Developing a Physiologically-Based Pharmacokinetic Model Knowledgebase in Support of Provisional Model Construction

<div><p>Developing physiologically-based pharmacokinetic (PBPK) models for chemicals can be resource-intensive, as neither chemical-specific parameters nor <i>in vivo</i> pharmacokinetic data are easily available for model construction. Previously developed, well-parameterized, and thoroughly-vetted models can be a great resource for the construction of models pertaining to new chemicals. A PBPK knowledgebase was compiled and developed from existing PBPK-related articles and used to develop new models. From 2,039 PBPK-related articles published between 1977 and 2013, 307 unique chemicals were identified for use as the basis of our knowledgebase. Keywords related to species, gender, developmental stages, and organs were analyzed from the articles within the PBPK knowledgebase. A correlation matrix of the 307 chemicals in the PBPK knowledgebase was calculated based on pharmacokinetic-relevant molecular descriptors. Chemicals in the PBPK knowledgebase were ranked based on their correlation toward ethylbenzene and gefitinib. Next, multiple chemicals were selected to represent exact matches, close analogues, or non-analogues of the target case study chemicals. Parameters, equations, or experimental data relevant to existing models for these chemicals and their analogues were used to construct new models, and model predictions were compared to observed values. This compiled knowledgebase provides a chemical structure-based approach for identifying PBPK models relevant to other chemical entities. Using suitable correlation metrics, we demonstrated that models of chemical analogues in the PBPK knowledgebase can guide the construction of PBPK models for other chemicals.</p></div

Case study with gefitinib.

<p>Comparing simulated (solid lines) and experimentally observed (triangle symbols) blood concentrations for compounds. PBPK models were extracted from the gefitinib study [<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004495#pcbi.1004495.ref061" target="_blank">61</a>], and executed to predict pharmacokinetics of gefitinib’s close-analogues (itraconazole, cocaine, diclofenac, 3,3'-diindolylmethane) and non-analoguse (perchlorate, phosphorothioate oligonucleotide, melamine, carbamateon). The experimental observations were extracted from PBPK literature listed in <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004495#pcbi.1004495.t003" target="_blank">Table 3</a>.</p

Partition coefficients and metabolic parameters from existing PBPK models for ethylbenzene and selected analogues.

<p>Partition coefficients and metabolic parameters from existing PBPK models for ethylbenzene and selected analogues.</p

Case study with ethylbenzene.

<p>Comparing blood concentrations of ethylbenzene (triangle symbols) from rats exposed to 100 ppm ethylbenzene for four hours [<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004495#pcbi.1004495.ref029" target="_blank">29</a>] and simulated blood concentrations of ethylbenzene (solid lines) based on the (A) ethylbenzene PBPK model [<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004495#pcbi.1004495.ref058" target="_blank">58</a>]; (B) xylene PBPK model [<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004495#pcbi.1004495.ref058" target="_blank">58</a>]; (C) toluene PBPK model [<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004495#pcbi.1004495.ref058" target="_blank">58</a>]; (D) benzene PBPK model [<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004495#pcbi.1004495.ref058" target="_blank">58</a>]; (E) dichloromethane PBPK model [<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004495#pcbi.1004495.ref059" target="_blank">59</a>]; and (F) methyl iodide PBPK model [<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004495#pcbi.1004495.ref060" target="_blank">60</a>].</p

Trends of PBPK literatures.

<p>(A) The 2,039 PBPK-related articles are placed into one of three categories: (1) unique chemical PBPK papers (grey), pioneering articles in which specific chemical names have appeared for the first time; (2) non-unique chemical PBPK papers (yellow), articles in which chemical names have appeared in previous publications; or (3) PBPK related papers (green), articles that are not associated with specific chemical names. (B) Linear regression of the number of articles in three categories over time.</p

Model parameters for selected gefitinib’s close-analogues and non-analogues.

<p>Model parameters for selected gefitinib’s close-analogues and non-analogues.</p

Keywords extraction from PBPK literatures.

<p>The abstracts in the PBPK knowledgebase were analyzed to identify PBPK-associated word-stems: (A) Frequency of the top 10 species; (B) Frequency of the top 10 life stages; (C) Frequency of the top 10 compartments.</p

Physicochemical molecular descriptors.

<p>Summary of the values of eight physicochemical molecular descriptors, calculated using the Molecular Operating Environment (MOE), for 307 chemicals in the PBPK knowledgebase. The eight descriptors are molecular weight (MW), hydrogen bond acceptor count (hba), hydrogen bond donor count (hbd), number of rotatable bonds (nRotB), polar surface area or topological polar surface area (PSA), octanol:water partition coefficient (LogP), log transformation of solubility (logS), and area of van der Waal surface (vdw_area). (A) The original calculated descriptor values; (B) The normalized descriptor values using <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004495#pcbi.1004495.e001" target="_blank">Eq 1</a> from the Methods section.</p

Selected analogues^* of ethylbenzene.

<p>Selected analogues<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004495#t001fn001" target="_blank">^*</a> of ethylbenzene.</p