Population pharmacokinetic analysis and dosing optimization of polymyxin B in critically ill patients.

Objectives: Since the global broadcast of multidrug-resistant gram-negative bacteria is accelerating, the use of Polymyxin B is sharply increasing, especially in critically ill patients. Unsatisfactory therapeutic effects were obtained because of the abnormal physiological function in critically ill patients. Therefore, the determination of optimal polymyxin B dosage becomes highly urgent. This study aimed to illustrate the polymyxin B pharmacokinetic characteristics by defining the influencing factors and optimizing the dosing regimens to achieve clinical effectiveness. Methods: Steady-state concentrations of polymyxin B from twenty-two critically ill patients were detected by a verified liquid chromatography-tandem mass spectrometry approach. The information on age, weight, serum creatinine, albumin levels, and Acute Physiology and Chronic Health Evaluation-II (APACHE-II) score was also collected. The population PK parameters were calculated by the non-parametric adaptive grid method in Pmetrics software, and the pharmacokinetic/pharmacodynamics target attainment rate was determined by the Monte Carlo simulation method. Results: The central clearance and apparent volume of distribution for polymyxin B were lower in critically ill patients (1.24 ± 0.38 L h-1 and 16.64 ± 12.74 L, respectively). Moreover, albumin (ALB) levels can be used to explain the variability in clearance, and age can be used to describe the variability in the apparent volume of distribution. For maintaining clinical effectiveness and lowering toxicity, 75 mg q12 h is the recommended dosing regimen for most patients suffering from severe infections. Conclusion: This study has clearly defined that in critically ill patients, age and ALB levels are potentially important factors for the PK parameters of polymyxin B. Since older critically ill patients tend to have lower ALB levels, so higher dosages of polymyxin B are necessary for efficacy.

Gene sequencing and result analysis of balanced translocation carriers by third-generation gene sequencing technology.

Because the total gene copy number remains constant and all genes are normally expressed, carriers of balanced chromosomal translocations usually have a normal phenotype but are able to produce many different types of gametes during meiosis, and unbalanced gametes lead to increased risks of infertility, recurrent spontaneous abortion, stillbirth, neonatal death or malformations and intellectual abnormalities in offspring. The key to balanced translocations lies in finding the breakpoints, but current genetic testing techniques are all short-read sequencing, with the disadvantage of procedural complexity and imprecision for precisely identifying the breakpoints. The latest third-generation sequencing technology overcomes these drawbacks and uses robust long-read sequencing to accurately and rapidly detect genome-wide information and identify breakpoint locations. In this paper, we performed whole genome long-read sequencing using an Oxford Nanopore sequencer to detect the breakpoints of 4 balanced chromosomal translocation carriers. The results showed that employing about ~ 10× coverage confirmed 6 of the 8 breakpoints, of which, 2 had microdeletions/insertions identified near the breakpoints and 4 had breakpoints that disrupted the normal gene structure and were simultaneously tested for genome-wide structural variation (SV). The results show that whole genome long-read sequencing is an efficient method for pinpointing translocation breakpoints and providing genome-wide information, which is essential for medical genetics and preimplantation genetic testing.

Physiologically based pharmacokinetic-pharmacodynamic evaluation of meropenem in CKD and hemodialysis individuals.

Objective: Chronic kidney disease (CKD) has significant effects on renal clearance of drugs. The application of antibiotics in CKD patients to achieve the desired therapeutic effect is challenging. This study aims to determine meropenem plasma exposure in the CKD population and further investigate optimal dosing regimens. Methods: A healthy adult PBPK model was established using the meropenem's physicochemical parameters, pharmacokinetic parameters, and available clinical data, and it was scaled to the populations with CKD and dialysis. The differences between the predicted concentration, Cmax, and AUClast predicted and observed model values were assessed by mean relative deviations (MRD) and geometric mean fold errors (GMFE) values and plotting the goodness of fit plot to evaluate the model's performance. Finally, dose recommendations for CKD and hemodialysis populations were performed by Monte Carlo simulations. Results: The PBPK models of meropenem in healthy, CKD, and hemodialysis populations were successfully established. The MRD values of the predicted concentration and the GMFE values of Cmax and AUClast were within 0.5-2.0-fold of the observed data. The simulation results of the PBPK model showed the increase in meropenem exposure with declining kidney function in CKD populations. The dosing regimen of meropenem needs to be further adjusted according to the renal function of CKD patients. In patients receiving hemodialysis, since meropenem declined more rapidly during the on-dialysis session than the off-dialysis session, pharmacodynamic evaluations were performed for two periods separately, and respective optimal dosing regimens were determined. Conclusion: The established PBPK model successfully predicted meropenem pharmacokinetics in patients with CKD and hemodialysis and could further be used to optimize dosing recommendations, providing a reference for personalized clinical medication.

AromTool: predicting aromatic stacking energy using an atomic neural network model.

Aromatic stacking exists widely and plays important roles in protein-ligand interactions. Computational tools to automatically analyze the geometry and accurately calculate the energy of stacking interactions are desired for structure-based drug design. Herein, we employed a Behler-Parrinello neural network (BPNN) to build predictive models for aromatic stacking interactions and further integrated it into an open-source Python package named AromTool for benzene-containing aromatic stacking analysis. Based on extensive testing, AromTool presents desirable precision in comparison to DFT calculations and excellent efficiency for high-throughput aromatic stacking analysis of protein-ligand complexes.

TeroKit: A Database-Driven Web Server for Terpenome Research.

Natural products are the major resource of drug discovery, and terpenoids represent the largest family of natural products. Terpenome is defined as all terpenoid-like and terpenoid-derived natural compounds, including the terpenoids, steroids, and their derivatives. Herein, aiming to navigate the chemical and biological space of terpenome, the first comprehensive database dedicated to terpenome research has been developed by collecting over 110 000 terpenome molecules from various resources, distributed in 14 351 species, belonging to 1109 families, and showing activity against 1366 biological targets. Much of the publically available information or computationally predicted properties for each terpenome molecule is annotated and integrated into TeroKit (http://terokit.qmclab.com/), serving as free Web server for academic use. Moreover, several practical toolkits, such as target profiling and conformer generation modules, are also implemented to facilitate the drug discovery of terpenome.