Traditional Development Process Research Articles

Philanthropic drug development: understanding its importance, mechanisms, and future prospects.

Philanthropic drug development (PDD) addresses gaps in traditional pharmaceutical innovation, particularly for rare and underserved diseases. Cost and timeline challenges discourage new investments, especially in niche therapeutic areas. Patient organizations (POs) are uniquely positioned to help to reduce development challenges by providing expertise, supporting early research, fostering collaborations, and driving patient-centered clinical trials. PDD relies on effective partnerships between POs, pharmaceutical companies, and other stakeholders, ensuring that patient perspectives inform the drug development process. PDD is poised to relieve the pressure on the traditional drug development process and thereby foster beneficial patient-focused innovations. In doing so, PDD allows pharmaceutical companies to expand their drug development activities into commercially unrewarding {AuQ: Edit OK?} areas, diversifying their portfolios beyond competitive fields.

Sustainability using Artificial Intelligence: Transformative Solutions in the IT Sector

Sustainability has become a critical priority for businesses and governments as we address environmental changes such as climate change, resource depletion, waste management, and water level increases. This paper investigates the transformative role of artificial intelligence (AI) in sustainable product development, highlighting how advancements in AI technologies are reshaping traditional practices. Traditional product development processes often relied on resource-intensive methods that generated significant waste and offered limited adaptability to sustainability goals. As a cornerstone of the digital economy, the IT sector faces increasing pressure to address its environmental impact, particularly in areas like server management, network optimization, and data transmission. This paper explores the transformative role of Artificial Intelligence (AI) in driving sustainable practices within IT operations. AI-powered solutions, such as dynamic workload management, predictive maintenance, and traffic optimization, enable energy-efficient server and network operations, significantly reducing power consumption. Advanced AI-driven algorithms facilitate data compression, prioritize transmission, and optimize content delivery, minimizing carbon emissions from data-intensive activities. Additionally, AI supports renewable energy integration, tracks carbon footprints, and provides actionable insights for developing sustainable software and IT infrastructure. These innovative applications of AI enhance operational efficiency and contribute to global sustainability goals by reducing the environmental footprint of IT systems. This research highlights the potential of AI to create a greener IT landscape while maintaining the sector’s growth and performance imperatives.

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.

Artificial Intelligence in Clinical Practice: Unlocking New Horizons in Drug Repurposing for Disease Treatment

Artificial intelligence (AI) has emerged as a transformative force in drug repurposing, offering innovative solutions to expedite the identification of new therapeutic uses for existing medications. This review explores the multifaceted applications of AI, particularly in the context of complex and rare diseases, where traditional drug development processes are often hindered by high costs and lengthy timelines. AI-driven methodologies, including machine learning (ML), deep learning (DL), and natural language processing (NLP), facilitate the analysis of vast datasets from clinical trials, electronic health records, and scientific literature, enabling researchers to uncover novel drug-disease relationships. The integration of AI with genomics and proteomics further enhances the precision of drug repurposing efforts by identifying genetic and proteomic markers that predict patient responses to therapies. Despite its potential, the field faces challenges related to data quality, regulatory hurdles, and the need for interdisciplinary collaboration among researchers, clinicians, and policymakers. This review highlights recent advancements in AI applications for drug repurposing, emphasizing their role in addressing unmet medical needs, particularly in rare diseases where treatment options are limited. By harnessing the capabilities of AI, the drug repurposing landscape is poised for significant transformation, ultimately leading to more efficient pathways for delivering effective therapies to patients.

Human-AI Synergy in Survey Development: Implications from Large Language Models in Business and Research

This study examines the novel integration of Large Language Models (LLMs) into the survey development process in business and research through the development and evaluation of the Behavioral Research ASSistant (BRASS) Bot. We first analyzed the traditional scale development process to identify tasks suitable for LLM integration, including both human-in-the-loop and automated LLM data collection methods. Following this analysis, we developed the details of BRASS Bot, incorporating design principles of falsifiability and reproducibility. We then conducted a comprehensive evaluation of the BRASS Bot across a diverse set of LLMs, including GPT, Claude, Gemini, and Llama, to assess its usability, validity, and reliability. We further demonstrated the practical utility of the BRASS Bot by conducting a user study and a predictive validity simulation. Our research presents both theoretical and practical implications. The augmentation approach of the BRASS Bot enriches the theoretical foundations of behavioral constructs by identifying previously overlooked patterns. Additionally, the BRASS Bot offers significant time and resource efficiency gains while enhancing scale validity. Our work lays the foundation for future research on the broader application of LLMs as both assistants and collaborators in survey analysis and behavioral research design and execution, highlighting their potential for a transformative impact on the field.

