Development of a Sensitive Fluorimetric Method for Atomoxetine Determination in Tablets and Plasma Using a Dansyl Chloride Derivatization Approach.

New, eco-friendly method for analyzing atomoxetine (AXT) was developed using dansyl chloride as a fluorescent derivative. The method demonstrated a linear range from 50.0 to 900.

Advanced hybrid intelligence evaluation of nanomedicine delivery to various organs using machine learning and adaptive tree structured Parzen estimator.

We have examined the efficiency of drug delivery for targeted therapy by theoretical models. Machine learning strategy was tested to analyze the drug delivery of nanomedicines to the desired sites for efficient treatment. The inputs to the models are properties of nanoparticles, tumor model, cancer type, administration dose of drug, while the outputs are delivery efficiency of drug in various organs.

Combination of machine learning and Raman spectroscopy for prediction of drug release in targeted drug delivery formulations.

In this research, advanced regression techniques are investigated for modeling intricate release patterns utilizing a high-dimensional dataset comprising more than 1500 spectrum-based variables and categorical inputs. The spectral data are collected from Raman spectroscopy for analysis of drug release from a solid dosage formulation coated with Polysaccharides (a high-dimensional dataset of 155 samples, with drug release measured at 2, 8, and 24 h). The considered drug is 5-aminosalicylic acid for colonic drug delivery, and its release was estimated using Raman data as inputs along with other categorical parameters.

Machine learning-based analysis on pharmaceutical compounds interaction with polymer to estimate drug solubility in formulations.

This study introduces a sophisticated predictive framework for determining drug solubility and activity values in formulations via machine learning. The framework utilizes a comprehensive dataset consisting of more than 12,000 data rows and 24 input features containing a wide range of parameters to estimate drug solubility in formulation. The primary goal is to improve the accuracy of predictions by using ensemble learning techniques.

Computational investigation of Belzutifan drug delivery mechanisms using SiC nanocrystals via combined DFT and molecular dynamics approaches.

This study employs density functional theory (DFT) and molecular dynamics (MD) simulations to investigate silicon carbide (SiC) nanocrystals as carriers for the anticancer drug Belzutifan. Among tested functional groups (-H, -OH, -NH₂, -COOH), carboxyl-functionalized SiC (SiC-COOH) exhibits superior drug loading capacity with an adsorption energy of -1.03 eV, representing a 25% improvement over conventional carbon-based carriers.

Enhancement of piperine solubility by solid dispersion using Kollidone VA64 and Soluplus as polymers: physicochemical characterization, molecular docking and cell viability assay.

Piperine (PRN) is a water-insoluble alkaloidal drug reported for different biological activities. As part of this study, Kollidone VA64 (KLD) and Soluplus (SLP) were used as carriers to develop piperine solid dispersions (PRN SDs) to enhance their solubility. The stability constant of the drug-polymer composition was determined by the phase solubility study.

Analysis of drug crystallization by evaluation of pharmaceutical solubility in various solvents by optimization of artificial intelligence models.

For analysis of crystallization, the solubility of drug in solvents should be correlated to input parameters. In this investigation, the solubility of salicylic acid as drug model in a variety of solvents is predicted through the utilization of multiple machine learning techniques. The dataset consists of 217 data points, each of which contains 15 input features, including pressure, temperature, and a variety of solvents.

A novel ultrasensitive derivatization-free synchronous fluorescence approach for the simultaneous analysis of propofol and nalbuphine in human plasma and dosage forms: Compliance with greenness and blueness metrics.

This study presents a very sensitive and eco-friendly synchronous spectrofluorimetric method for the simultaneous quantification of propofol (PRP) and nalbuphine (NAL) for the first time. The technique used the intrinsic fluorescence characteristics of the two drugs, providing enhanced sensitivity and specificity. The two drugs were assessed simultaneously at 217 nm and 281 nm for PRP and NAL, respectively, with a synchronous wavelength difference (Δλ) of 80 nm.

Computational study of carboplatin interaction with PEG-functionalized C fullerene as a drug carrier using DFT and molecular dynamics simulations.

In this research, the interaction of carboplatin with polyethylene glycol (PEG) functionalized iron-encapsulated fullerene (Fe@C) molecule was investigated using Density Functional Theory (DFT) and molecular dynamics simulations (MD). Our results indicate that the inclusion of PEG enhances the stability of the Fe@C molecule, leading to a shift in the formation energy of the structures from approximately - 3.4 to - 4.

Estimation and validation of solubility of recombinant protein in E. coli strains via various advanced machine learning models.

