Journal of Pharmaceutical Innovation最新文献

Discrete Element Method (DEM) simulations of cohesive particles are of great importance in understanding powder flows in processing equipment. Calibration of the particle–particle or particle–wall interaction parameters is important, and although there are many studies on DEM simulations of cohesive powders, most of them do not differentiate between particle–particle cohesive and particle–wall adhesive forces. In this work, a bench-top experimental setup was designed to demonstrate the effect of particle–wall adhesive forces. A simulation-based sensitivity analysis was also performed using a commercial scale tablet press feeder (TPF) and hopper screw feeder (HSF), by independently varying the particle–particle and particle–wall cohesive energy density values to illustrate their effect on powder flowability. Both the feeders experienced blockages for highly cohesive particles whose cohesive energy density exceeds 80 kJ/m³ in case of TPF and 110 kJ/m³ for HSF. The occurrence of such blockages was eliminated by reducing the particle–wall adhesion to 30 kJ/m³ in case of TPF and 50 kJ/m³ for HSF while keeping the particle–particle cohesion at the same high value. Thus, this work demonstrates that the particle–particle cohesion and particle wall adhesion should be considered as separate entities and must be calibrated separately in DEM so that the materials interaction effects of both powder and equipment can be captured adequately.

Purpose

The purpose of this work is to show the results of automating with AI, the interpretation of human albumin and immunoglobulin identity tests in commercial drugs for intravenous application, and to evaluate its incorporation into routine laboratory analytical activities, in compliance with Mexican health regulations.

Research Question

Is it possible to apply AI to determine the identity of human immunoglobulin and human albumin in commercial drugs for intravenous application, as a support for issuing analytical certificates, in compliance with Mexican health regulations?

Methods

In this work we developed an AI method that supports the determination of the identity of human immunoglobulin and human albumin in commercial drugs for intravenous application.

The identity of human albumin and immunoglobulin was determined using the validated pharmacopeial method of immunoelectrophoresis on agarose plates. The agarose plates were photographed using a webcam. The photographs were processed by a pre-trained AI algorithm. The data obtained by the AI were compared with the results from the agarose plates.

Results

The selectivity, sensitivity, specificity and predictive value were determined, as well as the performance evaluation with respect to the results obtained on the agarose plates. In all cases, the parameters evaluated were satisfactory > 95%. The analysts who performed the analytical execution considered it satisfactory to incorporate AI into the identity tests of human immunoglobulin and albumin in commercial drugs for intravenous application.

Conclusions

The AI method for identifying human immunoglobulin and human albumin in commercial intravenous medications is reliable and will be perfected for submission to Mexican regulatory authorities.

In today’s competitive landscape, the demand for scheduling optimization to enhance processes and industrial plants is rapidly increasing. To address this need, we introduce a scheduling optimization approach for a compact continuous pharmaceutical manufacturing line by developing a mixed integer linear programming (MILP) model that incorporates all necessary constraints and bounds. The MILP employs a continuous time representation and an extension to a multi-product manufacturing line to improve the capabilities of current equipment and the model itself is shown. To reinforce the ideas and exemplify the overall concept, we present simulation results, demonstrating the optimal scheduling plans for various scenarios. In a case study, we applied the MILP to a manufacturing line and successfully integrated the optimal scheduling plan into the plant. Our results show that the time constraints, expected mass flows, and mass hold-ups align with those of the optimal scheduling plan, confirming that fully automated and integrated operations are feasible.

Purpose

Pollutants, including the excessive accumulation of copper, provide considerable health problems by causing various physiological diseases. Although necessary for certain body activities, excessive amounts of copper can be toxic and lead to damage in the gastrointestinal, hepatic, renal, and neurological systems, among other effects. The current study investigated the therapeutic benefits of probiotics and postbiotics in reducing the adverse effects of copper in a rat model.

Methods

Twenty-eight male Wistar rats were randomly divided into four groups: control, CuSO4-control, third (probiotic) experimental group, and fourth (postbiotic) experimental group. Copper toxicity was induced by administering 200 mg CuSO4/kg. Then, the probiotic and postbiotic groups received daily doses containing Lactobacillus casei, Lactobacillus rhamnosus, and Lactobacillus helveticus. Serum biochemical parameters, histopathological changes, oxidative stress markers, and gene expression in renal and intestinal tissues were evaluated.

