In-silico analysis reveals Quinic acid as a multitargeted inhibitor against Cervical Cancer

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.

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.

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.

SPP1, a potential therapeutic target and biomarker for lung cancer: functional insights through computational studies

NIH reported 128 different types of cancer of which lung cancer is the leading cause of mortality. Globally, it is estimated that on average one in every seventeen hospitalized patients was deceased. There are plenty of studies that have been reported on lung cancer draggability and therapeutics, but yet a protein that plays a central specific to cure the disease remains unclear. So, this study is designed to identify the possible therapeutic targets and biomarkers that can be used for the potential treatment of lung cancers. In order to identify differentially expressed genes, 39 microarray datasets of lung cancer patients were obtained from various demographic regions of the GEO database available at NCBI. After annotating statistically, 6229 up-regulated genes and 10324 down-regulated genes were found. Out of 17 up-regulated genes and significant genes, we selected SPP1 (osteopontin) through virtual screening studies. We found functional interactions with the other cancer-associated genes such as VEGF, FGA, JUN, EGFR, and TGFB1. For the virtual screening studies,198 biological compounds were retrieved from the ACNPD database and docked with SPP1 protein (PDBID: 3DSF). In the results, two highly potential compounds secoisolariciresinol diglucoside (-12.9 kcal/mol), and Hesperidin (-12.0 kcal/mol) showed the highest binding affinity. The stability of the complex was accessed by 100 ns simulation in an SPC water model. From the functional insights obtained through these computational studies, we report that SPP1 could be a potential biomarker and successive therapeutic protein target for lung cancer treatment.

Molecular screening reveals Variolin B as a multitargeted inhibitor of lung cancer: a molecular docking-based fingerprinting and molecular dynamics simulation study

Lung Cancer is the topmost death causing cancer and results from smoking, air pollution, cigar, exposure to asbestos or radon-like substances, and genetic factors. The cases of Lung Cancer in south Asian developing nations are being seen most due to heavy pollution and unbalanced lifestyle and putting a considerable burden on healthcare systems. The Food and Drug Administration of the USA has approved almost 100 drugs against SCLC and NSLC and a few drugs that are given to minimise the side effect of anticancer drugs. However, the drugs are shown to be resistant at significantly higher stages and non-affective on cancerous cells and have long-term side effects due to designing the drug by keeping one protein/gene target while designing or repurposing the drugs. In this study, we have taken five main lung cancer protein targets- Nerve growth factor protein (1SG1), Apoptosis inhibitor survivin (1XOX), Heat shock protein (3IUC), Protein tyrosine phosphate (3ZM3), Aldo-keto reductase (4XZL) and screened the complete prepared Drug Bank library of 155888 compounds and identified Variolin B (DB08694) as a multitargeted inhibitor against lung cancer using HTVS, SP and XP sampling algorithms followed by MM\GBSA calculation to sort the best pose. Variolin B is a natural marine antitumor and antiviral compound, so we analysed the ADMET properties and interaction patterns and then simulated all five P-L complexes for 100 ns in water using the NPT ensemble to check its selves against lung cancer. The docking results, ADMET and fingerprints have shown a good performance, and RMSD and RMSF results were with least deviation and fluctuations (<2Å) and produced a huge contact with other residues making the complex stable. The complexes initially fluctuated and deviated due to changes in the solute medium and sudden heat and stabilise after a few ns. However, extensive experimental validation is required before human use.Communicated by Ramaswamy H. Sarma.

Computational analysis of potential drug-like compounds from Solanum torvum - A promising phytotherapeutics approach for the treatment of diabetes

Diabetes mellitus (DM) is a global pandemic that is characterized by high blood glucose levels. Conventional treatments have limitations, leading to the search for natural alternatives. This study focused on Solanum torvum (STV), a medicinal plant, to identify potential anti-diabetic compounds using molecular docking and molecular dynamics simulations. We focused on identifying natural inhibitors of two key enzymes involved in glucose metabolism: α-amylase (1HNY) and α-glucosidase (4J5T). In our preliminary docking study, rutin showed the highest binding affinity (-11.58 kcal/mol) to α-amylase, followed by chlorogenin (-7.58 kcal/mol) and myricetin (-5.82 kcal/mol). For α-glucosidase, rutin had the highest binding affinity (-11.78 kcal/mol), followed by chlorogenin (-7.11 kcal/mol) and fisetin (-6.44 kcal/mol). Hence, chlorogenin and rutin were selected for further analysis and compared with acarbose, an FDA-approved antidiabetic drug. Comparative docking revealed that chlorogenin had the highest binding affinity of (-9.9 kcal/mol) > rutin (-8.7 kcal/mol) and > acarbose (-7.7 kcal/mol) for α-amylase. While docking with α-glucosidase, chlorogenin again had the highest binding affinity of (-9.8 kcal/mol) > compared to rutin (-9.5 kcal/mol) and acarbose (-7.9 kcal/mol). Molecular dynamics (MD) simulations were conducted to assess their stability. We simulated 100 nanoseconds (ns) trajectories to analyze their stability on various parameters, including RMSD, RMSF, RG, SASA, H-bond analysis, PCA, FEL, and MM-PBSA on the six docked proteins. In conclusion, our study suggests that chlorogenin and rutin derived from STV may be effective natural therapeutic agents for diabetes management because of their strong binding affinities for the α-amylase and α-glucosidase enzymes.Communicated by Ramaswamy H. Sarma.

