Development Timelines Research Articles

Are Exposure Recommendations for QT Evaluation Being Fulfilled?

Pharmaceutical companies have several options to evaluate drug-induced QT prolongation, often referred to as QT pathways, during clinical development. Current regulatory practices recommend achieving high clinical exposure (HCE) for conventional thorough QT (TQT) studies. An alternative to the TQT study, commonly known as the Q&A 5.1 pathway, recommends a two-fold HCE as the exposure margin for concentration-corrected QT(C-QTc) analysis. To assess the impact of these recommendations, we analyzed the exposure margins of 166 new active substances approved in Japan since 2015. Among these, 28.3% of substances in conventional TQT studies (n = 92) did not achieve HCE, and 50.0% of substances in the C-QTc analysis (n = 22) did not achieve two-fold HCE. In the integrated risk assessment, C-QTc analysis, often incorporated into first-in-human studies, is recommended to cover HCE for substances showing no QT prolongation risks in both in vitro and in vivo non-clinical studies, and we analyzed whether the Cmax achieved in single-ascending dose (SAD) and multiple-ascending dose (MAD) studies reached HCE. The result showed that 51.1% and 47.7% of substances did not achieve HCE in SAD and MAD studies, respectively. Our findings highlight the need for dose-ascending strategy targeting two-fold therapeutic exposure to ensure HCE. Insufficient exposure may lead to failure to waive the TQT study, and delays in development timelines. To address these challenges, we propose strategies for optimizing early clinical study designs to meet the exposure recommendations and reduce the risk of additional requirements from the regulatory authorities at a later stage.

Interfacial fluid manipulation with bioinspired strategies: special wettability and asymmetric structures.

The inspirations from nature always enlighten us to develop advanced science and technology. To survive in complicated and harsh environments, plants and animals have evolved remarkable capabilities to control fluid transfer via sophisticated designs such as wettability contrast, oriented micro-/nano-structures, and geometry gradients. Based on the bioinspired structures, the on-surface fluid manipulation exhibits spontaneous, continuous, smart, and integrated performances, which can promote the applications in the fields of heat transfer, microfluidics, heterogeneous catalysis, water harvesting, etc. Although fluid manipulating interfaces (FMIs) have provided plenty of ideas to optimize the current systems, a comprehensive review of history, classification, fabrication, and integration focusing on their interfacial chemistry and asymmetric structure is highly required. In this review, we systematically introduce development and highlight the state-of-the-art progress of bioinspired FMIs. Firstly, the biological prototype and development timeline are presented, and the underlying mechanism of on-surface fluid control on versatile structures is analyzed. Secondly, the definition and classification of FMIs as well as the strategy for controlling fluid/interface interaction are discussed. Thirdly, emergent applications of FMIs in practical scenarios including fog/vapor collection, fluid diodes, interfacial catalysis, etc. are presented. Furthermore, the challenges and prospects of interfacial liquid manipulation are concluded. We envision that this review should provide guidance for the incorporation of FMIs into suitable situations, which enlightens interdisciplinary research and practical applications in the fields of interface chemistry, materials design, bionic science, fluid dynamics, etc.

Managing strategies of chemotherapy and radiotherapy-induced oral mucositis.

Radiotherapy and chemotherapy are widely employed as primary non-surgical cancer treatments; however, their non-selective cytotoxicity often leads to adverse events such as oral mucositis (OM), particularly in head and neck cancer therapies. International guidelines provide recommendations for managing chemoradiotherapy-induced OM in various clinical contexts. Subsequently, emerging researches have introduced evidence supporting novel approaches or existing regimens for OM prevention and treatment. The repurposing of established drugs has garnered significant interest due to its shorter development timeline, improved safety profiles, and lower costs compared to new drug development. For example, clinical trials assessing established drugs such as melatonin, clonidine, and pentoxifylline indicate promising potential for managing OM. Additionally, several emerging pharmacological interventions have demonstrated considerable efficacy; SAMITAL and rhIL-11 are supported by phase II clinical trials and prospective studies, while probiotics like Streptococcus salivarius K12 and curcumin have shown effectiveness in randomized clinical trials. Furthermore, recent high-level studies have reinforced the efficacy of non-pharmacological interventions, such as photobiomodulation (PBM) and cryotherapy, over the past two years. In all, given the evidence supporting different strategies, PBM and oral cryotherapy are highly recommended for managing OM when feasible. Topical clonidine, melatonin, oral pentoxifylline, topical SAMITAL or rhIL-11, oral SsK12, and curcumin may also be utilized but would benefit from validation in larger trials. Besides, Verbascoside, Palifermin, Amifostine, and Avasopasem manganese can be suggested for OM management, while the side effects should be monitored. The accessibility and cost/effectiveness of specific managing strategies of OM should be considered when selecting appropriate options.

