Background: The global alliance for genomics and healthcare facilities provides innovative solutions to expedite research and clinical practices for complex and incurable health conditions. Precision oncology is an emerging field explicitly tailored to facilitate cancer diagnosis, prevention, and treatment based on patients’ genetic profiles. Advancements in “omics” techniques, next-generation sequencing, artificial intelligence, and clinical trial designs provide a platform for assessing the efficacy and safety of combination therapies and diagnostic procedures. Methods: Data were collected from PubMed and Google Scholar using

Aim/Hypothesis: The complexity and heterogeneity of multiple pathological features make Alzheimer’s disease (AD) a major culprit to global health. Drug repurposing is an inexpensive and reliable approach to redirect the existing drugs for new indications. The current study aims to study the possibility of repurposing approved anticancer drugs for AD treatment. We proposed an in silico pipeline based on “omics” data mining that combines genomics, transcriptomics, and metabolomics studies. We aimed to validate the neuroprotective properties of repurposed drugs and to identify the possible mechanism of action of the proposed drugs in AD. Results: We generated a list of AD-related genes and then searched DrugBank database and Therapeutic Target Database to find anticancer drugs related to potential AD targets. Specifically, we researched the available approved anticancer drugs and excluded the information of investigational and experimental drugs. We developed a computational pipeline to prioritize the anticancer drugs having a close association with AD targets. From data mining, we generated a list of 2914 AD-related genes and obtained 49 potential druggable targets by functional enrichment analysis. The protein–protein interaction (PPI) studies for these genes revealed 641 interactions. We found that 15 AD risk/direct PPI genes were associated with 30 approved oncology drugs. The computational validation of candidate drug–target interactions, structural and functional analysis, investigation of related molecular mechanisms, and literature-based analysis resulted in four repurposing candidates, of which three drugs were epidermal growth factor receptor (EGFR) inhibitors. Conclusion: Our computational drug repurposing approach proposed EGFR inhibitors as potential repurposing drugs for AD. Consequently, our proposed framework could be used for drug repurposing for different indications in an economical and efficient way.

The regenerative capacity of mesenchymal stem cells (MSCs) is now attributed to their ability to release paracrine factors into the extracellular matrix that boost tissue regeneration, reduce inflammation and encourage healing. Understanding the MSC secretome is crucial for shifting the prototypic conventional cell-based therapies to cell-free regenerative treatments. This systematic review aimed to analyse the functional annotations of the secretome of human adult adipose tissue and bone marrow MSCs and unveil the gene clusters responsible for bone formation. Bioinformatics tools were used to identify the biological processes, molecular functions, hallmarks and KEGG pathways of adipose and bone marrow MSC secretome proteins. We found a substantial overlap in the functional annotations and protein compositions of both adipose and bone marrow MSC secretome indicating that MSC source may be noninfluencial with regards to tissue regeneration. Additionally, a novel network pharmacology-based analysis of the secreted proteins revealed that the commonly secreted proteins within a single source interact with multiple drugable targets of bone diseases and regulate various KEGG pathway. This study unravels the secretome profile of human adult adipose and bone marrow MSCs based on the current literature and provides valuable insights into the therapeutic use of the MSC secretome for cell-free therapies.