Research Article - Onkologia i Radioterapia ( 2023) Volume 17, Issue 1

High-throughput virtual screening of novel CHK1 inhibitors

Abhijit Debnath1*, Hema Chaudhary2, Siddhartha Roy2 and Shikha Srivastava3
 
1Assistant Professor, Noida Institute of Engineering and Technology (Pharmacy Institute), 19 Knowledge Park-II, Institutional Area Greater Noida, Uttar Pradesh, India
2Faculty of Pharmaceutical Sciences, PDM University, Delhi, India
3Bhaskaracharya College of Applied Sciences, University of Delhi, Delhi, India
 
*Corresponding Author:
Abhijit Debnath, Assistant Professor, Noida Institute of Engineering and Technology (Pharmacy Institute), 19 Knowledge Park-II, Institutional Area Greater Noida, Uttar Pradesh, India, Email: theabhijitdebnath@gmail.com

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

Molecule Id SMILE Binding Energy
ZINC12132957 Cc1cc(=O)c(c2n1-c3ccccc3S[C@H](C2)c4ccccn4)C(=O)NC[C@H]5COCCO5 -10.1
ZINC20600602 c1ccc(cc1)c2c3ccccc3c(=O)n(n2)CC(=O)N[C@@H]4CCCN(C4)c5ncccn5 -10.1
ZINC12516005 Cc1cc(n(n1)c2nc3c(n2CCCc4ccccc4)c(=O)n(c(=O)n3C)CC(=O)C)C -9
ZINC01056864 c1ccc2c(c1)CCN(C2)C(=O)c3cnn4c3nc(cc4C(F)(F)F)c5cccs5 -8.9
ZINC01056864 c1ccc2c(c1)CCN(C2)C(=O)c3cnn4c3nc(cc4C(F)(F)F)c5cccs5 -8.9
ZINC11840098 Cc1cc(n(n1)c2cccc(c2)C(=O)NC[C@H]3Cc4cc(ccc4O3)c5ccc(nn5)OC)C -8.8
ZINC14992739 CCOC(=O)[C@@H]1CCCCN1C(=O)c2cc(cc(c2)n3cnnn3)c4cc(ccc4OC)Cl -8.8
ZINC11784547 COc1ccc2c(c1)[nH]c(n2)CCNC(=O)CC[C@@]3(CCC(=O)N3)Cc4ccc(cc4)Cl -8.6
ZINC00945916 Cn1c2ccccc2nc1SCC(=O)N/N=C/c3ccc(cc3)OCc4ccccc4 -8.5
ZINC12034833 CN(Cc1nc2ccccc2s1)C(=O)C[C@]3(CC(=O)N(C3=O)C4CC4)c5ccc(cc5)OC -8.4
ZINC12447659 Cc1ccc(cc1)C2=NN(c3nc4c(n3[C@@H]2C)c(=O)n(c(=O)n4C)C)[C@@H]5CCS(=O)(=O)C5 -8.3
ZINC14885414 Cc1ccc(nc1)c2ccc3c(c2)C[C@H](O3)CNC(=O)CCN(C)[C@@H]4CCS(=O)(=O)C4 -8.3
ZINC14733310 Cc1ccc(s1)c2cc(cc(c2)S(=O)(=O)N3CCOCC3)C(=O)N(C)Cc4nccn4C -8.3
ZINC01216760 c1ccc(cc1)C[NH+]2CCN(CC2)C(=O)c3cnn4c3nc(cc4C(F)(F)F)c5ccco5 -8.1
ZINC02859380 CCOc1ccc(cc1)NC(=O)CSc2nnc(n2C)COc3ccc4c(c3)CCCC4 -8.1
ZINC14885974 C[C@H](c1cccs1)N(C)C(=O)c2cc(cc(c2)n3cnnn3)c4cccc5c4nccc5 -8.1
ZINC19774479 c1ccc(cc1)C[NH+]2CCN(CC2)C(=O)c3cnn4c3nc(cc4C(F)(F)F)c5ccco5ÃÃÂ?? ÃÃÂ?? ÃÃÂ??  -8.1
ZINC08925969 c1cc(cc(c1)Oc2ccc(cc2[N+](=O)[O-])C(F)(F)F)C(=O)N -8
ZINC12038620 c1ccc-2c(c1)Cc3c2ccc(c3)C[NH+]4CCC[C@@H](C4)n5cc(nn5)C(=O)NCCCO -8
ZINC00955034 CS(=O)(=O)c1ccc2c(c1)sc(n2)NC(=O)/C=C/c3ccc(cc3)OCc4ccccc4 -8