Examination of AI Enhanced Distributed Systems and its Effects on Software Engineering

Distributed systems are groups of independent computers that appear to the system's users as a single coherent system. These systems involve several critical factors, including network communication, concurrency, and fault tolerance, which are vital in software engineering. For example, cloud computing platforms like Amazon Web Services (AWS) use distributed systems to offer scalable, on-demand resources to millions of users worldwide. The introduction of Artificial Intelligence (AI) into software engineering marks a transformative period that reshapes traditional development processes and breaks down the complexity of distributed systems. AI automates routine tasks and simplifies complex processes, serving as a digital collaborator that enables developers to focus on strategic thinking and creativity. One significant advantage of using AI in distributed systems within software engineering is enhanced resource optimization. AI can identify irregularities that might indicate hardware malfunctions and take preventative measures to address them, such as rerouting traffic to healthier servers. However, distributed systems also have challenges, including increased complexity in system design, difficulty ensuring data consistency, and potential security vulnerabilities. The intricate structure of these systems can result in problems with high startup costs, safety risks, and the accuracy of the data provided. Integrating AI into distributed systems offers both significant advantages and disadvantages. This study evaluates how AI impacts the efficiency and security of distributed systems in software engineering. By analyzing the advantages and disadvantages of AI-enhanced distributed systems, we can gain a comprehensive understanding of their overall effectiveness and implications in the field.

MolCFL: A personalized and privacy-preserving drug discovery framework based on generative clustered federated learning

In today’s era of rapid development of large models, the traditional drug development process is undergoing a profound transformation. The vast demand for data and consumption of computational resources are making independent drug discovery increasingly difficult. By integrating federated learning technology into the drug discovery field, we have found a solution that both protects privacy and shares computational power. However, the differences in data held by various pharmaceutical institutions and the diversity in drug design objectives have exacerbated the issue of data heterogeneity, making traditional federated learning consensus models unable to meet the personalized needs of all parties. In this study, we introduce and evaluate an innovative drug discovery framework, MolCFL, which utilizes a multi-layer perceptron (MLP) as the generator and a graph convolutional network (GCN) as the discriminator in a generative adversarial network (GAN). By learning the graph structure of molecules, it generates new molecules in a highly personalized manner and then optimizes the learning process by clustering federated learning, grouping compound data with high similarity. MolCFL not only enhances the model’s ability to protect privacy but also significantly improves the efficiency and personalization of molecular design. MolCFL exhibits superior performance when handling non-independently and identically distributed data compared to traditional models. Experimental results show that the framework demonstrates outstanding performance on two benchmark datasets, with the generated new molecules achieving over 90% in Uniqueness and close to 100% in Novelty. MolCFL not only improves the quality and efficiency of drug molecule design but also, through its highly customized clustered federated learning environment, promotes collaboration and specialization in the drug discovery process while ensuring data privacy. These features make MolCFL a powerful tool suitable for addressing the various challenges faced in the modern drug research and development field.

Research on software reliability enhancement methods based on iterative development

Iterative development is an agile software development methodology that speeds up the development process and increases the flexibility of software delivery. However, the current software requirements change extremely fast, which leads to its increasing uncertainty, and iterative development also faces the challenge of software reliability. The software reliability enhancement method based on iterative development is researched and discussed for the current situation of insufficient reliability in the traditional software development process. In traditional software development, one-time delivery pressure and overall design complexity often lead to software functional defects and instability, affecting software reliability. The iterative development method, on the other hand, improves software reliability by decomposing the software development process into multiple iterative cycles, each of which contains requirements analysis, design, coding, and testing, updating the software functionality in smaller increments, and gradually improving the software product. This study proposes a software reliability improvement methodology by exploring and analyzing the key factors related to reliability in the iterative development process. This dissertation can establish a reliable and high-quality software development process through the steps of meticulous requirements planning and management, iterative development and testing, automated testing and continuous integration, code quality control, defect management and continuous improvement, user feedback and user engagement, and continuous learning and technology enhancement.

