Development Paradigm Research Articles

Evolution of the Innovative Therapies for Children With Cancer Consortium Trial Portfolio for Drug Development for Children With Cancer.

The aim of the Innovative Therapies for Children with Cancer (ITCC) consortium is to improve access to novel therapies for children and adolescents with cancer. The evolution of the ITCC clinical trial portfolio since 2003 was reviewed. All ITCC-labeled phase I/II trials opened between January 1, 2003 and February 3, 2018 were analyzed in two periods (2003-2010 and 2011-2018), and data were extracted from the ITCC database, regulatory agencies' registries, and publications. Sixty-one trials (62% industry-sponsored) enrolled 3,198 patients. The number of trials in the second period increased by almost 300% (16 v 45). All biomarker-driven trials (n = 14) were conducted in the second period. The use of rolling six and model-based designs increased (1 of 9, 11% v 21 of 31, 68%), and that of 3 + 3 designs decreased (5 of 9, 55% v 5 of 31, 16%; P = .014). The proportion of studies evaluating chemotherapeutics only decreased (5 of 16, 31% v 4 of 45, 9%), the proportion of single-agent targeted therapies did not change (9 of 16, 56.2% v 24 of 45, 53.3%), the proportion of combination targeted therapies trials increased (2 of 16, 12%, v 17 of 45, 38%), the proportion of randomized phase II trials increased (1 of 7, 14% v 8 of 14, 57%). More trials were part of a pediatric investigation plan in the second period (4 of 16, 25% v 21 of 45, 46%). The median time for Ethics Committees' approvals was 1.7 times longer for academic compared with industry-sponsored trials. This study reports a shift in the paradigm of early drug development for childhood cancers, with more biologically relevant targets evaluated in biomarker-driven trials or in combination with other therapies and with more model-based or randomized designs and a greater focus on fulfilling regulatory requirements. Improvement of trial setup and recruitment could increase the number of patients benefiting from novel agents.

Design Strategy and Consideration for Oncology Dose-Optimization: An Industry Perspective

The conventional more-is-better dose selection paradigm is often inappropriate for novel molecularly targeted agents. In response, the US Food and Drug Administration (FDA) initiated Project Optimus to reform the dose optimization and dose selection paradigm in oncology drug development. Furthermore, the FDA recently also published draft guidance “Optimizing the dosage of human prescription drugs and biological products for the treatment of oncologic diseases guidance for industry,” providing overviews on the rationale, importance, and principles of dose optimization from the regulatory perspective. However, detailed guidance on how to design dose optimization trials is still lacking; it is a key issue faced by drug developers. The article discusses statistical and design strategies of dose optimization in oncology by sharing current industry practice on dose-finding, delineating key statistical components for dose optimization trials, describing available design strategies, and providing recommendations and future directions. This article is a joint work of the Statistical Methods in Oncology Scientific Working Group (Dose-Optimization Workstream) of the American Statistical Association Biopharmaceutical Section.

Does Reduced Space Result in Fewer Rights? Controlled Shrinking in the Urban Renewal of Genoa

This article explores three examples of urban design initiatives in Genoa in an attempt to highlight the potential and possible contradictions that controlled shrinking projects pose for the future of contemporary cities. Genoa, a symbol of Italian shrinking cities, has been strongly defined over the years by post-industrialisation transformations and by long-standing conditions of urban shrinkage. Despite facing continuous shrinkage, local urban development policies have historically focused exclusively on urban growth and expansion. Only recently have some areas in Genoa started to adopt spatial planning experiences that actively pursue degrowth policies, aiming to reduce existing urban fabric and decrease urban density. These initiatives are adopted in specific areas affected by demographic decline, hydrogeological risks, pollution, or catastrophic events. These spatial strategies justify their existence by invoking concepts like smart shrinkage and degrowth, promising improvements in both environmental and social conditions. However, this article notes how these concepts in Genoa are not aligned with the actual social and environmental challenges that these considerations and positions pose. In fact, the urban renewal initiatives introduced by institutions, in reality, lean towards a strategy of shrinkage and demolition of residential complexes, transportation infrastructure, and productive spaces, with diverse and conflicting results. The observed controlled shrinking projects neglect the synthesis of the territory as a palimpsest, ignore new ecological sensitivities, and lack awareness of the social implications associated with the concepts of smart shrinkage and degrowth. Instead, the three instances introduce a spatial project that still adheres to the underlying principles of growth and exploitation, presenting a shrinkage of the existing urban fabric that is mere illusion. It involves clearing out the deteriorated spaces only to fill them with capitalist rhetoric and models that, instead of creating space, undermine fundamental rights. Nonetheless, a closer examination of these three missed opportunities sheds light on the necessary knowledge, actions, and design approaches for a city to navigate urban shrinkage adeptly. This exploration also reveals the potential for the city to transform into a framework and platform, inspiring and guiding new urban planning paradigms for sustainable development.

