The severity assessment of Parkinson's disease based on plasma inflammatory factors and third ventricle width by transcranial sonography.

BACKGROUND: Predicting Parkinson's disease (PD) can provide patients with targeted therapies. However, disease severity can be roughly evaluated in clinical practice based on the patient's symptoms and signs. OBJECTIVE: The current study attempted to explore the factors linked with PD severity and construct a predictive model. METHOD: The PD patients and healthy controls were recruited from our study center while recording their basic demographic information. The serum inflammatory markers levels, such as Cystatin C (Cys C), C-reactive protein (CRP), RANTES (regulated on activation, normal T cell expressed and secreted), Interleukin-10 (IL-10), and Interleukin-6 (IL-6) were determined for all the participants. PD patients were categorized into early and mid-advanced groups based on the Hoehn and Yahr (H-Y) scale and evaluated using PD-related scales. LASSO logistic regression analysis (Model C) helped select variables based on clinical scale evaluations, serum inflammatory factor levels, and transcranial sonography measurements. The optimal harmonious model coefficient λ was determined via 10-fold cross-validation. Moreover, Model C was compared with multivariate (Model A) and stepwise (Model B) logistic regression. The area under the curve (AUC) of a receiver operator characteristic (ROC), brier score, calibration curve, and decision curve analysis (DCA) helped determine the discrimination and calibration of the predictive model, followed by configuring a forest plot and column chart. RESULTS: The study included 113 healthy individuals and 102 PD patients, with 26 early and 76 mid-advanced patients. Univariate analysis of variance screened out statistically significant differences among inflammatory markers Cys C and RANTES. The average Cys C level in the mid-advanced stage was significantly higher than in the early stage (p < 0.001) but not for RANTES (p = 0.740). The LASSO logistic regression model (λ.1se = 0.061) associated with UPDRS-I, UPDRS-II, UPDRS-III, HAMA, PDQ-39, and Cys C as the included independent variables revealed that the Model C discrimination and calibration (AUC = 0.968, Brier = 0.049) were superior to Model A (AUC = 0.926, Brier = 0.079) and Model B (AUC = 0.929, Brier = 0.071) models. CONCLUSION: The study results show multiple factors are linked with PD assessment. Moreover, the inflammatory marker Cys C and transcranial sonography measurement could objectively predict PD symptom severity, helping doctors monitor PD evolution in patients while targeting interventions.

Combining fecal immunochemical testing and questionnaire-based risk assessment in selecting participants for colonoscopy screening in the Chinese National Colorectal Cancer Screening Programs: A population-based cohort study.

