AI-Driven Enhancements in Drug Screening and Optimization

AI-Based solutions for current challenges in regenerative medicine

The emergence of Artificial Intelligence (AI) and its usage in regenerative medicine represents a significant opportunity that holds the promise of tackling critical challenges and improving therapeutic outcomes. This article examines the ways in which AI, including machine learning and data fusion techniques, can contribute to regenerative medicine, particularly in gene therapy, stem cell therapy, and tissue engineering. In gene therapy, AI tools can boost the accuracy and safety of treatments by analyzing extensive genomic datasets to target and modify genetic material in a precise manner. In cell therapy, AI improves the characterization and optimization of cell products like mesenchymal stem cells (MSCs) by predicting their function and potency. Additionally, AI enhances advanced microscopy techniques, enabling accurate, non-invasive and quantitative analyses of live cell cultures. AI enhances tissue engineering by optimizing biomaterial and scaffold designs, predicting interactions with tissues, and streamlining development. This leads to faster and more cost-effective innovations by decreasing trial and error. The convergence of AI and regenerative medicine holds great transformative potential, promising effective treatments and innovative therapeutic strategies. This review highlights the importance of interdisciplinary collaboration and the continued integration of AI-based technologies, such as data fusion methods, to overcome current challenges and advance regenerative medicine.

Animals in Respiratory Research

The respiratory barrier, a thin epithelial barrier that separates the interior of the human body from the environment, is easily damaged by toxicants, and chronic respiratory diseases are common. It also allows the permeation of drugs for topical treatment. Animal experimentation is used to train medical technicians, evaluate toxicants, and develop inhaled formulations. Species differences in the architecture of the respiratory tract explain why some species are better at predicting human toxicity than others. Some species are useful as disease models. This review describes the anatomical differences between the human and mammalian lungs and lists the characteristics of currently used mammalian models for the most relevant chronic respiratory diseases (asthma, chronic obstructive pulmonary disease, cystic fibrosis, pulmonary hypertension, pulmonary fibrosis, and tuberculosis). The generation of animal models is not easy because they do not develop these diseases spontaneously. Mouse models are common, but other species are more appropriate for some diseases. Zebrafish and fruit flies can help study immunological aspects. It is expected that combinations of in silico, in vitro, and in vivo (mammalian and invertebrate) models will be used in the future for drug development.

AI-driven GPCR analysis, engineering, and targeting

This article investigates the role of recent advances in Artificial Intelligence (AI) to revolutionise the study of G protein-coupled receptors (GPCRs). AI has been applied to many areas of GPCR research, including the application of machine learning (ML) in GPCR classification, prediction of GPCR activation levels, modelling GPCR 3D structures and interactions, understanding G-protein selectivity, aiding elucidation of GPCRs structures, and drug design. Despite progress, challenges in predicting GPCR structures and addressing the complex nature of GPCRs remain, providing avenues for future research and development.