Related Experiment Video
Updated: Jul 2, 2025
Kinase Inhibitor Screening In Self-assembled Human Protein Microarrays
Published on: October 23, 2019
Dissecting the Interaction Fingerprints and Binding Affinity of BYL719 Analogs Targeting PI3Kα
Sepehr Dehghani-Ghahnaviyeh1, Cihan Soylu1, Pascal Furet2
1Novartis Institutes for BioMedical Research, 181 Massachusetts Avenue, Cambridge, Massachusetts 02139, United States.
Abstract:
Phosphatidylinositol-3-kinase Alpha (PI3Kα) is a lipid kinase which regulates signaling pathways involved in cell proliferation. Dysregulation of these pathways promotes several human cancers, pushing for the development of anticancer drugs to target PI3Kα. One such medicinal chemistry campaign at Novartis led to the discovery of BYL719 (Piqray, Alpelicib), a PI3Kα inhibitor approved by the FDA in 2019 for treatment of HR+/HER2-advanced breast cancer with a PIK3CA mutation. Structure-based drug design played a key role in compound design and optimization throughout the discovery process. However, further characterization of potency drivers via structural dynamics and energetic analyses can be advantageous for ensuing PI3Kα programs. Here, our goal is to employ various in-silico techniques, including molecular simulations and machine learning, to characterize 14 ligands from the BYL719 analogs and predict their binding affinities. The structural insights from molecular simulations suggest that although the ligand-hinge interaction is the primary driver of ligand stability at the pocket, the R group positioning at C2 or C6 of pyridine/pyrimidine also plays a major role. Binding affinities predicted via thermodynamic integration (TI) are in good agreement with previously reported IC50s. Yet, computationally demanding techniques such as TI might not always be the most efficient approach for affinity prediction, as in our case study, fast high-throughput techniques were capable of classifying compounds as active or inactive, and one docking approach showed accuracy comparable to TI.
Insights
This study used computational methods to analyze PI3Kα inhibitors, like BYL719. Molecular simulations and machine learning helped predict binding affinities, revealing key interactions for drug design.
Area of Science:
- Biochemistry
- Pharmacology
- Computational Chemistry
Background:
- Phosphatidylinositol-3-kinase Alpha (PI3Kα) is crucial for cell proliferation pathways implicated in human cancers.
- BYL719 (Alpelicib) is an FDA-approved PI3Kα inhibitor for specific breast cancers, developed using structure-based drug design.
- Further characterization of PI3Kα inhibitors using structural dynamics and energetics can enhance future drug discovery programs.
Purpose of the Study:
- To employ in-silico techniques, including molecular simulations and machine learning, for characterizing 14 BYL719 analogs.
- To predict the binding affinities of these ligands to PI3Kα.
- To gain structural insights into the drivers of ligand binding and potency.
Main Methods:
- Utilized molecular simulations to analyze the structural dynamics of PI3Kα-ligand interactions.
- Applied machine learning algorithms for binding affinity prediction.
- Employed thermodynamic integration (TI) and docking approaches for computational affinity prediction.
Main Results:
- Molecular simulations identified ligand-hinge interactions as critical for stability, with R group positioning at C2/C6 of pyridine/pyrimidine also significantly influencing binding.
- Binding affinities predicted by thermodynamic integration correlated well with reported IC50 values.
- Fast high-throughput screening methods effectively classified compounds as active or inactive, with one docking approach achieving accuracy comparable to TI.
Conclusions:
- In-silico methods, including molecular dynamics and machine learning, are valuable for characterizing PI3Kα inhibitors and predicting binding affinities.
- Ligand-hinge interactions and specific R group positioning are key determinants of PI3Kα inhibitor potency.
- While computationally intensive methods like TI provide accurate predictions, faster high-throughput techniques can be efficient alternatives for compound classification and initial screening in drug discovery programs.