BACKGROUND: Screening reduces colorectal cancer (CRC) burden by allowing early resection of precancerous and cancerous lesions. An adequate selection of high-risk individuals and a high uptake rate for colonoscopy screening are critical to identifying people more likely to benefit from screening and allocating healthcare resources properly. We evaluated whether combining a questionnaire-based interview for risk factors with fecal immunochemical test (FIT) outcomes for high-risk assessment is more efficient and economical than a questionnaire-based interview-only strategy. METHODS AND FINDINGS: In this multicenter, population-based, prospective cohort study, we enrolled community residents aged 40 to 74 years in 29 provinces across China. From 2016 to 2020, a total of 1,526,824 eligible participants were consecutively enrolled in the Cancer Screening Program in Urban China (CanSPUC) cohort, and 940,605 were enrolled in the Whole Life Cycle of Cancer Screening Program (WHOLE) cohort, with follow-up to December 31, 2022. The mean ages were 56.89 and 58.61 years in CanSPUC and WHOLE, respectively. In the WHOLE cohort, high-risk individuals were identified by combining questionnaire-based interviews to collect data on risk factors (demographics, diet history, family history of CRC, etc.) with FIT outcomes (RF-FIT strategy), whereas in the CanSPUC cohort, high-risk individuals were identified using only interview-based data on risk factors (RF strategy). The primary outcomes were participation rate and yield (detection rate of advanced neoplasm, early-stage detection rate of CRCs [stage I/II], screening yield per 10,000 invitees), which were reported for the entire population and for different gender and age groups. The secondary outcome was the cost per case detected. In total, 71,967 (7.65%) and 281,985 (18.47%) individuals were identified as high-risk and were invited to undergo colonoscopy in the RF-FIT group and RF group, respectively. The colonoscopy participation rate in the RF-FIT group was 26.50% (19,071 of 71,967) and in the RF group was 19.54% (55,106 of 281,985; chi-squared test, p < 0.001). A total of 102 (0.53%) CRCs and 2,074 (10.88%) advanced adenomas were detected by the RF-FIT, versus 90 (0.16%) and 3,593 (6.52%) by the RF strategy (chi-squared test, both p < 0.001). The early-stage detection rate using the RF-FIT strategy was significantly higher than that by the RF strategy (67.05% versus 47.95%, Fisher's exact test, p = 0.016). The cost per CRC detected was $24,849 by the RF-FIT strategy versus $55,846 by the RF strategy. A limitation of the study was lack of balance between groups with regard to family history of CRC (3.5% versus 0.7%). CONCLUSIONS: Colonoscopy participation and screening yield were better with the RF-FIT strategy. The association with CRC incidence and mortality reduction should be evaluated after long-term follow-up.

Integrating visual large language model and reasoning chain for driver behavior analysis and risk assessment.

Driver behavior is a critical factor in driving safety, making the development of sophisticated distraction classification methods essential. Our study presents a Distracted Driving Classification (DDC) approach utilizing a visual Large Language Model (LLM), named the Distracted Driving Language Model (DDLM). The DDLM introduces whole-body human pose estimation to isolate and analyze key postural features-head, right hand, and left hand-for precise behavior classification and better interpretability. Recognizing the inherent limitations of LLMs, particularly their lack of logical reasoning abilities, we have integrated a reasoning chain framework within the DDLM, allowing it to generate clear, reasoned explanations for its assessments. Tailored specifically with relevant data, the DDLM demonstrates enhanced performance, providing detailed, context-aware evaluations of driver behaviors and corresponding risk levels. Notably outperforming standard models in both zero-shot and few-shot learning scenarios, as evidenced by tests on the 100-Driver dataset, the DDLM stands out as an advanced tool that promises significant contributions to driving safety by accurately detecting and analyzing driving distractions.

Experience Learned and Perspectives on Using Model-Integrated Evidence in the Regulatory Context for Generic Drug Products-a Meeting Report.

This report summarizes relevant insights and discussions from a 2022 FDA public workshop titled Best Practices for Utilizing Modeling Approaches to Support Generic Product Development which illustrated how model-integrated evidence has been used and can be leveraged further to inform generic drug product development and regulatory decisions during the assessment of generic drug applications submitted to the FDA. The workshop attendees discussed that model-integrated evidence (MIE) approaches for generics are being applied in the space of long-acting injectable (LAI) products to develop shorter and more cost-effective alternative study designs for LAI products. Modeling and simulation approaches are utilized to support virtual BE assessments at the site of action for locally acting drug products and to assess the impact of food on BE assessments for oral dosage forms. The factors contributing to the success of the model-informed drug development program under PDUFA VI were discussed. The generic drug industry shared that decisions on formulation candidate/formulation variant selection, on pilot in vivo bioavailability studies, and on alternative study designs for BE assessment are informed by modeling and simulation approaches. There was agreement that interactions between the regulatory agencies and the industry are desirable because they improve the industry's understanding of scientific and other regulatory considerations on implementing modeling and simulation approaches in drug development and regulatory submissions.

Geographic disparities in trends of thyroid cancer incidence and mortality from 1990 to 2019 and a projection to 2030 across income-classified countries and territories.

