Recent advances in JAK inhibitors for the treatment of metabolic syndrome.

With an epidemic spread, metabolic syndrome represents an increasingly emerging risk for the population globally, and is currently recognized as a pathological entity. It is represented by a cluster of different conditions including increased blood pressure, high blood sugar, excess body fat around the waist and abnormal cholesterol or triglyceride levels. These conditions lead directly to several disorders, including obesity, dyslipidemia, hyperglycaemia, insulin resistance, impaired glucose tolerance and hypertension causing an increase in cardiovascular risk and in particular atherosclerotic disease. Despite efforts to promote healthier lifestyles through exercise, reduced caloric intake, and improved dietary choices, the incidence and prevalence of metabolic syndrome continue to rise worldwide. Recent research has highlighted the involvement of signaling pathways in chronic inflammatory conditions like obesity and type 2 diabetes mellitus, revealing the significance of the JAK/STAT pathway in atherosclerotic events. This pathway serves as a rapid membrane-to-nucleus signaling module that regulates the expression of critical mediators. Consequently, JAK inhibitors (JAKi) have emerged as potential therapeutic options for metabolic diseases, offering a promising avenue for intervention. The aim of this review is to shed light on the emerging indications of JAK inhibitors in metabolic syndrome, emphasizing their potential role in attenuating associated inflammatory processes, improving insulin sensitivity, and addressing cross-talk with the insulin pathway, with the intention of contributing to efforts in the field of inflammation pharmacology.

Pharmacological Approaches to Modulate the Scarring Process after Glaucoma Surgery.

Glaucoma is an acquired optic neuropathy that results in a characteristic optic nerve head appearance and visual field loss. Reducing the IOP is the only factor that can be modified, and the progression of the disease can be managed through medication, laser treatment, or surgery. Filtering procedures are used when target pressure cannot be obtained with less invasive methods. Nevertheless, these procedures require accurate control of the fibrotic process, which can hamper filtration, thus, negatively affecting the surgical success. This review explores the available and potential pharmacological treatments that modulate the scarring process after glaucoma surgery, analyzing the most critical evidence available in the literature. The modulation of scarring is based on non-steroidal anti-inflammatory drugs (NSAIDs), mitomycin, and 5-fluorouracil. In the long term, the failure rate of filtering surgery is mainly due to the limitations of the current strategies caused by the complexity of the fibrotic process and the pharmacological and toxicological aspects of the drugs that are currently in use. Considering these limitations, new potential treatments were investigated. This review suggests that a better approach to tackle the fibrotic process may be to hit multiple targets, thus increasing the inhibitory potential against excessive scarring following surgery.

Machine Learning in Early Prediction of Metabolism of Drugs.

Machine learning (ML) has increasingly been applied to predict properties of drugs. Particularly, metabolism can be predicted with ML methods, which can be exploited during drug discovery and development. The prediction of metabolism is a crucial bottleneck in the early identification of toxic metabolites or biotransformation pathways that can affect elimination of the drug and potentially hinder the development of future new drugs. Metabolism prediction can be addressed with the application of ML models trained on large and validated dataset, from early stages of lead optimization to latest stage of drug development. ML methods rely on molecular descriptors that allow to identify and learn chemical and molecular features to predict sites of metabolism (SoMs) or activity associated with mechanism of inhibition (e.g., CYP inhibition). The application of ML methods in the prediction of drug metabolism represents a powerful resource to be exploited during drug discovery and development. ML allows to improve in silico screening and safety assessments of drugs in advance, steering their path to marketing authorization. Prediction of biotransformation reactions and metabolites allows to shorten the time, save the cost, and reduce animal testing. In this context, ML methods represent a technique to fill data gaps and an opportunity to reduce animal testing, calling for the 3R principles within the Big Data era.