From Field to Pharmacy: Isolation, Characterization and Tableting Behaviour of Microcrystalline Cellulose from Wheat and Corn Harvest Residues.

A lack of strategies for the utilization of harvest residues (HRs) has led to serious environmental problems due to an accumulation of these residues or their burning in the field. In this study, wheat and corn HRs were used as feedstock for the production of microcrystalline cellulose (MCC) by treatment with 2-8% sodium hydroxide, 10% hydrogen peroxide and further hydrolysis with 1-2 M hydrochloric acid. The changes in the FT-IR spectra and PXRD diffractograms after chemical treatment confirmed the removal of most of the lignin, hemicellulose and amorphous fraction of cellulose. A higher degree of crystallinity was observed for MCC obtained from corn HRs, which was attributed to a more efficient removal of lignin and hemicellulose by a higher sodium hydroxide concentration, which facilitates the dissolution of amorphous cellulose during acid hydrolysis. MCC obtained from HRs exhibited lower bulk density and poorer flow properties but similar or better tableting properties compared to commercial MCC (CeolusTM PH101). The lower ejection and detachment stress suggests that MCC isolated from HRs requires less lubricant compared to commercial MCC. This study showed that MCC isolated from wheat and corn HRs exhibits comparable tableting behaviour like commercial sample, further supporting this type of agricultural waste utilization.

A comprehensive assessment of machine learning algorithms for enhanced characterization and prediction in orodispersible film development.

Orodispersible films (ODFs) have emerged as innovative pharmaceutical dosage forms, offering patient-specific treatment through adjustable dosing and the combination of diverse active ingredients. This expanding field generates vast datasets, requiring advanced analytical techniques for deeper understanding of data itself. Machine learning is becoming an important tool in the rapidly changing field of pharmaceutical research, particularly in drug preformulation studies. This work aims to explore into the application of machine learning methods for the analysis of experimental data obtained by ODF characterization in order to obtain an insight into the factors governing ODF performance and use it as guidance in pharmaceutical development. Using a dataset derived from extensive experimental studies, various machine learning algorithms were employed to cluster and predict critical properties of ODFs. Our results demonstrate that machine learning models, including Support vector machine, Random forest and Deep learning, exhibit high accuracy in predicting the mechanical properties of ODFs, such as flexibility and rigidity. The predictive models offered insights into the complex interaction of formulation variables. This research is a pilot study that highlights the potential of machine learning as a transformative approach in the pharmaceutical field, paving the way for more efficient and informed drug development processes.

Processability evaluation of multiparticulate units prepared by selective laser sintering using the SeDeM Expert System approach.

3D printing in dosage forms fabrication is in the focus of researchers, however, the attempts in multiparticulate units (MPUs) preparation are scarce. The aim of this study was to fabricate different size MPUs by selective laser sintering (SLS), using different polymers, and investigate their processability based on the SeDeM Expert System approach. MPUs (1- or 2-mm size) were prepared with model drug (ibuprofen or caffeine), polymer (poly(ethylene)oxide (PEO), ethyl cellulose (EC) or methacrylic acid-ethyl acrylate copolymer (MA-EA)) and printing aid. Comprehensive sample characterization was performed and experimentally obtained parameters were mathematically transformed and evaluated using the SeDeM Expert System framework. The obtained samples exhibited irregular shape, despite the spherical printing object design. Polymer incorporated notably affected MPUs properties. The obtained samples exhibited low bulk density, good flowability-, as well as stability-related parameters, which indicated their suitability for filling into capsules or sachets. Low density values implied that compressibility enhancing excipients may be required for MPUs incorporation in tablets. Samples containing EC and MA-EA were found suitable for compression, due to high compacts tensile strength. The obtained results indicate that SeDeM Expert System may extended from powder compressibility evaluation tool to framework facilitating powders/multiparticulate units processing.

Investigation into liquisolid system processability based on the SeDeM Expert System approach.

Liquisolid systems are emerging formulation approach for poorly soluble drugs, based on adsorption/absorption of drug dispersion and obtaining free-flowing powder with good compressibility. SeDeM Expert System represents a powder processability evaluation method. It may provide additional insight into liquisolid systems critical quality attributes, but the contribution of this approach remains to be explored. The aims of this study were: pellet preparation by combination of liquisolid technology and water granulation/extrusion, evaluation of liquisolid based systems (pellets/admixtures) and investigation into the applicability of SeDeM Expert System in liquisolid systems characterization. Pellets/admixtures were prepared with microcrystalline cellulose as carrier and crospovidone/silicon dioxide as coating agent. Ibuprofen solution in polyethylene glycol 400 was used as liquid phase. After comprehensive sample characterization, experimentally obtained parameters were mathematically transformed and evaluated in the SeDeM Expert System framework. Pellets exhibited low aspect ratio and excellent flowability, despite liquid load up to 52.2%. The investigated liquisolid admixtures exhibited good flowability and faster drug dissolution than pellets. Single pellet crushing test results exhibited strong correlation with compact indentation hardness and may be used as indentation hardness predictor. SeDeM Expert System provides useful insight into liquisolid system processability and comparative evaluation and it may facilitate final solid dosage form development.

An Investigation into Mechanical Properties and Printability of Potential Substrates for Inkjet Printing of Orodispersible Films.

Inkjet printing is novel approach in drug manufacturing that enables dispensing precise volumes of ink onto substrates. Optimal substrate properties including suitable mechanical characteristic are recognized as crucial to achieve desired dosage form performance upon administration. Identification of relevant quality attributes and their quantification is subject of intensive scientific research. The aim of this work was to explore applicability of different materials as printing substrates and explore contribution of the investigated substrate properties to its printability. Substrates were characterized with regards to uniformity, porosity, disintegration time, mechanical properties and drug dissolution. Experimentally obtained values were mathematically transformed and the obtained results were presented as relevant radar charts. It was shown that structurally different substrates may be employed for orodispersible films inkjet printing. Main disadvantage of single-polymer films was low drug load, and their printability was dependent on film flexibility and mechanical strength. Structured orodispersible film templates exhibited favorable mechanical properties and drug load capacity. Wafer edible sheets were characterized with high mechanical resistance and brittleness which somewhat diminished printability, but did not hinder high drug load. Obtained results provide insight into application of different materials as printing substrates and contribute to understanding of substrate properties which can affect printability.