Delineating Pixantrone Maleate's adroit activity against cervical cancer proteins through multitargeted docking-based MM\GBSA, QM-DFT and MD simulation

Cervical cancer poses a substantial worldwide health challenge, especially in low- and middle-income nations, caused by high-risk types of human papillomavirus. It accounted for a significant percentage of cancer-related deaths among women, particularly in areas with limited healthcare resources, necessitating innovative therapeutic approaches, and single-targeted studies have produced significant results, with a considerable chance of developing resistance. Therefore, the multitargeted studies can work as a beacon of hope. This study is focused on performing the multitargeted molecular docking of FDA-approved drugs with the three crucial proteins TBK1, DNA polymerase epsilon, and integrin α-V β-8 of cervical cancer. The docking studies using multisampling algorithms HTVS, SP, and XP reveal Pixantrone Maleate (DB06193) as a multitargeted inhibitor with docking scores of -8.147, -8.206 and -7.31 Kcal/mol and pose filtration with MM\GBSA computations with scores -40.55, -33.67, and -37.64 Kcal/mol. We also have performed QM-based DFT and pharmacokinetics studies of the compound and compared it with the standard values, which results in the compound being entirely suitable against cervical cancer proteins. The interaction fingerprints have revealed that PHE, VAL, SER and ALA are the residues among most interactions. We also explore the stability of the multitargeted potential of Pixantrone Maleate through 100ns MD simulations and investigate the RMSD, RMSF and intermolecular interactions between all three proteins-ligand complexes. All computational studies favour Pixantrone Maleate as a multitargeted inhibitor of the TBK1, DNA polymerase epsilon, and integrin α-V β-8 and can be validated experimentally before use.

Unraveling the therapeutic potential of Senna singueana phytochemicals to attenuate pancreatic cancer using protein-protein interactions, molecular docking, and MD simulation

Pancreatic cancer (PC) presents challenges due to limited treatment options and patients seek complementary therapies alongside conventional treatments to improve well-being. This study uses computational drug discovery approaches to find potential phytochemicals from S. singueana for PC treatment. Among the 38 phytochemicals screened from S. singueana, specific inhibitors against PC were selected. Protein-protein interaction (PPI) network analysis highlighted key targets with high degrees, including PTEN (8) and PTK2 (7) genes, along with their respective proteins 5BZX and 3BZ3, which were employed for molecular docking studies. 1-methylchrysene and 3-methyl-1,8,9-anthracenetriol showed strong binding affinities of - 9.2 and - 8.1 Kcal/mol, respectively. Molecular dynamics simulations lasting 300 ns assessed structural stability and interaction energy of compound-target dockings: 1-methylchrysene-PTEN and 3-methyl-1,8,9-anthracenetriol-PTK2. In molecular dynamics simulations, the 3-methyl-1,8,9-anthracenetriol-PTK2 complex showed lower RMSD, RMSF, radius of gyration, solvent-accessible surface area, and more hydrogen bonds than the 1-methylchrysene-PTEN complex. The 3-methyl-1,8,9-anthracenetriol-PTK2 complex exhibited significantly stronger binding with a binding free energy (ΔG) of - 21.92 kcal/mol compared to the less favourable ΔG of - 10.65 kcal/mol for the 1-methylchrysene-PTEN complex. The consistent and stable binding interaction observed in the 3-methyl-1,8,9-anthracenetriol-PTK2 complex highlights its potential as a potent inhibitor of Focal Adhesion Kinase 1. Consequently, it emerges as a promising lead compound for the development of pancreatic cancer therapeutics. Conversely, the fluctuations observed in the 1-methylchrysene-PTEN complex indicate a less stable binding interaction. This indicates the potential of 3-methyl-1,8,9-anthracenetriol as a primary candidate for pancreatic cancer treatment. These findings improve our grasp of S. singueana's multi-target effects and its promise in addressing pancreatic cancer. Nevertheless, additional in-vivo and in-vitro studies are required to validate their effectiveness and therapeutic potential.

