This article reviews prior work studying reaction kinetics in solution, with the goal of using this information to improve detailed kinetic modeling in the solvent phase.Both experimental and computational methods for calculating reaction rates in liquids are reviewed. Previous studies, which used such methods to determine solvent effects, are then analyzed based on reaction family. Many of these studies correlate kinetic solvent effect with one or more solvent parameters or properties of reacting species, but it is not always possible, and investigations are usually done on too few reactions and solvents to truly generalize. From these studies, we present suggestions on how best to use data to generalize solvent effects for many different reaction types in a high throughput manner. Lead-in

Background Investigation of the Alzheimer’s Disease (AD) brain proteome, along with weighted co‐expression network analysis, revealed a module enriched with proteins involved in inflammation (PMID: 27989508). This module progressively increases in brains from Controls to Asymptomatic AD to those with Symptomatic AD. Moesin (MSN) and CD44 emerged as key inflammation module hub proteins. MSN is a cytoskeletal protein with roles in focal adhesion‐mediated cell motility; CD44 is a transmembrane receptor that interacts with MSN. This project aims to develop chemical tools to disrupt this pathway. Method To investigate the AD‐relevant distribution of MSN, tissue sections and cells were stained with MSN‐specific antibodies as well as with Iba1, 4G8 and pHF‐Tau antibodies. Purified FERM domains of MSN and EPB41L3 were produced in E. coli and co‐crystallized or soaked with ligands after crystallization. The CD44 ectodomain was expressed in bacteria and refolded or secreted from HEK293 cells. DNA‐encoded library screens were performed as described (http://www.x‐chemrx.com/our‐science/). Result MSN‐staining in cortical human brain sections localised to endothelial cells and microglia‐like cells. MSN‐positive cells also stained for microglia‐specific marker, Iba1. In AD brains, MSN‐positive microglia‐like cells were present around amyloid plaques and tangle‐like structures. These immunohistochemical studies confirmed the association of MSN with AD pathology. Co‐crystal structures of Moesin and the related FERM protein EPB41L3 revealed the binding mode of the intracellular tail of CD44 to Moesin. This binding interaction was then used to develop HTRF‐based assays for high‐throughput screening for molecules that disrupt the CD44‐MSN interaction. In parallel, a crystal‐based fragment screen using EPB41L3 identified binding pockets and initial hits. Hits and chemical staring points will be further developed with the Open‐AD Medicinal Chemistry core at UNC. To identify inhibitors of CD44 ectodomain binding to hyaluronic acid we screened DNA‐encoded libraries. The screens identified micromolar hits which are being further developed, aiming to obtain potent brain‐penetrant inhibitors of CD44. Conclusion (1) Moesin expression is associated with AD pathology. (2) The CD44‐Moesin signalling axis was targeted for potential points of intervention. (3) We have developed the assays, structural biology and chemical starting points to develop small‐molecule inhibitors of the CD44 ectodomain and the CD44‐MSN interaction.

<div> <div> <div> <p>This article reviews prior work studying reaction kinetics in solution, with the goal of using this information to improve detailed kinetic modeling in the solvent phase. Both experimental and computational methods for calculating reaction rates in liquids are reviewed. Previous studies, which used such methods to determine solvent effects, are then analyzed based on reaction family. Many of these studies correlate kinetic solvent effect with one or more solvent parameters or properties of reacting species, but it is not always possible, and investigations are usually done on too few reactions and solvents to truly generalize. From these studies, we present suggestions on how best to use data to generalize solvent effects for many different reaction types in a high throughput manner. </p> </div> </div> </div>