Advanced modeling based on machine learning for evaluation of drug nanoparticle preparation via green technology: Theoretical assessment of solubility variations

Preparation of solid-dosage nanomedicine via green chemistry route: Advanced computational simulation of nanodrug solubility prediction using machine learning models

Computational simulation of molecular separation in liquid phase using membrane systems: Combination of computational fluid dynamics and machine learning

Experimental validation and modeling study on the drug solubility in supercritical solvent: Case study on Exemestane drug

Computational estimation of drug’s concentration distribution through a microporous membrane using artificial intelligence approach

We have developed ANFIS (adaptive neuro-fuzzy inference system) modeling strategy for investigations on molecular separation using membrane systems. The results can be useful for purification and isolation of pharmaceutical molecules during manufacturing, and also for purification of water effluents. The developed methodology is based on combination of machine learning model which is ANFIS in this case and also computational fluid dynamics (CFD). The integration between two models is built through prediction of drug concentration in the system via both method in which the CFD results are considered as inputs to the ANFIS model. In order to analyze the existing data set, which has 2 inputs r and z and one output C , we used the optimized ANFIS model. A total of over 8 thousand data rows are included in this data set which are produced using the CFD simulation of membrane separation. The ANFIS model has been optimized using three population-based methods, namely Genetic Algorithm (GA), Firefly Algorithm (FA), and Bat Algorithm (BA), in order to achieve desired results. The optimization of hyper-parameters with all three techniques has had significant results and all have shown an R 2 score higher than 0.99. In terms of MSE, the error rate decreased to 3.907 and the RMSE optimized to 1.02E+01. • Computational simulation of drug separation using membranes. • ANFIS simulation of molecular separation via polymeric membranes. • Optimization of ANFIS parameters using three optimization methods. • Combination of CFD and ANFIS in membrane simulation. 

Theoretical investigations on the liquid-phase molecular separation in isolation and purification of pharmaceutical molecules from aqueous solutions via polymeric membranes

Green processing based on supercritical carbon dioxide for preparation of nanomedicine: Model development using machine learning and experimental validation

Docetaxel (DTX) is classified as a class IV drug that exhibits poor aqueous solubility (6–7 µg/mL in water) and permeability (P-glycoprotein substrate). The main objective of this study was to construct, characterize, and evaluate docetaxel loaded nanomicellar formulation in vitro for oral delivery to enhance the absorption and bioavailability of DTX, as well as to circumvent P-gp efflux inhibition. Formulations were prepared with two polymeric surfactants, hydrogenated castor oil-40 (HCO-40) and D-α-Tocopherol polyethylene glycol 1000 succinate (VIT E TPGS) with solvent evaporation technique, and the resulting DTX nanomicellar formulations were characterized by proton nuclear magnetic resonance spectroscopy (1H NMR), Fourier Transform Infrared Spectroscopy (FT–IR), X-ray powder diffraction (XRD), and transmission electron microscopy (TEM). Proton NMR, FT–IR, and XRD data indicated that DTX was completely encapsulated within the hydrophobic core of the nanomicelles in its amorphous state. TEM data revealed a smooth spherical shape of the nanomicellar formulation. The optimized formulation (F-2) possessed a mean diameter of 13.42 nm, a zeta potential of −0.19 mV, with a 99.3% entrapment efficiency. Dilution stability study indicated that nanomicelles were stable up to 100-fold dilution with minimal change in size, poly dispersity index (PDI), and zeta potential. In vitro cytotoxicity study revealed higher anticancer activity of DTX nanomicelles at 5 µM compared to the native drug against breast cancer cell line (MCF-7) cells. The LC–MS data confirmed the chemical stability of DTX within the nanomicelles. In vitro drug release study demonstrated faster dissolution of DTX from the nanomicelles compared to the naked drug. Our experimental results exhibit that nanomicelles could be a drug delivery system of choice to encapsulate drugs with low aqueous solubility and permeability that can preserve the stability of the active constituents to provide anticancer activity.

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Self-Assembled Nanomicellar Formulation of Docetaxel as a Potential Breast Cancer Chemotherapeutic System

BACKGROUND: Reading can be described as a complex cognitive process of decrypting signs to create meaning. Eventually, it is a way of language achievement, communication, and sharing information and ideas. Changing lighting and color are known to improve visual comfort and the perceptual difficulties that affect reading for those with poor vision.
AIM: This study aims to investigate the effect of changing the wavelengths and different levels of positional noise on reading performance for participants with best-corrected distant visual acuity (BCVA) of 6/6 or better.
METHODOLOGY: Twenty English speakers with BCVA 6/6 or better were asked to read words presented in a printed format. The stimuli were black print words in a horizontal arrangement on matte white card. They were degraded using positional noise produced by random vertical displacements of the letter position below or above the horizontal line on three levels.
RESULTS: Introducing positional noise affected word recognition differently with different wavelengths. The role of short wavelength in enhancing orthographic reading and word recognition is clear – they reduce the effects of positional noise. The error rate and duration time have different effects with different wavelengths, even when positional noise is introduced.
CONCLUSION: The reading rate is not affected by changing the wavelength of the light. However, the mean differences in wpm were affected by changing the wavelengths. Also, introducing positional noise affects word recognition differently with different wavelengths.

/pdf/the-effect-of-color-and-positional-noise-on-reading-2zh336fo7y.pdf

The Effect of Color and Positional Noise on Reading Performance in Human Vision

We have developed computational modeling based on machine learning and computational fluid dynamics for purification and isolation of Ibuprofen from aqueous solutions. The considered separation and purification system is a membrane-based process which is used to selectively remove Ibuprofen from liquid phase. The computational fluid dynamics (CFD) method was performed to obtain the concentration of the drug in the membrane system, and then the concentration values were used as inputs to a number of machine learning models to build the hybrid model of process. Indeed, we dealt with a dataset of 8K data points generated from a CFD simulation. As core models, Multilayer Perceptron (MLP), Lasso, and Support Vector Regression (SVR) were used. To improve efficiency, a technique known as bagging has been added to these models. We optimized the models to find optimal hyper-parameters. With R 2 metric, all three models have scores above 0.995 so all models have acceptable performances. When we consider the MAE metric, the lowest error is related to the BAGGING+MLP model with a value of 5.805 × 10 1 , and the BAGGING+LASSO and BAGGING+SVR models have an error rate of 1.401 × 10 2 and 1.055 × 10 2 , respectively. Finally BAGGING+MLP can be introduced as the most accurate model. • Machine learning modeling of molecular separation via membranes. • Optimization of machine learning using Bagging technique. • All three optimized machine learning models had scores above 0.995. • The method of MLP indicated to perform better than other models. 

Abdullah Alsalhi

Papers

Theoretical investigations on the liquid-phase molecular separation in isolation and purification of pharmaceutical molecules from aqueous solutions via polymeric membranes

Green processing based on supercritical carbon dioxide for preparation of nanomedicine: Model development using machine learning and experimental validation

Self-Assembled Nanomicellar Formulation of Docetaxel as a Potential Breast Cancer Chemotherapeutic System

The Effect of Color and Positional Noise on Reading Performance in Human Vision

Computational simulation and investigations on the molecular separation of Ibuprofen from aqueous solution using polymeric membranes