Pharmacokinetic model

Assessment of RVG aptitude


The blood brain barrier (BBB) is formed by many components including endothelial cells of the capillary wall, astrocyte and pericytes. It is the largest interface between the blood and the central nervous system and is situated between the blood and the interstitial fluid. Our RVG construct is designed to steer exosome to cross the obstacle Delivering exosome through BBB is an extremely complex process. The affinity of RVG to nicotinic acetylcholine receptor (nAchR) is critical to clathrin and caveolae-mediated transcytosis, which is the main mechanism of guiding exosome through the barrier. To study the ability of our engineered exosomes to pass the BBB, we simplify the whole process into passing endothelial cells and study the aptitude of RVG by modeling the binding properties to the receptor side of the endothelial cell membrane.

CX43, a family member of gap junction proteins, can only help us to deliver exosome if it is open and exchange fusion signal with neuron cell. Here, we present a model demonstrating a macroscopic situation in gap junctions, and thereby ensuring the functionality of CX43 as a cytosolic delivery helper.


1. Interacting bonding rupture does not occur during cellular trafficking of endothelial cell, so transcytosis is successful once endocytosis happens
2. Influences of hyperaemia are neglected.
3. endosome can be considered as spheres.
4. Influences resulting from PH、 enzyme and salt concentration in blood are neglected.

Process and Result

The binding of the ligands on the surface of exosomes to the receptors of endothelial cells of capillary wall is affected by the blood flow. Blood flow creates a shearing force at the vessel walls that strains the receptor-ligand bondings and therefore increases the chances of disrupting the interaction. The following equations are given to measure dislodging force, including drag force (Fd) in the direction of blood flow and a rotating force called the torque (T). The two major forces hinder our exosome to be bound and thus transported through the endothelial cell.

Figure 1:drag force(Fd)

Figure 2:Rotation force(T)

The whole forces caused by blood flow could be measured by Fdis = Fd + Tq/2 [11].

The dislodging force (Fdis) to different size of nanoparticle is then tested.

There have been studies showing that exosome with 60nm diameter is most suitable for passing the vivo BBB model [11]. So Fdis of 10-14 could be used as the force acting on exosome. The probability of forming the interaction and breaking the interaction under shear flow can be given as:


Pbonds represents the steady bonds forming during ligand - receptor contact. We set 10 matching receptor-ligand pairs with a series of association constants for one exosome to test the binding status of 0.5s when it comes to exosome-endothelial contact. If RVG-carried exosome has higher affinity, in other words, transcytosis ability than non-RVG exosome, it should adhere to endothelial cell membrane with more steady interaction.

Figure: RVG-carried exosome has the better binding ability under different association constant. Extra steady bonds are always formed on engineered exosome, which increases the probability of successful transcytosis and theoretically proves the aptitude of RVG-carried exosomes to across BBB by receptor-mediated transcytosis. Furthermore, more blood-injected exosome will enter into brain than the rest of the body due to the brain-guiding efficiency of RVG.

Parameters Definitions Value Units Note
Rd Receptor-ligand density for exosome/RVG-carried exosome 105/2X105 nm-2 [11]
Ac The interfacial contact surface area 225 nm2 Estimated from most-fit exosome
LD ligand density 105 nm2
l distance from the particle center of mass to the cell membrane 35.8 nm
𝑺𝝁 wall shear stress 0.25 Pa [11]
𝜸 particle aspect ratio 1
kB Boltzmann constant 1.0X10-3 min-1
T temperature 310.15 K
BN nanoparticle shared over the number of bonds 5
k0/a the association constant at zero load per receptor-ligand pair 1000/2000/3000/4000 Tested value
kD dissociation constant 10-6
BRL bond length 7.2 nm Estimated from most-fit exosome
𝜹eq closest distance of the nanoparticle relative to the cell 5.8 nm [11]
r Radius of exosome 30 nm Estimated from most-fit exosome
Variables Meaning
Pa The probability of a spherical nanoparticle adhering to the cell membrane
FD Drag force generated by the blood flow
Fs is the force as a function of aspect ratio
Ts the torque force as a function of aspect ratio
FRL dislodging force per receptor ligand pair
Tq Torque
Pbonds The probability to form a stable ligand-receptor bond
Fdis The total dissociating force

