Latest revision as of 18:53, 18 September 2015

model

1 Delivery module

1.1 Introduction

Pharmacokinetics is the quantitative study of drug absorption, distribution and metabolism in the body. Pharmacokinetic data are indispensable for phase I clinical trials to evaluate the tissue distribution and safety of drugs. To construct a strategy for developing efficient and safe in vivo RNAi therapy systems, pharmacokinetics at whole body, organ, cellular and sub-cellular levels need to be considered [1].

In our laboratory study (GFP experiment), we obtained a qualitative description of in vivo drug distribution after systematic administration. A computational and compartmental model was built to provide mechanistic insights into a quantitative explanation of the experimental results.

Three primary aspects were counted in this pharmacokinetic model:
i) theoretically predicting the effect of RVG modification of the targeting ability of exosomes,
ii) approximating time-series exosome (siRNA) concentration data for use in modeling RNAi kinetics in target tissue and subsequently calculating the effective dose, and
iii) determining what portion of the delivery system could be improved based on simulation data.

1.2 Model methods

The process of drug delivery in humans and mice is quite complex. Physiologically speaking, drug delivery after administration can be simplified into two separate phases:

i) circulation from a central compartment (blood) to a peripheral compartment (body tissues), and
ii) uptake and trafficking at cellular and sub-cellular levels in target tissues.
Although physiologically based pharmacokinetic (PBPK) models have been widely used in clinical trials, few described the cellular uptake behavior because most of the available drugs, at present, are chemically synthesized and have different biological properties compared with exosomes. Exosomes differ from conventional chemical drugs because of their distinct biological characteristics as microvesicles [2]. Thus, we would like to modify the current PBPK model and add details regarding cellular uptake behavior based on the biological nature of exosomes.

1.2.1 Modeling multi-compartmental transport

In our laboratory work, we measured the relative level of GFP in the brain, liver, lung and spleen after injecting anti-GFP siRNA into mouse. Thus, we examined separate compartments for the brain, liver, lung and spleen. Other tissues were merged into one compartment. Each peripheral compartment had blood exchange with the central blood circulation, during which a certain percentage of exosomes were captured into the extracellular matrix of endothelial cells in different tissues.

Figure 1. Schematic diagram of the arrangement of different tissues in the pharmacokinetic model. The blood, along with exosomes, circulates from the central compartment to five peripheral compartments.

As membrane vesicles, exosomes may rapidly shift from associating with other complexes and disassociating into a free format during blood circulation. Moreover, the ultimate fate of exosomes, similar to other microvesicles, is degradation by lysosomes after internalization via a common process discussed later. Research has shown that microvesicles, containing miRNAs or siRNAs, are stable in serum and play significant biological roles in cell communication [3]. Furthermore, the elimination of exosomes occurs primarily in specific tissues rather than in blood circulation, albeit that the half-life of exosomes in blood circulation is much shorter [4]. These two findings suggest that the elimination rate of exosomes in blood circulation is negligible compared with that in target tissues and does not need to be considered in this portion of the pharmacokinetic model.

Using standard mass action kinetics, the equations below describe the change in the concentration (mass) of free exosomes over time in blood and target tissues. Here, kblooddis and kbloodbind represents the association and disassociation, respectively, of exosomes to other complexes in the blood circulation.

Notably, not all exosomes are effective or completely absorbed by tissues. Therefore, partitiontissue is included to describe the effective fraction of the dose. Additionally, Et represents the quantity of exosomes captured by the extracellular matrix of cells in tissues, but does not represent the final quantity of exosomes in tissues, which will be discussed in the next portion of the model. Qtissue and Qc represents the velocity of blood flowing in peripheral and central compartments, respectively.

This work is supported by model of IGEM Slovenia 2012, IGEM NJU-China 2013 and other literatures [5,6].

1.2.2 Modeling cellular uptake and intracellular trafficking

Extracellular vesicles can be internalized by cells via a variety of pathways, namely, phagocytosis, clathrin- and caveolin-mediated endocytosis and macropinocytosis [7]. We assume that receptor-mediated endocytosis is the major pathway of primary exosome internalization.

The cellular uptake pathway is summarized in Figure_2. Exosomes bind to the membranes of target cells after being captured by the extracellular matrix and then internalized through endocytosis. The receptor-ligand interaction may facilitate this process. After internalization, the RISC complex may escape from endosomes, and endosomes may be ultimately eliminated by lysosomes. Although other pathways such as transcytosis and exocytosis following endocytosis may occur, we did not take them into account for simplification.

Figure 2. Pathways that participate in exosomes uptake by target cells. Exosomes are transported from the extracellular matrix to the cell surface and undergo intracellular trafficking after internalization. The RISC complex in exosomes is released, and exosomes are ultimately degraded.

We used several equations to describe the above pathway. RVG modification helps exosomes bind acetylcholine receptors specifically expressed in neuronal cells. Exosomes internalization is much easier provided that more exosomes bind target cells. The binding process is modeled using mass action kinetics. AR denotes the number of acetylcholine receptors on target cells, and km represents the specific binding constant. Non-receptor-ligand interaction--mediated binding is summarized using kbindtissue.

The internalization and elimination of exosomes are formulated below using the parameters kinttissue and kelimttissue, respectively. Note that different tissues have different internalization and elimination rates.