Background: The rising incidence of thyroid cancer (TC) has generated growing concern globally; yet there are no studies examining whether this incidence was followed by a rise in related mortality. We aimed to comprehensively quantify current trends and future projections of TC incidence and mortality, and to explore the association between the TC burden and socioeconomic inequality in different income strata. Methods: We obtained incidence and mortality data on TC and population from the 2019 Global Burden of Disease (GBD) study and the United Nations' World Population Prospects 2022. We applied an age-period-cohort (APC) model to estimate the overall annual percentage change (net drift) and age, period, and cohort effects from 1990 to 2019, and also constructed a Bayesian APC model to predict the TC burden through 2030. Results: Over a third of global TC cases belonged to the high-income group. From 1990 to 2019, net drifts of TC incidence were >0 in all income groups, while a modest reduction (net drift <0) in mortality was observed in most income groups, except for the lower-middle-income group. Unfavourable age, period, and cohort effects were most notable in Vietnam, China, and Korea. The age-standardised incidence rate (ASIR) is predicted to increase whereas the age-standardized mortality rate (ASMR) is expected to decrease globally between 2020 and 2030, with geographic heterogeneity being detected across income groups. We observed a positive correlation between ASIR and universal health coverage index and health worker density, but a negative one between ASMR and the two indicators, primarily in upper-middle-income and high-income countries. Conclusions: Opposite patterns in incidence and mortality of TC raise concerns about overdiagnosis, particularly in upper-middle-income and high-income countries. Discrepancies in the distribution of health service accessibility, including diagnostic techniques and therapeutic care, should be addressed by narrowing health inequalities in the TC burden across countries.

Who drives carbon emissions and what emission reduction potential in the resource curse agglomeration: a case of Xinjiang.

The primary problem in achieving carbon emission reduction and carbon peak is to identify the key driving factors and emission reduction potential of the industrial sector, especially in resource-cursed regions like Xinjiang. This study aimed to explore the key driving factors and abatement potential of carbon emissions based on the "energy-environment-economy" hybrid input-output model of Xinjiang during 1997-2017. The result showed that: (1) Industrial carbon emissions have experienced three stages: slow growth-rapid growth-stable growth. (2) Demand change effect and energy intensity effect were the determinants of Xinjiang industrial carbon emission change; Capital formation and domestic trade were the biggest drivers of carbon emissions growth; Especially after entering the "new normal",the driving force of imports in Xinjiang's international trade increased gradually over time. (3) The coal-based energy structure was both the biggest obstacle and the best entry point in carbon emission reduction. (4) Of the 28 key industry sectors, heavy industry including the production and supply of electricity and heat (S22), petroleum processing, coking and nuclear fuel processing (S11), chemical industry (S12), metal smelting and rolling (S14), and energy industries had the greatest potential for carbon reduction. The research findings provide scientific decision-making reference for Xinjiang to accurately grasp the carbon emission reduction potential of the industry and formulate a targeted carbon peak action plan.

Occurrence, distribution and risk assessment of polycyclic aromatic hydrocarbons in soils around main water source areas of Beijing, China.

Polycyclic aromatic hydrocarbons (PAHs) in urban environments have been globally concerned due to their significant health impacts on residents. However, little is known about potential risks of PAHs from centralized water source areas. In the present study, 326 soils samples from the main water source areas of Beijing were collected and the occurrence, source appointment, and risks of PAHs were systematically investigated based on the monitoring results from high-performance liquid chromatography (HPLC). The total PAHs (∑16 PAHs) concentrations ranged from 5.70 to 1512 ng/g with median value of 44.2 ng/g, in which 4-ring and 5-ring groups were the major components. PAHs concentrations in the cultivated land were significantly higher than other areas, which could reflect significant impact of soil organic matter and total nitrogen contents on the spatial variations of PAHs. Further source identifications through positive matrix factorization model (PMF) revealed that biomass (22.5%), coal (21.4%), gasoline (17.6%) and diesel (16.4%) combustion were dominant sources of soil PAHs in the study area. Moreover, the risk assessment indicated that total ecological and health risk of PAHs were negligible, but individual PAH, including pyrene and benzo(b)fluoranthene, should be concerned due to their potential risks in several monitored stations located in the secondary protection area of four reservoirs. Our study provided new insights into environmental risks of soils in main water source areas from PAHs and could be helpful for organic micropollutant controlling and drinking water safety in rapidly urbanizing cities.

