Transcriptome profiling and metabolic pathway analysis towards reliable biomarker discovery in early-stage lung cancer

Earlier diagnosis of lung cancer is crucial for reducing mortality and morbidity in high-risk patients. Liquid biopsy is a critical technique for detecting the cancer earlier and tracking the treatment outcomes. However, noninvasive biomarkers are desperately needed due to the lack of therapeutic sensitivity and early-stage diagnosis. Therefore, we have utilized transcriptomic profiling of early-stage lung cancer patients to discover promising biomarkers and their associated metabolic functions. Initially, PCA highlights the diversity level of gene expression in three stages of lung cancer samples. We have identified two major clusters consisting of highly variant genes among the three stages. Further, a total of 7742, 6611, and 643 genes were identified as DGE for stages I-III respectively. Topological analysis of the protein-protein interaction network resulted in seven candidate biomarkers such as JUN, LYN, PTK2, UBC, HSP90AA1, TP53, and UBB cumulatively for the three stages of lung cancers. Gene enrichment and KEGG pathway analyses aid in the comprehension of pathway mechanisms and regulation of identified hub genes in lung cancer. Importantly, the medial survival rates up to ~ 70 months were identified for hub genes during the Kaplan-Meier survival analysis. Moreover, the hub genes displayed the significance of risk factors during gene expression analysis using TIMER2.0 analysis. Therefore, we have reason that these biomarkers may serve as a prospective targeting candidate with higher treatment efficacy in early-stage lung cancer patients.

In-silico bioprospecting of secondary metabolites from endophytic Streptomyces spp. against Magnaporthe oryzae, a cereal killer fungus

Rice blast disease, caused by Magnaporthe oryzae, is the most devastating cereal killer worldwide. Note that melanin pigment is an essential factor of M. oryzae virulence, thus fungicides interfering with melanin biosynthesizing enzymes would reduce the pathogenicity. Scytalone dehydratase (SDH) is the key target for commercial fungicides, like carpropamid, due to its role in the dehydration reaction of the fungal melanin pathway. However, a single-point mutation (V75M) in SDH elicits resistance to carpropamid. A lack of effective fungicides against this resistant strain expedited the quest for novel bioactive inhibitors. Currently, bacterial endophytes like Streptomyces have been heralded for synthesizing bioactive metabolites to protect plants from phytopathogens. The literature search led to the identification of 21 Streptomyces spp. symbionts of paddy that can suppress M. oryzae growth. An antiSMASH server was used to explore Streptomyces spp. gene clusters and found 4463 putative metabolites. Besides, 745 unique metabolites were subjected to a series of virtual screening techniques. Ideally, this process identified five potential SDH inhibitors. The docking result highlights that the metabolite pseudopyronine A interacted hydrophobically with both Val75 of SDHWT and Met75 of SDHV75M targets. Moreover, pseudopyronine A has a higher binding free energy with SDHWT (- 89.94 kcal/mol) and SDHV75M (- 71.95 kcal/mol). Interestingly, the pyranones scaffold of pseudopyronine A was reported for antifungal activity against phytopathogens. Dynamic behavior confirms that pseudopyronine A has excellent conformational states with both SDHWT and SDHV75M. Altogether, we hope that this study creates a new avenue for the discovery of novel phytopathogen inhibitors from endophytes.

Supplementary Information

The online version contains supplementary material available at 10.1007/s13205-023-03859-7.

Sequential virtual screening collaborated with machine-learning strategies for the discovery of precise medicine against non-small cell lung cancer

Dysregulation of MAPK pathway receptors are crucial in causing uncontrolled cell proliferation in many cancer types including non-small cell lung cancer. Due to the complications in targeting the upstream components, MEK is an appealing target to diminish this pathway activity. Hence, we have aimed to discover potent MEK inhibitors by integrating virtual screening and machine learning-based strategies. Preliminary screening was conducted on 11,808 compounds using the cavity-based pharmacophore model AADDRRR. Further, seven ML models were accessed to predict the MEK active compounds using six molecular representations. The LGB model with morgan2 fingerprints surpasses other models ensuing 0.92 accuracy and 0.83 MCC value versus test set and 0.85 accuracy and 0.70 MCC value with external set. Further, the binding ability of screened hits were examined using glide XP docking and prime-MM/GBSA calculations. Note that we have utilized three ML-based scoring functions to predict the various biological properties of the compounds. The two hit compounds such as DB06920 and DB08010 resulted excellent binding mechanism with acceptable toxicity properties against MEK. Further, 200 ns of MD simulation combined with MM-GBSA/PBSA calculations confirms that DB06920 may have stable binding conformations with MEK thus step forwarded to the experimental studies in the near future.Communicated by Ramaswamy H. Sarma.