Research on the vibration noise matching strategy of range-extended electric vehicle

Range-extended electric vehicles (REEVs) face more intricate noise, vibration, and harshness (NVH) challenges compared to traditional fuel and pure electric vehicles, primarily due to the complexities introduced by range extender control strategies. Poorly designed control logic can result in issues such as excessive noise during acceleration and engine roar, which are not only costly to rectify but also time-consuming. This article simplifies the theoretical model of range extender systems by treating the range extender as a single excitation source. By monitoring the vehicle's overall status, the system adjusts the output torque and crankshaft speed to evaluate the vibration and noise at critical points within the vehicle. This proactive approach helps to preemptively optimize NVH performance under various operating conditions, thereby minimizing the need for subsequent adjustments to the suspension, transmission, intake, and exhaust systems. As a result, it significantly reduces costs and accelerates the project's development timeline.

Attribute ranging as a coordinated strategy between drug substance and drug product to accelerate commercial process nomination.

To make investigational drug candidates available to patients sooner, timelines for drug development are becoming shorter. Synthesis route scouting for active pharmaceutical ingredients (API) and drug product development often must occur simultaneously, requiring formulators to make decisions regarding drug product process selection before commercial API route finalization. Alternatively, the formulation strategy may be locked, thereby constraining drug substance processes with strict API attribute requirements. Critical quality attributes of the drug product can depend heavily on the API, yet final physical attributes may not be known early on in development. Furthermore, the desire to reduce pill burden means higher drug loading in formulations, leaving little room for excipients to compensate for suboptimal API performance. The opposing challenges of API synthetic route and drug product formulation development typically lead to elongated development timelines requiring an iterative approach. In this work, a coordinated strategy was designed and implemented to deliberately range API attributes via crystallization and milling techniques to enable robust assessment of downstream manufacturing and significantly reduce the time for final process selection. The study presented was conducted on a protease inhibitor targeted for treatment of Covid-19. Given the emergent need for treatment options, this dramatically accelerated approach was crucial for potential emergency use authorization (EUA).

HPR253 Drug Development and Launch Timeline Analysis Across 6 High-Income Countries: From Pivotal Trial to HTA

HPR253 Drug Development and Launch Timeline Analysis Across 6 High-Income Countries: From Pivotal Trial to HTA

Revolutionizing Pharma: How AI is Accelerating the Path to New Drugs

The pharmaceutical industry has seen a shift in recent times, with the rise of Artificial Intelligence (AI) which has played a pivotal role in revolutionizing the way drugs are discovered and developed. This article delves into how AI technologies have played a role in transforming the way new medications are studied created and eventually made available to consumers. Through leveraging AI capabilities pharmaceutical firms can significantly speed up the drug development timeline improve the precision of drug targeting and cut down on expenses. The incorporation of AI does not simplify the identification of promising drug candidates but also boosts the efficiency of clinical trials by using predictive analytics and sophisticated data management methods. Additionally, AIs capacity to analyze amounts of biological data has paved the way for tailored medicine approaches that cater to individual genetic profiles. This emerging field does not promise to enhance therapeutic effectiveness but also aims to fast track innovative treatments to patients who require them most urgently. AI is now a player in modern pharmaceutical strategies propelling advancements that could redefine healthcare practices globally by enhancing patient outcomes through more precise and effective treatments. Keywords: Artificial Intelligence, Drug Discovery, Pharmaceutical Industry, Machine Learning, Clinical Trials

A Ku-Band Compact Offset Cylindrical Reflector Antenna with High Gain for Low-Earth Orbit Sensing Applications.

