Quantifying the content of biomedical semantic resources as a core for drug discovery platforms

Role of bioinformatics in different steps of drug discovery and drug designing. Various tools and software used in Computer-aided drug designing.

Medical innovation calls for new models for collaborations that facilitates, government, academia and industry. Barriers to research and ultimate commercialization will be lowered by bringing best practices from industry and academic settings. Hippocrates platform facilitates early drug development extending from basic research to drug invention and commercialization significantly saving time and money. The platform is designed in such way to facilitate collaboration amongst stakeholders as well as taking advantage of the vast resources currently available on the web to generate and aggregate content based on the needs of the research of the end-user.

This study aims to identify drug candidates that can be repurposed to treat three subtypes of leukemia by analyzing drug, protein, and disease interaction networks. The researchers gathered data on FDA-approved drugs and drugs in clinical trials for leukemia and related diseases. They then constructed networks showing interactions between drugs, proteins, and diseases. The top related diseases to leukemia were identified, and their associated drugs were considered candidates for repurposing. The researchers developed a website to import and analyze the collected data to identify the most suitable drug candidates based on the interaction networks.

1) Personalized medicine currently faces challenges in processing large-scale genomic data, interpreting the functional effects of genomic variations, integrating systems-level data, and translating discoveries into medical practice. 2) Bioinformatics can help address these challenges through algorithms for mapping and aligning sequencing data, predicting functional effects, prioritizing genes, integrating multi-omics data into networks, and disseminating discoveries through databases to inform medical practice. 3) Fully realizing personalized medicine will require overcoming limitations of current approaches, validating computational predictions, and updating medical practice and education to routinely incorporate genomic information.

1) Researchers have created a new online resource called the IUPHAR/MMV Guide to Malaria Pharmacology (GtoMPdb) to curate information on antimalarial compounds and their molecular targets in Plasmodium. 2) The database currently contains 25 Plasmodium molecular targets and 57 antimalarial ligands that were manually curated from scientific literature. 3) A new customized online portal provides open access to the antimalarial data and allows browsing by parasite lifecycle stage, target species, and other features to help malaria research.

tranSMART Community Meeting 5-7 Nov 13 - Session 3: tranSMART a Data Warehouse for Translational Medicine at Takeda Pharmaceuticals International Dave Marberg, Takeda We have used the tranSMART platform to construct a warehouse containing data from several Takeda clinical trials, proprietary preclinical drug activity studies, 1600 Gene Expression Omnibus studies, and data from TCGA, CCLE, and other sources. All gene expression data has been globally normalized. We extended the tranSMART platform with a set of R function calls to enable cross-study queries and analysis via the rich toolset available in R. The utility of the data warehouse is exemplified by a study in which we built a predictive model for drug sensitivities. The model was trained on gene expression and IC50 data from cell lines and was found to correctly predict drug activity in oncology indications.

Bioinformatics plays an important role in drug discovery and development by enabling target identification, rational drug design, compound refinement, and other processes. Key applications of bioinformatics include virtual screening of large compound libraries to identify potential drug leads, homology modeling of protein structures to inform drug design, and similarity searches to find analogs of existing drug molecules. The overall drug development process involves studying the disease, identifying drug targets, designing compounds, testing and refining candidates, and conducting clinical trials. Computational techniques expedite many steps but experimental validation is still needed.

Workshop on Clusters, Clouds, and Data for Scientific Computing, September 6, 2018 The need to create to label information and segment regions in individual sensor data sources and to create synthesizes from multiple disparate data sources span many areas of science, biomedicine and technology. The rapid evolution in sensor technologies – from digital microscopes to UAVs drive requirements in this area. I will describe a variety of use cases, describe technical challenges as well as tools, algorithms and techniques developed by our group and collaborators.

This study aimed to identify existing drug therapies that could potentially be repurposed to treat gastric cancer. Differentially expressed genes from microarray analyses of gastric cancer tissue samples were submitted to the Connectivity Map database to identify candidate drugs that could reverse disease signatures. Vorinostat and trichostatin A, both histone deacetylase inhibitors, were predicted as potential therapies based on their expression profiles opposing those of gastric cancer tumors. Future work will integrate biological pathway knowledge to link predicted drugs to relevant pathways.

