Emergence of Meropenem Resistance Among Cefotaxime Non-susceptible Streptococcus pneumoniae: Evidence and Challenges

Respiratory tract infections: an update on the complexity of bacterial diversity, therapeutic interventions and breakthroughs

Respiratory tract infections (RTIs) have a significant impact on global health, especially among children and the elderly. The key bacterial pathogens Streptococcus pneumoniae, Haemophilus influenzae, Klebsiella pneumoniae, Staphylococcus aureus and non-fermenting Gram Negative bacteria such as Acinetobacter baumannii and Pseudomonas aeruginosa are most commonly associated with RTIs. These bacterial pathogens have evolved a diverse array of resistance mechanisms through horizontal gene transfer, often mediated by mobile genetic elements and environmental acquisition. Treatment failures are primarily due to antimicrobial resistance and inadequate bacterial engagement, which necessitates the development of alternative treatment strategies. To overcome this, our review mainly focuses on different virulence mechanisms and their resulting pathogenicity, highlighting different therapeutic interventions to combat resistance. To prevent the antimicrobial resistance crisis, we also focused on leveraging the application of artificial intelligence and machine learning to manage RTIs. Integrative approaches combining mechanistic insights are crucial for addressing the global challenge of antimicrobial resistance in respiratory infections.

Bacterial pathogens and antimicrobial susceptibility in ocular infections: A study at Boru-Meda General Hospital, Dessie, Ethiopia

INTRODUCTION

The eye consists of both internal and external compartments. Several variables, including microbes, dust, and high temperatures can cause eye illnesses that can result in blindness. Bacterial eye infections continue to be a major cause of ocular morbidity and blindness, and their prevalence is periodically rising. The objective of the study was to detect bacterial pathogens and assess their susceptibility profiles to antibiotics in the ophthalmology unit of Boru-meda Hospital in Dessie, Ethiopia.

METHODS

A hospital-based cross-sectional study was conducted from February 1 to April 30, 2021, among 319 study participants with symptomatic ocular or peri-ocular infections who were enrolled using a consecutive sampling technique. After proper specimen collection, the specimen was immediately inoculated with chocolate, blood, and MacConkey agar. After pure colonies were obtained, they were identified using standard microbiological methods. The Kirby Bauer disk diffusion method was used to test antimicrobial susceptibility patterns, based on the guidelines of the Clinical and Laboratory Standards Institute.

RESULTS

The majority of participants developed conjunctivitis 126 (39.5%), followed by blepharitis 47 (14.73%), and dacryocystitis 45 (14.1%). Overall, 164 (51.4%) participants were culture positive, six (1.9%) participants had mixed bacterial isolates, giving a total of 170 bacterial isolates with an isolation rate of 53.3%. The predominant species was CoNS 47 (27.6%), followed by S. aureus 38 (22.4%) and Moraxella species 32 (18.8%). The overall Multi-Drug Resistance (MDR) rate was 62.9%, with 33 (44.6%) being gram-negative and 74 (77.1%) being gram-positive isolates.

CONCLUSION

Conjunctivitis was the dominant clinical case and CoNS, was the predominant isolate. A higher rate of MDR isolates, particularly gram-positive ones, was observed. Efficient peri-ocular or ocular bacterial infection surveillance, including microbiological laboratory data, is necessary for monitoring disease trends.

Cefiderocol susceptibility endows hope in treating carbapenem-resistant Pseudomonas aeruginosa: insights from in vitro and in silico evidence