This study presents a comprehensive approach to predicting solubility of recombinant protein in four E. coli samples by employing machine learning techniques and optimization algorithms. Various models, including AdaBoost, Decision Tree Regression (DT), Gaussian Process Regression (GPR), and K-Nearest Neighbors (KNN) are applied to capture the intricate relationships between experimental factors and protein solubility.

Advanced analysis on the correlation of salicylic acid solubility to solvent composition, temperature and pressure via machine learning approach.

This work aims to use powerful machine learning methods to predict salicylic acid solubility in various solvents as function of pressure and temperature. Using a dataset consisting of 217 data points and 15 input features, the analysis was performed using variables including pressure, temperature, and 13 different solvents as integral aspects. The considered solvents for this study included: ethanol, water, methanol, ethyl acetate, PEG 300, 1,4-dioxane, 1-propanol, 1-butanol, 1-pentanol, 1-hexanol, 1-heptanol, acetonitrile, and acetone.

A DFT insight into the potential of cycloparaphenylenes as efficient sensors for detecting Paracetamol.

Paracetamol (PCT) frequently contaminates natural water sources, posing potential risks to both human health and ecosystems. This study presents a computational investigation into the sensing capabilities of methylene-bridged [n]cycloparaphenylene ([n]MCPP, where n= 6, 8, and 10) nanorings for the detection of paracetamol using density functional theory (DFT) calculations. It was found that the stability of PCT@[n]MCPP complexes increases with the size of the [n]MCPP nanorings.

Computational intelligence investigations on evaluation of salicylic acid solubility in various solvents at different temperatures.

This research shows the utilization of various tree-based machine learning algorithms with a specific focus on predicting Salicylic acid solubility values in 13 solvents. We employed four distinct models: cubist regression, gradient boosting (GB), extreme gradient boosting (XGB), and extra trees (ET) for correlation of drug solubility to pressure, temperature, and solvent composition. The dataset was preprocessed using the Standard Scaler to standardize it, ensuring each feature has a mean of zero and a standard deviation of one, followed by outlier detection with Cook's distance.

Spectacular Application of Isoindole-Based Fluorophore for Mirabegron Assay in Tablets and Biological Fluids (Urine/Plasma). Assessment of Method Blueness and Whiteness.

The FDA has approved mirabegron (MBG) for alleviating urgency in micturition and incontinence. Its mechanism of action involves the activation of β-adrenoreceptors. The present research outlines a novel, highly sensitive, and cost-effective protocol for analyzing MBG.

Formulation and Evaluation of a Silymarin Inclusion Complex-Based Gel for Skin Cancer.

Silymarin (SLM) is a bioactive, water-insoluble flavonoid reported against different types of cancer. In the present research, the SLM inclusion complex was prepared by the freeze-drying method using different cyclodextrins. The phase solubility study was performed to assess the stability constant and complexation efficiency.

Development of a CFD simulation for the analysis of CO separation percentage using novel [emim][CN] ionic liquid solution inside the gas-liquid contactor.

Growing emission of environmentally-hazardous greenhouse pollutants (especially CO) has motivated the researchers to apply gas-liquid membrane contactors as an easy-to-operate and cost-effective technique for increasing their separation efficiency from different sources. In the current decades, ionic liquids (ILs) have shown their potential in the gas separation industry owing to their noteworthy advantages such as great capacity, excellent adjustability and suitable thermal/chemical stability compared to commonly-employed amine absorbents. This investigation aims to analytically/numerically determine the separation yield of CO from CO₂/N gaseous flow using novel -Ethyl-3-methylimidazolium dicyanamide ([emim][CN]) IL inside the gas-liquid contactor.

A New and Safe Delivery of Sildenafil Citrate Co-Evaporate Loaded Emulgels for the Cure of Certain Male Sexual Dysfunctions.

The present work focuses on the production of sildenafil co-evaporates loaded emulgels as topical dosage forms for the treatment of premature ejaculation and erectile dysfunction. Topical administration of sildenafil citrate (SILD) co-evaporates is expected to improve the bioavailability profile of the drug and to avoid the severe side effects accompanying the traditional SILD dosage forms, especially for prohibited cardiovascular cases. Firstly, the solubility of SILD was improved via solid dispersion via co-evaporation technique using PEG-5KDa and PVP-K90 as hydrophilic carriers.

Intelligence analysis of drug nanoparticles delivery efficiency to cancer tumor sites using machine learning models.

