Pharmaceutical Research最新文献

Purpose: Olmesartan medoxomil (olmesartan-MX), an ester-type prodrug of the angiotensin II receptor blocker (ARB) olmesartan, is predominantly anionic at intestinal pH. Human organic anion transporting polypeptide 2B1 (OATP2B1) is expressed in the small intestine and is involved in the absorption of various acidic drugs. This study was designed to test the hypothesis that OATP2B1-mediated uptake contributes to the enhanced intestinal absorption of olmesartan-MX, even though olmesartan itself is not a substrate of OATP2B1.

Methods: Tetracycline-inducible human OATP2B1- and rat Oatp2b1-overexpressing HEK 293 cell lines (hOATP2B1/T-REx-293 and rOatp2b1/T-REx-293, respectively) were established to characterize OATP2B1-mediated uptake. Rat jejunal permeability was measured using Ussing chambers. ARBs were quantified by liquid chromatography-tandem mass spectrometry.

Results: Significant olmesartan-MX uptake was observed in hOATP2B1/T-REx-293 and rOatp2b1/T-REx-293 cells, whereas olmesartan uptake was undetectable or much lower than olmesartan-MX uptake, respectively. Furthermore, olmesartan-MX exhibited several-fold higher uptake in Caco-2 cells and greater permeability in rat jejunum compared to olmesartan. Olmesartan-MX uptake in hOATP2B1/T-REx-293 cells and in Caco-2 cells was significantly decreased by OATP2B1 substrates/inhibitors such as 1 mM estrone-3-sulfate, 100 µM rifamycin SV, and 100 µM fluvastatin. Rat Oatp2b1-mediated uptake and rat jejunal permeability of olmesartan-MX were significantly decreased by 50 µM naringin, an OATP2B1 inhibitor. Oral administration of olmesartan-MX with 50 µM naringin to rats significantly reduced the area under the plasma concentration-time curve of olmesartan to 76.9%.

Conclusion: Olmesartan-MX is a substrate for OATP2B1, and the naringin-sensitive transport system contributes to the improved intestinal absorption of olmesartan-MX compared with its parent drug, olmesartan.

Objective: To utilize the global system analysis (GSA) in oral absorption modeling to gain a deeper understanding of system behavior, improve model accuracy, and make informed decisions during drug development.

Methods: GSA was utilized to give insight into which drug substance (DS), drug product (DP), and/or physiological parameter would have an impact on peak plasma concentration (C_max) and area under the curve (AUC) of dipyridamole as a model weakly basic compound. GSA guided the design of in vitro experiments and oral absorption risk assessment using FormulatedProducts v2202.1.0. The solubility and precipitation profiles of dipyridamole in different bile salt concentrations were measured. The results were then used to build a mechanistic oral absorption model.

Results: GSA warranted further investigation into the precipitation kinetics and its link to the levels of bile salt concentrations. Mechanistic modeling studies demonstrated that a precipitation-integrated modeling approach appropriately predicted the mean plasma profiles, C_max, and AUC from the clinical studies.

Conclusions: This work shows the value of GSA utilization in early development to guide in vitro experimentation and build more confidence in identifying the critical parameters for the mathematical models.

The challenge of antimicrobial resistance is broadly appreciated by the clinical and scientific communities. To assess progress in the development of medical countermeasures to combat bacterial infections, we deployed information gleaned from clinical trials conducted from 2000 to 2021. Whereas private sector interest in cancer grew dramatically over this period, activity to combat bacterial infections remained stagnant. The comparative ambivalence to antimicrobial resistance is reflected in the number of investigative drugs under clinical investigation, their stage of development and most troublingly, a declining number of organizations that are actively involved in the development of new products to treat bacterial infections. This drop reflects the exits of many companies that had previously developed antibacterial agents.

Background: Phosphate buffer is often used as a replacement for the physiological bicarbonate buffer in pharmaceutical dissolution testing, although there are some discrepancies in their properties making it complicated to extrapolate dissolution results in phosphate to the in vivo situation. This study aims to characterize these discrepancies regarding solubility and dissolution behavior of ionizable compounds.

Methods: The dissolution of an ibuprofen powder with a known particle size distribution was simulated in silico and verified experimentally in vitro at two different doses and in two different buffers (5 mM pH 6.8 bicarbonate and phosphate).

Results: The results showed that there is a solubility vs. dissolution mismatch in the two buffers. This was accurately predicted by the in-house simulations based on the reversible non-equilibrium (RNE) and the Mooney models.

