Revision as of 18:35, 17 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.

——————这里放Figure.2，就是红色的点点exosome那个图——————

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.

——————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——— —
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.

————这里放Figure.3的图，就是那个Control-Without RVG modification——

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.

——这里放Figure.4的那三张连着的图——————

   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

————表格还有表格底下的注释你自己弄哦————

1.7 Model Parameters

————表格还有表格底下的注释你自己弄哦——

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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-	你在火中涅槃，灵魂不死；你在花间长眠，记忆不消。绚烂的阳光是迎接你西去的光环，不知你独自一人去那方可会感到孤单？
+	<h1> 1 Delivery module </h1> <br>
-[一]魂去
-      起初听到你的死，我是愕然的。毕竟听说你的病情好了不少，却不料那只是最后的回光返照。
+	<h2> 1.1 Introduction </h2> <br><br>
-      我没有流泪，甚至没有悲伤，脑子里只是混混沌沌的一团。死？那是我们所有人最后的归宿吧。或许于你更是能够减少痛苦，未尝不是一件好事。
-      一切被我想得太简单，甚至太过理所当然了。理智，真是一个可怕的局。
+&nbsp;&nbsp;&nbsp;Pharmacokinetics is the quantitative study of drug absorption,
-[二]席间
-      小号、大鼓敲个不停，那种声音不得不说是一种噪音。良久声止，开始一个个念悼词。
+distribution and metabolism in the body. Pharmacokinetic data are indispensable for
-      我这时才发现，原来我并没有那么冷漠，缘是城市的喧嚣麻木了我的心。想起那年你的温言软语、慈爱笑容，泪竟是就这样忍不住地从眼眶中溢出了。我撇过头，不愿让人看见，嘴角仍是挂着笑容。
-      呵呵，我竟觉着在这葬礼上哭泣都是可耻的。望着周围人谈笑风生，我的心有点寒。到场的那么多人，究竟又有几个是真心的呢？可怜，还是可悲啊？
+phase I clinical trials to evaluate the tissue distribution and safety of drugs. To
-       原来人可以铁石心肠这种程度，可毕竟他们中很多人比我和你的关系更近啊！听着那个花钱雇来哭的人呜咽，看着那人热泪纵横，我终是忍不住苦笑，竟是一个毫不相识的外人哭得最伤心。这是讽刺，还是笑话？
-	  [三]送葬
+construct a strategy for developing efficient and safe in vivo RNAi therapy systems,
-       你说，为什么他们还能那样淡然，可我偏偏忍不住泪流满面？为什么他们那个惊讶的表情让我竟是感到羞愧？好像自己做了什么惊天动地的事一样？
-       执着一根香，撒下一碗酒，三拜三叩，泪融于酒，我如若痴人。
+pharmacokinetics at whole body, organ, cellular and sub-cellular levels need to be
-       照礼我是不必披麻戴孝的，只需戴着那顶白色的帽子即可。沿着幽僻的小路，我跟着吹着小号开路的队伍走在末尾。
-       风景甚好，埋在那棵大树底下，你会不会成为那树的枝，树的叶？
+considered [1]. <br><br>
-[四]感怀
-      死生有命，无可更改。谋事在人，成事在天。任你生前浮华万千，到头来不过一抔尘土，一捧青灰。愿逝者安息，生者恬淡。
+&nbsp;&nbsp;&nbsp;In our laboratory study (GFP experiment), we obtained a qualitative
-      有时候我们看不到自己拥有的美好，直到失去才恍然叹息。逝者已矣，页已掀过，惜福、感恩，生活其实温暖如花。
-      花开半夏，倾尽繁华，愿你回眸半刹。你可听到我在同你夜话？
+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. <br><br>
+<B> Three primary aspects were counted in this pharmacokinetic model: <br>
+i) theoretically predicting the effect of RVG modification of the targeting ability of
+exosomes,<br>
+ii) approximating time-series exosome (siRNA) concentration data for use in modeling
+RNAi kinetics in target tissue and subsequently calculating the effective dose, and
+<br>
+iii) determining what portion of the delivery system could be improved based on
+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
+into two separate phases: <br>
+	<B>&nbsp;&nbsp;&nbsp; i) circulation from a central compartment (blood) to a
+peripheral compartment (body tissues), and</B> <br>
+	<B>&nbsp;&nbsp;&nbsp; ii) uptake and trafficking at cellular and sub-cellular
+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
+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]. <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>
+	&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.
+<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
+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.<br><br>
+       <img src="https://2015.igem.org/File:NJU-China-Model_Figure1.jpg"> <!--插入第一幅图--> <br><br>
+	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.<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
+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].<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>
+	&nbsp;&nbsp;&nbsp;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>
+ <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
+absorbed by tissues. Therefore, <I>partitiontissue</I> is included to describe the
+effective fraction of the dose. Additionally, <I>Et</I> 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. <I>Qtissue</I> and <I>Qc</I> represents the velocity of
+blood flowing in peripheral and central compartments, respectively.<br><br>
+<img src="https://static.igem.org/mediawiki/2015/7/7c/NJU-China-Equation-
+Equation_delivery_2.jpg">
+<!-- 这里要插第三张图，是第二个出现的一条公式 -->
+<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>
+	&nbsp;&nbsp;&nbsp;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.<br> <br>
+	&nbsp;&nbsp;&nbsp;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.<br><br>
+——————这里放Figure.2，就是红色的点点exosome那个图——————<br><br>
+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.<br><br>
+	&nbsp;&nbsp;&nbsp;We used several equations to describe the above pathway. RVG
+modification helps exosomes bind acetylcholine receptors specifically expressed in
+neuronal cells. <B>Exosomes internalization is much easier provided that more exosomes
+bind target cells.</B> The binding process is modeled using mass action kinetics.
