A public–private partnership to enrich the development of in silico predictive models for pharmacokinetic and cardiotoxic properties

Kono, Hiroshi; Watanabe, Reiko; Kawashima, Hitoshi; Ohashi, Rikiya; Kuroda, Masataka; Sato, Tomohiro; Honma, Teruki; Mizuguchi, Kenji

doi:10.1016/j.drudis.2021.01.024

Cited by 9 publications

(5 citation statements)

References 38 publications

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“…GlaxoSmithKline released raw clinical data on new drugs in 2012, which was welcomed as the first step toward information disclosure. The movement to follow this lead is spreading worldwide, and there has been a large range of open biomedical datasets available for training new machine learning algorithms developed by governments, medical societies, and international research collaborations. − The possibility of learning from a large amount of data containing similar structures is increasing. By using this method to build a prediction model using in-company data, we expect to develop a prediction model with a prediction accuracy higher than that of in vitro tests.…”

Section: Discussionmentioning

confidence: 99%

Prediction of the Contribution Ratio of a Target Metabolic Enzyme to Clearance from Chemical Structure Information

et al. 2022

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The contribution ratio of metabolic enzymes such as cytochrome P450 to in vivo clearance (fraction metabolized: fm) is a pharmacokinetic index that is particularly important for the quantitative evaluation of drug–drug interactions. Since obtaining experimental in vivo fm values is challenging, those derived from in vitro experiments have often been used alternatively. This study aimed to explore the possibility of constructing machine learning models for predicting in vivo fm using chemical structure information alone. We collected in vivo fm values and chemical structures of 319 compounds from a public database with careful manual curation and constructed predictive models using several machine learning methods. The results showed that in vivo fm values can be obtained from structural information alone with a performance comparable to that based on in vitro experimental values and that the prediction accuracy for the compounds involved in CYP induction or inhibition is significantly higher than that by using in vitro values. Our new approach to predicting in vivo fm values in the early stages of drug discovery should help improve the efficiency of the drug optimization process.

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Section: Discussionmentioning

confidence: 99%

Prediction of the Contribution Ratio of a Target Metabolic Enzyme to Clearance from Chemical Structure Information

et al. 2022

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“…The most important factor to create accurate prediction models is to collect high-quality experimental data as much as possible. To this end, we have established a consortium with seven pharmaceutical companies and obtained chemical descriptors and experimental data for several pharmacokinetic parameters from these companies, , although these data are proprietary to the companies and we cannot provide them in DruMAP. Another approach to increase experimental data is to collect data from documents on new drug applications, such as Common Technical Document (CTD), package inserts, or Interview Form (IF), which is a supplementary document of the package insert of a launched drug specifically in Japan.…”

Section: Discussionmentioning

confidence: 99%

DruMAP: A Novel Drug Metabolism and Pharmacokinetics Analysis Platform

et al. 2023

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We developed a novel drug metabolism and pharmacokinetics (DMPK) analysis platform named DruMAP. This platform consists of a database for DMPK parameters and programs that can predict many DMPK parameters based on the chemical structure of a compound. The DruMAP database includes curated DMPK parameters from public sources and in-house experimental data obtained under standardized conditions; it also stores predicted DMPK parameters produced by our prediction programs. Users can predict several DMPK parameters simultaneously for novel compounds not found in the database. Furthermore, the highly flexible search system enables users to search for compounds as they desire. The current version of DruMAP comprises more than 30,000 chemical compounds, about 40,000 activity values (collected from public databases and inhouse data), and about 600,000 predicted values. Our platform provides a simple tool for searching and predicting DMPK parameters and is expected to contribute to the acceleration of new drug development. DruMAP can be freely accessed at: https://drumap.nibiohn.go.jp/ .

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“…ADMETlab 2.0, which is a completely redesigned version of the widely used AMDETlab web server, enables the prediction of pharmacokinetic and toxicity properties, including 17 physicochemical properties, 13 medicinal chemistry properties, 23 ADME properties, 27 toxicity endpoints, and 8 toxicophore rules. The AMED established the iD3-INST with financial support, with the goal of constructing a drug discovery platform with the components of a high-quality open-access database including public data and in silico prediction models for ADME, cardiotoxicity, and a drug-induced liver injury model [11,135].…”

Section: In Silico Prediction Models Applicable To Academic Researchmentioning

confidence: 99%

“…Based on the blueprint, the iD3-INST received data on solubility, human plasma protein binding, and metabolic stability from the private sectors and also obtained data for net ER using compounds provided by the private sectors [143]. The establishment of the PP database by integrating private data in a public database led to the creation of seven in silico prediction models for solubility, f u,p in humans, f u,p in rats, net ER, f u,brain , CL int in humans, and CL int in rats [11]. These in silico models have been made available for academic drug discovery and incorporated into SCIQUICK to build an ecosystem that maintains the iD3-INST integrated platform.…”

Section: In Silico Prediction Models Applicable To Academic Researchmentioning

confidence: 99%

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The Trends and Future Prospective of In Silico Models from the Viewpoint of ADME Evaluation in Drug Discovery

Komura,

Watanabe,

Mizuguchi

2023

Pharmaceutics

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Drug discovery and development are aimed at identifying new chemical molecular entities (NCEs) with desirable pharmacokinetic profiles for high therapeutic efficacy. The plasma concentrations of NCEs are a biomarker of their efficacy and are governed by pharmacokinetic processes such as absorption, distribution, metabolism, and excretion (ADME). Poor ADME properties of NCEs are a major cause of attrition in drug development. ADME screening is used to identify and optimize lead compounds in the drug discovery process. Computational models predicting ADME properties have been developed with evolving model-building technologies from a simplified relationship between ADME endpoints and physicochemical properties to machine learning, including support vector machines, random forests, and convolution neural networks. Recently, in the field of in silico ADME research, there has been a shift toward evaluating the in vivo parameters or plasma concentrations of NCEs instead of using predictive results to guide chemical structure design. Another research hotspot is the establishment of a computational prediction platform to strengthen academic drug discovery. Bioinformatics projects have produced a series of in silico ADME models using free software and open-access databases. In this review, we introduce prediction models for various ADME parameters and discuss the currently available academic drug discovery platforms.

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A public–private partnership to enrich the development of in silico predictive models for pharmacokinetic and cardiotoxic properties

Cited by 9 publications

References 38 publications

Prediction of the Contribution Ratio of a Target Metabolic Enzyme to Clearance from Chemical Structure Information

Prediction of the Contribution Ratio of a Target Metabolic Enzyme to Clearance from Chemical Structure Information

DruMAP: A Novel Drug Metabolism and Pharmacokinetics Analysis Platform

The Trends and Future Prospective of In Silico Models from the Viewpoint of ADME Evaluation in Drug Discovery

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