Identification of 5-nitroindazole as a multitargeted inhibitor for CDK and transferase kinase in lung cancer: a multisampling algorithm-based structural study

Harnessing machine learning potential for personalised drug design and overcoming drug resistance

Drug resistance in cancer treatment presents a significant challenge, necessitating innovative approaches to improve therapeutic efficacy. Integrating machine learning (ML) in cancer research is promising as ML algorithms outrival in analysing complex datasets, identifying patterns, and predicting treatment outcomes. Leveraging diverse data sources such as genomic profiles, clinical records, and drug response assays, ML uncovers molecular mechanisms of drug resistance, enabling personalised treatment, maximising efficacy and minimising adverse effects. Various ML algorithms contribute to the drug discovery process - Random Forest and Decision Trees predict drug-target interactions and aid in virtual screening, and SVM classify leads on bioactivity data. Neural Networks model QSAR to optimise lead compounds and K-means clustering group compounds with similar chemical properties aiding compound selection. Gaussian Processes predict drug responses, Bayesian Networks infer causal relationships, Autoencoders generate novel compounds, and Genetic Algorithms optimise molecular structures. These algorithms collectively enhance efficiency and success rates in drug design endeavours, from lead identification to optimisation and are cost-effective, empowering clinicians with real-time treatment monitoring and improving patient outcomes. This review highlights the immense potential of ML in revolutionising cancer care through effective drug design to reduce drug resistance, and we have also discussed various limitations and research gaps to understand better.

Unveiling the potency of FDA-approved oxidopamine HBr for cervical cancer regulation and replication proteins

Cervical Cancer remains a women's health concern worldwide and ranks among the most prevalent cancers, particularly in developing countries. Many women are diagnosed with cervical cancer, with a substantial number succumbing to the disease even after the availability of vaccines and drugs. The tumour microenvironment often exhibits immune evasion, including suppression of T-cell activity and altered cytokine, impacting the efficacy of therapeutic interventions and highlighting the need for treatments to modulate the immune response. Despite efforts to promote HPV vaccination and regular screenings, it causes many deaths, underscoring the urgent need for continued research, healthcare access, and rapid drug development or repurposing. In this study, we identified various proteins involved in cervical cancer cell cycle regulation and DNA replication proteins, performed the multitargeted docking with an FDA-approved library, and identified Oxidopamine HBr as a multitargeted drug. Studies extended with pharmacokinetics and compared with the standard values followed by DFT, which supported the compound as a multitargeted inhibitor. Further, the docked complexes were taken for the interaction fingerprints, and it was identified that there are many 9 polar, 5 hydrophobic, 2 aromatic, and 2 basic residues. We extended our studies for 100ns MD Simulation in water, and the computations explored the deviation and fluctuations under 2Å and many intermolecular interactions; the same trajectory files were used for the MM\GBSA studies. All the studies have supported the Oxidopamine HBr as a cervical cancer multitargeted inhibitor-however, experimental studies are needed before human use.

S-(N,N-diethyldithiocarbamoyl)-N-acetyl-l-cysteine for the treatment of non-small cell lung cancer through regulating NF-κB signalling pathway without neurotoxicity

The discovery of novel targeted agents for non-small cell lung cancer (NSCLC) remains an important research landscape due to the limited efficacy, side effects and drug resistance of current treatment options. Among many repurposed drugs, disulphiram (DSF) has shown the potential to target tumours. However, its unpleasant neurotoxicity greatly limits its use. A DSF derivative, S-(N,N-diethyldithiocarbamoyl)-N-acetyl-l-cysteine (DS-NAC), was synthesised against NSCLC. The therapeutic effects, mechanism and toxicities of DS-NAC were evaluated in A549 and H460 cells and the mouse model of in situ lung cancer. The in vitro results exhibited that DS-NAC had potent anti-proliferation, apoptotic, anti-metastasis and epithelial-mesenchymal transition (EMT) inhibition effects. In the orthotopic lung cancer mouse model, therapeutic effects of DS-NAC were better than those of DSF and were similar to docetaxel (DTX). Also, results from western blot and immunohistochemistry showed that DS-NAC in combination with copper exerted therapeutic effects via regulating NF-κB signalling pathway and ROS-related proteins such as HIF-1α, Nrf2 and PKC-δ rather than regulating ROS level directly. Moreover, the safety evaluation study showed that DS-NAC had low haematologic and hepatic toxicities in comparison with DTX as well as low neurological toxicity compared with DSF. DS-NAC could be a promising anti-lung cancer agent with a favourable safety profile.

An extensive review on lung cancer therapeutics using machine learning techniques: state-of-the-art and perspectives

There are over 100 types of human cancer, accounting for millions of deaths every year. Lung cancer alone claims over 1.8 million lives per year and is expected to surpass 3.2 million by 2050, which underscores the urgent need for rapid drug development and repurposing initiatives. The application of AI emerges as a pivotal solution to developing anti-cancer therapeutics. This state-of-the-art review aims to explore the various applications of AI in lung cancer therapeutics. Predictive models can analyse large datasets, including clinical data, genetic information, and treatment outcomes, for novel drug design and to generate personalised treatment recommendations, potentially optimising therapeutic strategies, enhancing treatment efficacy, and minimising adverse effects. A thorough literature review study was conducted based on articles indexed in PubMed and Scopus. We compiled the use of various machine learning approaches, including CNN, RNN, GAN, VAEs, and other AI techniques, enhancing efficiency with accuracy exceeding 95%, which is validated through a computer-aided drug design process. AI can revolutionise lung cancer therapeutics, streamlining processes and saving biological scientists' time and effort-however, further research is needed to overcome challenges and fully unlock AI's potential in Lung Cancer Therapeutics.

Systems biology approach: identification of hub genes, signaling pathways, and molecular docking of COL1A1 gene in cervical insufficiency

The collagen type I alpha 1 (COL1A1, OMIM #120,150) gene, encoding the alpha-1 chain of type I collagen (UniProt #P02452), plays a key role in life-homeostasis due to its remarkable involvement in collagen synthesis. It is a promising candidate gene implicated in the pathogenesis of cervical insufficiency (CI). This study aimed to identify genetic variations within the COL1A1 gene that contribute to the development of CI. Polymerase chain reaction (PCR) and amplicon sequencing were implemented for single nucleotide polymorphisms (SNPs) detection (+ 1245G/T, SP1 rs1800012), which revealed wild-type sequence for targeted SNPs in enrolled proband indicated negative results regarding COL1A1 gene involvement for current form of CI. It allows further investigation of other closely connected genes probed in this study. Computational approaches viz. Protein-protein interaction (PPI), gene ontology (GO), and pathway participation were used to identify the crucial hub genes and signaling pathways for COL1A1 and CI. Using the Yet Another Scientific Artificial Reality Application (YASARA) software, molecular docking, and molecular dynamic (MD) simulation with the oxytocin (CID 439,302), estradiol (CID 129,728,744), progesterone (CID 5994) and hydroxyprogesterone (CID 150,788) were done. Interactive bioinformatics analysis demonstrated that the COL1A1 and more than 10 collagen sister genes had a strong connection with CI. In sum, the findings of this study provide insights into a modus operandi that can be utilized to illuminate the path toward studying sister genes and smooth diagnosis of CI. These findings have implications for understanding the foundational process of the condition and potentially developing screening, diagnostic, and therapeutic interventions.

Graphical Abstract

Molecular interaction and MD-simulations: investigation of Sizofiran as a promising anti-cancer agent targeting eIF4E in colorectal cancer

CRC has a major global health impact due to high mortality rates. CRC shows high expression of eukaryotic translation initiation factor (eIF4E) protein, the rapid development of lung, bladder, colon, prostate, breast, head, and neck cancer is attributed to the dysregulation of eIF4E making an important target for treatment. Targeting eIF4E-mediated translation is a promising anti-cancer strategy. Many organic compounds that inhibit eIF4E are being studied clinically. The compound Sizofiran has emerged as a promising eIF4E inhibitor candidate, but its exact mechanism of action is unclear. In an effort to close this discrepancy by clarifying the mechanism of the interactions between phytochemical substances and eIF4E, molecular docking and dynamics studies were conducted. Molecular docking studies found Sizofiran (- 12.513 kcal/mol) has the most affinity eIF4E binding energy out of 93 phytochemicals, 5 current drugs, and 4 known inhibitors. This positions it as a top eIF4E inhibitor candidate. An alignment of eIF4E protein sequences from multiple pathogens revealed that the glutamate103 interacting residues are evolutionarily conserved across the different eIF4E proteins. Further insights from 100 ns of MD simulations supported Sizofiran having superior stability and eIF4E inhibition compared to reference compounds. Designed Sizofiran-related compounds showed better activity than the current drugs such as Camptosar, Sorafenib, Regorafenib, Doxorubicin, and Kenpaullone, indicating strong potential to suppress CRC progression by targeting eIF4E. This research aims to significantly aid development of improved eIF4E-targeting drugs for cancer treatment.

Graphical abstract

Showing the Graphical abstract of the complete study.

Supplementary Information

The online version contains supplementary material available at 10.1007/s40203-024-00206-3.

Exploring the anticancer potential of fluoro flavone analogues: insights from molecular docking and dynamics studies with Aurora Kinase B

Aurora Kinase B belongs to the serine kinase family. It plays an essential role in cell division and participates in mitosis and chromatid segregation. Overexpression, polymorphism, and splicing variants in the protein lead to tumorigenesis, leading to cancer. Flavones belong to the class of flavonoids and are derived from plants and show anti-cancer activities. Fluoro flavones and their analogs are taken from the PubChem database, resulting in 3882 compounds which is 90% similar to the fluoro flavones. Lipinski's rule of five, REOS and PAINS drug-like filters were applied which resulted 2448 compounds. These compounds are docked with Aurora Kinase B using SP and XP modules of Glide software. The best binding scores for SP docking were - 9.153 kcal/mol for the compound with CID: 44298667, and XP docking was - 10.287 kcal/mol with CID: 101664315. Enrichment calculations were done using Aurora Kinase B's decoys to validate the docking result. The resulting R2 = 0.96 from enrichment calculations suggests that the docking protocol is valid. The SP and XP docking lead compounds and the Fluoro flavone were subjected to 100 ns MD simulation to probe the protein-ligand complex stability. Also, the binding free energies between the Aurora kinase B and lead compounds were computed by Prime MM/GBSA module. The result suggests that the lead compounds bind more strongly with Aurora Kinase B than the Fluoro flavone. These lead compounds can be further evaluated in vitro and in vivo and can be used as future novel drugs for the curation of cancer.

Graphical abstract

Variolin B from sea sponge against lung cancer: a multitargeted molecular docking with fingerprinting and molecular dynamics simulation study

Lung Cancer is the one that causes more fatalities in the world compared to other cancers, and its uniqueness is that it can be found in both males and females. However, recent data has shown that males are more affected due to lifestyle habits like smoking, tobacco consumption and inhaling polluted air. The World Health Organization has kept lung cancer on its priority list as it causes 1.8 million deaths worldwide each year, and the predictions show that the cases are going to increase year by year, and by 2050, there can be 3.8 million new cases and 3.2 million deaths, and the global health system is not prepared for it. Also, finding drug candidates that can help shrink cancerous cells and lead to their death is essential to reduce global mortality. The system needs drug compounds that can inhibit multiple paths together not to enter drug resistance quickly and to reduce costs. Our study identified a compound named Variolin B (DB08694) that belongs to the organic compounds class of pyrrolopyridines. The identified compound can inhibit multiple proteins, drastically reducing the global burden. Variolin B was identified as a potential candidate against lung cancer using the multisampling algorithm such as HTVS, SP, and XP, followed by MM\GBSA calculations showing the docking score of -9.245 Kcal/mol to -5.92 Kcal/mol. Also, we have validated it with ADMET predictions and molecular fingerprinting to analyse the interaction patterns. Further, the study was extended to molecular dynamics simulations for 100 ns to understand the complex stability and simulative interactions. The complex's overall molecular dynamics simulation helped us understand that the identified candidate is stable with the lowest deviation and fluctuations.Communicated by Ramaswamy H. Sarma.

Structure-based multitargeted docking screening, pharmacokinetics, DFT, and dynamics simulation studies reveal mitoglitazone as a potent inhibitor of cellular survival and stress response proteins of lung cancer

Lung cancer is a disease in which lung cells grow abnormally and uncontrollably, and the cause of it is direct smoking, secondhand smoke, radon, asbestos, and certain chemicals. The worldwide leading cause of death is lung cancer, which is responsible for more than 1.8 million deaths yearly and is expected to rise to 2.2 million by 2030. The most common type of lung cancer is non-small cell lung cancer (NSCLC), which accounts for about 80% and small cell lung cancer (SCLC), which is more aggressive than NSCLC and is often diagnosed later and accounts for 20% of cases. The global concern for lung cancer demands efficient drugs with the slightest chance of developing resistance, and the idea of multitargeted drug designing came up with the solution. In this study, we have performed multitargeted molecular docking studies of Drug Bank compounds with HTVS, SP and XP algorithms followed by MM\GBSA against the four proteins of lung cancer cellular survival and stress responses, which revealed Mitoglitazone as a multitargeted inhibitor with a docking and MM\GBSA score ranging from - 5.784 to - 7.739 kcal/mol and - 25.81 to - 47.65kcal/mol, respectively. Moreover, we performed pharmacokinetics studies and QM-based DFT analysis, showing suitable candidate and interaction pattern analysis revealed the most count of interacting residues was 4GLY, 5PHE, 6ASP, 6GLU, 6LYS, and 6THR. Further, the results were validated with SPC water model-based MD simulation for 100ns in neutralised condition, showing the cumulative deviation and fluctuation < 2Å with many intermolecular interactions. The whole analysis has suggested that Mitoglitazone can be used as a multitargeted inhibitor against lung cancer-however, experimental studies are needed before human use.

Mitoxantrone 2HCl's adroit activity against cervical cancer replication and maintenance proteins: a multitargeted approach

Cervical cancer poses a significant global health challenge, ranking as the fourth most common cancer among women worldwide and resulting in approximately 300,000 deaths yearly, predominantly caused by high-risk human papillomavirus strains (HPV), mainly types 16 and 18. The scenario poses the urgent need of the hour to develop effective treatment strategies that can address the complexity of cervical cancer and multitargeted inhibitor designing that holds promise as it can simultaneously target multiple proteins and pathways involved in its progression and have the potential to enhance treatment efficacy, reduce the likelihood of drug resistance. In this study, we have performed multitargeted molecular docking of FDA-approved drugs against cervical cancer replication and maintenance proteins- Xenopus kinesin-like protein-2 (3KND), cell division cycle protein-20 (4N14), MCM2-histone complex (4UUZ) and MCM6 Minichromosome maintenance (2KLQ) with HTVS, SP and XP algorithms and have obtained the docking and MM\GBSA score ranging from -8.492 to -5.189 Kcal/mol and -58.16 to -39.07 Kcal/mol. Further, the molecular interaction fingerprints identified ALA, THR, SER, ASN, LEU, and ILE were among the most interacted residues, leaning towards hydrophobic and polar amino acids. The pharmacokinetics and DFT of the compound have shown promising results. The complexes were simulated for 100 ns to study the stability by computing the deviation, fluctuations, and intermolecular interactions formed during the simulation. This study produced promising results, satisfying the criteria that Mitoxantrone 2HCl can be a multitargeted inhibitor against cervical cancer proteins-however, experimental validation is a must before human use.Communicated by Ramaswamy H. Sarma.

Structure-Based In Silico Approaches Reveal IRESSA as a Multitargeted Breast Cancer Regulatory, Signalling, and Receptor Protein Inhibitor

Breast cancer begins in the breast cells, mainly impacting women. It starts in the cells that line the milk ducts or lobules responsible for producing milk and can spread to nearby tissues and other body parts. In 2020, around 2.3 million women across the globe received a diagnosis, with an estimated 685,000 deaths. Additionally, 7.8 million women were living with breast cancer, making it the fifth leading cause of cancer-related deaths among women. The mutational changes, overexpression of drug efflux pumps, activation of alternative signalling pathways, tumour microenvironment, and cancer stem cells are causing higher levels of drug resistance, and one of the major solutions is to identify multitargeted drugs. In our research, we conducted a comprehensive screening using HTVS, SP, and XP, followed by an MM/GBSA computation of human-approved drugs targeting HER2/neu, BRCA1, PIK3CA, and ESR1. Our analysis pinpointed IRESSA (Gefitinib-DB00317) as a multitargeted inhibitor for these proteins, revealing docking scores ranging from -4.527 to -8.809 Kcal/mol and MM/GBSA scores between -49.09 and -61.74 Kcal/mol. We selected interacting residues as fingerprints, pinpointing 8LEU, 6VAL, 6LYS, 6ASN, 5ILE, and 5GLU as the most prevalent in interactions. Subsequently, we analysed the ADMET properties and compared them with the standard values of QikProp. We extended our study for DFT computations with Jaguar and plotted the electrostatic potential, HOMO and LUMO regions, and electron density, followed by a molecular dynamics simulation for 100 ns in water, showing an utterly stable performance, making it a suitable drug candidate. IRESSA is FDA-approved for lung cancer, which shares some pathways with breast cancers, clearing the hurdles of multitargeted drugs against breast and lung cancer. This has the potential to be groundbreaking; however, more studies are needed to concreate IRESSA's role.

Curcumin hybrid molecules for the treatment of Alzheimer's disease: Structure and pharmacological activities

Alzheimer's disease (AD) is the most common neurodegenerative disease among the elderly. Contemporary treatments can only relieve symptoms but fail to delay disease progression. Curcumin is a naturally derived compound that has demonstrated significant therapeutic effects in AD treatment. Recently, molecular hybridization has been utilized to combine the pharmacophoric groups present in curcumin with those of other AD drugs, resulting in a series of novel compounds that enhance the therapeutic efficacy through multiple mechanisms. In this review, we firstly provide a concise summary of various pathogenetic hypotheses of AD and the mechanism of action of curcumin in AD, as well as the concept of molecular hybridization. Subsequently, we focus on the recent development of hybrid molecules derived from curcumin, summarizing their structures and pharmacological activities, including cholinesterase inhibitory activity, Aβ aggregation inhibitory activity, antioxidant activity, and other activities. The structure-activity relationships were further discussed.

Delineated 3-1-BenCarMethInYlPro-Phosphonic Acid's Adroit Activity against Lung Cancer through Multitargeted Docking, MM\GBSA, QM-DFT and Multiscale Simulations

Lung cancer is a pervasive and challenging disease with limited treatment options, with global health challenges often present with complex molecular profiles necessitating the exploration of innovative therapeutic strategies. Single-target drugs have shown limited success due to the heterogeneity of this disease. Multitargeted drug designing is imperative to combat this complexity by simultaneously targeting multiple target proteins and pathways, which can enhance treatment efficacy and overcome resistance by addressing the dynamic nature of the disease and stopping tumour growth and spread. In this study, we performed the molecular docking studies of Drug Bank compounds with a multitargeted approach against crucial proteins of lung cancer such as heat shock protein 5 (BIP/GRP78) ATPase, myosin 9B RhoGAP, EYA2 phosphatase inhibitor, RSK4 N-terminal kinase, and collapsin response mediator protein-1 (CRMP-1) using HTVS, SP with XP algorithms, and poses were filtered using MM\GBSA which identified [3-(1-Benzyl-3-Carbamoylmethyl-2-Methyl-1h-Indol-5-Yloxy)-Propyl-]-Phosphonic Acid (3-1-BenCarMethIn YlPro-Phosphonic Acid) (DB02504) as multitargeted drug candidate with docking and MM\GBSA score ranges from -5.83 to -10.66 and -7.56 to -50.14 Kcal/mol, respectively. Further, the pharmacokinetic and QM-based DFT studies have shown complete acceptance results, and interaction fingerprinting reveals that ILE, GLY, VAL, TYR, LEU, and GLN were among the most interacting residues. The 100 ns MD simulation in the SPC water model with NPT ensemble showed stable performance with deviation and fluctuations <2 Å with huge interactions, making it a promising multitargeted drug candidate; however, experimental studies are needed before use.

The application of WaterMap-guided structure-based virtual screening in novel drug discovery

INTRODUCTION

Nowadays, it is widely accepted that water molecules play a key role in binding a ligand to a molecular target. Neglecting water molecules in the process of molecular recognition was the result of several failures of the structure-based drug discovery campaigns. The application of WaterMap, in particular WaterMap-guided molecular docking, enables the reasonably accurate and quick description of the location and energetics of water molecules at the ligand-protein interface.

AREAS COVERED

In this review, the authors shortly discuss the importance of water in drug design and discovery and provide a brief overview of the computational approaches used to predict the solvent-related effects for the purposes of presenting WaterMap in the context of other available techniques and tools. A concise description of WaterMap concept is followed by the presentation of WaterMap-assisted virtual screening literature published between 2013 and 2023.

EXPERT OPINION

In recent years, WaterMap software has been extensively used to support structure-based drug design, in particular structure-based virtual screening. Indeed, it is a useful tool to rescore docking results considering water molecules in the binding pocket. Although WaterMap allows for the consideration of the dynamic behavior of water molecules in the binding site, for best accuracy, its application in conjunction with other techniques such as molecular mechanics-generalized Born surface area of FEP (Free Energy Perturbation) is recommended.

Design of new Mcl-1 inhibitors for cancer using fragments hybridization, molecular docking, and molecular dynamics studies

Apoptosis is a critical process that regulates cell survival and death and plays an essential role in cancer development. The Bcl-2 protein family, including myeloid leukemia 1 (Mcl-1), is a key regulator of the intrinsic apoptosis pathway, and its overexpression in many human cancers has prompted efforts to develop Mcl-1 inhibitors as potential anticancer agents. In this study, we aimed to design new Mcl-1 inhibitors using various computational techniques. First, we used the Mcl-1 receptor-ligand complex to build an e-pharmacophore hypothesis and screened a library of 567,000 fragments from the Enamine database. We obtained 410 fragments and used them to design 92,384 novel compounds, which we then docked into the Mcl-1 binding cavity using HTVS, SP, and XP docking modes of Glide. To assess their suitability as drug candidates, we conducted MM-GBSA calculations and ADME prediction, leading to the identification of 10 compounds with excellent binding affinity and favorable pharmacokinetic properties. To further investigate the interaction strength, we performed molecular dynamics simulations on the top three Mcl-1 receptor-ligand complexes to study their interaction stability. Overall, our findings suggest that these compounds have promising potential as anticancer agents, pending further experimental validation such as Mcl-1 apoptosis Assay. By combining experimental methods with various in silico approaches, these techniques prove to be invaluable for identifying novel drug candidates with distinct therapeutic applications using fragment-based drug design. This methodology has the potential to expedite the drug discovery process while also reducing its costs.Communicated by Ramaswamy H. Sarma.

Cheminformatics approach for identification of N-HyMenatPimeMelly as a novel potential ligand against RAS and renal chloride channel

Some angiotensin receptor (AR) blockers interfere with the renal chloride channel (ClC-K), which plays an important role in urine concentration. Identifying ligands targeting this channel, whether activating or blocking, is highly desirable because it could open the way for interventions that modulate their activity. In this study, the Asinex (BioDesign) complete library was screened to identify a compound with favorable physicochemical and pharmacokinetic properties, which have both AR blocking and ClC-Ka-modulating activities to present it as a novel potential oral candidate which could be useful for treatment of salt-sensitive hypertension without major ClC-K affection. A compound, N-{[4-Hydroxy-1-(2-methyl-1,6-naphthyridin-4-yl)-4-piperidinyl]methyl}-N-methyl-L-lysinamide (N-HyMenatPimeMelly) (Chem Spider ID 68416221), was identified as a potent potential oral ligand of the renin-angiotensin system (RAS) and ClC-Ka with docking scores ranging from -10.978 to -7.324 with the four selected proteins (4YAY: AR type 1, 2PFI: Cytoplasmic domain of ClC-Ka, 6JOD: AR type 2 and 6M0J: Angiotensin-converting enzyme 2). The protein-ligand complex was used to perform molecular dynamics (MD) simulation for 100 ns. The QikProp and SwissADME tools' results showed that the compound has ADME/T and drug-likeness properties, which are within the permissible ranges for 95% of known drugs. The density functional theory (DFT) analysis and MD simulation extended the study toward computational validation. Throughout the study, N-HyMenatPimeMelly has shown good interactions and stable performance in MD simulation and DFT analysis. The whole analysis has produced promising results, and N-HyMenatPimeMelly can be treated as a novel potential RAS and ClC-K oral ligand, however, experimental validation is needed before human use.Communicated by Ramaswamy H. Sarma.

