In silico approaches on Bruton’s Tyrosine Kinase Inhibitors: The ligand binding mechanism and the stability of the protein-ligand complexes

Abstract

Bruton’s tyrosine kinase (BTK) is a clinically validated therapeutic target for autoimmune and lymphoproliferative disorders. In this study, we conducted a comprehensive computational analysis to evaluate the binding properties, structural stability, and drug-likeness of three investigational BTK inhibitors: Tolebrutinib, Poseltinib, and JDB175, in comparison with the approved reference drug, Ibrutinib. Density functional theory (DFT) calculations revealed that JDB175 possesses the highest electrophilicity index and a relatively narrow HOMO–LUMO energy gap among the investigated inhibitors, indicating its high chemical reactivity potential. Molecular docking studies demonstrated strong interactions for Tolebrutinib and JDB175 with key BTK residues (Cys481, Glu475, and Met477), with binding energies of −11.8 and −12.1 kcal/mol, respectively. Molecular dynamics simulations (200 ns) confirmed the structural stability of the Tolebrutinib and JDB175 complexes, consistent with their MM-PBSA free energy values (−48.05 and −47.28 kcal/mol, respectively). ADME/T analysis showed favorable pharmacokinetic profiles for all candidates. These findings suggest that Tolebrutinib and JDB175 are promising BTK inhibitor candidates with optimal interaction profiles and drug-like properties, supporting their potential for further development in BTK-targeted therapy.

Keywords:
Bruton’s tyrosine kinase inhibitors; DFT; Molecular docking; Molecular dynamics; ADME/T

INTRODUCTION

The development of anti-cancer drugs continues to have a high failure rate of 95% in clinical trials, with only 5% of compounds that show anti-cancer activity in pre-clinical development ultimately being approved following the demonstration of efficacy in a phase III clinical trials. Molecular targets have reduced attrition rates (to 55% for kinase inhibitors), but these remain too high. This is unsustainable for the pharmaceutical industry and continuously drives up drug prices continuously (Sun et al., 2022).

Protein kinases are promising drug targets, with the majority of the world’s drug development efforts focused on these enzymes (Roskoski, 2024). In biological processes, BTK is a crucial enzyme, particularly in immune system function and cancer development, as well as in the B-cell receptor signaling. Recent studies (Pal Singh et al., 2018; Roskoski, 2021; Vetrie et al., 1993) have also shown the importance of BTK in solid tumours (i.e., prostate, colorectal, ovarian, and breast cancers).

B-cell hematologic malignancies encompass mantle cell lymphoma (MCL), chronic lymphocytic leukemia (CLL), and small lymphocytic lymphoma (SLL). These are types of non-Hodgkin lymphoma (NHL) (Krause, Van Etten, 2005). Recent progress in cancer research shows that novel small-molecule inhibitors (ligands) targeting BTK are improving the treatment of NHL and may offer a chemo-free approach, representing a significant shift in B-cell malignancy management.

BTK inhibitors (BTKis) can be classified as irreversible or reversible. Irreversible inhibitors covalently bind to a cysteine residue (Cys 481). Irreversible inhibitors covalently bind to a cysteine residue (Cys481) in the ATP-binding site, while reversible inhibitors interact via non-covalent forces, including hydrogen bonding, ionic interactions, and hydrophobic contacts (Cool et al., 2024; Feng et al., 2019; Hendriks, Yuvaraj, Kil, 2014; Rozkiewicz et al., 2023; Tasso et al., 2021; Wang et al., 2021; Zain, Vihinen, 2021).

To date, five irreversible BTKi drugs (acalabrutinib, ibrutinib, zanubrutinib, orelabrutinib, and tirabrutinib) have been approved and marketed. (Table I). While most reversible BTK inhibitors are still in preclinical and clinical investigations. The first FDA-approved drug in the reversible class is pirtobrutinib. (De Claro et al., 2015; Davids, Brown, 2014; Dhillon, 2020; Joseph et al., 2024; Zain, Vihinen, 2021).

TABLE I
Approved and in clinical trials, not yet approved irreversible Bruton’s tyrosine kinase (BTK) inhibitors

BTK Inhibitor		Approval/Phase	Indications
Ibrutinib C25H24N6O2		2013 /2014 FDA	CLL, SLL, MCL
Acalabrutinib C26H23N7O2		2017 / 2019 FDA	MCL / CLL
Zanubrutinib C27H29N5O3		2019 / 2023 2024	MCL / CLL, SLL FL
Tirabrutinib C25H22N6O3		2020 Japan Pharmaceuticals	CLL, DLBCL and RA
Orelabrutinib C26H25N3O3		2020 CFDA	MCL, CLL
Tolebrutinib C26H25N5O3		Phase III	MS
Poseltinib C26H26N6O		Phase II under development	SLE, SLL, and FL
JDB175 C26H21F3N4O2		Phase I	PCNSL

• Abbreviations: Food and Drug Administration (FDA); China Food and Drug Administration (CFDA); Primary central nervous system lymphoma (PCNSL); Chronic lymphocytic leukemia (CLL); Diffuse large B-cell lymphoma (DLBCL); Follicular lymphoma (FL), mantle cell lymphoma (MCL); marginal zone lymphoma (MZL); Systemic lupus erythematosus (SLE); Small lymphocytic lymphoma (SLL), Rheumatoid arthritis (RA)

Despite the success of these BTKi drugs in treating hematologic malignancies, many patients eventually develop resistance, leading to treatment failure and disease progression. The BTKi binding site, cysteine 481, is often affected by resistance mutations (serine C481S) (Nakhoda, Vistarop, Wang, 2023).

