The influence of drug solubility and sampling frequency on metformin and glibenclamide release from double-layered particles: experimental analysis and mathematical modelling

Co-axial electrohydrodynamic atomization was used to prepare core/shell polymethylsilsesquioxane particles for co-delivery of metformin and glibenclamide in a sustained release manner. The drug-loaded microparticles were mostly spherical and uniformly distributed in size, with average diameters between 3 and 5 µm across various batches. FTIR was used to confirm the presence of drugs within the particles while X-ray diffraction studies revealed drugs encapsulated existed predominantly in the amorphous state. Intended as systems that potentially can act as depot formulations for long-term release of antidiabetics, a detailed analysis of drug release from these particles was necessary. Drugs of different solubilities were selected in order to study the effects of drug solubility from a core/shell particle system. Further analyses to determine how conditions such as release into a limited volume of media, sampling rate and partitioning of drug between the core and shell layers influenced drug release were conducted by comparing experimental and mathematically modelled outcomes. It was found that while the solubility of drug may affect release from such systems, rate of removal of drug (sampling frequency) which upsets local equilibrium at the particle/solution interface prompting a rapid release to redress the equilibrium influenced release more

Physio-chemical characterization of three-component co-amorphous systems generated by a melt-quench method

The purpose of this work was to evaluate the possibility of creating a ternary co-amorphous system and to determine how the properties of a co-amorphous material are altered by the addition of a selected third component. Piroxicam and indomethacin form a stable co-amorphous with the Tg above room temperature. The third component added was selected based on tendency to crystallise (benzamide, caffeine) or form amorphous (acetaminophen, clotrimazole) on cooling. Generated co-amorphous systems were characterised with TGA, HSM, DSC, FTIR, and XRD. Stable ternary co-amorphous systems were successfully generated, which was confirmed using XRD, DSC and FTIR analysis. In all cases, Tg of the ternary system was lower than the Tg of the binary system, although higher than that of the individual third compound. Upon storage for 4 weeks all created ternary systems showed significantly smaller variation in Tg compared to the binary system. Stable three-component co-amorphous systems can be generated via melt quench method using either a crystalline or amorphous third component. Addition of third component can alter the Tg of co-amorphous system and in all cases created more stable co-amorphous system upon storage. Physical parameters may not be sufficient in predicting the resulting Tg, therefore knowledge of chemical interaction must be brought into equation as well

Using random forest and decision tree models for a new vehicle prediction approach in computational toxicology

yesDrug vehicles are chemical carriers that provide beneficial aid to the drugs they bear. Taking advantage of their favourable properties can potentially allow the safer use of drugs that are considered highly toxic. A means for vehicle selection without experimental trial would therefore be of benefit in saving time and money for the industry. Although machine learning is increasingly used in predictive toxicology, to our knowledge there is no reported work in using machine learning techniques to model drug-vehicle relationships for vehicle selection to minimise toxicity. In this paper we demonstrate the use of data mining and machine learning techniques to process, extract and build models based on classifiers (decision trees and random forests) that allow us to predict which vehicle would be most suited to reduce a drug’s toxicity. Using data acquired from the National Institute of Health’s (NIH) Developmental Therapeutics Program (DTP) we propose a methodology using an area under a curve (AUC) approach that allows us to distinguish which vehicle provides the best toxicity profile for a drug and build classification models based on this knowledge. Our results show that we can achieve prediction accuracies of 80 % using random forest models whilst the decision tree models produce accuracies in the 70 % region. We consider our methodology widely applicable within the scientific domain and beyond for comprehensively building classification models for the comparison of functional relationships between two variables

Towards eco-friendly crop protection: natural deep eutectic solvents and defensive secondary metabolites

Plant science