Advancements and Perspectives in Folate‐Based Anticancer Drugs: Bridging Quantum and Classical Mechanics in Folate Receptor Research

AbstractThis review highlights the role of computational chemistry, specifically quantum and molecular mechanics, in the development of folate‐based anticancer drugs. Folate receptors (FRs) are overexpressed in cancerous cells, rendering these receptors a key focus in the design of targeted drug delivery systems. These computational tools are fundamental for analyzing drug–receptor interactions and overcoming the limitations of traditional drug development processes. A 10‐year literature survey demonstrated advancements in employing FRs for targeted cancer therapy. Key findings reveal that structural modifications to folate derivatives consistently enhance binding affinities and specificity toward FRα and FRβ. Computational methodologies predicted and analyzed molecular interactions, validated by experimental data. Functional groups play a crucial role in enhancing binding stability and interaction strength within FR binding pockets. Detailed structural insights into folate derivatives and antifolates interacting with FRs have identified critical residues involved in binding, aiding the design of targeted therapeutics.

ENHANCING DRUG DISCOVERY THROUGH A META-PATH BASED OVERSAMPLING APPROACH FOR IMBALANCED DATA

This study proposes a new method to improve the efficiency of drug discovery by repurposing existing drugs, aiming to reduce the time and costs associated with traditional drug development processes, which can span 10 to 15 years and cost billions of dollars. Current approaches focus on leveraging heterogeneous data, such as drug-protein and disease-protein interactions, to construct complex networks that link drugs, proteins, and diseases. However, a significant challenge is the imbalance in data, where numerous unconfirmed potential drug-disease interactions (the majority class) outnumber approved drugs (the minority class), severely impacting the predictive performance of machine learning models. Previous attempts to address this issue have shown limited success. This study introduces a novel approach that integrates meta-paths in heterogeneous information networks with data balancing techniques to tackle this imbalance. Experimental results demonstrate that the proposed method enhances model performance and reliability in identifying new relationships between drugs and diseases. This research represents a promising advancement by leveraging network-based strategies and data balancing techniques to facilitate the rediscovery of drug applications, thereby potentially revolutionizing the pharmaceutical industry’s approach to drug development.

Analysis of Traditional and Agile Software Development Process for Developing Recommender Model using Machine Learning

Objective: To create an AI-powered recommendation system that is designed for IT professionals to help them choose the best software development approaches. Through the use of specified data parameters. Methods: The recommendation system will make use of machine learning algorithms and data analysis methods to examine team dynamics, project needs, and other variables. The technology will enable developers to improve the quality of products and speed up the development process by recommending suitable development methodologies. Data parameters considered for the development of the recommendation model fall into four categories: requirements, user involvement, development team, type of project, and risk associated with it. Findings: Existing recommendation systems developed by different researchers are applicable for only requirement elicitation and to recommend different phases of the development process, whereas systems that will help select development methodology are not available in the existing systems. Among the five machine learning algorithms applied in the recommender system building process, the DecisionTree Classifier and RandomForest Classifier exhibit superior performance, achieving 100% accuracy, while the Kneighbors Classifier indicates 94.74% accuracy. Novelty: This study of systems introduces a novel approach to software development methodology, a recommender system, which helps IT developers select the best appropriate development approach for the development of a software product or project based on the type of project to be built and other data parameters. Keywords: Agile, Development, Requirements, Methodology, User, Customer

Representation of some principles of the Agile Manifesto in the Semat Essence Kernel

The Agile Manifesto (AM) provides principles guiding agile software development as an alternative to traditional software development processes. While attempts have been madeto adapt processes to the agile context, representation of AM principles remains underdeveloped and subjective. The Semat (Software Engineering Method and Theory) Essence kernel offers a common ground for representing software development endeavors. In this paper, we represent some AM principles using the language provided by the Semat Essence kernel to establish a common ground. Additionally, we define constraints in OCL (Object Constraint Language) to enhance the Semat Essence kernel, enabling the introduction of time management in our representation. Such a representation will allow us for adapting and assessing different processes in an agile context.