The rise of CubeSats has unlocked opportunities for cutting-edge space missions with reduced costs and accelerated development timelines. CubeSats necessitate a high-gain antenna that can fit within a tightly confined space. This paper is primarily concerned with designing a compact Ku-band offset cylindrical reflector antenna for a CubeSat-based Earth Observation mission, with the goal of monitoring Arctic snow and sea ice. The development of a Ku-band offset cylindrical reflector, with a compact aperture of 110 × 149 mm2 (6.3λ × 8.5λ), is described alongside a patch array feed consisting of 2 × 8 elements. The patch array feed is designed using a lightweight Rogers substrate and is utilized to test the reflector. Adopting an offset configuration helped prevent gain loss due to feed blockage. Analyzing the reflector antenna, including the feed, thorough simulations and measurements indicates that achieving a gain of 25 dBi and an aperture efficiency of 52% at 17.2 GHz is attainable. The reflector's cylindrical shape and compact size facilitate the design of a simple mechanism for reflector deployment, enabling the antenna to be stored within 1U. The array feed and reflector antenna have been fabricated and tested, demonstrating good consistency between the simulation and measurement outcomes.

The benefits of Indigenous-led social science: a mindset for Arctic sustainability

The Peoples of the Arctic and Arctic health and sustainability are highly interconnected and essentially one and the same. An appropriate path to a sustainable Arctic involves a shift away from individual learning and achieving toward community leadership and the betterment of society. This article draws upon mindset theory from Western psychology and Indigenous relational accountability to propose and outline a model for achieving sustainability in the Arctic. The geographic focus is the North American Arctic. The principles of the argument and the foundations of the model may apply across the Circumpolar North. The paper is a call to action for social scientists and policy makers in the Arctic to implement an Indigenous-led and self-determined social science. Empowering and supporting Indigenous leaders and scholars to direct and conduct autonomous social science research would inherently produce well-being and sustainability for Indigenous communities and regions. The arguments are supported by an inductive analysis of peer-reviewed literature, and the model is organized and illustrated using a schematic of concentric circles. The foundational elements of the model include: Indigenous sovereignty, Indigenous ontology, Indigenous models of sustainability, and Indigenous scholarship. Environmental scientists, resource managers, and policy makers are directed to better understand, accept, and support Indigenous science as a comprehensive and valid knowledge system; change how they use key terminology in research; rethink research roles; and amend processes and timelines for research development and funding. To achieve the desired outcomes for community well-being and Arctic sustainability, Arctic social scientists should seriously consider centering Indigenous science, especially in Indigenous communities.

Unraveling Chemical Degradation in PEM Water Electrolyzers: Factors, Stressors, and Membrane-Level Solutions for Enhanced System Lifespan

Proton Exchange Membrane (PEM) water electrolyzers stand at the forefront of sustainable energy generation, offering a promising pathway toward carbon-neutral fuel production. Commercial systems require operation times > 50,000 hours. However, chemical degradation of PEMs remain a critical challenge affecting the longevity of the systems. One expected mode of degradation is that hydrogen peroxide is formed at the cathode in PEM water electrolyzers due to the low potentials and presence of platinum, hydrogen, and crossover oxygen. The hydrogen peroxide can participate in Fenton’s reactions to produce radicals that attack vulnerable sites of the ionomer. The radicals may be formed in the membrane or the electrodes and degrade the ionomer in both locations. Furthermore, the impact of degradation products, like short-chain ionomer fragments or hydrogen fluoride, on other system components is unknown. Overall, the underlying mechanisms and the key impact factors involved are not yet fully understood.Moreover, new product development for PEMs face the challenge to deliver robust products within shorter development timelines than the actual system operation allows. Consequently, there’s a pressing need for accelerated stress testing (AST) to evaluate new materials and assess mitigation effectiveness against specific membrane failure modes. Understanding the factors that accelerate chemical degradation and deconvoluting sources of performance loss is necessary for developing AST protocols.In this study, we intended to elucidate the intricate mechanisms underlying chemical degradation in PEM water electrolyzers. Chemical degradation is monitored through the loss of fluoride ions and post-mortem characterizations like SEM and FTIR. We explore the impact of various environment and operating parameters on degradation rate, such as water quality/contamination, water flow rate, operating temperature and pressure, and current density / voltage. These factors are explored in a flow-through system where concentration of water is needed for a detectable fluoride signal. Methods for concentrating water will be discussed.By elucidating the factors impacting chemical degradation and identifying key stressors, insights from this study can be used to develop informative AST protocols, essential for accelerating the evaluation of materials with improved conductance-crossover-durability tradeoff, and therefore enabling more robust and efficient PEM water electrolyzer systems.