The quantity of the endosomal RISC complex and escape behavior is modeled using the following equation. The concentration of siRNA in exosomes is determined by real-time RT-PCR in the literature [8] and represented by kc. kescendvec represents the escape rate of the RISC complex from exosomes (endosomes) to the cytosol.

This part of work is based on literature [5].

1.3 Parameter finding and adjustment

The most challenging part of modeling is finding and adjusting parameters. After reviewing the literature, we unfortunately found that few of the parameters have been measured or reported directly. The original paper written by Bartlett and Davis uses synthetic polyplexes as carriers to deliver siRNA [5]. The stability and targeting ability of synthetic polyplexes diverge considerably from exosomes due to their different biochemical nature. Using all the parameters in the original paper without adjustment would not be appropriate because of different biochemical natures and consequences of these delivery systems.

Parameter adjustment is not unusual in modeling biological processes. This endeavor is a somewhat uncertain endeavor and lacks specific procedures. In an iterative process, each set of parameters must be run through the model and modified to bring the output of the model into better and better agreement with observed experiment and literature results [9]. Following this doctrine, we ran our simulation and attempted to fit the results to the experimental and literature data.

You can access the description of model variables and parameters here . The determination of the parameters is also described in the list. When one parameter was reported in the literature, we cited the literature directly; when the parameter was not accessible but could be estimated and fitted to the literature or experimental results, we used the terms “estimated from literature and experimental results”.

1.4 Results

We simulated the pharmacokinetic model and obtained initial results. Unfortunately, the results showed that the model was not accurate. Distinguishing the effects of RVG modification on the tissue distribution of exosomes was difficult, as shown in the figure below.

Figure 3. Effect of RVG modification on the tissue distribution of exosomes. A: Without RVG modification; B: With RVG modification. The initial results are simulated with partitionbrain set at 1×10^-1.

Why did we obtain unrealistic simulation results? The answer simply lies in the parameter set we chose. After performing parameter sensitivity analysis, we were surprised to find that exosome bindings to the neuronal cell surface does not determine the internalization rate. In contrast, paritionbrain is more sensitive, indicating that the rate limiting step for exosome internalization is its effective dose fraction to targeted cells.

We next carefully investigated the presence of BBB and the effect of RGV modification on paritionbrain. The blood brain barrier is formed by endothelial cells at the level of cerebral capillaries [10]. The cerebral endothelial cells may form complex tight junctions that interfere with permeability. The binding of RVG to acetylcholine receptors, which are present in high density at the neuromuscular junction, would provide a mechanism whereby exosomes could be locally concentrated at sites in proximity to peripheral nerves, facilitating subsequent uptake and transfer to the central nervous system [11]. The local concentrating of exosomes at proximal sites may significantly increase the effective dose fraction available to targeted cells, resulting in a greater number of exosomes passing through the BBB and captured by the extracellular matrix of target cells. To our knowledge, this mechanism is why exosomes may pass through the BBB much more easily after RVG modification. Thus, we hypothesized that partitionbrain may also be influenced by RVG modification.

With partitionbrain increased by 6-fold, we finally obtained optimized simulation results. The biological meaning of this parameter adjustment is that RVG modification helps exosomes bind acetyl-choline receptors, not only facilitating internalization into target cells but also increasing the ability of exosomes to pass though the BBB by at least 6-fold.

Figure 4. Effect of RVG modification on the tissue distribution of exosomes. The results are simulated with partitionbrain increased by 6-fold. A-B: Control study of the time course of the tissue-distribution of exosomes without RVG modification. C-D: Case study of the time course of the tissue-distribution of exosomes with RVG modification and MOR-siRNA as cargo. E: In situ simulation of the tissue-distribution of exosomes.

We now better understand our delivery device using computational simulation data. The half-life of exosomes in blood is short, which is consistent with findings with the literature [12]. The tissue distribution pattern of exosomes with or without RVG modifications is also consistent with findings in the literature [13] and our GFP experiment.

Furthermore, the simulation data shows that a small portion of exosomes may also pass into non-targeted tissues due to circulation. We could improve the targeting precision by further modifying the exosomes.

1.5 Conclusion and Remarks

In this module, we created a pharmacokinetic model to simulate the time-dependent tissue distribution of exosomes at whole organ and cellular levels. We theoretically tested the effect of RVG modification on the capability of exosomes to pass through the BBB. The simulation results are consistent with experimental measurements, and provide clues regarding improvements to the delivery device.

1.6 Model Variables

*: Exosomes become endosomes after interanalization. Here we still use term exosomes for clear illustration.

1.7 Model Parameters

?: We are very uncertain about these parameters. However, the parameter sensitivity analysis showed these parameters were not notable for the accuracy of the result.

