Data Curation can Improve the Prediction Accuracy of Metabolic Intrinsic Clearance

Esaki, Tsuyoshi; Watanabe, Reiko; Kawashima, Hitoshi; Ohashi, Rikiya; Natsume-Kitatani, Yayoi; Nagao, Chioko; Mizuguchi, Kenji

doi:10.1002/minf.201800086

Cited by 26 publications

(34 citation statements)

References 27 publications

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“…Biocurators are also the professionals that transform data into database-compliant information that can be easily extracted and used with the database’s various bioinformatics tools [ 4 ]. For instance, the improvement in accuracy of prediction models for drug discovery trained with manual curated data [ 98 ] or the curation of genetic variants employing curated biomedical literature [ 99 ] as well as the detection of misannotated enzymes through curation [ 100 ] has proven the value of manually curated data. However, manual curation of databases is not undisputed.…”

Section: Resultsmentioning

confidence: 99%

Metabolic networks of the Nicotiana genus in the spotlight: content, progress and outlook

Foerster¹,

Battey

Sierro

et al. 2020

Briefings in Bioinformatics

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Manually curated metabolic databases residing at the Sol Genomics Network comprise two taxon-specific databases for the Solanaceae family, i.e. SolanaCyc and the genus Nicotiana, i.e. NicotianaCyc as well as six species-specific databases for Nicotiana tabacum TN90, N. tabacum K326, Nicotiana benthamiana, N. sylvestris, N. tomentosiformis and N. attenuata. New pathways were created through the extraction, examination and verification of related data from the literature and the aid of external database guided by an expert-led curation process. Here we describe the curation progress that has been achieved in these databases since the first release version 1.0 in 2016, the curation flow and the curation process using the example metabolic pathway for cholesterol in plants. The current content of our databases comprises 266 pathways and 36 superpathways in SolanaCyc and 143 pathways plus 21 superpathways in NicotianaCyc, manually curated and validated specifically for the Solanaceae family and Nicotiana genus, respectively. The curated data have been propagated to the respective Nicotiana-specific databases, which resulted in the enrichment and more accurate presentation of their metabolic networks. The quality and coverage in those databases have been compared with related external databases and discussed in terms of literature support and metabolic content.

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Section: Resultsmentioning

confidence: 99%

Metabolic networks of the Nicotiana genus in the spotlight: content, progress and outlook

Foerster¹,

Battey

Sierro

et al. 2020

Briefings in Bioinformatics

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“…We calculated the properties by Pipeline Pilot [19], and the results met the above-mentioned criteria (log P: 2.31, molecular weight: 210.2, number of H-bonding acceptors: 5, number of H-bonding donors: 0). In addition, the prediction model of human microsomal stability developed by Esaki et al [20] categorized RK-0404678 as “Stable (CLint < 20 μL/min/mg)”. Based on these preliminary assessments, RK-0404678 appears to be a promising starting point for future drug discovery.…”

Section: Discussionmentioning

confidence: 99%

Discovery of a small molecule inhibitor targeting dengue virus NS5 RNA-dependent RNA polymerase

et al. 2019

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Dengue is a mosquito-borne viral infection that has spread globally in recent years. Around half of the world’s population, especially in the tropics and subtropics, is at risk of infection. Every year, 50–100 million clinical cases are reported, and more than 500,000 patients develop the symptoms of severe dengue infection: dengue haemorrhagic fever and dengue shock syndrome, which threaten life in Asia and Latin America. No antiviral drug for dengue is available. The dengue virus (DENV) non-structural protein 5 (NS5), which possesses the RNA-dependent RNA polymerase (RdRp) activity and is responsible for viral replication and transcription, is an attractive target for anti-dengue drug development. In the present study, 16,240 small-molecule compounds in a fragment library were screened for their capabilities to inhibit the DENV type 2 (DENV2) RdRp activities in vitro. Based on in cellulo antiviral and cytotoxity assays, we selected the compound RK-0404678 with the EC50 value of 6.0 μM for DENV2. Crystallographic analyses revealed two unique binding sites for RK-0404678 within the RdRp, which are conserved in flavivirus NS5 proteins. No resistant viruses emerged after nine rounds of serial passage of DENV2 in the presence of RK-0404678, suggesting the high genetic barrier of this compound to the emergence of a resistant virus. Collectively, RK-0404678 and its binding sites provide a new framework for antiviral drug development.

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“…5 . Both datasets were created after careful curation to select the values of f e or CL r in healthy adult humans for a single administration to obtain higher prediction accuracy 28 . The details of curation are provided in Supplementary Methods.…”

Section: Methodsmentioning

confidence: 99%

Development of an in silico prediction system of human renal excretion and clearance from chemical structure information incorporating fraction unbound in plasma as a descriptor

Watanabe

Ohashi²,

Esaki

et al. 2019

Sci Rep

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Prediction of pharmacokinetic profiles of new chemical entities is essential in drug development to minimize the risks of potential withdrawals. The excretion of unchanged compounds by the kidney constitutes a major route in drug elimination and plays an important role in pharmacokinetics. Herein, we created in silico prediction models of the fraction of drug excreted unchanged in the urine (fe) and renal clearance (CLr), with datasets of 411 and 401 compounds using freely available software; notably, all models require chemical structure information alone. The binary classification model for fe demonstrated a balanced accuracy of 0.74. The two-step prediction system for CLr was generated using a combination of the classification model to predict excretion-type compounds and regression models to predict the CLr value for each excretion type. The accuracies of the regression models increased upon adding a descriptor, which was the observed and predicted fraction unbound in plasma (fu,p); 78.6% of the samples in the higher range of renal clearance fell within 2-fold error with predicted fu,p value. Our prediction system for renal excretion is freely available to the public and can be used as a practical tool for prioritization and optimization of compound synthesis in the early stage of drug discovery.

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Data Curation can Improve the Prediction Accuracy of Metabolic Intrinsic Clearance

Cited by 26 publications

References 27 publications

Metabolic networks of the Nicotiana genus in the spotlight: content, progress and outlook

Metabolic networks of the Nicotiana genus in the spotlight: content, progress and outlook

Discovery of a small molecule inhibitor targeting dengue virus NS5 RNA-dependent RNA polymerase

Development of an in silico prediction system of human renal excretion and clearance from chemical structure information incorporating fraction unbound in plasma as a descriptor

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