Revisiting blood-brain barrier: A chromatographic approach

Combining SILCS and Artificial Intelligence for High-Throughput Prediction of the Passive Permeability of Drug Molecules

Membrane permeability of drug molecules plays a significant role in the development of new therapeutic agents. Accordingly, methods to predict the passive permeability of drug candidates during a medicinal chemistry campaign offer the potential to accelerate the drug design process. In this work, we combine the physics-based site identification by ligand competitive saturation (SILCS) method and data-driven artificial intelligence (AI) to create a high-throughput predictive model for the passive permeability of druglike molecules. In this study, we present a comparative analysis of four regression models to predict membrane permeabilities of small druglike molecules; of the tested models, Random Forest was the most predictive yielding an R2 of 0.81 for the independent data set. The input feature vector used to train the developed prediction model includes absolute free energy profiles of ligands through a POPC-cholesterol bilayer based on ligand grid free energy (LGFE) profiles obtained from the SILCS approach. The use of the membrane free energy profiles from SILCS offers information on the physical forces contributing to ligand permeability, while the use of AI yields a more predictive model trained on experimental PAMPA permeability data for a collection of 229 molecules. This combination allows for rapid estimations of ligand permeability at a level of accuracy beyond currently available predictive models while offering insights into the contributions of the functional groups in the ligands to the permeability barrier, thereby offering quantitative information to facilitate rational ligand design.

Solute–Solvent Interactions in Hydrophilic Interaction Liquid Chromatography: Characterization of the Retention in a Silica Column by the Abraham Linear Free Energy Relationship Model

The Abraham linear free energy relationship model has been used to characterize a hydrophilic interaction liquid chromatography (HILIC) silica column with acetonitrile/water and methanol/water mobile phases. Analysis by the model for acetonitrile/water mobile phases points to solute volume and hydrogen bond basicity as the main properties affecting retention, whereas solute hydrogen bond acidity, dipolarity and polarizability practically do not affect it. Formation of a cavity is easier in acetonitrile-rich mobile phases than in the aqueous stationary phase, and hence increase of solute volume decreases retention. Conversely, hydrogen bond acidity is stronger in the aqueous stationary phase than in the acetonitrile-rich mobile phase and thus an increase of solute hydrogen bond basicity increases retention. Results are similar for methanol/water mobile phases with the difference that solute hydrogen bond acidity is significant too. Increase in hydrogen bond acidity of the solute decreases retention showing that methanol mobile phases must be better hydrogen bond acceptors than acetonitrile ones, and even than water-rich stationary phases. The results are like the ones obtained in zwitterionic HILIC columns bonded to silica or polymer supports for acetonitrile/water mobile phases, but different for solute hydrogen bond acidity for a polymer bonded zwitterionic column with methanol/water mobile phases, indicating that bonding support plays an important role in HILIC retention. Comparison to RPLC characterized systems confirms the complementarity of HILIC systems to RPLC ones because the main properties affecting retention are the same but with reversed coefficients. The least retained solutes in RPLC are the most retained in HILIC.

Linear free energy relationship models for the retention of partially ionized acid-base compounds in reversed-phase liquid chromatography

The LFER model of Abraham is applied to the retention of the neutral and ionic forms of 94 solutes in a C18 column and 40% v/v acetonitrile/water mobile phase. The results show that polarizability and cavity formation interactions increase retention, whereas dipole and hydrogen bonding interactions favours partition to the mobile phase and thus, they decrease retention. The coefficients of the ionic descriptors measure the effect of the electrostatic interactions and their contribution to partition of the cation or anion between the two mobile and stationary chromatographic phases. A new LFER model for application to the retention of partially dissociated acids and bases is derived averaging the descriptors of the neutral and ionic forms according to their degrees of ionization in the mobile phase. This new LFER model is satisfactorily compared to other literature modified Abraham models for a set of 498 retention data of partially dissociated acids and bases. All tested models require the calculation of the ionization degrees of the compounds at the measuring pH. Calculation of the ionization degrees in the chromatographic mobile phase (i.e. from pH and pK_a in the eluent) give good correlations for all tested models. However, estimation of these ionization degrees from pH - pK_a data in pure water gives biased estimations of the retention of the partially ionized solutes.

Application of separation methods for in vitro prediction of blood-brain barrier permeability-The state of the art

Despite many efforts, drug discovery pipeline is still a highly inefficient process. Nowadays, when combinatorial chemistry enables to synthesize hundreds of new drugs candidates, methods for rapid assessment of biopharmaceutical parameters of new compounds are highly desired. Over one-third of drugs candidates is rejected because of unsatisfactory pharmacokinetic properties. In the drug discovery process, the blood-brain barrier (BBB) permeability plays a critical role for central nervous system active drugs candidates as well as non-central nervous system active drugs. For this reason, knowledge on the BBB permeability of compounds is essential in the development of new medicines. The review was focused on the application of different separation methods for BBB permeability assessment. Both chromatographic and electrophoretic methods were described. In the article, the advantages and limitations of well-established chromatographic methods like immobilized artificial membrane chromatography or micellar liquid chromatography, and less common techniques were discussed. Special attention was devoted to methods were microemulsion is used as mobile or pseudostationary phases.

PerMM: A Web Tool and Database for Analysis of Passive Membrane Permeability and Translocation Pathways of Bioactive Molecules

The PerMM web server and database were developed for quantitative analysis and visualization of passive translocation of bioactive molecules across lipid membranes. The server is the first physics-based web tool that calculates membrane binding energies and permeability coefficients of diverse molecules through artificial and natural membranes (phospholipid bilayers, PAMPA-DS, blood-brain barrier, and Caco-2/MDCK cell membranes). It also visualizes the transmembrane translocation pathway as a sequence of translational and rotational positions of a permeant as it moves across the lipid bilayer, along with the corresponding changes in solvation energy. The server can be applied for prediction of permeability coefficients of compounds with diverse chemical scaffolds to facilitate selection and optimization of potential drug leads. The complementary PerMM database allows comparison of computationally and experimentally determined permeability coefficients for more than 500 compounds in different membrane systems. The website and database are freely accessible at https://permm.phar.umich.edu/ .