Dynamic task offloading edge-aware optimization framework for enhanced UAV operations on edge computing platform.

Resource optimization, timely data capture, and efficient unmanned aerial vehicle (UAV) operations are of utmost importance for mission success. Latency, bandwidth constraints, and scalability problems are the problems that conventional centralized processing architectures encounter. In addition, optimizing for robust communication between ground stations and UAVs while protecting data privacy and security is a daunting task in and of itself. Employing edge computing infrastructure, artificial intelligence-driven decision-making, and dynamic task offloading mechanisms, this research proposes the dynamic task offloading edge-aware optimization framework (DTOE-AOF) for UAV operations optimization. Edge computing and artificial intelligence (AI) algorithms integrate to decrease latency, increase mission efficiency, and conserve onboard resources. This system dynamically assigns computing duties to edge nodes and UAVs according to proximity, available resources, and the urgency of the tasks. Reduced latency, increased mission efficiency, and onboard resource conservation result from dynamic task offloading edge-aware implementation framework (DTOE-AIF)'s integration of AI algorithms with edge computing. DTOE-AOF is useful in many fields, such as precision agriculture, emergency management, infrastructure inspection, and monitoring. UAVs powered by AI and outfitted with DTOE-AOF can swiftly survey the damage, find survivors, and launch rescue missions. By comparing DTOE-AOF to conventional centralized methods, thorough simulation research confirms that it improves mission efficiency, response time, and resource utilization.

Application of DMSFNN-COA technique for brand image design.

Color plays a pivotal role in product design, as it can evoke emotional responses from users. Understanding these emotional needs is crucial for effective brand image design. This paper introduces a novel approach, the Brand Image Design using Deep Multi-Scale Fusion Neural Network optimized with Cheetah Optimization Algorithm (BID-DMSFNN-COA), for classifying product color brand images as "Stylish" and "Natural". By leveraging deep learning techniques and optimization algorithms, the proposed method aims to enhance brand image accuracy and address existing challenges in product color trend forecasting research. Initially, data are collected from the Mnist Data Set. The data are then supplied into the pre-processing section. In the pre-processing segment, it removes the noise and enhances the input image utilizing master slave adaptive notch filter. The Deep Multi-Scale Fusion Neural Network optimized with cheetah optimization algorithm effectively classifies the product colour brand image as "Stylish" and "Natural". Implemented on the MATLAB platform, the BID-DMSFNN-COA technique achieves remarkable accuracy rates of 99 % for both "Natural" and "Stylish" classifications. In comparison, existing methods such as BID-GNN, BID-ANN, and BID-CNN yield lower accuracy rates ranging from 65 % to 85 % for "Stylish" and 65 %-70 % for "Natural" product color brand image design. The simulation outcomes reveal the superior performance of the BID-DMSFNN-COA technique across various metrics including accuracy, F-score, precision, recall, sensitivity, specificity, and ROC analysis. Notably, the proposed method consistently outperforms existing approaches, providing higher values across all evaluation criteria. These findings underscore the effectiveness of the BID-DMSFNN-COA technique in enhancing brand image design through accurate product color classification.

Multi-variate multi-objective optimization of production conditions for electro-spun skin scaffold using RSM and investigation of gamma irradiation effects on the properties of the optimized sample.

Developing electro-spun scaffolds with ideal mechanical properties for skin purposes can profit from using the Response Surface Methodology technique to define and optimize the outcome quality and required sterilization for use in vivo. This study investigated the effects of four main independent electrospinning variables for polycaprolactone nanofibers scaffold using multi-variable and multi-objective optimization. It was done to determine significant parameters on responses and find optimal conditions to reach the preferred properties. Young's modulus, elongation-at-break, and tensile strength were the responses. After obtaining appropriate models, the impact share of variables on the responses was determined using Sobol sensitivity analysis. The results showed that flow rate is the most significant parameter of elastic modulus and tensile strength responses, with 76.45 % and 41.27 % impact shares, respectively. The polymer concentration is the following significant parameter on elongation at break, tensile strength and, Young's modulus responses with 64.35 %, 39.485 and, 14.28 % impact share, respectively. Based on the optimized results, a skin scaffold with desired mechanical properties was achieved (under solution concentration of 10 % w/v, flow rate of 2 mL/h, nuzzle-collector distance of 15 cm, and applied voltage of 20 kV). Then it was sterilized with gamma radiation of various doses (25, 40, and 55 kGy) to use in vivo. The SEM analysis indicated no significant change in fibrous morphology due to gamma irradiation at any dosage. FTIR analysis demonstrated the breakup of ester bonds due to gamma irradiation. For samples irradiated by 25 kGy, the crystallinity percentage decreased and chains crosslinking without losing the mechanical stability was dominant. The studies demonstrated that 25 kGy of gamma irradiation could improve the mechanical properties of the optimized PCL skin scaffold, which is very promising for wound healing.