Exploring the potential of Halalkalibacterium halodurans laccase for endosulfan and chlorophacinone degradation: insights from molecular docking and molecular dynamics simulations

Pesticides are widely used in agriculture but at the same time, a majority of them are known to cause serious harm to health and the environment. In the recent past, laccases have been reported as key enzymes having the ability to degrade pollutants by converting them into less toxic forms. In this investigation, laccase from polyextremophilic bacterium Halalkalibacterium halodurans C-125 was analyzed for its structural, physicochemical, and functional characterization using in silico approaches. The 3D model of the said enzyme is unknown; therefore, the model was generated by template-independent modeling using ROBETTA, I-TASSER, and Alphafold server. The best-generated model from Alphafold with a confidence of 0.95 was validated from ERRAT and Verify 3D scores of 89.95 and 91.80%, respectively. The Ramachandran plot generated using the PROCHECK server further predicted the accuracy of the model with 93.7% and 5.9% of residues present in most favored and additional allowed regions of the plot respectively. The active sites, ion binding sites, and subcellular localization of laccase were also predicted. The generated model was docked with 121 pollutants (pesticides, insecticides, herbicides, fungicides, and rodenticides) for its degradation potential towards these pollutants. Two ligands chlorophacinone (based on the highest binding energy) and endosulfan (based on agricultural uses) were selected for molecular dynamic simulation studies. Endosulfan as a pesticide is banned but in some countries governments allow its use for special purposes which need serious consideration on developing bioremediation approaches for endosulfan degradation. MD simulation studies revealed that both chlorophacinone and endosulfan form hydrogen bonds and hydrophobic bonds with the active site of laccase and chlorophacinone-laccase complex were more stable in comparison to endosulfan. The present investigation provides insight into the structural features of laccase and its potential for the degradation of pesticides which can be further validated by experimental data.Communicated by Ramaswamy H. Sarma.

Bioherbicides for sustainable barnyard grass management in paddy field: an in-silico perspective

Paddy (Oryza sativa) yield is greatly influenced by the insidious presence of rice-mimicking weed, widely known as barnyard grass. This study explores the promising natural ACCase inhibitors that could enhance paddy yield by controlling weeds. A total of 2828 natural compounds were examined using diverse computational techniques. The results of this study depict that CNP0390839 (xanthoangelol) exhibited a better XP Gscore (-7.328 kcal/mol) and MM/GBSA score (-84.24 kcal/mol) than other investigated compounds. Importantly, ACCase-xanthoangelol complexes was thermodynamically stable with an RMSD value of ∼1.2 nm. Of note, 72% xanthoangelol resides in the Angelica keiskei plant root which exhibits 55% weed-inhibitory action. The A. keiskei plant mainly inhibits the hypocotyl (71.8 ± 5.4%) and root region (55.3 ± 4.7%) of weeds. Moreover, the existence of dihydroxyphenyl scaffold in xanthoangelol was also witnessed in literatures for weed inhibitory action. Overall, xanthoangelol might prove to be an effective ACCase herbicide in paddy weed management.

Computational biophysics approach towards the discovery of multi-kinase blockers for the management of MAPK pathway dysregulation

The MAPK pathway is important in human lung cancer and is improperly activated in a substantial proportion through number of ways. Strategies on dual-targeting RAF and MEK are an alternative option to diminish the limitations in this pathway inhibition. Hence, we implemented parallel pharmacophore screening of 11,808 DrugBank compounds against RAF and MEK. ADHRR and DHHRR were modeled as a pharmacophore hypothesis for RAF and MEK respectively. Importantly, these hypotheses resulted an AUC value of > 0.90 with the external data set. As a result of phase screening, glide docking, and prime-MM/GBSA scoring, it is determined that DB08424 and DB08907 have the best chances of acting as multi-kinase inhibitors. The pi-cation interaction with key amino acid residues of both target receptors may responsible for the stronger binding with these kinases. Cumulative 600 ns MD simulation studies validate the binding ability of these compounds. Significantly, the hit compounds resulted higher number of stable conformational state with less atomic movements than the reference compound against both targets. The anti-cancer efficacy of the lead compounds was validated through machine learning-based approaches. These findings suggest that DB08424 and DB08907 might be novel molecules to be explored further experimentally to block the MAPK signaling in lung cancer patients.