Integrating financial literacy, regulatory technology, and decentralized finance: A new paradigm in Fintech evolution

This study investigates the implications of the interaction of financial literacy, regulatory technology, and decentralized finance applications for financial sector development. A two-step analytical regression approach on EViews 10 was used, which performs a one-factor analysis for each variable to identify the individual impact of each factor. A linear FMOLS approach was used to evaluate the cooperative effect of integration. The methodology was implemented on a dataset comprising 2,880 observations from 23 financial institutions in Jordan.The findings support the hypothesized dynamic interrelations between the essential Fintech factors relevant to the sustainable development of the financial sector, including significant and insignificant factors with the impact of inflation, which provides an adequate understanding of Fintech’s evolution. Additionally, the outcomes consider post-2017 regulatory changes that reflect the role of supervision and regulation for the financial sector’s flexibility and efficiency. Therefore, the results reveal the essential contribution of integrating decentralized finance applications, financial literacy, and regulatory technology to the development of Jordan’s financial sector. Financial literacy serves as a facilitator, regulatory technology is a compliance enabler, and decentralized finance applications are driving forces of innovation and financial inclusion, ensuring a robust and sustainable financial ecosystem. It is shown that the interaction of factors forces the sector’s development, reflecting the world’s trend in digital inclusion and viable financial development.

Drug discovery and development in the era of artificial intelligence: From machine learning to large language models

Drug Research and Development (R&D) is a complex and difficult process, and current drug R&D faces the challenges of long time span, high investment, and high failure rate. Machine learning, with its powerful learning ability to characterize big data and complex networks, is increasingly effective to improve the efficiency and success rate of drug R&D. Here we review some recent examples of the application of machine learning methods in six areas: disease gene prediction, virtual screening, drug molecule generation, molecular attribute prediction, and prediction of drug combination synergism. We also discuss the advantages of integrative learning in multi-attribute prediction. Integrative models based on base learners constructed from data of different dimensions on the one hand fully utilize the information contained in these data, and on the other hand improve the average prediction performance. Finally, we envision a new paradigm for drug discovery and development: a large language model acts as a central hub to organize public resources into a knowledge base, validating the knowledge with computational software and smaller predictive models, as well as high-throughput automated screening platforms based on organoidal technologies, to speed up development and reduce the differences in efficacy between disease models and humans to improve the success rate of a drug.

Personalized anesthesia and precision medicine: a comprehensive review of genetic factors, artificial intelligence, and patient-specific factors

Precision medicine, characterized by the personalized integration of a patient’s genetic blueprint and clinical history, represents a dynamic paradigm in healthcare evolution. The emerging field of personalized anesthesia is at the intersection of genetics and anesthesiology, where anesthetic care will be tailored to an individual’s genetic make-up, comorbidities and patient-specific factors. Genomics and biomarkers can provide more accurate anesthetic protocols, while artificial intelligence can simplify anesthetic procedures and reduce anesthetic risks, and real-time monitoring tools can improve perioperative safety and efficacy. The aim of this paper is to present and summarize the applications of these related fields in anesthesiology by reviewing them, exploring the potential of advanced technologies in the implementation and development of personalized anesthesia, realizing the future integration of new technologies into clinical practice, and promoting multidisciplinary collaboration between anesthesiology and disciplines such as genomics and artificial intelligence.