'High-risk' hypermutable clones of Pseudomonas aeruginosa disseminating extensive drug-resistance (XDR) have raised global health concerns with escalating mortality rates in immunocompromised patients. Mutations in conventional drug-targets under antibiotic stress necessitate structural understanding to formulate sustainable therapeutics. In the present study, the major β-lactam antibiotic target, penicillin-binding protein-3 (PBP3) with mutations F533L and T91A, were identified in carbapenemase-positive P. aeruginosa isolates (n = 6) using whole genome sequencing. Antibiotic susceptibility tests showed susceptibility to cefiderocol (MIC ≤ 4 μg ml-1) despite pan-β-lactam resistance in the isolates. Both the mutations reduced local intra-chain interactions in PBP3 that marginally increased the local flexibility (∼1%) in the structures to affect antibiotic-interactions. Molecular dynamics simulations confirmed the overall stability of the PBP3 mutants through root-mean square deviations, radius of gyration, solvent-accessibility and density curves, which favored their selection. Docking studies unveiled that the mutations in PBP3 elicited unfavorable stereochemical clashes with the conventional antibiotics thereby increasing their inhibition constants (IC) up to ∼50 fold. It was deciphered that cefiderocol retained its susceptibility despite mutations in PBP3, due to its higher average binding affinity (ΔG: -8.2 ± 0.4 kcal mol-1) towards multiple PBP-targets and lower average binding affinity (ΔG: -6.7 ± 0.7 kcal mol-1) to β-lactamases than the other β-lactam antibiotics. The molecular dynamics simulations and molecular mechanics Poisson Boltzmann surface area calculations further indicated energetically favorable binding for cefiderocol with PBP3 proteins. The study gave structural insight into emerging non-polar amino acid substitutions in PBP3 causing XDR and recommends prioritizing available antibiotics based on multi-target affinities to overcome challenges imposed by target-protein mutations.

Integrating pan-genome and reverse vaccinology to design multi-epitope vaccine against Herpes simplex virus type-1

Herpes simplex virus type-1 (HSV-1), the etiological agent of sporadic encephalitis and recurring oral (sometimes genital) infections in humans, affects millions each year. The evolving viral genome reduces susceptibility to existing antivirals and, thus, necessitates new therapeutic strategies. Immunoinformatics strategies have shown promise in designing novel vaccine candidates in the absence of a clinically licensed vaccine to prevent HSV-1. However, to encourage clinical translation, the HSV-1 pan-genome was integrated with the reverse-vaccinology pipeline for rigorous screening of universal vaccine candidates. Viral targets were screened from 104 available complete genomes. Among 364 proteins, envelope glycoprotein D being an outer membrane protein with a high antigenicity score (> 0.4) and solubility (> 0.6) was selected for epitope screening. A total of 17 T-cell and 4 B-cell epitopes with highly antigenic, immunogenic, non-toxic properties and high global population coverage were identified. Furthermore, 8 vaccine constructs were designed using different combinations of epitopes and suitable linkers. VC-8 was identified as the most potential vaccine candidate regarding chemical and structural stability. Molecular docking revealed high interactive affinity (low binding energy: - 56.25 kcal/mol) of VC-8 with the target elicited by firm intermolecular H-bonds, salt-bridges, and hydrophobic interactions, which was validated with simulations. Compatibility of the vaccine candidate to be expressed in pET-29(a) + plasmid was established by in silico cloning studies. Immune simulations confirmed the potential of VC-8 to trigger robust B-cell, T-cell, cytokine, and antibody-mediated responses, thereby suggesting a promising candidate for the future of HSV-1 prevention.

Supplementary Information

The online version contains supplementary material available at 10.1007/s13205-024-04022-6.

Unravelling the molecular mechanistic pathway underlying the anticancer effects of kaempferol in colorectal cancer: a reverse pharmacology network approach

Colorectal cancer (CRC) is the third most diagnosed and highly fatal malignancy, presenting serious health concerns worldwide. The search for an effective cure for CRC is challenging and poses a serious concern. Kaempferol is a potent anti-cancerous bioactive compound often suggested for treating various cancers, including CRC. However, its underlying molecular mechanism against CRC remains unclear. The present study delves into kaempferol's molecular pathways and underlying molecular mechanisms against CRC targets. The target protein-coding genes for kaempferol were retrieved, and the CRC-associated genes were curated. Twelve common targets with a disease specificity index of > 0.6 were validated for their protein expression at different stages of CRC. Over-expressed USP1, SETD7, POLH, TDP1 and RACGAP1 were selected for further studies. The binding affinities of kaempferol to the corresponding proteins were evaluated using molecular docking and Molecular Dynamics (MD) simulations. SETD7 exhibited the highest binding affinity with the lowest binding energy (- 8.06 kcal/mol). Additionally, the MD simulation, and MM-PBSA conferred SETD7-kaempferol complex had the least root-mean-square deviation with lower interaction energy and higher conformational stability. The protein-protein interaction of SETD7 constructed revealed direct interactors, namely, DNMT1, FOXO1, FOXO3, FOXO4, H3-3B, H3-4, H3C12, H3C13, SETD7, SIRT1 and TP53, have a potential role in cancer progression through FOXO signalling. In summary, our study revealed kaempferol's multi-target and synergistic effect on multiple CRC targets and its underlying mechanisms. Finally, the study recommends in-vitro and in-vivo trials for validation of anti-cancerous drugs for CRC.

