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Data Digitizing: Accurate and Precise Data Extraction for Quantitative Systems Pharmacology and Physiologically-Based
Jan-Georg Wojtyniak1,2, Hannah Britz1, Dominik Selzer1
1Clinical Pharmacy, Saarland University, Saarbrücken, Germany.
Data digitizing is a precise tool for quantitative systems pharmacology (QSP) and physiologically-based pharmacokinetic (PBPK) modeling. To improve accuracy, researchers recommend publishing raw data, as digitized data can contain pre-existing errors.
Area of Science:
- Pharmacology and Computational Biology
- Data Science in Scientific Research
Background:
- Data digitizing is crucial for extracting numerical data from graphs in quantitative systems pharmacology (QSP) and physiologically-based pharmacokinetic (PBPK) modeling.
- The use of digitizing software in QSP and PBPK research has shown a significant annual increase of 16% in publications.
Purpose of the Study:
- To evaluate the accuracy, precision, and potential confounders of data digitizing in QSP and PBPK modeling.
- To assess the discrepancy between reported and digitized data in published literature.
Main Methods:
- Utilized scaled median symmetric accuracy (ζ) to quantify accuracy and precision.
- Analyzed 181 peak plasma concentration values from scientific literature.
- Investigated the influence of confounders on data digitizing accuracy.
Main Results:
- Data digitizing demonstrated excellent accuracy, with a mean ζ of 0.99%.
- While confounders were present, they were not found to be significantly impactful (mean ζ ± SD circles = 0.69% ± 0.68% vs. triangles = 1.3% ± 0.62%).
- A notable discrepancy was observed in 85% of analyzed peak plasma concentration values (ζ > 5%) between reported and digitized data.
Conclusions:
- Data digitizing is a precise and valuable tool for QSP and PBPK modeling.
- Pre-existing errors in published graphs are a major source of inaccuracy in digitized data.
- It is recommended that researchers always publish raw data to ensure the integrity of quantitative analyses.
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