Carbasalate Calcium

In this study, we present an approach to neuropharmacological research by integrating few-shot meta-learning algorithms with brain activity mapping (BAMing) to enhance the discovery of central nervous system (CNS) therapeutics. By utilizing patterns from previously validated CNS drugs, our approach facilitates the rapid identification and prediction of potential drug candidates from limited datasets, thereby accelerating the drug discovery process. The application of few-shot meta-learning algorithms allows us to adeptly navigate the challenges of limited sample sizes prevalent in neuropharmacology. The study reveals that our meta-learning-based convolutional neural network (Meta-CNN) models demonstrate enhanced stability and improved prediction accuracy over traditional machine-learning methods. Moreover, our BAM library proves instrumental in classifying CNS drugs and aiding in pharmaceutical repurposing and repositioning. Overall, this research not only demonstrates the effectiveness in overcoming data limitations but also highlights the significant potential of combining BAM with advanced meta-learning techniques in CNS drug discovery.

Pulmonary toxicity has been associated with drug use. This is often not recognized in clinical practice, and underestimated.

We aimed to establish whether polymorphisms in certain genes corresponding with a metabolic pathway of drug(s) used are associated with pulmonary toxicity in patients with suspected drug-induced interstitial lung disease (DI-ILD).

This retrospective observational study explored genetic variations in three clinically relevant cytochrome P450 (CYP) iso-enzymes (i.e., CYP2D6, CYP2C9, and CYP2C19) in a group of patients with a fibroticinterstitial lung disease, either non-specific interstitial pneumonia (n = 211) or idiopathic pulmonary fibrosis (n = 256), with a suspected drug-induced origin.

Of the 467 patients, 79.0% showed one or more polymorphisms in the tested genes accompanied by the use of drug(s) metabolized by a corresponding affected metabolic pathway (60.0% poor metabolizers and/or using two or more drugs [likely DI-ILD], 37.5% using three or more [highly likely DI-ILD]). Most commonly used drugs were statins (63.1%) with a predominance among men (69.4 vs 47.1%, p < 0.0001). Nitrofurantoin, not metabolized by the tested pathways, was prescribed more frequently among women (51.9 vs 4.5%, p < 0.00001).

In our cohort with suspected DI-ILD, 79% carried one or more genetic variants accompanied by the use of drugs metabolized by a corresponding affected pathway. In 60%, the diagnosis of DI-ILD was likely, whereas in 37.5%, it was highly likely, based on CYP analyses. This study underlines the importance of considering both drug use and genetic make-up as a possible cause, or at least a contributing factor, in the development and/or progression of fibrotic lung diseases.

ClinicalTrials.gov identifier NCT00267800, registered in 2005.