***: Details and Reasons of this adjustment for RVG modification are discussed in the text.

References:
1. Takakura, Y., Nishikawa, M., Yamashita, F. and Hashida, M. (2001) Development of gene drug delivery systems based on pharmacokinetic studies. European journal of pharmaceutical sciences : official journal of the European Federation for Pharmaceutical Sciences, 13, 71-76.
2.El Andaloussi, S., Lakhal, S., Mager, I. and Wood, M.J. (2013) Exosomes for targeted siRNA delivery across biological barriers. Adv Drug Deliv Rev, 65, 391-397.
3.Zhang, Y., Liu, D., Chen, X., Li, J., Li, L., Bian, Z., Sun, F., Lu, J., Yin, Y., Cai, X. et al. (2010) Secreted monocytic miR-150 enhances targeted endothelial cell migration. Molecular cell, 39, 133-144.
4. Takahashi, Y., Nishikawa, M., Shinotsuka, H., Matsui, Y., Ohara, S., Imai, T. and Takakura, Y. (2013) Visualization and in vivo tracking of the exosomes of murine melanoma B16-BL6 cells in mice after intravenous injection. Journal of Biotechnology, 165, 77-84.
5. Bartlett, D.W. and Davis, M.E. (2006) Insights into the kinetics of siRNA-mediated gene silencing from live-cell and live-animal bioluminescent imaging. Nucleic Acids Res, 34, 322-333.
6.Levitt, D.G. and Schoemaker, R.C. (2006) Human physiologically based pharmacokinetic model for ACE inhibitors: ramipril and ramiprilat. BMC clinical pharmacology, 6, 1.
7.Mulcahy, L.A., Pink, R.C. and Carter, D.R. (2014) Routes and mechanisms of extracellular vesicle uptake. J Extracell Vesicles, 3.
8. Alvarez-Erviti, L., Seow, Y., Yin, H., Betts, C., Lakhal, S. and Wood, M.J. (2011) Delivery of siRNA to the mouse brain by systemic injection of targeted exosomes. Nature biotechnology, 29, 341-345.
9. Sible, J.C. and Tyson, J.J. (2007) Mathematical modeling as a tool for investigating cell cycle control networks. Methods (San Diego, Calif.), 41, 238- 247.
10. Cecchelli, R., Berezowski, V., Lundquist, S., Culot, M., Renftel, M., Dehouck, M.P. and Fenart, L. (2007) Modelling of the blood-brain barrier in drug discovery and development. Nat Rev Drug Discov, 6, 650-661.
11. Lentz, T.L., Burrage, T.G., Smith, A.L., Crick, J. and Tignor, G.H. (1982) Is the acetylcholine receptor a rabies virus receptor? Science, 215, 182-184.
12. Morishita, M., Takahashi, Y., Nishikawa, M., Sano, K., Kato, K., Yamashita, T., Imai, T., Saji, H. and Takakura, Y. (2015) Quantitative analysis of tissue distribution of the B16BL6-derived exosomes using a streptavidin-lactadherin fusion protein and iodine-125-labeled biotin derivative after intravenous injection in mice. Journal of pharmaceutical sciences, 104, 705-713.
13. Kumar, P., Wu, H., McBride, J.L., Jung, K.E., Kim, M.H., Davidson, B.L., Lee, S.K., Shankar, P. and Manjunath, N. (2007) Transvascular delivery of small interfering RNA to the central nervous system. Nature, 448, 39-43.
14. Lai, C.P., Mardini, O., Ericsson, M., Prabhakar, S., Maguire, C.A., Chen, J.W., Tannous, B.A. and Breakefield, X.O. (2014) Dynamic biodistribution of extracellular vesicles in vivo using a multimodal imaging reporter. ACS Nano, 8, 483- 494.
15. Banks, G.A., Roselli, R.J., Chen, R. and Giorgio, T.D. (2003) A model for the analysis of nonviral gene therapy. Gene Ther, 10, 1766-1775.

