Computational simulation and target prediction studies of solubility optimization of decitabine through supercritical solvent.

Computational analysis of drug solubility was carried out using machine learning approach. The solubility of Decitabine as model drug in supercritical CO2 was studied as function of pressure and temperature to assess the feasibility of that for production of nanomedicine to enhance the solubility. The data was collected for solubility optimization of Decitabine at the temperature 308-338 K, and pressure 120-400 bar used as the inputs to the machine learning models. A dataset of 32 data points and two inputs (P and T) have been applied to optimize the solubility. The only output is Y = solubility, which is Decitabine mole fraction solubility in the solvent. The developed models are three models including Kernel Ridge Regression (KRR), Decision tree Regression (DTR), and Gaussian process (GPR), which are used for the first time as a novel model. These models are optimized using their hyper-parameters tuning and then assessed using standard metrics, which shows R2-score, KRR, DTR, and GPR equal to 0.806, 0.891, and 0.998. Also, the MAE metric shows 1.08E-04, 7.40E-05, and 9.73E-06 error rates in the same order. The other metric is MAPE, in which the KRR error rate is 4.64E-01, DTR shows an error rate equal to 1.63E-01, and GPR as the best mode illustrates 5.06E-02. Finally, analysis using the best model (GPR) reveals that increasing both inputs results in an increase in the solubility of Decitabine. The optimal values are (P = 400, T = 3.38E + 02, Y = 1.07E-03).

Application of CO2 Supercritical Fluid to Optimize the Solubility of Oxaprozin: Development of Novel Machine Learning Predictive Models.

Over the last years, extensive motivation has emerged towards the application of supercritical carbon dioxide (SCCO2) for particle engineering. SCCO2 has great potential for application as a green and eco-friendly technique to reach small crystalline particles with narrow particle size distribution. In this paper, an artificial intelligence (AI) method has been used as an efficient and versatile tool to predict and consequently optimize the solubility of oxaprozin in SCCO2 systems. Three learning methods, including multi-layer perceptron (MLP), Kriging or Gaussian process regression (GPR), and k-nearest neighbors (KNN) are selected to make models on the tiny dataset. The dataset includes 32 data points with two input parameters (temperature and pressure) and one output (solubility). The optimized models were tested with standard metrics. MLP, GPR, and KNN have error rates of 2.079 × 10-8, 2.173 × 10-9, and 1.372 × 10-8, respectively, using MSE metrics. Additionally, in terms of R-squared, they have scores of 0.868, 0.997, and 0.999, respectively. The optimal inputs are the same as the maximum possible values and are paired with a solubility of 1.26 × 10-3 as an output.

A Novel Use of Allopurinol as A Quorum-Sensing Inhibitor in Pseudomonas aeruginosa.

Pseudomonas aeruginosa can cause a variety of healthcare-associated infections by its arsenal of virulence factors. Virulence factor production is largely controlled by the cell-to-cell communication system termed quorum sensing (QS). Targeting QS may be a good approach to inhibit the production of virulence factors and attenuate pathogenicity without exerting selective stress on bacterial growth. This will greatly reduce the emergence of resistant mutants. In this work, we investigated the anti-virulence and anti-QS activities of the FDA-approved drug allopurinol against the P. aeruginosa PAO1 strain. Allopurinol at 200 µg/mL (1/10 MIC) significantly decreased the production of the QS-controlled Chromobacterium violaceum CV026 violet pigment violacein and other P. aeruginosa QS-controlled virulence factors phenotypically. Furthermore, allopurinol reduced the infiltration of P. aeruginosa and leucocytes and diminished the congestion in the liver and kidney tissues of infected mice. In silico study showed that allopurinol could compete with the autoinducers on binding to the receptors LasR and RhlR by hydrogen bonding. On the molecular level, qRT-PCR proved that allopurinol showed a significant downregulating effect on all tested QS-encoding genes that regulate virulence factor production. In summary, allopurinol is a promising QS inhibitor that may be useful in the future treatment of P. aeruginosa infection.

Tackling Virulence of Pseudomonas aeruginosa by the Natural Furanone Sotolon.

The bacterial resistance development due to the incessant administration of antibiotics has led to difficulty in their treatment. Natural adjuvant compounds can be co-administered to hinder the pathogenesis of resistant bacteria. Sotolon is the prevailing aromatic compound that gives fenugreek its typical smell. In the current work, the anti-virulence activities of sotolon on Pseudomonas aeruginosa have been evaluated. P. aeruginosa has been treated with sotolon at sub-minimum inhibitory concentration (MIC), and production of biofilm and other virulence factors were assessed. Moreover, the anti-quorum sensing (QS) activity of sotolon was in-silico evaluated by evaluating the affinity of sotolon to bind to QS receptors, and the expression of QS genes was measured in the presence of sotolon sub-MIC. Furthermore, the sotolon in-vivo capability to protect mice against P. aeruginosa was assessed. Significantly, sotolon decreased the production of bacterial biofilm and virulence factors, the expression of QS genes, and protected mice from P. aeruginosa. Conclusively, the plant natural substance sotolon attenuated the pathogenicity of P. aeruginosa, locating it as a plausible potential therapeutic agent for the treatment of its infections. Sotolon can be used in the treatment of bacterial infections as an alternative or adjuvant to antibiotics to combat their high resistance to antibiotics.

Development of Sustained Release Baricitinib Loaded Lipid-Polymer Hybrid Nanoparticles with Improved Oral Bioavailability.

Baricitinib (BTB) is an orally administered Janus kinase inhibitor, therapeutically used for the treatment of rheumatoid arthritis. Recently it has also been approved for the treatment of COVID-19 infection. In this study, four different BTB-loaded lipids (stearin)-polymer (Poly(d,l-lactide-co-glycolide)) hybrid nanoparticles (B-PLN1 to B-PLN4) were prepared by the single-step nanoprecipitation method. Next, they were characterised in terms of physicochemical properties such as particle size, zeta potential (ζP), polydispersity index (PDI), entrapment efficiency (EE) and drug loading (DL). Based on preliminary evaluation, the B-PLN4 was regarded as the optimised formulation with particle size (272 ± 7.6 nm), PDI (0.225), ζP (-36.5 ± 3.1 mV), %EE (71.6 ± 1.5%) and %DL (2.87 ± 0.42%). This formulation (B-PLN4) was further assessed concerning morphology, in vitro release, and in vivo pharmacokinetic studies in rats. The in vitro release profile exhibited a sustained release pattern well-fitted by the Korsmeyer-Peppas kinetic model (R2 = 0.879). The in vivo pharmacokinetic data showed an enhancement (2.92 times more) in bioavailability in comparison to the normal suspension of pure BTB. These data concluded that the formulated lipid-polymer hybrid nanoparticles could be a promising drug delivery option to enhance the bioavailability of BTB. Overall, this study provides a scientific basis for future studies on the entrapment efficiency of lipid-polymer hybrid systems as promising carriers for overcoming pharmacokinetic limitations.