The schematic PK-PD model of the AZI intervention on the LPS -induced depressive-like behavior.

<p>Parameters and symbols were described in the text. Solid Lines with arrows indicated the disposition and turnover of the AZI, LPS, PCs, KYN and depressive-like behavior. Dashed lines with black dot meant the stimulatory effect being exerted by the connected factors. Dashed lines with white dot meant the inhibitory effect being exerted by the AZI. Long closed triangle symbols denoted the administration site for the AZI and LPS, respectively.</p

The Pharmacokinetic-Pharmacodynamic Model of Azithromycin for Lipopolysaccharide-Induced Depressive-Like Behavior in Mice

<div><p>A mechanism-based model was developed to describe the time course of lipopolysaccharide-induced depressive-like behavior and azithromycin pharmacodynamics in mice. The lipopolysaccharide-induced disease progression was monitored by lipopolysaccharide, proinflammatory cytokines, and kynrenine concentration in plasma. The depressive-like behavior was investigated by forced swimming test and tail suspension test. Azithromycin was selected to inhibit the surge of proinflammatory cytokines induced by lipopolysaccharide. Disease progression model and azithromycin pharmacodynamics were constructed from transduction and indirect response models. A delay in the onset of increased proinflammatory cytokines, kynrenine, and behavior test compared to lipopolysaccharide was successfully characterized by series transduction models. The inhibition of azithromycin on proinflammatory cytokines was described by an indirect response model. After lipopolysaccharide challenging, the proinflammatory cytokines, kynrenine and behavior tests would peak approximately at 3, 12, and 24 h respectively, and then the time courses slowly declined toward a baseline state after peak response. During azithromycin administration, the peak levels of proinflammatory cytokines, kynrenine and behavior indexes decreased. Model parameters indicated that azithromycin significantly inhibited the proinflammatory cytokines level in plasma and improved the depressive-like behavior induced by inflammation. The integrated model for disease progression and drug intervention captures turnovers of proinflammatory cytokines, kynrenine and the behavior results in the different time phases and conditions.</p> </div

The behavioral profiles of FST (A) and TST (B) after an i.p. LPS (0.8 mg/kg) and AZI treatment (100 mg/kg) in mice.

<p>Measured values and model fittings were shown in symbols and lines, respectively. Control mice were shown in closed squares and solid line. The model group was shown in closed circles and dot line. The regulative effect following a single oral dose of AZI was shown in closed triangle with dash line. All observations were reported as Mean ±SD (n = 8).</p

Parameter Estimates for the LPS-Induced Depressive like Model and Azithromycin Pharmacodynamics.

<p>Parameter Estimates for the LPS-Induced Depressive like Model and Azithromycin Pharmacodynamics.</p

Concentration-time profiles of AZI (A), LPS (B), total PCs (C), and KYN(D) after an i.p. LPS (0.8 mg/kg) and an i.g. AZI (100 mg/kg) in mice.

<p>(A): Closed squares and open squares indicated measured values in normal and LPS loading mice, respectively. Model fittings were presented as solid line for the normal mice and dash line for the LPS loading mice, respectively. (B): Closed squares and open squares indicated measured values in normal and AZI-treated mice, respectively. Model fittings were presented as solid line for normal mice and dash line for AZI-treated mice, respectively. (C) and (D): control mice were shown in closed squares and solid line. The model group was shown in closed circles and dot line. The regulative effect of AZI was shown in closed triangle with dash line. All observations were reported as Mean ±SD (n = 8) in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0054981#pone-0054981-g002" target="_blank">Figure 2</a>.</p

The Pharmacokinetic Parameters of Azithromycin in Normal Mice and LPS-Challenging Mice.

<p>The Pharmacokinetic Parameters of Azithromycin in Normal Mice and LPS-Challenging Mice.</p

The Pharmacokinetic Parameters of LPS in Normal Mice and Azithromycin-Treating Mice.

<p>The Pharmacokinetic Parameters of LPS in Normal Mice and Azithromycin-Treating Mice.</p

Chemicalome and Metabolome Matching Approach to Elucidating Biological Metabolic Networks of Complex Mixtures