ZINC12464790 CCN(CCn1cccn1)C(=O)C[C@H]2C(=O)NCC[NH+]2Cc3ccc4ccccc4c3 -8
ZINC14530440 COc1cccc(c1)c2c3n(c([nH+]2)[C@H]4CCOC4)CCN(C3)Cc5cc6c(cc5Cl)OCO6ÃÃÂ?? ÃÃÂ?? ÃÃÂ??  -8
ZINC14538250 CCn1c2c(c(n1)C(=O)N3CCOCC3)C[C@H](CC2)N4CCOc5ccc(cc5C4)Cl -8
ZINC14740689 CN(C)C(=O)c1c2c(n(n1)Cc3ccccc3)CCN(C2)Cc4ccnc5c4cccc5 -8
ZINC12041004 Cc1ccccc1[C@@]2(CC(=O)N(C2=O)Cc3cccnc3)CC(=O)N(C)Cc4ccsc4 -8
ZINC01245157 Cc1ccc(cc1)n2c(nnc2SCC(=O)Nc3ccccc3F)c4ccncc4 -8
ZINC14987901 c1ccc(cc1)c2ccc(cc2)C[NH+]3CCC[C@H](C3)n4cc(nn4)C(=O)NCCCO -8
ZINC02504256 c1ccc(cc1)N2CCN(CC2)C(=O)c3cnn4c3nc(cc4C(F)(F)F)c5ccco5 -8
ZINC08680620 c1ccc(cc1)C[NH+]2CCN(CC2)C(=O)c3cnn4c3nc(cc4C(F)(F)F)c5cccs5 -8
ZINC14733139 CCOC(=O)c1c2c(n(n1)Cc3ccccn3)CCN(C2)Cc4ccccc4c5ccco5 -8
ZINC12038301 CCOCCC[NH2+][C@@H]1CCc2c(sc3c2c(=O)n(cn3)C4Cc5ccccc5C4)C1 -8
ZINC14954144 CC1([C@H]2CC=C([C@@H]1C2)CN3C[C@H](C[C@H]3C(=O)OC)NC(=O)c4ccccc4n5cccn5)C -8
ZINC12037267 COc1ccc(cc1OCc2cccs2)CN(C3CCCC3)C(=O)c4cnn5c4nccc5 -8
ZINC14753959 CCN(Cc1ccncc1)[C@@H]2CCc3c(c(nn3C)C(=O)N(C)Cc4ccccc4)C2 -8
ZINC12036079 CC1(COC1)COc2cc(ccc2OC)CN(C[C@@H]3CCCO3)C(=O)c4nc5ncccn5n4 -8
ZINC12279677 Cc1ccccc1c2cnc(nc2[C@H]3CCCN(C3)C(=O)[C@@H]4CCOC4)c5ccncc5 -7.9
ZINC12300378 Cc1ccccc1C[NH+]2CCC(CC2)CN(C[C@H]3CCCO3)C(=O)c4cc(nn4C)C -7.9
ZINC12150749 CCNC(=O)c1cn(cc(c1=O)C(=O)N2CCOc3ccc(cc3C2)Cl)Cc4ccccc4 -7.9
ZINC12278958 Cn1cc(c(=O)c2c1cccc2)C(=O)N(Cc3ccc(c(c3)OCc4ccco4)OC)C5CC5 -7.9
ZINC12450775 C[C@@H](c1cccs1)N(C)C(=O)c2cc(cc(c2)n3cnnn3)c4ccc(c(c4OC)OC)OC -7.9
ZINC14542446 Cc1c(sc(n1)C)C(=O)N2C[C@@H](CN(C(=O)C2)Cc3cnn(c3)C)OCc4ccncc4 -7.9
ZINC14956070 CN(C)c1c(cc2cc3c(cc2n1)OCO3)CN(C[C@H]4CCCO4)C(=O)Cc5cccs5 -7.8
ZINC22077949 Cc1ccccc1n2c(nnn2)[C@H](c3ccccc3)[NH+](C)Cc4cc5c(c(c4)OC)OCO5 -7.8
ZINC14885515 Cc1ccc([nH+]c1)c2ccc3c(c2)C[C@H](O3)CNC(=O)CC[NH+](C)[C@@H]4CCS(=O)(=O)C4ÃÃÂ?? ÃÃÂ?? ÃÃÂ??  -7.8
ZINC02825769 COCCCN1C(=O)c2ccc(cc2C1=O)C(=O)OCC(=O)c3ccc(cc3)c4ccccc4 -7.8
ZINC14541675 Cc1c(c(on1)C)CC(=O)N2CCc3c(c(nn3CC4CC4)C(=O)N(C)Cc5cscn5)C2 -7.7
ZINC02833795 CS(=O)(=O)CC[C@@H](C(=O)OC(c1ccccc1)c2ccccc2)N3C(=O)c4ccccc4C3=O -7.7
ZINC00294396 CCOC(=O)[C@H]1CCC[NH+](C1)Cc2ccc(cc2)C -7.7
ZINC14879900 Cc1csc(n1)[C@H](C)N(C)C(=O)C[C@@]2(CC(=O)N(C2=O)CCOC)c3ccccc3OC -7.6
ZINC19853115 COCCN(Cc1cc2cc(c(cc2nc1N3CCOCC3)OC)OC)C(=O)[C@@H]4CCCO4 -7.6
ZINC20995059 Cc1cccn2c1nc(c2CN(C)C[C@@H](C)C[NH+]3CCCC3)C(=O)N4CCOCC4 -7.6

