin label in Australia over a similar period. We used data on numbers and proportions of products carrying health stars and country-of-origin labelling recorded annually between 2015 and 2023 through surveys of four large Australian food retailers. We determined the proportion of products with health stars and country-of-origin labels for each year by dividing the number of products carrying each label by the total number eligible to carry that label. The uptake of the voluntary Health Star Rating increased steadily between 2014 and 2018, reaching a maximum of 42% (8587/20 286) of products in 2021 before decreasing to 39% (8572/22 147) in 2023. Mandatory country-of-origin labelling uptake rose rapidly and was found on 93% (17 567/18 923) of products in 2023. In categories where country-of-origin labelling was voluntary, uptake by 2023 was 48% (3313/6925). In our 2023 sample of 22 147 products, 11 055 (50%) carried country-of-origin labelling only, 7466 (35%) carried both health star and country-of-origin labelling, 1106 (5%) carried health star labels only and 2520 (11%) carried neither label. The experience with country-of-origin labelling shows that widespread and rapid food labelling change can be achieved when required by law. The Australian government should mandate the Health Star Rating without delay. Australia's experience supports other jurisdictions in implementing mandatory front-of-pack nutrition labelling as well as updates to global guidance to recognize mandatory labelling as best-practice in delivering benefits to consumers.

Current opinion: antiretrovirals during pregnancy and breastfeeding

Purpose of review To review the most important literature from the past 2 years on the pharmacokinetics of antiretrovirals in pregnancy, placental transfer, and breastmilk. Recent findings Concentrations of antiretrovirals frequently used in pregnancy and their placental transfer are described, together with infant exposure through breastmilk. Also, applications of ex-vivo and in-silico studies, such as placenta perfusion studies and PBPK models, are discussed. Summary Great efforts were made in the past 2 years to accelerate the availability of data on antiretrovirals during pregnancy and lactation. Bictegravir showed decreased but still sufficient concentrations during pregnancy, leading to a label change by the FDA. In-silico and clinical studies on long-acting cabotegravir and rilpivirine generated information leading to cautious use of these formulations in pregnancy. Low infant exposure to antiretrovirals through breastmilk is expected for most compounds. Despite the impact of these studies, more incentives are needed for earlier implementation, for instance, during the developmental phase of drugs, to provide women antenatally with proper information on their drugs.

Bee Together: Joining Bee Audio Datasets for Hive Extrapolation in AI-Based Monitoring.

Beehive health monitoring has gained interest in the study of bees in biology, ecology, and agriculture. As audio sensors are less intrusive, a number of audio datasets (mainly labeled with the presence of a queen in the hive) have appeared in the literature, and interest in their classification has been raised. All studies have exhibited good accuracy, and a few have questioned and revealed that classification cannot be generalized to unseen hives. To increase the number of known hives, a review of open datasets is described, and a merger in the form of the "BeeTogether" dataset on the open Kaggle platform is proposed. This common framework standardizes the data format and features while providing data augmentation techniques and a methodology for measuring hives' extrapolation properties. A classical classifier is proposed to benchmark the whole dataset, achieving the same good accuracy and poor hive generalization as those found in the literature. Insight into the role of the frequency of the classification of the presence of a queen is provided, and it is shown that this frequency mostly depends on a colony's belonging. New classifiers inspired by contrastive learning are introduced to circumvent the effect of colony belonging and obtain both good accuracy and hive extrapolation abilities when learning changes in labels. A process for obtaining absolute labels was prototyped on an unsupervised dataset. Solving hive extrapolation with a common open platform and contrastive approach can result in effective applications in agriculture.

Inflation of crisis coverage? Tracking and explaining the changes in crisis labeling and crisis news wave salience 1785–2020

Abstract Has there been an inflation in crisis coverage in newspapers over the last centuries, and if so, what structural factors drive this change? We utilize computational text analyses along with our own signal detection algorithm to measure the presence of crisis keywords and the emergence of crisis news waves. An analysis of crisis coverage in The Times (U.K., 1785–2020, 183,239 news stories) shows that the share of coverage that uses crisis keywords has increased, though not steadily. The number and salience of crisis news waves tied to discernible events has increased at a slower pace. The hypothesized driving forces—government expansion, mediatization of politics, and the activity of crisis frame sponsors—explain the development well and allow accurate predictions even when ignoring time in the forecasting model. Crisis coverage seems to reflect not so much the problems society faces, but society’s identity, priorities, and outlook on the world.

