Articles

The synthesis and optimization of Active Pharmaceutical Ingredients (APIs) is fundamental to pharmaceutical drug development, directly influencing drug efficacy, safety, and cost-effectiveness. Over recent years, significant advancements in synthetic methodologies and manufacturing technologies have transformed API production. This manuscript provides an overview of the latest innovations in API synthesis, focusing on key techniques such as green chemistry, continuous flow chemistry, biocatalysis, and automation. Green chemistry principles, including solvent substitution and catalytic reactions, have enhanced sustainability by reducing waste and energy consumption. Continuous flow chemistry offers improved reaction control, scalability, and safety, while biocatalysis provides an eco-friendly alternative for synthesizing complex and chiral APIs. Additionally, the integration of automation and advanced process control using machine learning and real-time monitoring has optimized production efficiency and consistency. The manuscript also discusses the challenges associated with regulatory compliance and quality assurance, highlighting the role of advanced analytical techniques such as HPLC, NMR, and mass spectrometry in ensuring API purity. Looking ahead, personalized medicine and smart manufacturing technologies, including blockchain for traceability, are expected to drive further innovation in API production. This review concludes by emphasizing the need for continued advancements in sustainability, efficiency, and scalability to meet the evolving demands of the pharmaceutical industry, ultimately enabling the development of safer, more effective, and environmentally responsible medicines.

Over the past decades, quality assurance has received significant prominence in higher education management across the world. While the concept is pertinent to all areas of higher education management, nowhere is it considered more crucial than in curriculum development, given the importance of curriculum in supporting students to achieve the needed learning outcomes. In this study, we explored how quality is ensured in curriculum development in Ghana, using a STEM university, University of Mines and Technology (UMaT), as a case study. We specifically examined the procedure for curriculum development in the university, how quality assurance is ensured during the process, and the challenges associated with the process. We explore the case using qualitative techniques, particularly in-depth interviews. Fourteen (14) participants were purposively sampled from four (4) functional levels responsible for curriculum development in the university. The study found that the quality of curriculum in UMaT is largely determined by both national and institutional quality assurance frameworks. The major challenges that hamper quality assurance are the need to design curriculum at a shorter notice to fulfil accreditation requirement, lack of experts to support curriculum development, and less consultation with other relevant stakeholders as required by the regulator, Ghana Tertiary Education Commission (GTEC).

This essay explores how AI can enhance clinical research and, particularly, its pivotal role in the development of medical devices. A data-driven approach to medical device development that can streamline regulatory compliance and quality assurance is discussed. Methods that generate insights from pre-stage data and utilize it during development are detailed. The effectiveness of this approach in compliance audits, 510(k) submissions, and quality system audits - reducing time, effort, and risks is analyzed. The findings are illustrated with practical examples and takeaway recommendations. When reading a scientific article, how many times have you judged the quality of the research by looking at the methodology section? Artificial intelligence algorithms can be developed with the most robust and innovative technology, but if they are not properly validated, they will be worthless in the eyes of regulatory authorities. Conversely, outdated and simplistic models can still gain regulatory clearance if robustness is effectively demonstrated. For better or worse, ethics, economics, and robustness are often sacrificed in the constant government struggle to keep up with the technological edge of AI development. The slow crawl of lawmakers is constant in every field. Automating small tasks can save time and reduce risks when playing catch-up with a changing regulatory framework so the rest of the AI development can continue uninhibitedly. This dives into using FDA open data to collaborate with a food and drug law company and develop several bottom-up initiatives that supply knowledge needed for regulatory compliance and quality systems development. Methods that input pre-stage data and output actionable insights as models are provided. By sharing these resources and advice as academic researchers, efficiency in streamlining processes is maximized, thereby letting more time and resources be allocated to the actual development [1].

Automated testing and software quality assurance (SQA) practices are essential for ensuring the reliability, scalability, and maintainability of modern software systems. This paper presents a review of widely used automated testing frameworks, including Driven, Data-Driven, Behavior-Driven Development (BDD), and Record/Playback approaches, outlining their methodologies, benefits, and limitations in different development contexts. In parallel, it examines established SQA techniques such as Test-Driven Development, static analysis, and white-box testing, which provide systematic methods for defect detection and quality improvement. The study further examines the role of practical tools, such as Selenium, TestNG, and JUnit, in supporting test automation and validation activities. In addition to highlighting technical capabilities, the paper identifies common challenges faced in automation, including incomplete requirements, integration complexities, and maintaining evolving test suites. Recommended best practices are provided to address these issues, offering guidance for organizations seeking to strengthen their software testing processes through structured frameworks, adaptive techniques, and reliable automation tools.

The automation of IT Service Management (ITSM) workflows using explicit rules and data has been established for years. Domain-specific rule engines interpret rules written in declarative rule modelling languages and generate forwarding arrows to process event streams and support decision making. Such automation is augmented by rule-driven Quality Assurance for correctness, safety, and risk management. The service desk is the onshore base of an ITSM supply chain. An end-to-end incident response service resolves incidents using only onshore resources and employs back office teams to help with unresolvable incidents. The forward factories of rule-based automation for ticket processing service are identified. Several rule-based workflows in incident and change management have been published. Further glimpses of the future across all ITSM workflows are provided based on training in an online ITSM service with automated operations. Rule engines are specialised components that direct the processing of data flows according to pre-defined rules. Decision factories complement the more common event-driven rule engines. While event processing occurs below the polling frequency of the source, rules in decision factories are triggered based on the arrival of data. These factories are applied in ITSM for risk and safety evaluation and quality assurance. Rule-enriched architectures incorporate domain-specific modelling languages to ensure correctness with respect to qualitative quality attributes. Dedicated factories provide resilience, detect slack or over-utilisation, and offer point-in-time assurance and testing.