A multitier virtual screening of antagonists targeting PD-1/PD-L1 interface for the management of triple-negative breast cancer

Immunotherapies are promising therapeutic options for the management of triple-negative breast cancer because of its high mutation rate and genomic instability. Of note, the blockade of the immune checkpoint protein PD-1 and its ligand PD-L1 has been proven to be an efficient and potent strategy to combat triple-negative breast cancer. To date, various anti-PD-1/anti-PD-L1 antibodies have been approved. However, the intrinsic constraints of these therapeutic antibodies significantly limit their application, making small molecules a potentially significant option for PD-1/PD-L1 inhibition. In light of this, the current study aims to use a high-throughput virtual screening technique to identify potential repurposed candidates as PD-L1 inhibitors. Thus, the present study explored binding efficiency of 2509 FDA-approved compounds retrieved from the drug bank database against PD-L1 protein. The binding affinity of the compounds was determined using the glide XP docking programme. Furthermore, prime-MM/GBSA, DFT calculations, and RF score were used to precisely re-score the binding free energy of the docked complexes. In addition, the ADME and toxicity profiles for the lead compounds were also examined to address PK/PD characteristics. Altogether, the screening process identified three molecules, namely DB01238, DB06016 and DB01167 as potential therapeutics for the PD-L1 protein. To conclude, a molecular dynamic simulation of 100 ns was run to characterise the stability and inhibitory action of the three lead compounds. The results from the simulation study confirm the robust structural and thermodynamic stability of DB01238 than other investigated molecules. Thus, our findings hypothesize that DB01238 could serve as potential PD-L1 inhibitor in the near future for triple-negative breast cancer patients.

Designing a novel SOX9 based multi-epitope vaccine to combat metastatic triple-negative breast cancer using immunoinformatics approach

Immunotherapies are a promising treatment option especially for the management of TNBC owing to its higher levels of tumour-associated antigens together with higher mutational load. Of note, the administration of preventive vaccines in the early stage of the cancer holds promise for effective disease management. Therefore, the present study aimed to develop a novel multi-epitope peptide-based vaccination against TNBC employing SOX9, which has recently been recognized as a key regulator of TNBC metastasis. The immunodominant regions from the SOX9 protein were computed and assessed based on their ability to elicit both T and B lymphocyte mediated responses. The resultant epitopes were fused using appropriate linkers (EAAAK, KK, AAY and GPGPG) and adjuvant (50S ribosomal protein L7/L12) to enhance the vaccine's immunogenicity. The physicochemical properties and population coverage were also anticipated for the constructed vaccine. Adding together, docking and dynamics simulation studies were performed on the modelled vaccine against TLR-4 to provide insight into the stability. Finally, the designed vaccine was cloned into the pET28 (+) vector and immunological simulation studies were carried out. These results demonstrate that our designed vaccine had the potency to trigger humoral and cellular immune responses. Based on these collective evidences, the final proposed vaccine could be an interesting therapeutics for the management of TNBC in the near future. Schematic representation of an efficient vaccine design framework by combining the range of immunoinformatics strategies.

Design and In Silico Validation of a Novel MZF-1-Based Multi-Epitope Vaccine to Combat Metastatic Triple Negative Breast Cancer

Immunotherapy is emerging as a potential therapeutic strategy for triple negative breast cancer (TNBC) owing to the immunogenic landscape of its tumor microenvironment. Interestingly, peptide-based cancer vaccines have garnered a lot of attention as one of the most promising cancer immunotherapy regimens. Thus, the present study intended to design a novel, efficacious peptide-based vaccine against TNBC targeting myeloid zinc finger 1 (MZF1), a transcription factor that has been described as an oncogenic inducer of TNBC metastasis. Initially, the antigenic peptides from MZF1 were identified and evaluated based on their likelihood to induce immunological responses. The promiscuous epitopes were then combined using a suitable adjuvant (50S ribosomal L7/L12 protein) and linkers (AAY, GPGPG, KK, and EAAAK) to reduce junctional immunogenicity. Furthermore, docking and dynamics analyses against TLR-4 and TLR-9 were carried out to understand more about their structural stability and integrity. Finally, the constructed vaccine was subjected to in silico cloning and immune simulation studies. Overall, the findings imply that the designed chimeric vaccine could induce strong humoral and cellular immune responses in the desired organism. In light of these findings, the final multi-epitope vaccine could be used as an effective prophylactic treatment for TNBC and may pave the way for future research.

Imidazole and biphenyl derivatives as anti-cancer agents for glioma therapeutics: Computational Drug Repurposing Strategy

BACKGROUND

Targeting mutated isocitrate dehydrogenase 1 (mIDH1) is one of the key therapeutic strategies for the treatment of glioma. Few inhibitors, such as ivosidenib and vorasidenib, have been identified as selective inhibitors of mIDH1. However, dose-dependent toxicity and limited brain penetration of the blood-brain barrier remain the major limitations of the treatment procedures using these inhibitors.

OBJECTIVE

In the present study, computational drug repurposing strategies were employed to identify potent mIDH1-specific inhibitors from the 11,808 small molecules listed in the DrugBank repository.

