开发可靠的人类肝脏微粒体稳定性预测模型:利用大鼠数据的种间相关性

IF 4.9 3区 医学 Q1 PHARMACOLOGY & PHARMACY
Pranav Shah, Vishal B Siramshetty, Ewy Mathé, Xin Xu
{"title":"开发可靠的人类肝脏微粒体稳定性预测模型:利用大鼠数据的种间相关性","authors":"Pranav Shah, Vishal B Siramshetty, Ewy Mathé, Xin Xu","doi":"10.3390/pharmaceutics16101257","DOIUrl":null,"url":null,"abstract":"<p><p><b>Objectives:</b> Pharmacokinetic issues were the leading cause of drug attrition, accounting for approximately 40% of all cases before the turn of the century. To this end, several high-throughput in vitro assays like microsomal stability have been developed to evaluate the pharmacokinetic profiles of compounds in the early stages of drug discovery. At NCATS, a single-point rat liver microsomal (RLM) stability assay is used as a Tier I assay, while human liver microsomal (HLM) stability is employed as a Tier II assay. We experimentally screened and collected data on over 30,000 compounds for RLM stability and over 7000 compounds for HLM stability. Although HLM stability screening provides valuable insights, the increasing number of hits generated, along with the time- and resource-intensive nature of the assay, highlights the need for alternative strategies. One promising approach is leveraging in silico models trained on these experimental datasets. <b>Methods:</b> We describe the development of an HLM stability prediction model using our in-house HLM stability dataset. <b>Results:</b> Employing both classical machine learning methods and advanced techniques, such as neural networks, we achieved model accuracies exceeding 80%. Moreover, we validated our model using external test sets and found that our models are comparable to some of the best models in literature. Additionally, the strong correlation observed between our RLM and HLM data was further reinforced by the fact that our HLM model performance improved when using RLM stability predictions as an input descriptor. <b>Conclusions:</b> The best model along with a subset of our dataset (PubChem AID: 1963597) has been made publicly accessible on the ADME@NCATS website for the benefit of the greater drug discovery community. To the best of our knowledge, it is the largest open-source model of its kind and the first to leverage cross-species data.</p>","PeriodicalId":19894,"journal":{"name":"Pharmaceutics","volume":null,"pages":null},"PeriodicalIF":4.9000,"publicationDate":"2024-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11510424/pdf/","citationCount":"0","resultStr":"{\"title\":\"Developing Robust Human Liver Microsomal Stability Prediction Models: Leveraging Inter-Species Correlation with Rat Data.\",\"authors\":\"Pranav Shah, Vishal B Siramshetty, Ewy Mathé, Xin Xu\",\"doi\":\"10.3390/pharmaceutics16101257\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p><b>Objectives:</b> Pharmacokinetic issues were the leading cause of drug attrition, accounting for approximately 40% of all cases before the turn of the century. To this end, several high-throughput in vitro assays like microsomal stability have been developed to evaluate the pharmacokinetic profiles of compounds in the early stages of drug discovery. At NCATS, a single-point rat liver microsomal (RLM) stability assay is used as a Tier I assay, while human liver microsomal (HLM) stability is employed as a Tier II assay. We experimentally screened and collected data on over 30,000 compounds for RLM stability and over 7000 compounds for HLM stability. Although HLM stability screening provides valuable insights, the increasing number of hits generated, along with the time- and resource-intensive nature of the assay, highlights the need for alternative strategies. One promising approach is leveraging in silico models trained on these experimental datasets. <b>Methods:</b> We describe the development of an HLM stability prediction model using our in-house HLM stability dataset. <b>Results:</b> Employing both classical machine learning methods and advanced techniques, such as neural networks, we achieved model accuracies exceeding 80%. Moreover, we validated our model using external test sets and found that our models are comparable to some of the best models in literature. Additionally, the strong correlation observed between our RLM and HLM data was further reinforced by the fact that our HLM model performance improved when using RLM stability predictions as an input descriptor. <b>Conclusions:</b> The best model along with a subset of our dataset (PubChem AID: 1963597) has been made publicly accessible on the ADME@NCATS website for the benefit of the greater drug discovery community. To the best of our knowledge, it is the largest open-source model of its kind and the first to leverage cross-species data.</p>\",\"PeriodicalId\":19894,\"journal\":{\"name\":\"Pharmaceutics\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":4.9000,\"publicationDate\":\"2024-09-27\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11510424/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Pharmaceutics\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.3390/pharmaceutics16101257\",\"RegionNum\":3,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"PHARMACOLOGY & PHARMACY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Pharmaceutics","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.3390/pharmaceutics16101257","RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"PHARMACOLOGY & PHARMACY","Score":null,"Total":0}
引用次数: 0

