Affinity of Antifungal Isoxazolo[3,4-b]pyridine-3(1H)-Ones to Phospholipids in Immobilized Artificial Membrane (IAM) Chromatography

Currently, rapid evaluation of the physicochemical parameters of drug candidates, such as lipophilicity, is in high demand owing to it enabling the approximation of the processes of absorption, distribution, metabolism, and elimination. Although the lipophilicity of drug candidates is determined using the shake flash method (n-octanol/water system) or reversed phase liquid chromatography (RP-LC), more biosimilar alternatives to classical lipophilicity measurement are currently available. One of the alternatives is immobilized artificial membrane (IAM) chromatography. The present study is a continuation of our research focused on physiochemical characterization of biologically active derivatives of isoxazolo[3,4-b]pyridine-3(1H)-ones. The main goal of this study was to assess the affinity of isoxazolones to phospholipids using IAM chromatography and compare it with the lipophilicity parameters established by reversed phase chromatography. Quantitative structure–retention relationship (QSRR) modeling of IAM retention using differential evolution coupled with partial least squares (DE-PLS) regression was performed. The results indicate that in the studied group of structurally related isoxazolone derivatives, discrepancies occur between the retention under IAM and RP-LC conditions. Although some correlation between these two chromatographic methods can be found, lipophilicity does not fully explain the affinities of the investigated molecules to phospholipids. QSRR analysis also shows common factors that contribute to retention under IAM and RP-LC conditions. In this context, the significant influences of WHIM and GETAWAY descriptors in all the obtained models should be highlighted.

QSAR and Docking Study of Isatin Analogues as Cytotoxic Agents

Computational chemistry is a unique method in the drug discovery process?? Explain Why?. In this study 109 molecules containing the isatin backbone were subjected to quantitative structure-activity relationship analysis to find the structure requirements for ligand binding. The structures were sketched and optimized in Hyperchem. The structural invariants used in this study were those obtained from whole molecular structures: by both hyperchem and dragon software (16 types of descriptors). Four chemometrics methods including MLR, FA-MLR, PCR and GA-PLS were employed to make connections between structural parameters and anticancer effects. MLR models revealed the effects of constitutional, functional, geometrical, WHIM and GETAWAY descriptors having higher impact on anticancer activity of the compounds. GA-PLS showed functional, constitutional and chemical descriptor indices to be the most significant parameters on anticancer activity. Moreover, the result of FA-MLR analysis revealed the effects of functional descriptors on the anticancer activity. A comparison between the different statistical methods employed and the results indicated that GA-PLS represented superior results and could explain and predict 81% and 78% variances in the PIC50 data, respectively. Docking studies of these compounds were also investigated and promising results were obtained showing that some compounds were introduced as a good candidate for cancer agents.

MLR-Based QSAR Models for Predicting Inhibitory Activity of Reverse Transcriptase by HEPT Derivatives using GETAWAY Descriptors

In this study, quantitative structure-activity relationship (QSAR) models for non-nucleoside reverse transcriptase inhibitors based on 1-[(2-hydroxyethoxy)-methyl]-6-(phenylthio)thymine (HEPT) derivatives were generated. The structures of the compounds and their activities were obtained from the literature. The data set were divided into two sets: training set (N=91) and validating set (N=10). All 3-D structures of these inhibitors were optimized by semi-empirical method, AM1 prior to calculations of 3-D molecular descriptors, GETAWAY. Multiple linear regression (MLR) using stepwise method was applied to determined significant descriptors. Out of 197 GETAWAY descriptors, 4-14 molecular descriptors have significant relationships with the activities (expressed as log (1/EC50)) of HEPT. The MLR method generated 14 models. The predictive power of these models were evaluated internally by applying the following statistical parameters for the training set and test set: root-mean-square error for prediction (RMSE), correlation coefficient (R), squared correlation coefficient (R2), adjusted squared correlation coefficient (R2adj), difference between R2 and R2adj (R2 – R2adj), squared cross-validation correlation coefficient (Q2). External validation was performed by employing Golbraikh and Tropsha criteria. Moreover, residual analysis was performed. Internal validation of Model XX (N = 91) revealed that it has the highest predictive power (RMSE = 0.4288, R = 0.9393, R2 = 0.882, R2adj = 0.8620, R2 - R2adj = 0.0203, Q2 = 0.8317). However, external validation (using the validating set, N=10) showed that Model XII has the highest predictive power (R2 = 0.961, R20 = 0.9565, k = 0.8648, k’ = 0.9800, [R2 - R20] = 0.0066, [R2 - R20] /R2 = 0.0069, R2pred = 0.9481) based on Golbraikh and Tropsha criteria. Residual analysis confirmed that both models are valid.