COVID-19 has become a global pandemic that threatens millions of people worldwide. There is an urgent call for developing effective drugs against the virus (SARS-CoV-2) causing this disease. The main protease of SARS-CoV-2, 3C-like protease (3CL ^pro ), is highly conserved across coronaviruses and is essential for the maturation process of viral polyprotein. Scutellariae radix (Huangqin in Chinese), the root of Scutellaria baicalensis has been widely used in traditional Chinese medicine to treat viral infection related symptoms. The extracts of S. baicalensis have exhibited broad spectrum antiviral activities. We studied the anti-SARS-CoV-2 activity of S. baicalensis and its ingredient compounds. We found that the ethanol extract of S. baicalensis inhibits SARS-CoV-2 3CL ^pro activity in vitro and the replication of SARS-CoV-2 in Vero cells with an EC ₅₀ of 0.74 μg/ml. Among the major components of S. baicalensis , baicalein strongly inhibits SARS-CoV-2 3CL ^pro activity with an IC ₅₀ of 0.39 μM. We further identified four baicalein analogue compounds from other herbs that inhibit SARS-CoV-2 3CL ^pro activity at microM concentration. Our study demonstrates that the extract of S. baicalensis has effective anti-SARS-CoV-2 activity and baicalein and analogue compounds are strong SARS-CoV-2 3CL ^pro inhibitors.

The objectives of this study were to support dose selection of a novel FXR agonist XZP-5610 in first-in-human (FIH) trials and to predict its liver concentrations in Chinese healthy adults. Key parameters for extrapolation were measured using in vitro and in vivo models. Allometric scaling methods were employed to predict human pharmacokinetics (PK) parameters and doses for FIH clinical trials. To simulate the PK profiles, a physiologically based pharmacokinetic (PBPK) model was developed using animal data and subsequently validated with clinical data. The PBPK model was employed to simulate XZP-5610 concentrations in the human liver across different dose groups. XZP-5610 exhibited high permeability, poor solubility, and extensive binding to plasma proteins. After a single intravenous or oral administration of XZP-5610, the PK parameters obtained from rats and beagle dogs were used to extrapolate human parameters, resulting in a clearance of 138 mL/min and an apparent volume of distribution of 41.8 L. The predicted maximum recommended starting dose (MRSD), minimal anticipated biological effect level (MABEL), and maximum tolerated dose (MTD) were 0.15, 2, and 3 mg, respectively. The PK profiles and parameters of XZP-5610, predicted using the PBPK model, demonstrated good consistency with the clinical data. By using allometric scaling and PBPK models, the doses, PK profile, and especially the liver concentrations were successfully predicted in the FIH study.

OATP1B, P‐gp, BCRP, and CYP3A are the most contributing drug‐metabolizing enzymes or transporters (DMETs) for commonly prescribed medication. Their activities may change in end‐stage renal disease (ESRD) patients with large inter‐individual variabilities (IIVs), leading to altered substrate drug exposure and ultimately elevated safety risk. However, the changing extent and indictive influencing factors are not quantified so far. Here, a microdose cocktail regimen containing five sensitive substrate drugs (pitavastatin, dabigatran etexilate, rosuvastatin, midazolam, and atorvastatin) for these DMETs was administrated to Chinese healthy volunteers and ESRD patients. Drug pharmacokinetics profiles were determined, together with physiological, pharmacogenetic, and gut microbiome signature. Population pharmacokinetic and machine learning model were established to identify key influencing factors and quantify their contribution to drug exposure change. The exposure of pitavastatin, dabigatran, rosuvastatin, and atorvastatin increased to 1.8‐, 3.1‐, 1.1‐, and 1.3‐fold, respectively, whereas midazolam exposure decreased by 72% in ESRD patients. Notably, in addition to disease state, the relative abundance of genus Veillonella and Clostridium_XIVb were firstly identified as significant influencing factors for PTV and RSV apparent clearance, respectively, suggesting their indicative role for OATP and BCRP activity evaluation. Moreover, several genera were found to strongly associate with drug clearance and reduce unexplained IIVs. Accordingly, it was estimated that OATP1B and intestine P‐gp activity decreased by 35–75% and 29–44%, respectively, whereas BCRP and CYP3A4 activity may upregulate to some extent. Our study provides a quantitative and mechanistic understanding of individual DMET activity and could support precision medicine of substrate drugs in ESRD patients.

Von Hippel–Lindau (VHL) disease is a rare autosomal dominant inherited cancer syndrome that is characterized by hemangioblastomas in the central nervous system and retina, renal cell carcinoma and cysts, pancreatic tumors and cysts, and pheochromocytoma. The underlying gene in this disease is the VHL tumor suppressor gene. We characterized a family with 2 affected siblings. The brother and sister displayed VHL type 2B and type 2A features, respectively. Renal lesions in the brother exhibited 3 different phenotypes, including simple renal cysts, multilocular cystic renal cell carcinoma and clear cell renal cell carcinoma. The phenotypes of the 3 concurrent renal lesions were first reported in this study. Mutation detection of the VHL gene revealed 2 recurrent mutations, namely c.256C>T (p.P86S) and c.340 + 5G > C. The former was predicted to be deleterious and to destabilize the hydrophobic core and lead to VHL dysfunction; however, the latter was predicted to be a benign variant. Our findings provided new data for the genotype-phenotype of VHL diseases and elucidated the pathogenic mechanism with in silico analysis.

Background/Objective: Physiologically based pharmacokinetic (PBPK) modeling is a powerful tool for predicting pharmacokinetics (PK) to support drug development and precision medicine. However, it has not been established for non-renal clearance pathways in patients with end-stage renal disease (ESRD), a population that bears heavy medication burden and is thereby at high risk for drug–drug–disease interactions (DDDIs). Furthermore, the pronounced inter-individual variability in PK observed in ESRD patients highlights the urgent need for individualized PBPK models. Methods: In this study, we developed a PBPK population model for ESRD patients, incorporating functional changes in key drug-metabolizing enzymes and transporters (DMETs), including CYP3A4, OATP1B1/3, P-gp, and BCRP. The model was initially constructed using the recalibrated demographic and physiological parameters of ESRD patients. Then, we used five well-validated substrates (midazolam, dabigatran etexilate, pitavastatin, rosuvastatin, and atorvastatin) and their corresponding PK profiles from ESRD patients taking a microdose cocktail regimen to simultaneously estimate the abundance of all these DMETs. Lastly, machine learning was employed to identify potential factors influencing individual clearance. Results: Our study suggested a significant reduction in hepatic OATP1B1/3 (75%) and intestinal P-gp abundance (34%) in ESRD patients. Ileum BCRP abundance was estimated to increase by 100%, while change in hepatic CYP3A4 abundance is minimal. Notably, simulations of drug combinations revealed potential DDDI risks that were not observed in healthy volunteers. Machine learning further identified Clostridium XVIII and Escherichia genus abundances as significant factors influencing dabigatran clearance. For rosuvastatin, aspartate aminotransferase, total bilirubin, Bacteroides, and Megamonas genus abundances were key influencers. No significant factors were identified for midazolam, pitavastatin, or atorvastatin. Conclusions: Our study proposes a feasible strategy for individualized PK prediction by integrating PBPK modeling with machine learning to support the development and precise use of the aforementioned DMET substrates in ESRD patients.