Pursuing European Green Deal milestones in times of war in Ukraine – a context of energy transition in Poland

Within the sustainable development (SD) paradigm, the European Green Deal (EGD) sets dynamic energy transition goals for the European Union (EU). However, diverse dynamics among member states, exacerbated by events like the COVID-19 pandemic and the war in Ukraine, directly influence this progress. This paper aims to present the energy transition course in EU countries up against the implementation of the objectives of the EGD. This paper conducts a thematic (scoping) literature review and comparative analysis of energy transition progress in all EU countries, concentrating on Poland, which faces unique challenges, including a sluggish pace of energy transition and the effects of the Russian invasion on Ukraine in this regard. Recommendations for Poland include an intensified focus on investments in renewable energy sources (RES) and low-carbon installations. The results demonstrate that collective success in SD necessitates each EU member fulfilling national climate goals, emphasising the need for a unified commitment to navigate complexities and build a sustainable future.

The impact of the proportion of renewable energy consumption on geopolitical risks in the United States and the United Kingdom

Renewable energy serves as an effective alternative to traditional fossil fuels, reducing reliance on energy imports from specific countries, thereby alleviating geopolitical risks and ensuring national energy security. The development and utilization of renewable energy have profound implications for the global energy consumption structure and geopolitical landscape. This paper utilizes time-series and panel data from 1990 to 2020 to examine the causal relationship between renewable energy consumption and geopolitical risks. Focusing primarily on the United States and the United Kingdom, a multivariate regression model was developed for empirical analysis, with a comparative analysis conducted against other Group of Seven (G7) member countries. Subsequent robustness checks were performed to further validate the model's robustness. Additionally, this study explores the role of military strength as a moderating variable in the relationship between renewable energy and geopolitics. Lastly, a vector autoregression model was constructed to analyze the dynamic relationships between renewable energy and related variables. The findings reveal that (1) consumption of clean renewable energies, such as solar, wind, and tidal energy, significantly reduces geopolitical risks in the United States and the United Kingdom, with differing empirical results for France, Canada, and Japan, interpreted according to their developmental contexts and (2) increased defense expenditure in the US and the UK significantly enhances the positive impact of renewable energy consumption on mitigating geopolitical risks. Utilizing renewable energy not only diversifies national energy options but also reduces reliance on fossil fuels and fosters international cooperation, thereby easing geopolitical tensions. This research enriches the literature on the relationship between renewable energy consumption and national energy security in key global nations and offers theoretical insights for the formulation and implementation of national energy development strategies in the new world development paradigm.

PHARMACEUTICAL INDUSTRY ANALYSIS: A COMPREHENSIVE REVIEW OF MARKET TRENDS, COMPETITIVE LANDSCAPE, AND FUTURE OUTLOOK

The pharmaceutical industry, a cornerstone of modern healthcare, undergoes constant evolution to meet the dynamic demands of the healthcare landscape. This comprehensive review delves into the multifaceted dimensions of the industry, encompassing market trends, competitive dynamics, and future projections. Market trends reveal a shift towards personalized medicine, leveraging advancements in genomics and biotechnology, alongside a growing focus on specialty drugs catering to niche patient populations. The prevalence of chronic diseases and an aging populace accentuate the necessity for innovative therapies, while the digital revolution introduces new paradigms in patient care and drug development, propelled by telemedicine, digital therapeutics, and artificial intelligence. Simultaneously, regulatory pressures and pricing constraints exert significant influence, necessitating adaptability among industry players. In the competitive landscape, established pharmaceutical giants vie for market dominance amidst patent expirations and generic competition, while biotech startups and niche players disrupt traditional models. Mergers and acquisitions serve as strategic imperatives for pipeline fortification and market expansion. Looking forward, the industry must navigate a terrain characterized by rapid technological advancements, evolving patient needs, and regulatory complexities, all while striving for innovation, accessibility, and sustainability in the delivery of healthcare solutions. Keywords: Pharmaceutical industry, healthcare landscape, market trends, personalized medicine, specialty drugs, chronic diseases.

Arbitrary Digital DNA Computing: A Programmable Molecular Perceptron Driven by Lambda Exonuclease for Lighting up Concatenated Circuits.

