Automated Extraction of Information on Chemical–P-glycoprotein Interactions from the Literature

Knowledge of the interactions between drugs and transporters is important for drug discovery and development as well as for the evaluation of their clinical safety. We recently developed a text-mining system for the automatic extraction of information on chemical–CYP3A4 interactions from the literature. This system is based on natural language processing and can extract chemical names and their interaction patterns according to sentence context. The present study aimed to extend this system to the extraction of information regarding chemical–transporter interactions. For this purpose, the key verb list designed for cytochrome P450 enzymes was replaced with that for known drug transporters. The performance of the system was then tested by examining the accuracy of information on chemical–P-glycoprotein (P-gp) interactions extracted from randomly selected PubMed abstracts. The system achieved 89.8% recall and 84.2% precision for the identification of chemical names and 71.7% recall and 78.6% precision for the extraction of chemical–P-gp interactions

Automated Extraction of Information on Chemical–P-glycoprotein Interactions from the Literature

Knowledge of the interactions between drugs and transporters is important for drug discovery and development as well as for the evaluation of their clinical safety. We recently developed a text-mining system for the automatic extraction of information on chemical–CYP3A4 interactions from the literature. This system is based on natural language processing and can extract chemical names and their interaction patterns according to sentence context. The present study aimed to extend this system to the extraction of information regarding chemical–transporter interactions. For this purpose, the key verb list designed for cytochrome P450 enzymes was replaced with that for known drug transporters. The performance of the system was then tested by examining the accuracy of information on chemical–P-glycoprotein (P-gp) interactions extracted from randomly selected PubMed abstracts. The system achieved 89.8% recall and 84.2% precision for the identification of chemical names and 71.7% recall and 78.6% precision for the extraction of chemical–P-gp interactions

Curve-fitting to experimental data of the induction of CYP3A4 by rifampicin in human hepatocytes.

<p>Fig.-normalized data and corresponding equations, i.e., <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0070330#pone.0070330.e008" target="_blank">Equations 6</a>–<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0070330#pone.0070330.e010" target="_blank">8</a>, were used for this analysis, assuming that inter-individual variability for induction is because of differences in baseline CYP3A4 activity. The surface curves represent the averages.</p

Prediction of DDIs for various CYP3A4 substrate drugs with concomitantly administered rifampicin.

<p>a) Fraction of the drug metabolized by CYP3A4 (fmCYP3A4) and clinical DDI data were taken from the article of Ohno et al. <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0070330#pone.0070330-Ohno2" target="_blank">[24]</a>.</p><p>b) Clinical data were obtained from the articles shown with the reference ID (Ref. ID).</p><p>c) Induction ratio (IR) of CYP3A4 activity was calculated from daily dose and days of administration of rifampicin by using Eqs. 6–8 and 15–17. The values for IR were represented as an average and upper and lower limits when one S.D. for inter-individual variability of CYP3A4 baseline activity was considered.</p

Snapshots of DDI models implemented in multi-hierarchical physiology simulation platforms.

<p>Fig./translation dynamics model for CYP3A4 following administration of the drug, as implemented on CellDesigner. Fig. 1C represents a PBPK-based DDI model, where the enzyme induction model was hybridized. Yellow and white rectangles represent the capsule module and functional module, respectively. Modules can communicate by connecting their ports with an edge.</p

Nonlinear curve-fitting to the blood concentration of rifampicin with repeated oral administration.

<p>Clinical data measured on day(Ref. 40) were simultaneously analyzed based on a PBPK model considering an auto-inducible metabolic process (Eqs. 15–17). Theoretical curves are represented for each data set. Keys: 300 mg, b.i.d. (▴, dotted line); 600 mg, q.d. (•, broken line); 900 mg q.d. (▪, solid line).</p

Simulation of the induction of CYP3A4 following repeated oral dosing of rifampicin.

<p>Fig.<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0070330#pone.0070330.e008" target="_blank">Equations 6</a>–<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0070330#pone.0070330.e010" target="_blank">8</a> and <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0070330#pone.0070330.e018" target="_blank">15</a>–<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0070330#pone.0070330.e020" target="_blank">17</a> were used for this simulation.</p

Simulation of DDI between alprazolam and rifampicin using a PHML/SBML hybrid model.

<p>Fig.(solid line) and presence (broken line) of rifampicin. Fig. 7B represents the comparison between the predicted blood concentration of alprazolam with the corresponding clinical data (Ref. 35). Keys: 1 mg alprazolam alone (•, solid line); 1 mg alprazolam with 4-day pretreatment with daily doses of 450 mg rifampicin (▴, broken line). The pharmacokinetic parameters for alprazolam were estimated by curve-fitting to the blood concentrations following the sole administration (standard deviation of residuals, <i>RSD</i>: 0.483), and then used for predicting those following the concomitant administration (standard deviation of prediction errors, <i>SDEP</i>: 0.760). Both <i>RSD</i> and <i>SDEP</i> was the same in terms of formula: .</p