Alleviation of Autophagic Deficits and Neuroinflammation by Histamine H3 Receptor Antagonist E159 Ameliorates Autism-Related Behaviors in Mice.

Background/objectives: Autism spectrum disorder (ASD) is a neurodevelopmental condition marked by social interaction difficulties, repetitive behaviors, and immune dysregulation with elevated pro-inflammatory markers. Autophagic deficiency also contributes to social behavior deficits in ASD. Histamine H3 receptor (H3R) antagonism is a potential treatment strategy for brain disorders with features overlapping ASD, such as schizophrenia and Alzheimer's disease.

Perry Disease: Current Outlook and Advances in Drug Discovery Approach to Symptomatic Treatment.

Perry disease (PeD) is a rare, neurodegenerative, genetic disorder inherited in an autosomal dominant manner. The disease manifests as parkinsonism, with psychiatric symptoms on top, such as depression or sleep disorders, accompanied by unexpected weight loss, central hypoventilation, and aggregation of DNA-binding protein (TDP-43) in the brain. Due to the genetic cause, no causal treatment for PeD is currently available.

AR71, Histamine H Receptor Ligand-In Vitro and In Vivo Evaluation (Anti-Inflammatory Activity, Metabolic Stability, Toxicity, and Analgesic Action).

The future of therapy for neurodegenerative diseases (NDs) relies on new strategies targeting multiple pharmacological pathways. Our research led to obtaining the compound AR71 [(E)-3-(3,4,5-trimethoxyphenyl)-1-(4-(3-(piperidin-1-yl)propoxy)phenyl)prop-2-en-1-one], which has high affinity for human HR (K = 24 nM) and selectivity towards histamine H and H receptors (K > 2500 nM), and showed anti-inflammatory activity in a model of lipopolysaccharide-induced inflammation in BV-2 cells. The presented tests confirmed its antagonist/inverse agonist activity profile and good metabolic stability while docking studies showed the binding mode to histamine H, H, and H receptors.

Machine Learning Methods in Protein-Protein Docking.

An exponential increase in the number of publications that address artificial intelligence (AI) usage in life sciences has been noticed in recent years, while new modeling techniques are constantly being reported. The potential of these methods is vast-from understanding fundamental cellular processes to discovering new drugs and breakthrough therapies. Computational studies of protein-protein interactions, crucial for understanding the operation of biological systems, are no exception in this field.

Docking Foundations: From Rigid to Flexible Docking.

Despite the development of methods for the experimental determination of protein structures, the dissonance between the number of known sequences and their solved structures is still enormous. This is particularly evident in protein-protein complexes. To fill this gap, diverse technologies have been developed to study protein-protein interactions (PPIs) in a cellular context including a range of biological and computational methods.