Docking Screens for Dual Inhibitors of Disparate Drug Targets for Parkinson’s Disease

Modulation of multiple biological targets with a single drug can lead to synergistic therapeutic effects and has been demonstrated to be essential for efficient treatment of CNS disorders. However, rational design of compounds that interact with several targets is very challenging. Here, we demonstrate that structure-based virtual screening can guide the discovery of multi-target ligands of unrelated proteins relevant for Parkinson’s disease. A library with 5.4 million molecules was docked to crystal structures of the A_2A adenosine receptor (A_2AAR) and monoamine oxidase B (MAO-B). Twenty-four compounds that were among the highest ranked for both binding sites were evaluated experimentally, resulting in the discovery of four dual-target ligands. The most potent compound was an A_2AAR antagonist with nanomolar affinity (<i>K</i>_i = 19 nM) and inhibited MAO-B with an IC₅₀ of 100 nM. Optimization guided by the predicted binding modes led to the identification of a second potent dual-target scaffold. The two discovered scaffolds were shown to counteract 6-hydroxy­dopamine-induced neurotoxicity in dopaminergic neuronal-like SH-SY5Y cells. Structure-based screening can hence be used to identify ligands with specific polypharmacological profiles, providing new avenues for drug development against complex diseases

Docking Screens for Dual Inhibitors of Disparate Drug Targets for Parkinson’s Disease

Modulation of multiple biological targets with a single drug can lead to synergistic therapeutic effects and has been demonstrated to be essential for efficient treatment of CNS disorders. However, rational design of compounds that interact with several targets is very challenging. Here, we demonstrate that structure-based virtual screening can guide the discovery of multi-target ligands of unrelated proteins relevant for Parkinson’s disease. A library with 5.4 million molecules was docked to crystal structures of the A_2A adenosine receptor (A_2AAR) and monoamine oxidase B (MAO-B). Twenty-four compounds that were among the highest ranked for both binding sites were evaluated experimentally, resulting in the discovery of four dual-target ligands. The most potent compound was an A_2AAR antagonist with nanomolar affinity (<i>K</i>_i = 19 nM) and inhibited MAO-B with an IC₅₀ of 100 nM. Optimization guided by the predicted binding modes led to the identification of a second potent dual-target scaffold. The two discovered scaffolds were shown to counteract 6-hydroxy­dopamine-induced neurotoxicity in dopaminergic neuronal-like SH-SY5Y cells. Structure-based screening can hence be used to identify ligands with specific polypharmacological profiles, providing new avenues for drug development against complex diseases

Docking Screens for Dual Inhibitors of Disparate Drug Targets for Parkinson’s Disease

Modulation of multiple biological targets with a single drug can lead to synergistic therapeutic effects and has been demonstrated to be essential for efficient treatment of CNS disorders. However, rational design of compounds that interact with several targets is very challenging. Here, we demonstrate that structure-based virtual screening can guide the discovery of multi-target ligands of unrelated proteins relevant for Parkinson’s disease. A library with 5.4 million molecules was docked to crystal structures of the A_2A adenosine receptor (A_2AAR) and monoamine oxidase B (MAO-B). Twenty-four compounds that were among the highest ranked for both binding sites were evaluated experimentally, resulting in the discovery of four dual-target ligands. The most potent compound was an A_2AAR antagonist with nanomolar affinity (<i>K</i>_i = 19 nM) and inhibited MAO-B with an IC₅₀ of 100 nM. Optimization guided by the predicted binding modes led to the identification of a second potent dual-target scaffold. The two discovered scaffolds were shown to counteract 6-hydroxy­dopamine-induced neurotoxicity in dopaminergic neuronal-like SH-SY5Y cells. Structure-based screening can hence be used to identify ligands with specific polypharmacological profiles, providing new avenues for drug development against complex diseases

Docking Screens for Dual Inhibitors of Disparate Drug Targets for Parkinson’s Disease

Modulation of multiple biological targets with a single drug can lead to synergistic therapeutic effects and has been demonstrated to be essential for efficient treatment of CNS disorders. However, rational design of compounds that interact with several targets is very challenging. Here, we demonstrate that structure-based virtual screening can guide the discovery of multi-target ligands of unrelated proteins relevant for Parkinson’s disease. A library with 5.4 million molecules was docked to crystal structures of the A_2A adenosine receptor (A_2AAR) and monoamine oxidase B (MAO-B). Twenty-four compounds that were among the highest ranked for both binding sites were evaluated experimentally, resulting in the discovery of four dual-target ligands. The most potent compound was an A_2AAR antagonist with nanomolar affinity (<i>K</i>_i = 19 nM) and inhibited MAO-B with an IC₅₀ of 100 nM. Optimization guided by the predicted binding modes led to the identification of a second potent dual-target scaffold. The two discovered scaffolds were shown to counteract 6-hydroxy­dopamine-induced neurotoxicity in dopaminergic neuronal-like SH-SY5Y cells. Structure-based screening can hence be used to identify ligands with specific polypharmacological profiles, providing new avenues for drug development against complex diseases

The Application of the Open Pharmacological Concepts Triple Store (Open PHACTS) to Support Drug Discovery Research

