1
|
Stefan SM, Rafehi M. Medicinal polypharmacology: Exploration and exploitation of the polypharmacolome in modern drug development. Drug Dev Res 2024; 85:e22125. [PMID: 37920929 DOI: 10.1002/ddr.22125] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2023] [Revised: 09/23/2023] [Accepted: 10/12/2023] [Indexed: 11/04/2023]
Abstract
At the core of complex and multifactorial human diseases, such as cancer, metabolic syndrome, or neurodegeneration, are multiple players that cross-talk in robust biological networks which are intrinsically resilient to alterations. These multifactorial diseases are characterized by sophisticated feedback mechanisms which manifest cellular imbalance and resistance to drug therapy. By adhering to the specificity paradigm ("one target-one drug concept"), research focused for many years on drugs with very narrow mechanisms of action. This narrow focus promoted therapy ineffectiveness and resistance. However, modern drug discovery has evolved over the last years, increasingly emphasizing integral strategies for the development of clinically effective drugs. These integral strategies include the controlled engagement of multiple targets to overcome therapy resistance. Apart from the additive or even synergistic effects in therapy, multitarget drugs harbor molecular-structural attributes to explore orphan targets of which intrinsic substrates/physiological role(s) and/or modulators are unknown for future therapy purposes. We designated this multidisciplinary and translational research field between medicinal chemistry, chemical biology, and molecular pharmacology as 'medicinal polypharmacology'. Medicinal polypharmacology emerged as alternative approach to common single-targeted pharmacology stretching from basic drug and target identification processes to clinical evaluation of multitarget drugs, and the exploration and exploitation of the 'polypharmacolome' is at the forefront of modern drug development research.
Collapse
Affiliation(s)
- Sven Marcel Stefan
- Drug Development and Chemical Biology, Lübeck Institute of Experimental Dermatology (LIED), University of Lübeck and University Medical Center Schleswig-Holstein, Lübeck, Germany
- Translational Neurodegeneration Research and Neuropathology Lab, Department of Pathology, Section of Neuropathology and Oslo University Hospital, University of Oslo, Oslo, Norway
- School of Medical Sciences, Faculty of Medicine and Health, The University of Sydney, Camperdown, New South Wales, Australia
| | - Muhammad Rafehi
- Department of Medical Education, Augsburg University Medicine, Augsburg, Germany
- Institute of Clinical Pharmacology, University Medical Center Göttingen, Göttingen, Germany
| |
Collapse
|
2
|
Mathai N, Stork C, Kirchmair J. BonMOLière: Small-Sized Libraries of Readily Purchasable Compounds, Optimized to Produce Genuine Hits in Biological Screens across the Protein Space. Int J Mol Sci 2021; 22:ijms22157773. [PMID: 34360558 PMCID: PMC8346018 DOI: 10.3390/ijms22157773] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2021] [Revised: 07/13/2021] [Accepted: 07/15/2021] [Indexed: 12/21/2022] Open
Abstract
Experimental screening of large sets of compounds against macromolecular targets is a key strategy to identify novel bioactivities. However, large-scale screening requires substantial experimental resources and is time-consuming and challenging. Therefore, small to medium-sized compound libraries with a high chance of producing genuine hits on an arbitrary protein of interest would be of great value to fields related to early drug discovery, in particular biochemical and cell research. Here, we present a computational approach that incorporates drug-likeness, predicted bioactivities, biological space coverage, and target novelty, to generate optimized compound libraries with maximized chances of producing genuine hits for a wide range of proteins. The computational approach evaluates drug-likeness with a set of established rules, predicts bioactivities with a validated, similarity-based approach, and optimizes the composition of small sets of compounds towards maximum target coverage and novelty. We found that, in comparison to the random selection of compounds for a library, our approach generates substantially improved compound sets. Quantified as the "fitness" of compound libraries, the calculated improvements ranged from +60% (for a library of 15,000 compounds) to +184% (for a library of 1000 compounds). The best of the optimized compound libraries prepared in this work are available for download as a dataset bundle ("BonMOLière").
Collapse
Affiliation(s)
- Neann Mathai
- Computational Biology Unit (CBU) and Department of Chemistry, University of Bergen, N-5020 Bergen, Norway;
| | - Conrad Stork
- Center for Bioinformatics (ZBH), Department of Informatics, Universität Hamburg, 20146 Hamburg, Germany;
| | - Johannes Kirchmair
- Computational Biology Unit (CBU) and Department of Chemistry, University of Bergen, N-5020 Bergen, Norway;
- Division of Pharmaceutical Chemistry, Department of Pharmaceutical Sciences, University of Vienna, 1090 Vienna, Austria
- Correspondence:
| |
Collapse
|
3
|
Stork C, Embruch G, Šícho M, de Bruyn Kops C, Chen Y, Svozil D, Kirchmair J. NERDD: a web portal providing access to in silico tools for drug discovery. Bioinformatics 2020; 36:1291-1292. [PMID: 32077475 DOI: 10.1093/bioinformatics/btz695] [Citation(s) in RCA: 30] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2019] [Revised: 08/16/2019] [Accepted: 09/03/2019] [Indexed: 12/15/2022] Open
Abstract
SUMMARY The New E-Resource for Drug Discovery (NERDD) is a quickly expanding web portal focused on the provision of peer-reviewed in silico tools for drug discovery. NERDD currently hosts tools for predicting the sites of metabolism (FAME) and metabolites (GLORY) of small organic molecules, for flagging compounds that are likely to interfere with biological assays (Hit Dexter), and for identifying natural products and natural product derivatives in large compound collections (NP-Scout). Several additional models and components are currently in development. AVAILABILITY AND IMPLEMENTATION The NERDD web server is available at https://nerdd.zbh.uni-hamburg.de. Most tools are also available as software packages for local installation.
