The SLC6A15-SLC6A20 Neutral Amino Acid Transporter Subfamily: Functions, Diseases, and Their Therapeutic Relevance.

The neutral amino acid transporter subfamily that consists of six members, consecutively SLC6A15-SLC620, also called orphan transporters, represents membrane, sodium-dependent symporter proteins that belong to the family of solute carrier 6 (SLC6). Primarily, they mediate the transport of neutral amino acids from the extracellular milieu toward cell or storage vesicles utilizing an electric membrane potential as the driving force. Orphan transporters are widely distributed throughout the body, covering many systems; for instance, the central nervous, renal, or intestinal system, supplying cells into molecules used in biochemical, signaling, and building pathways afterward. They are responsible for intestinal absorption and renal reabsorption of amino acids. In the central nervous system, orphan transporters constitute a significant medium for the provision of neurotransmitter precursors. Diseases related with aforementioned transporters highlight their significance; SLC6A19 mutations are associated with metabolic Hartnup disorder, whereas altered expression of SLC6A15 has been associated with a depression/stress-related disorders. Mutations of SLC6A18-SLCA20 cause iminoglycinuria and/or hyperglycinuria. SLC6A18-SLC6A20 to reach the cellular membrane require an ancillary unit ACE2 that is a molecular target for the spike protein of the SARS-CoV-2 virus. SLC6A19 has been proposed as a molecular target for the treatment of metabolic disorders resembling gastric surgery bypass. Inhibition of SLC6A15 appears to have a promising outcome in the treatment of psychiatric disorders. SLC6A19 and SLC6A20 have been suggested as potential targets in the treatment of COVID-19. In this review, we gathered recent advances on orphan transporters, their structure, functions, related disorders, and diseases, and in particular their relevance as therapeutic targets. SIGNIFICANCE STATEMENT: The following review systematizes current knowledge about the SLC6A15-SLCA20 neutral amino acid transporter subfamily and their therapeutic relevance in the treatment of different diseases.

Artificial Intelligence-Based Quantitative Structure-Property Relationship Model for Predicting Human Intestinal Absorption of Compounds with Serotonergic Activity.

Oral medicines represent the largest pharmaceutical market area. To achieve a therapeutic effect, a drug must penetrate the intestinal walls, the main absorption site for orally delivered active pharmaceutical ingredients (APIs). Indeed, predicting drug absorption can facilitate candidate screening and reduce time to market. Algorithms are available with good prediction accuracy that however focus only on solubility. In this work, we focused on drug permeability looking at human intestinal absorption as a marker for intestinal bioavailability. Being of considerable therapeutic relevance, APIs with serotonergic activity were selected as a dataset. Due to process complexity, experimental data scarcity, and variability, we turned toward an artificial intelligence (AI)-based system, which is a hierarchical combination of classification and regression models. This combination of seemingly two models into a single system widens the space of molecules classified as highly permeable with high accuracy. The specialized and optimized system enables in silico and structure-based prediction with a high degree of certainty. Predictions in external validation allowed correct selection of the 38% of highly permeable molecules without any false positives. The proposed system based on AI represents a promising tool useful for oral drug screening at an early stage of drug discovery and development. Datasets and the obtained models are available on the GitHub platform (https://github.com/nczub/HIA_5-HT).