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

Zinc id Solubility Class GI Absorption Human intestinal absorption (HIA %) Madin-Darby Canine Kidney (MDCK) Caco-2 Permeability Partition Coefficent (LogP) Distribution Coefficient (logD) BBB (Cbrain/Cblood) Pgp Inhibition P-gp Substrate Plasma protein binding [%PPB] Human Liver Microsomes (HLM) CYP1A2 inhibitor CYP3A4 inhibitor CYP3A4 Substrate
ZINC12464790 Soluble High 92.131957 0.91684 37.1475 1.02 0.42643 No Non Yes 43.736916 Yes No No Substrate
ZINC14885414 Soluble High 97.30467 0.16601 9.80078 2.4 0.07455 No Non Yes 64.319963 Yes No Yes Weakly
ZINC14538250 Soluble High 97.428477 0.145629 50.5155 2.61 1.38984 Yes Inhibitor No 72.34835 Yes No Yes Substrate
ZINC14733310 Soluble High 100 0.350966 21.713 2.29 2.18163 No Non Yes 93.160059 Yes No Yes Substrate
ZINC12036079 Soluble High 99.446545 0.0747592 52.8658 2.22 1.5696 No Inhibitor Yes 69.966481 Yes No Yes Substrate
ZINC12041004 Soluble High 99.229839 3.83683 30.5199 3.11 2.74565 No Non Yes 91.555344 Yes No Yes Substrate
ZINC01216760 Soluble High 93.448984 0.404492 29.9402 2.27 1.53396 Yes Inhibitor Yes 46.438777 Yes No No Substrate
ZINC19774479 Soluble High 97.585841 0.213209 38.3199 2.27 1.8019 Yes Inhibitor Yes 75.010224 Yes No No Weakly
ZINC12279677 Soluble High 97.605255 2.37906 48.2635 3.41 2.82912 Yes Non Yes 87.991134 Yes No Yes Substrate
ZINC14753959 Soluble High 97.380853 0.896182 53.2898 3 1.67318 Yes Inhibitor Yes 83.909847 Yes No Yes Substrate
ZINC12038620 Moderately soluble High 89.753368 1.0062 19.2858 1.84 1.04455 No Non Yes 50.512485 Yes No No Substrate
ZINC14987901 Moderately soluble High 89.32498 1.89877 18.9232 1.72 1.22128 No Non Yes 54.274299 No No No Substrate
ZINC14530440 Moderately soluble High 97.475122 0.0586677 55.9159 3.39 3.06295 Yes Inhibitor Yes 84.471095 Yes No Yes Substrate
ZINC14740689 Moderately soluble High 97.669741 0.0579102 50.5699 3.2 1.98879 Yes Inhibitor Yes 82.007294 Yes No Yes Substrate
ZINC02504256 Moderately soluble High 97.586951 0.268624 37.2832 3.09 3.42467 Yes Inhibitor Yes 92.882846 Yes No Yes Weakly
ZINC12447659 Moderately soluble High 99.602877 2.13039 1.37961 1.76 2.71327 No Inhibitor No 100 Yes No No Substrate
