An in silico Approach for Integrating Phenotypic and Target-based Approaches in Drug Discovery.
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ABSTRACT:
SUBMITTER: Iwata H
PROVIDER: S-EPMC7050533 | biostudies-literature | 2020 Jan
REPOSITORIES: biostudies-literature
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Project description:Drug discovery for neglected tropical diseases is carried out using both target-based and phenotypic approaches. In this paper, target-based approaches are discussed, with a particular focus on human African trypanosomiasis. Target-based drug discovery can be successful, but careful selection of targets is required. There are still very few fully validated drug targets in neglected diseases, and there is a high attrition rate in target-based drug discovery for these diseases. Phenotypic screening is a powerful method in both neglected and non-neglected diseases and has been very successfully used. Identification of molecular targets from phenotypic approaches can be a way to identify potential new drug targets.
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Project description:Cardiovascular diseases (CVDs), including thrombosis, which is induced by platelet aggregation, are the leading cause of mortality worldwide. The P2Y1 receptor (P2Y1R) facilitates platelet aggregation and is thus an important potential anti-thrombotic drug target. The P2Y1R protein structure contains a binding site for receptor antagonist MRS2500 within its seven-transmembrane bundle, which also provides suitable pockets for numerous other ligands to act as nucleotide antagonists of P2Y1R. The Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform (TCMSP) comprises 499 Chinese Pharmacopoeia-registered herbs and the structure information for 29,384 ingredients. In silico docking of these compounds into the P2Y1R protein structure within the MRS2500 pocket can identify potential antithrombotic drugs from natural medicinal plants. Docking studies were performed and scored to evaluate ligand-binding affinities. In this study, a total of 8987 compounds from Traditional Chinese Medicine (TCM) were filtered by Lipinski's rule of five, and their ideal oral-intake properties were evaluated. Of these, 1656 compounds distributed in 443 herbs docked into the P2Y1R-MRS2500 structure in 16,317 poses. A total of 38 compounds were ranked with a DockScore above 70, and these may have significant potential for development into anti-thrombosis drugs. These computational results suggested that licorice (Glycyrrhiza uralensis Fisch), cimicifugae (Cimicifuga foetida L.), and ganoderma (Ganoderma lucidum Karst) and their chemical constituents, which have not previously been widely used for anti-thrombosis, may have unexpected effects on platelet aggregation. Moreover, two types of triterpene scaffolds summarized from 10 compounds were distributed in these three herbs and also docked into P2Y1R. These scaffold structures may be utilized for the development of drugs to inhibit platelet aggregation.
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Project description:The work began with the screening of a library of 700,000 small molecules for inhibitors of Trypanosoma brucei growth (a phenotypic screen). The resulting set of 1035 hit compounds was reviewed by a team of medicinal chemists, leading to the nomination of 17 chemically distinct scaffolds for further investigation. The first triage step was the assessment for brain permeability (looking for brain levels at least 20% of plasma levels) in order to optimize the chances of developing candidates for treating late-stage human African trypanosomiasis. Eleven scaffolds subsequently underwent hit-to-lead optimization using standard medicinal chemistry approaches. Over a period of six years in an academic setting, 1539 analogs to the 11 scaffolds were synthesized. Eight scaffolds were discontinued either due to insufficient improvement in antiparasitic activity (5), poor pharmacokinetic properties (2), or a slow (static) antiparasitic activity (1). Three scaffolds were optimized to the point of curing the acute and/or chronic T. brucei infection model in mice. The progress was accomplished without knowledge of the mechanism of action (MOA) for the compounds, although the MOA has been discovered in the interim for one compound series. Studies on the safety and toxicity of the compounds are planned to help select candidates for potential clinical development. This research demonstrates the power of the phenotypic drug discovery approach for neglected tropical diseases.