<div><p>Integration of open access, curated, high-quality information from multiple disciplines in the Life and Biomedical Sciences provides a holistic understanding of the domain. Additionally, the effective linking of diverse data sources can unearth hidden relationships and guide potential research strategies. However, given the lack of consistency between descriptors and identifiers used in different resources and the absence of a simple mechanism to link them, gathering and combining relevant, comprehensive information from diverse databases remains a challenge. The Open Pharmacological Concepts Triple Store (Open PHACTS) is an Innovative Medicines Initiative project that uses semantic web technology approaches to enable scientists to easily access and process data from multiple sources to solve real-world drug discovery problems. The project draws together sources of publicly-available pharmacological, physicochemical and biomolecular data, represents it in a stable infrastructure and provides well-defined information exploration and retrieval methods. Here, we highlight the utility of this platform in conjunction with workflow tools to solve pharmacological research questions that require interoperability between target, compound, and pathway data. Use cases presented herein cover 1) the comprehensive identification of chemical matter for a dopamine receptor drug discovery program 2) the identification of compounds active against all targets in the Epidermal growth factor receptor (ErbB) signaling pathway that have a relevance to disease and 3) the evaluation of established targets in the Vitamin D metabolism pathway to aid novel Vitamin D analogue design. The example workflows presented illustrate how the Open PHACTS Discovery Platform can be used to exploit existing knowledge and generate new hypotheses in the process of drug discovery.</p></div

Benefits of using the Open PHACTS Discovery Platform for drug discovery research.

<p>Benefits of using the Open PHACTS Discovery Platform for drug discovery research.</p

Regulators of Vitamin D signaling obtained from Workflow 3.

<p>Terms in bold are discussed in the text.</p><p>Regulators of Vitamin D signaling obtained from Workflow 3.</p

Use case C workflows 1 and 2.

<p>Open PHACTS v 1.3 API calls are shown in orange boxes along with the results obtained. Bioactivity filters and other data processing operations are shown in yellow boxes with results obtained in light grey boxes. Blue colored boxes show results included in the manuscript. Sample input URLs are shown in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0115460#pone.0115460.s005" target="_blank">S2 Table</a>. For workflow 1, a description of the pathway and targets contained were obtained using the ‘Pathway information’ and ‘Pathway Information: Get targets’ API calls. Other pathways where these targets are present were obtained using ‘Pathways for Target: List’ API call. Approved drugs against single protein targets were obtained using ‘Target Information’ API call by specifying target type - approved. Compounds tested against all targets in the pathway were retrieved using ‘Target Pharmacology: List’ API call. Approved drugs targeting protein complexes (containing any member of the pathway) were identified by filtering for protein complexes and ‘approved’ target type via the ‘Compound Information’ API call. For workflow 2, compounds hitting CYP24A1 from the previous results were used as input to find additional targets using the ‘Compound Pharmacology: List’ API. Additional pathways containing these new targets were obtained using ‘Pathways for Target: List’ API.</p

case B workflow.

<p>Open PHACTS v 1.3 API calls are shown in orange boxes along with the results obtained. Bioactivity filters and other data processing operations are shown in yellow boxes with results obtained in light grey boxes. Blue colored boxes show results included in the manuscript. Compound pharmacology at the pathway level was retrieved by consecutive execution of the API calls ‘Pathway Information: Get targets’ and ‘Target Pharmacology: List’ - the latter includes a filtering for desired activity endpoints and units - and other filtering, transformation, and normalization steps: transformation into ‘- logActivity values [molar]’, setting a threshold for binary representation, and subsequent filtering by keeping only the max. activity value for each compound/target pair. Retrieving GO annotations for a list of targets, and ChEBI annotations for compounds that have been tested against those targets was achieved by using the API calls ‘Target Classifications’ and ‘Compound Classifications’ and subsequent restriction to terms of the type ‘biological process’ and ‘has role’, respectively.</p

Use case A workflow.

<p>Schematic representation of the workflow for use case A. Starting with a free text search for the desired target(s), Uniprot AC identifiers, protein sequences and gene symbols are obtained using ‘Free Text to Concept’ and ‘Target Information’ API calls. A gene symbol list is obtained for targets from the same family (based on GO) using a ‘Target Classification’ API call. Alternatively, UniProt ACs obtained for related protein sequences via a BLAST search are used to get corresponding gene symbols using the ‘Target Information’ API call. Using this gene list, corresponding pharmacology records in the public domain are obtained via the ‘Pharmacology by Target’ API. In parallel, the gene symbol list is used to retrieve target pharmacology information in Thomson Reuters Integrity, World Drug Index, PharmaProjects, GVKBio GOSTAR, and Janssen pharmacology proprietary databases. Public pharmacology records (additional targets) for the retrieved compounds are then obtained using the ‘Pharmacology by compound’ API call with equivalent searches in Janssen pharmacology proprietary databases. If required, a structure similarity search is performed with the retrieved compounds to identify additional compounds, followed by another round of searches in Open PHACTS and proprietary databases as before. A Pipeline Pilot script was developed to run the above steps and produce an integrated list of compounds, activity data and target information from all databases. Proprietary components developed at Janssen were used to parse Janssen pharmacology data. All data processing was performed within the Pipeline Pilot framework.</p