High-throughput virtual screening of novel CHK1 inhibitors

Received: 03-Jan-2023, Manuscript No. OAR-22-76413; Accepted: 27-Jan-2023, Pre QC No. OAR-22-76413 (PQ); Editor assigned: 05-Jan-2023, Pre QC No. OAR-22-76413 (PQ); Reviewed: 19-Jan-2023, QC No. OAR-22-76413 (Q); Revised: 26-Jan-2023, Manuscript No. OAR-22-76413 (R); Published: 28-Jan-2023

Introduction

Numerous anticancer treatments induce DNA damage and activate cell cycle checkpoints, giving cancer cells time to repair their DNA and recover [1]. As potential therapeutic targets, these checkpoints have been the subject of extensive research, and Chk1 inhibitors have emerged as fascinating novel therapeutic drugs [2]. Through inactivation of p53 or Rb or amplification of proto-oncogenes, cancer cells usually lack one or more genes for G1 checkpoint regulation (cyclins and CDKs). Chk1 inhibitors that inhibit the remaining checkpoints, S and G2, ought to render cancer cells more susceptible to anticancer therapies, such as c-radiation or DNA-damaging drugs [3-5]. Chk1 was initially recognized as a regulator of the G2/M checkpoint, but it has now been demonstrated to serve other roles in replication fork stability, origin firing, and homologous recombination. Inhibition of these systems can greatly increase the sensitivity of cells to specific antimetabolites [6,7]. Inhibition of CHK-1 is particularly effective in cancer cells devoid of p53 [8]. Consequently, the selective efficacy of CHK-1 inhibitors in combination with cytotoxic, such as DNA-damaging chemicals, is a significant advantage of these medications as cancer therapy [9-11]. Even if several small molecule-based CHK-1 inhibitors are undergoing clinical testing, there is always the possibility of identifying novel CHK1 inhibitors. Using computer-assisted drug design, we have attempted to identify effective CHK1 inhibitors in this study. Million Compounds Database, Natural Product Database, NCI Database has been examined and three molecules has been found by structure-based virtual screening followed by filtering for various drug Likeness, ADME, toxicity, Molecular docking. Our research led to the development of lead compounds with high binding affinity, efficient ADME characteristics, low toxicity, and high stability.

Materials and Methods

Identification of Hits

For the Identification of Hit molecules; Million Molecules Database, Natural Product database and NCI Database available at RASPD were screened by following RASPD protocol [12]. A cut off was set at -7.0 Kcal/mol. Those molecules have successfully passed the cut off they were taken for further studies.

Filtering Hits based on Drug Likeness Properties

Drug-likeness properties were evaluated by using Swiss ADME server [13]. To exclude molecules that are incompatible with pharmacokinetics parameters Lipinski’s rule of five, Ghose rule, Veber rule, Muegge rule was applied. The molecules those have passed all these rules and having “Drug Like” properties were taken for further studies [14-17].

Lead Optimization

Docking:

To understand the molecular level interaction and get accurate poses Molecular Docking was carried out by using Auto Dock Vina implemented in AMDock [18, 19]. The Crystal Structure of CHK1 was obtained from RCSB- Protein Data Bank (PDB id: 1nvq, resolution: 2.00 Å [20, 21]. The crystal structure was freed from water molecules, Co-Factors, ions and covalent ligand by using the Dock-prep procedure implemented in the UCSF Chimera program [22]. Charges were computed, polar hydrogen atoms were subsequently added. As no active site was mentioned so we preferred for bind docking. The grid box centred in (X=5.062639, Y=6.464194, Z=16.857611) based on the active sites of the protein (CYS87, ALA36, LEU15, GLY16, GLU91, LEU137, GLU85, VAL23, LYS38, SER147, ASN135) [21,23]. Grid box centre points and dimensions were set to target the substrate binding-binding pocket of the protein. The best docked pose was selected based on its binding energy score and significant interactions in Active sites. Based on the ΔG, the best result was subjected to ADME and Toxicity.

ADME:

T he Pharmacokinetic profile was checked by using Swiss ADME, Pre-ADMET, vnnadmet [13,24,25]. Parameters such as Solubility (LogS), Water solubility (mg/ml), Solubility Class, SKlogS buffer, Bioavailability, GI Absorption, Human Intestinal Absorption (HIA %), Madin-Darby Canine Kidney (MDCK), Caco-2 Permeability, Skin permeability (logKp) (cm/s), Partition Coefficent (LogP), Distribution Coefficient (logD), BBB (Cbrain/Cblood), BBB, Pgp Inhibition, P-gp Substrate, Plasma protein binding (%PPB), Human Liver Microsomes (HLM), CYP1A2 inhibitor, CYP3A4 inhibitor, CYP3A4 Substrate, CYP2D6 inhibitor, CYP2D6 substrate, CYP2C9 inhibitor, CYP2C19 inhibitor were selected for the studies. Those molecules have shown proper pharmacokinetic profile taken for further studies.