CRISPR and Gene Editing: A Game-changer in Drug Development.

CRISPR and gene editing technologies have emerged as transformative tools in medicine, offering unprecedented precision in targeting genetic disorders and revolutionizing drug development. This review explores the multifaceted impact of CRISPR across various medical domains, from hereditary diseases to infectious diseases and cancer. The potential of CRISPR in personalized medicine, therapeutic innovation, and pandemic prevention is highlighted, along with its role in reshaping traditional drug development processes. However, alongside its promise, ethical considerations loom large, particularly regarding germline editing and equitable access to treatments. The commercialization of CRISPR poses further challenges, raising questions about affordability and healthcare equity. Collaboration among scientists, policymakers, and the public is emphasized to navigate the ethical and societal implications of CRISPR responsibly. As the field advances, it is essential to ensure that the benefits of CRISPR are realized while addressing potential risks and maintaining a commitment to the well-being of future generations.

Challenges of Requirements Engineering in Agile Projects: A Systematic Review

The practice of Requirements Engineering (RE) within Agile development contexts markedly differs from that within traditional development process. As the RE discipline in Agile Software Development continues to evolve, this research highlights and assesses various challenges to refine its impact on software product development. This study describes inductive and empirical research on Agile RE challenges and practices based on an analysis of data collected in the binding academic and scientific literature. This research is also based on the guidelines of a Systematic Review of the Literature (SRL) proposed by Kitchenham. Our results indicate eight type of challenges that we grouped into four themes: suitability delimitations, on software product development projects, the formation of the development team, and the efficient participation of the client during the process. Finally, this paper contributes detailed insights into RE challenges specific to Agile project development, evaluates their resolution extent, and outlines existing research gaps in RE practices

Model‐based analysis and specification of functional requirements and tests for complex automotive systems

AbstractThe specification of requirements and tests are crucial activities in automotive development projects. However, due to the increasing complexity of automotive systems, practitioners fail to specify requirements and tests for distributed and evolving systems with complex interactions when following traditional development processes. To address this research gap, we propose a technique that starts with the early identification of validation concerns from a stakeholder perspective, which we use to systematically design tests that drive a scenario‐based modeling and analysis of system requirements. To ensure complete and consistent requirements and test specifications in a form that is required in automotive development projects, we develop a Model‐Based Systems Engineering (MBSE) methodology. This methodology supports system architects and test designers in the collaborative application of our technique and in maintaining a central system model, in order to automatically derive the required specifications. We evaluate our methodology by applying it at KOSTAL (Tier1 supplier) and within student projects as part of the masters program Embedded Systems Engineering. Our study corroborates that our methodology is applicable and improves existing requirements and test specification processes by supporting the integrated and stakeholder‐focused modeling of product and validation systems, where the early definition of stakeholder and validation concerns fosters a problem‐oriented, iterative and test‐driven requirements modeling.

Current Scenario of Digitalization in Pharmaceutical Industry

Digitalization is playing an important role in all business sectors. Every sector and industry first oppose the reforms being introduced but law of life is always for the better. Indian pharma industry is still in the early stages of digitalization, it induces transparency, productivity and speeding up the activities. Hence, there is an urgent need of digitalization in traditional and new drug development process. The success of digitalization in pharmaceutical industry depends upon implementation of aspects of good manufacturing practice. In this review, we focused on importance of digitalization in pharma sector in sales improvement, process digitalization, principles of GMP and CDMO digitalization process.

Advancements in software engineering using AI

The integration of Artificial Intelligence (AI) into the space of software engineering marks a transformative period that reshapes traditional development processes and propels the industry into a new era of innovation. This exploration delves into the multifaceted impact of AI, from its roots in early symbolic AI to the contemporary dominance of machine learning and deep learning. AI’s applications span various domains, but its significance in software engineering lies in its ability to enhance efficiency, improve software quality, and introduce novel approaches to problem-solving. From automating routine tasks to streamlining complex development workflows, AI acts as a virtual collaborator, allowing human developers to focus on higher-order thinking and creativity. This study introduces the application of AI in software engineering. reverse-engineering, and development environments. Moreover, ethical considerations, challenges, and future trends, including explainable AI, reinforcement learning, and human-AI collaboration, are presented.

