Discovery of Potent ALK Inhibitors Using Pharmacophore-Informatics Strategy

Identification of NS2B-NS3 Protease Inhibitors for Therapeutic Application in ZIKV Infection: A Pharmacophore-Based High-Throughput Virtual Screening and MD Simulations Approaches

Zika virus (ZIKV) pandemic and its implication in congenital malformations and severe neurological disorders had created serious threats to global health. ZIKV is a mosquito-borne flavivirus which spread rapidly and infect a large number of people in a shorter time-span. Due to the lack of effective therapeutics, this had become paramount urgency to discover effective drug molecules to encounter the viral infection. Various anti-ZIKV drug discovery efforts during the past several years had been unsuccessful to develop an effective cure. The NS2B-NS3 protein was reported as an attractive therapeutic target for inhibiting viral proliferation, due to its central role in viral replication and maturation of non-structural viral proteins. Therefore, the current in silico drug exploration aimed to identify the novel inhibitors of Zika NS2B-NS3 protease by implementing an e-pharmacophore-based high-throughput virtual screening. A 3D e-pharmacophore model was generated based on the five-featured (ADPRR) pharmacophore hypothesis. Subsequently, the predicted model is further subjected to the high-throughput virtual screening to reveal top hit molecules from the various small molecule databases. Initial hits were examined in terms of binding free energies and ADME properties to identify the candidate hit exhibiting a favourable pharmacokinetic profile. Eventually, molecular dynamic (MD) simulations studies were conducted to evaluate the binding stability of the hit molecule inside the receptor cavity. The findings of the in silico analysis manifested affirmative evidence for three hit molecules with -64.28, -55.15 and -50.16 kcal/mol binding free energies, as potent inhibitors of Zika NS2B-NS3 protease. Hence, these molecules holds the promising potential to serve as a prospective candidates to design effective drugs against ZIKV and related viral infections.

Innovative, rapid, high-throughput method for drug repurposing in a pandemic-A case study of SARS-CoV-2 and COVID-19

Several efforts to repurpose drugs for COVID-19 treatment have largely either failed to identify a suitable agent or agents identified did not translate to clinical use. Reasons that have been suggested to explain the failures include use of inappropriate doses, that are not clinically achievable, in the screening experiments, and the use of inappropriate pre-clinical laboratory surrogates to predict efficacy. In this study, we used an innovative algorithm, that incorporates dissemination and implementation considerations, to identify potential drugs for COVID-19 using iterative computational and wet laboratory methods. The drugs were screened at doses that are known to be achievable in humans. Furthermore, inhibition of viral induced cytopathic effect (CPE) was used as the laboratory surrogate to predict efficacy. Erythromycin, pyridoxine, folic acid and retapamulin were found to inhibit SARS-CoV-2 induced CPE in Vero cells at concentrations that are clinically achievable. Additional studies may be required to further characterize the inhibitions of CPE and the possible mechanisms.

Discovery of potent Covid-19 main protease inhibitors using integrated drug-repurposing strategy

The emergence and rapid spreading of novel SARS-CoV-2 across the globe represent an imminent threat to public health. Novel antiviral therapies are urgently needed to overcome this pandemic. Given the significant role of the main protease of Covid-19 for virus replication, we performed a drug-repurposing study using the recently deposited main protease structure, 6LU7. For instance, pharmacophore- and e-pharmacophore-based hypotheses such as AARRH and AARR, respectively, were developed using available small molecule inhibitors and utilized in the screening of the DrugBank repository. Further, a hierarchical docking protocol was implemented with the support of the Glide algorithm. The resultant compounds were then examined for their binding free energy against the main protease of Covid-19 by means of the Prime-MM/GBSA algorithm. Most importantly, the machine learning-based AutoQSAR algorithm was used to predict the antiviral activities of resultant compounds. The hit molecules were also examined for their drug-likeness and toxicity parameters through the QikProp algorithm. Finally, the hit compounds activity against the main protease was validated using molecular dynamics simulation studies. Overall, the present analysis yielded two potential inhibitors (DB02986 and DB08573) that are predicted to bind with the main protease of Covid-19 better than currently used drug molecules such as N3 (cocrystallized native ligand), lopinavir, and ritonavir.

Exploring the antidiabetic potential of compounds isolated from Anacardium occidentale using computational aproach: ligand-based virtual screening

Diabetes mellitus is becoming an important public health challenge worldwide and especially in developing nations. About 8.8 percent of the world adult population has been reported to have diabetes. Glutamine-fructose-6-phosphate amidotransferase 1 (GFAT1) catalyses the first committed step in the pathway for biosynthesis of hexosamines in mammals, and its inhibition has been thought to prevent hyperglycaemia. Dipeptidyl peptidase-4 (DPP-4), on the other hand, degrades hormone glucagon-like peptide-1 (GLP-1), an enzyme that plays a major role in the enhancement of glucose-dependent insulin secretion, making these two proteins candidate targets for diabetes. To find potential inhibitors of DPP-4 and GFAT1 from Anacardium occidentale using a computational approach, glide XP (extra precision) docking, Induced Fit Docking (IFD), Binding free energy of the compounds were determined against prepared crystal structure of DPP-4 and GFAT1 using the Maestro molecular interface of Schrödinger suites. The Lipinski's rule of five (RO5) and ADME properties of the compounds were assessed. Predictive models for both protein targets were built using AutoQSAR. This study identified 8 hit compounds. Most of these compounds passed the RO5 and were within the recommended range for defined ADME parameters. In addition, the predicted pIC50 for the hit compounds were promising. The results obtained from the present study can be used to design an antidiabetic drug.