Identifying potent inhibitors of BTK is crucial because of its role in the treatment of various B-cell malignancies and autoimmune diseases (Alu et al., 2022; Rozkiewicz et al., 2023; Tavakoli, Yazdanpanah, Rezaei, 2024; Xia et al., 2023; Younes et al., 2017).

The experimental determination of the high-resolution three-dimensional (3D) structures of protein targets by X-ray crystallography (Ilari, Savino, 2008; Lin, Andreotti, 2023; Maveyraud, Mourey, 2020) and cryo-electron microscopy (Giri, Cheng, 2023; Nakane et al., 2020) supports the drug discovery process. NMR-based structure-activity relationship (SAR) studies also contribute to identifying ligand-binding sites and affinities (Moore et al., 2004; Muchmore, Hajduk, 2003).

Technical difficulties, time constraints, and cost of distribution limit the development of a drug for a disease.

At the beginning of drug design processes, computational approaches are used to analyze drug-receptor interactions and the pharmacological and pharmacokinetic properties of drugs. As a complementary framework for validating experimental methods, many computational approaches have been developed as a complementary framework (Ding, Kihara, 2018; Moumbock et al., 2019; Vogel et al., 2013).

Molecular modeling approaches are the most frequently used in silico prediction methods for finding potential binding sites on protein surfaces, assessing ligand binding affinity with protein receptors, and predicting molecular interactions and protein stability (Anderson, 2003; Batool, Ahmad, Choi, 2019; Chang et al., 2022; Childers, Daggett, 2017; Collier, Piggot, Allison, 2020; Ekins, Mestres, Testa, 2007; Gilbert, 2004; Halperin et al., 2002; Hernndez-Santoyo et al., 2013; Hospital et al., 2015; Karplus, McCammon, 2002).

Currently, high-resolution structures of the BTK kinase domain and drug-ligand co-crystals have been determined for Ibrutinib (PDB: 5P9J), Zanubrutinib (PDB: 6J6M), Acalabrutinib (PDB: 8FD9), and Tirabrutinib (PDB: 8FF0, 5P9M). However, no such structures are currently available for Tolebrutinib, Poseltinib, and JDB175 (Lin, Andreotti, 2023).

In this study, three irreversible BTKi were selected for comparative in silico analysis based on their distinct structural and pharmacological characteristics: Tolebrutinib, Poseltinib, and JDB175. Tolebrutinib is a second-generation covalent inhibitor with improved selectivity (Yu et al., 2021); Poseltinib is a conventional irreversible BTKi evaluated in B-cell malignancies (Wen et al., 2021); and JDB175 is a novel compound with potent BTK inhibition, minimal off-target activity, and potential to overcome resistance caused by Cys481 and Thr474 mutations (Xia et al., 2023). Ibrutinib, a widely used FDA-approved BTKi, was included as a reference molecule due to its broad clinical application despite known limitations in selectivity.

Density Functional Theory (DFT) calculations were performed to evaluate molecular properties, electronic structures, and chemical reactivity descriptors of the selected ligands. Molecular docking and molecular dynamics simulations were used to investigate the ligand-BTK interactions and provide structural insights into binding mechanisms. In addition, ADME/T properties were predicted to assess the pharmacokinetic behavior and safety profiles of the compounds.

Given that in vivo preclinical pharmacokinetic-pharmacodynamic testing is costly, time-consuming, and ethically challenging, in silico approaches offer a faster, safer, and more economical alternative. Notably, over 80% of drug candidates fail due to poor ADME properties or high toxicity (Clark, 2005; Myung, de Sá, Ascher, 2024). Therefore, predicting these parameters early in the design process is essential for identifying drug candidates with high therapeutic potential and favorable safety margins.

METHODS

Density functional theory (DFT)

The geometry optimizations in the gas phase were performed using B3LYP (Becke, 1993) with the 6-31G(d,p) basis set using the Gaussian 16W (Frisch et al., 2016).

To better understand the chemical stability of the BTKis, the energy levels of the highest occupied molecular orbitals (HOMOs) and lowest unoccupied molecular orbitals (LUMOs) were determined using time-dependent density functional theory (DFT) with the B3LYP method and the 6-31G(d,p) basis set. Some chemical reactivity descriptors, including electron affinity (EA), global electrophilicity index (ω), and electronegativity (χ), were evaluated using the HOMO and LUMO data, applying the formulations of Parr and Person as well as Koopmans’ theorem. (Koopmans, 1934; Parr, Pearson, 1983).

The molecular electrostatic potential (MEP) is a widely used tool for identifying the electrophilic and nucleophilic regions of ligand molecules involved in hydrogen bonding and biological interactions. MEPs of BTKi molecules were evaluated from the optimized molecular geometries (Murray, Politzer, 2017).

Molecular docking simulations

The first step in the docking procedure involves obtaining the three-dimensional (3D) PDB structure of the macromolecule from protein databases. Experimentally determined X-ray 3D structural data of BTK co-crystallized with Ibrutinib (PDB code: 5P9J) were obtained from the Protein Data Bank (PDB). Unneeded water molecules, the inhibitor molecule, and other heteroatoms were removed from the protein.