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+<li style="line-height:250%;margin-left:10%"><a href="https://2015.igem.org/NJU-China-model.html" style="font-weight:bold;font-family:幼圆;color:black">Delivery Module</a></li>
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+<li style="line-height:250%;margin-left:10%"><a href="https://2015.igem.org/Team:NJU-China/RNAi" style="font-weight:bold;font-family:幼圆;color:black">RNAi Module</a></li>
+<li style="line-height:250%;margin-left:10%"><a href="https://2015.igem.org/Team:NJU-China/signaling" style="font-weight:bold;font-family:幼圆;color:black">Signaling Module</a></li>
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-	<h1> 1 Delivery module </h1> <br>
+				<h1> 1 Delivery module </h1> <br>
 	<h2> 1.1 Introduction </h2> <br><br>
-&nbsp;&nbsp;&nbsp;Pharmacokinetics is the quantitative study of drug absorption,
-distribution and metabolism in the body. Pharmacokinetic data are indispensable for
+Pharmacokinetics is the quantitative study of drug absorption, distribution and
-phase I clinical trials to evaluate the tissue distribution and safety of drugs. To
+metabolism in the body. Pharmacokinetic data are indispensable for phase I clinical
-construct a strategy for developing efficient and safe in vivo RNAi therapy systems,
+trials to evaluate the tissue distribution and safety of drugs. To construct a strategy
-pharmacokinetics at whole body, organ, cellular and sub-cellular levels need to be
+for developing efficient and safe in vivo RNAi therapy systems, pharmacokinetics at
-considered [1]. <br><br>
+whole body, organ, cellular and sub-cellular levels need to be considered [1].
-&nbsp;&nbsp;&nbsp;In our laboratory study (GFP experiment), we obtained a qualitative
+<br><br>
-description of in vivo drug distribution after systematic administration. A
-computational and compartmental model was built to provide mechanistic insights into a
+In our laboratory study (GFP experiment), we obtained a qualitative description of in
-quantitative explanation of the experimental results. <br><br>
+vivo drug distribution after systematic administration. A computational and
+compartmental model was built to provide mechanistic insights into a quantitative
+explanation of the experimental results.
+<br><br>
 <B> Three primary aspects were counted in this pharmacokinetic model: <br>
@@ Line 237: / Line 247: @@
 ii) approximating time-series exosome (siRNA) concentration data for use in modeling
-RNAi kinetics in target tissue and subsequently calculating the effective dose, and
+RNAi kinetics in target tissue and subsequently calculating the effective dose, and <br>
-<br>
 iii) determining what portion of the delivery system could be improved based on
-simulation data. </B> <br><br>
+simulation data.
+</B>
+<br><br>
 	<h2> 1.2 Model methods </h2> <br><br>
-	&nbsp;&nbsp;&nbsp;The process of drug delivery in humans and mice is quite
-complex. Physiologically speaking, drug delivery after administration can be simplified
+The process of drug delivery in humans and mice is quite complex. Physiologically
-into two separate phases: <br>
+speaking, drug delivery after administration can be simplified into two separate phases:
-	<B>&nbsp;&nbsp;&nbsp; i) circulation from a central compartment (blood) to a
-peripheral compartment (body tissues), and</B> <br>
+<br><br>
-	<B>&nbsp;&nbsp;&nbsp; ii) uptake and trafficking at cellular and sub-cellular
+<B>
-levels in target tissues.</B> <br>
-	&nbsp;&nbsp;&nbsp;Although physiologically based pharmacokinetic (PBPK) models
-have been widely used in clinical trials, few described the cellular uptake behavior
+i) circulation from a central compartment (blood) to a peripheral compartment (body
-because most of the available drugs, at present, are chemically synthesized and have
+tissues), and
-different biological properties compared with exosomes. Exosomes differ from
+</B>
-conventional chemical drugs because of their distinct biological characteristics as
+<br>
+<B>
-microvesicles [2]. <B>Thus, we would like to modify the current PBPK model and add
+ii) uptake and trafficking at cellular and sub-cellular levels in target tissues.
-details regarding cellular uptake behavior based on the biological nature of
+</B> <br>
+Although physiologically based pharmacokinetic (PBPK) models have been widely used in
-exosomes.</B><br><br>
+clinical trials, few described the cellular uptake behavior because most of the
-<h3> 1.2.1 Modeling multi-compartmental transport</h3> <br>
+available drugs, at present, are chemically synthesized and have different biological
-	&nbsp;&nbsp;&nbsp;In our laboratory work, we measured the relative level of GFP
-in the brain, liver, lung and spleen after injecting anti-GFP siRNA into mouse.
+properties compared with exosomes. Exosomes differ from conventional chemical drugs
-<B>Thus, we examined separate compartments for the brain, liver, lung and spleen.</B>
+because of their distinct biological characteristics as microvesicles [2]. <B>Thus, we
+would like to modify the current PBPK model and add details regarding cellular uptake
+behavior based on the biological nature of exosomes.
+</B>
+<br><br>
+	<h3> 1.2.1 Modeling multi-compartmental transport</h3> <br>
+In our laboratory work, we measured the relative level of GFP in the brain, liver, lung
+and spleen after injecting anti-GFP siRNA into mouse.
+ <B> Thus, we
+examined separate compartments for the brain, liver, lung and spleen.</B>
 Other tissues were merged into one compartment. Each peripheral compartment had blood
@@ Line 285: / Line 315: @@
 exosomes were captured into the extracellular matrix of endothelial cells in different
-tissues.<br><br>
+tissues.
+<br><br>
-      <img src="https://2015.igem.org/File:NJU-China-Model_Figure1.jpg"> <!--插入第一幅图--> <br><br>