摘要

目标:药代动力学问题是药物减产的主要原因,在本世纪初之前约占所有减产案例的 40%。为此,我们开发了几种高通量体外检测方法(如微粒体稳定性),用于在药物发现的早期阶段评估化合物的药代动力学特征。在 NCATS,单点大鼠肝微粒体(RLM)稳定性测定被用作一级测定,而人肝微粒体(HLM)稳定性测定被用作二级测定。我们对 30,000 多种化合物进行了 RLM 稳定性实验筛选并收集了数据,对 7,000 多种化合物进行了 HLM 稳定性实验筛选并收集了数据。虽然 HLM 稳定性筛选提供了有价值的见解,但由于产生的命中数量越来越多,加上该检测需要大量的时间和资源,因此需要采用替代策略。一种很有前景的方法是利用在这些实验数据集上训练的硅学模型。方法:我们介绍了利用内部 HLM 稳定性数据集开发 HLM 稳定性预测模型的情况。结果:通过采用经典的机器学习方法和神经网络等先进技术,我们的模型准确率超过了 80%。此外,我们还利用外部测试集对我们的模型进行了验证,发现我们的模型可与文献中的一些最佳模型相媲美。此外,当使用 RLM 稳定性预测作为输入描述符时,我们的 HLM 模型性能得到了提高,这进一步加强了 RLM 和 HLM 数据之间的强相关性。结论最佳模型和我们的数据集子集(PubChem AID:1963597)已在 ADME@NCATS 网站上公开,供广大药物发现界使用。据我们所知,这是同类中最大的开源模型,也是第一个利用跨物种数据的模型。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Developing Robust Human Liver Microsomal Stability Prediction Models: Leveraging Inter-Species Correlation with Rat Data.

Objectives: Pharmacokinetic issues were the leading cause of drug attrition, accounting for approximately 40% of all cases before the turn of the century. To this end, several high-throughput in vitro assays like microsomal stability have been developed to evaluate the pharmacokinetic profiles of compounds in the early stages of drug discovery. At NCATS, a single-point rat liver microsomal (RLM) stability assay is used as a Tier I assay, while human liver microsomal (HLM) stability is employed as a Tier II assay. We experimentally screened and collected data on over 30,000 compounds for RLM stability and over 7000 compounds for HLM stability. Although HLM stability screening provides valuable insights, the increasing number of hits generated, along with the time- and resource-intensive nature of the assay, highlights the need for alternative strategies. One promising approach is leveraging in silico models trained on these experimental datasets. Methods: We describe the development of an HLM stability prediction model using our in-house HLM stability dataset. Results: Employing both classical machine learning methods and advanced techniques, such as neural networks, we achieved model accuracies exceeding 80%. Moreover, we validated our model using external test sets and found that our models are comparable to some of the best models in literature. Additionally, the strong correlation observed between our RLM and HLM data was further reinforced by the fact that our HLM model performance improved when using RLM stability predictions as an input descriptor. Conclusions: The best model along with a subset of our dataset (PubChem AID: 1963597) has been made publicly accessible on the ADME@NCATS website for the benefit of the greater drug discovery community. To the best of our knowledge, it is the largest open-source model of its kind and the first to leverage cross-species data.

求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
Pharmaceutics
Pharmaceutics Pharmacology, Toxicology and Pharmaceutics-Pharmaceutical Science
CiteScore
7.90
自引率
11.10%
发文量
2379
审稿时长
16.41 days
期刊介绍: Pharmaceutics (ISSN 1999-4923) is an open access journal which provides an advanced forum for the science and technology of pharmaceutics and biopharmaceutics. It publishes reviews, regular research papers, communications,  and short notes. Covered topics include pharmacokinetics, toxicokinetics, pharmacodynamics, pharmacogenetics and pharmacogenomics, and pharmaceutical formulation. Our aim is to encourage scientists to publish their experimental and theoretical details in as much detail as possible. There is no restriction on the length of the papers. The full experimental details must be provided so that the results can be reproduced.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信