Impact of PmrB mutations on clinical Klebsiella pneumoniae with variable colistin-susceptibilities: Structural insights and potent therapeutic solutions

Carbapenem-resistant Klebsiella pneumoniae (CRKP) infections continue to impose high morbidity threats to hospitalized patients worldwide, limiting therapeutic options to last-resort antibiotics like colistin. However, the dynamic genomic landscape of colistin-resistant K. pneumoniae (COLR-Kp) invoked ardent exploration of underlying molecular signatures for therapeutic propositions/designs. We unveiled the structural impact of the widespread and emerging PmrB mutations involved in colistin resistance (COLR) in K. pneumoniae. In the present study, clinical isolates of K. pneumoniae expressed variable susceptibilities to colistin (>0.5 μg/mL for resistant and ≤0.25 μg/mL for susceptible) despite mutations such as T157P, G207D and T246A. The protein sequences extracted from in-house sequenced genomes were used to model mutant PmrB proteins and analyze the underlying structural alterations. The mutations were contrasted based on molecular dynamics simulation trajectories, free-energy landscapes and structural flexibility profiles. The altered backbone flexibilities can be an essential factor for mutant selection by COLR K. pneumoniae and can provide clues to deal with emerging mutants. Furthermore, PmrB having high druggability confidence (>0.99), was explored as a potential target for 1396 virtually screened FDA-approved drug candidates. Among the top-10 compounds (scores >70), amphotericin B was found to be potential candidate with high affinity (Binding energy <-8 kcal/mol) and stable interactions (RMSF <0.7 Å) against PmrB druggable pockets, despite the mutations, which encourages future adjunct therapeutic research against COLR-Kp.

FN1 and cancer-associated fibroblasts markers influence immune microenvironment in clear cell renal cell carcinoma

BACKGROUND

Altered tumor microenvironment (TME) is characterized in clear cell renal cell carcinoma (ccRCC) as a result of the heterogeneity observed in the TME. Modulations in TME have shown tumor metastasis promotion; hence, identifying TME-based biomarkers can be critical for theranostics application.

METHODS

Here, we performed an integrated systems biology approach utilizing differential gene expression, network metrics and clinical samples cohorts to prioritize the major deregulated genes and their associated pathways specific for metastasis.

RESULTS

The gene expression profiling of 140 ccRCC samples resulted in 3657 differentially expressed genes, from which a network of 1867 up-regulated genes were further computed using network metrics for screening hub-genes. The specific pathways of ccRCC entailed through functional enrichment analysis of the hub-gene clusters indicated the role of the identified hub-genes in the enriched pathways, further validating the functional significance of the hub-genes. The positive correlation of TME cells, namely cancer-associated fibroblasts (CAFs) and its biomarkers (FAP and S100A4) with FN1, signified the role of hub-gene signaling for promoting metastasis in ccRCC. Thereafter, comparative expression, differential methylation, genetic alteration and overall survival analysis were analyzed to validate the screened hub-genes.

CONCLUSIONS

The hub-genes were validated and prioritized by correlating with expression-based parameters, including histological grades, tumor, metastatic and pathological stages (based on median transcript per million; analysis of variance [ANOVA], P ≤ 0.05) from a clinically curated ccRCC dataset to further substantiate the translational benefits of the screened hub-genes as potential diagnostic biomarkers for ccRCC.

Repurposing FDA approved drug molecules against A B C classes of β-lactamases: a computational biology and molecular dynamics simulations study

β-lactamase are the main resistance factor for β-lactam antibiotics in Gram-negative bacteria. Since β-lactam antibiotics are being utilised as an antimicrobial agents extensively for the past 70 years, a large number of β-lactam-inactivating β-lactamases have been produced by bacteria. Here, we employed a structure-based drug discovery approach to identify and assess the efficacy of a potential medication that might block the β-lactamases which hydrolyse antibiotics. The FDA-approved medications were subjected to virtual screening, molecular docking, molecular dynamics simulations, density functional theory, and covalent docking against the β-lactamases. We identified diosmin, hidrosmin, monoxuritin and solasulfone as β-lactamase inhibitors which are authorised for therapeutic use in humans. These medications interact in a remarkable variety of non-covalent ways with the conserved residues in the substrate-binding pocket of the β-lactamases. Diosmin has been identified as an inhibitor that binds covalently to the NDM-1 a class B metallo-betalactamase. After experimental validation and clinical demonstration, this study offers adequate evidence for the therapeutic use of these drugs for controlling multidrug resistance.Communicated by Ramaswamy H. Sarma.