+     <img src="https://static.igem.org/mediawiki/2015/2/22/NJU-China-Model_Figure1.jpg"
+style="width:500px"> <!--插入第一幅图--> <br><br>
-	Figure 1. Schematic diagram of the arrangement of different tissues in the
+Figure 1. Schematic diagram of the arrangement of different tissues in the
 pharmacokinetic model. The blood, along with exosomes, circulates from the central
@@ Line 297: / Line 330: @@
 compartment to five peripheral compartments.<br><br>
-	&nbsp;&nbsp;&nbsp;As membrane vesicles, exosomes may rapidly shift from
-associating with other complexes and disassociating into a free format during blood
+As membrane vesicles, exosomes may rapidly shift from associating with other complexes
-circulation. Moreover, the ultimate fate of exosomes, similar to other microvesicles,
+and disassociating into a free format during blood circulation. Moreover, the ultimate
-is degradation by lysosomes after internalization via a common process discussed later.
+fate of exosomes, similar to other microvesicles, is degradation by lysosomes after
-Research has shown that microvesicles, containing miRNAs or siRNAs, are stable in serum
+internalization via a common process discussed later. Research has shown that
-and play significant biological roles in cell communication [3]. Furthermore, the
+microvesicles, containing miRNAs or siRNAs, are stable in serum and play significant
-elimination of exosomes occurs primarily in specific tissues rather than in blood
+biological roles in cell communication [3]. Furthermore, the elimination of exosomes
-circulation, albeit that the half-life of exosomes in blood circulation is much shorter
+occurs primarily in specific tissues rather than in blood circulation, albeit that the
-[4].<B> These two findings suggest that the elimination rate of exosomes in blood
+half-life of exosomes in blood circulation is much shorter [4].
+<B> These two findings suggest that the elimination rate of exosomes in blood
 circulation is negligible compared with that in target tissues and does not need to be
-considered in this portion of the pharmacokinetic model.</B><br><br>
+considered in this portion of the pharmacokinetic model.
+</B>
-	&nbsp;&nbsp;&nbsp;Using standard mass action kinetics, the equations below
+<br><br>
-describe the change in the concentration (mass) of free exosomes over time in blood and
-target tissues. Here, <I>kblooddis</I> and <I>kbloodbind</I> represents the association
+Using standard mass action kinetics, the equations below describe the change in the
+concentration (mass) of free exosomes over time in blood and target tissues. Here,
+<I>kblooddis</I> and <I>kbloodbind</I> represents the association and disassociation,
+respectively, of exosomes to other complexes in the blood circulation.
+<br><br>
-and disassociation, respectively, of exosomes to other complexes in the blood
+ <img src="https://static.igem.org/mediawiki/2015/6/62/NJU-China-Equation_delivery_1.jpg"
-circulation.<br><br>
+style="width:350px">  <!-- delivery公式1 -->
- <img src="https://static.igem.org/mediawiki/2015/6/62/NJU-China-Equation_delivery_1.jpg">
-<!-- delivery公式1 -->
 <br><br>
-	&nbsp;&nbsp;&nbsp;Notably, not all exosomes are effective or completely
+&nbsp;&nbsp;&nbsp;Notably, not all exosomes are effective or completely absorbed by
-absorbed by tissues. Therefore, <I>partitiontissue</I> is included to describe the
+tissues. Therefore, <I>partitiontissue</I> is included to describe the effective
-effective fraction of the dose. Additionally, <I>Et</I> represents the quantity of
+fraction of the dose. Additionally, <I>Et</I> represents the quantity of exosomes
-exosomes captured by the extracellular matrix of cells in tissues, but does not
+captured by the extracellular matrix of cells in tissues, but does not represent the
-represent the final quantity of exosomes in tissues, which will be discussed in the
+final quantity of exosomes in tissues, which will be discussed in the next portion of
-next portion of the model. <I>Qtissue</I> and <I>Qc</I> represents the velocity of
+the model. <I>Qtissue</I> and <I>Qc</I> represents the velocity of blood flowing in
-blood flowing in peripheral and central compartments, respectively.<br><br>
+peripheral and central compartments, respectively.
+<br><br>
 <img src="https://static.igem.org/mediawiki/2015/7/7c/NJU-China-Equation-
-Equation_delivery_2.jpg">
+Equation_delivery_2.jpg" style="width:500px">
-<!-- 这里要插第三张图，是第二个出现的一条公式 -->
+<!-- 这里要插第三张图，是第二个出现的
+一条公式 --> <br><br>
+This work is supported by model of IGEM Slovenia 2012, IGEM NJU-China 2013 and other
+literatures [5,6].
 <br><br>
-	&nbsp;&nbsp;&nbsp;This work is supported by model of IGEM Slovenia 2012, IGEM
-NJU-China 2013 and other literatures [5,6].<br><br>
+	<h3>1.2.2 Modeling cellular uptake and intracellular trafficking</h3> <br><br>
-<h3>1.2.2 Modeling cellular uptake and intracellular trafficking</h3> <br><br>
+Extracellular vesicles can be internalized by cells via a variety of pathways, namely,
-	&nbsp;&nbsp;&nbsp;Extracellular vesicles can be internalized by cells via a
+phagocytosis, clathrin- and caveolin-mediated endocytosis and macropinocytosis [7]. We
-variety of pathways, namely, phagocytosis, clathrin- and caveolin-mediated endocytosis
+assume that receptor-mediated endocytosis is the major pathway of primary exosome
-and macropinocytosis [7]. We assume that receptor-mediated endocytosis is the major
+internalization.
-pathway of primary exosome internalization.<br> <br>
+<br> <br>
-	&nbsp;&nbsp;&nbsp;The cellular uptake pathway is summarized in Figure_2.
+The cellular uptake pathway is summarized in Figure_2. Exosomes bind to the membranes of
-Exosomes bind to the membranes of target cells after being captured by the
+target cells after being captured by the extracellular matrix and then internalized
-extracellular matrix and then internalized through endocytosis. The receptor-ligand