Global metabolite identification of complex compound mixtures in biological systems is a very challenging task. Herein, we developed and validated a chemicalome to metabolome matching approach by taking herbal medicine as an example to delineate the metabolic networks of complex systems. This approach consists of five steps of data processing including raw data output, endogenous background subtraction, parent compound and metabolite differentiation, chemicalome to metabolome correlation, and the final validation via manual fragment comparison. Chemicalome to metabolome correlation, the core step of this approach, was performed based on matching the accurate mass differences of pseudomolecular ions between them with the accurate mass changes of known metabolic pathways and validating the matches by validation ions. A step-forward approach that confers a gradual identification of metabolites generated from different steps (1–4) and types (degradation, phase I/II, or mixed) of metabolic reactions was further proposed for chemicalome to metabolome matching. This approach was validated to be very useful and powerful for the metabolite identification of a single compound, a homologous compound mixture, and a complex herbal system. Using this approach, all metabolites (162) detected from urine samples of rats treated with Mai-Luo-Ning injection could be linked to their respective parent compounds, and 143 of them were supported by the final validation via manual fragment analysis. In most cases, more than 80% of the automatic matching results could be supported by the manual fragment validations. A complex metabolic network showing all the possible links between precursors and metabolites was successfully constructed. This study provides a generally applicable approach to global metabolite identification of complex compound mixtures in complex matrixes

Chemicalome and Metabolome Matching Approach to Elucidating Biological Metabolic Networks of Complex Mixtures

Global metabolite identification of complex compound mixtures in biological systems is a very challenging task. Herein, we developed and validated a chemicalome to metabolome matching approach by taking herbal medicine as an example to delineate the metabolic networks of complex systems. This approach consists of five steps of data processing including raw data output, endogenous background subtraction, parent compound and metabolite differentiation, chemicalome to metabolome correlation, and the final validation via manual fragment comparison. Chemicalome to metabolome correlation, the core step of this approach, was performed based on matching the accurate mass differences of pseudomolecular ions between them with the accurate mass changes of known metabolic pathways and validating the matches by validation ions. A step-forward approach that confers a gradual identification of metabolites generated from different steps (1–4) and types (degradation, phase I/II, or mixed) of metabolic reactions was further proposed for chemicalome to metabolome matching. This approach was validated to be very useful and powerful for the metabolite identification of a single compound, a homologous compound mixture, and a complex herbal system. Using this approach, all metabolites (162) detected from urine samples of rats treated with Mai-Luo-Ning injection could be linked to their respective parent compounds, and 143 of them were supported by the final validation via manual fragment analysis. In most cases, more than 80% of the automatic matching results could be supported by the manual fragment validations. A complex metabolic network showing all the possible links between precursors and metabolites was successfully constructed. This study provides a generally applicable approach to global metabolite identification of complex compound mixtures in complex matrixes

Pharmacokinetic Compatibility of Ginsenosides and <i>Schisandra</i> Lignans in <i>Shengmai-san</i>: From the Perspective of P-Glycoprotein

<div><p>Background</p><p>Phytochemical-mediated alterations in P-glycoprotein (P-gp) activity may result in herb-drug interactions by altering drug pharmacokinetics. <i>Shengmai-san</i>, a traditional Chinese herbal medicine composed by <i>Panax Ginseng, Ophiopogon Japonicus, and Schisandra Chinensis</i>, is routinely being used for treating various coronary heart diseases. In our previous studies, S<i>chisandra Lignans Extract</i> (SLE) was proved as a strong P-gp inhibitor, and herein, the compatibility of <i>Shengmai-san</i> was studied by investigating the influence of SLE on the pharmacokinetics of the ginsenosides from the perspective of P-gp.</p><p>Methodology</p><p>Pharmacokinetic experiments were firstly performed based on <i>in vitro</i> uptake, efflux and transport experiments in Caco-2, LLC-PK1 wild-type and MDR1-overexpressing L-MDR1 cells. During the whole experiment, digoxin, a classical P-gp substrate, was used as a positive control drug to verify the cells used are the valid models. Meanwhile, the effects of SLE on the pharmacokinetics of ginsenosides were further investigated in rats after single-dose and multi-dose of SLE.</p><p>Results and Conclusions</p><p>The efflux ratios of ginsenoside Rb2, Rc, Rg2, Rg3, Rd and Rb1 were found more than 3.5 in L-MDR1 cells and can be decreased significantly by verapamil (a classical P-gp inhibitor). Contrarily, the efflux ratios of other ginsenosides (Rh1, F1, Re, and Rg1) were lower than 2.0 and not affected by verapamil. Then, the effects of SLE on the uptake and transport of ginsenosides were investigated, and SLE was found can significantly enhance the uptake and inhibit the efflux ratio of ginsenoside Rb2, Rc, Rg2, Rg3, Rd and Rb1 in Caco-2 and L-MDR1 cells. Besides, <i>In vivo</i> experiments showed that single-dose and multi-dose of SLE at 500 mg/kg could increase the area under the plasma concentration time curve of Rb2, Rc and Rd significantly without affecting terminal elimination half-time. In conclusion, SLE could enhance the exposure of ginsenosides Rb2, Rc, Rg2, Rg3, Rd and Rb1 significantly.</p></div