Computational modeling of novel quinazoline derivatives as potent epidermal growth factor receptor inhibitors

Unveiling potent inhibitors for schistosomiasis through ligand-based drug design, molecular docking, molecular dynamics simulations and pharmacokinetics predictions

Schistosomiasis is a neglected tropical disease which imposes a considerable and enduring impact on affected regions, leading to persistent morbidity, hindering child development, diminishing productivity, and imposing economic burdens. Due to the emergence of drug resistance and limited management options, there is need to develop additional effective inhibitors for schistosomiasis. In view of this, quantitative structure-activity relationship studies, molecular docking, molecular dynamics simulations, drug-likeness and pharmacokinetics predictions were applied to 39 Schistosoma mansoni Thioredoxin Glutathione Reductase (SmTGR) inhibitors. The chosen QSAR model demonstrated robust statistical parameters, including an R2 of 0.798, R2adj of 0.767, Q2cv of 0.681, LOF of 0.930, R2test of 0.776, and cR2p of 0.746, confirming its reliability. The most active derivative (compound 40) was identified as a lead candidate for the development of new potential non-covalent inhibitors through ligand-based design. Subsequently, 12 novel compounds (40a-40l) were designed with enhanced anti-schistosomiasis activity and binding affinity. Molecular docking studies revealed strong and stable interactions, including hydrogen bonding, between the designed compounds and the target receptor. Molecular dynamics simulations over 100 nanoseconds and MM-PBSA free binding energy (ΔGbind) calculations validated the stability of the two best-designed molecules. Furthermore, drug-likeness and pharmacokinetics prediction analyses affirmed the potential of these designed compounds, suggesting their promise as innovative agents for the treatment of schistosomiasis.

Versatile in silico modelling of microplastics adsorption capacity in aqueous environment based on molecular descriptor and machine learning

To comprehensively evaluate the hazards of microplastics and their coexisting organic pollutants, the sorption capacity of microplastics is a major issue that is quantified through the microplastic-aqueous sorption coefficient (K_d). Almost all quantitative structure-property relationship (QSPR) models that describe K_d apply only to narrow, relatively homogeneous groups of reactants. Herein, non-hybrid QSPR-based models were developed to predict PE-water (K_PE-w), PE-seawater (K_PE-sw), PVC-water (K_PVC-w) and PP-seawater (K_PP-sw) sorption coefficients at different temperatures, with eight machine learning algorithms. Moreover, novel hybrid intelligent models for predicting K_d more accurately were innovatively developed by applying GA, PSO and AdaBoost algorithms to optimize MLP and ELM models. The results indicated that all three optimization algorithms could improve the robustness and predictability of the standalone MLP and ELM models. In all models trained with K_PE-w, K_PE-sw, K_PVC-w and K_PP-sw data sets, GBDT-1 and XGBoost-1 models, MLP-GA-2 and MLP-PSO-2 models, MLR-3 and MLR-4 models performed better in terms of goodness of fit (R_adj²: 0.907-0.999), robustness (Q_BOOT²: 0.900-0.937) and predictability (R_ext²: 0.889-0.970), respectively. Analyzing the descriptors revealed that temperature, lipophilicity, ionization potential and molecular size were correlated closely with the adsorption capacity of microplastics to organic pollutants. The proposed QSPR models may assist in initial environmental exposure assessments without imposing heavy costs in the early experimental phase.

Combining structure-based and 3D QSAR pharmacophore models to discover diverse ligands against EGFR in oral cancer

Background: Epidermal growth factor receptor-tyrosine kinase (EGFR-TK) is a well-known hallmark of oral and oropharyngeal cancers, as its overexpression leads to poor prognosis and malignancy. The activating EGFR mutations (particularly T790M and L858R double mutant) are a major challenge causing drug resistance, especially in the treatment of oral cancers. Methodology: This paper is an effort to exploit both structure-based and ligand-based pharmacophore modeling to discover EGFR-TK inhibitors, which show inhibition of proliferation of erlotinib-resistant FaDu and Cal27 oral cancer cells. Interestingly, the hit compound H2 also showed an effect on the downstream glucose and lactate metabolism pathways. Conclusion: The results indicate the potential of H2 to be developed as an EGFR-based metabolic inhibitor for oral cancer treatment.

