Avacopan (Tavneos®) is approved as an oral adjunctive treatment at a dose of 30 mg twice daily with food for adult patients with severe active granulomatosis with polyangiitis (GPA) and microscopic polyangiitis (MPA) in combination with standard therapy including glucocorticoids. In this pharmacokinetic (PK) study, the absorption, metabolism, and excretion of avacopan were evaluated following a single 100 mg/400 μCi oral 14C-avacopan dose solution in six healthy male participants. The mass balance recovery, plasma concentrations, and metabolite profile in plasma, urine, and feces were determined. Fecal and renal excretion accounted for 77.2% and 9.5%, respectively, of the total administered radioactivity, with none of the mono- or bis-oxidation metabolites present at greater than 7% of the total radioactive dose. In urine, intact avacopan was present at <1% of the radioactive dose. In feces, intact avacopan was present at 8.7%, which represented 6.7% of the total radioactive dose, suggesting at least 93.3% of the radioactive dose was absorbed. The predominant component in plasma was avacopan, which accounted for 18.0% of the dose. The major circulating metabolite, M1, a monohydroxylation metabolite with similar potency in C5a receptor inhibition as avacopan, accounted for 11.9% of the total radioactivity. The primary route of elimination of avacopan is phase I metabolism, followed by biliary excretion of the metabolites. CYP3A4 is the primary isozyme involved in the in vitro metabolism of avacopan and formation of metabolite M1. Study results provide a definitive assessment of the absorption, elimination, and nature of metabolism of avacopan in humans.

Waiganfengsha Granule, an over-the-counter drug, is commonly used for treating windheat cold and sore throat in clinical settings. However, its material basis of medicinal efficacy is still unclear. In this study, an efficient integrated analytical strategy was established for its chemical and metabolite profiles study. Firstly, to avoid the possible false-positive results of structural elucidation, an in-house component library that contains chemical constituents reported in the literature from the six individual medicines of Waiganfengsha Granule was established. Secondary, mass data post-processing techniques, including precursor ion list and neutral loss filtering, were applied to enhance the identification accuracy. Thirdly, for the rapid characterization of those absorbed components after oral administration in rats, the identified chemical constituents were used as candidate components for the serum analysis. By comparing the retention time and analyzing mass data, the metabolites in rat plasma were identified. As a result, 57 chemical ingredients were identified, including 21 phenolic acids, 9 alkaloids, 2 flavonoids, 5 lignins, 13 saponins, and 7 other compounds. Among these, 12 compounds were unambiguously identified by comparison with reference standards, and 45 were tentatively characterized by analyzing their accurate MS data, MS/MS fragmentation patterns, and also by comparison with those data reported in the literature. Additionally, 46 metabolites were detected and identified in rat plasma. This study is beneficial for understanding the chemical composition and metabolic profiles of Waiganfengsha Granule, and the results obtained might provide a solid basis for further studies on its functional mechanism.

One of the biggest obstacles to the treatment of diseases, particularly serious conditions like cancer, is therapeutic resistance. The process of drug resistance is influenced by a number of important variables, including MDR genes, drug efflux, low-quality medications, inadequate dosage, etc. Drug resistance must be addressed, and new combinations based on the pharmacokinetics/pharmacodynamics (PK-PD) characteristics of the partner pharmaceuticals must be developed in order to extend the half-lives of already available medications. The primary mechanism of drug elimination is hepatic biotransformation of medicines by cytochrome P450 (CYP) enzymes; of these CYPs, CYP3A4 makes up 30-40% of all known cytochromes that metabolize medications. Induction or inhibition of CYP3A4-mediated metabolism affects the pharmacokinetics of most anticancer drugs, but these details are not fully understood and highlighted because of the complexity of tumor microenvironments and various influencing patient related factors. The involvement of CYPs, particularly CYP3A4 and other drug-metabolizing enzymes, in cancer medication resistance will be covered in the current review.

Drug-Protein Interaction (DPI) identification is crucial in drug discovery. The high dimensionality of drug and protein features poses challenges for accurate interaction prediction, necessitating the use of computational techniques. Docking-based methods rely on 3D structures, while ligand-based methods have limitations such as reliance on known ligands and neglecting protein structure. Therefore, the preferred approach is the chemogenomics-based approach using machine learning, which considers both drug and protein characteristics for DPI prediction. In machine learning, feature selection plays a vital role in improving model performance, reducing overfitting, enhancing interpretability, and making the learning process more efficient. It helps extract meaningful patterns from drug and protein data while eliminating irrelevant or redundant information, resulting in more effective machine-learning models. On the other hand, classification is of great importance as it enables pattern recognition, decision-making, predictive modeling, anomaly detection, data exploration, and automation. It empowers machines to make accurate predictions and facilitates efficient decision-making in DPI prediction. For this research work, protein data was sourced from the KEGG database, while drug data was obtained from the DrugBank data machine-learning base. To address the issue of imbalanced Drug Protein Pairs (DPP), different balancing techniques like Random Over Sampling (ROS), Synthetic Minority Over-sampling Technique (SMOTE), and Adaptive SMOTE were employed. Given the large number of features associated with drugs and proteins, feature selection becomes necessary. Various feature selection methods were evaluated: Correlation, Information Gain (IG), Chi-Square (CS), and Relief. Multiple classification methods, including Support Vector Machines (SVM), Random Forest (RF), Adaboost, and Logistic Regression (LR), were used to predict DPI. Finally, this research identifies the best balancing, feature selection, and classification methods for accurate DPI prediction. This comprehensive approach aims to overcome the limitations of existing methods and provide more reliable and efficient predictions in drug-protein interaction studies.