Toxicity:

Toxicity causes 30% of lead candidates to fail. The toxicity study was carried out by using Pre-ADMET, vnnadmet and lazar [24-26]. Toxicity parameters such Acute Oral Toxicity, Human Ether-a-Go-Related Gene Inhibition, Liver Toxicity: Cytotoxicity, Mitochondrial Toxicity, Acute algae toxicity, AMES, Carcinogenicity (Mouse), Carcinogenicity(Rat), Carcinogenicity (Rodent), Acute daphina toxicity, hERG Blocker

Honey Bee Toxicity, Acute fish toxicity (medaka), Acute fish toxicity (minnow), Ames TA100 (-S9), Ames TA1535 (-S9), Biodegradation, MRTD (mg/day) were predicted. Only nontoxic molecules have been reported as therapeutic potential for CHK1 inhibitors.

Results and Discussion

Identification of Hits

After the Screening and removing the duplicate molecule a total 3313 unique hit molecules were found that are binding with the receptor having binding affinity less than -7.0 Kcal/mol which were taken for further studies.,

Filtering Hits based on Drug Likeness Properties

To get lead like molecule Swiss ADME server was used to calculate all the hits in multiple batches. Microsoft Excel was employed for process and analysis of the data generated by Swiss ADME. Out of 3313 hit molecules only 51 Molecules Obeyed multiple drug likeness Rules such as Lipinski rule, Ghose rule, Veber rule, Muegge rule. Among them 2010 Molecules obeyed Lipinski Rule of 5 followed by 1775 molecules obeyed Veber rule, 1710 Molecules obeyed Egan rule, 1507 molecules obeyed Muegge rule, and 51 molecules obeyed Ghose rule.

Lead Optimization

Docking:

The docking was carried out to find the most suitable Confirmation of the molecule that can bind with CHK1 with lowest being energy. Out of all 51 drug like molecules, top 35 molecules were taken for further studies based on their binding energy and chemical interactions. The Molecular Docking Results of all the 53 molecules along with SMILEs and binding energy has been reported in Table 1.

Tab. 1. Docking Results

ADME:

The reason behind the failure of lead molecules in the Clinical trial are low Poor ADME properties. To eliminate such molecules which having poor pharmacokinetic profile Insilco Pharmacokinetics study was conducted. Out of 35, only 17 molecules have passed all the criteria of ADME Profile. A detail view has shown in Table 2.

Tab. 2. ADME Results

Toxicity:

To be an effective drug compound, a highly biologically active lead molecule must possess low toxicity. In-silico Toxicity predictions are gaining acceptance in toxicological risk assessment. Out of 19 molecules, only 5 molecules have shown Non-Toxic properties (rows highlighted in Green) such as: Liver Toxicity: DILI, Mitochondrial Toxicity (MMP), Acute algae toxicity, AMES, Carcinogenicity (Mouse), Carcinogenicity(Rat), Carcinogenicity (Rodent), Acute daphina toxicity, in vitro hERG inhibition,Acute fish toxicity (medaka), Acute fish toxicity (minnow), Ames TA100 (+S9), Ames TA100 (-S9), Ames TA1535 (-S9). The Toxicity Prediction of the top 39 molecules listed Table 3.

Tab. 3. Toxicity Results

Molecular Interaction Analysis:

To understand the molecular level interaction all, the top three molecules (ZINC08925969, ZINC11784547, and ZINC12516005) that have successfully passed all the Drug Likeness, ADME and Toxicity study has been taken for Molecular Interaction Analysis. All the molecules have been found that they are effectively binding with the same amino acids present in the active site of CHK1 (CYS87, ALA36, LEU15, GLY16, GLU91, LEU137, GLU85, VAL23, LYS38, SER147, ASN135) and they have formed sufficient Hydrogen bonds to make complex. The interaction details of CHK1 all the molecules have been reported in ribbon representation and 2D Depiction in Figure 1-3.

Figure 1: CDK1-ZINC08925969 interaction depicted in Ribbon representation and 2D Depiction

Figure 2: CHK1- ZINC11784547 interaction depicted in Ribbon representation and 2D Depiction

Figure 3: CHK1- ZINC12516005 interaction depicted in Ribbon representation and 2D Depiction

Conclusion

The identified molecules ZINC08925969, ZINC11784547, ZINC11972241, and ZINC12516005 exhibit drug-like properties, ADME, and non-toxicity with strong binding energy at the active site of CHK1 and interacting Key amino acid residues with stable hydrogen bonds and a thermodynamically favourable receptor-ligand interaction. Therefore, we wish to report that these compounds may be effective CHK1 inhibitors.

Funding Resources

The authors have not received no funds for this work.

Declaration of Interests

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper

Conflicts of Interest

The authors report no conflicts of interest. The authors alone are responsible for the content and writing of this article.

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Abstract

Check point kinase 1 (Chk1) is an essential protein in G2 phase checkpoint arrest, which cancer cells need to sustain the cell cycle and prevent cell death. Chk1 inhibitors have been shown to eliminate the S and G2 checkpoints and change the DNA repair pathway, resulting in immature mitotic progression, mitotic catastrophe, and cell death. Normal cells remain in the G1 phase to repair DNA damage as a result of p53 and are less affected by the deletion of the S and G2 checkpoints. Due of its function in this research we have tried to target CHK1 to identify potent CHK1 inhibitors by employing computer aided drug design. Million Molecules Database, Natural Product Database, NCI Database has been screened and three molecules has been identified by structure-based virtual screening followed by filtering for various drug likeness, ADME, toxicity, Molecular docking. Our research work resulted in lead molecules that have shown strong binding affinity with effective ADME properties, low toxicity, and high stability.

Graphical Abstract: