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

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

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

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

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 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

Synthesis and Functional Characterization of Novel Sialyl LewisX Mimic-Decorated Liposomes for E‑selectin-Mediated Targeting to Inflamed Endothelial Cells

Sialyl LewisX (sLeX) is a natural ligand of E-selectin that is overexpressed by inflamed and tumor endothelium. Although sLeX is a potential ligand for drug targeting, synthesis of the tetrasaccharide is complicated with many reaction steps. In this study, structurally simplified novel sLeX analogues were designed and linked with 1,2-distearoyl-<i>sn</i>-glycero-3-phosphoethanolamine-polyethylene glycol-2000 (DSPE-PEG) for E-selectin-mediated liposomal delivery. The sLeX structural simplification strategies include (1) replacement of the Gal-GlcNAc disaccharide unit with lactose to reduce many initial steps and (2) substitution of neuraminic acid with a negatively charged group, i.e., 3′-sulfo, 3′-carboxymethyl (3′-CM), or 3′-(1-carboxy)­ethyl (3′-CE). While all the liposomes developed were similar in particle size and charge, the 3′-CE sLeX mimic liposome demonstrated the highest uptake in inflammatory cytokine-treated human umbilical vein endothelial cells (HUVECs), being even more potent than native sLeX-decorated liposomes. Inhibition studies using antiselectin antibodies revealed that their uptake was mediated primarily by overexpressed E-selectin on inflamed HUVECs. Molecular dynamics simulations were performed to gain mechanistic insight into the E-selectin binding differences among native and mimic sLeX. The terminally branched methyl group of the 3′-CE sLeX mimic oriented and faced the bulk hydrophilic solution during E-selectin binding. Since this state is entropically unfavorable, the 3′-CE sLeX mimic molecule might be pushed toward the binding pocket of E-selectin by a hydrophobic effect, leading to a higher probability of hydrogen-bond formation than native sLeX and the 3′-CM sLeX mimic. This corresponded with the fact that the 3′-CE sLeX mimic liposome exhibited much greater uptake than the 3′-CM sLeX mimic liposome