+through endocytosis. The receptor-ligand interaction may facilitate this process. After
-interaction may facilitate this process. After internalization, the RISC complex may
+internalization, the RISC complex may escape from endosomes, and endosomes may be
-escape from endosomes, and endosomes may be ultimately eliminated by lysosomes.
+ultimately eliminated by lysosomes. Although other pathways such as transcytosis and
-Although other pathways such as transcytosis and exocytosis following endocytosis may
+exocytosis following endocytosis may occur, we did not take them into account for
-occur, we did not take them into account for simplification.<br><br>
+simplification.
+<br><br>
-——————这里放Figure.2，就是红色的点点exosome那个图——————<br><br>
+<img src="https://static.igem.org/mediawiki/2015/6/65/NJU-China-Model_Figure2.jpg"
+style="width:500px"> <!--插入第二幅图--> <br><br>
 Figure 2. Pathways that participate in exosomes uptake by target cells. Exosomes are
@@ Line 394: / Line 445: @@
 trafficking after internalization. The RISC complex in exosomes is released, and
-exosomes are ultimately degraded.<br><br>
+exosomes are ultimately degraded.
+<br><br>
-	&nbsp;&nbsp;&nbsp;We used several equations to describe the above pathway. RVG
+We used several equations to describe the above pathway. RVG
 modification helps exosomes bind acetylcholine receptors specifically expressed in
@@ Line 408: / Line 461: @@
 represents the specific binding constant. Non-receptor-ligand interaction--mediated
-binding is summarized using <I>kbindtissue</I>.<br><br>
+binding is summarized using <I>kbindtissue</I>.
+<br><br>
 <img src="https://static.igem.org/mediawiki/2015/9/93/NJU-China-Equation-
-Equation_delivery_3.jpg">
+Equation_delivery_3.jpg" style="width:350px">
       <!-- 这里要插第三个公式 -->
-      <br><br>
-	&nbsp;&nbsp;&nbsp;The internalization and elimination of exosomes are
+<br><br>
-formulated below using the parameters <I>kinttissue</I> and <I>kelimttissue</I>,
+The internalization and elimination of exosomes are formulated below using the
-respectively. Note that different tissues have different internalization and
+parameters <I>kinttissue</I> and <I>kelimttissue</I>, respectively. Note that different
-elimination rates.<br> <br>
+tissues have different internalization and elimination rates.
-<img src="https://static.igem.org/mediawiki/2015/9/93/NJU-China-Equation-
+<br> <br>
-Equation_delivery_3.jpg">
+<img src="https://static.igem.org/mediawiki/2015/2/27/NJU-China-Equation-
+Equation_delivery_4.jpg" style="width:350px">
       <!-- 这里要插第四个公式 -->
-      <br><br>
-	&nbsp;&nbsp;&nbsp;The quantity of the endosomal RISC complex and escape
+<br><br>
-behavior is modeled using the following equation. The concentration of siRNA in
-exosomes is determined by real-time RT-PCR in the literature [8] and represented by
-<I>kc</I>. <I>kescendvec</I>  represents the escape rate of the RISC complex from
+The quantity of the endosomal RISC complex and escape behavior is modeled using the
-exosomes (endosomes) to the cytosol.<br> <br>
+following equation. The concentration of siRNA in exosomes is determined by real-time
-——————这里放第五个出现的公式的图，该图在word里面的figure.2下面的下面的下面———
+RT-PCR in the literature [8] and represented by <I>kc</I>. <I>kescendvec</I>  represents
-—<br>
+the escape rate of the RISC complex from exosomes (endosomes) to the cytosol.
-&nbsp;&nbsp;&nbsp;This part of work is based on literature [5].<br> <br>
+<br> <br>
-<h2> 1.3 Parameter finding and adjustment </h2> <br><br>
+<img src="https://static.igem.org/mediawiki/2015/6/6d/NJU-China-Equation-
-	&nbsp;&nbsp;&nbsp;The most challenging part of modeling is finding and
-adjusting parameters. After reviewing the literature, we unfortunately found that few
+Equation_delivery_5.jpg" style="width:350px">
+     <!-- 这里要插第五个公式 -->
-of the parameters have been measured or reported directly. The original paper written
+<br><br>
-by Bartlett and Davis uses synthetic polyplexes as carriers to deliver siRNA [5]. The
+This part of work is based on literature [5].<br> <br>
-stability and targeting ability of synthetic polyplexes diverge considerably from
-exosomes due to their different biochemical nature. Using all the parameters in the
+	<h2> 1.3 Parameter finding and adjustment </h2> <br><br>
+The most challenging part of modeling is finding and adjusting parameters. After
-original paper without adjustment would not be appropriate because of different
+reviewing the literature, we unfortunately found that few of the parameters have been
-biochemical natures and consequences of these delivery systems. <br><br>
+measured or reported directly. The original paper written by Bartlett and Davis uses
-	&nbsp;&nbsp;&nbsp;Parameter adjustment is not unusual in modeling biological
+synthetic polyplexes as carriers to deliver siRNA [5]. The stability and targeting
-processes. This endeavor is a somewhat uncertain endeavor and lacks specific
+ability of synthetic polyplexes diverge considerably from exosomes due to their
-procedures. In an iterative process, each set of parameters must be run through the
+different biochemical nature. Using all the parameters in the original paper without
-model and modified to bring the output of the model into better and better agreement
+adjustment would not be appropriate because of different biochemical natures and
-with observed experiment and literature results [9]. <B>Following this doctrine, we ran
+consequences of these delivery systems.
-our simulation and attempted to fit the results to the experimental and literature
+<br><br>
-data. </B><br><br>
-	&nbsp;&nbsp;&nbsp;<B>You can access the description of model variables and
-parameters here.</B> The determination of the parameters is also described in the list.
+Parameter adjustment is not unusual in modeling biological processes. This endeavor is a