Advancements in rice disease detection through convolutional neural networks: A comprehensive review.

This review paper addresses the critical need for advanced rice disease detection methods by integrating artificial intelligence, specifically convolutional neural networks (CNNs). Rice, being a staple food for a large part of the global population, is susceptible to various diseases that threaten food security and agricultural sustainability. This research is significant as it leverages technological advancements to tackle these challenges effectively. Drawing upon diverse datasets collected across regions including India, Bangladesh, Türkiye, China, and Pakistan, this paper offers a comprehensive analysis of global research efforts in rice disease detection using CNNs. While some rice diseases are universally prevalent, many vary significantly by growing region due to differences in climate, soil conditions, and agricultural practices. The primary objective is to explore the application of AI, particularly CNNs, for precise and early identification of rice diseases. The literature review includes a detailed examination of data sources, datasets, and preprocessing strategies, shedding light on the geographic distribution of data collection and the profiles of contributing researchers. Additionally, the review synthesizes information on various algorithms and models employed in rice disease detection, highlighting their effectiveness in addressing diverse data complexities. The paper thoroughly evaluates hyperparameter optimization techniques and their impact on model performance, emphasizing the importance of fine-tuning for optimal results. Performance metrics such as accuracy, precision, recall, and F1 score are rigorously analyzed to assess model effectiveness. Furthermore, the discussion section critically examines challenges associated with current methodologies, identifies opportunities for improvement, and outlines future research directions at the intersection of machine learning and rice disease detection. This comprehensive review, analyzing a total of 121 papers, underscores the significance of ongoing interdisciplinary research to meet evolving agricultural technology needs and enhance global food security.

A clustering mining method for sports behavior characteristics of athletes based on the ant colony optimization.

This study aims to enhance the precision of analyzing athlete behavior characteristics, thereby optimizing sports training and competitive strategies. This study introduces an innovative Ant Colony Optimization (ACO) clustering model designed to address the high-dimensional clustering issues in athlete behavior data by simulating the path selection mechanism of ants searching for food. The development process of this model includes fine-tuning ACO parameters, optimizing for features specific to sports data, and comparing it with traditional clustering algorithms, and similar research models based on the neural network, support vector machines, and deep learning. The results indicate that the ACO model significantly outperforms the comparison algorithms in terms of silhouette coefficient (0.72) and Davies-Bouldin index (1.05), demonstrating higher clustering effectiveness and model stability. Particularly noteworthy is the recall rate (0.82), a key performance indicator, where the ACO model accurately captures different behavioral characteristics of athletes, validating its effectiveness and reliability in athlete behavior analysis. The innovation lies not only in the application of the ACO algorithm to address practical issues in the field of sports but also in showcasing the advantages of the ACO algorithm in handling complex, high-dimensional sports data. However, its generality and efficiency on a larger scale or different types of sports data still need further validation. In conclusion, through the introduction and optimization of the ACO clustering model, this study provides a novel and effective approach for a deeper understanding and analysis of athlete behavior characteristics. This study holds significant importance in advancing sports science research and practical applications.

Optimization of above-ground environmental factors in greenhouses using a multi-objective adaptive annealing genetic algorithm.

