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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 (?), thus finding excellent accuracy (mean ? = 0.99%). Although significant, no relevant confounders were found (mean ? ± 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 ? > 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.

SUBMITTER: Wojtyniak JG

PROVIDER: S-EPMC7306621 | biostudies-literature | 2020 Jun

REPOSITORIES: biostudies-literature

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

Wojtyniak Jan-Georg JG Britz Hannah H Selzer Dominik D Schwab Matthias M Lehr Thorsten T

CPT: pharmacometrics & systems pharmacology 20200616 6

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 ...[more]

PMID: 32543786

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Project description:More than 50 million adults in America suffer from chronic pain. Opioids are commonly prescribed for their effectiveness in relieving many types of pain. However, excessive prescribing of opioids can lead to abuse, addiction, and death. Non-steroidal anti-inflammatory drugs (NSAIDs), another major class of analgesic, also have many problematic side effects including headache, dizziness, vomiting, diarrhea, nausea, constipation, reduced appetite, and drowsiness. There is an urgent need for the understanding of molecular mechanisms that underlie drug abuse and addiction to aid in the design of new preventive or therapeutic agents for pain management. To facilitate pain related small-molecule signaling pathway studies and the prediction of potential therapeutic target(s) for the treatment of pain, we have constructed a comprehensive platform of a pain domain-specific chemogenomics knowledgebase (Pain-CKB) with integrated data mining computing tools. Our new computing platform describes the chemical molecules, genes, proteins, and signaling pathways involved in pain regulation. Pain-CKB is implemented with a friendly user interface for the prediction of the relevant protein targets and analysis and visualization of the outputs, including HTDocking, TargetHunter, BBB predictor, and Spider Plot. Combining these with other novel tools, we performed three case studies to systematically demonstrate how further studies can be conducted based on the data generated from Pain-CKB and its algorithms and tools. First, systems pharmacology target mapping was carried out for four FDA approved analgesics in order to identify the known target and predict off-target interactions. Subsequently, the target mapping outcomes were applied to build physiologically based pharmacokinetic (PBPK) models for acetaminophen and fentanyl to explore the drug-drug interaction (DDI) between this pair of drugs. Finally, pharmaco-analytics was conducted to explore the detailed interaction pattern of acetaminophen reactive metabolite and its hepatotoxicity target, thioredoxin reductase.

Project description:BackgroundModeling of pharmacokinetic parameters and pharmacodynamic actions requires knowledge of the arterial blood concentration. In most cases, experimental measurements are only available for a peripheral vein (usually antecubital) whose concentration may differ significantly from both arterial and central vein concentration.MethodsA physiologically based pharmacokinetic (PBPK) model for the tissues drained by the antecubital vein (referred to as "arm") is developed. It is assumed that the "arm" is composed of tissues with identical properties (partition coefficient, blood flow/gm) as the whole body tissues plus a new "tissue" representing skin arteriovenous shunts. The antecubital vein concentration depends on the following parameters: the fraction of "arm" blood flow contributed by muscle, skin, adipose, connective tissue and arteriovenous shunts, and the flow per gram of the arteriovenous shunt. The value of these parameters was investigated using simultaneous experimental measurements of arterial and antecubital concentrations for eight solutes: ethanol, thiopental, 99Tcm-diethylene triamine pentaacetate (DTPA), ketamine, D2O, acetone, methylene chloride and toluene. A new procedure is described that can be used to determine the arterial concentration for an arbitrary solute by deconvolution of the antecubital concentration. These procedures are implemented in PKQuest, a general PBPK program that is freely distributed http://www.pkquest.com.ResultsOne set of "standard arm" parameters provides an adequate description of the arterial/antecubital vein concentration for ethanol, DTPA, thiopental and ketamine. A significantly different set of "arm" parameters was required to describe the data for D2O, acetone, methylene chloride and toluene - probably because the "arm" is in a different physiological state.ConclusionsUsing the set of "standard arm" parameters, the antecubital vein concentration can be used to determine the whole body PBPK model parameters for an arbitrary solute without any additional adjustable parameters. Also, the antecubital vein concentration can be used to estimate the arterial concentration for an arbitrary input for solutes for which no arterial concentration data is available.

Dataset Information

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

Publications

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

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