This study focuses on the use of machine learning (ML) models to predict the biodistribution of nanoparticles in various organs, using a dataset derived from research on nanoparticle behavior for cancer treatment. The dataset includes both categorical and numerical variables related to nanoparticle properties, with a focus on their distribution across organs such as the tumor, heart, liver, spleen, lung, and kidney tissues. In order to address the complex and non-linear nature of the data, three machine learning models were utilized: Bayesian Ridge Regression (BRR), Kernel Ridge Regression (KRR), and K-Nearest Neighbors (KNN).

Ultrasensitive analysis of the commonly abused CNS antitussive, dextromethorphan in biological fluids and dosage forms using a novel micellar-sensitized spectrofluorimetric approach: Compliance with greenness and blueness metrics.

The current study introduces the first micellar-enhanced spectrofluorimetric approach for the estimation of the commonly abused CNS antitussive, dextromethorphan (DXM) in its syrup and biological fluids. A micellar solution of sodium dodecyl sulfate (SDS) containing DXM showed high native fluorescence emission at 305 nm following excitation at 224 nm. Using SDS as a micellar system resulted in about a 2.

A Novel Ultrasensitive Second Derivative Synchronous Spectrofluorimetric Approach for the Simultaneous Analysis of the Co-Administered Antihypertensives, Telmisartan and Carvedilol in Biological Fluids and Tablets: Greenness and Blueness Assessment.

Hypertension is the predominant risk factor for cardiovascular diseases and mortality. This study presents the first method for the simultaneous analysis of the co-administered antihypertensive drugs, Carvedilol (CAR) and Telmisartan (TEL) using a fast, highly sensitive, environmentally friendly, and cost-effective second derivative synchronous spectrofluorimetric approach. The fluorescence of CAR and TEL was quantified at 243 nm and 274.

Computational intelligence analysis on drug solubility using thermodynamics and interaction mechanism via models comparison and validation.

This study investigates the application of various regression models for predicting drug solubility in polymer and API-polymer interactions in complex datasets. Four models-Gaussian Process Regression (GPR), Support Vector Regression (SVR), Bayesian Ridge Regression (BRR), and Kernel Ridge Regression (KRR)-are evaluated. Preprocessing the dataset using the Z-score approach helped to detect outliers, further improving the accuracy and dependability of the analysis.

Streamlined turn-off fluorescence sensing of Donepezil: Enhancing evaluation in pharmaceutical formulations, quality control labs, and biological fluids at nano levels.

Donepezil (DPZ) is used to treat Alzheimer's disease by increasing acetylcholine in the brain, necessitating precise analytical methods for its evaluation. This study aims to develop and validate a new turn-off fluorescence sensing method for quantifying DPZ, enhancing its evaluation in pharmaceutical formulations, quality control laboratories, and biological fluids. By leveraging the fluorescence-quenching interplay between DPZ and tetrabromofluorescein, the assay parameters were fine-tuned to enhance the development of an ion-associated complex.

Nano-scale multi-spectroscopic analysis of the anti-fibromyalgia drug pregabalin based on dihydropyridine ring formation with sustainability and whiteness evaluation.

This research addresses the challenge of analyzing pregabalin, a primary amine in a zwitterionic structure, which is difficult to evaluate due to its lack of chromatophore. The study introduces a derivatization assessment using Hantzsch's multicomponent organic reaction to enhance the detectability of pregabalin by forming a highly fluorescent dihydropyridine derivative. This process involves the condensation of pregabalin with acetylacetone and formaldehyde, yielding a yellowish-green compound measurable both colorimetrically at 338 nm and fluorimetrically at an emission wavelength of 486 nm (λ = 408 nm).

Design and evaluation of sulfadiazine derivatives as potent dual inhibitors of EGFR and EGFR: integrating biological, molecular docking, and ADMET analysis.

A series of derivatives (5-14) were synthesized through the diazotization of sulfadiazine with active methylene compounds. The chemical structures of these newly designed compounds were validated through spectral and elemental analysis techniques. The antiproliferative potential of derivatives 5-14 was assessed against three distinct cancer cell lines (A431, A549, and H1975) using the MTT assay.

Design and synthesis of high quantum yield doped carbon nano probe derived from household sources for sensing of the anti-GERD drug pantoprazole.

This contribution aims to design and validate a new green, cheap, and fast approach for determining the anti-GERD drug pantoprazole in different matrices. New S and N-doped carbon nanomaterials (S,N-CNMs) have been prepared via microwave irradiation of a mixture of widely available household sources. Remarkably, the utilization of a blend of carbamide and thiocarbamide with table sugar yields S,N-CNMs exhibiting the utmost quantum yield (54 %), hydrophilicity, as well as stable, homogeneous, and diminutive particle size distribution.