Integration of Biorelevant Pediatric Dissolution Methodology into PBPK Modeling to Predict In Vivo Performance and Bioequivalence of Generic Drugs in Pediatric Populations: a Carbamazepine Case Study.

This study investigated the impact of gastro-intestinal fluid volume and bile salt (BS) concentration on the dissolution of carbamazepine (CBZ) immediate release (IR) 100 mg tablets and to integrate these in vitro biorelevant dissolution profiles into physiologically based pharmacokinetic modelling (PBPK) in pediatric and adult populations to determine the biopredictive dissolution profile. Dissolution profiles of CBZ IR tablets (100 mg) were generated in 50-900 mL biorelevant adult fasted state simulated gastric and intestinal fluid (Ad-FaSSGF and Ad-FaSSIF), also in three alternative compositions of biorelevant pediatric FaSSGF and FaSSIF medias at 200 mL. This study found that CBZ dissolution was poorly sensitive to changes in the composition of the biorelevant media, where dissimilar dissolution (F2 = 46.2) was only observed when the BS concentration was changed from 3000 to 89 µM (Ad-FaSSIF vs Ped-FaSSIF 50% 14 BS). PBPK modeling demonstrated the most predictive dissolution volume and media composition to forecast the PK was 500 mL of Ad-FaSSGF/Ad-FaSSIF media for adults and 200 mL Ped-FaSSGF/FaSSIF media for pediatrics. A virtual bioequivalence simulation was conducted by using Ad-FaSSGF and/or Ad-FaSSIF 500 mL or Ped-FaSSGF and/or Ped-FaSSIF 200 mL dissolution data for CBZ 100 mg (reference and generic test) IR product. The CBZ PBPK models showed bioequivalence of the product. This study demonstrates that the integration of biorelevant dissolution data can predict the PK profile of a poorly soluble drug in both populations. Further work using more pediatric drug products is needed to verify biorelevant dissolution data to predict the in vivo performance in pediatrics.

Weakly-Interactive-Mixed Learning: Less Labelling Cost for Better Medical Image Segmentation.

Common medical image segmentation tasks require large training datasets with pixel-level annotations which are very expensive and time-consuming to prepare. To overcome such limitation and achieve the desired segmentation accuracy, a novel Weakly-Interactive-Mixed Learning (WIML) framework is proposed by efficiently using weak labels. On one hand, utilize weak labels to reduce annotation time for high-quality strong labels by designing a Weakly-Interactive Annotation (WIA) part of the WIML which prudently introduces interactive learning into the weakly-supervised segmentation strategy. On the other hand, utilize weak labels and very few strong labels to achieve desired segmentation accuracy by designing a Mixed-Supervised Learning (MSL) part of the WIML which can boost the segmentation accuracy by providing strong prior knowledge during training. Besides, a multi-task Full-Parameter-Sharing Network (FPSNet) is proposed to better implement this framework. Specifically, to further reduce annotation time, attention modules (scSE) are integrated into FPSNet to improve the class activation map (CAM) performance for the first time. To further improve segmentation accuracy, a Full-Parameter-Sharing (FPS) strategy is designed in FPSNet to alleviate the overfitting of the segmentation task supervised by very few strong labels. The proposed method is validated on the BraTS 2019 and LiTS 2017 datasets, and experiments demonstrate that the proposed method WIML-FPSNet outperforms several state-of-the-art segmentation methods with minimal annotation efforts.

