Difference between revisions of "Team:Tec-Chihuahua/Model"

Line 43: Line 43:
  
 
<h3 align="center">MODELING</h3>
 
<h3 align="center">MODELING</h3>
   <div class="a"><p align="justify"> The model is based on the diffusion from the antimicrobial peptide in the interior of the PLGA nanoparticle to the exterior of it. Since out peptides will be delivered through PLGA 50:50 nanoencapsulation, we decided to use a simple Frickian diffusion model suggested by previous research<sup>2, 3, 4 </sup>. The diffusion equation can be seen here: </p></div>
+
   <div class="a"><p align="justify"> The model is based on the diffusion from the antimicrobial peptide in the interior of the PLGA nanoparticle to the exterior of it. Since out peptides will be delivered through PLGA 50:50 nanoencapsulation, we decided to use a simple Frickian diffusion model suggested by previous research<sup>2, 3, 4 </sup>. The diffusion equation can be seen here: </p>
  
 
<center><img src="https://static.igem.org/mediawiki/2018/8/84/T--Tec-Chihuahua--EQ1.png"></center>
 
<center><img src="https://static.igem.org/mediawiki/2018/8/84/T--Tec-Chihuahua--EQ1.png"></center>

Revision as of 01:56, 28 September 2018

Erwinions

From the first moment we made this our project, we repeatedly heard the same question: “how can you make sure AMPs get to bee larvae?” This is a valid and central concern. One of the key elements to our project’s success is that the antimicrobial peptides encapsulated within PLGA nanoparticles reach the bee larvae with enough peptide concentration to inhibit pathogenic bacteria.

In order to measure the final amount of AMPs that will be delivered to each larva, we model the amount of peptide released from the nanoparticles in their path through the insides of the nurse bee and into the jelly fed to larvae.

The model will help us to determine the initial dosage of AMPs necessary to reach their final destination with high enough concentration.


THEORETICAL FOUNDATION

The polylactic-co-glycolic acid (PLGA) is a biodegradable and biocompatible polymer that is used for the fabrication of drug delivery devices. PLGA could be used to transport proteins, peptides, and macromolecules such as DNA and RNA.

PLGA is ideal for drug delivery because its degradation can be used to control the drug’s release profile by manipulating the factors involved in it. Some relevant parameters that influence the release are the polymer molecular weight, the ratio of the particle and the drug concentration.

The release behavior of PLGA is given by its degradation which occurs by hydrolysis. The degradation profile is through bulk degradation as a consequence of the water penetration into the particle and the polymer degradation. However, the degradation of water penetrations is way faster than the PLGA degradation.

The release behavior of PLGA is given by its degradation which occurs by hydrolysis. The degradation profile is through bulk degradation as a consequence of the water penetration into the particle and the polymer degradation. However, the degradation of water penetrations is way faster than the PLGA degradation.

The process can be divided into two phases

  1. The initial burst where there is a quantity of drug released at time zero related to the drug type, drug concentration, and polymer hydrophobicity.
  2. The progressive drug liberation through the weakened PLGA layer. The water hydrolyzes the polymer and creates a channel for where the drug can be released by diffusion and erosion until the complete polymer degradation1.

The factors that influence the PLGA degradation are the following:

  1. The most important factor is the polymer composition because it determines the hydrophilicity and the rate of degradation of the particles. A high percentage of glycolic acid in the composition increases the weight loss due to this component is critical to control the hydrophilicity and as a consequence the rate of degradation.
  2. The average molecular weight is related to the polymer chain size, a higher average of molecular weight shows a longer polymer chain thus there is a lower degradation rate and lower average molecular weight exhibits a higher degradation rate.
  3. The drug or molecule type in the PLGA matrix modify the degradation mechanism and the rate degradation. However, there is not a clear correlation between the drug chemistry and the behavior. However, it is clear that the chemical properties of the drug affect the release profile.
  4. The size and shape is also an important factor that impacts on the matrix degradation. In larger devices of liberation, the ratio of surface area is really significant. A higher surface area in comparison to volume shows higher matrix degradation making faster the drug liberation.
  5. The pH also may affect the rate degradation of PLGA. Strongly alkaline and acidic media leads to a faster degradation whilst less acidic and neutral media exhibits slower acceleration on the rate degradation.
  6. Also, another important parameter is the amount of drug load. Matrices of PLGA loaded with a high drug concentration in ratio to the polymer size increase the initial burst release.
  7. Finally, in biological systems, the degradation may be influenced by the enzymatic activity. However, there are not conclusive results in how the enzymes play role in the degradation process1.

MODELING

The model is based on the diffusion from the antimicrobial peptide in the interior of the PLGA nanoparticle to the exterior of it. Since out peptides will be delivered through PLGA 50:50 nanoencapsulation, we decided to use a simple Frickian diffusion model suggested by previous research2, 3, 4 . The diffusion equation can be seen here:

Where:

Symbol Variable
Mt/M∞ Drug released in time t in relation to the total amount of nanoencapsulated drug Unitless, from 0 to 1
T Time Seconds
D Diffusion coefficient, dependent on the drug-polymer interations Centimeter squared per second
R Radius Centimeters
The initial burst, which is a normal behavior of PLGA-drug release systems where quantity of initial drug is released independently of time. A fraction, in relation to the total amount of drug

It was first necessary to define the appropriate boundary conditions for the equation. Before receiving the data for the radii range in our nanoparticles, we picked boundary conditions based on a wide range of pre-reported2 diffusion coefficients and radii, as well as time (from 0 to 35 days, far beyond what we need).