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Background: The integration of artificial intelligence (AI) with the internet of things (IoTs) represents a significant advancement in pharmaceutical manufacturing and effectively bridges the gap between digital and physical worlds. With AI algorithms integrated into IoTs sensors, there is an improvement in the production process and quality contro...
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... Moreover, by evaluating parameters such as glycosylation patterns, these computational tools support robust process validation and agility in production environments, ensuring that product quality consistently meets regulatory standards [111,112]. AI facilitates real-time process monitoring and control in biosimilar production by integrating advanced analytics with PAT systems to analyze bioprocess data streams, improving adaptive feedback mechanisms that minimize variability and optimize yields [2,113]. Although still in the early stages, the convergence of these advanced techniques promises to optimize process variables, thereby reducing deviations and accelerating scale-up while advancing quality control measures in biosimilar development [37,114]. ...
The formulation of biosimilar products critically determines their stability, safety, immunogenicity, and market accessibility. This article presents a novel integrative framework for biosimilar formulation that balances scientific, regulatory, and intellectual property dimensions, offering a holistic perspective rarely unified in the literature. It highlights the growing trend toward buffer-free, high-concentration systems that leverage protein self-buffering to improve patient comfort and formulation stability. The article also addresses regulatory flexibility from the FDA and EMA, which allows scientifically justified deviations from reference formulations to ensure pharmaceutical equivalence and minimize immunogenicity. A novelty of this article is its comprehensive analysis of how digital innovations, such as Quality-by-Design, Process-Analytical-Technology, and AI-based in silico simulations, are transforming formulation design and bioprocess optimization to reduce immunogenic risks and enhance bioequivalence. Two important key takeaways emerge: (1) strategic innovation in formulation, especially using buffer-free and high concentration systems, improve product stability and patient tolerability while complying with regulatory standards; and (2) intellectual property challenges, including patent thickets, strongly influence formulation decisions, making early legal-strategic alignment essential for market entry. The article confirms that practical recommendations for the selection of recombinant therapeutic protein formulations can effectively guide developers and regulators toward safer, more efficient, and commercially viable biosimi-lar products.
... However, in pharmaceutical manufacturing, the AI-IoT systems have led to much promise of bridging the digital physical divide by real time quality monitoring and predictiveness. Automation and predictive maintenance of manufacturing devices are supported by these systems which help increase system efficiency as well as compliance with quality assurance protocols [6]. However, case studies indicate that integration of AI/ML with IoT sensors can lead to a massive reduction of downtime and improve optimization of operational parameters, and quality of products in pharmaceutical production lines. ...
In this paper, we study the application of AI/ML based predictive maintenance in IoT devices in healthcare sector in regulated environments. They discuss the operational advantages, the impact to model effectiveness, and regulatory issues using both literature and data analysis. Results show that AI-based systems can make the substantial reduction of downtime and cost while keeping compliance and safety of patients, and can have its scalability potential in modern healthcare systems.
... Innovative IoT-enabled systems improve drug management and patient safety by enhancing tracking and monitoring capabilities [4]. The convergence of IoT with artificial intelligence (AI) fosters smarter manufacturing processes that optimize product quality and reduce waste [5]. ...
... Data analysis is key to proactive maintenance and operational efficiency. By using advanced analytics tools, pharmaceutical companies can anticipate equipment failures, optimize maintenance schedules, and reduce downtime [5]. ...
... These sensors provide precise control over production parameters, leading to improved efficiency and reduced waste. The ability to maintain consistency in processes like tablet compression and capsule filling enhances product reliability and consumer satisfaction [5]. improved delivery times by 25%. ...
old-chain IoT sensors have been shown to cut vaccine spoilage by 30% in real-world trials. This review examines how these principles apply to predictive maintenance in tablet production, enabling faster drug discovery, more targeted disease treatment, and enhanced manufacturing efficiency. By automating temperature logs via Wi-Fi, each Pfizer site saved 5 labor hours daily. The integration of IoT and AI enhances real-time monitoring, supply chain management, and product quality, contributing to increased operational productivity and flexibility. However, challenges remain, including high installation costs, the need for specialized expertise and training, and strict regulatory requirements for quality, safety, and efficacy. Overcoming these barriers is essential for pharmaceutical companies to remain competitive, ensure patient safety, and achieve sustainable growth in a rapidly evolving healthcare landscape
Artificial intelligence and machine learning integration in biomedical research has tremendously benefitted precision medicine, disease diagnosis, and drug discovery. On the basis of these four advanced algorithms, this study investigates how AI-driven methodologies can be used for analysis in medical imaging, processing of genomic data and the prediction of drug response. Results from the experimental results show that traditional methods fail with a diagnostic accuracy of 82.7 % while Deep Learning-based medical imaging models attain a diagnostic accuracy of 97.3% outperforming the traditional methods by 15%. AI based genomic data mining had helped improve the mutation detection rate by 18%, which improved precision medicine approaches. Predictive models in cancer immunotherapy also increased treatment success rates by 22% in AI’s study. In addition, applying reinforcement learning in drug discovery led to compound screening efficiency of 40% improvement and reduced total drug development time. This underscores AI’s ability to increase diagnostic precision, improve treatment strategies and improve biomedical research efficiency. Meanwhile, much more attention will be needed for challenges so as cloud providers will need to meet requirements for data privacy, model interpretability as well as regulatory compliance. The future research should pursue the enhancement of AI explainability, the integration of multi-modal biomedical data, and the improvement of AI driven personalized treatment recommendations. Therefore, this study can contribute to the advancement of AI driven healthcare innovations and help create more accurate and accessible and personalized medical solutions.
