ABSTRACT
Herbal medications have an extensive history of use in treating various diseases, attributed to their perceived efficacy and safety. Traditional medicine practitioners and contemporary healthcare providers have shown particular interest in herbal syrups, especially for respiratory illnesses associated with the SARS-CoV-2 virus. However, the current understanding of the pharmacokinetic and toxicological properties of phytochemicals in these herbal mixtures is limited. This study presents a comprehensive computational analysis utilizing novel approach methodologies (NAMs) to investigate the pharmacokinetic and toxicological profiles of phytochemicals in herbal syrup, leveraging in-silico techniques and prediction tools such as PubChem, SwissADME, and Molsoft's database. Although molecular dynamics, docking, and broader system-wide analyses were not considered, future studies hold potential for further investigation in these areas. By combining drug-likeness with molecular simulation, researchers identify diverse phytochemicals suitable for complex medication development examining their pharmacokinetic-toxicological profiles in phytopharmaceutical syrup. The study focuses on herbal solutions for respiratory infections, with the goal of adding to the pool of all-natural treatments for such ailments. This research has the potential to revolutionize environmental and alternative medicine by leveraging in-silico models and innovative analytical techniques to identify novel phytochemicals with enhanced therapeutic benefits and explore network-based and systems biology approaches for a deeper understanding of their interactions with biological systems. Overall, our study offers valuable insights into the computational analysis of the pharmacokinetic and toxicological profiles of herbal concoction. This paves the way for advancements in environmental and alternative medicine. However, we acknowledge the need for future studies to address the aforementioned topics that were not adequately covered in this research.
ABSTRACT
Recent advances in the orthopedic prostheses design have significantly improved the quality of life for individuals with orthopedic disabilities. However, there are still critical challenges that need to be addressed to further enhance the functionality of orthopedic prostheses improving biocompatibility to promote better integration with natural tissues, enhancing durability to withstand the demands of daily use, and improving sensory feedback for better control of movement are the most pressing issues. To address these challenges, promising emerging solutions such as smart prosthetics, 3D printing, regenerative medicine, and artificial intelligence have been developed. These innovative technologies hold the potential to significantly enhance the functionality of orthopedic prostheses. Realizing the full potential of these next-generation orthopedic prostheses requires addressing several critical factors. These include interdisciplinary collaboration between experts in orthopedics, materials science, biology, and engineering, increased investment in research and development, standardization of components to ensure quality and reliability, and improved access to prosthetics. A comprehensive review of these challenges and considerations for future orthopedic prosthesis design is s provided in this paper addressing the further advances to the field. By addressing these issues, we can continue to improve the lives of individuals with orthopedic disabilities and further enhance the field of orthopedic prosthetics.
ABSTRACT
More information about a person's genetic makeup, drug response, multi-omics response, and genomic response is now available leading to a gradual shift towards personalized treatment. Additionally, the promotion of non-animal testing has fueled the computational toxicogenomics as a pivotal part of the next-gen risk assessment paradigm. Artificial Intelligence (AI) has the potential to provid new ways analyzing the patient data and making predictions about treatment outcomes or toxicity. As personalized medicine and toxicogenomics involve huge data processing, AI can expedite this process by providing powerful data processing, analysis, and interpretation algorithms. AI can process and integrate a multitude of data including genome data, patient records, clinical data and identify patterns to derive predictive models anticipating clinical outcomes and assessing the risk of any personalized medicine approaches. In this article, we have studied the current trends and future perspectives in personalized medicine & toxicology, the role of toxicogenomics in connecting the two fields, and the impact of AI on personalized medicine & toxicology. In this work, we also study the key challenges and limitations in personalized medicine, toxicogenomics, and AI in order to fully realize their potential.