Conclusions: The results can be explained by the existence of a relatively large gap between the initial surface pH of the drug and the bulk pH at saturation in bicarbonate but not in phosphate, which is caused by not all the interfacial reactions reaching equilibrium in bicarbonate prior to bulk saturation. This means that slurry pH measurements, while providing surface pH estimates for buffers like phosphate, are poor indicators of surface pH in the intestinal bicarbonate buffer. In addition, it showcases the importance of accounting for the H₂CO₃-CO₂ interconversion kinetics to achieve good predictions of intestinal drug dissolution.

Purpose

This study examined the effects of nicotinamide mononucleotide (NMN) and nicotinamide riboside (NR) on folliculogenesis and mitochondrial dynamics (fission and fusion mechanisms) in ovaries of middle-aged female rats.

Methods

Experimental groups were young, middle-aged (control), middle-aged + NMN and middle-aged + NR. NMN was administered at a concentration of 500 mg/kg intraperitoneally but NR at a concentration of 200 mg/kg by gavage. Follicle stimulating hormone (FSH) and luteinizing hormone (LH) levels were analyzed by ELISA. Hematoxylin-eosin staining sections were used for histopathological examination and follicles-counting. Expression levels of mitochondrial fission (Drp1, Mff and Fis1) and fusion (Mfn1, Mfn2, Opa1, Fam73a and Fam73b) genes as well as Sirt1 gene were analyzed by RT-PCR. Expression levels of fission-related proteins (DRP1, MFF, FIS1 and SIRT1) were analyzed by Western Blot.

Results

Higher ovarian index, more corpus luteum and antral follicles were detected in NMN and NR groups compared to the control. NMN or NR could rebalance LH/FSH ratio. The control group was determined to possess higher expression levels of fission genes and lower expression levels of fusion genes when compared the young group. In comparison with the control group, both NMN and NR group were found to exhibit less mitochondrial fission but more mitochondrial fussion. Higher gene and protein levels for Sirt1 were measured in NMN and NR groups compared to the control group.

Conclusion

This study reveals that NMN alone or NR alone can rebalance mitochondrial dynamics by decreasing excessive fission in middle-aged rat ovaries, thus alleviating mitochondrial stress and correcting aging-induced folliculogenesis abnormalities.

Background

Evaluating drug transplacental clearance is vital for forecasting fetal drug exposure. Ex vivo human placenta perfusion experiments are the most suitable approach for this assessment. Various in silico methods are also proposed. This study aims to compare these prediction methods for drug transplacental clearance, focusing on the large molecular weight drug vancomycin (1449.3 g/mol), using maternal–fetal physiologically based pharmacokinetic (m-f PBPK) modeling.

Methods

Ex vivo human placenta perfusion experiments, in silico approaches using intestinal permeability as a substitute (quantitative structure property relationship (QSPR) model and Caco-2 permeability in vitro-in vivo correlation model) and midazolam calibration model with Caco-2 scaling were assessed for determining the transplacental clearance (CL_PD) of vancomycin. The m-f PBPK model was developed stepwise using Simcyp, incorporating the determined CL_PD values as a crucial input parameter for transplacental kinetics.

Results

The developed PBPK model of vancomycin for non-pregnant adults demonstrated excellent predictive performance. By incorporating the CL_PD parameterization derived from ex vivo human placenta perfusion experiments, the extrapolated m-f PBPK model consistently predicted maternal and fetal concentrations of vancomycin across diverse doses and distinct gestational ages. However, when the CL_PD parameter was derived from alternative prediction methods, none of the extrapolated maternal–fetal PBPK models produced fetal predictions in line with the observed data.

Conclusion

Our study showcased that combination of ex vivo human placenta perfusion experiments and m-f PBPK model has the capability to predict fetal exposure for the large molecular weight drug vancomycin, whereas other in silico approaches failed to achieve the same level of accuracy.

Purpose

A comparative assessment was performed to evaluate the potential of particle sizing by an ensemble based conventional dynamic light scattering (DLS) technique and an emerging technology based on tunable resistive pulse sensing (TRPS) using particle by particle approach by evaluating three different types of vaccine formulations representing three case studies and showing the limitation of each technique, instrument variability, sensitivity, and the resolution in mixed population.

Methods

Three types of in-house vaccine formulations- a protein antigen, an outer membrane vesicle and viral particles were simultaneously evaluated by TRPS based Exoid and two DLS instruments-Zetatrac and Zetasizer for particle size distribution, aggregates, and resolution of polydisperse species.

Results

The data from first case study show the risk of possible size overestimation and size averaging in polydisperse samples in DLS measurements which can be addressed by the TRPS analysis. It also shows how TRPS may be utilized only to large size antigens due to its limited size range. The second case study highlights the difference in the sensitivities of two DLS instruments working on the same principle. The third case study show that how TRPS can better resolve the large aggregate species compare to DLS in polydisperse samples.

