Exploring cutting-edge strategies for drug repurposing in female cancers - An insight into the tools of the trade.

Female cancers, which include breast and gynaecological cancers, represent a significant global health burden for women. Despite advancements in research pertinent to unearthing crucial pathological characteristics of these cancers, challenges persist in discovering potential therapeutic strategies. This is further exacerbated by economic burdens associated with de novo drug discovery and clinical intricacies such as development of drug resistance and metastasis. Drug repurposing, an innovative approach leveraging existing FDA-approved drugs for new indications, presents a promising avenue to expedite therapeutic development. Computational techniques, including virtual screening and analysis of drug-target-disease relationships, enable the identification of potential candidate drugs. Integration of diverse data types, such as omics and clinical information, enhances the precision and efficacy of drug repurposing strategies. Experimental approaches, including high-throughput screening assays, in vitro, and in vivo models, complement computational methods, facilitating the validation of repurposed drugs. This review highlights various target mining strategies based on analysis of differential gene expression, weighted gene co-expression, protein-protein interaction network, and host-pathogen interaction, among others. To unearth drug candidates, the technicalities of leveraging information from databases such as DrugBank, STITCH, LINCS, and ChEMBL, among others are discussed. Further in silico validation techniques encompassing molecular docking, pharmacophore modelling, molecular dynamic simulations, and ADMET analysis are elaborated. Overall, this review delves into the exploration of individual case studies to offer a wide perspective of the ever-evolving field of drug repurposing, emphasizing the multifaceted approaches and methodologies employed for the same to confront female cancers.

Innovative target mining stratagems to navigate drug repurposing endeavours.

The conventional theory linking a single gene with a particular disease and a specific drug contributes to the dwindling success rates of traditional drug discovery. This requires a substantial shift focussing on contemporary drug design or drug repurposing, which entails linking multiple genes to diverse physiological or pathological pathways and drugs. Lately, drug repurposing, the art of discovering new/unlabelled indications for existing drugs or candidates in clinical trials, is gaining attention owing to its success rates. The rate-limiting phase of this strategy lies in target identification, which is generally driven through disease-centric and/or drug-centric approaches. The disease-centric approach is based on exploration of crucial biomolecules such as genes or proteins underlying pathological cascades of the disease of interest. Investigating these pathological interplays aids in the identification of potential drug targets that can be leveraged for novel therapeutic interventions. The drug-centric approach involves various strategies such as exploring the mechanism of adverse drug reactions that can unearth potential targets, as these untoward reactions might be considered desirable therapeutic actions in other disease conditions. Currently, artificial intelligence is an emerging robust tool that can be used to translate the aforementioned intricate biological networks to render interpretable data for extracting precise molecular targets. Integration of multiple approaches, big data analytics, and clinical corroboration are essential for successful target mining. This chapter highlights the contemporary strategies steering target identification and diverse frameworks for drug repurposing. These strategies are illustrated through case studies curated from recent drug repurposing research inclined towards neurodegenerative diseases, cancer, infections, immunological, and cardiovascular disorders.

Leveraging technology-driven strategies to untangle omics big data: circumventing roadblocks in clinical facets of oral cancer.

Oral cancer is one of the 19most rapidly progressing cancers associated with significant mortality, owing to its extreme degree of invasiveness and aggressive inclination. The early occurrences of this cancer can be clinically deceiving leading to a poor overall survival rate. The primary concerns from a clinical perspective include delayed diagnosis, rapid disease progression, resistance to various chemotherapeutic regimens, and aggressive metastasis, which collectively pose a substantial threat to prognosis. Conventional clinical practices observed since antiquity no longer offer the best possible options to circumvent these roadblocks. The world of current cancer research has been revolutionized with the advent of state-of-the-art technology-driven strategies that offer a ray of hope in confronting said challenges by highlighting the crucial underlying molecular mechanisms and drivers. In recent years, bioinformatics and Machine Learning (ML) techniques have enhanced the possibility of early detection, evaluation of prognosis, and individualization of therapy. This review elaborates on the application of the aforesaid techniques in unraveling potential hints from omics big data to address the complexities existing in various clinical facets of oral cancer. The first section demonstrates the utilization of omics data and ML to disentangle the impediments related to diagnosis. This includes the application of technology-based strategies to optimize early detection, classification, and staging via uncovering biomarkers and molecular signatures. Furthermore, breakthrough concepts such as salivaomics-driven non-invasive biomarker discovery and omics-complemented surgical interventions are articulated in detail. In the following part, the identification of novel disease-specific targets alongside potential therapeutic agents to confront oral cancer via omics-based methodologies is presented. Additionally, a special emphasis is placed on drug resistance, precision medicine, and drug repurposing. In the final section, we discuss the research approaches oriented toward unveiling the prognostic biomarkers and constructing prediction models to capture the metastatic potential of the tumors. Overall, we intend to provide a bird's eye view of the various omics, bioinformatics, and ML approaches currently being used in oral cancer research through relevant case studies.