The greenhouse environment represents a dynamic, nonlinear system characterized by hysteresis and is influenced by a myriad of interacting environmental parameters, posing a complex multi-variable optimization challenge. This study proposes a multi-objective adaptive annealing genetic algorithm to optimize above-ground environmental factors in greenhouses, addressing the challenges of variable environmental conditions and extensive heating and humidity infrastructure. Initially, after analyzing the multi-objective model of greenhouse above-ground environmental factors, including temperature, relative humidity, and CO2 concentration, a comprehensive multi-objective, multi-constraint model was developed to encapsulate these factors in greenhouse environments. Subsequently, the model optimization incorporated multi-parameter coding of decision variables, a fitness function, and an annealing dynamic penalty factor. Validation conducted at Yangling Agricultural Demonstration Park revealed that the application of multi-objective adaptive annealing genetic algorithms (schemes 1 and 2) significantly outperformed the single-objective genetic algorithm (scheme 3) and the traditional genetic algorithm (scheme 4). Specifically, the improvements included a reduction in average temperature rise by 2.64 °C and 5.29 °C for schemes 1 and 2, respectively, equating to 20 % and 34 % decreases. Additionally, average humidification reductions of 2.39 % and 3.9 % were observed, alongside decreases in the total lengths of heating and humidification pipes by up to 2.99 km and 0.443 km, respectively, with a maximum reduction of 14 % in heating pipes. The integration of an annealing dynamic penalty factor enhanced the adaptive climbing ability of schemes 1 and 2, improving static stability and robustness. Furthermore, the number of iterations required to achieve convergence was reduced by approximately 170-240 times compared to schemes 3 and 4. This reduction in iterations also resulted in a significant decrease in running time by 5-13 min, corresponding to time savings of 31 %-56 %, thereby achieving further optimization.

Urban drone stations siting optimization based on hybrid algorithm of MILP and machine learning.

Urban environments, characterized by high population density and intricate infrastructures, are susceptible to a range of emergencies such as fires and traffic accidents. Optimal placement and distribution of fire stations and ambulance centers are thus imperative for safeguarding both life and property. An investigation into the distribution inefficiencies of emergency service facilities in selected districts of Chengdu reveals that imbalanced distribution of these facilities results in suboptimal response times during critical incidents. To address this challenge, a two-stage clustering method, incorporating X-means and K-means algorithms, is employed to identify optimal number and locations for Unmanned Aerial Vehicle (UAV) fire stations and drone ambulance centers. A Mixed-Integer Linear Programming (MILP) model is subsequently constructed and solved using the Gurobi optimization platform. Bayesian optimization-a machine learning technique-is exploited to elucidate the interplay between response speed and service capacity of these UAV-based emergency service stations under an optimized layout. Results affirm that integration of MILP and machine learning provides a robust framework for solving complex problems related to the siting and allocation of emergency service facilities. The proposed hybrid algorithm demonstrates substantial potential for enhancing emergency preparedness and response in urban settings.

Many-objective ant lion optimizer (MaOALO): A new many-objective optimizer with its engineering applications.

Many-objective optimization (MaO) is an important aspect of engineering scenarios. In many-objective optimization algorithms (MaOAs), a key challenge is to strike a balance between diversity and convergence. MaOAs employs various tactics to either enhance selection pressure for better convergence and/or implements additional measures for sustaining diversity. With increase in number of objectives, the process becomes more complex, mainly due to challenges in achieving convergence during population selection. This paper introduces a novel Many-Objective Ant Lion Optimizer (MaOALO), featuring the widely-popular ant lion optimizer algorithm. This method utilizes reference point, niche preserve and information feedback mechanism (IFM), to enhance the convergence and diversity of the population. Extensive experimental tests on five real-world (RWMaOP1- RWMaOP5) optimization problems and standard problem classes, including MaF1-MaF15 (for 5, 9 and 15 objectives), DTLZ1-DTLZ7 (for 8 objectives) has been carried out. It is shown that MaOALO is superior compared to ARMOEA, NSGA-III, MaOTLBO, RVEA, MaOABC-TA, DSAE, RL-RVEA and MaOEA-IH algorithms in terms of GD, IGD, SP, SD, HV and RT metrics. The MaOALO source code is available at: https://github.com/kanak02/MaOALO.

Screening, characterization and optimization of potential dichlorodiphenyl trichloroethane (DDT) degrading fungi.