DNA circuits, as a type of biochemical system, have the capability to synchronize the perception of molecular information with a chemical reaction response and directly process the molecular characteristic information in biological activities, making them a crucial area in molecular digital computing and smart bioanalytical applications. Instead of cascading logic gates, the traditional research approach achieves multiple logic operations which limits the scalability of DNA circuits and increases the development costs. Based on the interface reaction mechanism of Lambda exonuclease, the molecular perceptron proposed in this study, with the need for only adjusting weight and bias parameters to alter the corresponding logic expressions, enhances the versatility of the molecular circuits. We also establish a mathematical model and an improved heuristic algorithm for solving weights and bias parameters for arbitrary logic operations. The simulation and FRET experiment results of a series of logic operations demonstrate the universality of molecular perceptron. We hope the proposed molecular perceptron can introduce a new design paradigm for molecular circuits, fostering innovation and development in biomedical research related to biosensing, targeted therapy, and nanomachines.

New paradigm for watershed model development by coupling machine learning algorithm and mechanistic model

The development of watershed models faces double pressure of data requirements and physical interpretation. The simulation of nonpoint source (NPS) pollution is an important application of watershed model, and the best management practices (BMPs) have attracted wide attention as the main control approach of NPS pollution. In this study, a new paradigm was proposed based on the integration of data-driven and mechanistic methods, taking BMP evaluation as an example. Specifically, comprehensive databases were constructed for filter and retention BMPs by collecting, classifying and analyzing published data. Twelve machine learning algorithms were employed for regression analysis between BMPs efficiency and their influencing factors, while the constructed equations were coupled with physical-based models. A case study was performed in a typical catchment of Chaohu Lake Watershed, China. The results demonstrated total interception area, soil type, and vegetation biomass had significant impacts on BMPs performances, while the multilayer perceptron regression (MLPR), K nearest neighbor regression (KNRD), and extremely randomized tree (ETR) methods had the best performances of nutrients removal. The study generated over ten thousand datasets using mechanistic processes, resulting in more efficient and interpretable BMPs evaluator. Compare to traditional methods, this new paradigm has shown advantages in the model application at the watershed scale, enhancing the BMPs evaluator with a higher level of interpretability by coupling the approach with mechanistic models.

Drug Classification and Repurposing Using Decision Tree Algorithm and Data Analysis

Abstract: Fundamentally, drugs are substances that exhibit the capacity to alter biological functions, often serving as interventions to mitigate ailments or enhance physiological processes. However, the landscape of drug development and usage is undergoing transformation, fueled by innovative strategies that hold the promise of revolutionizing healthcare solutions. This research paper explores drug classification and repurposing through the utilization of decision tree algorithms and data analysis. Specifically, the aim is to classify drugs into three distinct types: Drug X which is most used drugs, Drug Y which is less frequently used drugs, and Drug C includes drugs like narcotics. Decision tree algorithms are employed to discern the defining attributes that categorize drugs into these types. By analyzing comprehensive datasets encompassing drug properties, interactions, and clinical outcomes, the study harnesses decision tree models to predict drug classifications accurately. This approach holds the potential to accelerate drug discovery, optimize treatment strategies, and contribute to more efficient healthcare solutions. The proposed algorithm is compared with both Naïve Bayes and K - Nearest Neighbors algorithms to prove it is more accurate. Ultimately, the significance of this paper transcends the boundaries of conventional drug development paradigms and to overcome the problem of shortage of drugs.

학습목표지향성과 무형식학습은 통합사례관리 슈퍼바이저 역량에 영향을 미치는가? -팀장과 통합사례관리사 다중집단분석-

The study examined whether learning goal orientation and informal learning influence the competencies of integrated case management supervisors, and whether there are differences in this pathway between supervisor types, namely, public service team leaders and case managers. A survey involving 155 public service team leaders and 100 case managers was conducted, and a multi-group path analysis was performed. First, it was found that learning goal orientation positively affects informal learning and each sub-dimension of supervisor competency, including knowledge level, skill level, and attitude level. Second, informal learning positively influences the skill level and attitude level among the sub-dimensions of supervisor competency. Third, in examining the differences between team leaders and case managers, a statistically significant difference was found in the path from informal learning to skill level competency among supervisors. Based on the findings, this study suggests ways to enhance the competencies of case management supervisors by applying the latest human resource development paradigms.

