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Bioengineering Statistics Meredith Goolsby, Melody Keith, Swati Vishnubhakat

Bioengineering Statistics Meredith Goolsby, Melody Keith, Swati Vishnubhakat. Targeted Liposomal Drug Delivery for the Treatment of Glioma In Vivo Katie McNeeley, Ravi Bellamkonda

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Bioengineering Statistics Meredith Goolsby, Melody Keith, Swati Vishnubhakat

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  1. Bioengineering Statistics Meredith Goolsby, Melody Keith, Swati Vishnubhakat Targeted Liposomal Drug Delivery for the Treatment of Glioma In Vivo Katie McNeeley, Ravi Bellamkonda Wallace H. Coulter Department of Biomedical Engineering, Laboratory for Neuroengineering, Georgia Institute of Technology Results Introduction Statistical Method • The GLM ANOVA statistical method yielded p-values greater than 19.8%. • The residual plots demonstrates the difference between the observed values and predicted or fitted values. All plots for each data set show large residual values. • In the uptake clearance, the p-values tended to be lower than the p-values of the other data sets, but still no statistical significance occurred. • The mode for the uptake clearance is zero; however, the outliers cause the uptake clearance mean to lack significance • There also seemed to be a fairly large number of outliers in each data set. These outliers cause the ANOVA to be less accurate than it could be. These outlier cannot be excluded because we do not have a sufficient filter to exclude them. • The data tends to skew right because of the high numbers of residuals to the right of the mean. • After conducting a 2-level Factorial Design at 90% power using MINITAB 14, the ideal sample size was determined to be 12. The experimental data ranged from between 7-10 subjects. • Cancer Treatments • Current cancer treatments are often unsuccessful because they do not specifically target the tumor site. As a result, non cancerous cells are disrupted by the treatment causing invasive side effects. In order to create an effective treatment, it will be necessary to develop a drug delivery technique that will specifically target the tumor cells. • Specific targeting is achieved through the attachment of transferrin, a ligand whose corresponding receptor is overexpressed by glioma cells, to the outer membrane of PEGylated liposomes. • The transferrin receptor is also known to aid in transport across the blood-brain barrier, thereby increasing Tf-liposomal access to glioma. • Targeting the folate receptor has shown considerable promise in mediating uptake of a variety of drugs when folic acid is conjugated to the drug or delivery vehicle. 1 • Drug Delivery • Folate targeted liposomes are loaded with a chemotherapeutic agent, doxorubicin (DXR), and administered to tumor bearing rats. The control treatment will be the DXR contained within a non-specific liposome. • Due to the increased therapeutic efficacy, it is expected that tumor growth will be slowed and survival times will be significantly increased for animals receiving this treatment than for animals treated with the non-targeted equivalent. • The effect of the drug on tumor growth in rats will be monitored at 20 and 50 hrs. after the drug is delivered through magnetic resonance imaging and histology. • Animal survival times will be compared between rats treated with targeted and non-targeted liposomal DXR. • Uptake Clearance • The evaluation of uptake clearance will signify whether the drug is actually being delivered to the tumor as opposed to other organs of the body. The uptake clearance takes into account the fact that circulating levels of drug are different between the 2 formulations of the treatments. • It demonstrates the uptake of drug considering the levels in the blood. This value is calculated by taking the amount of drug in the tumor and dividing it by the area under the clearance curve, which is the amount of drug in the blood versus time. It should be noted that the lower the amount of drug in the blood, the higher the uptake clearance. • Based on the multi-leveled nature of the data, a 2-way ANOVA would be ideal in analyzing the data. However, this particular set does not have an equal number of data points in each level. Therefore, it was necessary to use a General Linear Model (GLM). This model analyzes balanced or unbalanced ANOVA models with crossed or nested and fixed or random factors. • The data was processed by MINITAB 14 using the GLM ANOVA technique. Results GLM ANOVA for DXR Content (ng/tumor) Figure 1a. Residual Plots of Response Conclusions GLM ANOVA for Corrected DXR Content (ng/mg) • Liposomes with folate targeted ligands do not increase the specificity of the target site on the tumor. • Normally, in vivo studies require p-value of 2% or less to be considered significant. However, the GLM ANOVA shows that the data is not significant because the p-values are significantly higher than 5%. • The small size for each data set could have skewed the results. As with any in vivo study, additional trials are recommended. It might be beneficial to conduct the same experiment with a larger sample size. This increase may yield higher significance. • As the 2-level Factorial Design indicates, the number of repetitions necessary to obtain a 90% power is 12. The number of experimental subjects was less than optimal. • The data tends toward a lower mean residual for the uptake clearance. This shows that the drug is being delivered to the tumor more often when the liposome contains the transferrin to specifically bind to the tumor. Figure 1b. Residual Plots of Response GLM ANOVA for Uptake Clearance References • Gabizon et al. Clinical Cancer Research. 9 (2003) 6551–6559. • J .M . Saul et al. Journal of Controlled Release. 92 (2003) 49–67. Acknowledgements • We would like to thank Dr. Ravi Bellamkonda and Ms. Katie McNeeley for allowing us to use their experimental data. We would also like to extend our appreciation to Dr. Brani Vidakovic for sharing his statistical expertise. Figure 1c. Residual Plots of Response

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