Investigating Protein-Nucleic Acid Binding Interactions with Diethylpyrocarbonate Covalent Labeling-Mass Spectrometry.

Nucleic acids are important biomolecules that facilitate numerous cellular functions and have in recent years become promising candidates for treating disease. Consequently, there is a need for methods to characterize protein interactions with these molecules. Here, we demonstrate that diethylpyrocarbonate (DEPC) covalent labeling-mass spectrometry (CL-MS) can provide structural information for protein-nucleic acid binding by characterizing the binding sites of two DNA aptamers specific to thrombin. Reductions in thrombin labeling are observed at the pair's binding interfaces. Furthermore, we find that binding of the aptamers causes changes in labeling at residues in the thrombin active site and known exosites for each aptamer, showcasing the sensitivity of DEPC CL-MS to significant allosteric changes.

Summaries of safety labeling changes approved by FDA-boxed warnings highlights, April-June 2024.

Summaries of safety labeling changes approved by FDA-boxed warnings highlights, April-June 2024.

Development of a genotoxicity/carcinogenicity assessment method by DNA adductome analysis

Safety evaluation is essential for the development of chemical substances. Since in vivo safety evaluation tests, such as carcinogenesis tests, require long-term observation using large numbers of experimental animals, it is necessary to develop alternative methods that can predict genotoxicity/carcinogenicity in the short term, taking into account the 3Rs (replacement, reduction, and refinement). We established a prediction model of the hepatotoxicity of chemicals using a DNA adductome, which is a comprehensive analysis of DNA adducts that may be used as an indicator of DNA damage in the liver. An adductome was generated with LC-high-resolution accurate mass spectrometer (HRAM) on liver of rats exposed to various chemicals for 24 h, based on two independent experimental protocols. The resulting adductome dataset obtained from each independent experiment (experiments 1 and 2) and integrated dataset were analyzed by linear discriminant analysis (LDA) and found to correctly classify the chemicals into the following four categories: non-genotoxic/non-hepatocarcinogens (−/−), genotoxic/non-hepatocarcinogens (+/−), non-genotoxic/hepatocarcinogens (−/+), and genotoxic/hepatocarcinogens (+/+), based on their genotoxicity/carcinogenicity properties. A prototype model for predicting the genotoxicity/carcinogenicity of the chemicals was established using machine learning methods (using random forest algorithm). When the prototype genotoxicity/carcinogenicity prediction model was used to make predictions for experiments 1 and 2 as well as the integrated dataset, the correct response rates were 89 % (genotoxicity), 94 % (carcinogenicity) and 87 % (genotoxicity/carcinogenicity) for experiment 1, 47 % (genotoxicity), 62 % (carcinogenicity) and 42 % (genotoxicity/carcinogenicity) for experiment 2, and 52 % (genotoxicity), 62 % (carcinogenicity), and 48 % (genotoxicity/carcinogenicity) for the integrated dataset. To improve the accuracy of the toxicity prediction model, the toxicity label was reconstructed as follows; Pattern 1: when +/+ and −/− chemicals were used from the toxicity labels +/+, +/−, −/+ and −/−; and Pattern 2: when +/+, +/−, and −/+ other than −/− were replaced with the label "Others". As a result, chemicals with only +/+ and −/− toxicity labels were used and the correct response rates were approximately 100 % for the measured data in experiment 1, 53 %–66 % for the data in experiment 2, and 59–73 % for the integrated data, all of which were 10 %–30 % higher compared with the data before the label change. In contrast, when the toxicity labels were replaced with −/− and “Others”, they reached nearly 100 % in the measured data from experiment 1, 65 %–75 % in the data from experiment 2, and 70 %–78 % in the integrated data, all of which were 10 %–50 % higher compared with the data before the label change.