Structure Elucidation and Interaction Dynamics of MefA-MsrD Efflux Proteins in Streptococcus pneumoniae: Impact on Macrolide Susceptibility

Macrolides are empirically used to treat bacterial community-acquired pneumonia (CAP). Streptococcus pneumoniae, being the major pathogen responsible for bacterial CAP with high mortality rates, express MefA-MsrD efflux pumps to hinder macrolide susceptibility. Despite its importance, the structural features of the efflux-protein complex and its impact on macrolide susceptibility have not yet been elucidated explicitly. Therefore, in the present study, combining homology, threading, and dynamics approaches, MefA and MsrD proteins in pathogenic S. pneumoniae were modeled. Both membrane (lipid-bilayer) and cytoplasmic (aqueous) environments were considered to simulate the MefA and MsrD proteins in their ideal cellular conditions followed by dynamics analyses. The simulated MefA structure represented a typical major facilitator superfamily protein structure with 13 transmembrane helices. MefA-MsrD interaction via clustering-based docking revealed low-energy conformers with stable intermolecular interactions. The higher clinical MIC value of azithromycin over erythromycin was reflected upon erythromycin eliciting stronger interactions (dissociation constant or ki = ∼52 μM) with the cytoplasmic ATP-binding MsrD than azithromycin (ki = ∼112 μM). The strong (binding energy = -132.1 ± 9.5 kcal/mol) and highly stable (root-mean-square fluctuation < 1.0 Å) physical association between MefA with MsrD was validated and was found to be unaffected by the antibiotic binding. Higher propensity of the macrolides to interact with MsrD than MefA established the importance of the former in macrolide susceptibility. Ours is probably the first report on the structural arrangements in the MefA-MsrD efflux complex and the macrolide susceptibility in S. pneumoniae. This study provides a novel lead for experimental explorations and efflux-pump inhibitor designs.

Consolidated knowledge-guided computational pipeline for therapeutic intervention against bacterial biofilms - a review

Biofilm-associated bacterial infections attributed to multifactorial antimicrobial resistance have caused worldwide challenges in formulating successful treatment strategies. In search of accelerated yet cost-effective therapeutics, several researchers have opted for bioinformatics-based protocols to systemize targeted therapies against biofilm-producing strains. The present review investigated the up-to-date computational databases and servers dedicated to anti-biofilm research to design/screen novel biofilm inhibitors (antimicrobial peptides/phytocompounds/synthetic compounds) and predict their biofilm-inhibition efficacy. Scrutinizing the contemporary in silico methods, a consolidated approach has been highlighted, referred to as a knowledge-guided computational pipeline for biofilm-targeted therapy. The proposed pipeline has amalgamated prominently employed methodologies in genomics, transcriptomics, interactomics and proteomics to identify potential target proteins and their complementary anti-biofilm compounds for effective functional inhibition of biofilm-linked pathways. This review can pave the way for new portals to formulate successful therapeutic interventions against biofilm-producing pathogens.

Discovery of Small Molecule PARKIN Activator from Antipsychotic/Anti-neuropsychiatric Drugs as Therapeutics for PD: an In Silico Repurposing Approach

Although there is presently no cure for Parkinson's disease (PD), the available therapies are only able to lessen symptoms and preserve the quality of life. Around 10 million people globally had PD as of 2020. The widely used standard drug has recently been revealed to have several negative effects. Additionally, there is a dearth of innovative compounds entering the market as a result of subpar ADMET characteristics. Drug repurposing provides a chance to reenergize the sluggish drug discovery process by identifying new applications for already-approved medications. As this strategy offers a practical way to speed up the process of developing alternative medications for PD. This study used a computer-aided technique to select therapeutic agent(s) from FDA-approved neuropsychiatric/psychotic drugs that can be adopted in the treatment of Parkinson's disease. In the current work, a computational approach via molecular docking, density functional theory (DFT), and pharmacokinetics were used to identify possible (anti)neuropsychiatric/psychotic medications for the treatment of PD. By using molecular docking, about eight (anti)neuropsychiatric/psychotic medications were tested against PARKIN, a key protein in PD. Based on the docking score, the best ligand in the trial was determined. The top hits were compared to the reference ligand levodopa (L-DOPA). A large proportion of the drugs displayed binding affinity that was relatively higher than L-DOPA. Also, DFT analysis confirms the ligand-receptor interactions and the molecular charge transfer. All the compounds were found to obey Lipinski's rule with acceptable pharmacokinetic properties. The current study has revealed the effectiveness of antineuropsychiatric/antipsychotic drugs against PARKIN in the treatment of PD and lumateperone was revealed to be the most promising candidate interacting with PARKIN.

