In silico evaluation of binding interaction and ADME study of new 1,3-diazetidin-2-one derivatives with high antiproliferative activity

ABSTRACT

A series of eight novels’ 1,3-diazetidin-2-ones have been proposed to assess their potential activities. They are intended to examine antiproliferative effects through inhibition of epidermal growth factor receptor (EGFR) expression. These eight compounds strongly interact with the EGFR protein, responsible for the activity. As part of a present study, these compounds were docked to the crystal structure of the EGFR (Protein Data Bank code: 1 M17) to determine their binding affinity at the active site. Based on computer predictions, two compounds were demonstrated high scores of 80.80 and 85.89. After analyzing ADME properties, these compounds were found to have significant potential for binding. Consequently, the abilities of gefitinib, erlotinib, imatinib, and sorafenib were selected for comparison as controls. Computational methods were performed to predict the critical disposition of eight novels’ 1,3-diazetidin-2-one derivatives to the EGFR. Moreover, a docking technique employing the Genetic Optimization for Ligand Docking program was conducted. Compounds 2 and 7 demonstrate a high docking peace-wise scoring function (PLP) fitness of 85.89 and 80.80, respectively. They fulfilled the Lipinski’s rule, topological descriptors, and fingerprints of drug-like molecular structure keys. These compounds can be used as lead compounds to develop novel antiproliferative agents. The outcome of applying this study is novel series of 1,3-diazetidin-2-one compounds as new analogs were designed and evaluated for their antiproliferative activity with a higher potency profile and binding affinity within the active sites of EGFR.

INTRODUCTION

According to the World Health Organization, cancer has the most significant global burden.[1] More people die yearly from this illness than from malaria, tuberculosis, or AIDS. According to the International Agency for Research on Cancer, 9.7% of all cases are colorectal cancer. The agency also reports that 1.8 million people were diagnosed with lung cancer and 1.7 million were diagnosed with breast cancer cases.[2]

Protein kinases are one of the essential gene families, and their overexpression, translocation, and fusing cause significant problems for downstream signaling.[3] Several mechanisms disrupt upstream signaling and increase the risk of carcinogenesis; as a result, epidermal growth factor receptor (EGFR) is the first cancer cell receptor to be identified as a kinase family oncogene.[4]

A reversible tyrosine kinase inhibitor of the EGFR is erlotinib, which has been approved by the Food and Drug Administration for the treatment of non-small cell lung cancer (NSCLC).[5] It displaces adenosine triphosphate, preventing EGFR autophosphorylation and downstream signaling. Since these tumors’ overexpression of EGFR has a poor prognostic value, EGFR has been a target for anticancer therapy.[6,7]

The significance of inflammation in the development and aggressiveness of several malignancies, including NSCLC, has drawn much attention. Numerous studies have demonstrated the effectiveness of nonsteroidal anti-inflammatory drugs (NSAIDs) in preventing cancer in animal models, partly because of their capacity to suppress cyclooxygenase activity. Combining different NSAIDs with the EGFR tyrosine kinase inhibitor (erlotinib) resulted in the design and manufacturing of several novel anticancer medicines.[8]

In medicinal chemistry, heterocycle analogs based on nitrogen are a valuable source of therapeutic drugs. Because nitrogen readily creates hydrogen bonds with biological targets. Numerous nitrogen-containing heterocyclic compounds have diverse therapeutic effects, including those against cancer, HIV, malaria, tuberculosis, other microorganisms, and diabetes. These chemicals are referred to as diazetidines, diazetines, and diazetes as shown in Figure 1, and they have been the subject of extensive investigation. Due to the near resemblance between these compounds and lactam antibiotics, they are sometimes known as aza-lactams.[9]

Figure 1.

Structures of diazetidines, diazetes, and diazetines

With increased importance, the quaternary ring sector enhances the chemical composition of projects already in progress. Their innovative structural design adds tremendous value to an untapped field of chemical possibility. Modern researchers have largely ignored the unique chemistry surrounding this ring system. However, it recently received renewed attention due to its use to inhibit the protein phosphatase methylesterase-1.[10]

Zuhl et al., in 2012, had been found a group of Aza-β-lactams (ABL) that effectively inhibit the proliferation of Ishikawa cells and the migration of ECC-1 endometrial cancer cells, as shown in Figure 2.[11]

Figure 2.