Dichlorodiphenyltrichloroethane is an organo-chlorine insecticide used for malaria and agricultural pest control, but it is the most persistent pollutant, endangering both human and environmental health. The primary aim of the research is to screen, characterize, and assess putative fungi that degrade DDT for mycoremediation. Samples of soil and wastewater were gathered from Addis Ababa, Koka, and Ziway. Fungi were isolated and purified using potato dextrose media. Matrix-Assisted Laser Desorption, Ionization, and Flight Duration The technique of mass spectrometry was employed to identify fungi. It was found that the finally selected isolate, AS1, was Aspergillus niger. Based on growth factor optimization at DDT concentrations (0, 3500, and 7000 ppm), temperatures (25, 30, and 35 °C), and pH levels (4, 7, and 10), the potential DDT-tolerant fungal isolates were investigated. A Box-Behnken experimental design was used to analyze and optimize fungal biomass and sporulation. The highest biomass (0.981 ± 0.22 g) and spore count (5.60 ± 0.32 log/mL) of A. niger were found through optimization assessment, and this fungus was chosen as a potential DDT-degrader. For DDT degradation investigations by A. niger in DDT-amended liquid media, gas chromatograph-electron capture detector technology was employed. DDT and its main metabolites, DDE and DDD, were eliminated from both media to the tune of 96-99 % at initial DDT concentrations of 1750, 3500, 5250, and 7000 ppm. In conclusion, it is a promising candidate for detoxifying and/or removing DDT and its breakdown products from contaminated environments.

A multi-center study of the MicroNET-covered stent in consecutive patients with acute carotid-related stroke: SAFEGUARD-STROKE.

Introduction: Acute carotid-related stroke (CRS), with its large thrombo-embolic load and large volume of affected brain tissue, poses significant management challenges. First generation (single-layer) carotid stents fail to insulate the athero-thrombotic material; thus they are often non-optimized (increasing thrombosis risk), yet their use is associated with a significant (20-30%) risk of new cerebral embolism. Aim: To evaluate, in a multi-center multi-specialty investigator-initiated study, outcomes of the MicroNET-covered (cell area ≈ 0.02-0.03 mm2) carotid stent (CGuard, InspireMD) in consecutive CRS patients eligible for emergency recanalization. Treatment, other than study device use, was according to center/operator routine. Material and methods: Seventy-five patients (age 40-89 years, 26.7% women) were enrolled in 7 interventional stroke centers. Results: The median Alberta Stroke Program Early CT Score (ASPECTS) was 9 (6-10). Study stent use was 100% (no other stent types implanted); retrograde strategy predominated (69.2%) in tandem lesions. Technical success was 100%. Post-dilatation balloon diameter was 4.0 to 8.0 mm. 89% of patients achieved final modified Thrombolysis in Cerebral Infarction (mTICI) 2b-c/3. Glycoprotein IIb/IIIa inhibitor use as intraarterial (IA) bolus + intravenous (IV) infusion was an independent predictor of symptomatic intracranial hemorrhage (OR = 13.9, 95% CI: 5.1-84.5, p < 0.001). The mortality rate was 9.4% in-hospital and 12.2% at 90 days. Ninety-day mRS0-2 was 74.3%, mRS3-5 13.5%; stent patency was 93.2%. Heparin-limited-to-flush predicted patency loss on univariate (OR = 14.3, 95% CI: 1.5-53.1, p < 0.007) but not on multivariate analysis. Small-diameter balloon/absent post-dilatation was an independent predictor of stent patency loss (OR = 15.2, 95% CI: 5.7-73.2, p < 0.001). Conclusions: This largest to-date study of the MicroNET-covered stent in consecutive CRS patients demonstrated a high acute angiographic success rate, high 90-day patency and favorable clinical outcomes despite variability in procedural strategies and pharmacotherapy (SAFEGUARD-STROKE NCT05195658).

An innovative approach to detecting the freshness of fruits and vegetables through the integration of convolutional neural networks and bidirectional long short-term memory network.

Fruit and vegetable freshness testing can improve the efficiency of agricultural product management, reduce resource waste and economic losses, and plays a vital role in increasing the added value of fruit and vegetable agricultural products. At present, the detection of fruit and vegetable freshness mainly relies on manual feature extraction combined with machine learning. However, manual extraction of features has the problem of poor adaptability, resulting in low efficiency in fruit and vegetable freshness detection. Although exist some studies that have introduced deep learning methods to automatically learn deep features that characterize the freshness of fruits and vegetables to cope with the diversity and variability in complex scenes. However, the performance of these studies on fruit and vegetable freshness detection needs to be further improved. Based on this, this paper proposes a novel method that fusion of different deep learning models to extract the features of fruit and vegetable images and the correlation between various areas in the image, so as to detect the freshness of fruits and vegetables more objectively and accurately. First, the image size in the dataset is resized to meet the input requirements of the deep learning model. Then, deep features characterizing the freshness of fruits and vegetables are extracted by the fused deep learning model. Finally, the parameters of the fusion model were optimized based on the detection performance of the fused deep learning model, and the performance of fruit and vegetable freshness detection was evaluated. Experimental results show that the CNN_BiLSTM deep learning model, which fusion convolutional neural network (CNN) and bidirectional long-short term memory neural network (BiLSTM), is combined with parameter optimization processing to achieve an accuracy of 97.76% in detecting the freshness of fruits and vegetables. The research results show that this method is promising to improve the performance of fruit and vegetable freshness detection.