Crocin is one of the main components of Crocus sativus L. and can alleviate oxidative stress and inflammation in diabetic nephropathy (DN). However, the specific mechanism by which crocin treats DN still needs to be further elucidated. In the present study, a mouse model of DN was first established to investigate the therapeutic effect of crocin on DN mice. Subsequently, non-targeted metabolomics techniques were used to analyze the mechanisms of action of crocin in the treatment of DN. The effects of crocin on CYP4A11/PPARγ and TGF-β/Smad pathway were also investigated. Results showed that crocin exhibited significant therapeutic and anti-inflammatory, and anti-oxidative effects on DN mice. In addition, the non-targeted metabolomics results indicated that crocin treatment affected several metabolites in kidney. These metabolites were mainly associated with biotin metabolism, riboflavin metabolism, and arachidonic acid metabolism. Furthermore, crocin treatment upregulated the decreased levels of CYP4A11 and phosphorylated PPARγ, and reduced the increased levels of TGF-β1 and phosphorylated Smad2/3 in the kidneys of DN mice. In conclusion, our study validated the considerable therapeutic, anti-inflammatory, and antioxidative impacts of crocin on DN mice. The mechanism of crocin treatment may be related to the regulation of biotin riboflavin and arachidonic acid metabolism, the activation of CYP4A11/PPARγ pathway, and the inhibition of TGF-β/Smad pathway in the kidney.

Osthole (OST) is a bioactive natural coumarin derived from the plant Cnidium monnieri (L.) Cusson fruit (She Chuang Zi), which has various pharmacological and biological activities. OST contains an α,β- unsaturated lactone, which is an electrophilic group that tends to be metabolized into reactive metabolites (RMs). Then, RMs are able to covalently modify nucleophilic amino acid (AA) residues of target proteins. However, few researchers considered the contribution of the covalent modification induced by OST or its metabolites. This study aims to investigate the metabolic profile and the metabolites-protein modification of OST. The metabolites of OST were qualitatively identified using UHPLC-Q-TOF-MS. The RMs modification patterns and potentially modified AA residues were confirmed by UHPLC-Q-TOF-MS using rat liver microsomes (RLMs) and model AAs. Finally, the modified peptides derived from high-abundance microsomal peptides were separated via nano-LC-Orbitrap-MS, and then RM-modified proteins were identified using a proteome discoverer. In the presence of RLMs, OST could rapidly be metabolized within 1 h and hardly identified at 4 h. We detected 10 OST metabolites, 13 OST metabolites-NAC (N-acetyl cysteine) adducts, 3 NAL (N-acetyl lysine) adducts, and 11 GSH (glutathione) adducts. Furthermore, 16 RM-modified protein targets were identified, many of which are included in the essential biological processes of OST's anti-Alzheimer's disease (AD) and anti-tumor. This study provides a novel perspective on the molecular mechanism of OST's pharmacological activities, as well as identifies potential targets for further development and application of OST and other Natural products (NPs).

Chebulinic acid (CA) is an active constituent of Terminalia chebula fruits with therapeutic potential against multiple metabolic diseases, including dementia, benign prostate hyperplasia, and osteoporosis. The present work intends to explore the preclinical pharmacokinetics, including the absolute bioavailability of CA and its influence on the gene expression of cytochrome P450 enzymes in the liver. Quantifying CA and probe drugs in vitro samples and preclinical serum samples of male SD rats were performed using LC-MS/MS. The influence of CA on the hepatic CYPs and their gene expression was analyzed in rat liver by quantitative real-time polymerase chain reaction. The plasma protein binding was found to be 84.81 ± 7.70 and 96.34 ± 3.12, blood-to-plasma ratio of 0.62 ± 0.16 and 0.80 ± 0.23 at 1 μM and 10 μM concentrations, respectively. Again, the absolute oral bioavailability of CA at 100 mg/kg was found to be 37.56 ± 7.3%. The in-vivo pharmacokinetic profile of probe drugs revealed CA to have significant inducing effects on CYP1A2, 2C11, 2D2, and 2E1 after 14 days, which correlates to both in-vitro rat microsomal data and gene expression results. Altogether, pharmacokinetic parameters reveal CA to have an affinity to distribute across different extravascular tissues and induce rat liver CYP enzymes.

Atherosclerosis (AS) is one of the major risk factors for cardiovascular disease pathogenesis, and current studies have found that the development of atherosclerosis is closely related to the intestinal microbiome. This review describes the relationship between the development of atherosclerosis and the gut microbiome with its metabolites and reviews the interactions between atherosclerosis-related drugs and the intestinal microbiome, especially the in vivo metabolic effects of the intestinal microbiome on drugs related to the treatment of atherosclerosis, to provide further understanding for the development of drugs based on the intestinal microbiome to treat atherosclerosis.