Prediction models with multiple machine learning algorithms for POPs: The calculation of PDMS-air partition coefficient from molecular descriptor

Polydimethylsiloxane-air partition coefficient (K_PDMS-air) is a key parameter for passive sampling to measure POPs concentrations. In this study, 13 QSPR models were developed to predict K_PDMS-air, with two descriptor selection methods (MLR and RF) and seven algorithms (MLR, LASSO, ANN, SVM, kNN, RF and GBDT). All models were based on a data set of 244 POPs from 13 different categories. The diverse model evaluation parameters calculated from training and test set were used for internal and external verification. Notably, the R_adj², Q_BOOT² and Q_ext² are 0.995, 0.980 and 0.951 respectively for GBDT model, showing remarkable superiority in fitting, robustness and predictability compared with other models. The discovery that molecular size, branches and types of the bonds were the main internal factors affecting the partition process was revealed by mechanism explanation. Different from the existing QSPR models based on single category compounds, the models developed herein considered multiple classes compounds, so that its application domain was more comprehensive. Therefore, the obtained models can fill the data gap of missing experimental K_PDMS-air values for compounds in the application range, and help researchers better understand the distribution behavior of POPs from the perspective of molecular structure.

Computer-aided design of some quinazoline analogues as epidermal growth factor receptor inhibitors

Background

The treatment of epidermal growth factor receptor (EGFR)-muted non-small cell lung cancer (NSCLC) remains among the utmost important unachieved therapeutic need worldwide. Development of EGFR inhibitors to treat NSCLC mutations has been among the difficult tasks faced by researchers in this area. As such, there is a need to discover more EGFR inhibitors. The purpose of this work is to perform computer-aided/structure-based design of novel EGFR inhibitors, elucidate their nature of interactions with their target, and also assess their ADMET properties as well as their drug-likeness, respectively. Compound 17 with a highest binding affinity of −9.5kcal/mol was identified as the template hit compound using molecular docking virtual screening in our previous work. The compound interacted with the active site of the EGFR receptor via hydrogen bond with the following amino acid residues MET793, MET793, THR854, and ASP855 with bond distances of 2.61394 (Å), 2.18464 (Å), 2.57601 (Å), and 2.68794 (Å), respectively. It also interacted with the active site of the EGFR receptor via halogen bond (GLN791), hydrophobic bond (LEU718, CYS797, LYS745, ALA743, ALA743, and VAL726), electrostatic bond (LYS745), and others (MET766), respectively. Furthermore, from our previous study, the following descriptors (ATSC6m, ATSC8e, MATS7m, SpMax3_Bhp, SpMax5_Bhs, and MaxHBint10) contained in the reported model were found to be responsible for the inhibitory activities of the studied compounds. In this research, the template (compound 17) was modified manually by attaching halo-phenyl and halo-phenyl-amino rings on the para position of the flouro-nitro-benzamide moiety of the template compound, respectively.

Results

A computer-aided design/structure-based approach was used to design six new EGFR inhibitors using molecule 17 as the template compound for the design identified in our previously reported work. Molecular docking investigation was performed to elucidate the binding mode of these newly designed EGFR inhibitors with the binding pose of EGFR receptor (pdb code 4ZAU) and found to have better affinities which range from −9.5 to −10.4 kcal/mol than the template compound and gefitinib, the control, respectively. The ADMET property assessment of these newly designed EGFR inhibitors indicated that they were orally bioavailable with good absorption, distribution, metabolism, and excretory properties with no toxicity. And for their drug-likeness, they were seen to have a higher molecular weight which might be as a result of halo-phenyl-amino ring attachments. Based on this finding, halo-phenyl-amino rings might be responsible for the inhibitory activities of these newly designed compounds.

Conclusion

The six newly designed EGFR inhibitors were found to have higher binding affinities toward their target EGFR receptor than the template compound and gefitinib which was used as the control in this research. They were seen to have good ADMET and drug-like properties which indicate that they might be orally bioavailable. Furthermore, according to their synthetic accessibility score, they can be easily synthesized in the laboratory because the values were found to be less than five which fall within the easy portion of the scale. Therefore, this research recommends that these newly designed EGFR inhibitors should be synthesized most especially those with higher binding affinities, good ADMET, and drug-likeness properties than the template compound.

