Volume 19, Issue 12 e202300644
Research Article

Developing Dynamic Structure-Based Pharmacophore and ML-Trained QSAR Models for the Discovery of Novel Resistance-Free RET Tyrosine Kinase Inhibitors Through Extensive MD Trajectories and NRI Analysis

Ehsan Sayyah

Ehsan Sayyah

Computational Biology and Molecular Simulations Lab, Department of Biophysics, School of Medicine, Bahçeşehir University, Istanbul, Turkey

Computational Drug Design Center (HITMER), Bahçeşehir University, Istanbul, Turkey

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Lalehan Oktay

Lalehan Oktay

Computational Biology and Molecular Simulations Lab, Department of Biophysics, School of Medicine, Bahçeşehir University, Istanbul, Turkey

Computational Drug Design Center (HITMER), Bahçeşehir University, Istanbul, Turkey

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Assist. Prof. Huseyin Tunc

Assist. Prof. Huseyin Tunc

Department of Biostatistics and Medical Informatics, School of Medicine, Bahçeşehir University, Istanbul, Turkey

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Prof. Dr. Serdar Durdagi

Corresponding Author

Prof. Dr. Serdar Durdagi

Computational Biology and Molecular Simulations Lab, Department of Biophysics, School of Medicine, Bahçeşehir University, Istanbul, Turkey

Computational Drug Design Center (HITMER), Bahçeşehir University, Istanbul, Turkey

Molecular Therapy Lab, Department of Pharmaceutical Chemistry, School of Pharmacy, Bahçeşehir University, Istanbul, Turkey

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First published: 24 March 2024

Graphical Abstract

This study introduces a methodology that integrates a dynamic structure-based pharmacophore-driven approach, utilizing E-pharmacophore modeling derived from MD trajectories, to identify energetically favorable hypotheses. Additionally, ML-trained QSAR models are constructed and employed to predict the pIC50 values of ultra-large ligand libraries against RET tyrosine kinase. Moreover, a NRI model is utilized to predict interaction maps between wild-type and mutant RET proteins. Our in silico results highlight that one of the identified compounds (Hit9) demonstrates promising binding potential to the ATP binding pocket of the RET protein without inducing drug resistance.

Abstract

Activation of RET tyrosine kinase plays a critical role in the pathogenesis of various cancers, including non-small cell lung cancer, papillary thyroid cancers, multiple endocrine neoplasia type 2A and 2B (MEN2A, MEN2B), and familial medullary thyroid cancer. Gene fusions and point mutations in the RET proto-oncogene result in constitutive activation of RET signaling pathways. Consequently, developing effective inhibitors to target RET is of utmost importance. Small molecules have shown promise as inhibitors by binding to the kinase domain of RET and blocking its enzymatic activity. However, the emergence of resistance due to single amino acid changes poses a significant challenge. In this study, a structure-based dynamic pharmacophore-driven approach using E-pharmacophore modeling from molecular dynamics trajectories is proposed to select low-energy favorable hypotheses, and ML-trained QSAR models to predict pIC50 values of compounds. For this aim, extensive small molecule libraries were screened using developed ligand-based models, and potent compounds that are capable of inhibiting RET activation were proposed.

Conflict of interests

The authors declare no conflict of interest.

Data Availability Statement

The data that support the findings of this study are available from the corresponding author upon reasonable request.