Predicting the molecular mechanism-driven progression of breast cancer through comprehensive network pharmacology and molecular docking approach

Identification of key regulators is a critical step toward discovering biomarker that participate in BC. A gene expression dataset of breast cancer patients was used to construct a network identifying key regulators in breast cancer. Overexpressed genes were identified with BioXpress, and then curated genes were used to construct the BC interactome network. As a result of selecting the genes with the highest degree from the BC network and tracing them, three of them were identified as novel key regulators, since they were involved at all network levels, thus serving as the backbone. There is some evidence in the literature that these genes are associated with BC. In order to treat BC, drugs that can simultaneously interact with multiple targets are promising. When compared with single-target drugs, multi-target drugs have higher efficacy, improved safety profile, and are easier to administer. The haplotype and LD studies of the FN1 gene revealed that the identified variations rs6707530 and rs1250248 may both cause TB, and endometriosis respectively. Interethnic differences in SNP and haplotype frequencies might explain the unpredictability in association studies and may contribute to predicting the pharmacokinetics and pharmacodynamics of drugs using FN1.

Translational protein RpsE as an alternative target for novel nucleoside analogues to treat MDR Enterobacter cloacae ATCC 13047: network analysis and molecular dynamics study

The pathogenic Enterobacter cloacae subsp. cloacae str. ATCC 13047 has contemporarily emerged as a multi-drug resistant strain. To formulate an effective treatment option, alternative therapeutic methods need to be explored. The present study focused on Gene Interaction Network study of 46 antimicrobial resistance genes to reveal the densely interconnecting and functional hub genes in E. cloacae ATCC 13047. The AMR genes were subjected to clustering, topological and functional enrichment analysis, revealing rpsE (RpsE), acrA (AcrA) and arnT (ArnT) as novel therapeutic drug targets for hindering drug resistance in the pathogenic strain. Network topology further indicated translational protein RpsE to be exploited as a promising drug-target candidate for which the structure was predicted, optimized and validated through molecular dynamics simulations (MDS). Absorption, distribution, metabolism and excretion screening recognized ZINC5441082 (N-Isopentyladenosine) (Lead_1) and ZINC1319816 (cyclopentyl-aminopurinyl-hydroxymethyl-oxolanediol) (Lead_2) as orally bioavailable compounds against RpsE. Molecular docking and MDS confirmed the binding efficacy and protein-ligand complex stability. Furthermore, binding free energy (G_bind) calculations, principal component and free energy landscape analyses affirmed the predicted nucleoside analogues against RpsE protein to be comprehensively examined as a potential treatment strategy against E. cloacae ATCC 13047.

Mechanistic insights into the role of vitamin D and computational identification of potential lead compounds for Parkinson's disease

Parkinson's disease (PD) is the second most common neurodegenerative disorder that affects dopaminergic neurons in the midbrain. A recent study suggests that Orphan Nuclear Receptor 1 (NURR1) impairment may contribute to PD pathogenesis. Our study found three potent agonists for NURR1 protein based on structural and ligand-based screening methods. The pharmacophore is comprised of a hydrogen bond donor, a hydrophobic group, and two aromatic rings (DHRR). The Pharmacophore screening method screened 3142 compounds, of which 3 were screened using structure-based screening. An analysis of the molecules using Molecular Mechanics-Generalized Born Surface Area (binding free energy) revealed a range of -46.77 to -59.06 Kcal/mol. After that, chemical reactivity was investigated by density functional theory, and molecular dynamics simulation was performed (protein-ligand stability). Based on the computational studies, Lifechemical_16901310, Maybridge_2815310, and NPACT_392450 are promising agonists with respect to NURR1. To confirm the potency of the identified compounds, further validation and experiments must be conducted.

