9 research outputs found
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)Âethyl
(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