Traditional Development Process Research Articles

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.

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.

Semantic-enhanced Graph Contrastive Learning with Adaptive Denoising for Drug Repositioning.

The traditional drug development process requires a significant investment in workforce and financial resources. Drug repositioning as an efficient alternative has attracted much attention during the last few years. Despite the wide application and success of the method, there are still many shortcomings in the existing model. For example, sparse datasets will seriously affect the existing methods' performance. Additionally, these methods do not pay attention to the noise in datasets. In response to the above defects, we propose a semantic-enriched augmented graph contrastive learning with an adaptive denoising method, called SGCD. This method enhances data from the perspective of the embedding layer, deeply mines potential neighborhood relation-ships in semantic space, and combines similar drugs in the semantic neighborhoods into prototype comparison targets, thus effectively mitigating the impact of data sparsity on the model. Moreover, to enhance the model's robustness to noisy data, we use the adaptive denoising method, which can effectively identify noisy data in the training process. Exhaustive experiments on multiple real datasets show the effectiveness of the proposed model. The code implementation is available at https://github.com/yuhuimin11/SGCD-master.

Gene expression profiles of precursor cells identify compounds that reduce NRP1 surface expression in macrophages: Implication for drug repositioning for COVID-19.

Coronavirus disease 2019 (COVID-19) is transitioning from a pandemic to an endemic phase through recurring mutations. Initial efforts focused on developing strategies to mitigate infection of lung epithelial cells which are the primary targets of the SARS-CoV-2 virus using the affinity of the spike protein to human ACE2 receptor. SARS-CoV-2, however, infects additional cell types present in the lung such as macrophages through the alternate entry receptor Neuropilin 1 (NRP1). Developing novel therapeutic strategies to prevent SARS-CoV-2 infection of cells crucial for immunosurveillance could thus be integral to treat post-acute sequelae of COVID-19 (PASC). Since traditional drug development process takes a long time, it is imperative to establish new strategies that can be rapidly deployed to combat the dynamic nature of COVID-19 evolution and to contribute to prevention of future pandemics. We obtained the gene expression profiles of THP-1 monocytes from L1000-based Connectivity Map using CLUE, cloud- based software platform for the analysis of perturbational datasets to identify compounds that could reduce the expression level of NRP1. Out of 33,590 compounds, we analyzed the profiles of 45 compounds for their ability to reduce NRP1 expression. We selected the top five small molecule inhibitors predicted to decrease the expression of NRP1 for validation studies. All five selected compounds showed low cytotoxicity at tested doses and their ability to reduce NRP1 surface expression was evaluated in THP-1 monocytes, THP-1-derived macrophage like cells and human peripheral blood mononuclear cell (PBMC)-derived primary macrophages. Five compounds with the largest predicted reduction of NRP1 expression decreased macrophage NRP1 surface expression measured using flow cytometry and fluorescent microscopy assays in both cell line and primary macrophages. Using our computational approach, we identified 45 compounds that could potentially decrease NRP1 surface expression in macrophages based on their effect on THP-1 monocytes. Validation studies showed that such an approach can help to identify compounds for drug repositioning in target cells that are absent in the L1000 database. Our proposed approach can be applicable for the rapid compound exploration to combat novel cell types that SARS-CoV-2 targets for infection and could provide molecular bases for the development of new drugs.

Drug Repositioning and Response Detection Based on Randomized Control Trials for Cancer Treatment - A Review

Drug repositioning is vital in cancer treatment, offering a swift alternative to identify existing drugs repurposable for cancer treatment, bypassing the lengthy and costly traditional drug development process. This approach not only saves resources for the pharmaceutical sector and healthcare systems but also accelerates the discovery of new drugs. Overcoming challenges like data integration and patient classification is crucial in drug repositioning, where methodological advancements utilizing randomized control trials (RCTs) become essential. RCTs provide a systematic way to assess medication efficacy in diverse cancer subpopulations, enhancing the credibility of drug repositioning outcomes. The current study integrates RCTs with advanced data analytics and machine learning to establish a Bayesian Network response detection based on randomized control (BNRD-RC). This approach allows researchers to identify promising drug candidates, predict patient responses, and optimize treatment plans by analyzing diverse datasets, including genomes, proteomics, and clinical records. Beyond personalized treatment, drug repositioning explores medication synergy and combination therapy for rare cancer types. Simulation analysis significantly aids in validating the efficacy and safety of repositioned drugs. Through simulations of clinical scenarios and treatment outcomes, researchers can assess the impact of drug repositioning on patient survival, quality of life, and healthcare costs.