DFT optimized 3D geometries of BTKi (ligand molecules) and the crystal structure of the cleaned BTK protein (receptor) were used as the docking simulation starting structures. The residues within the loop region spanning positions 552-557 were absent in the crystal structure. This missing segment was reconstructed and refined using SwissPdbViewer 4.1.0 (Guex, Peitsch, 1997).

CB-Dock2, the protein-ligand blind docking server that utilizes AutoDock Vina (version 1.1.2) docking software (Liu et al., 2022), was used for molecular docking studies. The lowest binding energy conformation was considered the most favorable docking pose. CB-Dock2 tool can be reached from https://cadd.labshare.cn/cb-dock2.

BIOVIA Discovery Studio (DS) visualizer was used to create the 2D maps for the highest binding energy of the protein-ligand combination. (BIOVIA, 2012).

Molecular dynamics simulations (MD)

Following molecular docking studies, the best-docked score for each protein-ligand complex was selected for MD simulations. GROMACS package (version 2021) was used to perform all MD simulations (Abraham et al., 2015; Berendsen, van der Spoel, van Drunen, 1995; Lindahl et al., 2001; Van Der Spoel et al., 2005).

The SwissParam server (Bugnon et al., 2023; Zoeteet al., 2011) (https://www.swissparam.ch/) was used to generate the topology file for the ligands using the CHARMM27 force field in GROMACS (Lindahl et al. , 2010). The TIP3P water model (Jorgensen et al., 1983) was used, and sodium (Na⁺) and chlorine (Cl^-) ions were added to neutralize the system.

Following energy minimization, the systems were then equilibrated in two stages. The first stage, the isothermal-isochoric (NVT) ensemble, a leapfrog integrator was employed in conjunction with a modified Berendsen thermostat for a reference temperature of 300 K. This process was conducted to achieve equilibrium within the system for 200 ps. In the second stage, constant pressure and isothermal-isobaric (NPT) ensemble equilibration was conducted for another 200 ps to stabilize the system’s pressure at 1 bar. The compressibility was set to 4.5 ><10^-5 bar^-1, and the isotropic position scaling protocol was applied to control the pressure (Berendsen et al., 1984; Olademehin, Shuford, Kim, 2022).

Free energy calculation

Molecular Mechanics Poisson-Boltzmann Surface Area (MM/PBSA) method (Foloppe, Hubbard, 2006; Genheden, Ryde, 2015; Ji et al., 2020; Pearlman, 2005; Wang et al., 2019) was used for calculation of the free binding energy of ligand-protein complexes using the gmx_MMPBSA module of GROMACS (Miller et al., 2012; Van Der Spoel et al., 2005; Valdés-Tresanco et al., 2021)

The binding free energy, denoted by ΔG_binding, can be expressed by the following terms:

(1) $\Delta {\text{G}}_{\text{binding}}={\text{G}}_{\text{complex}}-\left({\text{G}}_{\text{protein}}+{\text{G}}_{\text{lingand}}\right)$

The total free energy of the ligand-protein complex is represented by G_complex,, while the free energies complex of the isolated protein and ligand are represented as G_protein and G_ligand, respectively (Kollman et al., 2000). protein ligand Subsequently, the polar and non-polar solvation energies were calculated consecutively, employing the solvent-Tugba Gõcen, Ali Kemal Garip, Serkan Akin, Sevgi Haman Bayari accessible surface area model (Mahmoudi Gomari et al., 2021).

MD simulations were then performed for 200 ns with a time step of 2 fs. Data were recorded every 10 ps, and a cut-off distance of 12 Å was applied for short-range interactions. The trajectories were analyzed using GROMACS standard tools.(Carugo, 2007; Damm, Carlson, 2006; Kufareva, Abagyan, 2012; Martinez. Andreani, Martinez, 2007)

In this work, the analyses were performed on MD trajectories using RMSD and RMSF calculations in comparison with a reference structure.

All molecular dynamics simulations and calculations were performed utilizing the TÜBÍTAK TRUBA infrastructure.

Physicochemical and ADMET properties

For predicting physicochemical properties, noncommercial online web tools SwissADME (http://www.swissadme.ch/) (Daina, Michielin, Zoete, 2017) and pkCSM (https://biosig.lab.uq.edu.au/pkcsm/prediction) (Pires, Blundell, Ascher, 2015) were used, which provide free access to different properties of compounds.

RESULTS AND DISCUSSION DFT calculations

Protein-ligand interactions may take place between LUMOs of the protein and HOMO of the ligand. (Pang et al., 2012). The HOMO energy indicates the region of small molecules capable of donating electrons during complex formation, whereas the LUMO energy reflects the molecule’s capability for accepting electrons from the interacting protein.

Since the protein molecule is large and its energy levels are closely packed, we considered only the ligand molecules for DFT calculations. As illustrated in Figure 1, the HOMO and LUMO isosurfaces provide a visual depiction of the electron distribution and the energy gap. The 3D surface maps of the molecular electrostatic potential of the BTKis are also shown in Figure 1.

FIGURE 1
Frontier molecular orbitals, HOMO-LUMO band gaps (DEg, eV), and molecular electrostatic potential maps of Ibrutinib, Tolebrutinib, Poseltinib and JDB175.