Results

The CuSO₄ significantly increased serum creatinine, urea, and sodium levels and reduced potassium levels. Also, histopathological analysis indicated severe renal and intestinal tissue damage, increased oxidative stress, and altered inflammatory gene expression. Probiotic and postbiotic treatments significantly ameliorated these effects, as evidenced by improved serum biochemical profiles, reduced histopathological damage, and ameliorated oxidative and inflammatory markers. The administration of probiotics and postbiotics significantly decreased CuSO₄ toxicity in renal and intestinal systems.

Conclusion

These findings suggest that taking probiotics and postbiotics could be a novel strategy for heavy metal toxicity.

Objective

This study evaluated various mixing, homogenization, and dosing techniques for laboratory-scale semi-solid formulations, focusing on parameters such as volume, rpm, container morphology, and paddle design to determine efficiency and efficacy.

Methods

Three three-dimensional vessels were analyzed: mortar and pestle, blender, and a custom-designed conical reactor. Simulations using computational fluid dynamics (CFD) and the finite volume method were conducted to study particle trajectories and the homogenization process during the mixing, agitation, and emptying phases. Additionally, experimental validation was performed in parallel with the simulations, using similar operational conditions for each system.

Results

The CFD simulations revealed that the conical reactor was the most efficient under all conditions (high and low volumes and revolutions). Further evaluations showed that the helical (screw) paddle inside the reactor provided the highest efficiency in mixing and significantly accelerated the complete emptying of the reactor. Experimental validation corroborated the simulation results, showing a high degree of agreement between both methods and demonstrating the reliability of CFD simulations under real operational conditions.

Conclusions

The results demonstrate the relevance of the conical reactor and its helical screw paddle for improving the efficiency of mixing and dosing in semi-solid formulations, offering valuable insights for pharmacists involved in therapy customization. The findings emphasize the need for further research to refine these systems and enhance their efficiency in pharmaceutical applications.

Background

The rise of multidrug-resistant tuberculosis (MDR-TB) poses a significant challenge to global health. This study aimed to develop a novel drug delivery system using mesoporous silica nanoparticles (MSNPs) loaded with dual drug conjugates to combat MDR-TB.

Objective

To design, optimize, and evaluate MSNPs loaded with dual drug conjugates for enhanced treatment of MDR-TB.

Methods

Two dual drug conjugates, D-Cycloserine-Chloroacetyl chloride-Moxifloxacin (DCM) and D-Cycloserine-Succinyl chloride-Moxifloxacin (DSM), were synthesized via nucleophilic substitution reactions. The conjugates were evaluated for hydrolysability and loaded into MSNPs, which were optimized using Design Expert software. The MSNPs were characterized using FTIR, NMR, particle size analysis, zeta potential measurements, and SEM. In vitro drug release, in vivo pharmacokinetic studies, and in vitro microbiological investigations against M. tuberculosis H37Rv were conducted.

Results

The optimized MSNPs exhibited desirable characteristics, including a uniform mesoporous structure, high drug loading, controlled release, and enhanced bioavailability. DSM-loaded MSNPs demonstrated superior synergistic inhibitory activity against M. tuberculosis H37Rv compared to DCM and the free drug mixture.

Conclusion

This study highlights the potential of MSNPs loaded with dual drug conjugates, particularly DSM, as an effective strategy for MDR-TB treatment. This approach offers enhanced drug delivery, synergistic activity, and improved bioavailability, potentially overcoming challenges associated with MDR-TB.

Background

Ibuprofen is a widely used non-steroidal anti-inflammatory drug (NSAID) with analgesic and antipyretic properties. However, its clinical efficacy is limited by poor solubility and bioavailability.

Objectives

This study aimed to: Select an appropriate co-former for ibuprofen using computational tools; synthesize and characterize an ibuprofen-salicylic acid co-crystal and evaluate its solubility and dissolution properties.

Methods

Computational tools, including Avogadro, AutoDock Vina, and MGL Tools, were used for molecular docking, employing the Lamarckian genetic algorithm to determine docking scores to search for the selection of a co-former. Salicylic acid showed the best interaction and was selected. Four different methods were used to make ibuprofen-salicylic acid co-crystals. The co-crystal was analyzed using X-ray diffraction (XRD), thermal analysis, and spectroscopic techniques to confirm its structure. The aqueous solubility and dissolution rate of the co-crystal were evaluated and compared with pure ibuprofen.

Results

The ibuprofen-salicylic acid co-crystals synthesized using solvent-assisted co-crystallization technique exhibited a distinct crystalline structure and thermal behavior compared to their individual components. Aqueous solubility was significantly enhanced in comparison to pure ibuprofen.