Analysis of reconstructed modified [formula omitted] gravity

This paper studies the reconstruction criterion in the framework of f(Q,T) gravity using a ghost dark energy model, where Q represents the non-metricity and T is the trace of energy–momentum tensor. In this regard, the correspondence scenario for a non-interacting scheme is used to construct the ghost dark energy f(Q,T) model. We consider the flat Friedmann–Robertson–Walker universe with a power-law scale factor and pressure-less matter. We investigate the behavior of the equation of state parameter and examine the stability of the ghost dark energy model via the squared speed of sound parameter. The equation of state parameter depicts the phantom era, while the squared speed of sound reveals a stable ghost dark energy model for the entire cosmic evolutionary paradigm. Finally, we study the cosmography of the ωGDE−ωGDE′ and r−s planes that correspond to a freezing region and Chaplygin gas model, respectively. It is concluded that the reconstructed ghost dark energy model represents the evolution of the cosmos for suitable choices of the parametric values.

Redefining sustainable urban development: A critical analysis in the wake of COVID‐19

AbstractThis paper critically examines the concept of sustainable development, particularly in the context of urban challenges exacerbated by the COVID‐19 pandemic. Although the concept of sustainable development has been around for more than three decades, it faces ongoing global socio‐environmental crises that have been exacerbated by the rapid spread of COVID‐19 since 2019, which has highlighted the vulnerability of urban and interconnected cities. Our approach includes a comparative literature review to analyze the evolution and critique of sustainable development, focusing on its conceptual foundations, complementary strategies, and political‐economic implications. The paper addresses the urban implications of COVID‐19, emphasizing its direct impact on the Sustainable Development Goals and arguing for a comprehensive reassessment of sustainable urban development practices. We propose a transformative agenda that recognizes local specificities and draws on cities' experiences during the pandemic to build a more equitable and crisis‐resilient global society. This research highlights the urgency to rethink current sustainable development paradigms, especially in light of the human‐environment imbalance and lessons learned from the COVID‐19 crisis. It calls for a reformed global approach that incorporates local dimensions and draws on the experience of cities to address the inequalities and crisis‐prone structures in our societies, with the aim of a fundamental shift toward a more sustainable future in the post‐COVID‐19 era.

When Industry meets Trustworthy AI: A Systematic Review of AI for Industry 5.0

We are focusing on analyzing the current industrial evolution paradigm, aiming to make it more sustainable and trustworthy. In Industry 5.0, Artificial Intelligence (AI) is one of the key technologies utilized to develop services with a sustainable, human-centric, and resilient approach. Understanding the factors enabling AI adoption in industry, while adhering to trustworthy principles, involves examining its incorporation in early stages, assessing its impact, and identifying emerging trends. Additionally, we aim to grasp the challenges and gaps in transitioning from Industry 4.0 to Industry 5.0,providing insights into the industry's readiness for new technologies. This offers practitioners new avenues to explore for fostering the adoption of trustworthy AI within the sector

Sustainable Indigenous Governance: Catalysts for Economic Development in India

In India we have too many different communities with vast tradition and culture. In order to enhance their own way of life we have to protect the indigenous nature of governance. The idea of indigenous governance for economic development in India combines traditional knowledge and governance structures with modern development strategies. This model of indigenous governance is based on the central role of community-led initiatives, practices and management to promote sustainable economic growth. This research paper aims to make a correlation between indigenous governance and economic development. This paper also advocates a top-down approach to prioritizes the aspirations, needs and values of indigenous communities, to ensure their active participation in decision-making processes. This indigenous governance model is important to rethink development paradigms and make them more inclusive and equitable. By following this indigenous governance and leadership models, our country can find the way for a development strategy that is not only economically valuable but also socially and environmentally sustainable. India can harness the potential of its diverse cultures for inclusive and sustainable economic development by creating an enabling political environment that respects and takes into account indigenous governance systems. The principle of self-determination is a key to this indigenous model, which can allow different communities to follow their own development paths in accordance with their cultural heritage and environmental protection. To improve the economic well-being of indigenous peoples and to preserve their natural resources, cultural identity and traditional knowledge for future generations, the government should follow the indigenous approach of governance.