The global reactivity descriptors such as ionization potential (IP=-E_HOMO) electron affinity (EA), chemical hardness (η), electronegativity (χ) electrophilicity index (ω) electronic chemical potential (μ) were calculated and given in Table SI to gain insights into the structural stability and reactivity of the studied BTKi molecules.

The highest and lowest energy gaps were found for Ibrutinib and Poseltinib, respectively (Table SI). A large HOMO-LUMO energy gap indicates a more stable molecule, while a small energy gap indicates lesser stability/a more reactive molecule. JDB175 and Poseltinib have the highest and lowest IP values, respectively. The highest EA and electrophilicity index values were found for JDB175, suggesting a stronger tendency to accept electrons.

According to the Mulliken charge distribution, red regions in the MEP map correspond to negative electrostatic potential (electrophilic sites), blue regions correspond to positive electrostatic potential(nucleophilic sites), and green regions represent neutral potential areas (Jaramillo et al., 2006). Figure 1 illustrates that the red regions are concentrated on the oxygen atoms of the acrylamide warhead in all BTKis.

The results of the molecular docking calculations showed that the interactions formed between the ligand and protein involved these positive and negative centers, as discussed below.

Structure of BTK and interaction domain with inhibitors

BTK contains five different protein interaction domains from N terminal to C-terminal with 659 amino acids (Figure 2). These domains include an N-terminal pleckstrin homology (PH) domain, a proline-rich TEC homology (TH) domain, two SRC homology (SH) domains, SH2 and SH3, and a C-terminal kinase domain (Mao, Zhou, Uckun, 2001; Marcotte et al., 2010; Rozkiewicz et al., 2023; Vetrie et al., 1993).

FIGURE 2
The structural diagram of BTK.

The BTK kinase domain consists of approximately 250 residues. In spite of extensive research, the reasons for BTK binding to the kinase domain rather than to the full-length protein remain unclear. The activity of irreversible BTKis on the BTK kinase domain is quite different (Brullo et al., 2021; Lin Andreotti, 2023) Irreversible BTKis are composed of four principal components, including a large hydrophobic group, an aromatic heterocyclic nucleus, a linker, and a warhead terminal group (Tasso et al., 2021).

The local environment surrounding Cys481, as well as the chemical nature of the ‘warhead’, has the potential to contribute to the specificity of the drug. The aliphatic thiol (SH) group within Cys-481 is an excellent nucleophile, capable of reacting with an electrophilic functional group in the inhibitor that

Tugba Gõcen, Ali Kemal Garip, Serkan Akin, Sevgi Haman Bayari has been designed to form a stable covalent bond. The attachment of an acrylamide electrophilic group (warhead) to the piperidine nitrogen of BTK is studied here and is responsible for its irreversible inhibitory action on BTK, which interrupts the B cell signalling pathway, hinders its adhesion and migration, and finally causes the B cell to die.

Molecular docking-preferential binding modes

Each ligand’s binding affinity was evaluated based on its predicted interaction energy. The conformation exhibiting the lowest binding energy was selected as the optimal docked structure, and the ligands were ranked according to their scores in Table II.

TABLE II
Binding interactions of potential inhibitors and BTK compounds in molecular docking

		Hydrogen Bonds [H bond length (Å)]					Hyrophobic
					Electrostatic
Compound	Docking score kcal/mol	H bond (Classic)	Carbon-H	Pi-donor	Pi cation interaction	Pi-anion	Pi-sigma	T-shaped Pi-Pi	Alkyl	Pi-Alkyl
Ibrutinib	-10.9	Cys481 (3.2) Met 477 (3.0) Glu 475 (2.0) Thr:474 (2.7)	Met477(3.1)	-	Lys430	Asp539	Val416 Lys430	Phe540	Val416 Cys481	Leu408, Val416, Ala 428, Leu:528
Tolebrutinib	-11.8	Cys 481(3.0) Met 477(3.1) Glu 475 (2.4) Thr:474 (2.7)	Met 477 (3.2)	Thr474	Lys430	Asp539	Val 416, Lys430	Phe540	Val 416	Leu408 Val416 Ala428 Ile472 Leu528 Leu 542
Poseltinib	-10.2	Gln 412 (3.2) Ser 538 (3.1)	Asp521(3.6)	Thr 474	Lys430	Asp539	Val 416 Lys 430	-	Ile472, Leu542	Leu408 Val416 Ala428 Leu:528
JDB175	-12.1	Met477 (3.2)	Met477(3.1)	-	Lys430	Asp A:539	Val416 Lys430	Phe:540	Val416 Cys481	Leu:408 Val:416 Ala:428 Leu:528 Lys:430

Based on molecular docking results, Tolebrutinib and JDB175 are energetically the most favorable with binding energy scores of -11.8 kcal/mol and -12.1 kcal/ mol, respectively. Fluorination of ligand molecules has previously been demonstrated to contribute significantly to binding affinity (Zhou et al., 2009).

Molecular docking studies are valuable for predicting how ligands bind to proteins by providing potential binding modes. Visual representations of the 2D docked poses of the studied BTKi with the active site region of the BTK protein and interactions between the BTK protein and BTKis (Ibrutinib, Tolebrutinib, Poseltinib, and JDB175) are given in Figure 3.

