The Tetrahydroisoquinoline Scaffold in ABC Transporter Inhibitors that Act as Multidrug Resistance (MDR) Reversers

BACKGROUND

The failure of anticancer chemotherapy is often due to the development of resistance to a variety of anticancer drugs. This phenomenon is called multidrug resistance (MDR) and is related to the overexpression of ABC transporters, such as P-glycoprotein, multidrug resistance- associated protein 1 and breast cancer resistance protein. Over the past few decades, several ABC protein modulators have been discovered and studied as a possible approach to evade MDR and increase the success of anticancer chemotherapy. Nevertheless, the co-administration of pump inhibitors with cytotoxic drugs, which are substrates of the transporters, does not appear to be associated with an improvement in the therapeutic efficacy of antitumor agents. However, more recently discovered MDR reversing agents, such as the two tetrahydroisoquinoline derivatives tariquidar and elacridar, are characterized by high affinity towards the ABC proteins and by reduced negative properties. Consequently, many analogs of these two derivatives have been synthesized, with the aim of optimizing their MDR reversal properties.

OBJECTIVE

This review aims to describe the MDR modulators carrying the tetraidroisoquinoline scaffold reported in the literature in the period 2009-2021, highlighting the structural characteristics that confer potency and/or selectivity towards the three ABC transport proteins.

RESULTS AND CONCLUSION

Many compounds have been synthesized in the last twelve years showing interesting properties, both in terms of potency and selectivity. Although clear structure-activity relationships can be drawn only by considering strictly related compounds, some of the compounds reviewed could be promising starting points for the design of new ABC protein inhibitors.

Image-based QSAR Model for the Prediction of P-gp Inhibitory Activity of Epigallocatechin and Gallocatechin Derivatives

BACKGROUND

Permeability glycoprotein (P-gp) is one of the cell membrane proteins that can push some drugs out of the cell causing drug tolerance and its inhibition can prevent drug resistance.

OBJECTIVE

In this study, we used image-based Quantitative Structure-Activity Relationship (QSAR) models to predict the P-gp inhibitory activity of epigallocatechin and gallocatechin derivatives.

METHODS

The 2D-chemical structures and their P-gp inhibitory activity were taken from literature. The pixels of images and their Principal Components (PCs) were calculated using MATLAB software. Principle Component Regression (PCR), Artificial Neural Network (ANN) and Support Vector Machine (SVM) approaches were used to develop QSAR models. Statistical parameters included the leave one out cross-validated correlation coefficient (q2) for internal validation of the models and R2 of test set, Root Mean Square Error (RMSE) and Concordance Correlation Coefficient (CCC) were applied for external validation.

RESULTS

Six PCs from image analysis method were selected by stepwise regression for developing linear and non-linear models. Non-linear models i.e. ANN (with the R2 of 0.80 for test set) were chosen as the best for the established QSAR models.

CONCLUSION

According to the result of the external validation, ANN model based on image analysis method can predict the P-gp inhibitory activity of epigallocatechin and gallocatechin derivatives better than the PCR and SVM models.

MIA-QSAR based model for bioactivity prediction of flavonoid derivatives as acetylcholinesterase inhibitors

Alzheimer's disease is a common form of dementia, which considered to be a major health concern. Multivariate Image Analysis - Quantitative Structure-Activity Relationship (MIA-QSAR) is a simple and quite accessible QSAR method for predicting biological activities of unstudied compounds based on 2D image analysis. This study focuses on constructing an efficient QSAR model using a dataset of 52 flavonoid derivatives (substituted with amino-alkyl, alkoxy, alkyl-amines, and piperidine groups) as active compounds against acetylcholinesterase inhibitors (AChE). The model was constructed by PLS (Partial Least Square) using NIPALS (Non-Linear iterative Partial Least Square) algorithm. The comparable values obtained from calibration of training set using five latent variables (R² = 0.955) and external validation of test set (Q² = 0.948) confirmed the precision in the prediction of bioactivities for the set of flavonoid derivatives used in designing the model.