The rapid pace of scientific advances is enabling a greater understanding of diseases at the molecular level. In turn, the process for researching and developing new medicines is growing in difficulty, costs, and length as a result of the scientific, technical, and regulatory challenges related to the development process.
In light of these challenges, drug repositioning, the utilization of known drugs for a new medical indication, has emerged as an increasingly important strategy for the new drug discovery. Availability of prior knowledge regarding
safety, efficacy and the appropriate administration route significantly reduces the development costs and cuts down the development time resulting in less effort to successfully bring a repositioned drug to market.
In another aspect, a protein’s shape is closely linked with its function; thereby, the ability to predict this structure unlocks a greater understanding of what it does and how it works. Nowadays, more than 10,000 biologically relevant protein structures are yearly released and available to the scientific community. A number suspected to triple over the following years due to the recent breakthroughs in structure prediction techniques.
This work introduces a novel structure-based drug repositioning approach, exploiting the similarities of drugs’ binding mode via identification and virtual screening of interaction patterns. Such patterns are uncovered with the use of PLIP, an automated tool for the in silico detection of non-covalent interactions defining the binding mode between drugs and their protein targets. Besides, the approach has been applied to a series of case studies with tangible results: the uncovering of an antimalarial drug as potential chemoresistance treatment, the explained binding mode of ibrutinib to the target VEGR2 as potential B-cells deactivator in autoimmune diseases, and three over the counter drugs with a proved anti-trypanocidal activity as treatments for Chagas disease.
Overall the structure-based approach with interaction patterns proved to be a suitable framework for identifying novel repositioning candidates. The uncovered candidates were structurally unrelated to the currently available treatments, and experimental assays successfully demonstrated their inhibitory activity on the protein targets of interest. Furthermore, the approach represents a promising option for the 'in high demand' diseases and all rare and neglected diseases for which no reliable treatment has yet been found and for which the pharmaceutical industry makes only a little investment.
| Identifer | oai:union.ndltd.org:DRESDEN/oai:qucosa:de:qucosa:75443 |
| Date | 20 July 2021 |
| Creators | Adasme, Melissa F. |
| Contributors | Schroeder, Michael, Kalinina, Olga, Technische Universität Dresden |
| Source Sets | Hochschulschriftenserver (HSSS) der SLUB Dresden |
| Language | English |
| Detected Language | English |
| Type | info:eu-repo/semantics/publishedVersion, doc-type:doctoralThesis, info:eu-repo/semantics/doctoralThesis, doc-type:Text |
| Rights | info:eu-repo/semantics/openAccess |
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