METHODS

Tanimoto coefficient (Tc) calculations were initially used to retrieve compounds with structurally similar scaffolds to ivosidenib. The resultant compounds were then subjected to molecular docking to discriminate the binders from the non-binders. The binding affinities and pharmacokinetic properties of the screened compounds were examined using prime Molecular Mechanics-Generalized Born Surface Area (MM-GBSA) and QikProp algorithm, respectively. The conformational stability of these molecules was validated using 100ns molecular dynamics simulation.

RESULTS

Together, these processes led to the identification of three-hit molecules, namely DB12001, DB08026, and DB03346, as potential inhibitors of the mIDH1 protein. Of note, the binding free energy calculations and MD simulation studies emphasized the greater binding affinity and structural stability of the hit compounds towards the mIDH1 protein.

CONCLUSION

The collective evidence from our study indicates the activity of DB12001 against recurrent glioblastoma, which, in turn, highlights the accuracy of our adapted strategy. Hence, we hypothesize that the identified lead molecules could be translated for the development of mIDH1 inhibitors in the near future.

In Silico Design and Investigation of Novel Thiazetidine Derivatives as Potent Inhibitors of PrpR in Mycobacterium tuberculosis

Tuberculosis is one of the most life-threatening acute infectious diseases diagnosed in humans. In the present investigation, a series of 16 new disubstituted 1,3-thiazetidines derivatives is designed, and investigated via various in silico methods for their potential as anti-tubercular agent by evaluating their ability to block the active site of PrpR transcription factor protein of Mycobacterium tuberculosis. The efficacy of the molecules was initially assessed with the help of AutoDock Vina algorithm. Further Glide module is used to redock the previously docked complexes. The binding energies and other physiochemical properties of the designed molecules were evaluated using the Prime-MM/GBSA and the QikProp module, respectively. The results of docking revealed the nature, site of interaction and the binding affinity between the proposed candidates and the active site of PrpR. Further the inhibitory effect of the scaffolds was predicted and evaluated employing a machine learning-based algorithm and was used accordingly. Further, the molecular dynamics simulation studies ascertained the binding characteristics of the unique 13, when analysed across a time frame of 100 ns with GROMACS software. The results show that the proposed 1,3-thiazetidine derivatives such as 10, 11, 13 and 14 could be potent and selective anti-tubercular agents as compared to the standard drug Pyrazinamide. Finally, this study concludes that designed thiazetidines can be employed as anti-tubercular agents. Undeniably, the results may guide the experimental biologists to develop safe and non-toxic drugs against tuberculosis by demanding further in vivo and in vitro analyses.

Machine learning driven drug repurposing strategy for identification of potential RET inhibitors against non-small cell lung cancer

Non-small cell lung cancer (NSCLC) remains the leading cause of mortality and morbidity worldwide accounting about 85% of total lung cancer cases. The receptor REarranged during Transfection (RET) plays an important role by ligand independent activation of kinase domain resulting in carcinogenesis. Presently, the treatment for RET driven NSCLC is limited to multiple kinase inhibitors. This situation necessitates the discovery of novel and potent RET specific inhibitors. Thus, we employed high throughput screening strategy to repurpose FDA approved compounds from DrugBank comprising of 2509 molecules. It is worth noting that the initial screening is accomplished with the aid of in-house machine learning model built using IC₅₀ values corresponding to 2854 compounds obtained from BindingDB repository. A total of 497 compounds (19%) were predicted as actives by our generated model. Subsequent in silico validation process such as molecular docking, MMGBSA and density function theory analysis resulted in identification of two lead compounds named DB09313 and DB00471. The simulation study highlights the potency of DB00471 (Montelukast) as potential RET inhibitor among the investigated compounds. In the end, the half-minimal inhibitory activity of montelukast was also predicted against RET protein expressing LC-2/ad cell lines demonstrated significant anticancer activity. Collective analysis from our study highlights that montelukast could be a promising candidate for the management of RET specific NSCLC.

Drug repurposing combined with MM/PBSA based validation strategies towards MEK inhibitors screening

Emergence of oncogenic mutations in the MAPK pathway gaining more impact in the recent years. Importantly, MEK is a core element of this pathway as it is easy to inhibit and is a gatekeeper of multiple malignancies. Therefore, we performed in-silico strategy to screen repurposed candidate for MEK protein using a library of 11,808 compounds from different clusters in the DrugBank database. Glide docking, Prime-MM/GBSA and QikProp analysis were implemented to retrieve the hits with high precision. The stability of the binding mode and binding affinity of the resultant hit were explored using molecular dynamic simulations and MM/PBSA approach. The results highlight that Nebivolol (DB04861) not only achieved a stable conformation in the MEK binding pocket but also displayed highest binding affinity than the other molecules investigated in our study. Taken together, we hypothesized that Nebivolol is an excellent candidate for the inhibition of MEK in NSCLC patients in future.Communicated by Ramaswamy H. Sarma.

Forecasting determinants of recurrence in lung cancer patients exploiting various machine learning models

Lung cancer recurrence seems to be the most leading cause of death as well as deterioration of lifespan. Proper assessment of the probability of recurrence in early-stage lung cancer is necessary to push up the treatment progress. We therefore employed machine-learning technologies to forecast post-operative recurrence risks using 174 lung cancer patient records. Six classification algorithms logistic regression, SVM, decision tree classification, random forest classification, XGBoost and lightGBM were used to predict the cancer recurrence. The patient samples were divided into training and test group with the split ratio of 3:1 for model generation and the accuracy were validated using k-fold cross-validation method. It is worth noting that the logistic regression model outperformed all the models in both training (Accuracy = 0.82) and test set (Accuracy = 0.79) on k-fold validation. Further, the optimal features (n = 7) identified using the RFE method is certainly helpful to improve the model in a high precision. The imperative risk factors associated with recurrence were identified using three feature selection methods. Importantly, our research showed that age is an important prognostic factor to be considered during the recurrence prediction. Indeed, severe concern on the identified risk factors combined with predictive models assists the physician to reduce the cancer recurrence rate in patients with lung cancer.

Identification of novel inhibitors for Prp protein of Mycobacterium tuberculosis by structure based drug design, and molecular dynamics simulations

In this study, we assess the effective inhibition of a series of thiazolidine derivatives (1a-1q) were adopting structure-based drug design. Thiazolidine is a five-membered ring structure with thioether and amino groups at positions 1 and 3. Although, thiazolidine may bind to a wide range of protein targets, it is a major heterocyclic core in medicinal chemistry. Different scoring utilities including AutoDock Vina, Glide, and MM/GBSA analysis were performed to commensurate the improvement of screening progress. The evaluated binding affinities were validated by molecular dynamics simulations over a period of 20 ns for the interactions between the Mycobacterium tuberculosis protein PrpR with three novel scaffolds (1b, 1j, and 1k). All the scaffolds exhibited distinct stable interactions with the significant residues like Arg169, Arg197, Tyr248, Arg308, and Gly311 respectively. Further, the inhibitory activities of scaffolds were predicted and evaluated by machine learning based algorithm to rank the above proposed compounds. This study reveals the potential of 1k and 1j as effective inhibitor candidates for the treatment of tuberculosis.

Envelope Glycoprotein based multi-epitope vaccine against a co-infection of Human Herpesvirus 5 and Human Herpesvirus 6 using in silico strategies

The Human Betaherpesviruses HHV-5 and HHV-6 are quite inimical in immunocompromised hosts individually. A co-infection of both has been surmised to be far more disastrous. This can be attributed to a synergetic effect of their combined pathologies. While there have been attempts to develop a vaccine against each virus, no efforts were made to contrive an effective prophylaxis for the highly detrimental co-infection. In this study, an ensemble of viral envelope glycoproteins from both the viruses was utilized to design a multi-epitope vaccine using immunoinformatics tools. A collection of bacterial protein toll-like receptor agonists (BPTAs) was screened to identify a highly immunogenic adjuvant for the vaccine construct. The constructed vaccine was analysed using an array of methodologies ranging from World population coverage analysis to Immune simulation, whose results indicate high vaccine efficacy and stability. Furthermore, codon optimization and in silico cloning analysis were performed to check for efficient expression in a bacterial system. Collectively, these findings demonstrate the potential of the constructed vaccine to elicit an immune response against HHV-5 and HHV-6, thus supporting the viability of in vitro and in vivo studies.

In Silico Screening of Available Drugs Targeting Non-Small Cell Lung Cancer Targets: A Drug Repurposing Approach

The RAS-RAF-MEK-ERK pathway plays a key role in malevolent cell progression in many tumors. The high structural complexity in the upstream kinases limits the treatment progress. Thus, MEK inhibition is a promising strategy since it is easy to inhibit and is a gatekeeper for the many malignant effects of its downstream effector. Even though MEK inhibitors are under investigation in many cancers, drug resistance continues to be the principal limiting factor to achieving cures in patients with cancer. Hence, we accomplished a high-throughput virtual screening to overcome this bottleneck by the discovery of dual-targeting therapy in cancer treatment. Here, a total of 11,808 DrugBank molecules were assessed through high-throughput virtual screening for their activity against MEK. Further, the Glide docking, MLSF and prime-MM/GBSA methods were implemented to extract the potential lead compounds from the database. Two compounds, DB012661 and DB07642, were outperformed in all the screening analyses. Further, the study results reveal that the lead compounds also have a significant binding capability with the co-target PIM1. Finally, the SIE-based free energy calculation reveals that the binding of compounds was majorly affected by the van der Waals interactions with MEK receptor. Overall, the in silico binding efficacy of these lead compounds against both MEK and PIM1 could be of significant therapeutic interest to overcome drug resistance in the near future.

Exploration of potential inhibitors for tuberculosis via structure-based drug design, molecular docking, and molecular dynamics simulation studies

Drug resistance in tuberculosis is major threat to human population. In the present investigation, we aimed to identify novel and potent benzimidazole molecules to overcome the resistance management. A series of 20 benzimidazole derivatives were examined for its activity as selective antitubercular agents. Initially, AutodockVina algorithm was performed to assess the efficacy of the molecules. The results are further enriched by redocking by means of Glide algorithm. The binding free energies of the compounds were then calculated by MM-generalized-born surface area method. Molecular docking studies elucidated that benzimidazole derivatives has revealed formation of hydrogen bond and strong binding affinity in the active site of Mycobacterium tuberculosis protein. Note that ARG308, GLY189, VAL312, LEU403, and LEU190 amino acid residues of Mycobacterium tuberculosis protein PrpR are involved in binding with ligands of benzimidazoles. Interestingly, the ligands exhibited same binding potential to the active site of protein complex PrpR in both the docking programs. In essence, the result portrays that benzimidazole derivatives such as 1p, 1q, and 1 t could be potent and selective antitubercular agents than the standard drug isoniazid. These compounds were then subjected to molecular dynamics simulation to validate the dynamics activity of the compounds against PrpR. Finally, the inhibitory behavior of compounds was predicted using a machine learning algorithm trained on a data collection of 15,000 compounds utilizing graph-based signatures. Overall, the study concludes that designed benzimidazoles can be employed as antitubercular agents. Indeed, the results are helpful for the experimental biologists to develop safe and non-toxic drugs against tuberculosis.

Exploring safe and potent bioactives for the treatment of non-small cell lung cancer

Activating and suppressing mutations in the MAPK pathway receptors are the primary causes of NSCLC. Of note, MEK inhibition is considered a promising strategy because of the diverse structures and harmful effects of upstream receptors in MAPK pathway. Thus, we explore a total of 1574 plant-based bioactive compounds activity against MEK using an energy-based virtual screening strategy. Molecular docking, binding free energy, and drug-likeness analysis were performed through GLIDE, Prime MM-GBSA, and QikProp module, respectively. The findings indicate that 5-O-caffeoylshikimic acid has an increased binding affinity to MEK protein. Further, molecular dynamic simulations and MM-PBSA analysis were performed to explore the ligand activity in real-life situations. In essence, compounds inhibitory activity was validated across 77 lung cancer cell lines using multimodal attention-based neural network algorithm. Eventually, our analysis highlight that 5-O-caffeoylshikimic acid obtained from the bark of Rhizoma smilacis glabrae would be developed as a potential compound for treating NSCLC.

Gene expression profiling of corona virus microarray datasets to identify crucial targets in COVID-19 patients

The current outbreak of coronavirus disease (COVID-19) has been affecting millions of people and has caused devastating mortality worldwide. Moreover, it is to be noted that cytokine storm has become an important cause for the rising mortality. However, the efforts for the development of drugs, vaccines and treatment has also been intervened due to poor understanding of host's defense mechanism and also due to the development of cytokine storm against this viral infection. Thus, a deeper understanding of the mechanism behind the immune dysregulation and cytokine storm development might give us clues for the clinical management of the severe cases. Hence, we have implemented differential gene expression analysis together with protein-protein interaction and Gene Ontology (GO) studies with the help of Severe Acute respiratory syndrome coronavirus (SARS-CoV) data sets such as GSE1739 and GSE33267 to give us more knowledge on the host immune response for the pathogenic coronavirus which in turn reduces the mortality. A total of 79 differentially-expressed genes (DEGs) were identified in our data set using the filters such as P-value and log2 fold change values of less than 0.05 and 1.5 respectively. Further, network analysis and GO studies showed that differential expression of two hub genes namely ELANE and LTF which could induce higher levels of pro-inflammatory cytokines in the lungs. We are certain that differential expression of ELANE and LTF results in an excessive inflammatory reaction known as the cytokine storm and ultimately leading to death. Therefore, targeting these key drivers of cytokine storm genes appears to be the potential therapeutic targets for combating the Severe Acute respiratory syndrome coronavirus - 2 (SARS-CoV-2) infection ultimately resulting in reduced mortality. Indeed, this predictive view may open new insights for designing an immune intervention for COVID-19 in the near future resulting in the mitigation of mortality rate.

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.

Discovery of Potent Covid-19 Main Protease Inhibitors Using Machine Learning Based Virtual Screening Strategy.. [DOI: 10.26434/chemrxiv.12090423] [Citation(s) in RCA: 0] [Impact Index Per Article: 0]

<p>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 great role of main protease of Covid-19 for virus replication, we performed drug repurposing study using 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 DrugBank repository. Further, hierarchical docking protocol was implemented with the support of Glide algorithm. The resultant compounds were then examined for its binding free energy against main protease of Covid-19 by means of Prime-MM/GBSA algorithm. Most importantly, the resultant compounds antiviral activities were further predicted by machine learning-based model generated by AutoQSAR algorithm. Finally, the hit molecules were examined for its drug likeness and its toxicity parameters through QikProp algorithm. Overall, the present analysis yielded four potential inhibitors (DB07800, DB08573, DB03744 and DB02986) that are predicted to bind with main protease of Covid-19 better than currently used drug molecules such as N3 (co-crystallized native ligand), Lopinavir and Ritonavir. </p>

Discovery of Potent Covid-19 Main Protease Inhibitors Using Machine Learning Based Virtual Screening Strategy.. [DOI: 10.26434/chemrxiv.12090423.v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0]

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 great role of main protease of Covid-19 for virus replication, we performed drug repurposing study using 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 DrugBank repository. Further, hierarchical docking protocol was implemented with the support of Glide algorithm. The resultant compounds were then examined for its binding free energy against main protease of Covid-19 by means of Prime-MM/GBSA algorithm. Most importantly, the resultant compounds antiviral activities were further predicted by machine learning-based model generated by AutoQSAR algorithm. Finally, the hit molecules were examined for its drug likeness and its toxicity parameters through QikProp algorithm. Overall, the present analysis yielded four potential inhibitors (DB07800, DB08573, DB03744 and DB02986) that are predicted to bind with main protease of Covid-19 better than currently used drug molecules such as N3 (co-crystallized native ligand), Lopinavir and Ritonavir.

Current progress and future perspectives of polypharmacology : From the view of non-small cell lung cancer

A pre-eminent subtype of lung carcinoma, Non-small cell lung cancer accounts for paramount causes of cancer-associated mortality worldwide. Undeterred by the endeavour in the treatment strategies, the overall cure and survival rates for NSCLC remain substandard, particularly in metastatic diseases. Moreover, the emergence of resistance to classic anticancer drugs further deteriorates the situation. These demanding circumstances culminate the need of extended and revamped research for the establishment of upcoming generation cancer therapeutics. Drug repositioning introduces an affordable and efficient strategy to discover novel drug action, especially when integrated with recent systems biology driven stratagem. This review illustrates the trendsetting approaches in repurposing along with their numerous success stories with an emphasize on the NSCLC therapeutics. Indeed, these novel hits, in combination with conventional anticancer agents, will ideally make their way the clinics and strengthen the therapeutic arsenal to combat drug resistance in the near future.

Discovery of human autophagy initiation kinase ULK1 inhibitors by multi-directional in silico screening strategies

Autophagy is a self-catabolic mechanism employed by cancer cells to acquire nutrients and energy in times of stress conditions, thereby leading to its progression and survival. Thus, autophagy inhibition has emerged as a new paradigm in the area of cancer treatment. Here, we leverage multi-dimensional screening campaigns aim to identify potent inhibitors against an early and an essential autophagic kinase, ULK1 from DrugBank database. In particular, receptor-based hypothesis, pharmacophore hypothesis, e-pharmacophore hypothesis and shape similarity-based screening algorithm were employed. Of note, the results of the different algorithm were then integrated to eliminate the false positive prediction. Moreover, the inhibitory activities and PK/PD parameters of the leads were tested by Glide and Qikprop algorithm. This resulted in a set of four hits namely; DB12686, DB08341, DB07936, and DB07163. Finally, molecular dynamics simulation was performed using the GROMACS package, to validate the binding kinetics of the hit compound. The compound activity in vitro was assessed by PASS algorithm, highlights the anti-cancer activities of the hits. The structural insights reveal existence of functional moieties such as piperidine carboxamide, benzenesulfonamide, benzamide, and isoindolone in the resultant hits which plays a major role in the anti-cancer activity. Overall, we strongly believe that these ULK1 antagonists could be novel and potent drug candidates for future cancer therapeutics.

Damping, Thermal and Mechanical Analyses of Polycarbonate/Cerium Oxide Composites for Structural Applications

Polycarbonate (PC)/cerium (IV) oxide (CeO2) composites are prepared by a melt-compounding method using a twin-screw extruder. The effect of the CeO2 content on the damping property of the composites was investigated using scanning electron microscopy, dynamic mechanical analysis (DMA), and thermogravimetric analysis (TGA). In addition, the composites' mechanical properties were studied through tensile and impact tests. The DMA results revealed that the addition of CeO2 (0.5 wt%) improved the damping property of the composite. TGA showed that the thermal stability was improved when the CeO2 became 1 wt%. Mechanical tests revealed that both the tensile and impact strengths were substantially improved when 1 wt% CeO2 was added. Finally, it can be concluded that the 0.5 wt% CeO2-filled PC composite can be used as a structural damping material.

Quinolones and Fluoroquinolones to Treat Salmonella Typhimurium: A Review of Metabolism and Pharmacokinetics

BACKGROUND

Salmonella enterica serovar typhimurium is the most important serotype of Salmonella transmitted from animals to humans in most parts of the world. They are associated with an estimated 1 million deaths annually. Quinolones, an important class of broad-spectrum antimicrobials, have been utilized as a treatment option for salmonellosis for over 40 years. Despite the number of available quinolone agents, many of them failed in the clinical stage and never make it to FDA approval. Despite considerable evidence reveals the importance of different drug discovery process of S. typhimurium, there are no systematic review outlining the pharmacokinetic and pharmacodynamic parameters of quinolones. Keeping this in mind, the present study aims to provide a systematic review on metabolism and pharmacokinetics of different quinolones.

METHODS

Information from all relevant bibliographic databases was used. Additionally, the recent journal articles and textbooks were searched manually in the preparation of this review article.

RESULTS

A total of 136 journal articles and textbooks were included in the preparation of the review. Majority of research articles defines about the metabolism, pharmacodymanics and pharmacokinetics of different quinolones. Twenty eight papers outlined about the mechanism and challenges faced by the quinolones and fluoroquinolones. Finally, information on drug interactions, adverse effects and drug resistance of quinolones and fluoroquinolones were supported with forty two research articles. Note that the importance of computational biology in the field of drug discovery was also addressed with appropriate literatures.

CONCLUSION

Overall, the findings of this review highlight the importance of pharmacokinetic profiling of different quinolones in developing novel drugs to overcome drug resistance in the near future.

Drug repurposing: An approach to tackle drug resistance in S. typhimurium

Drug resistant S. typhimurium pose important public health problem. The development of effective drugs with novel mechanism(s) of action is needed to overcome issues pertaining to drug resistance. Drug repurposing based on computational analyses is considered a viable alternative strategy to circumvent this issue. In this context, 1309 FDA-approved drugs molecules from Mantra 2.0 database were analyzed for this study, against S. typhimurium. Sixteen compounds having similar profiles of gene expression as quinolones were identified from the database, Mantra 2.0. Further, the pharmacophore characteristics of each resultant molecule were identified and compared with the features of nalidixic acid, using the PharamGist program. Subsequently, the activities of these compounds against S. typhimurium DNA gyrase were identified, using molecular docking study. Side effects analysis was also performed for the identified compounds. Molecular dynamics simulation was carried out for the compound to validate its binding efficiency. Further, characterization of screened compound revealed IC50 values in micromolar concentration range, of which flufenamic acid showed comparable in vitro activity alongside ciprofloxacin and nalidixic acid. Thus represent interesting starting points for further optimization against S. typhimurium infections. It may be noted that the results we have obtained are the first experimental evidence of flufenamic acid activity against S. typhimurium.

An Integrative Drug Repurposing Pipeline: Switching Viral Drugs to Breast Cancer

The growing incidence rate of breast cancer, coupled with cellular chemotherapeutic resistance, has made this disease one of the most prevalent cancers among women worldwide. Despite the recent efforts to understand the underlying cause of the resistance due to mutation, there are no feasible tactics to overcome this bottleneck. This issue could be addressed by the concept of polypharmacology-disguising drugs present in the pharmacopeia for novel purposes (drug repurposing). Of note, we have proposed a multi-modal computational drug-repositioning stratagem to predict drugs possessing anti-proliferative effect. Our results suggest that Ombitasvir, a Hepatitis C NS5B polymerase inhibitor, could be "repurposed" for the control and prevention of beta-tubulin-driven breast cancers. J. Cell. Biochem. 118: 1412-1422, 2017. © 2016 Wiley Periodicals, Inc.

Haematological responses to exposure to sublethal concentration of cadmium in air breathing fish, Channa punctatus (Bloch)

The effect of sublethal concentration (29 mg/l) of cadmium on haematological parameters of Channa punctatus after the exposure periods of 7, 15 and 30 days were studied. In treated groups of cadmium exposed fish have been found to significantly decreased in total erythrocyte count, haemoglobin content, packed cell volume, mean corpuscular haemoglobin concentration and oxygen carrying capacity of blood, and at the same time the white blood cell count, mean cell volume and mean corpuscular haemoglobin were found to significantly increased. Both increasing and decreasing of the above mentioned haematological characteristics of blood cells of cadmium intoxicant are mainly with time dependent effects. With regard to the pathological characteristics of blood cells of cadmium intoxicant fish shows fragility and rupture of erythrocytic membrane and leads to haemolysis, which are very severe on 30 day of exposure group.