Description and Validation of a Novel AI Tool, LabelComp, for the Identification of Adverse Event Changes in FDA Labeling.

The accurate identification and timely updating of adverse reactions in drug labeling are crucial for patient safety and effective drug use. Postmarketing surveillance plays a pivotal role in identifying previously undetected adverse events (AEs) that emerge when a drug is used in broader and more diverse patient populations. However, traditional methods of updating drug labeling with new AE information have been manual, time consuming, and error prone. This paper introduces the LabelComp tool, an innovative artificial intelligence (AI) tool designed to enhance the efficiency and accuracy of postmarketing drug safety surveillance. Utilizing a combination of text analytics and a trained Bidirectional Encoder Representations from Transformers (BERT) model, the LabelComp tool automatically identifies changes in AE terms from updated drug labeling documents. Our objective was to create and validate an AI tool with high accuracy that could enable researchers and FDA reviewers to efficiently identify safety-related drug labeling changes. Our validation study of 87 drug labeling PDF pairs demonstrates the tool's high accuracy, with F1 scores of overall performance ranging from 0.795 to 0.936 across different evaluation tiers and a recall of at least 0.997 with only one missed AE out of 483 total AEs detected, indicating the tool's efficacy in identifying new AEs. The LabelComp tool can support drug safety surveillance and inform regulatory decision-making. The publication of this tool also aims to encourage further community-driven enhancements, aligning with broader interests in applying AI to advance regulatory science and public health.

Leveraging foreground–background cues for semantically-driven, training-free moving object detection

Deep learning algorithms and semantic cue-based methods are increasingly used to detect moving objects. The semantic label changes in pixel location over time are used for moving object detection in semantic cue-based studies. In this approach, traditional background subtraction is employed only when there is a lack of semantic background. However, the change in semantic information in each pixel is insufficient for accurate detection of moving objects due to similar labels assignments for different objects and limited use of pixel intensity data. Moreover, challenges arise in detecting moving objects, such as a lack of diversity in labeled objects, an uneven distribution of labels or classes, and an insufficient amount of unseen datasets. In this study, we propose the Moving Semantic Object Detector (MSemOD), a train-less moving object detection method based on a traditional method enhanced with semantic information provided by pre-trained semantic segmentation models. The proposed method includes a region-based decision step, utilizing the traditional methods and semantic information as spatial–temporal cues. Threshold values are applied in the method to determine the temporal and spatial movements of each segment in the image. To improve the consistency of the semantic information, the proposed method converts the semantic segmentation output into a semantic probability map by enhancing the temporal and spatial state. The performance of the proposed method is evaluated on the LASIESTA, CDNet2014, and the BMC datasets and the results are compared with the state-of-the-art algorithms. The results show that using region-based semantic information can significantly reduce false positives and enhance foreground–background segmentation in moving object detection.

Engaging Mood Brain Circuits with Psilocybin (EMBRACE): a study protocol for a randomized, placebo-controlled and delayed-start, neuroimaging trial in depression