Collapse
Affiliation(s)
- Conrad Stork
- Department of Informatics, Universität Hamburg, Faculty of Mathematics, Informatics and Natural Sciences, Center for Bioinformatics (ZBH), Hamburg 20146, Germany
| | - Gerd Embruch
- Department of Informatics, Universität Hamburg, Faculty of Mathematics, Informatics and Natural Sciences, Center for Bioinformatics (ZBH), Hamburg 20146, Germany
| | - Martin Šícho
- Department of Informatics and Chemistry, CZ-OPENSCREEN: National Infrastructure for Chemical Biology, University of Chemistry and Technology Prague, Faculty of Chemical Technology, 166 28 Prague 6, Czech Republic
| | - Christina de Bruyn Kops
- Department of Informatics, Universität Hamburg, Faculty of Mathematics, Informatics and Natural Sciences, Center for Bioinformatics (ZBH), Hamburg 20146, Germany
| | - Ya Chen
- Department of Informatics, Universität Hamburg, Faculty of Mathematics, Informatics and Natural Sciences, Center for Bioinformatics (ZBH), Hamburg 20146, Germany
| | - Daniel Svozil
- Department of Informatics and Chemistry, CZ-OPENSCREEN: National Infrastructure for Chemical Biology, University of Chemistry and Technology Prague, Faculty of Chemical Technology, 166 28 Prague 6, Czech Republic
| | - Johannes Kirchmair
- Department of Informatics, Universität Hamburg, Faculty of Mathematics, Informatics and Natural Sciences, Center for Bioinformatics (ZBH), Hamburg 20146, Germany.,Department of Chemistry, University of Bergen, Bergen N-5020, Norway.,Computational Biology Unit (CBU), Bergen N-5020, Norway
| |
Collapse
|
4
|
Abstract
Aim: The druggability of epigenetic targets has prompted researchers to develop small-molecule therapeutics. However, no systematic assessment has ever been done to investigate the chemical space of epigenetic modulators. Herein, we report a comprehensive chemoinformatic analysis of epigenetic ligands from EpiDBase, HEMD, ChEMBL and PubChem databases. Results: Nearly, 0.45 × 106 ligands were analyzed for assay interference compounds, target profiling, drug-like properties and hit prioritization. After eliminating approximately 96,000 problematic compounds, the remaining 0.36 × 106 compounds were studied for their physicochemical distributions, principal component analysis and hit prioritization. More than 30% of assay interference compounds were determined for many proteins. Conclusion: This systematic assessment of epigenetic ligands will help in the enrichment of screening libraries with high-quality compounds and thus, the generation of efficacious drug candidates.
Collapse
|
5
|
Abstract
Targeted covalent modification is assuming consolidated importance in drug discovery. In this context, the electrophilic tuning of redox-dependent cell signaling is attracting major interest, as it opens prospect for treating numerous pathologic conditions. Herein, we discuss the rationale and the issues of electrophile-based approaches, focusing on the transcriptional Nrf2-Keap1 pathway as a test case. We also highlight relevant medicinal chemistry strategies researchers have devised to meet the ambitious goal, dwelling on the investigational and therapeutic potential of modulating redox-signaling networks through regulatory cysteine switches.
Collapse
|
6
|
|
7
|
Stork C, Chen Y, Šícho M, Kirchmair J. Hit Dexter 2.0: Machine-Learning Models for the Prediction of Frequent Hitters. J Chem Inf Model 2019; 59:1030-1043. [DOI: 10.1021/acs.jcim.8b00677] [Citation(s) in RCA: 54] [Impact Index Per Article: 10.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/26/2023]
Affiliation(s)
- Conrad Stork
- Center for Bioinformatics (ZBH), Department of Computer Science, Faculty of Mathematics, Informatics and Natural Sciences, Universität Hamburg, Hamburg, 20146, Germany
| | - Ya Chen
- Center for Bioinformatics (ZBH), Department of Computer Science, Faculty of Mathematics, Informatics and Natural Sciences, Universität Hamburg, Hamburg, 20146, Germany
| | - Martin Šícho
- Center for Bioinformatics (ZBH), Department of Computer Science, Faculty of Mathematics, Informatics and Natural Sciences, Universität Hamburg, Hamburg, 20146, Germany
- CZ-OPENSCREEN: National Infrastructure for Chemical Biology, Laboratory of Informatics and Chemistry, Faculty of Chemical Technology, University of Chemistry and Technology Prague, 166 28 Prague 6, Czech Republic
| | - Johannes Kirchmair
- Center for Bioinformatics (ZBH), Department of Computer Science, Faculty of Mathematics, Informatics and Natural Sciences, Universität Hamburg, Hamburg, 20146, Germany
- Department of Chemistry, University of Bergen, N-5020 Bergen, Norway
- Computational Biology Unit (CBU), University of Bergen, N-5020 Bergen, Norway
| |
Collapse
|