Structure of ABL

The new research focuses on heterocyclic four-member ring synthesis and activity. We proposed our work, supporting our results with a docking study after our team confirmed that the proposed compounds had never been synthesized. As the role of ABL in cancer and neurodegeneration,[11] inflammation has an age long been known as a mark of cancer and a significant factor in carcinogenesis and disease progression.[12] Although most anticancer drugs exhibit toxicity, lack selectivity, and frequently develop acquired resistance,[13] in this study, NSAIDs core was retained and linked to 1,3-diazetidine-2-one ring, hopefully achieving synergistic actions, with fewer side effects and more selectivity against EGFR.

MATERIALS AND METHODS

Modern drug research and development is costly due to the difficulty of creating new molecules with specific properties. Creating a new molecule provides insight into the complexity and cost of developing new drugs today. A holistic approach to designing new drugs that consider the interactions of potential compounds with the entire network of biomolecules in cells can be more successful.[14]

The structure of eight 1,3-diazetidin-2-one derivatives was designed based on a literature review made by our team. Molecular dockings were performed using the EGFR protein as the initial step due to its importance in reducing the danger of inadvertently picking an unsuitable protein model, enhancing pose prediction, and virtual screening (VS) enrichments.[15]

Preparation of protein receptor and ligand

The crystal structure of EGFR complexes with erlotinib (Protein Data Bank [PDB] code: 1 M17) was retrieved from the PDB. To get the proper ionization and tautomeric states of amino acid residues, the crystal structures of target proteins were prepared by removing water molecules and adding hydrogen atoms. In addition, CheBio3D (version 17.1) was also utilized to implement a molecular mechanic force field to decrease the energy of the eight chosen ligand molecules.

Docking procedures

Molecular docking was performed using the full license version of Genetic Optimization for Ligand Docking (GOLD is Cambridge Crystallographic Data Centre (CCDC), Chemdraw is also Cambridge software) (version 2021.2.0). In addition, the Hermes visualizer program (version 2021.2.0, Cambridge, England) from the GOLD suite was utilized to prepare the receptor for the docking procedure and obtain images. The GOLD docking binding site comprises all the protein residues within 10Å of the reference ligands in the downloaded protein structure complexes. ChemBioOffice (version 17.1) software drew the ligands’ chemical structures.

Three EGFR proteins were downloaded from the PDB website (1M17, 4HJO, and 2ITY) to perform the process of ensemble docking. As a result, 1M17, the crystal structure of EGFR protein complexes with erlotinib was chosen.

The generated pose number was maintained as ten, the top-ranked solution was preserved as default, and the early termination option was switched off. The configuration guide used was Chemscore kinase, and the scoring function was the ChemPLP fitness.

To investigate the interaction between the amino acid residues of the EGFR protein and our synthesized ligands, the docking results, including the binding mode, docked pose, and binding free energy, were analyzed.[16]

ADME procedure

Using the SwissADME server, the synthesized drugs’ pharmacokinetic profile includes absorption, distribution, metabolism, and excretion (ADME), along with other parameters, such as blood–brain barrier (BBB) penetration and affinity for P-gp, and bioavailability was assessed. It is used to identify the safest and most promising drug candidates to eliminate the compounds that are most likely to fail in later phases of drug development owing to poor ADME features.[17]

ChemDraw was used to design all ligands converted to SMILE names using the SwissADME tool. Using BIOLED-Egg, the lipophilicity and polarity of the compounds were determined.[18]

RESULTS AND DISCUSSION

Finding new medications aim to create compounds with potency and selective characteristics with high absorption, distribution, metabolism, excretion, and lower toxicity.[19]

New inhibitor ligands with increased binding affinity were intended to be produced by incorporating binding affinity techniques and protein docking. VS uses a computer program to select compounds based on how well they bind to target receptors.[20] The score of binding affinity for all screening compounds [Table 1], as determined by VS, ranged between 79 and 92 on EGFRs, while the binding ability of erlotinib was 76.20, gefitinib (78.24), imatinib (78.04), and sorafenib (67.04). Due to their strong binding affinity and ideal orientation inside the receptor active region, which is flanked by a critical amino acid for effective interactions, compounds 2 and 7 had high docking scores among the compounds mentioned earlier, as shown in Figure 3a and b.

Table 1.