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Project description:Computational methods for predicting the macromolecular targets of drugs and drug-like compounds have evolved as a key technology in drug discovery. However, the established validation protocols leave several key questions regarding the performance and scope of methods unaddressed. For example, prediction success rates are commonly reported as averages over all compounds of a test set and do not consider the structural relationship between the individual test compounds and the training instances. In order to obtain a better understanding of the value of ligand-based methods for target prediction, we benchmarked a similarity-based method and a random forest based machine learning approach (both employing 2D molecular fingerprints) under three testing scenarios: a standard testing scenario with external data, a standard time-split scenario, and a scenario that is designed to most closely resemble real-world conditions. In addition, we deconvoluted the results based on the distances of the individual test molecules from the training data. We found that, surprisingly, the similarity-based approach generally outperformed the machine learning approach in all testing scenarios, even in cases where queries were structurally clearly distinct from the instances in the training (or reference) data, and despite a much higher coverage of the known target space.
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Project description:BackgroundThe pharmaceutical field faces a significant challenge in validating drug target interactions (DTIs) due to the time and cost involved, leading to only a fraction being experimentally verified. To expedite drug discovery, accurate computational methods are essential for predicting potential interactions. Recently, machine learning techniques, particularly graph-based methods, have gained prominence. These methods utilize networks of drugs and targets, employing knowledge graph embedding (KGE) to represent structured information from knowledge graphs in a continuous vector space. This phenomenon highlights the growing inclination to utilize graph topologies as a means to improve the precision of predicting DTIs, hence addressing the pressing requirement for effective computational methodologies in the field of drug discovery.ResultsThe present study presents a novel approach called DTIOG for the prediction of DTIs. The methodology employed in this study involves the utilization of a KGE strategy, together with the incorporation of contextual information obtained from protein sequences. More specifically, the study makes use of Protein Bidirectional Encoder Representations from Transformers (ProtBERT) for this purpose. DTIOG utilizes a two-step process to compute embedding vectors using KGE techniques. Additionally, it employs ProtBERT to determine target-target similarity. Different similarity measures, such as Cosine similarity or Euclidean distance, are utilized in the prediction procedure. In addition to the contextual embedding, the proposed unique approach incorporates local representations obtained from the Simplified Molecular Input Line Entry Specification (SMILES) of drugs and the amino acid sequences of protein targets.ConclusionsThe effectiveness of the proposed approach was assessed through extensive experimentation on datasets pertaining to Enzymes, Ion Channels, and G-protein-coupled Receptors. The remarkable efficacy of DTIOG was showcased through the utilization of diverse similarity measures in order to calculate the similarities between drugs and targets. The combination of these factors, along with the incorporation of various classifiers, enabled the model to outperform existing algorithms in its ability to predict DTIs. The consistent observation of this advantage across all datasets underlines the robustness and accuracy of DTIOG in the domain of DTIs. Additionally, our case study suggests that the DTIOG can serve as a valuable tool for discovering new DTIs.
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Project description:The high morbidity and mortality of cancer make it one of the leading causes of global death, thus it is an urgent need to develop effective drugs for cancer therapy. Vascular endothelial growth factor receptor-2 (VEGFR2) acts as a central modulator of angiogenesis, and is therefore an important pharmaceutical target for developing anti-angiogenic agents. In this study, ligand-based naïve Bayesian (NB) models and structure-based molecular docking were combined to develop a virtual screening (VS) pipeline for identifying potential VEGFR2 inhibitors from FDA-approved drugs. The best validated naïve Bayesian model (NB-c) gave Matthews correlation coefficients of 0.966 and 0.951 for the test set and external validation set, respectively. 1841 FDA-approved drugs were sequentially screened by the optimal model NB-c and molecular docking module LibDock. By analyzing the results of VS, 9 top ranked drugs with EstPGood value ≥ 0.6 and LibDock Score ≥ 120 were chosen for biological validation. VEGFR2 kinase assay results demonstrated that flubendazole, rilpivirine and papaverine showed VEGFR2 inhibitory activities with IC50 values ranging from 0.47 to 6.29 μM. Binding mode analysis with CDOCKER revealed the action mechanism of the 3 hit drugs binding to VEGFR2. In summary, we not only proposed an integrated VS pipeline for potential VEGFR2 inhibitors screening, but also identified 3 FDA-approved drugs as novel VEGFR2 inhibitors, which could be used to design and develop new antiangiogenic agents.
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