Data Digitizing: Accurate and Precise Data Extraction for Quantitative Systems Pharmacology and Physiologically‐Based Pharmacokinetic Modeling

Abstract

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.

A Mechanistic, Enantioselective, Physiologically Based Pharmacokinetic Model of Verapamil and Norverapamil, Built and Evaluated for Drug–Drug Interaction Studies

“…Clinical studies of intravenous and oral administration in single- and multiple-dose regimens were collected and digitized from literature [ 21 ], complemented by unpublished verapamil plasma concentration-time profiles of two clinical trials previously conducted at Boehringer Ingelheim [ 22 , 23 ]. Both studies were performed in accordance with the Declaration of Helsinki and its later amendments.…”

Physiologically-Based Pharmacokinetic (PBPK) Modeling of Buprenorphine in Adults, Children and Preterm Neonates

“…Model input parameter optimization was accomplished using the Monte Carlo algorithm implemented in PK-Sim ® . Clinical data in scientific literature were digitized using GetData Graph Digitizer version 2.26.0.20 (S. Fedorov) according to best practices [ 30 ]. Allometric scaling was performed in NONMEM ® (Version 7.4.3), pharmacokinetic (PK) parameter analyses and graphics with the R programming language version 3.6.1 (R Foundation for Statistical Computing, Vienna, Austria) and R Studio ® version 1.2.5019 (R Studio, Inc., Boston, MA, USA).…”

A Physiologically-Based Pharmacokinetic Model of Trimethoprim for MATE1, OCT1, OCT2, and CYP2C8 Drug–Drug–Gene Interaction Predictions

“…The PBPK model of trimethoprim was developed using PK-Sim ® modeling software (Open Systems Pharmacology Suite 8.0, , 2019). Clinical study data from literature were digitized with Engauge Digitizer 10.12 (© M. Mitchell [ 17 ], 2019) according to best practices [ 18 ]. Model parameter optimization (Levenberg–Marquardt algorithm) and sensitivity analysis were performed within PK-Sim ® .…”