1. 0.034
0
0.05
0.1
0.15
80 200
PDI
Particle Size
Overlaid Contour PDI
vs. Particle Size
Overlaid Contour PDI
Design of Experiments Approach to Parameterize PLGA Nanoparticle Synthesis
Katya Hristova[1], Ramalingam Venkat Kalyana Sundaram[2], Rosemary Bastian[2], Dr. Elisabeth S. Papazoglou[2]
[1]College of Arts and Sciences, Drexel University, Philadelphia, PA 19104 USA
[2]School of Biomedical Engineering, Science and Health Systems , Drexel University, Philadelphia, PA 19104 USA
The null hypothesis was rejected for both PDI and particle size because
of p-values less than 0.05. These statistically significant results give
evidence to support the initial assumption that at least one of the
selected design factors contribute significantly to the response system.
Filippov et al. have recently tuned PDI in a range from 0.06 to 0.3
through addition of surfactant proportional to the particle surface [2]. Our
study, on the other hand, has improved on achieving PDI in a smaller
range without the addition of surfactant. The results from validation
experiments are considerably close to the predictions made by both the
response surface plot and overlaid contour plot.
While relevant nanoparticle studies using the OFAT method show PDI
and particle size responses with many experiments, the current study
completed it in 8 runs. It has achieved the goal of parameterizing the
synthesis of polymeric nanoparticles by altering the significant factors
concurrently. Our future work is focused on designing FFD that screens
for more PDI and particle size factors simultaneously.
ScreeningIntroduction
Materials: Poly(D,L-lactide-co-glycolide), (50:50), Mw 7,000-17,000 from
SigmaAldrich.
Methods: PLGA nanoparticles were prepared using spontaneous
emulsion-solvent diffusion. PLGA was dissolved in 100% acetone
(organic phase) and added drop wise to water (aqueous phase) which
was stirred using a magnetic stir bar. The sample was then exposed to
vacuum in a rotary evaporator for 15 minutes. Finally, the particles were
characterized on a DLS instrument (Zetasizer Nano from Malvern
Instruments) in terms of the diameter and PDI. The experimental data
were analyzed using Minitab statistical software.
A 23 full factorial design (FFD) leading
to 8 runs of each of two major
experiments, performed in triplicate,
was used to verify the most significant
factors affecting both the PDI and
particle size.
Polymeric nanparticle drug delivery devices are preferred over currently
administered drugs for their long-term drug circulation and low toxicity.
For the present study, poly(lactide-co-glycolide) (PLGA) was selected for
its biocompatibility and biodegradability. Two main physical properties
used to characterize PLGA nanoparticles are size and polydispersity.
Size in the nanometer range facilitates entry into a specific site.
Polydispersity index (PDI) on the other hand, describes the homogeneity
of particle size. Real-time monitoring and DOE analysis provides vital
information that enables prompt adjustments to the on-going process,
resulting in a greater understanding of nanosphere synthesis process. [1]
Every factor had two levels: low (-1) and
high (1). We used DOE as an alternative
to the widely used one-factor-at-a-time
(OFAT) approach. The levels of tested
factors for PDI are listed in the table
above.
Stirring Speed
(rpm)
Aqueous
Phase (ml)
PLGA
Concentration
(mg/ml)
A B C
350 6 2
700 6 2
350 21 2
700 21 2
350 6 6
700 6 6
350 21 6
700 21 6
Validation
Optimization
Statistical analysis: One Way analysis of variance (ANOVA), an extension of the
independent group t-test for more than two groups (runs), was used to compare the means
of eight runs. There are two hypothesis for the comparison of independent groups that are
being tested. Null hypothesis states that all the means of the groups are equal. Alternative
hypothesis states that the means of two or more groups are not equal. It is assumed that the
levels of a factor are randomly assigned to eight experimental runs.
Term Effect Coef SE Coef T P
Constant 0.16767 0.005526 30.34 0
A 0.01117 0.00558 0.005526 1.01 0.327
B -0.0535 -0.02675 0.005526 -4.84 0
C -0.06933 -0.03467 0.005526 -6.27 0
A*B 0.00833 0.00417 0.005526 0.75 0.462
A*C 0.0075 0.00375 0.005526 0.68 0.507
B*C -0.0265 -0.01325 0.005526 -2.4 0.029
A*B*C 0.02033 0.01017 0.005526 1.84 0.084
1 2 3 4 5 6
7
8
0
0.1
0.2
0.3
123456789
Aqueous Phase (ml)
PLGA
Concentration
(mg/ml)
PDI Response Surface
0.2-0.3 0.1-0.2 0-0.1
ANOVA Session Window
PLGA Concentration (mg/ml)
AqueousPhase(ml)
6.05.55.04.54.0
34
32
30
28
26
24
22
20
Stirring Speed (rpm) 350
Hold Values
0.03
0.07
PDI
110
120
Size
Particle
Overlaid Contour
AC
AB
A
ABC
BC
B
C
76543210
Term
Standardized Effect
2.120
A Stirring Speed
B A queous Phase
C PLGA C oncentration
Factor Name
PDI Pareto Chart
216 62
0.20
0.15
0.10
0.20
0.15
0.10
Stirring Speed (rpm)
Aqueous Phase (ml)
PLGA Concentration (mg/ml)
350
700
(rpm)
Speed
Stirring
6
21
Phase (ml)
Aqueous
PDI Interaction Plot
6
2
21
6 700350
PLGA Concentration (mg/ml)
Aqueous Phase (ml)
Stirring Speed (rpm)
0.11667
0.168000.17800
0.06933
0.18467
0.223670.20800
0.19300
PDI Cube Plot
1
-1
1
-1
1-1
Factor C
Factor B
Factor A
0.002480
0.02
0.04
0.06
0.08
0.1
0.12
0.14
0.16
0.18
1 2 3
PDI
Replicates
RSM PDI Experimental PDI
Response Surface
Methodology PDI
Materials and Methods
Design
Conclusion and Future Work
References
[1] Singh B, Bhatowa R, Tripathi CB, Kapil R. Developing micro-
/nanoparticulate drug delivery systems using "design of experiments". Int
J Pharma Investig 20111:75-87.
[2] Filippov, S.; Hruby, M.; Konak, C.; Mackova, H.; Stepanek, P.
Langmuir 2008, 24, 9295–9301.