Panoramic view of key cross-talks underpinning the oral squamous cell carcinoma stemness - unearthing the future opportunities.

The clinical management of oral cancer is often frequented with challenges that arise from relapse, recurrence, invasion and resistance towards the cornerstone chemo and radiation therapies. The recent conceptual advancement in oncology has substantiated the role of cancer stem cells (CSC) as a predominant player of these intricacies. CSC are a sub-group of tumor population with inherent adroitness to self-renew with high plasticity. During tumor evolution, the structural and functional reprogramming persuades the cancer cells to acquire stem-cell like properties, thus presenting them with higher survival abilities and treatment resistance. An appraisal on key features that govern the stemness is of prime importance to confront the current challenges encountered in oral cancer. The nurturing niche of CSC for maintaining its stemness characteristics is thought to be modulated by complex multi-layered components encompassing neoplastic cells, extracellular matrix, acellular components, circulatory vessels, various cascading signaling molecules and stromal cells. This review focuses on recapitulating both intrinsic and extrinsic mechanisms that impart the stemness. There are contemplating evidences that demonstrate the role of transcription factors (TF) in sustaining the neoplastic stem cell's pluripotency and plasticity alongside the miRNA in regulation of crucial genes involved in the transformation of normal oral mucosa to malignancy. This review illustrates the interplay between miRNA and various known TF of oral cancer such as c-Myc, SOX, STAT, NANOG and OCT in orchestrating the stemness and resistance features. Further, the cross-talks involved in tumor micro-environment inclusive of cytokines, macrophages, extra cellular matrix, angiogenesis leading pathways and influential factors of hypoxia on tumorigenesis and CSC survival have been elucidated. Finally, external factorial influence of oral microbiome gained due to the dysbiosis is also emphasized. There are growing confirmations of the possible roles of microbiomes in the progression of oral cancer. Given this, an attempt has been made to explore the potential links including EMT and signaling pathways towards resistance and stemness. This review provides a spectrum of understanding on stemness and progression of oral cancers at various regulatory levels along with their current therapeutic knowledge. These mechanisms could be exploited for future research to expand potential treatment strategies.

Application of comprehensive bioinformatics approaches to reconnoiter crucial genes and pathways underpinning hepatocellular carcinoma: a drug repurposing endeavor.

Hepatocellular carcinoma (HCC) is the fifth most common neoplasm in the world. Chronic inflammation of liver and associated wound healing processes collectively contribute to the development of cirrhosis which further progresses to dysplastic nodule and then to HCC. Etiological mediators and ongoing manipulations at cellular level in HCC are well established; however, key protein interactions and genetic alterations involved in stepwise hepatocarcinogenic pathways are seldom explored. This study aims to unravel novel targets of HCC and repurpose the FDA-approved drugs against the same. Genetic data pertinent to different stages of HCC were retrieved from GSE6764 dataset and analyzed via GEO2R. Subsequently, protein-protein interaction network analysis of differentially expressed genes was performed to identify the hub genes with significant interaction. Hub genes displaying higher interactions were considered as potential HCC targets and were validated thorough UALCAN and GEPIA databases. These targets were screened against FDA-approved drugs through molecular docking and dynamics simulation studies to capture the drugs with potential activity against HCC. Finally, cytotoxicity of the shortlisted drug was confirmed in vitro by MTT assay. CDC20 was identified as potential druggable target. Docking, binding energy calculations, and dynamic studies revealed significant interaction exhibited by Labetalol with CDC20. Further, in MTT assay, Labetalol demonstrated an IC50 of 200.29 µg/ml in inhibiting the cell growth of HepG2 cell line. In conclusion, this study discloses a series of key genetic underpinnings of HCC and recommends the pertinence of labetalol as a potential repurposable drug against HCC.

Early prediction of clinical response in first episode schizophrenia (FES) patients receiving olanzapine.