Low-coordinated Mn-N2 sites in graphene oxide induce peroxydisulfate activation for tetracycline degradation: Process optimization and theoretical calculation.

Atom-dispersed low-coordinated transition metal-Nx catalysts exhibit excellent efficiency in activating peroxydisulfate (PDS) for environmental remediation. However, their catalytic performance is limited due to metal-N coordination number and single-atom loading amount. In this study, low-coordinated nitrogen-doped graphene oxide (GO) confined single-atom Mn catalyst (Mn-SA/NGO) was synthesized by molten salt-assisted pyrolysis and coupled to PDS for degradation of tetracycline (TC) in water. Aberration-corrected high-angle annular dark-field scanning transmission electron microscopy (AC-HAADF-STEM) and X-ray absorption fine structure spectroscopy (XAFS) analysis showed the successful doping of single-atom Mn (weight percentage 1.6%) onto GO and the formation of low-coordinated Mn-N2 sites. The optimized parameters obtained by Box-Behnken Design achieved 100% TC removal in both prediction and experimental results. The Mn-SA/NGO + PDS system had strong anti-interference ability for TC removal in the presence of anions. Besides, Mn-SA/NGO possessed good reusability and stability. O2•-, •OH, and 1O2 were the main active species for TC degradation, and the TC mineralization reached 85.1%. Density functional theory (DFT) calculations confirmed that the introduction of single atoms Mn could effectively enhance adsorption and activation of PDS. The findings provide a reference for the synthesis of high-performance single-atom catalysts for effective removal of antibiotics.

Challenges and opportunities in bioprocessing of gellan gum: A review.

Gellan gum (GG) - the microbial exopolysaccharide is increasingly being adopted into drug development, tissue engineering, and food and pharmaceutical products. In spite of the commercial importance and expanding application horizon of GG, little attention has been directed toward the exploration of novel microbial cultures, development of advanced screening protocols, strain engineering, and robust upstream or downstream processes. This comprehensive review not only attempts to summarize the existing knowledge pool on GG bioprocess but also critically assesses their inherent challenges. The process optimization design augmented with advanced machine learning modeling tools, widely adopted in other microbial bioprocesses, should be extended to GG. The unification of mechanistic insight into data-driven modeling would help to formulate optimal feeding and process control strategies.

FSRW: fuzzy logic-based whale optimization algorithm for trust-aware routing in IoT-based healthcare.

The Internet of Things (IoT) is an extensive system of interrelated devices equipped with sensors to monitor and track real world objects, spanning several verticals, covering many different industries. The IoT's promise is capturing interest as its value in healthcare continues to grow, as it can overlay on top of challenges dealing with the rising burden of chronic disease management and an aging population. To address difficulties associated with IoT-enabled healthcare, we propose a secure routing protocol that combines a fuzzy logic system and the Whale Optimization Algorithm (WOA) hierarchically. The suggested method consists of two primary approaches: the fuzzy trust strategy and the WOA-inspired clustering methodology. The first methodology plays a critical role in determining the trustworthiness of connected IoT equipment. Furthermore, a WOA-based clustering framework is implemented. A fitness function assesses the likelihood of IoT devices acting as cluster heads. This formula considers factors such as centrality, range of communication, hop count, remaining energy, and trustworthiness. Compared with other algorithms, the proposed method outperformed them in terms of network lifespan, energy usage, and packet delivery ratio by 47%, 58%, and 17.7%, respectively.

Evaluating strategies to recruit health researchers to participate in online survey research.

