Current Topics in Medicinal Chemistry - Volume 24, Issue 8, 2024

Peptides acquire target affinity based on the combination of residues in their sequences and the conformation formed by their flexible folding, an ability that makes them very attractive biomaterials in therapeutic, diagnostic, and assay fields. With the development of computer technology, computer-aided design and screening of affinity peptides has become a more efficient and faster method. This review summarizes successful cases of computer-aided design and screening of affinity peptide ligands in recent years and lists the computer programs and online servers used in the process. In particular, the characteristics of different design and screening methods are summarized and categorized to help researchers choose between different methods. In addition, experimentally validated sequences are listed, and their applications are described, providing directions for the future development and application of computational peptide screening and design.

Nanocarriers have emerged as a promising class of nanoscale materials in the fields of drug delivery and biomedical applications. Their unique properties, such as high surface area- tovolume ratios and enhanced permeability and retention effects, enable targeted delivery of therapeutic agents to specific tissues or cells. However, the inherent instability of nanocarriers poses significant challenges to their successful application. This review highlights the importance of nanocarrier stability in biomedical applications and its impact on biocompatibility, targeted drug delivery, long shelf life, drug delivery performance, therapeutic efficacy, reduced side effects, prolonged circulation time, and targeted delivery. Enhancing nanocarrier stability requires careful design, engineering, and optimization of physical and chemical parameters. Various strategies and cutting-edge techniques employed to improve nanocarrier stability are explored, with a focus on their applications in drug delivery. By understanding the advances and challenges in nanocarrier stability, this review aims to contribute to the development and implementation of nanocarrier- based therapies in clinical settings, advancing the field of nanomedicine.

Hepatobiliary system cancers have demonstrated an increasing incidence rate in the past years. Without the presence of early symptoms, the majority of such cancers manifest with a set of similar symptoms, such as cholestasis resulting in posthepatic icterus. Differential diagnosis of hepatobiliary cancers is required for the therapy selection, however, the similarity of the symptoms complicates diagnostics. Thus, the search for molecular markers is of high interest for such patients. Cholangiocarcinoma (CCA) is characterized by a poor prognosis due to a low resectability rate, which occurs because this disease is frequently beyond the limits of surgical therapy at the time of diagnosis. The CCA is diagnosed by the combination of clinical/biochemical features, radiological methods, and non-specific serum tumor biomarkers, although invasive examination is still needed. The main disadvantage is limited specificity and sensitivity, which complicates early diagnostics. Therefore, prognostic and predictive biomarkers are still lacking and urgently needed for early diagnosis. In contrast to serum, bile is more accessible to identify biliary disease due to its simpler composition. Moreover, bile can contain higher concentrations of tumor biomarkers due to its direct contact with the tumor. It is known that the composition of the main bile component - bile acids, may vary during different diseases of the biliary tract. This review summarizes the recent developments in the current research on the diagnostic biomarkers for CCA in serum and bile and provides an overview of the methods of bile acids analysis.

Background: SARS-CoV-2, the unique coronavirus that causes COVID-19, has wreaked damage around the globe, with victims displaying a wide range of difficulties that have encouraged medical professionals to look for innovative technical solutions and therapeutic approaches. Artificial intelligence-based methods have contributed a significant part in tackling complicated issues, and some institutions have been quick to embrace and tailor these solutions in response to the COVID-19 pandemic's obstacles. Here, in this review article, we have covered a few DL techniques for COVID-19 detection and diagnosis, as well as ML techniques for COVID-19 identification, severity classification, vaccine and drug development, mortality rate prediction, contact tracing, risk assessment, and public distancing. This review illustrates the overall impact of AI/ML tools on tackling and managing the outbreak. Purpose: The focus of this research was to undertake a thorough evaluation of the literature on the part of Artificial Intelligence (AI) as a complete and efficient solution in the battle against the COVID-19 epidemic in the domains of detection and diagnostics of disease, mortality prediction and vaccine as well as drug development. Methods: A comprehensive exploration of PubMed, Web of Science, and Science Direct was conducted using PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analysis) regulations to find all possibly suitable papers conducted and made publicly available between December 1, 2019, and August 2023. COVID-19, along with AI-specific words, was used to create the query syntax. Results: During the period covered by the search strategy, 961 articles were published and released online. Out of these, a total of 135 papers were chosen for additional investigation. Mortality rate prediction, early detection and diagnosis, vaccine as well as drug development, and lastly, incorporation of AI for supervising and controlling the COVID-19 pandemic were the four main topics focused entirely on AI applications used to tackle the COVID-19 crisis. Out of 135, 60 research papers focused on the detection and diagnosis of the COVID-19 pandemic. Next, 19 of the 135 studies applied a machine-learning approach for mortality rate prediction. Another 22 research publications emphasized the vaccine as well as drug development. Finally, the remaining studies were concentrated on controlling the COVID-19 pandemic by applying AI AI-based approach to it. Conclusion: We compiled papers from the available COVID-19 literature that used AI-based methodologies to impart insights into various COVID-19 topics in this comprehensive study. Our results suggest crucial characteristics, data types, and COVID-19 tools that can aid in medical and translational research facilitation.