FIGURE 3
2D docked poses and ligand-protein interaction diagrams of BTK and ligands. A) Ibrutinib, B) Tolebrutinib, C) Poseltinib, and D) JDB175.

Ligand molecules can bind to proteins through the formation of chemical bonds between the ligand and the protein, as well as through non-covalent interactions. Various aspects of protein function, including ligand binding affinities and modes, and enzymatic reaction mechanisms, are influenced by non-covalent interactions, which play a crucial role in stabilizing the ligand-protein complexes.

Types of non-covalent interactions between ligands and the amino acid residues involve several bond types, such as conventional hydrogen bonds (strong/classical, e.g., O—H-O and N—H-N), weak hydrogen bonds (e.g., carbon C-H bonds), hydrophobic interactions (e.g., alkyl, pi-alkyl, pi-sigma), electrostatic interactions (e.g., pi-cation/anion), and halogen bonds. Ionic bonds and van der Waals interactions are also types of non-covalent noncovalent interactions that are frequently found in biomolecules. (Adhav Saikrishnan, 2023; Zhou, Huang, Tian, 2012) To gain detailed information about the protein structure, noncovalent interactions have been investigated. Besides the covalent bond formed between the acrylamide warhead of ligand molecules and the Cys481 residue, the compounds form several noncovalent bonds with the ATP binding pocket of the BTK kinase domain.

As demonstrated in Figure 3, the interaction analysis revealed that both the 4-amino and 5-nitro groups on the pyrazolopyrimidine ring in Ibrutinib formed hydrogen bonds with Glu475 and Met477. The 4-amino group was also within the distance of the H-bond to Thr474. Furthermore, Val416, Tyr476, and residues Ala428 and Leu528 were observed to encircle the pyrazolopyrimidine moiety, exhibiting a distance of interaction. A pi-donor H-bond was identified to be formed between the N of pyrazolopyrimidine and Lys430. The diphenyl ether moiety was also found to be surrounded by Met449, Asp539, and residues Tugba Gõcen, Ali Kemal Garip, Serkan Akin, Sevgi Haman Bayari Lys430, Ser538, Ile472, and Thr474 (pi-interaction), while the acrylamide carbonyl oxygen formed a H-bond with Cys481. Our molecular docking results are also confirmed by the experimental X-ray co-crystal structure of Ibrutinib with BTK (PDB: 5P9J).

The Tolebrutinib-BTK complex displayed four hydrogen bonds with Cys481, Met477, Glu 475, and Thr474 and nine hydrophobic interactions. The analysis indicated that the interaction profile between Tolebrutinib and BTK is analogous to that of Ibrutinib (Figure 3).

The Poseltinib-BTK complex exhibited two hydrogen bonds with Ser538 and Gln432, whilst also engaging in a total of nine hydrophobic interactions with Val 458, Thr410, Gly411, Lys 558, and Tyr551. Arg525, Asn526, Leu460, and Met449.

In comparison, JDB175-BTK complex has a single hydrogen bond with Met 477 and alongside ten hydrophobic interactions. Docking results showed that pi-pi interaction was involved with residue Phe540 for Ibrutinib and JDB175 (Figure 3).

The presence of fluorine (F) in a drug molecule significantly influences the drug’s bioavailability, lipophilicity, acidity/basicity, metabolic stability, and toxicity (Brown Wobst, 2021; Swallow, 2015). Although fluorine, with its high electronegativity and low polarizability, is not usually considered to form halogen bonds in aromatic systems, unlike other halogens. In a protein environment, fluorine can act as an acceptor for a hydrogen bonds (Politzer et al. , 2007) (Murray, Seybold, Politzer, 2021).

Fluorine usually bonds with non-polar amino acids (Leu, Val, Phe, Ile, Ala) via H-bonds, suggesting a preference for hydrophobic regions (Pietrus et al, 2022). A bond (3.21 Å) occurred between the fluorine (F) atom of the phenoxyphenyl group of JDB175 and Val458. (Table II).

MD simulation-based structural and dynamic analysis

A combination of molecular docking and MD simulations can be used to obtain more reliable results for protein-ligand complexes. MD simulations play a critical role in capturing the dynamic nature of protein-ligand time-dependent interactions, revealing how binding affinities evolve, and illustrating the effects of solvents and ions on stability. Furthermore, they have been shown to enable the identification of hidden states of the system that cannot be detected through experimental means.

To estimate the overall structural stability and divergence, RMSD and RMSF profiles of BTKi complexes were calculated.

Binding free energy calculations

MM/PBSA is used to study receptor-ligand binding free energy (Genheden, Ryde, 2015).

The method used in the study allows a comprehensive analysis of the energetic contributions arising from ligand-protein target interactions by decomposing the total binding energy (ΔG_binding) into its individual energy components (Table III).