BackgroundMajor depressive disorder (MDD) is a leading cause of disability worldwide across domains of health and cognition, affecting overall quality of life. Approximately one third of individuals with depression do not fully respond to treatments (e.g., conventional antidepressants, psychotherapy) and alternative strategies are needed. Recent early phase trials suggest psilocybin may be a safe and efficacious intervention with rapid-acting antidepressant properties. Psilocybin is thought to exert therapeutic benefits by altering brain network connectivity and inducing neuroplastic changes that endure for weeks post-treatment. Although early clinical results are encouraging, psilocybin’s acute neurobiological effects on neuroplasticity have not been fully investigated. We aim to examine for the first time how psilocybin acutely (intraday) and subacutely (weeks) alters functional brain networks implicated in depression.MethodsFifty participants diagnosed with MDD or persistent depressive disorder (PDD) will be recruited from a tertiary mood disorders clinic and undergo 1:1 randomization into either an experimental or control arm. Participants will be given either 25 mg psilocybin or 25 mg microcrystalline cellulose (MCC) placebo for the first treatment. Three weeks later, those in the control arm will transition to receiving 25 mg psilocybin. We will investigate whether treatments are associated with changes in arterial spin labelling and blood oxygenation level-dependent contrast neuroimaging assessments at acute and subacute timepoints. Primary outcomes include testing whether psilocybin demonstrates acute changes in (1) cerebral blood flow and (2) functional brain activity in networks associated with mood regulation and depression when compared to placebo, along with changes in MADRS score over time compared to placebo. Secondary outcomes include changes across complementary clinical psychiatric, cognitive, and functional scales from baseline to final follow-up. Serum peripheral neurotrophic and inflammatory biomarkers will be collected at baseline and follow-up to examine relationships with clinical response, and neuroimaging measures.DiscussionThis study will investigate the acute and additive subacute neuroplastic effects of psilocybin on brain networks affected by depression using advanced serial neuroimaging methods. Results will improve our understanding of psilocybin’s antidepressant mechanisms versus placebo response and whether biological measures of brain function can provide early predictors of treatment response.Trial registrationClinicalTrials.gov Identifier: NCT06072898. Registered on 6 October 2023.

Removing the FDA’s Boxed Hepatotoxicity Warning and Liver Function Testing Requirement for Ambrisentan

Endothelin receptor antagonists are first-line therapy for pulmonary arterial hypertension (PAH). The first 2 agents approved in the class, bosentan and ambrisentan, initially carried boxed warnings for hepatotoxicity and required monthly liver function tests (LFTs) as part of a risk evaluation and mitigation strategy (REMS); however, in 2011, as further safety data emerged on ambrisentan, the boxed hepatotoxicity warning and LFT requirements were removed. To analyze changes in the use of and LFT monitoring for ambrisentan and bosentan after changes to the ambrisentan labeling and REMS. This serial cross-sectional study used data from 3 longitudinal health care insurance claims databases-Medicaid, Optum's deidentified Clinformatics Data Mart, and Merative Marketscan-to perform an interrupted time series analysis of prescription fills and LFTs for patients taking ambrisentan and bosentan. Participants were patients filling prescriptions for ambrisentan and bosentan from July 1, 2007, to December 31, 2018. Data analysis was performed from April 2021 to August 2023. Removal of the boxed warning for hepatotoxicity and the REMS LFT monitoring requirements on ambrisentan in March 2011. The primary outcomes were use of ambrisentan (ie, individuals with at least 1 dispensing per 1 000 000 individuals enrolled in the 3 datasets) vs bosentan and LFT monitoring (ie, proportion of initiators with at least 1 ordered test) before initiation and before the first refill. A total of 10 261 patients received a prescription for ambrisentan during the study period (7442 women [72.5%]; mean [SD] age, 52.6 [17.6] years), and 11 159 patients received a prescription for bosentan (7931 women [71.1%]; mean [SD] age, 47.7 [23.7] years). Removal of the ambrisentan boxed hepatotoxicity warning and LFT monitoring requirement was associated with an immediate increase in the use of ambrisentan (1.50 patients per million enrollees; 95% CI, 1.08 to 1.92 patients per million enrollees) but no significant change in the use of bosentan. There were reductions in recorded LFTs before drug initiation (13.1% absolute decrease; 95% CI, -18.2% to -8.0%) and before the first refill (26.4% absolute decrease; 95% CI, -34.4% to -18.5%) of ambrisentan but not bosentan. In this serial cross-sectional study of ambrisentan, labeling changes and removal of the REMS-related LFT requirement were associated with shifts in prescribing and testing behavior for ambrisentan but not bosentan. Further clinician education may be needed to maximize the benefits of REMS programs and labeling warnings designed to ensure the safe administration of high-risk medications.

Label-guided seed-chain-extend alignment on annotated De Bruijn graphs.