Pharma in The Digital Era: The Role of Artificial Intelligence in Drug Development

The integration of Artificial Intelligence (AI) in the pharmaceutical sector marks a transformative era, where technology reshapes traditional drug development processes. This paper delves into the pivotal role AI plays in enhancing efficiency and precision in drug discovery, testing, and approval. Existing methods in drug development often suffer from high costs, extended timelines, and high attrition rates in clinical trials, posing significant barriers to timely patient care. To address these issues, we propose the Drug Development based on Artificial Intelligence (DD-AI) framework, leveraging AI-driven algorithms and machine learning models. The DD-AI framework enhances the identification and optimization of drug candidates, predicts clinical trial outcomes, and personalizes patient treatment plans. AI algorithms analyze vast datasets to discover potential drug molecules, simulate their interactions, and streamline the clinical trial process by identifying the most promising candidates. Implementing the DD-AI framework has demonstrated substantial improvements in reducing drug development costs and timelines while increasing the accuracy of trial outcomes. AI's predictive capabilities also personalize treatments, optimizing efficacy and minimizing adverse effects for patients. The DD-AI framework offers a robust solution to existing challenges in drug development, fostering a more efficient, cost-effective, and patient-centered approach. The findings underscore AI's transformative potential in revolutionizing pharmaceutical practices, ultimately benefiting patients with faster and more precise medical solutions.

Changing snow conditions are challenging moose (Alces alces) surveys in Alaska

AbstractSnow conditions are changing rapidly across our planet, which has important implications for wildlife managers. In Alaska, USA, the later arrival of snow is challenging wildlife managers' ability to conduct aerial fall (autumn) moose (Alces alces) surveys. Complete snow cover is required to reliably detect and count moose using visual observation from an aircraft. With inadequate snow to help generate high‐quality moose survey data, it is difficult for managers to determine if they are effectively meeting population goals and optimizing hunting opportunities. We quantified past relationships and projected future trends between snow conditions and moose survey success across 7 different moose management areas in Alaska using 32 years (1987–2019) of moose survey data and modeled snow data. We found that modeled mean snow depth was 15 cm (SD = 11) when moose surveys were initiated, and snow depths were greater in years when surveys were completed compared to years when surveys were canceled. Further, we found that mean snow depth toward the beginning of the survey season (1 November) was the best predictor of whether a survey was completed in any given year. Based on modeled conditions, the trend in mean snow depth on 1 November declined from 1980 to 2020 in 5 out of 7 survey areas. These findings, coupled with future projections, indicated that by 2055, the delayed onset of adequate snow accumulation in the fall will prevent the completion of moose surveys over roughly 60% of Alaska's managed moose areas at this time of the year. Our findings can be used by wildlife managers to guide decisions related to the future reliability of aerial fall moose surveys and help to identify timelines for development of alternate measurement and monitoring methods.

Emotion as Cause, Effect, Mediator, and Moderator in Marketing: An Integrative Review and Future Research Directions

ABSTRACTThe study aims to synthesize four decades of research on emotions in marketing by delving into the specifics of who, where, how, what and when. Using quantitative and qualitative methods, 453 articles from top marketing journals were analysed. The study first presents a descriptive account of publication and citation metrics and theoretical underpinning, resolving contestation around emotion function as cause, effect, mediator and moderator. Secondly, using science mapping, along with performance and content analysis landscape of emotion research is unravelled by identifying eight clusters. These clusters placed on development timelines provide a bird's eye view and are presented on a three‐level categorization: Theory, Characteristics and Context. Clusters 1 and 2 focus on theoretical underpinning of emotion interrelationship with cognition and construal. Clusters 3 and 4 emphasized unique characteristics of emotion as antecedents and outcomes to/of evaluations/behaviours. Clusters 5, 6, 7, and 8 emphasized distinct emotional settings—notably, cluster 5 accounts for emotion interaction across service encounters and elements of service interface. Cluster 6 emphasizes effectiveness, advantage and measurement of emotion in advertising. Cluster 7 highlights how emotions are profoundly shaped by and influence social interactions/behaviour, and Cluster 8 underscores emotion embedded in brands, products and life experiences. Finally, a diverse yet unified field of emotion research in marketing is advocated by reflecting on findings, identifying opportunities for cross‐pollination, highlighting cluster‐specific future research directions and developing an integrative framework. The framework, beyond decision‐making, synthesizes distinct ways emotion arises and permeates marketplace and accounts for alternate lenses to emotion functioning, promoting newer research.

