Unfortunately, published data are typically presented in aggregate form as plots or graphs without providing access to the underlying raw, uncondensed data. 1 However, for model development, time-dependent data of pharmacological relevant processes are a crucial requirement. Furthermore, they can protect the modeler from using biased data that could subsequently lead to false in silico predictions and hence hamper the drug discovery and development process or, even worse, harm patients as a result of erroneously derived therapy recommendations.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. ☑ The study findings could help improve the quality of the QSP and PBPK models, which were developed based on digitized literature data. HOW MIGHT THIS CHANGE DRUG DISCOVERY, DEVELOPMENT, AND/OR THERAPEUTICS?.
☑ The results of this study contributed to an improved understanding of the precision and accuracy of digitizing software. WHAT DOES THIS STUDY ADD TO OUR KNOWLEDGE?.In addition, the discrepancies between reported and graphically presented data were analyzed. Moreover, it evaluated the software accuracy, precision, confounder influence, and variability between software. ☑ This study investigated the usage of digitizing software in QSP and PBPK modeling. ☑ In quantitative systems pharmacology (QSP) and physiologically-based pharmacokinetic (PBPK) modeling, data digitizer becomes a valuable tool to translate literature data from a graphical representation into numerical values. WHAT IS THE CURRENT KNOWLEDGE ON THE TOPIC?.However, because the greatest pitfall comes from pre-existing errors, we recommend always making published data available as raw values. 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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