In recent years, many attempts have been made to enhance the drug bioavailability and therapeutic effectiveness of oral dosage forms. In this context, various gastroretentive drug delivery systems (GRDDS) have been used to improve the therapeutic efficacy of drugs that have a narrow absorption window, are unstable at alkaline pH, are soluble in acidic conditions, and are active locally in the stomach. In this review, we discuss the physiological state of the stomach and various factors that affect GRDDS. Recently applied gastrointestinal technologies such as expandable, superporous hydrogel; bio/mucoadhesive, magnetic, ion-exchange resin; and low- and high-density-systems have also been examined along with their merits and demerits. The significance of in vitro and in vivo evaluation parameters of various GRDDS is summarized along with their applications. Moreover, future perspectives on this technology are discussed to minimize the gastric emptying rate in both the fasted and fed states. Overall, this review may inform and guide formulation scientists in designing the GRDDS.

To enhance the efficiency of vaccine manufacturing, this study focuses on optimizing the microfluidic conditions and lipid mix ratios of messenger RNA - lipid nanoparticles (mRNA-LNP). Different mRNA - LNP formulations (n = 24) were developed using an I - optimal design, where machine learning tools (XGBoost/Bayesian optimization and self - validated ensemble (SVEM)) were used to optimize the process and predict lipid mix ratio. The investigation included material attributes, their respective ratios, and process attributes. The critical responses like particle size (PS), polydispersity index (PDI), Zeta potential, pKa, heat trend cycle, encapsulation efficiency (EE), recovery ratio, and encapsulated mRNA were evaluated. Overall prediction of SVEM (> 97%) was comparably better than that of XGBoost/Bayesian optimization (> 94%). Moreover, in actual experimental outcomes, SVEM prediction is close to the actual data as confirmed by the experimental PS (94∼96 nm) is close to the predicted one (95∼97 nm). The other parameters including PDI and EE were also close to the actual experimental data.

The convergence of artificial intelligence (AI) and nanomedicine has transformed cancer vaccine development, particularly in optimizing RNA-loaded lipid nanoparticles (LNPs). Stability and targeted delivery are major obstacles to the clinical translation of promising RNA-LNP vaccines for cancer immunotherapy. This systematic review analyzes the AI’s impact on LNP engineering through machine learning-driven predictive models, generative adversarial networks (GANs) for novel lipid design, and neural network-enhanced biodistribution prediction. AI reduces the therapeutic development timeline through accelerated virtual screening of millions of lipid combinations, compared to conventional high-throughput screening. Furthermore, AI-optimized LNPs demonstrate improved tumor targeting. GAN-generated lipids show structural novelty while maintaining higher encapsulation efficiency; graph neural networks predict RNA-LNP binding affinity with high accuracy vs. experimental data; digital twins reduce lyophilization optimization from years to months; and federated learning models enable multi-institutional data sharing. We propose a framework to address key technical challenges: training data quality (min. 15,000 lipid structures), model interpretability (SHAP > 0.65), and regulatory compliance (21CFR Part 11). AI integration reduces manufacturing costs and makes personalized cancer vaccine affordable. Future directions need to prioritize quantum machine learning for stability prediction and edge computing for real-time formulation modifications.

Digital Twins (DTs) represent a groundbreaking development tool in the pharmaceutical and biopharmaceutical industries, providing virtual representations of physical entities, processes, or systems. This review investigates the transformative roles of DTs by examining their applications throughout the entire drug development lifecycle, from discovery to continuous manufacturing. By facilitating real-time monitoring and predictive analytics, DTs enhance operational efficiency, reduce costs, and improve product quality. Integration with advanced technologies, such as artificial intelligence and machine learning, further amplifies their capabilities, enabling sophisticated data analysis for preventive maintenance and manufacturing optimization. Despite these advantages, the implementation of DTs faces significant challenges, including data integration, model accuracy, and regulatory complexity. This review discusses these barriers while highlighting opportunities for innovation and automation through emerging technologies, including blockchain, nanotechnology, and dark factory. It also explores the potential of DTs to support personalized medicine through individualized treatments based on patient-specific data. Overall, this review highlights the current state, key challenges, and future perspectives of DT applications in pharmaceutical systems, emphasizing their potential to improve efficiency, quality, and patient outcomes.