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On the relationship of anthranilic derivatives structure and the FXR (Farnesoid X receptor) agonist activity

Thales Kronenberger1,2, Björn Windshügel2, Carsten Wrenger1

  • 1a Unit for Drug Discovery, Department of Parasitology, Institute of Biomedical Sciences , University of São Paulo , São Paulo , Brazil.

Journal of Biomolecular Structure & Dynamics
|December 15, 2017
PubMed

Insights

Quantitative structure-activity relationship (QSAR) models incorporating standard deviation (SD) of EC50 values enhance predictions for Farnesoid X receptor (FXR) agonists. This approach improves accuracy for drug discovery targeting metabolic diseases.

Area of Science:

  • Medicinal Chemistry
  • Computational Chemistry
  • Pharmacology

Background:

  • Farnesoid X receptor (FXR) is a key regulator of lipid and glucose homeostasis, making it a significant therapeutic target for metabolic diseases like diabetes, dyslipidemia, and liver cancer.
  • Numerous FXR agonists have been identified, with some progressing to clinical trials for liver disorders, underscoring the need for advanced predictive modeling.

Purpose of the Study:

  • To develop quantitative structure-activity relationship (QSAR) models for anthranilic acid derivatives (AADs) to elucidate the structural basis of Farnesoid X receptor (FXR) activation.
  • To evaluate the impact of incorporating the standard deviation (SD) of EC50 values on QSAR model quality and predictive accuracy.

Main Methods:

  • Generation of 2D and 3D QSAR models using a dataset of AADs.
  • Comparison of QSAR models built with average EC50 values versus models incorporating both average and standard deviation (SD) of EC50 values.
  • Evaluation of model performance using metrics such as Q²LOO, Q²(F2), Q²(F3), and mean absolute error (MAE).

Main Results:

  • QSAR models incorporating SD values demonstrated comparable molecular interpretation maps and overall quality (Q² values) to models based solely on average values.
  • SD-based QSAR models exhibited superior predictive accuracy for test compounds, evidenced by lower MAE indices and a higher proportion of residuals near zero.
  • Visual interpretation of the QSAR models aligned with experimental data, identifying critical structural features influencing AAD biological activity.

Conclusions:

  • The inclusion of SD in QSAR modeling provides a more accurate assessment of predictive performance for FXR agonists.
  • SD-based QSAR models offer enhanced accuracy in predicting the activity of new compounds, potentially accelerating drug discovery efforts.
  • This methodology presents a valuable approach for developing more robust QSAR models using existing experimental data.
Keywords:
AAD, Anthranilic acid derivativeANOVA, Analysis of varianceBA, Bile acidCoMFACoMFA, Comparative molecular fields analysisCoMSIACoMSIA, Comparative molecular similarity indices analysisFXR, Farnesoid X receptorFarnesoid X receptorHL, Hologram lengthHQSARHQSAR, Hologram quantitative structure-activity relationshipLBD, Ligand binding domainLBP, Ligand binding pocketMAE, Mean absolute errorMIF, Molecular interaction fieldNR, Nuclear receptorPDB, Protein data bankQSAR, Quantitative structure-activity relationshipanthranilic acid derivativescpd. or CPD., Compounddrug designρc, Concordance correlation coefficient

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