Post-Disaster Restoration and Reconstruction Assessment of the Jiuzhaigou Lake Landscape and a Resilience Development Pathway.

The essence of post-disaster reconstruction is the restoration and rebirth of the affected areas. The earthquake hitting Jiuzhaigou was the first earthquake that had its epicenter in the World Natural Heritage located in China. Ecological restoration and landscape reconstruction are essential for the sustainable development of tourism. This study uses high-resolution remote sensing images to monitor and evaluate the post-disaster restoration and reconstruction process of the leading lakes in Jiuzhaigou. It was found that the lake water quality, vegetation, and road facilities have undergone moderate reconstruction. However, the restoration and reconstruction still faced severe challenges. The ecological environment's stability and balance are prerequisites for the sustainable development of the World Natural Heritage sites. This paper combines the "Build Back Better" concept that advocates risk reduction, scenic spot restoration, and efficient implementation to ensure Jiuzhaigou's restoration and sustainable development. It comes up with specific measures for the resilience development of Jiuzhaigou from the eight principles of overall planning, structural resilience, disaster prevention and mitigation, landscape facilities, social psychology, management mechanisms, policies and regulations, and monitoring and evaluation to provide a reference for the sustainable development of tourism.

Regulatory utility of mechanistic modeling to support alternative bioequivalence approaches: A workshop overview.

On September 30 and October 1, 2021, the US Food and Drug Administration (FDA) and the Center for Research on Complex Generics cosponsored a live virtual workshop titled "Regulatory Utility of Mechanistic Modeling to Support Alternative Bioequivalence Approaches." The overall aims of the workshop included (i) engaging the generic drug industry and other involved stakeholders regarding how mechanistic modeling and simulation can support their product development and regulatory submissions; (ii) sharing the current state of mechanistic modeling for bioequivalence (BE) assessment through case studies; (iii) establishing a consensus on best practices for using mechanistic modeling approaches, such as physiologically based pharmacokinetic modeling and computational fluid dynamics modeling, for BE assessment; and (iv) introducing the concept of a Model Master File to improve model sharing between model developers, industry, and the FDA. More than 1500 people registered for the workshop. Based on a postworkshop survey, the majority of participants reported that their fundamental scientific understanding of mechanistic models was enhanced, there was greater consensus on model validation and verification, and regulatory expectations for mechanistic modeling submitted in abbreviated new drug applications were clarified by the workshop.

Regulatory utility of physiologically-based pharmacokinetic modeling to support alternative bioequivalence approaches and risk assessment: A workshop summary report.

This report summarizes the proceedings for day 2 sessions 1 and 3 of the 2-day public workshop entitled "Regulatory Utility of Mechanistic Modeling to Support Alternative Bioequivalence Approaches," a jointly sponsored workshop by the US Food and Drug Administration (FDA) and the Center for Research on Complex Generics (CRCG). The aims of this workshop were: (1) to discuss how mechanistic modeling, including physiologically-based pharmacokinetic (PBPK) modeling and simulation, can support product development, and regulatory submissions; (2) to share the current state of mechanistic modeling for bioequivalence (BE) assessment through case studies; (3) to establish a consensus on best practices for using PBPK modeling for BE assessment to help drive further investment by the generic drug industry into mechanistic modeling and simulation; and (4) to introduce the concept of a Model Master File to improve model-sharing. The theme of day 2 covered PBPK absorption model for oral products as an alternative BE approach and a tool for supporting risk assessment and biowaiver (session 1), oral PBPK for evaluating the impact of food on BE (session 2), successful cases, and challenges for oral PBPK (session 3). This report summarizes the topics of the presentations of day 2 sessions 1 and session 3 from FDA, academia, and pharmaceutical industry, including the current status of oral PBPK, case examples as well as the challenges and opportunities in this area. In addition, panel discussions on the utility of oral PBPK in both new drugs and generic drugs from regulatory and industry perspective are also summarized.

