Arthritis is a chronic inflammatory condition that affects millions of individuals worldwide. The conventional treatment options for arthritis often come with limitations and potential side effects, leading to increased interest in herbal plants as alternative therapies. This article provides a comprehensive overview of the use of herbal plants in arthritis treatment, focusing on their traditional remedies, active components, mechanisms of action, and pharmaceutical approaches for enhancing their delivery. Various herbal plants, including turmeric, ginger, Boswellia, and willow bark, have shown anti-inflammatory and analgesic properties, making them valuable options for managing arthritis symptoms. The active components of these herbal plants, such as curcumin, gingerols, and boswellic acids, contribute to their therapeutic effects. To enhance the delivery of herbal medicines, pharmaceutical approaches like nanoparticle-based drug delivery systems, liposomes, polymeric nanoparticles, nanoemulsions, microneedles, and inhalation systems have been explored. These approaches aim to improve bioavailability, targeted delivery, and controlled release of herbal compounds. Safety considerations, including potential interactions with medications and the risk of allergic reactions, are also discussed. Future perspectives for this field involve conducting well-designed clinical studies, enhancing standardization and quality control measures, exploring novel drug delivery systems, and fostering collaborations between traditional medicine practitioners and healthcare professionals. Continued research and development in these areas will help unlock the full potential of herbal plants in arthritis treatment, offering personalized and effective care for affected individuals.
Being a candidate of BCS class II, dolutegravir (DTG), a recently approved antiretroviral drug, possesses solubility issues. The current research was aimed to improve the solubility of the DTG and thereby enhance its efficacy using the solid dispersion technique. In due course, the miscibility study of the drug was performed with different polymers, where Poloxamer 407 (P407) was found suitable to move forward. The solid dispersion of DTG and P407 was formulated using solvent evaporation technique with a 1:1 proportion of drug and polymer, where the solid-state characterization was performed using differential scanning calorimetry, Fourier transform infrared spectroscopy and X-ray diffraction. No physicochemical interaction was found between the DTG and P407 in the fabricated solid dispersion; however, crystalline state of the drug was changed to amorphous as evident from the X-ray diffractogram. A rapid release of DTG was observed from the solid dispersion (>95%), which is highly significant (p<0.05) as compared to pure drug (11.40%), physical mixture (20.07%) and marketed preparation of DTG (35.30%). The drug release from the formulated solid dispersion followed Weibull model kinetics. Finally, the rapid drug release from the solid dispersion formulation revealed increased Cmax (14.56 μg/mL) when compared to the physical mixture (4.12 μg/mL) and pure drug (3.45 μg/mL). This was further reflected by improved bioavailability of DTG (AUC: 105.99±10.07 μg/h/mL) in the experimental Wistar rats when compared to the AUC of animals administered with physical mixture (54.45±6.58 μg/h/mL) and pure drug (49.27±6.16 μg/h/mL). Therefore, it could be concluded that the dissolution profile and simultaneously the bioavailability of DTG could be enhanced by means of the solid dispersion platform using the hydrophilic polymer, P407, which could be projected towards improved efficacy of the drug in HIV/AIDS.
Drug discovery and development (DDD) is a highly complex process that necessitates precise monitoring and extensive data analysis at each stage. Furthermore, the DDD process is both time-consuming and costly. To tackle these concerns, artificial intelligence (AI) technology can be used, which facilitates rapid and precise analysis of extensive datasets within a limited timeframe. The pathophysiology of cancer disease is complicated and requires extensive research for novel drug discovery and development. The first stage in the process of drug discovery and development involves identifying targets. Cell structure and molecular functioning are complex due to the vast number of molecules that function constantly, performing various roles. Furthermore, scientists are continually discovering novel cellular mechanisms and molecules, expanding the range of potential targets. Accurately identifying the correct target is a crucial step in the preparation of a treatment strategy. Various forms of AI, such as machine learning, neural-based learning, deep learning, and network-based learning, are currently being utilised in applications, online services, and databases. These technologies facilitate the identification and validation of targets, ultimately contributing to the success of projects. This review focuses on the different types and subcategories of AI databases utilised in the field of drug discovery and target identification for cancer.
Premixed insulins are an important tool for glycemic control in persons with diabetes. Equally important in diabetes care is the selection of the most appropriate insulin regimen for a particular individual at a specific time. Currently, the choice of insulin regimens for initiation or intensification of therapy is a subjective decision. In this article, we share insights, which will help in rational and objective selection of premixed formulations for initiation and intensification of insulin therapy. The glycemic status and its variations in a person help to identify the most appropriate insulin regimen and formulation for him or her. The evolution of objective glucometric indices has enabled better glycemic monitoring of individuals with diabetes. Management of diabetes has evolved from a 'glucocentric' approach to a 'patient-centered' approach; patient characteristics, needs, and preferences should be evaluated when considering premixed insulin for treatment of diabetes.Funding: Novo Nordisk, India.