-When one parameter was reported in the literature, we cited the literature directly;
+somewhat uncertain endeavor and lacks specific procedures. In an iterative process, each
-when the parameter was not accessible but could be estimated and fitted to the
+set of parameters must be run through the model and modified to bring the output of the
-literature or experimental results, we used the terms “estimated from literature and
+model into better and better agreement with observed experiment and literature results
-experimental results”.<br><br>
+[9]. <B>Following this doctrine, we ran our simulation and attempted to fit the results
-<h2>1.4 Results</h2> <br><br>
+to the experimental and literature data.
-	&nbsp;&nbsp;&nbsp;We simulated the pharmacokinetic model and obtained initial
-results. Unfortunately, the results showed that the model was not accurate.
+</B><br><br>
-<B>Distinguishing the effects of RVG modification on the tissue distribution of
-exosomes was difficult, as shown in the figure below.</B><br><br>
+<B>
+You can access the description of model variables and parameters <a href="#var">
-————这里放Figure.3的图，就是那个Control-Without RVG modification——<br><br>
+here </a>.</B>
+The determination of the parameters is also described in the list. When one parameter
+was reported in the literature, we cited the literature directly; when the parameter was
+not accessible but could be estimated and fitted to the literature or experimental
+results, we used the terms “estimated from literature and experimental results”.
+<br><br>
+	<h2>1.4 Results</h2> <br><br>
+We simulated the pharmacokinetic model and obtained initial results. Unfortunately, the
+results showed that the model was not accurate. <B>Distinguishing the effects of RVG
+modification on the tissue distribution of exosomes was difficult, as shown in the
+figure below.
+</B><br><br>
+<img src="https://static.igem.org/mediawiki/2015/d/d1/NJU-China-Model_Figure3.jpg"
+style="width:500px"> <!--插入
+第三幅图--> <br><br>
 Figure 3. Effect of RVG modification on the tissue distribution of exosomes. A: Without
@@ Line 505: / Line 588: @@
 RVG modification; B: With RVG modification. The initial results are simulated with
-partitionbrain set at 1×10-1.<br><br>
+partitionbrain set at 1×10^-1.<br><br>
-	&nbsp;&nbsp;&nbsp;Why did we obtain unrealistic simulation results? The answer
+Why did we obtain unrealistic simulation results? The answer simply lies in the
-simply lies in the parameter set we chose. <B>After performing parameter sensitivity
+parameter set we chose. <B>After performing parameter sensitivity analysis, we were
-analysis, we were surprised to find that exosome bindings to the neuronal cell surface
+surprised to find that exosome bindings to the neuronal cell surface does not determine
-does not determine the internalization rate.</B> In contrast, <I>paritionbrain</I> is
+the internalization rate.</B> In contrast, <I>paritionbrain</I> is more sensitive,
-more sensitive, indicating that the rate limiting step for exosome internalization is
+indicating that the rate limiting step for exosome internalization is its effective dose
-its effective dose fraction to targeted cells.<br> <br>
+fraction to targeted cells.
-	&nbsp;&nbsp;&nbsp;We next carefully investigated the presence of BBB and the
+<br> <br>
-effect of RGV modification on paritionbrain. The blood brain barrier is formed by
+We next carefully investigated the presence of BBB and the effect of RGV modification on
-endothelial cells at the level of cerebral capillaries [10]. The cerebral endothelial
+paritionbrain. The blood brain barrier is formed by endothelial cells at the level of
-cells may form complex tight junctions that interfere with permeability. The binding of
+cerebral capillaries [10]. The cerebral endothelial cells may form complex tight
-RVG to acetylcholine receptors, which are present in high density at the neuromuscular
+junctions that interfere with permeability. The binding of RVG to acetylcholine
-junction, would provide a mechanism whereby exosomes could be locally concentrated at
+receptors, which are present in high density at the neuromuscular junction, would
-sites in proximity to peripheral nerves, facilitating subsequent uptake and transfer to
+provide a mechanism whereby exosomes could be locally concentrated at sites in proximity
-the central nervous system [11]. <B>The local concentrating of exosomes at proximal
+to peripheral nerves, facilitating subsequent uptake and transfer to the central nervous
-sites may significantly increase the effective dose fraction available to targeted
+system [11]. <B>The local concentrating of exosomes at proximal sites may significantly
-cells, resulting in a greater number of exosomes passing through the BBB and captured
+increase the effective dose fraction available to targeted cells, resulting in a greater
-by the extracellular matrix of target cells.</B> To our knowledge, this mechanism is
+number of exosomes passing through the BBB and captured by the extracellular matrix of
-why exosomes may pass through the BBB much more easily after RVG modification. <B>Thus,
+target cells.</B> To our knowledge, this mechanism is why exosomes may pass through the
-we hypothesized that <I>partitionbrain</I> may also be influenced by RVG
+BBB much more easily after RVG modification. <B>Thus, we hypothesized that
-modification.</B><br><br>
+<I>partitionbrain</I> may also be influenced by RVG modification.
-	&nbsp;&nbsp;&nbsp;With <I>partitionbrain</I> increased by 6-fold, we finally
+</B><br><br>
-obtained optimized simulation results. The biological meaning of this parameter
+With <I>partitionbrain</I> increased by 6-fold, we finally obtained optimized simulation
-adjustment is that RVG modification helps exosomes bind acetyl-choline receptors, not