Docking score for different 1,3-diazetidin-2-one derivatives inside epidermal growth factor receptor active site

Number of compound	Structure	Docking score	Binding interactions	3D representations of the interactions between ligands and amino acids in the EGFR active site
1		84.70	Hydrogen bond: CYS773 Hydrophobic interaction with LYS721, LEU820, VAL702
2		85.89	Hydrogen bond: THR830, ASP831 Hydrophobic interaction with GLY772, ASP831, THR766, LEU764
3		88.61	Hydrogen bond: THR830, two bonds with ASP831 Hydrophobic interaction with CYS721, ASP831
4		91.90	Hydrogen bond: THR766 Hydrophobic interaction with LYS721, LEU820, LEU768, MET769
5		92.77	Hydrogen bond: Two bonds with ASP831 Hydrophobic interaction with ASP831, MET769, LYS721, CYS773, LEU820
6		79.81	Hydrogen bond: THR766 Hydrophobic interaction with LEU768, GLY772, PHE832
7		80.80	Hydrogen bond: THR766 Hydrophobic interaction with GLY772, LEU764, VAL702, LEU768, LYS721, LEU820
8		81.57	Hydrogen bond: THR830, LYS721 Hydrophobic interaction with MET769, LEU768, LEU820, LYS721

EGFR: Epidermal growth factor receptor, 3D: Three dimensional

Figure 3.

Compounds 2 and 7 in ball and stick format, amino acid residues in wireframe format. (a) compound 2, (b) compound 7

Docking analysis showed that VAL 702, LYS 721, ASP831, THR830, LEU820, MET769, LYS704, LEU768, GLY772, THR766, LEU764, ASP776, CYS773, CYS751, LEU694, GLN767, and PHE832. They are listed in Table 1 for this receptor and interact through hydrogen bonding and transient contacts with the proposed final ligand. GOLD determines short contact and hydrogen bond distances between a protein atom and a proposed ligand.

Compound 2 demonstrates a high docking PLP fitness (85.89) against EGFR, H-bonds with THR830 and ASP831, and hydrophobic interactions with GLY772, ASP831, THR766, and LEU764. As shown in Figure 3a, compound 7 shows a high docking PLP fitness of 80.80 with EGFR through H-bond with THR766 and hydrophobic interactions between GLY772, LEU764, VAL702, LEU768, LYS,721, and LEU820; this amino acid is present in the EGFR active site binding of erlotinib, imatinib, and sorafenib as shown in Figure 3b. All of the interactions above have improved the capabilities of newly developed compounds and nominated them as potential anticancer agents with more excellent activity and binding affinity.

The biological activity rises as the number of hydrophobic interactions increases due to the dominance of these connections and H-bonding interactions, which are required for substrate binding at the active site.[20]

It is essential to assess the absorption, distribution, metabolism, and elimination (ADME) process early in the research. This is why the SwissADME server was used to predict the properties of the designed compounds.[21]

Figure 4 shows a representation of BOILED-Egg. It shows that compound 2 (red dots) does not cross the BBB, is strongly passively absorbed from the gastrointestinal tract, and is not exported from central nervous system (CNS) cells through p-glycoprotein. On the other hand, compound 7 (blue dot) is expected to cross the BBB and be cleared from the CNS by P-glycoprotein.

Figure 4.

BOILED-Egg for both compound 2 (red dot) and compound 7 (blue dot). Molecules within the yellow ovule are anticipated to permeate BBB passively. Likewise, it is envisaged that the GIT will passively absorb the molecules in the white ovule. P-glycoprotein: Blue dots show compounds that P-glycoprotein is anticipated to eliminate from the CNS. PGP: Red dots designate substances that P-glycoprotein does not expect excreting from the CNS. BBB: Blood–brain barrier, GIT: Gastrointestinal tract, CNS: Central nervous system

Lipinski’s rule of 5 states that oral drugs should have a molecular weight under 500, a P (o/w) <5, 5 H-bond donors, and 10 H-bond acceptors.[21] In addition, the drug’s polar surface area, one of the fundamental properties related to bioavailability, should be at least 140 Å. The higher the PSA, the lower the drug’s oral bioavailability; all compounds are passively absorbed with topological polar surface area (TPSA) <140 Å to increase oral bioavailability.[22]

According to our findings, the TPSA values of compounds 2 and 7 were lower than 140 (77.82 Å and 75.02 Å), respectively. Both compounds have bioavailability rates of 0.55, meaning that they can enter the systemic circulatory system. Tables 2 and 3 contain information about the suggested compounds’ chemical, pharmacokinetic, and physicochemical properties. In addition, these tables demonstrate that the other designed compounds have potential activity.

Table 2.