Multitargeted inhibitory effect of Mitoxantrone 2HCl on cervical cancer cell cycle regulatory proteins: a multitargeted docking-based MM\GBSA and MD simulation study

Cervical cancer remains a significant global health concern that starts in the cervix, the lower part of the uterus that connects to the vagina and is caused by the human papillomavirus (HPV), necessitating the development of effective multitargeted effective and resistance-proof therapies. In early-stage cervical cancer may not show any symptoms, however, as the cancer progresses, some people may experience- abnormal vaginal bleeding, watery or bloody vaginal discharge, pain in the pelvis or lower back, pain during sex, and frequent and painful urination. In this study, we screened the complete FDA-approved drug library using a multitargeted inhibitory approach against four cervical cancer proteins, namely mitotic arrest deficient -2, DNA polymerase epsilon B-subunit, benzimidazole-related -1, and threonine-protein kinase-1 which crucially plays its role for the in its development process. We employed the HTVS, SP and XP algorithms for efficient filtering and screening that helped to identify Mitoxantrone 2HCl against all of them with docking and MM\GBSA scores ranging from - 11.63 to - 7.802 kcal/mol and - 74.38 to - 47.73 kcal/mol, respectively. We also evaluated the interaction patterns of each complex and the pharmacokinetics properties that helped gain insight into interactions. Subsequently, we performed multiscale MD simulations for 100 ns to understand the dynamic behaviour and stability of the Mitoxantrone 2HCl -protein complexes that revealed the formation of stable drug-protein complexes and provided insights into the molecular interactions that contribute to Mitoxantrone's inhibitory effects on these proteins and can be a better drug for cervical cancer. However, experimental studies of these findings could pave the way for therapies to combat cervical cancer effectively.

In-silico and in-vitro study reveals ziprasidone as a potential aromatase inhibitor against breast carcinoma

Aromatase enzyme plays a fundamental role in the development of estrogen receptors, and due to this functionality, the enzyme has gained significant attention as a therapeutic for reproductive disorders and cancer diseases. The currently employed aromatase inhibitors have severe side effects whereas our novel aromatase inhibitor is more selective and less toxic, therefore has greater potential to be developed as a drug. The research framework of this study is to identify a potent inhibitor for the aromatase target by profiling molecular descriptors of the ligand and to find a functional pocket in the target by docking and MD simulations. For assessing cellular and metabolic activities as indicators of cell viability and cytotoxicity, in-vitro studies were performed by using the colorimetric MTT assay. Aromatase activities were determined by a fluorometric method. Cell morphology was assessed by phase-contrast light microscopy. Flow cytometry and Annexin V-FITC/PI staining assay determined cell cycle distribution and apoptosis. This study reports that CHEMBL708 (Ziprasidone) is the most promising compound that showed excellent aromatase inhibitory activity. By using better drug design methods and experimental studies, our study identified a novel compound that could be effective as a high-potential drug candidate against aromatase enzyme. We conclude that the compound ziprasidone effectively blocks the cell cycle at the G1-S phase and induces cancer cell death. Further, in-vivo studies are vital for developing ziprasidone as an anticancer agent. Lastly, our research outcomes based on the results of the in-silico experiments may pave the way for identifying effective drug candidates for therapeutic use in breast cancer.

Structural-functional analysis and molecular characterization of arsenate reductase from Enterobacter cloacae RSC3 for arsenic biotransformation

Enterobacter cloacae RSC3 isolated from an industrial pesticide site transformed arsenate into arsenite. The arsenate is transported by membrane-bound phosphate transporter and transformed to arsenite by arsenate reductase (arsC). E. cloacae RSC3 produced an arsenate reductase enzyme with a maximum activity of 354 U after 72 h of incubation. Arsenate reductase was found to be active and stable at a wide range of temperatures (20 and 45 °C) and pH (5-10), with maximum activity at 35 °C and pH 7.0. The arsenate reductase protein was further characterised molecularly using different bioinformatics tools. The 3D structure of ArsC protein was predicted by homology modelling and validated by the Ramachandran plot with 91.9% residues in the most favoured region. ArsC protein of E. cloacae RSC3 revealed structural homology with ArsC from PDB ID: 1S3C. The gene ontology results also showed that the ArsC protein had a molecular functionality of the arsenate reductase (glutaredoxin) activity and the biological function of cellular response to DNA damage stimulus. Molecular docking analysis of 3D structures using AutoDock vina-1.5.7 server predicted four ligand binding active site residues at Gln70, Asp68, Leu68, and Leu63. Strong ArsC-arsenate ion interaction was observed with binding energy -1.03 kcal/mol, indicating significant arsenate reductase activity and specificity of ArsC protein. On the basis of molecular dynamics simulation analysis, the RMSD and RMSF values revealed the stability of ArsC protein from E. cloacae RSC3.

Supplementary Information

The online version contains supplementary material available at 10.1007/s13205-023-03730-9.