Simulation of exosome in blood simulation


Circulation of the blood is a vital function in human body. The main significance of blood circulation is to guarantee a proper metabolism of every cell in the body. The various tissues of the body get various nutrients, water and oxygen from the blood. Exosome is transported without exception. However, injecting the exosomes into the vein is an efficient but risky approach of delivering exosome to the brain. Excess exosomes in the blood or other parts of the body can be harmful. To ensure a proper delivery of exosome, a pharmacokinetic model is built to simulate the kinetics of exosome circulation in blood after injection.


The formulation is connected with two pharmacokinetic ideas. Firstly, Exosome can be reversibly bounded with blood components, which repress them to enter into main parts of body. Also, Heart acts like a pump which determines the proportion and flow of blood to the body by blood circulation. After considering the two basic ideas above, the following formulas are produced.

Parameters Definitions Value Units Note
kblooddis Exosomes dissociation from blood components 1.7X10-4 min-1 IGEM NJU 2017
kbloodbind Exosomes binding to blood components 1.0X10-6 min-1 IGEM NJU 2017
kintbrain Effective fraction of dose available to brain 0.23 min-1
kintheart Effective fraction of dose available to heart 1.0X10-2 min-1 IGEM NJU 2017
kintliver Effective fraction of dose available to liver 1.0X10-2 min-1 IGEM NJU 2017
kspleen Effective fraction of dose available to spleen 1.0X10-3 min-1 IGEM NJU 2017
klung Effective fraction of dose available to lung 1.0X10-3 min-1 IGEM NJU 2017
kkidney Effective fraction of dose available to kidney 1.0X10-2 min-1 IGEM NJU 2017
kadipocyte Effective fraction of dose available to adipocyte 1.0X10-2 min-1 IGEM NJU 2017
kbbb Effective fraction of dose cross the blood brain barrier 0.4 min-1
Qc Cardiac Output 5.6 L/min IGEM Slovenia 2012
Qheart Blood following through heart tissue 5.6 L/min IGEM Slovenia 2012
Qspleen Blood following through spleen tissue 0.6 L/min IGEM Slovenia 2012
Qlung Blood following through lung tissue 5.6 L/min IGEM Slovenia 2012
Qkidney Blood following through heart tissue 1.7 L/min IGEM Slovenia 2012
Qadi Blood following through adipose tissue 2.8 L/min IGEM Slovenia 2012
Qliver Blood following through liver tissue 1.4 L/min IGEM Slovenia 2012
Variables Determination(Units)
DisExo Dissociated Exosome in blood(ug)
∑E Sum of exosome entering in tissues except brain(ug)
dFixedExo Exosome associated with blood component(ug)
dExoB Exosome after crossing the blood brain barrier(ug)



Figure: distribution of injected exosome

According to the figure, most of the injected exosomes are absorbed by brain and other tissues. Those who bind blood components finally go into other parts of body. Also, the sum of exosome (∑E) entering into other tissues is approaching the 10% of total injected exosome, which is far less than those entering into the brain (ExoB). This result shows the efficiency of our exosome design. Additionally, the remaining amount of exosome in the blood (DisExo and FixedExo) approach zero within 20 hours.


According to our model, RVG could be theoretically helpful to steer exosome on passing BBB. At the meantime, it Increases the proportion of exosomes entering the brain, which lower the risk of causing potential damage to the human body. However, the amount of safe dose of exosome injected into human for curing patient is still remain unknown.

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Xi'an Jiaotong-Liverpool University

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