Virtual properties of compound 2 that was predicted by the Swiss absorption, distribution, metabolism, and excretion website

SMILES	O=C(c1ccccc1Nc1cccc(c1C)C)NN1C(=O)N(C1c1ccco1)c1ccccc1
Physicochemical Properties
Formula	C27H24N4O3
Molecular weight	452.50 g/mol
Num. heavy atoms	34
Num. arom. heavy atoms	23
Fraction Csp3	0.11
Num. rotatable bonds	7
Num. H-bond acceptors	3
Num. H-bond donors	2
Molar Refractivity	137.62
TPSA	77.82 Ų
Lipophilicity
Log Po/w (iLOGP)	4.14
Log Po/w (XLOGP3)	5.78
Log Po/w (WLOGP)	4.84
Log Po/w (MLOGP)	4.00
Log Po/w (SILICOS-IT)	3.20
Consensus Log Po/w	4.39
Water Solubility
Log S (ESOL)	-6.33
Solubility	2.14e-04 mg/ml ; 4.73e-07 mol/l
Class	Poorly soluble
Log S (Ali)	-7.18
Solubility	2.97e-05 mg/ml ; 6.56e-08 mol/l
Class	Poorly soluble
Log S (SILICOS-IT)	-8.74
Solubility	8.18e-07 mg/ml ; 1.81e-09 mol/l
Class	Poorly soluble
Pharmacokinetics
GI absorption	High
BBB permeant	No
P-gp substrate	No
CYP1A2 inhibitor	No
CYP2C19 inhibitor	Yes
CYP2C9 inhibitor	Yes
CYP2D6 inhibitor	Yes
CYP3A4 inhibitor	Yes
Log Kp (skin permeation)	-4.96 cm/s
Druglikeness
Lipinski	Yes; 0 violation
Ghose	No; 1 violation: MR >130
Veber	Yes
Egan	Yes
Muegge	No; 1 violation: XLOGP3 >5
Bioavailability Score	0.55
Medicinal Chemistry
PAINS	0 alert
Brenk	0 alert
Leadlikeness	No; 2 violations: MW >350, XLOGP3 >3.5
Synthetic accessibility	4.26

Table 3.

Virtual properties of compound 7 that was predicted by the Swiss absorption, distribution, metabolism, and excretion website

SMILES	COc1ccc2c(c1)ccc(c2)[C@@H](C(=O)NN1C(=O)N(C1c1ccco1)c1ccccc1)C
Physicochemical Properties
Formula	C26H23N3O4
Molecular weight	441.48 g/mol
Num. heavy atoms	33
Num. arom. heavy atoms	21
Fraction Csp3	0.15
Num. rotatable bonds	7
Num. H-bond acceptors	4
Num. H-bond donors	1
Molar Refractivity	131.53
TPSA	75.02 Ų
Lipophilicity
Log Po/w (iLOGP)	3.58
Log Po/w (XLOGP3)	4.69
Log Po/w (WLOGP)	4.13
Log Po/w (MLOGP)	3.29
Log Po/w (SILICOS-IT)	3.07
Consensus Log Po/w	3.75
Water Solubility
Log S (ESOL)	-5.54
Solubility	1.27e-03 mg/ml; 2.88e-06 mol/l
Class	Moderately soluble
Log S (Ali)	-5.99
Solubility	4.48e-04 mg/ml; 1.02e-06 mol/l
Class	Moderately soluble
Log S (SILICOS-IT)	-7.65
Solubility	9.88e-06 mg/ml; 2.24e-08 mol/l
Class	Poorly soluble
Pharmacokinetics
GI absorption	High
BBB permeant	Yes
P-gp substrate	Yes
CYP1A2 inhibitor	No
CYP2C19 inhibitor	Yes
CYP2C9 inhibitor	Yes
CYP2D6 inhibitor	Yes
CYP3A4 inhibitor	Yes
Log Kp (skin permeation)	-5.66 cm/s
Druglikeness
Lipinski	Yes; 0 violation
Ghose	No; 1 violation: MR >130
Veber	Yes
Egan	Yes
Muegge	Yes
Bioavailability Score	0.55
Medicinal Chemistry
PAINS	0 alert
Brenk	0 alert
Leadlikeness	No; 2 violations: MW >350, XLOGP3 >3.5
Synthetic accessibility	4.25

CONCLUSION

Molecular docking is one of the most effective drug discovery methods that increase existing drugs’ efficacy and medicinal value enzyme binding affinity. The novelty of this study is the creation of a series of new 1,3-diazetidin-2-one derivatives and the evaluation of their potency profile. In addition, it increased binding affinity toward the EGFR active site compared to erlotinib. As a result, these compounds demonstrated more antiproliferative activity and a higher binding affinity. In addition, the physicochemical and pharmacokinetic indicators of compounds 2 and 7 were in line with Lipinski’s rules. Last but not least, these compounds can serve as lead substances for developing novel antiproliferative agents. Therefore, all proposed compounds should undergo biological and pharmacological evaluation studies to examine their potency, side effects, and toxicity profile.

Financial support and sponsorship

Nil.

Conflicts of interest

There are no conflicts of interest.