1/9/2023 0 Comments Datathief to digitize![]() ![]() Despite the potential to automatically data‐mine population average pharmacokinetic (PK) data for certain applications,ĭata extraction from graphical representations still requires manual efforts. As a result, researchers must extract the information of interest from the graphical representation to use the data for their modeling approaches. Unfortunately, published data are typically presented in aggregate form as plots or graphs without providing access to the underlying raw, uncondensed data. However, for model development, time‐dependent data of pharmacological relevant processes are a crucial requirement. However, because the greatest pitfall comes from pre‐existing errors, we recommend always making published data available as raw values.ĭuring the past few years, quantitative systems pharmacology (QSP) and especially physiologically‐based pharmacokinetics modeling (PBPK) have proven to be an important cornerstone of model‐informed drug discovery and development. Our findings suggest that data digitizing is precise and important. Analysis of 181 literature peak plasma concentration values revealed a considerable discrepancy between reported and post hoc digitized data with 85% having ζ > 5%. Although significant, no relevant confounders were found (mean ζ ± SD circles = 0.69% ± 0.68% vs. Accuracy, precision, confounder influence, and variability were investigated using scaled median symmetric accuracy (ζ), thus finding excellent accuracy (mean ζ = 0.99%). ![]() 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. 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.
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