Exponential growth in sequencing databases has motivated scalable De Bruijn graph-based (DBG) indexing for searching these data, using annotations to label nodes with sample IDs. Low-depth sequencing samples correspond to fragmented subgraphs, complicating finding the long contiguous walks required for alignment queries. Aligners that target single-labelled subgraphs reduce alignment lengths due to fragmentation, leading to low recall for long reads. While some (e.g. label-free) aligners partially overcome fragmentation by combining information from multiple samples, biologically irrelevant combinations in such approaches can inflate the search space or reduce accuracy. We introduce a new scoring model, 'multi-label alignment' (MLA), for annotated DBGs. MLA leverages two new operations: To promote biologically relevant sample combinations, 'Label Change' incorporates more informative global sample similarity into local scores. To improve connectivity, 'Node Length Change' dynamically adjusts the DBG node length during traversal. Our fast, approximate, yet accurate MLA implementation has two key steps: a single-label seed-chain-extend aligner (SCA) and a multi-label chainer (MLC). SCA uses a traditional scoring model adapting recent chaining improvements to assembly graphs and provides a curated pool of alignments. MLC extracts seed anchors from SCAs alignments, produces multi-label chains using MLA scoring, then finally forms multi-label alignments. We show via substantial improvements in taxonomic classification accuracy that MLA produces biologically relevant alignments, decreasing average weighted UniFrac errors by 63.1%-66.8% and covering 45.5%-47.4% (median) more long-read query characters than state-of-the-art aligners. MLAs runtimes are competitive with label-combining alignment and substantially faster than single-label alignment. The data, scripts, and instructions for generating our results are available at https://github.com/ratschlab/mla.

Association of outpatient fluoroquinolone prescribing with the National Medical Products Administration announcements of label changes in China

ABSTRACT Background In 2017 and 2021, the National Medical Products Administration (NMPA) announced to revise the drug label of fluoroquinolones. We aimed to evaluate the association of fluoroquinolone prescribing with the NMPA announcements of label changes. Research design and methods Monthly prevalence of fluoroquinolone prescriptions for uncomplicated urinary tract infections (uUTI), acute exacerbation of chronic bronchitis (AECB), and acute sinusitis (AS) between 2016 and 2022 was calculated, and interrupted time series analysis was applied to assess the impacts of NMPA label changes on fluoroquinolone use. Results Prevalence of fluoroquinolone prescriptions decreased by 2.39% (95% CI, −4.72% to −0.07%) for uUTI but increased by 3.02% (95% CI, 1.71% to 4.34%) for AS immediately after the 2017 label change. Moreover, after the 2021 label change, fluoroquinolone use decreased shortly in all the three indications. However, a significant increasing trend was observed in fluoroquinolone use for AECB episodes, and fluoroquinolons were used for 61.4% of treated uUTI, 31.6% of treated AECB, and 5.42% of treated AS at the end of 2022, respectively. Conclusions The label changes issued by the NMPA had no substantial impacts on fluoroquinolone prescribing in the study region in China. Fluoroquinolone prescribing was still highly prevalent for uUTI and AECB and thus requiring further antimicrobial stewardship.

Overview and Lifecycle Management of Generic Pharmaceutical Drugs in Mexico, Guatemala and Brazil