TABLE III
The MM/PBSA binding free energy components of inhibitor-BTK complexesa

Componentsb	Ibrutinib	Tolebrutinib	Poseltinib	JDB175
EvdW	-53.62 ± 0.96c	-59.63 ± 0.87	-51.22 ± 0.97	-62.40 ± 1.14
Eele	-24.24 ± 1.41	-28.43 ± 2.16	-62.76 ± 0.37	-16.07 ± 0.94
Egb	42.93 ± 0.36	47.54 ± 1.24	80.51 ± 0.82	39.04 ± 0.40
Esurf	-6.96 ± 0.03	-7.53 ± 0.05	-6.47 ± 0.06	-7.85 ± 0.06
Gvacuum	-77.86 ± 1.72	-88.06 ± 2.33	-113.98 ± 1.05	-78.47 ± 1.48
Gsolvation	35.97 ± 0.37	40.01 ± 1.25	74.05 ± 0.82	31.19 ± 0.40
ΔGbinding	-41.89 ± 1.76	-48.05 ± 2.64	-39.93 ± 1.34	-47.28 ± 1.54

• ^aAll values are given in kcal·mol^-1;
• ^bComponents
• E_vdW: van der Waals energy; E_ele: electrostatic energy in vacuum;
• E^gb = E^vdW + E^nonpolar + E^ele+polar the electrostatic contribution to the solvation energy;
• E_surf: the surface area-dependent solvation energy;
• E_gas: total gas phase energy
• G_solvation = G_polar + G_non-polar: the electrostatic and non-electrostatic contributions to the solvation free energy,
• ^c
  Standard errors of the mean;

The computational analysis predicted that the BTK would interact with tolebrutinib with a much higher affinity compared to that of Ibrutinib. Van der Waals energies were similar across all complexes, but electrostatic and solvation energies varied significantly (Table III).

E_vdW and E_ele interaction energies are close for Tolebrutinib and Ibrutinib, both sharing comparable binding profiles.

The binding energy of the Tolebrutinib with BTK was found to be -48.05 kcal/mol, which elucidates strong binding with the protein. The ΔG_binding energy value of the JDB175 protein complex is very close to that of Tolebrutinib.

Poseltinib shows a stronger E_ele of -62.76 kcal/ mol but a weaker E_vdW of -51.22 kcal/mol, resulting in a ΔG_binding of -39.93 kcal/mol, demonstrating that its binding profile differs from that of Ibrutinib.

RMSD and RMSF

The stability of each system during the simulation is evaluated by plotting the RMSD of backbone atoms as a function of time (Figure 4A).

FIGURE 4
The time evolution of RMDS values during 200 ns of all-atom (A) and RMSF graph as a function of amino acid residues (B) of the BTK and its structure upon binding with inhibitors.

Throughout the 200 ns MD simulations, all ligands remained generally stable in their complexes with BTK. Ibrutinib and Tolebrutinib exhibited similar RMSD values, maintaining the structural stability of BTK upon binding, indicating that both compounds form stable and strong interactions with BTK.

JDB175, on the other hand, demonstrated lower RMSD values compared to Ibrutinib and Tolebrutinib, suggesting a tighter binding to BTK and fewer structural changes in the protein. This implies that JDB175 potentially forms a more stable complex with BTK.

Although Poseltinib exhibited higher RMSD values (~0.1 nm) compared to the other ligands, the increase in RMSD was limited, and the stability of the complex was not significantly disrupted. This indicates that, while Poseltinib induces slightly more structural flexibility in BTK, it still maintains an acceptable level of stability. Overall, these results show that the candidate ligands do not cause significant structural perturbations in their interactions with BTK and all form stable binding complexes.

In conclusion, Tolebrutinib and JDB175, which exhibit stability similar to that of Ibrutinib, may serve as clinically relevant alternatives, while Poseltinib demonstrates a slightly more flexible binding behavior, as indicated by its higher RMSD values.

Based on the results of 200 ns molecular dynamics simulations, we compared the RMSF profiles of BTK in both the Ibrutinib-bound and unbound states to analyze rigidity changes in the ATP binding region and the activation loop (Figure 4B). Ibrutinib stabilized BTK by reducing flexibility, particularly at residues Val416, Thr474, Glu475, Met477, Cys481, Ser538, Asp539, and Phe540. Among the BTKi ligands, Tolebrutinib exhibited the most similar RMSF profile to Ibrutinib, providing rigidity close to that of Ibrutinib in both the ATP binding region and the activation loop, emerging as the most promising candidate.

JDB175 also showed high similarity to Ibrutinib in specific regions, suggesting it could be a strong candidate for stabilization. However, small differences were observed in the activation loop.

Poseltinib, on the other hand, exhibited greater flexibility in both the ATP binding region and the activation loop, showing the least mimicry of the rigidity provided by Ibrutinib.

The results of the RMSD and RMSF analyses were consistent with each other, further validating the stability of the complexes.

Intermolecular H-bond analysis

Monitoring the formation and fluctuations of hydrogen bonds over time provides insight into the binding of ligand to protein and the dynamics of protein-ligand interactions (Bitencourt-Ferreira et al., 2019).

Figure 5 shows the analysis of the time evolution of intermolecular hydrogen bonds, evaluating the strength of hydrogen bonding between the ligands and BTK. The average numbers of hydrogen bonds between the BTK protein and ligands were 3, 2, and 1 for Tolebrutinib, Poseltinib, and JDB175, respectively. Tolebrutinib has the same average number of hydrogen bonds as the reference drug, Ibrutinib. These results agree with the molecular docking findings, showing that the docking predictions are accurate and reliable.

FIGURE 5
Number of intermolecular hydrogen bonds at 200 ns of MD simulation A) BTK-Ibrutinib (green) B) BTK-Tolebrutinib (red), C) BTK-Poseltinib (blue), and D) BTK-JDB175 ( purple).