Generic lamotrigine extended-release tablets are bioequivalent to innovator drug in fully replicated crossover bioequivalence study.

OBJECTIVE: Lamotrigine is a commonly prescribed antiepileptic drug. U.S. Food and Drug Administration (FDA)-funded clinical studies have demonstrated bioequivalence (BE) for generic lamotrigine immediate-release (IR) products in epilepsy patients with generic substitution. To address the potential concerns about the risk of generic-brand substitution of lamotrigine extended-release (ER) products, considering the complexity of controlled release systems and pharmacokinetic variations associated with possible within-subject variability (WSV), this prospective study assessed (1) BE of generic and brand lamotrigine ER products in a fully replicated BE study design in healthy subjects and (2) whether such fully replicated study design and WSV data can better support the approval of generic lamotrigine ER products. METHODS: This open-label, single-dose, two-treatment, four-period, two-sequence, fully replicated crossover BE study compared generic lamotrigine ER tablet to brand Lamictal XR (200 mg) in 30 healthy subjects under fed conditions. Pharmacokinetics (PK) profiles were generated based on intensive blood sampling up to 144 h. RESULTS: The two products showed comparable peak plasma concentration (Cmax ), area under the concentration-time curve (AUC) from time zero to the last measurable time point (AUC0-t ) and AUC extrapolated to infinity (AUC0-inf ), whereas median time to Cmax (Tmax ) values differed, that is, 10 h for generic and 22 h for brand products, respectively. WSVs for PK metrics were small (~8% of Cmax and ~6% of AUC) and similar between these two products. PK simulation predicted equivalent PK measurements of both products at steady state and after brand-to-generic switch, except the first day upon switching. No serious adverse events were reported. SIGNIFICANCE: The generic lamotrigine ER tablet product demonstrates BE to the brand product in a fully replicated BE study design with healthy subjects, supporting the adequacy of the two-way crossover study design to demonstrate BE and generic-brand substitution of lamotrigine ER products.

Improved GSimp: A Flexible Missing Value Imputation Method to Support Regulatory Bioequivalence Assessment.

Missing values are not uncommon in in vivo bioequivalence (BE) studies and pose non-trivial challenges for BE assessment. Missing values typically appear as a mixture of different types, such as Missing Not at Random (MNAR) and Missing Completely at Random (MCAR), however, current data imputation methods were usually developed for a certain type of missing values (e.g., MNAR). Among them, an iterative Gibbs sampler-based left-censored missing value imputation approach (GSimp) was recently developed and showed superior performance over other methods in handling MNAR data. In this study, we introduce an improved GSimp ("Improved GSimp" thereafter) that offers flexibility in handling mixed types of missing data and better imputation accuracy to support BE assessment for studies with missing values. Simulations mimicking different missing value scenarios (e.g., mixture of different missing types and proportion of missing values) were conducted to compare performance of the Improved GSimp with other methods (e.g., original GSimp and half of minimal value). Normalized root mean square error (NRMSE) was used to evaluate imputation accuracy. Our results showed that the Improved GSimp always had the best accuracy in all simulated scenarios compared to other methods.

Spatial planning of urban communities via deep reinforcement learning.

Effective spatial planning of urban communities plays a critical role in the sustainable development of cities. Despite the convenience brought by geographic information systems and computer-aided design, determining the layout of land use and roads still heavily relies on human experts. Here we propose an artificial intelligence urban-planning model to generate spatial plans for urban communities. To overcome the difficulty of diverse and irregular urban geography, we construct a graph to describe the topology of cities in arbitrary forms and formulate urban planning as a sequential decision-making problem on the graph. To tackle the challenge of the vast solution space, we develop a reinforcement learning model based on graph neural networks. Experiments on both synthetic and real-world communities demonstrate that our computational model outperforms plans designed by human experts in objective metrics and that it can generate spatial plans responding to different circumstances and needs. We also propose a human-artificial intelligence collaborative workflow of urban planning, in which human designers can substantially benefit from our model to be more productive, generating more efficient spatial plans with much less time. Our method demonstrates the great potential of computational urban planning and paves the way for more explorations in leveraging computational methodologies to solve challenging real-world problems in urban science.