Multi-Epitope Vaccine for Monkeypox Using Pan-Genome and Reverse Vaccinology Approaches

Outbreaks of monkeypox virus infections have imposed major health concerns worldwide, with high morbidity threats to children and immunocompromised adults. Although repurposed drugs and vaccines are being used to curb the disease, the evolving traits of the virus, exhibiting considerable genetic dynamicity, challenge the limits of a targeted treatment. A pan-genome-based reverse vaccinology approach can provide fast and efficient solutions to resolve persistent inconveniences in experimental vaccine design during an outbreak-exigency. The approach encompassed screening of available monkeypox whole genomes (n = 910) to identify viral targets. From 102 screened viral targets, viral proteins L5L, A28, and L5 were finalized based on their location, solubility, and antigenicity. The potential T-cell and B-cell epitopes were extracted from the proteins using immunoinformatics tools and algorithms. Multiple vaccine constructs were designed by combining the epitopes. Based on immunological properties, chemical stability, and structural quality, a novel multi-epitopic vaccine construct, V4, was finalized. Flexible-docking and coarse-dynamics simulation portrayed that the V4 had high binding affinity towards human HLA-proteins (binding energy < -15.0 kcal/mol) with low conformational fluctuations (<1 Å). Thus, the vaccine construct (V4) may act as an efficient vaccine to induce immunity against monkeypox, which encourages experimental validation and similar approaches against emerging viral infections.

A comprehensive review on genomics, systems biology and structural biology approaches for combating antimicrobial resistance in ESKAPE pathogens: computational tools and recent advancements

In recent decades, antimicrobial resistance has been augmented as a global concern to public health owing to the global spread of multidrug-resistant strains from different ESKAPE pathogens. This alarming trend and the lack of new antibiotics with novel modes of action in the pipeline necessitate the development of non-antibiotic ways to treat illnesses caused by these isolates. In molecular biology, computational approaches have become crucial tools, particularly in one of the most challenging areas of multidrug resistance. The rapid advancements in bioinformatics have led to a plethora of computational approaches involving genomics, systems biology, and structural biology currently gaining momentum among molecular biologists since they can be useful and provide valuable information on the complex mechanisms of AMR research in ESKAPE pathogens. These computational approaches would be helpful in elucidating the AMR mechanisms, identifying important hub genes/proteins, and their promising targets together with their interactions with important drug targets, which is a crucial step in drug discovery. Therefore, the present review aims to provide holistic information on currently employed bioinformatic tools and their application in the discovery of multifunctional novel therapeutic drugs to combat the current problem of AMR in ESKAPE pathogens. The review also summarizes the recent advancement in the AMR research in ESKAPE pathogens utilizing the in silico approaches.

Statistics and network-based approaches to identify molecular mechanisms that drive the progression of breast cancer

Breast cancer (BC) is one of the most malignant tumors and the leading cause of cancer-related death in women worldwide. So, an in-depth investigation on the molecular mechanisms of BC progression is required for diagnosis, prognosis and therapies. In this study, we identified 127 common differentially expressed genes (cDEGs) between BC and control samples by analyzing five gene expression profiles with NCBI accession numbers GSE139038, GSE62931, GSE45827, GSE42568 and GSE54002, based-on two statistical methods LIMMA and SAM. Then we constructed protein-protein interaction (PPI) network of cDEGs through the STRING database and selected top-ranked 7 cDEGs (BUB1, ASPM, TTK, CCNA2, CENPF, RFC4, and CCNB1) as a set of key genes (KGs) by cytoHubba plugin in Cytoscape. Several BC-causing crucial biological processes, molecular functions, cellular components, and pathways were significantly enriched by the estimated cDEGs including at-least one KGs. The multivariate survival analysis showed that the proposed KGs have a strong prognosis power of BC. Moreover, we detected some transcriptional and post-transcriptional regulators of KGs by their regulatory network analysis. Finally, we suggested KGs-guided three repurposable candidate-drugs (Trametinib, selumetinib, and RDEA119) for BC treatment by using the GSCALite online web tool and validated them through molecular docking analysis, and found their strong binding affinities. Therefore, the findings of this study might be useful resources for BC diagnosis, prognosis and therapies.