BACKGROUND: Engaging researchers as research subjects is key to informing the development of effective and relevant research practices. It is important to understand how best to engage researchers as research subjects. METHODS: A 24 factorial experiment, as part of a Multiphase Optimization Strategy, was performed to evaluate effects of four recruitment strategy components on participant opening of an emailed survey link and survey completion. Participants were members of three US-based national health research consortia. A stratified simple random sample was used to assign potential survey participants to one of 16 recruitment scenarios. Recruitment strategy components were intended to address both intrinsic and extrinsic sources of motivation, including: $50 gift, $1,000 raffle, altruistic messaging, and egoistic messaging. Multivariable generalized linear regression analyses adjusting for consortium estimated component effects on outcomes. Potential interactions among components were tested. Results are reported as adjusted odds ratios (aOR) with 95% confidence intervals (95% CI). RESULTS: Surveys were collected from June to December 2023. A total of 418 participants were included from the consortia, with final analytical sample of 400 eligible participants. Out of the final sample, 82% (341) opened the survey link and 35% (147) completed the survey. Altruistic messaging increased the odds of opening the survey (aOR 2.02, 95% CI: 1.35-2.69, p = 0.033), while egoistic messaging significantly reduced the odds of opening the survey (aOR 0.56, 95%CI 0.38-0.75, p = 0.08). The receipt of egoistic messaging increased the odds of completing the survey once opened (aOR 1.81, 95%CI: 1.39-2.23, p < 0.05). There was a significant negative interaction effect between the altruistic appeal and egoistic messaging strategies for survey completion outcome. Monetary incentives did not a have a significant impact on survey completion. CONCLUSION: Intrinsic motivation is likely to be a greater driver of health researcher participation in survey research than extrinsic motivation. Altruistic and egoistic messaging may differentially impact initial interest and survey completion and when combined may lead to improved rates of recruitment, but not survey completion. Further research is needed to determine how to best optimize message content and whether the effects observed are modified by survey burden.

Recent Progress in Aqueous Zinc-ion Batteries at High Zinc Utilization.

Aqueous zinc ion batteries (AZIBs) are promising candidates for next-generation energy storage systems due to their low cost, high safety, and environmental friendliness. As the critical component, Zn metal with high theoretical capacity (5855 mAh cm-3), low redox potential (-0.763 V vs standard hydrogen electrode), and low cost has been widely applied in AZIBs. However, the low Zn utilization rate (ZUR) of Zn metal anode caused by the dendrite growth, hydrogen evolution, corrosion, and passivation require excess Zn installation in current AZIBs, thus leading to increased unnecessary battery weight and decreased energy density. Herein, approaches to the historical progress toward high ZUR AZIBs through the perspective of electrolyte optimization, anode protection, and substrate construction are comprehensively summarized, and an in-depth understanding of ZUR is highlighted. Specifically, the main challenges and failure mechanisms of Zn anode are analyzed. Then, the persisting issues and promising solutions in the reaction interface, aqueous electrolyte, and Zn anode are emphasized. Finally, the design of 100% ZUR AZIBs free of Zn metal is presented in detail. This review aims to provide a better understanding and fundamental guidelines on the high ZUR AZIBs design, which can shed light on research directions for realizing high energy density AZIBs.

Fault causes and its detection in standalone PV system using ANN and GEO technique.

Power generation systems using photovoltaic (PV) technology have become increasingly popular due to their high production efficiency. A partial shading defect is the most common defect in this system under the process of production, diminishing both the amount and quality of energy produced. This paper proposes an Artificial Neural Network and Golden Eagle Optimization based prediction of the fault and its detection in a standalone PV system to recover the optimum performance and diagnosis of the PV system. The proposed technique combines the Artificial Neural Network (ANN) and Golden Eagle Optimization (GEO) algorithm. The major contribution of this work is to raise PV systems' performance. The result is a defect in the classification and identification of an ANN is used. The use of GEO provides an efficient optimization technique for ANN training, which reduces the training time and improves the accuracy of the model. The proposed technique is executed on the MATLAB site and contrasted with different present techniques, like genetic algorithm (GA),Elephant Herding Optimization (EHO) and Particle Swarm Optimization (PSO). The findings displays that the proposed technique is more accurate and effective than the existing methodologies for detecting and diagnosing defects in PV systems.

Leveraging pharmacokinetic parameters as covariate in Bayesian logistic regression model to optimize dose selection in early phase oncology trial.

