Current Computer Science - Current Issue

The application of machine learning (ML) algorithms is causing a revolutionary change in the pharmaceutical production sector, providing previously unheard-of chances to improve productivity, precision, and creativity. From supply chain management and predictive maintenance to process optimization and quality assurance, machine learning approaches are transforming many aspects of drug manufacturing. These algorithms greatly lower production costs and minimize errors by utilizing large datasets to provide real-time analysis, anomaly detection, and predictive insights. The potential of these technologies to simplify intricate industrial processes is further enhanced by emerging trends like automation, sophisticated process control systems, and the integration of machine learning with digital twins. With an emphasis on important applications such as formulation optimization, adaptive process controls, and predictive modeling for regulatory compliance, this paper examines the changing field of machine learning in pharmaceutical production. It emphasizes how ML and Industry 4.0 technologies-like robotics and the Internet of Things (IoT)-work together to create smart industrial environments. The essay also discusses issues like algorithm openness, data privacy, and the requirement for a trained staff in order to fully utilize machine learning in this field. A key tool in addressing the rising demand for complicated biologics and tailored medications, machine learning (ML), is emerging as the pharmaceutical industry shifts to more flexible, economical, and sustainable production models. This review highlights the revolutionary impact of machine learning (ML)-driven production and offers a roadmap for its successful adoption in the pharmaceutical industry by analyzing its present status and potential future developments.

Pharmacovigilance (PV) is a data-driven method that quickly identifies medication safety risks by processing reports of suspected Adverse Events (AEs) and extracting health data. The first steps in the PV case processing cycle include data collection, data entry, coding, preliminary validity and completeness checks, and medical evaluation for severity, seriousness, expectation, and causality. Afterward, a report is submitted, quality is checked, and data storage and maintenance are performed. This process is costly and time-consuming, as it requires both a workforce and technology. Conversely, artificial intelligence (AI) is used to reduce this time investment and increase data accuracy. AI includes machine learning methods like deep learning and natural language processing, which can recognize and retrieve information on adverse drug occurrences. By doing so, it is possible to optimize the pharmacovigilance process and improve the tracking of documented adverse medication occurrences. AI's advancement in pharmacovigilance raises concerns about potential changes in drug safety professionals' roles, prompting curiosity about their future in an AI-assisted workplace. Artificial Intelligence (AI) should augment human intelligence, not replace human specialists. It's crucial to highlight and ensure AI improves PV more than it causes problems. The pharmaceutical business faces significant obstacles and opportunities, especially when it comes to implementing and employing advanced Information Technology (IT) in Pharmaceutical Monitoring Systems (PMS). Automation improves PV in several ways (e.g., boosting data quality or improving consistency). Several themes are discussed, outlining the challenges encountered, exploring potential solutions, and emphasizing the need for further research. The accepted use case involves automating the workflow in the case of ICRS.