ZINC12516005 Moderately soluble High 99.524021 0.0495903 24.3883 2.63 3.26059 No Inhibitor No 90.548132 Yes No Yes Substrate
ZINC12034833 Moderately soluble High 99.621884 0.0971435 38.3776 3.27 2.69902 No Non Yes 89.055638 Yes No Yes Substrate
ZINC08925969 Moderately soluble High 98.503336 0.0460023 21.3751 2.52 1.60789 No Non No 88.810932 Yes Yes Yes Weakly
ZINC14733139 Moderately soluble High 97.750533 0.734869 38.443 3.44 2.56431 Yes Inhibitor Yes 83.690586 Yes No Yes Substrate
ZINC14954144 Moderately soluble High 96.384572 0.0555437 25.3521 3.15 1.85147 Yes Inhibitor Yes 77.171736 Yes No Yes Substrate
ZINC12132957 Moderately soluble High 97.448023 1.47904 25.884 2.81 2.54095 No Non Yes 83.753715 No No Yes Substrate
ZINC08680620 Moderately soluble High 94.473155 0.190938 27.1701 2.83 1.98318 No Non Yes 72.27183 Yes No No Substrate
ZINC20600602 Moderately soluble High 96.70851 1.25893 24.49 2.69 2.97726 No Non Yes 93.709467 Yes No Yes Weakly
ZINC11784547 Moderately soluble High 91.29963 0.07203 17.3092 3.31 2.82744 No Non Yes 84.5226 No Yes Yes Weakly
ZINC01056864 Moderately soluble High 97.831212 0.0614135 48.0599 4.13 3.8651 No Inhibitor Yes 93.822916 Yes Yes Yes Weakly
ZINC11840098 Moderately soluble High 97.831212 0.0614135 48.0599 3.64 3.8651 No Inhibitor Yes 93.822916 Yes No Yes Weakly
ZINC12037267 Moderately soluble High 98.650061 0.0901468 54.503 3.91 4.08286 No Inhibitor Yes 93.073048 No No Yes Substrate
ZINC12038301 Moderately soluble High 94.346549 7.3874 23.5531 3.2 1.47678 No Non Yes 36.813913 Yes No Yes Substrate
ZINC01245157 Moderately soluble High 96.772975 0.0985069 44.7343 3.7 4.7439 No Inhibitor No 99.446876 No Yes Yes Weakly
ZINC14992739 Moderately soluble High 99.578491 0.0614725 22.5019 3.52 3.82613 No Inhibitor No 90.591818 Yes Yes Yes Substrate
ZINC14885974 Poorly soluble High 98.571171 0.457832 37.013 3.92 4.2395 No Inhibitor Yes 92.200846 Yes No Yes Substrate
ZINC02859380 Poorly soluble High 97.179052 20.0541 51.9786 3.92 5.40364 No Inhibitor No 96.692417 No No Yes Substrate
ZINC00945916 Poorly soluble High 96.871525 0.86525 45.2023 4.07 5.64578 No Inhibitor No 96.895471 Yes Yes Yes Substrate
ZINC00955034 Poorly soluble Low 97.149361 0.241538 19.746 4.33 4.69202 No Inhibitor No 100 Yes No Yes Weakly