Supplementary Information

The online version contains supplementary material available at 10.1007/s40203-021-00084-z.

Discovery of anaplastic lymphoma kinase inhibitors from natural product library: A holistic in silico approach

Over the years, phytochemical compounds have shown compelling evidences in exhibiting powerful antitumor properties. Moreover, due to the lack of safety and high cost of cancer therapies, opportunities are being sought out in these compounds as an alternative treatment modality. Therefore, in the present study, 1,574 compounds from NPACT library were examined to excavate potent and nontoxic anaplastic lymphoma kinase (ALK) inhibitors. Notably, two pharmacophore hypotheses (AAAHP and DDRRR) were generated using ligand-based and energy-based techniques, respectively, to eliminate false-positive prediction in database screening. Furthermore, molecular docking and Prime MM/GBSA analysis were performed on the screened compounds to examine inhibitory activity against ALK. The analysis revealed that the two hits, namely, NPACT00018 and NPACT01077, exhibited better docking scores, binding energies, and also ensured excellent drug-likeness properties than the reference compound, crizotinib. Finally, the results were subjected to molecular dynamics studies to gain insight into the stability of these compounds in the binding pocket of ALK protein. Indeed, the useful predictions generated by the present computational models are of immense importance and could further speed up the anticancer drug development in the near future.

Molecular simulation strategies for the discovery of selective inhibitors of β-catenin

Tumor dissemination and relapse in lung cancer were found to be due to the existence of cancer stem cells. In particular, the [Formula: see text]-catenin pathway is found to be one of the crucial pathways in maintaining the stem-like properties of the cells. Thus, targeting the [Formula: see text]-catenin family of proteins is a significant therapeutic route in the treatment of lung cancer. Therefore, in the present study, a pharmacophore-based drug repurposing approach was accomplished to pinpoint potent [Formula: see text]-catenin inhibitors from the DrugBank database. Primarily, ligand-based pharmacophore hypothesis (AAHHR) was generated using existing [Formula: see text]-catenin inhibitors available in the literature and utilized for library screening. Subsequently, the inhibitory activity of the screened compounds was examined by the hierarchical docking process and the Prime MM-GBSA algorithm. Moreover, quantum chemical calculations and molecular dynamics simulations were executed to analyze the inhibitory effects of the screened hit molecule. The results indicate that hit molecule, DB08047 was found to possess better binding free energy, favorable ligand strain energy, satisfactory pharmacokinetic properties and superior free energy landscape profile. Eventually, the pIC[Formula: see text] values of the lead compounds were predicted by the AutoQSAR algorithm. It is noteworthy to mention that DB08047 was found to possess pyrazole scaffolds which could downregulate [Formula: see text]-catenin pathway and thus facilitate the controlled cell growth/inhibit tumor growth.

E-pharmacophore hypothesis strategy to discover potent inhibitor for influenza treatment

The surface protein of Influenza virus, Neuraminidase (NA), is believed to play a critical role in the release of new viral particle and thus spreads infection. It has been recognized as a valid drug target for anti-influenza therapy. Despite the number of available approved drugs for the influenza infection treatment, the emergence of resistant variants with novel mutations are the foremost challenges for the currently used NA inhibitors. Thus, the current investigation was carried out to ascertain potent inhibitors using computational strategies such as e-pharmacophore based virtual screening and docking approach. A three-dimensional e-pharmacophore hypothesis was generated based on the chemical features of complexes of the drugs and NA protein using PHASE module of Schrödinger suite. The generated hypothesis consisted of one hydrogen bond acceptor (A), two hydrogen bond donors (D), one negatively charged group (N) and one aromatic ring (R), ADDNR. The hypothesis was further evaluated for its integrity using enrichment analysis and used to filter out molecules with similar pharmacophoric features from approved, investigational and experimental subsets of DrugBank and ZINC database. In addition, ligand filtration was performed to curb down the molecules to an efficient collection of hit molecules by using Lipinski “rule of five and ADME analysis by using Qikprop module. Overall, the results from our analysis suggest that compound lisinopril and formoterol could serve as potent antiviral compounds for the treatment of influenza A virus infection. It is worth mentioning that the results correlate well with literature evidences.