Conclusion

The ibuprofen-salicylic acid co-crystal demonstrated improved aqueous solubility and dissolution rate, suggesting its potential for enhancing the oral bioavailability of ibuprofen.

The targeted delivery of drugs to ocular tissue has garnered significant attention for treating ocular diseases. Traditional ophthalmic formulations have various drawbacks, such as low absorption, limited corneal permeation, and rapid removal of the dosage from the ocular cavity. Recently, the scientific community has shown interest in using oleogels for managing ocular infections, although there is a prerequisite for further enhancement in corneal permeation. Graphene oxide (GO) has gained significant attention in drug delivery due to its excellent biocompatibility, aptitude to enhance permeation, and surface tunability. In this research work, we developed an oleogel based on GO, jasmine floral wax (JFW), and castor oil (CO) to improve corneal permeation for targeted delivery of ketoconazole (KTZ). In brief, the prepared GO-KTZ-JFW/CO oleogel underwent various spectral characterizations, antifungal studies, in vitro release assessments, ex vivo permeation experiments, etc. The obtained results revealed that the GO-KTZ-JFW/CO oleogel exhibited good antifungal activity against Candida albicans, attributed to the combination of GO and KTZ. Furthermore, in vitro studies confirmed that 66.33 ± 1.88% of KTZ was released within 6 h (best-fit model: Korsmeyer peppas; 0.991), following a non-Fickian diffusion mechanism (n = 0.554) in oleogels. Notably, the GO-KTZ-JFW/CO oleogel (batch: JC5) significantly improved the ex vivo permeation of KTZ molecules through the caprine cornea. Therefore, this study highlights the presence of GO in oleogels contributes to the enhancement of trans-corneal permeation of KTZ. In conclusion, the GO-KTZ-JFW/CO oleogel demonstrates improved corneal permeation of KTZ and good antifungal ability, marking a potential avenue for ophthalmic oleogels.

Purpose

This study aimed to develop immediate-release Quetiapine Fumarate (QTF) tablets using twin-screw melt granulation (TSMG), a solvent-free and efficient granulation method, and compare their dissolution profiles with the marketed product.

Methods

QTF tablets were formulated using TSMG with varying concentrations of Ludiflash^® as a diluent and HPMC as a binder. Granules were prepared using a twin-screw extruder at 150 °C, optimized for feed rate and screw design. Granules were characterized for flow properties, thermal stability, and molecular interactions using differential scanning calorimetry (DSC) and Fourier transform infrared (FTIR) spectroscopy. Compressed tablets were evaluated for weight variation, hardness, friability, disintegration, and dissolution. Drug release profiles were compared with a commercial product.

Results

TSMG improved granule flow properties, with all formulations exhibiting excellent post-extrusion characteristics. DSC confirmed the retention of QTF crystallinity, and FTIR demonstrated excipient compatibility. The tablets passed pharmacopeial tests for weight variation, hardness, friability, and disintegration. Dissolution studies showed that formulations F4, F5, and F6 released over 85% of the drug within 15 min, comparable to the marketed product.

Conclusion

TSMG is a robust and cost-effective technology for manufacturing immediate-release QTF tablets. The solvent-free process ensures compatibility, thermal stability, and rapid drug release, making TSMG a promising alternative for continuous pharmaceutical production.

Purpose

Maintaining positive revenue performance and R&D productivity is crucial for the pharma industry, therefore identifying and predicting future revenue determinants is of upmost importance. The aim of the study was to identify factors that determine annual revenue and to use these factors to develop a model that predicts future revenues.

Methods

IDEA Pharma’s longitudinal Pharmaceutical Innovation and Invention Index data were used to conduct the analyses, looking at the financial, regulatory and pipeline data (2019–2023) of the top 24 pharmaceutical companies. Broader industry trends over the last 5 years were analysed using descriptive statistics. To validate the factors gleaned from the descriptive statistics, a predictive model was constructed. This was done through predictive statistical analysis (stepwise regression), producing a model that can predict future revenue.

Results

Three factors were determined to be significant determinants of higher future revenues: higher R&D spend, lower R&D spend as a proportion of current revenue, and higher number of Phase III clinical trials.

Conclusions

The resulting model combined financial, commercial, and strategic metrics from the world’s largest pharmaceutical companies to produce a statistically robust method of predicting revenues, that is critical for every pharma company’s growth and strategic planning.