DATATHIEF TOOL MANUAL
DATATHIEF TOOL SOFTWARE

Finally, recommendations regarding the creation of digitizable plots and graphs and the digitization process itself were proposed. Moreover, discrepancies between reported and graphically presented data were quantified, and the covariates influencing the digitizing process were identified. Second, this analysis aimed to evaluate the accuracy and precision of a relevant digitizing software.

DATATHIEF TOOL SOFTWARE

Thus, the first objective of this analysis was to assess the general usage of the data digitizing software for QSP and PBPK modeling. Consequently, these factors can potentially interfere with model development and evaluation processes and ultimately lead to false predictions and questionable model-based decisions. In addition, little is known about the extent of discrepancy between reported and graphically presented data that is typically only revealed after post hoc digitization and the nature of these errors and confounding factors when it comes to the digitization process. Moreover, to the best of our knowledge, there is no systematic evaluation of the accuracy and precision of these software solutions, nor have any interfering factors that could potentially bias the digitized output been identified. 14 However, neither for them nor for QSP or PBPK modeling is information available regarding the importance and use of digitizing software. 10- 13 These software solutions have been in active use for some time for the well-established population PK approaches. 3- 9 Fortunately, several off-the-shelf digitization software packages that allow the extraction of numerical information from their two-dimensional graphical representation are currently available. To illustrate the scale of this issue, it should be noted that PBPK projects not uncommonly rely on extracted data gathered from up to 50 articles.

DATATHIEF TOOL MANUAL

Despite the potential to automatically data-mine population average pharmacokinetic (PK) data for certain applications, 2 data 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. 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.

Datathief tool