AI-assisted autonomous manufacturing of tailored drug-loaded nanoparticles by multi-step continuous-flow platform

In the development of highly effective drug delivery systems (DDS), autonomous multi-step manufacturing of tailored nanoparticles in size and drug loading was achieved by seamlessly integrating combinatorial synthesis of drug-loaded nanoparticles, rapid flow-dialysis to purify them, followed by real-time characterization of size and encapsulation efficiency (EE) in a continuous-flow manner. To accomplish this, a newly developed ultrafast flow-dialysis module with multiple buffer injectors for efficient removal of impurities within 10 min was utilized. These integrated multi-step flow processes were autonomously controlled by leveraging a multi-objective Bayesian optimization (MOBO) algorithm. Consequently, this artificial intelligence (AI)-assisted rapid screening of multi-step parameters accomplished the swift manufacturing of both Doxorubicin (DOX)-loaded liposomes and polymeric nanoparticles, customized in terms of size and drug loading within 6 h for each. This AI-based autonomous platform envisions accelerating the development timeline for bespoke DDS applications, addressing diverse therapeutic needs in the field of nanomedicine.

Automation and innovation in home hemodialysis machines.

In this review, we discuss the timeline of innovation and technologic development in home hemodialysis (HHD) in the United States and the legislative approvals that accompanied them. The most recently FDA-approved home hemodialysis devices provide features that include on-demand and batch dialysate generation, access disconnect for venous needle dislodgement, touchscreen interface with visual and auditory prompts and animations, drop-in sterilized cartridges with prestrung tubing, hot water disinfection of tubing allowing extended-use, dialysate flow rates as high as 500 ml/min, as well as remote treatment monitoring capabilities. Furthermore, wearable/portable dialysis devices are currently under development to simplify dialysis delivery to patients with end-stage kidney disease. Home hemodialysis devices providing longitudinal hemodialysis across different clinical settings, virtual reality headsets for more personalized training, automated patient support, as well as wearable device development and innovations give hope for a future where home hemodialysis is more accessible and seamless.

Integrating AI with Chemical Engineering for Rapid Biomaterial Development in Health Applications

This research presents an innovative approach to healthcare biomaterial development by integrating AI with chemical engineering methodologies. This study combines AI’s predictive power with the precision of chemical engineering to accelerate the discovery and optimization of biomaterials tailored for health applications while focusing on biocompatibility, adaptability, and stringent biomedical functionality. By using machine learning frameworks and advanced simulation software, the project developed predictive models that streamline biomaterial synthesis. This reduced experimental cycles by up to 40% and shortened development timelines by over 30%. Through neural networks and hybrid AI models, material properties such as degradation rate, strength, and biocompatibility can be accurately predicted, which minimizes in vivo testing and accelerates the transition from laboratory research to clinical trials. Protocols optimized through AI-driven insights enhance biomaterial production’s reproducibility and scalability while reducing material costs by 20%, which supports economically viable synthesis methods for large-scale applications. This research contributes a customizable AI framework for health-focused biomaterial solutions and impacts areas such as regenerative medicine, drug delivery, and implantable devices. Future directions include integrating real-time clinical feedback into AI models to improve safety and adaptability, thus advancing sustainable and personalized approaches to biomaterial development. This interdisciplinary framework demonstrates how AI-enhanced chemical engineering workflows can yield adaptive biomaterials suited to specific biomedical needs and offers unprecedented precision in material selection. The model’s high predictive accuracy in biocompatibility screening, achieving over 90%, enables focused experimental validation while reducing the need to rely on animal studies, which fosters ethical research practices. The sustainability impact of this project is significant, as AI-driven optimization reduces resource consumption and waste and aligns with eco-friendly biomaterial development practices. Ultimately, this research paves the way for rapid, efficient, and sustainable innovations in healthcare materials and sets a foundation for future advancements in personalized medical applications.

Potential and challenges of clinical high-dimensional flow cytometry: A call to action.

Clinical biomarker strategies increasingly integrate translational research to gain new insights into disease mechanisms or to define better biomarkers in clinical trials. High-dimensional flow cytometry (HDFCM) holds the promise to enhance the exploratory potential beyond traditional, targeted biomarker strategies. However, the increased complexity of HDFCM poses several challenges, which need to be addressed in order to fully leverage its potential and to align with current regulatory requirements in clinical flow cytometry. These challenges include among others extended timelines for assay development and validation, the necessity for extensive knowledge in HDFCM, and sophisticated data analysis strategies. However, no guidelines exist on how to manage such challenges in adopting clinical HDFCM. Our CYTO 2024 workshop "Potential and challenges of clinical high-dimensional flow cytometry" aimed to find consensus across the pharmaceutical industry and broader scientific community on the overall benefits and most urgent challenges of HDFCM in clinical trials. Here, we summarize the insights we gained from our workshop. While this report does not provide a blueprint, it is a first step in defining and summarizing the most pressing challenges in implementing HDFCM in clinical trials. Furthermore, we compile current efforts with the goal to overcome some of these challenges. As such we bring the scientific community and health authorities together to build solutions, which will accelerate and simplify the full adoption of HDFCM in clinical trials.