Design of more potent quinazoline derivatives as EGFRWT inhibitors for the treatment of NSCLC: a computational approach

Background

Lung cancer remains the leading and deadly type of cancer worldwide. It was estimated to account for about 25% of the 7 million people that died as a result of cancer-related issues/mortality every year in the world. Non-small cell lung cancer (NSCLC) is the lethal/deadly class of lung cancer with nearly 1.5 million reported cases and less than 20% survival rate. Therefore, it becomes necessary to explore more effective NSCLC drugs.

Result

A computational approach was employed here to design ten new EGFRWT inhibitors using compound 18 as a template for the design identified with the best binding affinity and good pharmacokinetic properties previously reported in our work. The modeled inhibitory activities of these newly designed EGFRWT inhibitors (range from 7.746966 to 11.09261) were better than that of the hit compound with pIC50 of 7.5639 and gefitinib the positive control with pIC50 of 5.879426. The ligand-binding interaction between these newly designed EGFRWT inhibitors and the EGFR tyrosine kinase receptor as shown in Table 3 was investigated and elucidated using molecular docking protocol. Based on the molecular docking results, the binding affinities of these newly designed EGFRWT inhibitors were found to be between − 8.8 and − 9.5 kcal/mol. The designed compound SFD10 has the highest binding affinity of − 9.5 kcal/mol followed by compound SFD8 (with a binding affinity of − 9.3 kcal/mol), then by compound SFD9 and 4 (each with a binding affinity of − 9.3 kcal/mol). None of them was found to have more than one violation of the filtering criterion used in this study thereby showing good ADMET properties.

Conclusion

The modeled inhibitory activities and binding affinities of these newly designed EGFRWT inhibitors were found to be higher than that of the template compound and the control (gefitinib) used in this research. They were also seen to be non-toxic with good pharmacokinetic properties.

In silico prediction of polyethylene-aqueous and air partition coefficients of organic contaminants using linear and nonlinear approaches

Low-density polyethylene (LDPE) passive sampling is very attractive for use in determining chemicals concentrations. Crucial to the measurement is the coefficient (K_PE) describing partitioning between LDPE and environmental matrices. 255, 117 and 190 compounds were collected for the development of datasets in three different matrices, i.e., water, air and seawater, respectively. Further, 3 pp-LFER models and 9 QSPR models based on classical multiple linear regression (MLR) coupled with prevalent nonlinear algorithms (artificial neural network, ANN and support vector machine, SVM) were performed to predict LDPE-water (K_PE-W), LDPE-air (K_PE-A) and LDPE-seawater (K_PE-SW) partition coefficients. These developed models have satisfying predictability (R²_adj: 0.805-0.966, 0.963-0.991 and 0.817-0.941; RMSE_tra: 0.233-0.565, 0.200-0.406 and 0.260-0.459) and robustness (Q²_ext: 0.840-0.943, 0.968-0.984 and 0.797-0.842; RMSE_ext: 0.308-0.514, 0.299-0.426 and 0.407-0.462) in three datasets (water, air and seawater), respectively. In particular, the reasonable mechanism interpretations revealed that the molecular size, hydrophobicity, polarizability, ionization potential, and molecular stability were the most relevant properties, for governing chemicals partitioning between LDPE and environmental matrices. The application domains (ADs) assessed here exhibited the satisfactory applicability. As such, the derived models can act as intelligent tools to predict unknown K_PE values and fill the experimental gaps, which was further beneficial for the construction of enormous and reliable database to facilitate a distinct understanding of the distribution for organic contaminants in total environment.

