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.

Exploration of interaction interface of TRKß/BDNF through fingerprint analysis to disinter potential agonists.

Tyrosine Kinase beta (TRKß), is a type I membrane receptor which plays a major role in various signalling pathways. TRKß was found to be upregulated in various cancers and contrastingly downregulated in various neurodegenerative disorders. Hitherto, contemporary drug research is oriented towards discovery of TRKß inhibitors, thus neglecting the development of TRKß agonists. This research is aimed at identifying FDA approved drugs exhibiting repurposable potential as TRKß agonists by mapping them with fingerprints of the BDNF/TRKß interaction interface. Initially, crucial interacting residues were retrieved and a receptor grid was generated around it. TRKß agonists were retrieved from literature search and a drug library was created for each agonist based on its structural and side effect similarities. Subsequently, molecular docking and dynamics were performed for each library to identify the drugs possessing affinity towards the binding pocket of TRKß. The study revealed molecular interactions of Perospirone, Droperidol, Urapidil, and Clobenzorex with the crucial amino acids lining the active binding pocket of TRKß. Subsequent network pharmacological analysis of the above drugs revealed their interactions with key proteins involved in neurotransmitter signalling pathways. Clobenzorex displayed high stability in dynamics simulation and therefore this drug is recommended for further experimental evaluations to attain better mechanistic insights and predict its implications in correcting neuropathological aberrations. This study's focus on the interaction interface between TRKß and BDNF, combined with the utilization of fingerprint analysis for drug repurposing, contributes to our understanding of neurotrophic signalling and holds potential for identifying new therapeutic options for neurological 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.

Untangling huge literature to disinter genetic underpinnings of Alzheimer's Disease: A systematic review and meta-analysis.

Drug discovery for Alzheimer's Disease (AD) is channeled towards unravelling key disease specific drug targets/genes to predict promising therapeutic candidates. Though enormous literature on AD genetics is available, there exists dearth in data pertinent to drug targets and crucial pathological pathways intertwined in disease progression. Further, the research findings revealing genetic associations failed to demonstrate consistency across different studies. This scenario prompted us to initiate a systematic review and meta-analysis with an aim of unearthing significant genetic hallmarks of AD. Initially, a Boolean search strategy was developed to retrieve case-control studies from PubMed, Cochrane, ProQuest, Europe PMC, grey literature and HuGE navigator. Subsequently, certain inclusion and exclusion criteria were framed to shortlist the relevant studies. These studies were later critically appraised using New Castle Ottawa Scale and Q-Genie followed by data extraction. Later, meta-analysis was performed only for those Single Nucleotide Polymorphisms (SNPs) which were evaluated in at least two different ethnicities from two different reports. Among, 204,351 studies retrieved, 820 met our eligibility criteria and 117 were processed for systematic review after critical appraisal. Ultimately, meta-analysis was performed for 23 SNPs associated with 15 genes which revealed significant associations of rs3865444 (CD33), rs7561528 (BIN1) and rs1801133 (MTHFR) with AD risk.