+results. The biological meaning of this parameter adjustment is that RVG modification
-only facilitating internalization into target cells but also increasing the ability of
+helps exosomes bind acetyl-choline receptors, not only facilitating internalization into
-exosomes to pass though the BBB by at least 6-fold.<br><br>
+target cells but also increasing the ability of exosomes to pass though the BBB by at
+least 6-fold.
-——这里放Figure.4的那三张连着的图——————<br><br>
+<br><br>
+<table>
-	&nbsp;&nbsp;&nbsp;Figure 4. Effect of RVG modification on the tissue
+<tr>
+<td>
+<img src="https://static.igem.org/mediawiki/2015/b/b2/NJU-China-model-fix1.jpg"> <!--插入
-distribution of exosomes. The results are simulated with <I>partitionbrain</I>
+第四
-increased by 6-fold. A-B: Control study of the time course of the tissue-distribution
+幅图A B-->
+</td>
+</tr>
-of exosomes without RVG modification. C-D: Case study of the time course of the
+<tr>
+<td>
+<img src="https://static.igem.org/mediawiki/2015/a/a9/NJU-China-model-fix2.jpg"> <!--插入
-tissue-distribution of exosomes with RVG modification and MOR-siRNA as cargo. E: In
+第四
-situ simulation of the tissue-distribution of exosomes.<br><br>
+幅图C D-->
+</td>
+</tr>
-	&nbsp;&nbsp;&nbsp;We now better understand our delivery device using
+<tr>
+<td>
+<img src="https://static.igem.org/mediawiki/2015/2/26/NJU-China-model-fix3.jpg"> <!--插入
-computational simulation data. The half-life of exosomes in blood is short, which is
+第四
-consistent with findings with the literature [12]. The tissue distribution pattern of
+幅图E-->
+</td>
+</tr>
-exosomes with or without RVG modifications is also consistent with findings in the
+<br><br>
-literature [13] and our GFP experiment. <br><br>
+</table>
-	&nbsp;&nbsp;&nbsp;Furthermore, the simulation data shows that a small portion
+Figure 4. Effect of RVG modification on the tissue distribution of exosomes. The results
-of exosomes may also pass into non-targeted tissues due to circulation. We could
+are simulated with <I>partitionbrain</I> increased by 6-fold. A-B: Control study of the
-improve the targeting precision by further modifying the exosomes.<br><br>
+time course of the tissue-distribution of exosomes without RVG modification. C-D: Case
+study of the time course of the tissue-distribution of exosomes with RVG modification
-<h2>1.5 Conclusion and Remarks</h2> <br><br>
+and MOR-siRNA as cargo. E: In situ simulation of the tissue-distribution of exosomes.
-	&nbsp;&nbsp;&nbsp;<B>In this module, we created a pharmacokinetic model to
+<br><br>
-simulate the time-dependent tissue distribution of exosomes at whole organ and cellular
+We now better understand our delivery device using computational simulation data. The
-levels. We theoretically tested the effect of RVG modification on the capability of
+half-life of exosomes in blood is short, which is consistent with findings with the
-exosomes to pass through the BBB. The simulation results are consistent with
+literature [12]. The tissue distribution pattern of exosomes with or without RVG
-experimental measurements, and provide clues regarding improvements to the delivery
+modifications is also consistent with findings in the literature [13] and our GFP
-device.</B><br><br>
+experiment.
+<br><br>
-<h2>1.6  Model Variables</h2> <br><br>
+Furthermore, the simulation data shows that a small portion of exosomes may also pass
-————表格还有表格底下的注释你自己弄哦————<br><br>
+into non-targeted tissues due to circulation. We could improve the targeting precision
+by further modifying the exosomes.
-<h2>1.7	 Model Parameters</h2> <br><br>
+<br><br>
-————表格还有表格底下的注释你自己弄哦——<br><br>
+	<h2>1.5 Conclusion and Remarks</h2> <br><br>
+<B>In this module, we created a pharmacokinetic model to simulate the time-dependent
+tissue distribution of exosomes at whole organ and cellular levels. We theoretically
+tested the effect of RVG modification on the capability of exosomes to pass through the
+BBB. The simulation results are consistent with experimental measurements, and provide
+clues regarding improvements to the delivery device.</B>
 <br><br>
+	<h2 id="var">1.6  Model Variables</h2> <br><br>
+ <img src="https://static.igem.org/mediawiki/2015/a/a2/NJU-China-delivery-figure-2.jpg" style="width:500px">
+<br><br>
+*: Exosomes become endosomes after interanalization. Here we still use term exosomes for clear illustration.
+<br><br>
+	<h2>1.7	 Model Parameters</h2> <br><br>
+ <img src="https://static.igem.org/mediawiki/2015/6/6f/NJU-China-delivery-figure-vari.jpg" style="width:500">
+<br><br>
+?: We are very uncertain about these parameters. However, the parameter sensitivity analysis showed these parameters were not notable for the accuracy of the result. <br><br>
+***: Details and Reasons of this adjustment for RVG modification are discussed in the text.
+<br><br>
 References:<br>
-.&nbsp;&nbsp;&nbsp;Takakura, Y., Nishikawa, M., Yamashita, F. and Hashida, M. (2001)
+. Takakura, Y., Nishikawa, M., Yamashita, F. and Hashida, M. (2001)
 Development of gene drug delivery systems based on pharmacokinetic studies. European
@@ Line 623: / Line 760: @@
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-.&nbsp;&nbsp;&nbsp;El Andaloussi, S., Lakhal, S., Mager, I. and Wood, M.J. (2013)
+.El Andaloussi, S., Lakhal, S., Mager, I. and Wood, M.J. (2013)
-Exosomes for targeted siRNA delivery across biological barriers. Adv Drug Deliv Rev,
+Exosomes for targeted siRNA delivery across biological barriers. Adv Drug Deliv Rev, 65,
-, 391-397.<br>
+-397.<br>
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@@ Line 682: / Line 817: @@
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-.&nbsp;&nbsp;&nbsp;Kumar, P., Wu, H., McBride, J.L., Jung, K.E., Kim, M.H., Davidson,
+. Kumar, P., Wu, H., McBride, J.L., Jung, K.E., Kim, M.H., Davidson,
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