Unraveling the developmental heterogeneity within the human retina to reconstruct the continuity of retinal ganglion cell maturation and stage-specific intrinsic and extrinsic factors.

Tissue development is a complex spatiotemporal process with multiple interdependent components. Anatomical, histological, sequencing, and evolutional strategies can be used to profile and explain tissue development from different perspectives. The introduction of scRNAseq methods and the computational tools allows to deconvolute developmental heterogeneity and draw a decomposed uniform map. In this manuscript, we decomposed the development of a human retina with a focus on the retinal ganglion cells (RGC). To increase the temporal resolution of retinal cell classes maturation state we assumed the working hypothesis that that maturation of retinal ganglion cells is a continuous, non-discrete process. We have assembled the scRNAseq atlas of human fetal retina from fetal week 8 to week 27 and applied the computational methods to unravel maturation heterogeneity into a uniform maturation track. We align RGC transcriptomes in pseudotime to map RGC developmental fate trajectories against the broader timeline of retinal development. Through this analysis, we identified the continuous maturation track of RGC and described the cell-intrinsic (DEGs, maturation gene profiles, regulons, transcriptional motifs) and -extrinsic profiles (neurotrophic receptors across maturation, cell-cell interactions) of different RGC maturation states. We described the genes involved in the retina and RGC maturation, including de novo RGC maturation drivers. We demonstrate the application of the human fetal retina atlas as a reference tool, allowing automated annotation and universal embedding of scRNAseq data. Altogether, our findings deepen the current knowledge of the retina and RGC maturation by bringing in the maturation dimension for the cell class vs. state analysis. We show how the pseudotime application contributes to developmental-oriented analyses, allowing to order the cells by their maturation state. This approach not only improves the downstream computational analysis but also provides a true maturation track transcriptomics profile.

A review on drug repurposing applicable to obesity.

Obesity is a major public health concern and burden on individuals and healthcare systems. Due to the challenges and limitations of lifestyle adjustments, it is advisable to consider pharmacological treatment for people affected by obesity. However, the side effects and limited efficacy of available drugs make the obesity drug market far from sufficient. Drug repurposing involves identifying new applications for existing drugs and offers some advantages over traditional drug development approaches including lower costs and shorter development timelines. This review aims to provide an overview of drug repurposing for anti-obesity medications, including the rationale for repurposing, the challenges and approaches, and the potential drugs that are being investigated for repurposing. Through advanced computational techniques, researchers can unlock the potential of repurposed drugs to tackle the global obesity epidemic. Further research, clinical trials, and collaborative efforts are essential to fully explore and leverage the potential of drug repurposing in the fight against obesity.

A Comprehensive Survey on Edge Data Integrity Verification: Fundamentals and Future Trends

Recent advances in edge computing (EC) have pushed cloud-based data caching services to edge; however, such emerging edge storage comes with numerous challenging and unique security issues. One of them is the problem of edge data integrity verification (EDIV), which coordinates multiple participants (e.g., data owners and edge nodes) to inspect whether data cached on edge is authentic. To date, various solutions have been proposed to address the EDIV problem, while there is no systematic review. Thus, we offer a comprehensive survey for the first time, aiming to show current research status, open problems, and potentially promising insights for readers to further investigate this under-explored field. Specifically, we begin by stating the significance of the EDIV problem, the integrity verification difference between data cached on cloud and edge, and three typical system models with corresponding inspection processes. To thoroughly assess prior research efforts, we synthesize a universal criteria framework that an effective verification approach should satisfy. On top of it, a schematic development timeline is developed to reveal the research advance on EDIV in a sequential manner, followed by a detailed review of the existing EDIV solutions. Finally, we highlight intriguing research challenges and possible directions for future work, along with a discussion on how forthcoming technology, e.g., machine learning and context-aware security, can augment security in EC. Given our findings, some major observations are: there is a noticeable trend to equip EDIV solutions with various functions and diversify study scenarios; completing EDIV within two types of participants (i.e., data owner and edge nodes) is garnering escalating interest among researchers; although the majority of existing methods rely on cryptography, emerging technology is being explored to handle the EDIV problem.