Robust classification-based molecular modelling of diverse chemical entities as potential SARS-CoV-2 3CLpro inhibitors: theoretical justification in light of experimental evidences

COVID-19 is the most unanticipated incidence of 2020 affecting the human population worldwide. Currently, it is utmost important to produce novel small molecule anti-SARS-CoV-2 drugs urgently that can save human lives globally. Based on the earlier SARS-CoV and MERS-CoV infection along with the general characters of coronaviral replication, a number of drug molecules have been proposed. However, one of the major limitations is the lack of experimental observations with different drug molecules. In this article, 70 diverse chemicals having experimental SARS-CoV-2 3CL^proinhibitory activity were accounted for robust classification-based QSAR analysis statistically validated with 4 different methodologies to recognize the crucial structural features responsible for imparting the activity. Results obtained from all these methodologies supported and validated each other. Important observations obtained from these analyses were also justified with the ligand-bound crystal structure of SARS-CoV-2 3CL^pro enzyme. Our results suggest that molecules should contain a 2-oxopyrrolidine scaffold as well as a methylene (hydroxy) sulphonic acid warhead in proper orientation to achieve higher inhibitory potency against SARS-CoV-2 3CL^pro. Outcomes of our study may be able to design and discover highly effective SARS-CoV-2 3CL^pro inhibitors as potential anticoronaviral therapy to crusade against COVID-19.

Computer modeling of some anti-breast cancer compounds

The research was aimed at exploring the biological activities of novel series of β-lactam derivatives against MCF-7 breast cancer cell lines via computer modeling such as quantitative structure-activity relationship (QSAR), designing new compounds and analyzing the drug likeliness of designed compounds. The QSAR model was highly robust as it also conforms to the least minimum requirement for QSAR model from the statistical assessments with a correlation coefficient squared (R2) of 0.8706, correlation coefficient adjusted squared (R2adj) of 0.8411, and cross-validation coefficient (Q2) of 0.7844. The external validation of R2pred was calculated as 0.6083 for model 4. The model parameters (MATS5i and MATS1s) were used in designing new derivative compounds with higher potency against estrogen-positive breast cancer. The pharmacokinetics test on the restructured compounds revealed that all the compounds passed the drug likeness test and they could further proceed to clinical trials. These reveal a breakthrough in medicine, in the research for breast cancer drug with higher effectiveness against the MCF-7 cell line.

Quantitative structure-activity relationship, molecular docking, drug-likeness, and pharmacokinetic studies of some non-small cell lung cancer therapeutic agents

Background

Lung cancer has been reported to be among the leading cancer cases in the world. It was also reported to have caused a lot of death every year and accounted for about one-third of the whole cancer deaths in the globe. The main subset of lung cancers that accounts for about 85% of the problems of lung cancer raised above was non-small cell lung cancer (NSCLC). The most common cause of NSCLCs that mostly affects women and cigarette smokers was recognized to be overexpression of epidermal growth factor receptor tyrosine kinase (EGFR TK).

Results

Five models on thirty five (35) NSCLC therapeutic agents were developed via quantitative structure-activity relationship (QSAR) technique. The best model among them was selected and reported due to its fitness statistically with the following validation parameters: R2 of 0.8764, R2adj of 0.8370, Qcv2 of 0.7655, R2test of 0.7024, and LOF of 0.3312. Molecular docking was used to elucidate the mode of binding interactions between the thirty five (35) NSCLC therapeutic agents and the binding pose of EGFR tyrosine kinase receptor (3IKA) in this research. Compound 29 was recognized to have the most excellent binding affinity of − 8.8 kcal/mol among others. The drug-likeness and pharmacokinetic properties of all the NSCLC therapeutic agents were predicted using SWISSADME, and none among the molecules under investigation violated more than the permissible limit of the conditions stated by Lipinski’s RO5 filters. Five hit compounds were identified using molecular docking virtual screening. The five (5) hit compounds were further screened and identified compound 16 and 27 as excellent among them using their pharmacokinetic profiles and drug-likeness properties.