The present invention provides medicinally active extracts and fractions; and a method for preparing the same by extracting and fractioning constituents from the tissue of plant components of the Anoectochilus family. These active extracts and fractions are useful for preventing or inhibiting tumor groWth.

This document discusses the role of genomics and proteomics in drug discovery and development. It explains that genomics and proteomics technologies can help identify new drug targets by comparing gene and protein expression between healthy and diseased cells. Proteomics in particular analyzes changes in protein levels and can quantify individual proteins using techniques like 2D gel electrophoresis and mass spectrometry. The integration of genomics and proteomics provides a more comprehensive understanding of biological systems and is improving the drug discovery process.

Establishing other new medical usages for already known drugs, including approved drugs. Drug repurposing lies in repurposing an active pharmaceutical ingredient for a new indication that is already on the market. Drug repurposing is a promising approach and mainly applied for the treatment of both common and rare genetic diseases, and it also offers significant benefits to the pharmaceutical industries. "At its simplest, drug repurposing is taking an existing drug and seeing whether it can be used as an effective treatment for another condition.“ “Repurposing generally refers to studying drugs that are already approved to treat one disease or condition to see if they are safe and effective for treating other diseases”.

Drug repurposing involves finding new uses for existing drugs to treat different diseases. It provides a more efficient and lower cost alternative to traditional drug development. Computational approaches like network-based, text mining, and semantic methods are used to discover novel drug-disease relationships for drug repurposing. These include identifying modules in biological networks, propagating information across networks, extracting relationships from literature, and constructing semantic networks to predict new associations. Drug repurposing reduces costs and risks compared to de novo drug development.

INTRODUCTION A PERFECT THERAPEUTIC DRUG DRUG DISCOVERY- HISTORY MODERN DRUG DISCOVERY BIOINFORATICS IN DRUG DISCOVERY DRUG DISCOVERY BASED ON BIOINFORMATIC TOOLS BIOINFORMATICS IN COMPUTER-AIDED DRUG DISCOVERY ECONOMICS OF DRUG DISCOVERY CONCLUSION REFERENCES

Phil Lorenzi discusses pathway analysis approaches and their uses in biomedical research and drug development. He compares strategies for analyzing the autophagy and apoptosis pathways, finding that integrating multiple methods provides the most comprehensive understanding. Lorenzi also provides examples of how pathway analysis could have predicted problems with COX-2 inhibitors and helped explain past failures of AKT inhibitors. He concludes that pathway analysis is consistent with approvals of EGFR, MEK, RANKL and PARP inhibitors and may support development of GLS inhibitors.

Neglected and rare diseases traditionally have not been the focus of large pharmaceutical company research as biotech and academia have primarily been involved in drug discovery efforts for such diseases. This area certainly represents a new opportunity as the pharmaceutical industry investigates new markets. One approach to speed up drug discovery is to examine new uses for existing approved drugs; this is termed drug repositioning or drug repurposing and has become increasingly popular in recent years. Analysis of the literature reveals that using high-throughput screening there have been many examples of FDA approved drugs found to be active against additional targets that can be used to therapeutic advantage for repositioning for other diseases. To date there are far fewer such examples where in silico approaches have allowed for the derivation of new uses. It is suggested that with current technologies and databases of chemical compounds (drugs) and related data, as well as close integration with in vitro screening data, improved opportunities for drug repurposing will emerge. In this publication a review of the literature will highlight several proof of principle examples from areas such as finding new inhibitors for drug transporters with 3D pharmacophores and uncovering molecules active against Mycobacterium tuberculosis (Mtb) using Bayesian models of compound libraries. Research into neglected or rare/orphan diseases can likely benefit from in silico drug repositioning approaches and accelerate drug discovery for these diseases.

The document discusses the process of drug discovery, including target selection, lead discovery, medicinal chemistry, in vitro and in vivo studies, and clinical trials. Target selection involves identifying cellular or genetic targets involved in disease through techniques like genomics, proteomics, and bioinformatics. Lead discovery focuses on identifying small molecule modulators of protein function through methods like synthesis, combinatorial chemistry, assay development, and high-throughput screening. Medicinal chemistry then works to optimize these leads. [/SUMMARY]

Presented at the British Pharmacological Society Focused meeting in April 2015, this poster summarises the current coverage of our curation of enzyme drug targets and supplements our previous poster covering this target class