Synthesizing micro-/nano-sized pharmaceutical compounds with an appropriate size distribution is a method often followed to enhance drug delivery and reduce side effects. Supercritical CO₂ (carbon dioxide) is a well-known solvent utilized in the pharmaceutical synthesis process. Reliable knowledge of a drug’s solubility in supercritical CO₂ is necessary for feasible study, modeling, design, optimization, and control of such a process. Therefore, the current study constructs a stacked/ensemble model by combining three up-to-date machine learning tools (i.e., extra tree, gradient boosting, and random forest) to predict the solubility of twelve anticancer drugs in supercritical CO₂. An experimental databank comprising 311 phase equilibrium samples was gathered from the literature and applied to design the proposed stacked model. This model estimates the solubility of anticancer drugs in supercritical CO₂ as a function of solute and solvent properties and operating conditions. Several statistical indices, including average absolute relative deviation (AARD = 8.62%), mean absolute error (MAE = 2.86 × 10⁻⁶), relative absolute error (RAE = 2.42%), mean squared error (MSE = 1.26 × 10⁻¹⁰), and regression coefficient (R² = 0.99809) were used to validate the performance of the constructed model. The statistical, sensitivity, and trend analyses confirmed that the suggested stacked model demonstrates excellent performance for correlating and predicting the solubility of anticancer drugs in supercritical CO₂.

This study constructs a machine learning method to simultaneously analyze the thermodynamic behavior of many polymer–drug systems. The solubility temperature of Acetaminophen, Celecoxib, Chloramphenicol, D-Mannitol, Felodipine, Ibuprofen, Ibuprofen Sodium, Indomethacin, Itraconazole, Naproxen, Nifedipine, Paracetamol, Sulfadiazine, Sulfadimidine, Sulfamerazine, and Sulfathiazole in 1,3-bis[2-pyrrolidone-1-yl] butane, Polyvinyl Acetate, Polyvinylpyrrolidone (PVP), PVP K12, PVP K15, PVP K17, PVP K25, PVP/VA, PVP/VA 335, PVP/VA 535, PVP/VA 635, PVP/VA 735, Soluplus analyzes from a modeling perspective. The least-squares support vector regression (LS-SVR) designs to approximate the solubility temperature of drugs in polymers from polymer and drug types and drug loading in polymers. The structure of this machine learning model is well-tuned by conducting trial and error on the kernel type (i.e., Gaussian, polynomial, and linear) and methods used for adjusting the LS-SVR coefficients (i.e., leave-one-out and 10-fold cross-validation scenarios). Results of the sensitivity analysis showed that the Gaussian kernel and 10-fold cross-validation is the best candidate for developing an LS-SVR for the given task. The built model yielded results consistent with 278 experimental samples reported in the literature. Indeed, the mean absolute relative deviation percent of 8.35 and 7.25 is achieved in the training and testing stages, respectively. The performance on the largest available dataset confirms its applicability. Such a reliable tool is essential for monitoring polymer–drug systems’ stability and deliverability, especially for poorly soluble drugs in polymers, which can be further validated by adopting it to an actual implementation in the future.

A new glucosyl flavone, 5,7,2′,5′-tetrahydroxyflavone 7-O-β-d-glucopyranoside, named loeflingiin, together with apigenin 6-C-glucoside (isovitexin), coumarins citropten and isompinellin, triterpenoids betulin and betulinic acid, and a mixture of phytosterols β-sitosterol, stigmasterol and campesterol were isolated for the first time from the leaves of wild Plantago loeflingii L. (Plantaginaceae) collected in the Iraqi Kurdistan region. The plant is used by local people to treat wounds and as a vulnerary remedy. The structures of isolated compounds were determined by spectroscopic analysis. The activities of isovitexin and loeflingiinon the viability of breast (MCF7), ovarian (BG-1), endometrial (Ishikawa), and mesothelioma (IST-MES1) human cancer cells and two normal cell lines were determined with an MTT assay. Notably, the new 7-O-glucosyl flavone showed effects higher than cisplatin against the Ishikawa and IST-MESI cell lines. The significant biological activities exhibited by all the compounds isolated from P. loeflingii provided scientific evidence to support the use of the plant in the Kurdish traditional medicine.

The volatile oils hydrodistilled from the aerial parts and roots of O. carduchorum C.C Towns. (Fabaceae) have been chemically characterized for the first time. A total of 43 constituents with an abundance >0.03% were identified and quantified in the two oils by GC/MS and GC/FID analyses. They comprise 38 components (98.58%) of the oil isolated from the aerial parts (OCA) and 34 components (93.33%) of the oil from the roots (OCR). Six constituents, α-pinene (23.11 ± 0.1%), β-elemene (17.33 ± 0.1%), 1,8-cineole (12.15 ± 0.2%), furfural (7.91 ± 0.1%), terpineol-4-ol (6.32 ± 0.2%), and limonene (4.13 ± 0.1%), accounted for about 75% of the total OCA oil. On the other hand, 1,8-cineole (15.79 ± 0.1%), furfural (10.44 ± 0.1%), β-elemene (10.14 ± 0.2%), α-terpineol (7.74 ± 0.1%), linalool (7.45 ± 0.1%), and α-pinene (4.76 ± 0.1%) made up about 60% of the OCR oil. The IC₅₀ values of the scavenging activities of the OCA and OCR oils towards the DPPH radical and H₂O₂ were 79.8 ± 0.5 and 153.3 ± 0.6 μg/mL and 394.09 ± 0.2 and 311.67 ± 0.4 μg/mL, respectively. In addition, in the MTS assay, the OCA and OCR oils showed significant antiproliferative effects against T47D, MDA-MB-453, BG-1, and A549 human cancer cells that were more powerful than those against two normal human cell lines, HEK-293 and HFF-1. The abundant presence of β-elemene as an antiproliferative component of the two oils suggested the existence of a new chemotype of O. carduchorum.

Omicron variants have been classified as Variants of Concern (VOC) by the World Health Organization (WHO) ever since they first emerged as a result of a significant mutation in this variant, which showed to have an impact on transmissibility and virulence of the virus, as evidenced by the ongoing modifications in the SARS-CoV-2 virus. As a global pandemic, the Omicron variant also spread among the Kurdish population. This study aimed to analyze different strains from different cities of the Kurdistan region of Iraq to show the risk of infection and the impact of the various mutations on immune responses and vaccination. A total of 175 nasopharyngeal/oropharyngeal specimens were collected at West Erbil Emergency Hospital and confirmed for SARS-CoV-2 infection by RT-PCR. The genomes of the samples were sequenced using the Illumina COVID-Seq Method. The genome analysis was established based on previously published data in the GISAID database and compared to previously detected mutations in the Omicron variants, and that they belong to the BA.1 lineage and include most variations determined in other studies related to transmissibility, high infectivity and immune escape. Most of the mutations were found in the RBD (receptor binding domain), the region related to the escape from humoral immunity. Remarkably, these point mutations (G339D, S371L, S373P, S375F, T547K, D614G, H655Y, N679K and N969K) were also determined in this study, which were unique, and their impact should be addressed more. Overall, the Omicron variants were more contagious than other variants. However, the mortality rate was low, and most infectious cases were asymptomatic. The next step should address the potential of Omicron variants to develop the next-generation COVID-19 vaccine.