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

ZINC ID Acute Oral Toxicity Human Ether-a-go-go-Related Gene Inhibition Liver Toxicity: Cyto- toxicity Mitochondrial Toxicity (MMP) AMES Carcinogenicity (Mouse) Carcinogenicity( Rat) Carcinogenicity (Rodent) hERG Blocker Honey bee Toxicity Ames TA100 (+S9) Ames TA100 (-S9) Ames TA1535 (-S9)
ZINC01056864 III Weak inhibitor No No No positive negative non-carcinogenic Yes Low HBT positive negative negative
ZINC12037267 III Weak inhibitor No No No negative negative carcinogenic No Low HBT positive negative negative
ZINC14733310 III Weak inhibitor No No Yes negative negative non-carcinogenic Yes Low HBT positive negative negative
ZINC12034833 III Strong inhibitor No No No negative negative non-carcinogenic Yes Low HBT negative negative negative
ZINC14992739 III Weak inhibitor No No No negative negative non-carcinogenic Yes Low HBT positive negative negative
ZINC12516005 III Weak inhibitor No No No negative negative non-carcinogenic No Low HBT negative negative negative
ZINC12036079 III Weak inhibitor No No Yes negative positive carcinogenic Yes Low HBT positive positive negative
ZINC12041004 III Weak inhibitor No No No negative negative non-carcinogenic Yes Low HBT positive negative negative
ZINC08925969 III Weak inhibitor Yes Yes No negative negative non-carcinogenic No Low HBT negative negative negative
ZINC11840098 III Weak inhibitor No No Yes negative positive non-carcinogenic Yes Low HBT negative negative negative
ZINC12132957 III Weak inhibitor No No No negative negative non-carcinogenic Yes Low HBT positive positive negative
ZINC14885414 III Weak inhibitor No No Yes negative negative non-carcinogenic Yes Low HBT negative negative negative
ZINC01245157 III Weak inhibitor No No No negative positive carcinogenic No Low HBT negative negative negative
ZINC20600602 III Strong inhibitor No No No negative negative non-carcinogenic Yes Low HBT positive negative negative
ZINC08680620 III Weak inhibitor No No No negative positive non-carcinogenic Yes Low HBT positive negative negative
ZINC12038301 III Weak inhibitor No No No negative negative carcinogenic Yes Low HBT negative positive negative
ZINC11784547 III Weak inhibitor No No No negative negative non-carcinogenic No Low HBT negative negative negative

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.

OAR-17-1-CHK1-G001

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

OAR-17-1-CHK1-G002

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

OAR-17-1-CHK1-G003

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.

References

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:

OAR-17-1-Graphical-G001

Awards Nomination

Editors List

  • Prof. Elhadi Miskeen

    Obstetrics and Gynaecology Faculty of Medicine, University of Bisha, Saudi Arabia

  • Ahmed Hussien Alshewered

    University of Basrah College of Medicine, Iraq

  • Sudhakar Tummala

    Department of Electronics and Communication Engineering SRM University – AP, Andhra Pradesh

     

     

     

  • Alphonse Laya

    Supervisor of Biochemistry Lab and PhD. students of Faculty of Science, Department of Chemistry and Department of Chemis

     

  • Fava Maria Giovanna

     

Onkologia i Radioterapia peer review process verified at publons
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