Ligand-Residue Interaction Percentage

To evaluate the interaction frequency between the ligands and critical residues within the active site of BTK, ligand-residue group interaction percentages were calculated throughout a 200 ns molecular dynamics simulation using distance thresholds ranging from 0.150 nm to 0.400 nm. This analysis was performed using the gmx select module of the GROMACS software, which enabled the quantification of the time each ligand remained within a defined spatial proximity to a target residue group. The residue group consisted of Cys481, Thr474, Glu475, Met477, Val416, Lys430, Val458, Ile472, Ser538, and Phe540.

As the distance threshold increased, the frequency of contacts between ligands and the target residues also increased. For instance, Ibrutinib showed only 8% interaction at a 0.200 nm threshold, which increased to 82% at 0.225 nm and reached 100% at 0.250 nm. Poseltinib and JDB175 exhibited interaction rates exceeding 90% in the 0.225-0.250 nm range, indicating favorable binding at shorter distances. Tolebrutinib showed 80% interaction at 0.200 nm and reached 100% at 0.225 nm. These results indicate that all studied ligands, compared to the reference compound Ibrutinib, maintained a higher frequency of interaction with key residues in BTK throughout the simulation.

Physicochemical parameters and prediction of ADMET properties

Mutation of cysteine at position 481 (Cys-481) of the BTK protein to serine affects the efficacy of ibrutinib, resulting in drug resistance and challenges in targeted inhibition, which remain critical issues to be addressed (Bond, Woyach, 2019).

The progress in developing new drugs is significantly hindered by the incomplete understanding of their physico-chemical, pharmacokinetic, and intrinsic properties. To overcome the problems of toxicity and resistance that occur in patients undergoing continuous treatment, several next-generation inhibitors have been developed (Frustaci et al. , 2023).

Physicochemical properties, including solubility, size, structure, hydrogen bonds, polar surface area, stability, and enzyme susceptibility, influence efficacy, safety, and metabolism, and can be predicted using Lipinski’s Rule of Five (Ro5) (Lipinski et al., 2001; Pillai, Dhanikula, Panchagnula, 2001).

Ro5 predicts the molecular properties that determine how a pharmaceutical substance affects the body, including absorption and distribution.

This rule is the most well-known filter for drug-likeness, helping to determine whether a molecule is likely to be well absorbed when taken orally. According to Lipinski’s criteria, molecules with good absorption typically have a logP of less or equal to 5, hydrogen bond donors (HBD) of less or equal to 5, hydrogen bond acceptors (HBA) of less or equal to 10, molecular weight (MW) of less than 500 Da, and fewer than ten rotatable bonds.

Important pharmacokinetic properties ofthe studied BTKis are listed in Table SII. It was found that all Ro5 properties of BTKis were within the recommended/ acceptable range.

ADME/T analysis describes how drug candidates behave within the human body and is the assessment of the additional pharmacokinetics of a drug. Drug compounds with a poor ADMET profile are often eliminated in preclinical or clinical stages (Daina, Michielin, Zoete, 2017). The predicted ADMET profiles of the investigated BTKis are presented in Table IV.

TABLE IV
ADME/T prediction results of the studied BTK inhibitors

	Absorption		Distribution	Metabolism					Excretion	Toxicity
Ligand molecules	Intestinal absorption Numeric (%)	P-Gp substrate Categorical(Yes/No)	VDss Numeric (log L/kg)	CYP450 Categorical (Yes/No)					Total clearance Numeric (log ml/ min/kg)	AMES toxicity Categorical (Yes/No)	Hepatotoxicity Categorical(Yes/No)	Skin sensitization Categorical(Yes/No)
				Substrate		Inhibitor
				2D6	3A4	1A2	2D6	3A4
Ibrutinib	97.724	yes	0.083	no	yes	yes	no	yes	0.601	no	yes	no
Tolebrutinib	93.066	yes	0.511	no	yes	no	no	yes	0.316	no	yes	no
Poseltinib	91.726	yes	1.242	no	yes	yes	no	yes	0.550	no	yes	no
JDB175	92.478	yes	0.130	no	yes	no	no	yes	0.139	no	yes	no

The intestinal absorption values for the studied molecules in humans are predicted to exceed 90%, which is significantly higher than the threshold of 30%, below which poor absorption is typically indicated.

The volume of distribution at steady state (VDss) is an in vivo pharmacokinetic parameter that is used to represent the hypothetical volume into which a drug would need to be evenly distributed to produce the same concentration observed in the blood plasma. VDss is regarded as low if its logarithmic value is below -0.15 (logVdss < -0.15) and high if it exceeds 0.45 (logVdss > 0.45) (Aloui et al., 2024).

The calculated VDss of Ibrutinib is low, but the VDss of Poseltinib is high (Table IV). Low VDss values indicate high water solubility or strong plasma protein binding, as a greater proportion of the drug remains in the plasma. Conversely, high VDss values suggest significant tissue accumulation, which may result from tissue binding or high lipid solubility.

The cytochrome mono-oxygenase P450 (CYP) is a key enzyme system involved in drug metabolism in the liver. Knowledge of the interaction of potential drugs with cytochromes CYP450 enzymes is essential in Phase I studies during drug discovery. The metabolism prediction from the ADMET platform involves assessing whether an input can act as a substrate or inhibitor for CYP1A2, 2D6, and 3A4, the most crucial enzymes in humans, along with the corresponding prediction confidence. (Zanger, Schwab, 2013).