+<I>AR</I> denotes the number of acetylcholine receptors on target cells, and <I>km</I>
+represents the specific binding constant. Non-receptor-ligand interaction--mediated
+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">
+     <!-- 这里要插第三个公式 -->
+     <br><br>
+	&nbsp;&nbsp;&nbsp;The internalization and elimination of exosomes are
+formulated below using the parameters <I>kinttissue</I> and <I>kelimttissue</I>,
+respectively. Note that different tissues have different internalization and
+elimination rates.<br> <br>
+<img src="https://static.igem.org/mediawiki/2015/9/93/NJU-China-Equation-
+Equation_delivery_3.jpg">
+     <!-- 这里要插第四个公式 -->
+     <br><br>
+	&nbsp;&nbsp;&nbsp;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
+<I>kc</I>. <I>kescendvec</I>  represents the escape rate of the RISC complex from
+exosomes (endosomes) to the cytosol.<br> <br>
+——————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———
+—<br>
+&nbsp;&nbsp;&nbsp;This part of work is based on literature [5].<br> <br>
+<h2> 1.3 Parameter finding and adjustment </h2> <br><br>
+	&nbsp;&nbsp;&nbsp;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. <br><br>
+	&nbsp;&nbsp;&nbsp;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]. <B>Following this doctrine, we ran
+our simulation and attempted to fit the results to the experimental and literature
+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.
+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>
+	&nbsp;&nbsp;&nbsp;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>
+————这里放Figure.3的图，就是那个Control-Without RVG modification——<br><br>
+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.<br><br>
+	&nbsp;&nbsp;&nbsp;Why did we obtain unrealistic simulation results? The answer
+simply lies in the parameter set we chose. <B>After performing parameter sensitivity
+analysis, we were surprised to find that exosome bindings to the neuronal cell surface
+does not determine the internalization rate.</B> In contrast, <I>paritionbrain</I> is
+more sensitive, indicating that the rate limiting step for exosome internalization is
+its effective dose fraction to targeted cells.<br> <br>
+	&nbsp;&nbsp;&nbsp;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]. <B>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.</B> To our knowledge, this mechanism is
+why exosomes may pass through the BBB much more easily after RVG modification. <B>Thus,
+we hypothesized that <I>partitionbrain</I> may also be influenced by RVG
+modification.</B><br><br>
+	&nbsp;&nbsp;&nbsp;With <I>partitionbrain</I> 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.<br><br>
+——这里放Figure.4的那三张连着的图——————<br><br>
+	&nbsp;&nbsp;&nbsp;Figure 4. Effect of RVG modification on the tissue
+distribution of exosomes. The results are simulated with <I>partitionbrain</I>
+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.<br><br>
+	&nbsp;&nbsp;&nbsp;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. <br><br>
+	&nbsp;&nbsp;&nbsp;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.<br><br>
+<h2>1.5 Conclusion and Remarks</h2> <br><br>
+	&nbsp;&nbsp;&nbsp;<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>1.6  Model Variables</h2> <br><br>
+————表格还有表格底下的注释你自己弄哦————<br><br>
+<h2>1.7	 Model Parameters</h2> <br><br>
+————表格还有表格底下的注释你自己弄哦——<br><br>
+<br><br>
+References:<br>
+.&nbsp;&nbsp;&nbsp;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.<br>
+.&nbsp;&nbsp;&nbsp;El Andaloussi, S., Lakhal, S., Mager, I. and Wood, M.J. (2013)
+Exosomes for targeted siRNA delivery across biological barriers. Adv Drug Deliv Rev,
+, 391-397.<br>
+.&nbsp;&nbsp;&nbsp;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.<br>
+.&nbsp;&nbsp;&nbsp;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.<br>
+.&nbsp;&nbsp;&nbsp;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.<br>
+.&nbsp;&nbsp;&nbsp;Levitt, D.G. and Schoemaker, R.C. (2006) Human physiologically
+based pharmacokinetic model for ACE inhibitors: ramipril and ramiprilat. BMC clinical
+pharmacology, 6, 1.<br>
+.&nbsp;&nbsp;&nbsp;Mulcahy, L.A., Pink, R.C. and Carter, D.R. (2014) Routes and
+mechanisms of extracellular vesicle uptake. J Extracell Vesicles, 3.<br>
+.&nbsp;&nbsp;&nbsp;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.<br>
+.&nbsp;&nbsp;&nbsp;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-
+.<br>
+.&nbsp;&nbsp;&nbsp;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.<br>
+.&nbsp;&nbsp;&nbsp;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-
+.<br>
+.&nbsp;&nbsp;&nbsp;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.<br>
+.&nbsp;&nbsp;&nbsp;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.<br>
+.&nbsp;&nbsp;&nbsp;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-
+.<br>
+.&nbsp;&nbsp;&nbsp;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.<br>
 	</TD>
      </TR>

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