Revealing the mechanical behavior of Ti48-xZrxHf26Nb26 high-entropy alloy through zirconium content variations

Refractory high-entropy alloys (RHEAs) composed of Ti, Zr, Hf, and Nb have emerged as promising materials for high-temperature aerospace applications due to their exceptional balance of strength and toughness, coupled with a reduced density and softening resistance. This study delves into the influence of alloy composition on the mechanical properties, a pivotal aspect in the design of RHEAs. We investigate a series of Ti48-xZrxHf26Nb26 (x = 14, 18, 22, 26, 30, 34) alloys, prepared through suction casting and homogenization treatments. The phases, microstructure, and fracture morphology of these alloys are analyzed using X-ray diffraction (XRD), scanning electron microscopy (SEM), and electron backscattered diffraction (EBSD), subsequent tensile tests provide insights into the strength and elongation characteristics of alloys. The homogenized alloys consistently exhibit one BCC phase with equiaxed grains, whereas the fracture mode shifts from intergranular to transgranular, and return to intergranular as the Zr content increases. Experimental results reveal an asymmetry mechanical behavior, alloys with higher Ti content display superior toughness (elongation 17.83 % for Ti = 34 at%) compared to those with higher Zr content (elongation 16.05 % for Zr = 34 at%). Among them, the Ti22Zr26Hf26Nb26 alloy achieves the peak tensile strength of 695.25 MPa, indicating an inverse relationship between strength and toughness. Solid-solution strengthening and chemical short-range orderings emerge as the predominant contributors to the overall strength of the TiZrHfNb alloys. To gain a deeper understanding of the mechanical behavior at the atomic scale, we performed first-principles calculations to investigate the elastic properties and charge density of Ti48-xZrxHf26Nb26 alloys in the BCC structure. These calculations shed light on the underlying mechanisms governing the strength and toughness behavior. The present work offers valuable insights into the design of mechanical properties of TiZrHfNb RHEAs, serving as a paradigm for future materials development in this field.

Freshwater genome-reduced bacteria exhibit pervasive episodes of adaptive stasis

The emergence of bacterial species is rooted in their inherent potential for continuous evolution and adaptation to an ever-changing ecological landscape. The adaptive capacity of most species frequently resides within the repertoire of genes encoding the secreted proteome (SP), as it serves as a primary interface used to regulate survival/reproduction strategies. Here, by applying evolutionary genomics approaches to metagenomics data, we show that abundant freshwater bacteria exhibit biphasic adaptation states linked to the eco-evolutionary processes governing their genome sizes. While species with average to large genomes adhere to the dominant paradigm of evolution through niche adaptation by reducing the evolutionary pressure on their SPs (via the augmentation of functionally redundant genes that buffer mutational fitness loss) and increasing the phylogenetic distance of recombination events, most of the genome-reduced species exhibit a nonconforming state. In contrast, their SPs reflect a combination of low functional redundancy and high selection pressure, resulting in significantly higher levels of conservation and invariance. Our findings indicate that although niche adaptation is the principal mechanism driving speciation, freshwater genome-reduced bacteria often experience extended periods of adaptive stasis. Understanding the adaptive state of microbial species will lead to a better comprehension of their spatiotemporal dynamics, biogeography, and resilience to global change.

Social and Spatial Inequalities during COVID-19: Evidence from France and the Need for a New Sustainable Urban and Regional Development Paradigm

This study examines the impact of social and spatial inequalities on the spread of COVID-19 and the efficacy of lockdown measures in French regions during the third wave (22 March and 3 May 2021), with a focus on population density and poverty. It based on data from French government websites, adopting quantitative methods including the Pearson Coefficient and One-Way ANOVA. The results indicate a significant correlation between population density and COVID-19 deaths in 92 departments (R = 0.695), which is stronger than the correlation between density and infection rates (R = 0.633, p < 0.001). In the 25 most affected departments, this pattern shifts, with a weaker correlation between density and deaths (R = 0.644) compared to density/infections (R = 0.716, p < 0.001). Although insightful, these statistics do not capture regional disparities. A minor correlation between poverty and death rates was recorded in French regions. Spatial observations showed that areas exceeding the national poverty average experienced higher infection/death rates. The lockdowns were effective, leading to a reduction (51.7%) in infections. The present results showed that there is a significant need for a new social contract based on a new urban and regional paradigm focused on socio-spatial equality and Sustainable Development Goals locally and globally, a vital task for policymakers, planners, and researchers.