Objective: The generic pharmaceutical markets in Mexico, Guatemala, and Brazil are influenced by distinct regulatory frameworks, production and distribution dynamics, and marketing strategies, which shape lifecycle management. Regulatory policies, market competition, and healthcare initiatives impact accessibility, affordability, and quality, presenting both challenges and opportunities for enhancement in these areas. Summary: This thesis offers an in-depth examination of lifecycle management strategies in the generic pharmaceutical markets of Mexico, Guatemala, and Brazil. Generic drugs are crucial for increasing access to affordable healthcare, especially in emerging economies. Through comparative analysis, this research explores the regulatory frameworks, market dynamics, and key challenges in the development, approval, marketing, and post-marketing surveillance of generic drugs in these three Latin American countries. The study starts with an overview of the regulatory landscape for generic pharmaceuticals in Mexico, Guatemala, and Brazil, highlighting similarities and differences in registration requirements, approval processes, and post-approval obligations. It investigates the roles of regulatory agencies like COFEPRIS, DIGEMID, and ANVISA in ensuring the quality, safety, and efficacy of generic drugs throughout their lifecycle. Additionally, the research delves into the strategies used by pharmaceutical companies for lifecycle management of generic drugs, including variations, biowaivers, and labeling changes. Real-world case studies illustrate post-approval changes and their regulatory implications in each country, providing insights into the complexities of managing generic drug portfolios in different regulatory environments. The thesis also examines the market dynamics affecting the availability, pricing, and accessibility of generic drugs in Mexico, Guatemala, and Brazil. Factors such as patent expirations, competition, healthcare policies, and public procurement practices are analysed to understand their impact on market entry, competition, and the affordability of generic drugs. Furthermore, the study addresses the challenges and opportunities in pharmacovigilance and post-marketing surveillance of generic drugs, emphasizing the need for robust systems to detect and manage adverse drug reactions and ensure patient safety. Conclusion: The key findings highlight regulatory frameworks, market dynamics, and lifecycle management strategies in the generic pharmaceutical markets, identifying challenges and opportunities. The study reflects on implications for stakeholders and suggests areas for future research to enhance accessibility, affordability, and quality.

Potato oxidized hydroxypropyl starch/pectin-based indicator film with Clitoria ternatea anthocyanin and silver nanoparticles for monitoring chilled beef freshness

Potato oxidized hydroxypropyl starch (POHS)/pectin (P) functional and smart beef freshness indicator films were prepared using butterfly pea (Clitoria ternatea) anthocyanin (BA) and silver nanoparticles (AgNPs). BA exhibited significant pH-responsive color changes. BA and AgNPs were evenly distributed within a polymer matrix to create a compatible film with POHS/P. The films containing BA and AgNPs had good UV resistance and maintained strong mechanical strength, barrier properties, and color stability. The color of the indicator film changed from purple to green when exposed to ammonia, with the 1 % POHS/P/BA/AgNPs film showing the most sensitive response. The films also demonstrated strong antibacterial and antioxidant properties. The freshness of beef was monitored using 1 % POHS/P/BA/AgNPs films and was identified as sub-fresh and spoiled on days 4 and 7, respectively. The relationship between the color change of the indicator label and the freshness of chilled beef was established: purple for fresh meat, blue for less fresh meat, and green for spoiled meat. Thus, the new POHS/P/BA/AgNPs film can serve as a smart packaging material to indicate food freshness and extend shelf life. These results suggest that POHS/P/BA/AgNPs films have significant potential as an active and smart food packaging material.

Pediatric Pharmacology for the Primary Care Provider: Advances and Limitations.

Despite >1 in 5 children taking prescription drugs in the United States, off-label drug use is common. To increase the study of drugs in children, regulatory bodies have enacted legislation to incentivize and require pediatric drug studies. As a result of this legislation, novel trial approaches, and an increase in personnel with pediatric expertise, there have been numerous advancements in pediatric drug development. With this review, we aim to highlight developments in pediatric pharmacology over the past 6 years for the most common disease processes that may be treated pharmacologically by the pediatric primary care provider. Using information extracted from label changes between 2018 and 2023, the published literature, and Clinicaltrials.gov, we discuss advances across multiple therapeutic areas relevant to the pediatric primary care provider, including asthma, obesity and related disorders, mental health disorders, infections, and dermatologic conditions. We highlight instances in which new drugs have been developed on the basis of a deeper mechanistic understanding of illness and instances in which labels have been expanded in older drugs on the basis of newly available data. We then consider additional factors that affect pediatric drug use, including cost and nonpharmacologic therapies. Although there is work to be done, efforts focused on pediatric-specific drug development will increase the availability of evidence-based, labeled guidance for commonly prescribed drugs and improve outcomes through the safe and effective use of drugs in children.

Summaries of safety labeling changes approved by FDA-boxed warnings highlights, January-March 2024.

Summaries of safety labeling changes approved by FDA-boxed warnings highlights, January-March 2024.