DAPredict: a database for drug action phenotype prediction.

The phenotypes of drug action, including therapeutic actions and adverse drug reactions (ADRs), are important indicators for evaluating the druggability of new drugs and repositioning the approved drugs. Here, we provide a user-friendly database, DAPredict (http://bio-bigdata.hrbmu.edu.cn/DAPredict), in which our novel original drug action phenotypes prediction algorithm (Yang,J., Zhang,D., Liu,L. etal. (2021) Computational drug repositioning based on the relationships between substructure-indication. Brief. Bioinformatics, 22, bbaa348) was embedded. Our algorithm integrates characteristics of chemical genomics and pharmacogenomics, breaking through the limitations that traditional drug development process based on phenotype cannot analyze the mechanism of drug action. Predicting phenotypes of drug action based on the local active structures of drugs and proteins can achieve more innovative drug discovery across drug categories and simultaneously evaluate drug efficacy and safety, rather than traditional one-by-one evaluation. DAPredict contains 305 981 predicted relationships between 1748 approved drugs and 454 ADRs, 83 117 predicted relationships between 1478 approved drugs and 178 Anatomical Therapeutic Chemicals (ATC). More importantly, DAPredict provides an online prediction tool, which researchers can use to predict the action phenotypic spectrum of more than 110 000 000 compounds (including about 168 000 natural products) and corresponding proteins to analyze their potential effect mechanisms. DAPredict can also help researchers obtain the phenotype-corresponding active structures for structural optimization of new drug candidates, making it easier to evaluate the druggability of new drug candidates and develop more innovative drugs across drug categories. Database URL: http://bio-bigdata.hrbmu.edu.cn/DAPredict/.

Framework for metamodel-based design optimization considering product performance and assembly process complexity

This paper proposes a method for simultaneous evaluation of the assembly process complexity together with the performance of the future product. It allows for product design optimization, considering different aspects of the future design at the early stage of the development process. The proposed method, embodied in a fully automated framework, substitutes the traditional sequential development process with a more efficient and rapid combined procedure, which addresses multiple design aspects simultaneously. Design for assembly (DFA) rules, used as quantitative metrics of the ease-of-assembly of the whole product and individual assembly operations, are automatically evaluated together with performance metrics, estimated based on finite element (FE) simulations. The direct solution to this optimization problem might be inefficient or impossible since it requires the recurrent evaluation of computationally expensive discrete and continuous functions with unknown behavior that represent the optimization objectives and constraints. For that reason, the proposed framework employs regression models based on the Gaussian process and artificial neural networks, thus achieving the optimal design of a product as a result of metamodel-based design optimization (MBDO). The suggested approach is demonstrated in the optimization of a gearbox assembly, considering its mechanical performance and assembly process. Comparing the results of the metamodel-based and direct design optimization shows that MBDO allows finding a better solution using a three times smaller computational budget. In addition, analysis of the results obtained using stationary sampling data sets of different sizes highlighted the limitations of the employed sampling procedure.

Towards a sustainability-oriented development of biointelligent products

A sustainable and safe satisfaction of needs can be expected to be achieved through so-called biointelligent systems, resulting from the convergence of life, engineering, and computer sciences. In the context of product design, two fundamental challenges emerge. First, the integration of living elements represents a component that has been largely absent from traditional development processes. Second, a variety of additional challenges arise to ensure contributions to sustainability, resulting from its multidisciplinary nature, e.g. miscommunication, various methodological approaches or conflicts of objectives. Therefore, a product development process that integrates biology, technology and informatics while considering sustainability aspects from the outset is becoming necessary. Given that existing sustainable product development concepts like Design for Sustainability or Ecodesign primarily refer to conventional product development methods, addressing sustainability in the development of biointelligent products poses a novel challenge. This work aims to propose a "Design for Biointelligence" framework by elaborating conceptual thoughts on a sustainability-oriented guideline for the development of biointelligent products. As a result, we state that a biointelligent product integrates biological and technical parts, transmits information through interaction between these parts and promotes sustainability. Through a case study of a biointelligent sensor system, potential challenges associated with developing biointelligent products were identified and conceptual thoughts on a guideline were elaborated.