Predicting the Blood-Brain Barrier Permeability of New Drug-Like Compounds via HPLC with Various Stationary Phases

Abstract

The permeation of the blood-brain barrier is a very important consideration for new drug candidate molecules. In this research, the reversed-phase liquid chromatography with different columns (Purosphere RP-18e, IAM.PC.DD2 and Cosmosil Cholester) was used to predict the penetration of the blood-brain barrier by 65 newly-synthesized drug-like compounds. The linear free energy relationships (LFERs) model (log BB = c + eE + sS + aA + bB + vV) was established for a training set of 23 congeneric biologically active azole compounds with known experimental log BB (BB = Cblood/Cbrain) values (R2 = 0.9039). The reliability and predictive potency of the model were confirmed by leave-one-out cross validation as well as leave-50%-out cross validation. Multiple linear regression (MLR) was used to develop the quantitative structure-activity relationships (QSARs) to predict the log BB values of compounds that were tested, taking into account the chromatographic lipophilicity (log kw), polarizability and topological polar surface area. The excellent statistics of the developed MLR equations (R2 > 0.8 for all columns) showed that it is possible to use the HPLC technique and retention data to produce reliable blood-brain barrier permeability models and to predict the log BB values of our pharmaceutically important molecules.

Predicting Pharmacokinetic Properties of Potential Anticancer Agents via Their Chromatographic Behavior on Different Reversed Phase Materials

“…Retention parameters, especially log k w values, measured by a column or by planar Reversed-Phase Liquid Chromatography (RPLC), are the most popular, and they are accepted as chromatographic lipophilicity descriptors by the Organization of Economic Co-operation and Development. In addition, liquid chromatography techniques are very popular as indirect in vitro methods for the determination of lipophilicity [ 5 , 6 , 7 ]. The chromatographic methods used to assess lipophilicity have significant advantages, e.g., simplicity and reduced experimental time, good reproducibility, process automation, no need for quantitative analysis, and small amounts of samples required.…”

Towards Deep Neural Network Models for the Prediction of the Blood–Brain Barrier Permeability for Diverse Organic Compounds

“…Simple rules of thumb or scores to predict BBB-penetrating compounds, similar to the drug-likeness filters, have been formulated [ 27 , 28 ]. As additional descriptors, orbital and solvation energies calculated by quantum chemistry methods [ 29 ], membrane permeation parameters derived from molecular dynamics simulations [ 30 , 31 , 32 ], and even experimental parameters from chromatography [ 33 , 34 , 35 ] and ion mobility spectroscopy [ 36 ], can be used. External estimates of probability that a compound will undergo active efflux mediated by P‑glycoprotein (P-gp) can also be included [ 21 , 24 , 37 ].…”

Permeability Data of Organosulfur Garlic Compounds Estimated by Immobilized Artificial Membrane Chromatography: Correlation Across Several Biological Barriers

“…Thus, the combination of log k ’ (IAM) and TPSA yielded the best OSC-BBB model classificatory. TPSA has also been related to BBB permeation models in several investigations ( Luco, 1999 ; Luco and Marchevsky, 2006 ; Janicka et al, 2020 ) showing that the polar surface area is one of the most important factors determining the passage of molecules through the BBB.…”