Conclusion

This analysis shows that TRPS can be used as an orthogonal technique in addition to conventional DLS based methods for more precise and in-depth characterization. Both techniques are efficient in size characterization and produce comparable results, however the choice will depend on the type of formulation and size range to be evaluated.

Introduction

Diabetic foot infection (DFI) is one of the complications of diabetes mellitus. Clindamycin (CLY) is one of the antibiotics recommended to treat DFI, but CLY given orally and intravenously still causes many side effects.

Methods

In this study, we encapsulated CLY in a bacteria sensitive microparticle system (MP-CLY) using polycaprolactone (PCL) polymer. MP-CLY was then delivered in a separable effervescent microarray patch (MP-CLY-SEMAP), which has the ability to separate between the needle layer and separable layer due to the formation of air bubbles when interacting with interstitial fluid in the skin.

Result

The characterization results of MP-CLY proved that CLY was encapsulated in large amounts as the amount of PCL polymer used increased, and there was no change in the chemical structure of CLY. In vitro release test results showed increased CLY release in media cultured with Staphylococcus aureus bacteria and showed controlled release. The characterization results of MPCLY-SEMAP showed that the developed formula has optimal mechanical and penetration capabilities and can separate in 56 ± 5.099 s. An ex vivo dermatokinetic test on a bacterially infected skin model showed an improvement of CLY dermatokinetic profile from MP-CLY SEMAP and a decrease in bacterial viability by 99.99%.

Conclusion

This research offers proof of concept demonstrating the improved dermatokinetic profile of CLY encapsulated in a bacteria sensitive MP form and delivered via MP-CLY-SEMAP. The results of this research can be developed for future research by testing MP-CLY-SEMAP in vivo in appropriate animal models.

Graphical Abstract

Purpose

This study assesses the Multilayer Perceptron (MLP) neural network, complemented by other Machine Learning techniques (CART, PCA), in predicting the antimicrobial activity of 140 newly designed imidazolium chlorides against Klebsiella pneumoniae before synthesis. Emphasis is on leveraging molecular properties for predictive analysis.

Methods

Classification and regression decision trees (CART) identified the top 200 predictive molecular descriptors. Principal Component Analysis (PCA) reduced these descriptors to 5 components, retaining 99.57% of raw data information. Antimicrobial activity, categorized as high or low, was based on experimentally proven minimal inhibitory concentration (MIC), with a cut-point at MIC = 0.856 mol/L. A 12-fold cross-validation trained the MLP (architecture 5-12-2 with 5 Principal Components).

Results

The MLP exhibited commendable performance, achieving almost 90% correct classifications across learning, validation, and test sets, outperforming models without PCA dimension reduction. Key metrics, including accuracy (0.907), sensitivity (0.905), specificity (0.909), and precision (0.891), were notably high. These results highlight the MLP model's efficacy with PCA as a high-quality classifier for determining antimicrobial activity.

Conclusions

The study concludes that the MLP neural network, along with CART and PCA, is a robust tool for predicting the antimicrobial activity class of imidazolium chlorides against Klebsiella pneumoniae. CART and PCA, used in this study, allowed input variable reduction without significant information loss. High classification accuracy and associated metrics affirm the method’s potential utility in pre-synthesis assessments, offering valuable insights for antimicrobial compound design.

Objective

This study aimed to improve the efficiency of pharmacotherapy for CNS diseases by optimizing the ability of drug molecules to penetrate the Blood-Brain Barrier (BBB).

Methods

We established qualitative and quantitative databases of the ADME properties of drugs and derived characteristic features of compounds with efficient BBB penetration. Using these insights, we developed four machine learning models to predict a drug's BBB permeability by assessing ADME properties and molecular topology. We then validated the models using the B3DB database. For acyclovir and ceftriaxone, we modified the Hydrogen Bond Donors and Acceptors, and evaluated the BBB permeability using the predictive model.

Results

The machine learning models performed well in predicting BBB permeability on both internal and external validation sets. Reducing the number of Hydrogen Bond Donors and Acceptors generally improves BBB permeability. Modification only enhanced BBB penetration in the case of acyclovir and not ceftriaxone.

Conclusions

The machine learning models developed can accurately predict BBB permeability, and many drug molecules are likely to have increased BBB penetration if the number of Hydrogen Bond Donors and Acceptors are reduced. These findings suggest that molecular modifications can enhance the efficacy of CNS drugs and provide practical strategies for drug design and development. This is particularly relevant for improving drug penetration of the BBB.

Graphical Abstract