Development of novel CDK9 and CYP3A4 inhibitors for cancer therapy through field and computational approaches

Abstract

Cyclin-dependent kinase 9 (CDK9) and cytochrome P450 3A4 (CYP3A4) have emerged as promising targets in the development of anticancer drugs, presenting a consistent challenge in the quest for potent inhibitors. CDK9 inhibitors can selectively target fast-growing cancer cells by disrupting transcription elongation, which in turn hinders the production of proteins essential for cell cycle progression and survivaŚ. Understanding how CYP3A4 metabolizes specific chemotherapy drugs allows for personalized treatment plans, optimizing drug dosages according to a patient's metabolic profile. Since many cancer patients undergo combination therapies, and CYP3A4 is vital in drug metabolism, its inhibition or induction by one drug can alter the plasma levels of others, potentially leading to treatment failure or increased toxicity. Therefore, managing CYP3A4 activity is critical for effective cancer treatment. Employing a range of computational methodologies, this study systematically investigated the binding mechanisms of pyrimidine derivatives against CDK9 and CYP3A4. The field-based model demonstrated high R ² values (0.99), with Q² (0.66), demonstrating its ability to predict in silico inhibitory activity against the target of this study. The screening process followed in this work led to the discovery of powerful new inhibitor compounds. Of the 15 new compounds designed, three have a high affinity with the target (ranging from -8 to -9 kcal/mol kcal/mol) and were singled out through docking filtration for more detailed investigation. As well as, a reference compound with a substantial pIC₅₀ value of 8.4, serving as the foundation for the development of the new compounds, was included for comparative analysis. To elucidate the essential features of CDK9 and CYP3A4 inhibitor design, a comparative analysis was conducted between 3D-QSAR-generated contours and molecular docking conformations of ligands. Molecular dynamics simulations were carried out for a duration of 100 ns on selected docked complexes, specifically those involving novel compounds with CDK9 and CYP3A4 enzymes. Additionally, the binding free energy for these complexes was assessed using the MM/PBSA method, which evaluates the free energy landscape of protein-ligand interactions. The results of MM/PBSA highlighted the strength of the new compounds in enhancing interactions with the target protein, which favors the results of molecular docking and MD simulation. These insights contribute to a deeper understanding of the mechanisms underlying CDK9 and CYP3A4 inhibition, offering potential avenues for the development of innovative and effective CDK9 inhibitors.

Keywords: 3D-QSAR; CADD; CDK9; Cancer; drug design.

FIGURE 1

Contrasting the pIC₅₀ values predicted by field-based methodologies for ' training and test sets with their corresponding actual values.

FIGURE 2

The contour maps generated for the compounds in the test set illustrate distinct fields, with specific colors indicating various characteristics. (A) Red signifies Gaussian hydrogen bond acceptor fields, indicating favored regions, while magenta denotes disfavored regions. (B) Yellow is employed for Gaussian hydrophobic fields to denote favored regions, while white signifies disfavored regions. (C) Green illustrates Gaussian steric fields, highlighting favored regions, and yellow indicates unfavorable regions. (D) Purple is used to depict Gaussian hydrogen bond donor fields for favored regions, while cyan represents disfavored regions. (E) Blue represents Gaussian electrostatic fields, showcasing favored electropositive regions and disfavored electronegative regions.

FIGURE 3

Guide for the design of new molecules based on a Field-based model; the best molecules were selected based on their affinity using the field-based model, and their predicted pIC₅₀ values were utilized for the selection.

FIGURE 4

The representation of co-crystallized ligand in the active site of CDK9, the target with PDB: 7nwk.

FIGURE 5

The representation of HEM (Heme is a complex formed by a porphyrin ring (which contains carbon, hydrogen, and nitrogen) with an iron atom at its center.) in the active site of CYP3A4, the target with PDB: 8wes.

FIGURE 6

Non-covalent binding interactions of novel compounds (A) (T1) and (B) (T2) with CDK9 (PDB ID: 7NWK): 3D and 2D diagram illustrations.

FIGURE 7

Non-covalent binding interactions of novel compounds (A) (T3) and (B) (Reference) with CDK9 (PDB ID: 7NWK): 3D and 2D diagram illustrations.

FIGURE 8

Non-covalent binding interactions of novel compounds: (A) T1 and (B) T2 with CYP3A4 (PDB ID: 8EWS), illustrated in 3D and 2D diagrams.

FIGURE 9

Non-covalent binding interactions of novel compounds: (A) T1 and (B) T2 with CYP3A4 (PDB ID: 8EWS), illustrated in 3D and 2D diagrams.

FIGURE 10

The most stable conformations of the new compounds and ref with CYP3A4 corresponding to the energy minima for the new inhibitors.

FIGURE 11

Analysis of boiled egg using SwissADME: T1, T2, T3, and reference.