Discovery of novel anaplastic lymphoma kinase inhibitors: Structure and energy-based pharmacophore strategy

The clinical outcomes in patients with non-small cell lung cancer have improved, as a result of anaplastic lymphoma kinase (ALK) inhibition. Therefore in the current study, substantial effort has been made to identify ALK inhibitors through systematic virtual screening experiment consisting of e-pharmacophore and pharmacophore perception techniques. Initially, a pharmacophore model (AAAHP.193) and an e-pharmacophore model (DDRRR) encompassing the whole dataset of 12 known ALK inhibitors were developed. The hypotheses could retrieve effective compounds from DrugBank database (8621 molecules), which were then subjected to molecular docking and ADME prediction. These approaches resulted in the identification of five hits, namely, nebivolol, HDY, D42, 796, and LZE having higher Glide docking scores and promising ADME properties with augmented CNS involvement. Moreover, molecular dynamics simulations were performed to validate the inhibitory activity of the hit compounds, and density functional theory calculations were carried out to scrutinize the chemical reactivity of the hits. Subsequent interaction and scaffold analysis identified prominent interactions of the hits with ALK kinase domain and scaffolds with anti-tumor activity against lung cancer cell lines. We strongly believe that the study provides an outlook for the sighting of novel and potent ALK inhibitors in the near future.

In silico identification and in vitro validation of nogalamycin N-oxide (NSC116555) as a potent anticancer compound against non-small-cell lung cancer cells

The epidermal growth factor receptor (EGFR) was found to be overexpressed in several cancers, especially in lung cancers. Finding new effective drug against EGFR is the key to cancer treatment. In this study, the GOLD docking algorithm was used to virtually screen for novel human EGFR inhibitors from the NCI database. Thirty-four hit compounds were tested for EGFR-tyrosine kinase (TK) inhibition. Two potent compounds, 1-amino-4-(4-[4-amino-2-sulfophenyl]anilino)-9,10-dioxoanthracene-2-sulfonic acid (NSC125910), and nogalamycin N-oxide (NSC116555) were identified with IC₅₀ values against EGFR-TK comparable to gefitinib; 16.14 and 37.71 nM, respectively. However, only NSC116555 demonstrated cytotoxic effects against non-small-cell lung cancer, A549, shown in the cell cytotoxicity assay with an IC₅₀ of 0.19 + 0.01 µM, which was more potent than gefitinib. Furthermore, NSC116555 showed cytotoxicity against A549 via apoptosis in a dose-dependent manner.

Latest perspectives of orally bioavailable 2,4-diarylaminopyrimidine analogues (DAAPalogues) as anaplastic lymphoma kinase inhibitors: discovery and clinical developments

The course of anaplastic lymphoma kinase (ALK)-rearranged non-small-cell lung cancer (NSCLC) therapy has improved impressively. The Food and Drug Administration (FDA) has approved crizotinib (Xalkori, Pfizer) as a first-in-class tyrosine kinase inhibitor (TKI) that demonstrated a substantial objective response rate (ORR) and remarkable progression-free survival (PFS). However, acquired resistance to crizotinib is still a major concern especially as the central nervous system (CNS) remains the most common sites of relapse. To combat disease resistance, limited PFS and poor CNS exposure exhibited by crizotinib (Xalkori, Pfizer) led to the discovery of numerous next generation ALK-TKIs and surprisingly most of them are 2,4-Diarylaminopyrimidine Analogues (DAAPalogues). To date, DAAPalogues have been investigated extensively to display their superior potency against numerous kinase targets especially ALK/ROS1. This review describes hit-to-drug evolution strategies, activity spectra, milestones related to medicinal chemistry discovery efforts and scalable synthetic pathways of clinically emerging DAAPalouges which are either progressing as investigational or preclinical candidates. In addition, the significance of DAAPalogues to treat the patients with ALK+-NSCLC in clinical settings has been detailed. This review is beneficial for medicinal chemists and researchers contributing to discovering ALK-TKIs to overcome existing issues related to DAAPalouges in the drug discovery process.

How to Achieve Better Results Using PASS-Based Virtual Screening: Case Study for Kinase Inhibitors

Discovery of new pharmaceutical substances is currently boosted by the possibility of utilization of the Synthetically Accessible Virtual Inventory (SAVI) library, which includes about 283 million molecules, each annotated with a proposed synthetic one-step route from commercially available starting materials. The SAVI database is well-suited for ligand-based methods of virtual screening to select molecules for experimental testing. In this study, we compare the performance of three approaches for the analysis of structure-activity relationships that differ in their criteria for selecting of "active" and "inactive" compounds included in the training sets. PASS (Prediction of Activity Spectra for Substances), which is based on a modified Naïve Bayes algorithm, was applied since it had been shown to be robust and to provide good predictions of many biological activities based on just the structural formula of a compound even if the information in the training set is incomplete. We used different subsets of kinase inhibitors for this case study because many data are currently available on this important class of drug-like molecules. Based on the subsets of kinase inhibitors extracted from the ChEMBL 20 database we performed the PASS training, and then applied the model to ChEMBL 23 compounds not yet present in ChEMBL 20 to identify novel kinase inhibitors. As one may expect, the best prediction accuracy was obtained if only the experimentally confirmed active and inactive compounds for distinct kinases in the training procedure were used. However, for some kinases, reasonable results were obtained even if we used merged training sets, in which we designated as inactives the compounds not tested against the particular kinase. Thus, depending on the availability of data for a particular biological activity, one may choose the first or the second approach for creating ligand-based computational tools to achieve the best possible results in virtual screening.