Cancer is a group of disorders characterized by uncontrolled cell growth that affects around 11 million people each year globally. Nanocarrier-based systems are extensively used in cancer imaging, diagnostics as well as therapeutics; owing to their promising features and potential to augment therapeutic efficacy. The focal point of research remains to develop new-fangled smart nanocarriers that can selectively respond to cancer-specific conditions and deliver medications to target cells efficiently. Nanocarriers deliver loaded therapeutic cargos to the tumour site either in a passive or active mode, with the least drug elimination from the drug delivery systems. This review chiefly focuses on current advances allied to smart nanocarriers such as dendrimers, liposomes, mesoporous silica nanoparticles, quantum dots, micelles, superparamagnetic iron-oxide nanoparticles, gold nanoparticles and carbon nanotubes, to list a few. Exhaustive discussion on crucial topics like drug targeting, surface decorated smart-nanocarriers and stimuli-responsive cancer nanotherapeutics responding to temperature, enzyme, pH and redox stimuli have been covered.

Current discoveries as well as research findings on various types of carbon nanostructures have inspired research into their utilization in a number of fields. These carbon nanostructures offer uses in pharmacy, medicine and different therapies. One such unique carbon nanostructure includes carbon nanotubes (CNTs), which are one-dimensional allotropes of carbon nanostructure that can have a length-to-diameter ratio greater than 1,000,000. After their discovery, CNTs have drawn extensive research attention due to their excellent material properties. Their physical, chemical and electronic properties are excellent and their composites provide great possibilities for enormous nanometer applications. The current study provides a systematic review based on prior literature review and data gathered from various sources. The various research studies from many research labs and organizations were systematically retrieved, collected, compiled and written. The entire collection and compilation of this review concluded the use of CNT approaches and their efficacy and safety for the treatment of various diseases such as brain tumors or cancer via nanotechnology-based drug delivery, phototherapy, gene therapy, antiviral therapy, antifungal therapy, antibacterial therapy and other biomedical applications. The current review covers diverse applications of CNTs in designing a range of targeted drug delivery systems and application for various therapies. It concludes with a discussion on how CNTs based medicines can expand in the future.

Parkinson’s disease (PD) and Alzheimer’s disease (AD) are neurodegenerative disorders that have emerged as among the serious health problems of the 21st century. The medications currently available to treat AD and PD have limited efficacy and are associated with side effects. Natural products are one of the most vital and conservative sources of medicines for treating neurological problems. Karanjin is a furanoflavonoid, isolated mainly from Pongamia pinnata with several medicinal plants, and has been reported for numerous health benefits. However, the effect of karanjin on AD and PD has not yet been systematically investigated. To evaluate the neuroprotective effect of karanjin, extensive in silico studies starting with molecular docking against five putative targets for AD and four targets for PD were conducted. The findings were compared with three standard drugs using Auto Dock 4.1 and Molegro Virtual Docker software. Additionally, the physiochemical properties (Lipinski rule of five), drug-likeness and parameters including absorption, distribution, metabolism, elimination and toxicity (ADMET) profiles of karanjin were also studied. The molecular dynamics (MD) simulations were performed with two selective karanjin docking complexes to analyze the dynamic behaviors and binding free energy at 100 ns time scale. In addition, frontier molecular orbitals (FMOs) and density-functional theory (DFT) were also investigated from computational quantum mechanism perspectives using the Avogadro-ORCA 1.2.0 platform. Karanjin complies with all five of Lipinski’s drug-likeness rules with suitable ADMET profiles for therapeutic use. The docking scores (kcal/mol) showed comparatively higher potency against AD and PD associated targets than currently used standard drugs. Overall, the potential binding affinity from molecular docking, static thermodynamics feature from MD-simulation and other multiparametric drug-ability profiles suggest that karanjin could be considered as a suitable therapeutic lead for AD and PD treatment. Furthermore, the present results were strongly correlated with the earlier study on karanjin in an Alzheimer’s animal model. However, necessary in vivo studies, clinical trials, bioavailability, permeability and safe dose administration, etc. must be required to use karanjin as a potential drug against AD and PD treatment, where the in silico results are more helpful to accelerate the drug development.

In recent years, hormone therapy has been shown to be a remarkable treatment option for cancer. Hormone treatment for gynecological cancers involves the use of medications that reduce the level of hormones or inhibit their biological activity, thereby stopping or slowing cancer growth. Hormone treatment works by preventing hormones from causing cancer cells to multiply. Aromatase inhibitors, anti-estrogens, progestin, estrogen receptor (ER) antagonists, GnRH agonists, and progestogen are effectively used as therapeutics for vulvar cancer, cervical cancer, vaginal cancer, uterine cancer, and ovarian cancer. Hormone replacement therapy has a high success rate. In particular, progestogen and estrogen replacement are associated with a decreased incidence of gynecological cancers in women infected with human papillomavirus (HPV). The activation of estrogen via the transcriptional functionality of ERα may either be promoted or decreased by gene products of HPV. Hormonal treatment is frequently administered to patients with hormone-sensitive recurring or metastatic gynecologic malignancies, although response rates and therapeutic outcomes are inconsistent. Therefore, this review outlines the use of hormonal therapy for gynecological cancers and identifies the current knowledge gaps.

Diabetic foot ulceration is the most distressing complication of diabetes having no standard therapy. Nanofibers are an emerging and versatile nanotechnology-based drug-delivery system with unique wound-healing properties. This study aimed to prepare and evaluate silk-sericin based hybrid nanofibrous mats for diabetic foot ulcer. The nanofibrous mats were prepared by electrospinning using silk sericin mixed with different proportions of polycaprolactone (PCL) and cellulose acetate (CA) loaded with ferulic acid (FA). The in vitro characterizations, such as surface morphology, mechanical properties, swelling behavior, biodegradability, scanning electron microscopy, and drug release were carried out. The SEM images indicated that nanofibers formed with varied diameters, ranging from 100 to 250 nm, and their tensile strength was found to range from 7 to 15 MPa. In vitro release demonstrated that the nanofibers sustained FA release over an extended time of period. In vitro cytotoxicity showed that the nanofibers possessed a lower cytotoxicity in HaCaT cells. The in vivo wound-healing studies demonstrated an excellent wound-healing efficiency of the nanofibers in diabetic rats. Furthermore, the histopathological studies showed the nanofibers’ ability to restore the skin’s normal structure. Therefore, it was concluded that the prepared silk-sericin-based hybrid nanofibers loaded with FA could be a promising drug-delivery platform for the effective treatment of diabetic foot ulcers.

This work presents the results of using tree-based models, including Gradient Boosting, Extra Trees, and Random Forest, to model the solubility of hyoscine drug and solvent density based on pressure and temperature as inputs. The models were trained on a dataset of hyoscine drug with known solubility and density values, optimized with WCA algorithm, and their accuracy was evaluated using R², MSE, MAPE, and Max Error metrics. The results showed that Gradient Boosting and Extra Trees models had high accuracy, with R² values above 0.96 and low MAPE and Max Error values for both solubility and density output. The Random Forest model was less accurate than the other two models. These findings demonstrate the effectiveness of tree-based models for predicting the solubility and density of chemical compounds and have potential applications in determination of drug solubility prior to process design by correlation of solubility and density to input parameters including pressure and temperature.