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

Effects of tissue suction on hepatic toxicity.

<p>A) Alanine aminotransferase (ALT) in serum. ALT activity was measured at 0, 6, 24, and 48 h after transfection. Type III device was used. Each value represents mean ± SD (n  = 3 [sham operation], or n  = 4 [tissue suction]). *p<0.05 versus sham operation. B) Aspartate aminotransferase (AST) in serum. AST activity was measured at 0, 6, 24, and 48 h after transfection. Type III device was used. Each value represents mean ± SD (n  = 3 [sham operation], or n  = 4 [tissue suction]). *p<0.05 versus sham operation. C) HE staining of the liver section. The suctioned part of the liver (Part I in Fig. 4A) was sampled at 0 and 7 days after tissue suction. Type III device was used. All mice were alive at the end of the experiment.</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

<em>In vivo</em> Site-Specific Transfection of Naked Plasmid DNA and siRNAs in Mice by Using a Tissue Suction Device

<div><p>We have developed an <em>in vivo</em> transfection method for naked plasmid DNA (pDNA) and siRNA in mice by using a tissue suction device. The target tissue was suctioned by a device made of polydimethylsiloxane (PDMS) following the intravenous injection of naked pDNA or siRNA. Transfection of pDNA encoding luciferase was achieved by the suction of the kidney, liver, spleen, and heart, but not the duodenum, skeletal muscle, or stomach. Luciferase expression was specifically observed at the suctioned region of the tissue, and the highest luciferase expression was detected at the surface of the tissue (0.12±0.03 ng/mg protein in mice liver). Luciferase expression levels in the whole liver increased linearly with an increase in the number of times the liver was suctioned. Transfection of siRNA targeting glyceraldehyde 3-phosphate dehydrogenase (GAPDH) gene significantly suppressed the expression of GAPDH mRNA in the liver. Histological analysis shows that severe damage was not observed in the suctioned livers. Since the suction device can be mounted onto the head of the endoscope, this method is a minimally invasive. These results indicate that the <em>in vivo</em> transfection method developed in this study will be a viable approach for biological research and therapies using nucleic acids.</p> </div

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

Tissue suction devices used in this study.

<p>Tissue suction devices used in this study.</p

<i>In vivo</i> transfection of naked pDNA by tissue suction.

<p>A) <i>In vivo</i> imaging of luciferase activity in a mouse liver that was suctioned once by the type I device just after intravenous injection of pCMV-Luc. B) <i>Ex vivo</i> imaging of luciferase activity in the liver suctioned by the type I device. C) Bright field image of (B). D) Luciferase levels of various tissues. The right kidney in mice was suctioned once by the type III device. Each value represents means + SD (n  = 4). All mice were alive at the end of the experiment.</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