Measuring drug similarity using drug–drug interactions

Combination therapy is a promising approach to address the challenge of antimicrobial resistance,and computational models have been proposed for predicting drug–drug interactions.Most existing models rely on drug similarity measures based on characteristics such as chemical structure and the mechanism of action.In this study,we focus on the network structure itself and propose a drug similarity measure based on drug–drug interaction networks.We explore the potential applications of this measure by combining it with unsupervised learning and semi-supervised learning approaches.In unsupervised learning,drugs can be grouped based on their interactions,leading to almost monochromatic group–group interactions.In addition,drugs within the same group tend to have similar mechanisms of action(MoA).In semi-supervised learning,the similarity measure can be utilized to construct affinity matrices,enabling the prediction of unknown drug–drug interactions.Our method exceeds existing approaches in terms of performance.Overall,our experiments demonstrate the effectiveness and practicability of the proposed similarity measure.On the one hand,when combined with clustering algorithms,it can be used for functional annotation of compounds with unknown MoA.On the other hand,when combined with semi-supervised graph learning,it enables the prediction of unknown drug–drug interactions.

Analysis of interactions of immune checkpoint inhibitors with antibiotics in cancer therapy

The discovery of immune checkpoint inhibitors,such as PD-1/PD-L1 and CTLA-4,has played an important role in the development of cancer immunotherapy.However,immune-related adverse events often occur because of the enhanced immune response enabled by these agents.Antibiotics are widely applied in clinical treatment,and they are inevitably used in combination with immune checkpoint inhibitors.Clinical practice has revealed that antibiotics can weaken the therapeutic response to immune checkpoint inhibitors.Studies have shown that the gut microbiota is essential for the interaction between immune checkpoint inhibitors and antibiotics,although the exact mechanisms remain unclear.This review focuses on the interactions between immune checkpoint inhibitors and antibiotics,with an in-depth discussion about the mechanisms and therapeutic potential of modulating gut microbiota,as well as other new combination strategies.

Comparison of the inhibition potentials of icotinib and erlotinib against human UDP-glucuronosyltransferase 1A1

UDP-glucuronosyltransferase 1A1(UGT1A1) plays a key role in detoxification of many potentially harmful compounds and drugs. UGT1A1 inhibition may bring risks of drug–drug interactions(DDIs), hyperbilirubinemia and drug-induced liver injury. This study aimed to investigate and compare the inhibitory effects of icotinib and erlotinib against UGT1A1, as well as to evaluate their potential DDI risks via UGT1A1 inhibition. The results demonstrated that both icotinib and erlotinib are UGT1A1 inhibitors, but the inhibitory effect of icotinib on UGT1A1 is weaker than that of erlotinib. The IC_(50) values of icotinib and erlotinib against UGT1A1-mediated NCHN-O-glucuronidation in human liver microsomes(HLMs) were 5.15 and 0.68 μmol/L, respectively. Inhibition kinetic analyses demonstrated that both icotinib and erlotinib were non-competitive inhibitors against UGT1A1-mediated glucuronidation of NCHN in HLMs, with the Kivalues of 8.55 and 1.23 μmol/L, respectively. Furthermore, their potential DDI risks via UGT1A1 inhibition were quantitatively predicted by the ratio of the areas under the concentration–time curve(AUC) of NCHN. These findings are helpful for the medicinal chemists todesign and develop next generation tyrosine kinase inhibitors with improved safety, as well as to guide reasonable applications of icotinib and erlotinib in clinic, especially for avoiding their potential DDI risks via UGT1A1 inhibition.

Opportunities and challenges of incretin-based hypoglycemic agents treating type 2 diabetes mellitus from the perspective of physiological disposition

The treatment of patients with diabetes mellitus, which is characterized by defective insulin secretion and/or the inability of tissues to respond to insulin, has been studied for decades. Many studies have focused on the use of incretin-based hypoglycemic agents in treating type 2 diabetes mellitus(T2DM). These drugs are classified as GLP-1 receptor agonists, which mimic the function of GLP-1,and DPP-4 inhibitors, which avoid GLP-1 degradation. Many incretin-based hypoglycemic agents have been approved and are widely used, and their physiological disposition and structural characteristics are crucial in the discovery of more effective drugs and provide guidance for clinical treatment of T2DM.Here, we summarize the functional mechanisms and other information of the drugs that are currently approved or under research for T2DM treatment. In addition, their physiological disposition, including metabolism, excretion, and potential drug drug interactions, is thoroughly reviewed. We also discuss similarities and differences in metabolism and excretion between GLP-1 receptor agonists and DPP-4 inhibitors. This review may facilitate clinical decision making based on patients' physical conditions and the avoidance of drug drug interactions. Moreover, the identification and development of novel drugs with appropriate physiological dispositions might be inspired.