Supplementary Information

The online version contains supplementary material available at 10.1007/s40203-023-00179-9.

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.

Unveiling the multitargeted repurposing potential of taxifolin (dihydroquercetin) in cervical cancer: an extensive MM\GBSA-based screening, and MD simulation study

Cervical cancer is a significant cause of morbidity and mortality in women worldwide. Despite the availability of effective therapies, the development of drug resistance and adverse side effects remain significant challenges in cervical cancer treatment. Thus, repurposing existing drugs as multitargeted therapies for cervical cancer is an attractive approach. In this study, we extensively screened the complete prepared FDA-approved drugs and identified the repurposing potential of taxifolin, a flavonoid with known antioxidant and anti-inflammatory properties, as a multitargeted therapy for cervical cancer. We performed a computational analysis using molecular docking with various sampling algorithms, namely HTVS, SP, and XP algorithms, for robust sampling pose and filtered with MM/GBSA analysis to determine the binding affinity of taxifolin with potential targets involved in cervical cancer, such as Symmetric Mad2 Dimer, replication initiation factor MCM10-ID, TPX2, DNA polymerase epsilon B-subunit, human TBK1, and alpha-v beta-8. We then conducted MD simulations to investigate the stability and conformational changes of the complex formed between taxifolin and the mentioned proteins. Our results suggest that taxifolin has a high binding affinity ranging from - 6.094 to - 9.558 kcal/mol, indicating its potential as a multitargeted therapy for cervical cancer. Furthermore, interaction fingerprints, pharmacokinetics and MD simulations revealed that the Taxifolin-target complexes remained stable over the simulation period, indicating that taxifolin may bind to the targets for an extended period. Our study suggests that taxifolin has the potential as a multitargeted therapy for cervical cancer, and further experimental studies are necessary to validate our findings.

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

Lung cancer is the second most common cancer, which is the leading cause of cancer death worldwide. The FDA has approved almost 100 drugs against lung cancer, but it is still not curable as most drugs target a single protein and block a single pathway. In this study, we screened the Drug Bank library against three major proteins- ribosomal protein S6 kinase alpha-6 (6G77), cyclic-dependent protein kinase 2 (1AQ1), and insulin-like growth factor 1 (1K3A) of lung cancer and identified the compound 5-nitroindazole (DB04534) as a multitargeted inhibitor that potentially can treat lung cancer. For the screening, we deployed multisampling algorithms such as HTVS, SP and XP, followed by the MM\GBSA calculation, and the study was extended to molecular fingerprinting analysis, pharmacokinetics prediction, and Molecular Dynamics simulation to understand the complex's stability. The docking scores against the proteins 6G77, 1AQ1, and 1K3A were - 6.884 kcal/mol, - 7.515 kcal/mol, and - 6.754 kcal/mol, respectively. Also, the compound has shown all the values satisfying the ADMET criteria, and the fingerprint analysis has shown wide similarities and the water WaterMap analysis that helped justify the compound's suitability. The molecular dynamics of each complex have shown a cumulative deviation of less than 2 Å, which is considered best for the biomolecules, especially for the protein-ligand complexes. The best feature of the identified drug candidate is that it targets multiple proteins that control cell division and growth hormone mediates simultaneously, reducing the burden of the pharmaceutical industry by reducing the resistance chance.

Metabolism Pathways of Major Therapeutics for Treating Monkeypox Mono- and Co-infection with Human Immunodeficient Virus or SARS-CoV-2

Monkeypox is a zoonotic viral disease and remains endemic in tropical regions of Central and West Africa. Since May of 2022, cases of monkeypox have soared and spread worldwide. Confirmed cases have shown no travel history to the endemic regions as seen in the past. The World Health Organization declared monkeypox a global public health emergency in July 2022, and the United States government followed suit one month later. The current outbreak, in contrast to traditional epidemics, has high coinfection rates, particularly with HIV (human immunodeficiency virus), and to a lesser extent with SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2), the pathogen of COVID-19. No drugs have been approved specifically for monkeypox. However, there are therapeutic agents authorized to treat monkeypox under the Investigational New Drug protocol, including brincidofovir, cidofovir, and tecovirimat. In contrast to limited options for monkeypox treatment, there are available drugs specifically for HIV or SARS-CoV-2 infection. Interestingly, these HIV and COVID-19 medicines share metabolism pathways with those authorized to treat monkeypox, particularly of hydrolysis, phosphorylation, and active membrane transport. This review discusses how these pathways shared by these medicines should be considered to gain therapeutic synergy and maximize safety for treating monkeypox coinfections.