Clinical Pharmacy, Saarland University, Saarbrücken, Germany.
Dr. Margarete Fischer-Bosch-Institute of Clinical Pharmacology, Stuttgart, Germany.
CPT Pharmacometrics Syst Pharmacol. 2020 Jun;9(6):322-331. doi: 10.1002/psp4.12511. Epub 2020 Jun 16.
In quantitative systems pharmacology (QSP) and physiologically-based pharmacokinetic (PBPK) modeling, data digitizing is a valuable tool to extract numerical information from published data presented as graphs. To quantify their relevance, a literature search revealed a remarkable mean increase of 16% per year in publications citing digitizing software together with QSP or PBPK. Accuracy, precision, confounder influence, and variability were investigated using scaled median symmetric accuracy (ζ), thus finding excellent accuracy (mean ζ = 0.99%). Although significant, no relevant confounders were found (mean ζ ± SD circles = 0.69% ± 0.68% vs. triangles = 1.3% ± 0.62%). Analysis of 181 literature peak plasma concentration values revealed a considerable discrepancy between reported and post hoc digitized data with 85% having ζ > 5%. Our findings suggest that data digitizing is precise and important. However, because the greatest pitfall comes from pre-existing errors, we recommend always making published data available as raw values.
在定量系统药理学 (QSP) 和基于生理的药代动力学 (PBPK) 建模中，数据数字化是从以图形形式呈现的已发表数据中提取数值信息的一种有用工具。为了量化其相关性，文献检索显示，引用数字化软件与 QSP 或 PBPK 一起的出版物每年平均增长 16%。使用缩放中位数对称准确性 (ζ) 研究了准确性、精密度、混杂因素影响和可变性，结果发现准确性非常好（平均 ζ=0.99%）。虽然存在显著差异，但未发现相关混杂因素（平均 ζ±SD 圆=0.69%±0.68%与三角=1.3%±0.62%）。对 181 篇文献峰血浆浓度值的分析表明，报告值和事后数字化数据之间存在相当大的差异，85%的 ζ 值>5%。我们的研究结果表明，数据数字化是精确和重要的。然而，由于最大的陷阱来自于先前存在的错误，我们建议始终提供原始值作为已发表数据。