Dose selection and optimization in early phase of oncology drug development serves as the foundation for the success of late phases drug development. Bivariate Bayesian logistic regression model (BLRM) is a widely utilized model-based algorithm that has been shown to improve the accuracy for identifying recommended phase 2 dose (RP2D) based on dose-limiting-toxicity (DLT) over traditional method such as 3 + 3. However, it remains a challenge to optimize dose selection that strikes a proper balance between safety and efficacy in escalation and expansion phase of phase I trials. In this paper, we first use a phase I clinical trial to demonstrate how the variability of drug exposure related to pharmacokinetic (PK) parameters among trial participants may add to the difficulties of identifying optimal dose. We use simulation to show that concurrently or retrospectively fitting BLRM model for dose/toxicity data from escalation phase with dose-independent PK parameters as covariate lead to improved accuracy of identifying dose level at which DLT rate is within a prespecified toxicity interval. Furthermore, we proposed both model- and rule-based methods to modify dose at patient level in expansion cohorts based on their PK/exposure parameters. Simulation studies show this approach leads to higher likelihood for a dose level with a manageable toxicity and desirable efficacy margin to be advanced to late phase pipeline after being screened at expansion phase of phase I trial.

Predictors of the efficacy of His bundle pacing in patients with a prolonged PR interval: A stratified analysis of the HOPE-HF randomized controlled trial.

AIMS: The randomized, double-blind, placebo-controlled HOPE-HF trial assessed the benefit of atrio-ventricular (AV) delay optimization delivered using His bundle pacing. It recruited patients with left ventricular ejection fraction ≤40%, PR interval ≥200 ms, and baseline QRS ≤140 ms or right bundle branch block. Overall, there was no significant increase in peak oxygen uptake (VO2max) but there was significant improvement in heart failure specific quality of life. In this pre-specified secondary analysis, we evaluated the impact of baseline PR interval, echocardiographic E-A fusion, and the magnitude of acute high-precision haemodynamic response to pacing, on outcomes. METHODS AND RESULTS: All 167 randomized participants underwent measurement of PR interval, acute haemodynamic response at optimized AV delay, and assessment of presence of E-A fusion. We tested the impact of these baseline parameters using a Bayesian ordinal model on VO2max, quality of life and activity measures. There was strong evidence of a beneficial interaction between the baseline acute haemodynamic response and the blinded benefit of pacing for VO2 (Pr 99.9%), Minnesota Living With Heart Failure (MLWHF) (Pr 99.8%), MLWHF physical limitation score (Pr 98.9%), EQ-5D visual analogue scale (Pr 99.6%), and exercise time (Pr 99.4%). The baseline PR interval and the presence of baseline E-A fusion did not have this reliable ability to predict the clinical benefit of pacing over placebo across multiple endpoints. CONCLUSIONS: In the HOPE-HF trial, the acute haemodynamic response to pacing reliably identified patients who obtained clinical benefit. Patients with a long PR interval (≥200 ms) and left ventricular impairment who obtained acute haemodynamic improvement with AV-optimized His bundle pacing were likely to obtain clinical benefit, consistent across multiple endpoints. Importantly, this gradation can be reliably tested for before randomization, but does require high-precision AV-optimized haemodynamic assessment to be performed.

Can we do better with Mylotarg? Model-based assessment of opportunities to improve therapeutic index.

Late-stage clinical trial failures increase the overall cost and risk of bringing new drugs to market. Determining the pharmacokinetic (PK) drivers of toxicity and efficacy in preclinical studies and early clinical trials supports quantitative optimization of drug schedule and dose through computational modeling. Additionally, this approach permits prioritization of lead candidates with better PK properties early in development. Mylotarg is an antibody-drug conjugate (ADC) that attained U.S. Food and Drug Administration (FDA) approval under a fractionated dosing schedule after 17 years of clinical trials, including a 10-year period on the market resulting in hundreds of fatal adverse events. Although ADCs are often considered lower risk for toxicity due to their targeted nature, off-target activity and liberated payload can still constrain dosing and drive clinical failure. Under its original schedule, Mylotarg was dosed infrequently at high levels, which is typical for ADCs because of their long half-lives. However, our PK modeling suggests that these regimens increase maximum plasma concentration (Cmax)-related toxicities while producing suboptimal exposures to the target receptor. Our analysis demonstrates that the benefits of dose fractionation for Mylotarg tolerability should have been obvious early in the drug's clinical development and could have curtailed the proliferation of ineffective Phase III studies. We also identify schedules likely to be even more efficacious without compromising on tolerability. Alternatively, a longer-circulating Mylotarg formulation could obviate the need for dose fractionation, allowing superior patient convenience. Early-stage PK optimization through quantitative modeling methods can accelerate clinical development and prevent late-stage failures.