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.

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.

Medication targeting to subcellular organelles: Emphasizing mitochondria as a therapeutic marvel-Current situation and future prospects.

Subcellular disorders are linked with several diseases, specifically mitochondrial dysfunction linked to age, metabolic disorders, cancer, cardiovascular disease, and other mitochondrial diseases (MDs). Intracellular medication delivery is a promising option for effective therapy. This study aims to highlight subcellular delivery with focus on mitochondrial pharmacology, gene therapy, transplantation, and drug targeting.

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.

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.

From Molecular Therapies to Lysosomal Transplantation and Targeted Drug Strategies: Present Applications, Limitations, and Future Prospects of Lysosomal Medications.

Lysosomes are essential intracellular organelles involved in plentiful cellular processes such as cell signaling, metabolism, growth, apoptosis, autophagy, protein processing, and maintaining cellular homeostasis. Their dysfunction is linked to various diseases, including lysosomal storage disorders, inflammation, cancer, cardiovascular diseases, neurodegenerative conditions, and aging. This review focuses on current and emerging therapies for lysosomal diseases (LDs), including small medicines, enzyme replacement therapy (ERT), gene therapy, transplantation, and lysosomal drug targeting (LDT).

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.

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.

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).

Molybdenum Disulfide Nanosheet-Based Nanocomposite for the Topical Delivery of Umbelliferone: Evaluation of Anti-inflammatory and Analgesic Potentials.

This study aimed to develop a nanocomposite formulation comprising umbelliferone (UMB) and molybdenum disulfide (MoS) nanosheets as a carrier, termed as the UMB-MoS nanocomposite in gel for topical delivery. MoS nanosheets were successfully synthesized via a green-hydrothermal reaction of 10 mg of ammonium molybdate and 10 mg of thiourea in 80 mL of deionized water under predetermined conditions. The UMB-MoS nanocomposite was prepared by sonicating UMB and MoS nanosheets (each of 1 mg/mL) in dimethylformamide.

A Comprehensive Study on Folate-Targeted Mesoporous Silica Nanoparticles Loaded with 5-Fluorouracil for the Enhanced Treatment of Gynecological Cancers.

Background: Gynecological cancers are a significant public health concern, accounting for 40% of all cancer incidence and 30% of deaths in women. 5-Fluorouracil (5-FU) can be used with chemotherapy to improve treatment in advanced-stage gynecological cancer. Mesoporous silica nanoparticles (MSNs) can improve drug effectiveness and reduce toxicity.

Imiquimod-Loaded Chitosan-Decorated Di-Block and Tri-Block Polymeric Nanoparticles Loaded In Situ Gel for the Management of Cervical Cancer.

Background: Cervical intraepithelial neoplasia, the predisposing factor for cervical cancer (CC), is caused by human papillomavirus (HPV) infection and can be treated with imiquimod (IMQ). However, poor water solubility and side effects such as local inflammation can render IMQ ineffective. The aim of this study is to design a prolonged release nano system in combination with mucoadhesive-thermosensitive properties for an effective vaginal drug delivery.

In Vitro Safety Assessment of In-House Synthesized Titanium Dioxide Nanoparticles: Impact of Washing and Temperature Conditions.

Titanium dioxide nanoparticles (TiO NPs) have been widely used in food, cosmetics, and biomedical research. However, human safety following exposure to TiO NPs remains to be fully understood. The aim of this study was to evaluate the in vitro safety and toxicity of TiO NPs synthesized via the Stöber method under different washing and temperature conditions.

Mesoporous Silica Nanoparticles Coated with Carboxymethyl Chitosan for 5-Fluorouracil Ocular Delivery: Characterization, In Vitro and In Vivo Studies.

This study investigates the development of topically applied non-invasive amino-functionalized silica nanoparticles (AMSN) and O-Carboxymethyl chitosan-coated AMSN (AMSN-CMC) for ocular delivery of 5-Fluorouracil (5-FU). Particle characterization was performed by the DLS technique (Zeta-Sizer), and structural morphology was examined by SEM and TEM. The drug encapsulation and loading were determined by the indirect method using HPLC.

The Design of Anionic Surfactant-Based Amino-Functionalized Mesoporous Silica Nanoparticles and their Application in Transdermal Drug Delivery.

Melanoma remains the most lethal form of skin cancer and most challenging to treat despite advances in the oncology field. Our work describes the utilization of nanotechnology to target melanoma locally in an attempt to provide an advanced and efficient quality of therapy. Amino-functionalized mesoporous silica nanoparticles (MSN-NH) were developed in situ through the utilization of anionic surfactant and different volumes of 3-aminopropyltriethoxysilane (APTES) as a co-structure directing agent (CSDA).

Employing a PLGA-TPGS based nanoparticle to improve the ocular delivery of Acyclovir.

Delivering drugs via the ocular route has always been a challenge for poorly soluble drugs. The various anatomical and physiological barriers in the eye cavity hinder the residence of drugs within the corneal and precorneal regions. In this study, the nanosystem that could sufficiently deliver the poorly soluble Acyclovir topically via ocular route.

Antifungal efficacy of Itraconazole loaded PLGA-nanoparticles stabilized by vitamin-E TPGS: In vitro and ex vivo studies.

Itraconazole (ITZ) loaded Poly-(D, L-lactic-co-glycolic acid, PLGA) nanoparticles (PLGA-NPs) stabilized by D-α-Tocopherol polyethylene-glycol succinate-1000 (TPGS) were developed by nanoprecipitation and single emulsion solvent evaporation methods to improve antifungal activity of ITZ by enhancing its solubility, and hence bioavailability. Encapsulation efficiency, drug loading, in-vitro release, ex-vivo permeation and antifungal activity were performed for the optimized PLGA-NPs. Characterization of PLGA-NPs were performed by scanning electron microscopy, dynamic light scattering, differential scanning calorimetry, Fourier transform infrared spectroscopy, and powder X-ray diffractometry.

Cellular uptake and internalization of hyaluronan-based doxorubicin and cisplatin conjugates.

Background: Hyaluronan (HA) is a ligand for the CD44 receptor which is crucial to cancer cell proliferation and metastasis. High levels of CD44 expression in many cancers have encouraged the development of HA-based carriers for anti-cancer therapeutics.

Purpose: The objective of this study was to determine whether HA conjugation of anticancer drugs impacts CD44-specific HA-drug uptake and disposition by human head and neck cancer cells.