Can Inbound Tourism Improve Regional Ecological Efficiency? An Empirical Analysis from China.

Inbound tourism has an important impact on regional eco-efficiency. This paper uses the panel data of 31 provincial administrative units in China from 2005 to 2019; uses the improved DEA model to measure the regional ecological efficiency; and uses the panel threshold model to investigate input, output, and efficiency from the perspective of green technology innovation. Then, it explores the heterogeneous effects of inbound tourism on ecological efficiency. This paper finds that cross-border tourism has a positive impact on the ecological efficiency of tourist destinations. However, the degree of influence varies and will be changed with the level of regional green innovation. The main conclusions are as follows: (1) From an overall perspective, inbound tourism has a significant positive effect on ecological efficiency. (2) With the increase in green innovation investment and output, the promotion effect of inbound tourism on regional ecological efficiency first increases and then decreases. (3) The higher the green innovation efficiency, the greater the promotion effect of inbound tourism on ecological efficiency. Therefore, the Chinese government should encourage the development of inbound tourism, adopt greener innovative technologies that are cleaner and more environmentally friendly, and enhance the welfare effect of tourism on green economy.

Life Cycle Assessment and Multiobjective Optimization for Steam Cracking Process in Ethylene Plant.

With energy savings and emission reduction becoming national policies in recent years, the environmental impacts of industrial production are more and more critical. Most of the studies have concentrated on the environmental effects of the industrial production process. Little attention has been paid to the energy consumption and pollution emission in extracting, processing, and transporting the feedstock and other secondary materials. An integrated multiobjective optimization framework is proposed for the steam cracking process on the basis of a life cycle assessment and data-driven modeling methods. A multiobjective economic-environmental optimization model is developed on the basis of industrial and simulated data. A multiobjective optimization model combined with energy cost is also developed for comparative study. The nondominated sorting genetic algorithm-II is utilized to solve the problems, and the Pareto front is obtained. An industrial case study is carried out to indicate the effectiveness of the proposed method. The results show that the LCA-based method can better represent the environmental impacts in comparison with the standard energy cost model. Therefore, the proposed method can achieve a better tradeoff between economic benefits and environmental impacts for guiding ethylene production.

A randomized, cross-over trial of metoprolol succinate formulations to evaluate PK and PD end points for therapeutic equivalence.

There are limited comparison data throughout the dosing interval for generic versus brand metoprolol extended-release (ER) tablets. We compared the pharmacokinetics (PKs) and pharmacodynamics of brand name versus two generic formulations (drugs 1 and 2) of metoprolol ER tablets with different time to maximum concentration (Tmax ) in adults with hypertension. Participants were randomized to equal drug doses (50-150 mg/day) administered in one of two sequences (brand-drug1-brand-drug2 or brand-drug2-brand-drug1) and completed 24-h PK, digital heart rate (HR), ambulatory blood pressure (BP), and HR studies after taking each formulation for greater than or equal to 7 days. Metoprolol concentrations were determined by liquid chromatography tandem mass spectrometry, with noncompartmental analysis performed to obtain PK parameters in Phoenix WinNonlin. Heart rate variability (HRV) low-to-high frequency ratio was determined per quartile over the 24-h period. Thirty-six participants completed studies with the brand name and at least one generic product. Among 30 participants on the 50 mg dose, the primary PK end points of area under the concentration-time curve and Cmax were similar between products; Tmax was 6.1 ± 3.6 for the brand versus 3.5 ± 4.9 for drug 1 (p = 0.019) and 9.6 ± 3.2 for drug 2 (p < 0.001). Among all 36 participants, 24-h BPs and HRs were similar between products. Mean 24-h HRV low-to-high ratio was also similar for drug 1 (2.04 ± 1.35), drug 2 (1.86 ± 1.35), and brand (2.04 ± 1.77), but was more sustained over time for the brand versus drug 1 (drug × quartile interaction p = 0.017). Differences in Tmax between metoprolol ER products following repeated doses may have implications for drug effects on autonomic balance over the dosing interval.

