Advances in Drug Discovery based on Genomics, Proteomics and Bioinformatics in Malaria.

Malaria is one of the neglected infectious diseases, and drugs are the first line of action taken against the onset of malaria as therapeutics. The drugs can be of either natural or artificial origin. Drug development has multiple impediments grouped under three categories, a. drug discovery and screening, b. the drug's action on the host and the pathogen, and c. clinical trials. Drug development takes coon's age from discovery to the market after FDA approval. At the same time, targeted organisms develop drug resistance quicker than drug approval, raising the requirement for advancement in drug development. The approach to explore drug candidates using the classical methods from natural sources, computation-based docking, mathematical and machine learningbased high throughput in silico models or drug repurposing has been investigated and developed. Also, drug development with information about the interaction between Plasmodium species and its host, humans, may facilitate obtaining an efficient drug cohort for further drug discovery or repurposing expedition. However, drugs may have side effects on the host system. Hence, machine learning and systems-based approaches may provide a holistic view of genomic, proteomic, and transcriptomic data and their interaction with the selected drug candidates. This review comprehensively describes the drug discovery workflows using drug and target screening methodologies, followed by possible ways to check the binding affinity of the drug and targets using various docking software.

Comprehending Meningioma Signaling Cascades Using Multipronged Proteomics Approaches & Targeted Validation of Potential Markers.

Meningiomas are one of the most prevalent primary brain tumors. Our study aims to obtain mechanistic insights of meningioma pathobiology using mass spectrometry-based label-free quantitative proteome analysis to identifying druggable targets and perturbed pathways for therapeutic intervention. Label-free based proteomics study was done from peptide samples of 21 patients and 8 non-tumor controls which were followed up with Phosphoproteomics to identify the kinases and phosphorylated components of the perturbed pathways. In silico approaches revealed perturbations in extracellular matrix remodeling and associated cascades. To assess the extent of influence of Integrin and PI3K-Akt pathways, we used an Integrin Linked Kinase inhibitor on patient-derived meningioma cell line and performed a transcriptomic analysis of the components. Furthermore, we designed a Targeted proteomics assay which to the best of our knowledge for very first-time enables identification of peptides from 54 meningioma patients via SRM assay to validate the key proteins emerging from our study. This resulted in the identification of peptides from CLIC1, ES8L2, and AHNK many of which are receptors and kinases and are difficult to be characterized using conventional approaches. Furthermore, we were also able to monitor transitions for proteins like NEK9 and CKAP4 which have been reported to be associated with meningioma pathobiology. We believe, this study can aid in designing peptide-based validation assays for meningioma patients as well as IHC studies for clinical applications.