The results indicate that BTKis do not act as substrates or inhibitors of the 2D6 CYP 1 enzyme. However, they serve as both substrates and inhibitors of the 3A4 CYP enzyme. Tolebritunib and Poseltinib are inhibitors of the 1A2 enzyme.

All inhibitors demonstrate non-toxic properties, reinforcing their potential as promising therapeutic candidates.

CONCLUSION

In this study, computational method (in silico) was used to examine how three BTKis, Tolebrutinib, Poseltinib, and JDB175, interact with the BTK protein. These results were also compared with the FDA-approved drug Ibrutinib.

First, Density Functional Theory (DFT) calculations were performed to analyze the molecular and electronic properties of the compounds. The results showed that JDB175 exhibited the highest reactivity due to its electronic features, including the lowest LUMO energy and the highest electrophilicity index. These properties suggest that JDB175 can form strong and stable interactions with the BTK binding site.

Docking studies showed that Tolebrutinib and JDB175 bind more strongly to the BTK protein than Ibrutinib, especially by forming stable contacts with important amino acids like Cys481, Glu475, and Met477. Tolebrutinib had a binding pattern similar to Ibrutinib, while Poseltinib showed weaker binding.

To better understand how the molecules behave over time, molecular dynamics simulations were run for 200 ns using the GROMACS program. The results showed that all the drug-protein complexes remained stable, and Tolebrutinib and JDB175 had the lowest structural fluctuations and maintained stable hydrogen bonds. Energy calculations (MM-PBSA) confirmed that these two compounds had strong interactions.

Drug-likeness and safety were also assessed using Lipinski’s Rule of Five and ADME/T predictions. All compounds showed drug-like properties and were predicted to be non-toxic. In particular, JDB175 and Tolebrutinib had high GI absorption, good membrane permeability, and no CYP2D6 inhibition, indicating they may be safe and effective when taken orally.

In summary, the results suggest that Tolebrutinib and JDB175 are strong candidates for further research as next-generation BTKis. Their strong binding, good stability, and favorable drug-like properties make them promising for various therapeutic applications. These findings support future in vitro and in vivo studies to confirm their efficacy and safety.

ACKNOWLEDMENT

All molecular dynamics simulations and calculations were performed utilizing the TUBITAK TRUBA infrastructure.

DATA AVAILABILITY STATEMENT

Use of data not disclosed.

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TABLE SI
The chemical reactivity descriptors of BTKIs were calculated using the TD-DFT level with the 6-31G(d,p) basis set in the gas phase

Parameter* (eV)	Ibrutinib	Tolebrutinib	Poseltinib	JDB175
EHOMO	-5.775	-5.377	-4.939	-5.931
ELUMO	-1.143	-0.929	-1.236	-1.494
ΔEgap	4.632	4.448	3.703	4.437
EA	1.143	0.929	1.236	1.494
IP	5.775	5.377	4.939	5.931
η	2.316	2.224	1.851	2.218
μ	-3.459	-3.153	-3.087	-3.713
X	3.459	3.153	3.087	3.713
ω	2.583	2.235	2.574	3.107
Dipole moment (D)	5.957	2.441	3.497	6.283

• HOMO-LUMO energy band gap (ΔE_gap), electron affinity (A), ionization energy (I), global chemical hardness (η), global electrophilicity index (ω), electronic chemical potential (μ), electronegativity (χ),

Ionization energy (I), electron affinity (A) are critical molecular properties and can be used for the virtual design of molecules. The global electrophilicity index (ω) measures the stabilization in energy when the system acquires an additional electronic charge from the environment. η denotes the global chemical hardness, and μ represents the electronic chemical potential, which describes the charge transfer within a system in the ground state. Electronegativity (χ) is defined as the tendency of an atom participating in a covalent bond to attract the bonding electrons. These properties play an important role in determining the chemical reactivity of a system.

TABLE SII
Pharmacokinetic parameters of the studied BTK inhibitors

Compund	Mol. Wt. (g/ mol)	ADMET structure	Rotatable bonds	H-bond acceptors	H-bond donors	TPSA (Å)	iLOGP	Solubility	GI absorption	BBB permeant	Pgp substrate	CYP2D6 inhibitor	Lipinski violations
Ibrutinib	440.5		6	5	1	99.16	3.80	Moderately soluble	high	no	yes	yes	yes
Tolebrutinib	455.51		6	4	1	95.38	3.79	Moderately soluble	high	no	yes	no	yes
Poseltinib	470.52		8	6	2	95.76	3.88	Moderately soluble	high	no	yes	yes	yes
JDB175	478.47		6	7	0	60.25	4.16	Moderately soluble	high	no	yes	no	Yes

*

Correspondence: T. Gocen, Zonguldak Bulent Ecevit University, Ahmet Erdogan Vocational School of Health Services, 67100, Zonguldak / TURKEY, E-mail: [e-mail], A. K. Garip: [e-mail], S. Akın: [e-mail], S. H. Bayarı: [e-mail]

^1,2

These authors contributed equally to this study

Associate Editor:

Mateo Perra

Publication Dates

• Publication in this collection
  09 Nov 2026
• Date of issue
  2026

History

• Received
  16 Apr 2025
• Accepted
  11 July 2025