Medication Cost-Savings and Utilization of Generic Inhaled Corticosteroid (ICS) and Long-Acting Beta-Agonist (LABA) Drug Products in the USA.

BACKGROUND: In the USA, drug costs associated with the inhaled corticosteroid (ICS) and long acting ß agonist (LABA) combination products have been increasing since 2001. In January 2019, the first generic ICS/LABA drug product was approved by the U.S. Food and Drug Administration. METHODS: We investigated retrospectively the effects of the first approved generic ICS/LABA drug from 2019 to 2020 on the wholesale cost-savings and prescription dispensing using the IQVIA data system in the USA. RESULTS: The marketing of the first generic for fluticasone propionate and salmeterol xinafoate dry powder inhaler was associated with $941 million in drug cost-savings during the first year for this class of medications. Although the brand-name drug manufacturer concurrently introduced its authorized generic, these cost-savings were driven by the averaged unit cost of the approved generic at $115, compared to $169 for the authorized generic and $334 for the branded product. Generic initiation and substitution with the first generic were, respectively, higher compared to those with authorized generics; however, overall dispensing of the first generic was lower than that of its branded product. As in the case of budesonide and formoterol fumarate dry powder inhaler, marketing of authorized generics alone was not associated with any noticeable change in sales or prescription cost-saving. CONCLUSION: We estimated that more than 20% of prescription cost-saving was achieved for the ICS/LABA dry powder inhalers in the first year following the introduction of the first approved generic, even though generic utilization remained lower than that of the branded counterpart.

Rates of, and factors associated with, switching among generic levothyroxine preparations in commercially insured American adults.

IMPORTANCE: Some practice guidelines warn against generic L-thyroxine preparation switching. OBJECTIVE: To examine the rates of generic L-thyroxine preparation switching within one year of initiating L-thyroxine, and to examine factors associated with switching. DESIGN AND SETTING: Retrospective study using national data from a large administrative claims database from January 2008 through November 2018. PATIENTS: Medicare or commercially insured adults (≥18 years) who filled a generic L-thyroxine preparation. MAIN OUTCOME MEASURES: At least one switch from one generic L-thyroxine preparation to another within 1 year of L-thyroxine initiation defined by prescription fills. RESULTS: From January 2008 to November 2018, we included 483,390 patients who initiated generic L-thyroxine: mean (SD) age was 61.4 years (15.2), 75.2% were female, 72.6% were white. Within 1 year of initiating therapy, 98,013 (20%) switched to another L-thyroxine generic preparation at least once. In a multivariate logistic regression analysis, factors associated with switching included the number of pharmacies visited to fill L-thyroxine (>2 vs 1 adjusted OR [aOR] 7.15, 95% confidence interval [CI] 6.97-7.34), age ≥75 vs. <45 years (aOR 1.29, 95% CI 1.26-1.33), history of thyroid surgery (aOR 1.22, 95% CI 1.13-1.31), and first L-thyroxine fill date in 2018 vs. 2008 (aOR 3.32, 95% CI 3.14-3.51). CONCLUSIONS AND RELEVANCE: One in five patients switched among generic L-thyroxine manufacturers within one year of treatment initiation. Generic L-thyroxine switching occurred more often when more pharmacies were used to fill L-thyroxine. Given existing guideline recommendations, additional studies should clarify the impact of generic L-thyroxine switching on thyroid hormone values.