Systemic Sclerosis (SSc) is an autoimmune disease associated with changes in the skin's structure in which the immune system attacks the body. A recent meta-analysis has reported a high incidence of cancer prognosis including lung cancer (LC), leukemia (LK), and lymphoma (LP) in patients with SSc as comorbidity but its underlying mechanistic details are yet to be revealed. To address this research gap, bioinformatics methodologies were developed to explore the comorbidity interactions between a pair of diseases. Firstly, appropriate gene expression datasets from different repositories on SSc and its comorbidities were collected. Then the interconnection between SSc and its cancer comorbidities was identified by applying the developed pipelines. The pipeline was designed as a generic workflow to demonstrate a premise comorbid condition that integrate regarding gene expression data, tissue/organ meta-data, Gene Ontology (GO), Molecular pathways, and other online resources, and analyze them with Gene Set Enrichment Analysis (GSEA), Pathway enrichment and Semantic Similarity (SS). The pipeline was implemented in R and can be accessed through our Github repository: https://github.com/hiddenntreasure/comorbidity. Our result suggests that SSc and its cancer comorbidities share differentially expressed genes, functional terms (gene ontology), and pathways. The findings have led to a better understanding of disease pathways and our developed methodologies may be applied to any set of diseases for finding any association between them. This research may be used by physicians, researchers, biologists, and others.

Plasmodium falciparum , the prime causative agent of malaria, is responsible for 4, 05,000 deaths per year and fatality rates are higher among the children aged below 5 years. The emerging distribution of the multi-drug resistant P. falciparum becomes a worldwide concern, so the identification of unique targets and novel inhibitors is a prime need now. In the present study, we have employed pharmacoinformatics approaches to analyze 265 lead-like compounds from PubChem databases for virtual screening. Thereafter, 15 lead-like compounds were docked within the active side pocket of importin alpha. Comparative ligand properties and absorption, distribution, metabolism, excretion, and toxicity (ADMET) profile were also assessed. Finally, a novel inhibitor was designed and assessed computationally for its efficacy. From the comparative analysis we have found that our screened compounds possess better results than the existing lead ivermectin; having the highest binding energy of −15.6 kcal/mol, whereas ivermectin has −12.4kcal/mol. The novel lead compound possessed more fascinating output without deviating any of the rules of Lipinski. It also possessed higher bioavailability and the drug-likeness score of 0.55 and 0.71, respectively compared to ivermectin. Furthermore, the binding study was confirmed by molecular dynamics simulation over 25 ns by evaluating the stability of the complex. Finally, all the screened compounds and the novel compound showed promising ADMET properties likewise. To end, we hope that our proposed screened compounds, as well as the novel compound, might give some advances to treat malaria efficiently in vitro and in vivo.