Conclusion

QSAR technique was used to build five models on thirty five (35) NSCLC therapeutic agents. The best model among them was reported because it is statistically significant with good validation parameters. The molecular docking result has identified five (5) hit compounds. The most common amino acid residues to all hit compounds under investigation were Glu762, Leu718, Lys745, and Val726 which might be responsible for the higher inhibitory activities/binding affinities of the compounds under investigation. Furthermore, these five (5) hit compounds were further subjected to drug-likeness and pharmacokinetic properties prediction to determine which among them have the best pharmacokinetic profile. Compounds 16 and 27 among the hit compounds were observed to have high chance of passive absorption by the gastrointestinal tract while the other three have low tendency of passive absorption. More so, only compounds 16 and 27 have higher bioavailability scores, and none of the two have more than one violation of the RO5 criteria. The cause of efficiency of compounds 16 and 27 might be as a result of good pharmacokinetic profiles and drug-likeness properties possessed by the molecules when compared to other hit compounds.

Structure-based design of some quinazoline derivatives as epidermal growth factor receptor inhibitors

Background

The discovery of epidermal growth factor receptor (EGFR) inhibitors for the treatment of lung cancer, most especially non-small cell lung cancer (NSCLC), was one of the major challenges encountered by the medicinal chemist in the world. The treatment of EGFR tyrosine kinase to manage NSCLCs becomes an urgent therapeutic necessity. NSCLC was the foremost cause of cancer mortality worldwide. Therefore, there is a need to develop more EGFR inhibitors due to the development of drug resistance by the mutation. This research is aimed at designing new EGFR inhibitors using a structure-based design approach. Structure-based drug design comprises several steps such as protein structure retrieval and preparation, ligand library preparation, docking, and structural modification on the best hit compound to design new ones.

Result

Molecular docking virtual screening on fifty sets of quinazoline derivatives/epidermal growth factor receptor inhibitors against their target protein (EGFR tyrosine kinase receptor PDB entry: 3IKA) and pharmacokinetic profile predictions were performed to identify hit compounds with promising affinities toward their target and good pharmacokinetic profiles. The hit compounds identified were compound 6 with a binding affinity of − 9.3 kcal/mol, compounds 5 and 8, each with a binding affinity of − 9.1 kcal/mol, respectively. The three hit compounds bound to EGFR tyrosine kinase receptor via four different types of interactions which include conventional hydrogen bond, carbon-hydrogen bond, electrostatic, and hydrophobic interactions, respectively. The best hit (compound 6) among the 3 hit compounds was retained as a template and used to design sixteen new EGFR inhibitors. The sixteen newly designed compounds were also docked into the active site of EGFR tyrosine kinase receptor to study their mode of interactions with the receptor. The binding affinities of these newly designed compounds range from − 9.5 kcal/mol to − 10.2 kcal/mol. The pharmacokinetic profile predictions of these newly designed compounds were further examined and found to be orally bioavailable with good absorption, low toxicity level, and permeable properties.

Conclusion

The sixteen newly designed EGFR inhibitors were found to have better binding affinities than the template used in the designing process and afatinib the positive control (an FDA approved EGFR inhibitor). None of these designed compounds was found to violate more than the permissible limit set by RO5. More so, the newly designed compounds were found to have good synthetic accessibility which indicates that these newly designed compounds can be easily synthesized in the laboratory.

Versatile in silico modeling of partition coefficients of organic compounds in polydimethylsiloxane using linear and nonlinear methods

Environmental fate, behavior and effects of hazardous organic compounds have recently received great attention in diverse environmental phases, including water, atmosphere, soil and sediment. Considering polydimethylsiloxane (PDMS) fibers were validated for the wide application in the determination of partition behavior in passive sampling, in this work, several in silico models were established to predict PDMS-water (K_PDMS-w), PDMS-air (K_PDMS-a) and PDMS-seawater partition coefficients (K_PDMS-sw) of diverse chemicals. This is an attempt to combine conventional linear method and popular nonlinear algorithm for the estimation of partition coefficients between PDMS and different environmental media. All of the developed models showed satisfactory goodness-of-fit with high adjusted correlation coefficient (R²_adj) and were validated to be robust, stable and predictable by various internal and external validation techniques, deriving a wide series of statistical checks. Moreover, it was found that hydrophobicity, polarizability, charge distribution and molecular size of compounds contributed significantly to the model development by interpreting the selected descriptors. Based on the broad applicability domains (ADs), the current study provides suitable tools to fill the experimental data gap for other compounds and to help researchers better understand the mechanistic basis of adsorption behavior of PDMS.