Background: At present, schizophrenia guidelines recommend waiting for 8 weeks before considering a patient as non-responder. This study aims to detect the optimal early response threshold that best predict the final outcome of olanzapine.Methods: The study was conducted for 8-week, four points follow up (week 2,3,4, and 8) prospective observational study. A reduction of 20, 25, 30% in Positive and Negative Syndrome Scale (PANSS) score from the base line at week 2,3, and 4 respectively were considered as early response. A reduction of 50% at week 8 was considered as responders. Receiver Operating Characteristics (ROC) curves were performed to detect the optimal threshold.Results: Mean total baseline PANSS score was 106.66(95% CI; 100.4, 112.9). Week 2 (AUC = 50.5%, p > 0.964) and week 3 (AUC = 64.9, p > 0.13) responses failed to predict the 8th week response. Week 4 response (AUC = 92%, p < 0.001) can be taken for the prediction of 8th week response (specificity = 72%, sensitivity = 100%, Positive Predictive Value = 61.1%, Negative Predictive Value = 100% and Optimum Early Response (OER) = 29.4%). 25 patients (69%) achieved more than 50% reduction (responders) in PANSS score after 8 weeks of treatment.Conclusions: Our study suggests that patients with early response at week 4 are likely to achieve positive response after 8 weeks. This may help in appropriate clinical decision making for early non-responders.Key PointsThe early response can forecast the outcome at the endpoint for the treatment of FESA reduction of baseline PANSS score by 30% or more after four weeks are likely to have remission after week 8 with olanzapine therapy.

Deprescribing of benzodiazepines and Z-drugs amongst the psychiatric patients of a tertiary care Hospital.

BACKGROUND: In current clinical practice, regardless of the clinical guidelines, BZDs and Z drugs are used beyond the period of indication, resulting in undesirable effects. This study aimed to assess feasibility of deprescribing amongst patients utilizing BZDs and Z drugs inappropriately for longer duration than the prescribed period. The study also analysed the Quality of Sleep (QoS) and Cost Savings incurred amongst deprescribed patients. METHODS: It was a prospective interventional study conducted in IP and OP settings of Psychiatry Department, Bangalore, India. Based on inclusion criteria, 109 patients were recruited for the study for a period of 7 months. Deprescribing was advised to inappropriate BZD and Z-drug users by clinical pharmacist after discussing with the prescribing psychiatrist. The patients were followed-up twice in a month after deprescribing. QoS was assessed by using Pittsburg Sleep Quality Index (PSQI) scale. The total medications cost incurred per patient/month before and after the intervention among both the groups was measured. RESULTS: Post-intervention, 40(30.69%) BZD users were deprescribed i.e, either dose tapered 6(5.5%), completely ceased 27(24.8%) or on si opus sit (SOS) BZDs prescription 7(6.4%). A majority of 44(40.36%) patients continued BZDs according to the algorithm. Clonazepam 35(87.5%) was the most deprescribed BZD. Deprescribing of BZDs showed an association with QoS of patients, p-value (<0.05). A statistically significant cost reduction was observed after deprescribing BZDs, (Z = 5.465, p=<0.001). DISCUSSION: Deprescribing BZDs was associated with decline in its usage; implementing deprescribing practice amongst the inappropriate BZD users is feasible, provides an improved QoS and an economic benefit.

Graph Theoretical Analysis, Insilico Modeling and Formulation of Pyrimidine Nanoparticles as p38α MAP Kinases inhibitors: A Quantitative Proteomics Approach.

In this study, the optimized 4-(4-hydroxybenzyl)-2-amino-6-hydroxypyrimidine-5-carboxamide derivative was formulated as nanoparticles to evaluate for their anticancer activity. The response surface methodology (RSM) was performed with utilization of Box-Behnken statistical design (BBSD) to optimize the experimental conditions for identification of significant synthetic methodology. To explore the stability of the derivative was done by density functional theory (DFT). Graph theoretical analysis was introduced to identify the drug target p38α MAP Kinases and then insilico modeling was performed to provide straightforward information for further structural optimization. The experimental results under optimal experimental conditions obtained 74.55-76% yield of 4-(4-hydroxybenzyl)-2-amino-6-hydroxypyrimidine-5-carboxamide, 127oC melting point and Rf value 0.59 were well matched with the predicted results and this was gaining 95% of confidence level and suitability of RSM. The spectral data were reliable with the assigned structures of synthetic yields. The formulated nanoparticles were exhibited a good anticancer activity against used cancer cell line MCF7. Amusingly the observed docking scores and in-vitro anticancer activity was proving the compound significance and potential as a potent p38α inhibitor. Further, we have elucidated the mechanism of action at its functional level using label-free quantitative proteomics. Interestingly the observed results were indicating that the derived proteomics data involving in the alteration process in cancer-related regulatory pathways.