Tuning and Controlling the Release Profiles of Functional Biomolecules through Optimal

Learning

Jesse GoodmanSummer 2014

McAlpine Group

McAlpine Research Group

• Nanotechnology• Biology• Energy• Worked closely with:– Dr. Maneesh Gupta

My Project• Switch from encapsulating phase change materials• Protein loaded microspheres– Drug delivery, tissue engineering, etc.– Controlling release rate is difficult…

http://www.pharmaceutical-int.com/upload/image_files/news/0_multilayered-particles-for-drug-delivery-and-artificial-tissues_content_Multilayered-Particles-Drug-Delivery.jpg

http://www.rsc.org/images/Figure%201_tcm18-35157.jpg

Need for customizable release profiles

• Literature is very application specific

• Lack of discussion re the ability to create any desired release profile by altering certain parameters

PLGA-Based Microparticles for the Sustained Release of BMP-2 (Kirby et al., 2011) Controlled Release of Dexamethasone from PLGA Microspheres Embedded Within Polyacid-Containing PVA Hydrogels (Galeska et al., 2005)

Particle FormulationDouble Emulsion Solvent Evaporation

W1/O/W2 Emulsification• (W1/O)/W2 Volume Fraction• External PVA Concentration• Dispersion Speed

W1/O Emulsification• W1/O Volume Fraction• Polymer Concentration• Payload Concentration• Dispersion Speed

Drying Process• Dilution Ratio• Temperature

Varying Parameters

• Affects particle size & polydispersity• Should also affect release profile

• Settle on modifying only certain parameters– W1/O ratio, PLGA conc, BSA and HRP conc.

Measuring protein release

0 0.02 0.04 0.06 0.08 0.1 0.120

0.2

0.4

0.6

0.8

1

1.2

1.4

1.6

1.8

2

f(x) = 18.913893129771 x − 0.0250695504664972R² = 0.999111276231038

8.5.14: Standard Curve for HRP

HRP Concentration (U/ml)

Abso

rban

ce

Release Profiles

0 5 10 15 20 25 300

5

10

15

20

25

30

35

40

7.29.14: HRP release profile

Time (hours)

% H

RP re

leas

ed

0 5 10 15 20 25 300

2

4

6

8

10

12

14

8.4.14: HRP release profile

Time (hours)

% H

RP re

leas

ed

Optimization Process

Use chosen parameters to create particles & release profile

Email release profile data to Kris & Si in ORFE collaboration group

Plug into model & chose parameters (optimized to

develop model) for another experiment

Optimization Predictions & Comparisons

Further research• Target release profiles in order to develop

certain medicines• Apply this optimal learning technique to

similar projects

http://www.processingmagazine.com/ext/resources/News-Photos/2013/0813/TS_162264253_715x400.jpg

Acknowledgements

• Thanks to:– Dr. Maneesh Gupta for working with me on this

project throughout the summer– Dr. Kris Reyes and Si Chen for collaborating with

me and working on the optimization portion of this project

– Professor McAlpine for hosting me in his lab and guiding me through this project

– PEI for making this internship possible