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利用ROC分析从动物数据对人体口服生物利用度进行定性预测。

The use of ROC analysis for the qualitative prediction of human oral bioavailability from animal data.

作者信息

Olivares-Morales Andrés, Hatley Oliver J D, Turner David, Galetin Aleksandra, Aarons Leon, Rostami-Hodjegan Amin

机构信息

Centre for Applied Pharmacokinetic Research, School of Pharmacy and Pharmaceutical Sciences The University of Manchester, Stopford Building, Oxford Road, Manchester, M13 9PT, UK.

出版信息

Pharm Res. 2014 Mar;31(3):720-30. doi: 10.1007/s11095-013-1193-2. Epub 2013 Sep 27.

DOI:10.1007/s11095-013-1193-2
PMID:24072264
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC4250569/
Abstract

PURPOSE

To develop and evaluate a tool for the qualitative prediction of human oral bioavailability (Fhuman) from animal oral bioavailability (Fanimal) data employing ROC analysis and to identify the optimal thresholds for such predictions.

METHODS

A dataset of 184 compounds with known Fhuman and Fanimal in at least one species (mouse, rat, dog and non-human primates (NHP)) was employed. A binary classification model for Fhuman was built by setting a threshold for high/low Fhuman at 50%. The thresholds for high/low Fanimal were varied from 0 to 100 to generate the ROC curves. Optimal thresholds were derived from 'cost analysis' and the outcomes with respect to false negative and false positive predictions were analyzed against the BDDCS class distributions.

RESULTS

We successfully built ROC curves for the combined dataset and per individual species. Optimal Fanimal thresholds were found to be 67% (mouse), 22% (rat), 58% (dog), 35% (NHP) and 47% (combined dataset). No significant trends were observed when sub-categorizing the outcomes by the BDDCS.

CONCLUSIONS

Fanimal can predict high/low Fhuman with adequate sensitivity and specificity. This methodology and associated thresholds can be employed as part of decisions related to planning necessary studies during development of new drug candidates and lead selection.

摘要

目的

利用ROC分析开发并评估一种从动物口服生物利用度（Fanimal）数据定性预测人体口服生物利用度（Fhuman）的工具，并确定此类预测的最佳阈值。

方法

采用一个包含184种化合物的数据集，这些化合物在至少一种物种（小鼠、大鼠、狗和非人灵长类动物（NHP））中具有已知的Fhuman和Fanimal。通过将高/低Fhuman的阈值设定为50%，建立了Fhuman的二元分类模型。高/低Fanimal的阈值从0到100变化以生成ROC曲线。从“成本分析”中得出最佳阈值，并根据BDDCS类别分布分析假阴性和假阳性预测的结果。

结果

我们成功为合并数据集和每个单独物种构建了ROC曲线。发现最佳Fanimal阈值为67%（小鼠）、22%（大鼠）、58%（狗）、35%（NHP）和47%（合并数据集）。按BDDCS对结果进行子分类时，未观察到显著趋势。

结论

Fanimal能够以足够的敏感性和特异性预测高/低Fhuman。这种方法和相关阈值可作为新药候选物开发和先导物选择过程中规划必要研究的决策的一部分。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ff3e/4250569/2c8ed65d61ad/11095_2013_1193_Fig8_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ff3e/4250569/cd3002d6b832/11095_2013_1193_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ff3e/4250569/013bf6fd242f/11095_2013_1193_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ff3e/4250569/ab7edc635562/11095_2013_1193_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ff3e/4250569/650b03ba5b04/11095_2013_1193_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ff3e/4250569/61ec7413618f/11095_2013_1193_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ff3e/4250569/fbd21ed72d96/11095_2013_1193_Fig6_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ff3e/4250569/f3ae11eb0272/11095_2013_1193_Fig7_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ff3e/4250569/2c8ed65d61ad/11095_2013_1193_Fig8_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ff3e/4250569/cd3002d6b832/11095_2013_1193_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ff3e/4250569/013bf6fd242f/11095_2013_1193_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ff3e/4250569/ab7edc635562/11095_2013_1193_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ff3e/4250569/650b03ba5b04/11095_2013_1193_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ff3e/4250569/61ec7413618f/11095_2013_1193_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ff3e/4250569/fbd21ed72d96/11095_2013_1193_Fig6_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ff3e/4250569/f3ae11eb0272/11095_2013_1193_Fig7_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ff3e/4250569/2c8ed65d61ad/11095_2013_1193_Fig8_HTML.jpg

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