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研究生:曾韻宜
研究生(外文):Wan-I Chang
論文名稱:奈米顆粒在孤立及皮下腫瘤空間與時間分佈之研究
論文名稱(外文):Investigation of the Spatial and Temporal Distribution of Nanoparticles in Isolated and Subcutaneous Tumors
指導教授:周呈霙周呈霙引用關係
指導教授(外文):Cheng-Ying Chou
口試委員:林文澧洪子倫
口試委員(外文):Win-Li LinTzyy-Leng Horng
口試日期:2014-07-10
學位類別:碩士
校院名稱:國立臺灣大學
系所名稱:生物產業機電工程學研究所
學門:工程學門
學類:機械工程學類
論文種類:學術論文
論文出版年:2014
畢業學年度:102
語文別:英文
論文頁數:44
中文關鍵詞:奈米藥物載體藥物傳輸腫瘤模擬細胞存活率
外文關鍵詞:nanodrug carrierdrug deliverytumor modelcell survival rate
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腫瘤中的藥物載體濃度及其分佈是評估腫瘤治療效果的重要因子,而奈米藥物載體的傳輸受許多因素影響,如腫瘤的血管密度及分佈、腫瘤的大小、壞死區大小比例等。本研究利用流體流動方程式開發出腫瘤藥物運輸模型,模擬不同大小的奈米藥物在不均勻血管分佈的孤立腫瘤以及皮下腫瘤的傳輸情況和濃度分佈,並且分析不同的腫瘤生理條件對藥物濃度的影響。我們以阿黴素(doxorubicin)作為傳統藥物的代表並與奈米藥物比較其傳輸情況及治療效果。除此之外,我們從藥物濃度中推算出腫瘤細胞的單次和多次治療後存活率,藉此更客觀地評估腫瘤的治療效果。
研究結果顯示(1)大顆藥物載體在腫瘤血管附近有較高的累積劑量,而小顆載體雖累積劑量較低但較容易滲透至腫瘤壞死區及正常組織;(2)當腫瘤越小,藥物載體越容易滲透至壞死區及正常組織,但其最高累積劑量相對較低;(3)藥物載體在腫瘤中的累積劑量會隨著血管密度增加而提高,而血管密度對於大顆藥物載體的影響力較大;(4)奈米藥物的腫瘤細胞存活率較傳統藥物低,而正常組織的存活率較高且受影響的範圍較小。結果顯示出血管密度以及藥物的大小對於在腫瘤治療效果有較大的影響力,而細胞的存活率結果顯示使用奈米藥物載體有更好的治療效果並且對正常組織的傷害較少。


The distribution and accumulation of nanoparticle dosage in a tumor are important in evaluating the effectiveness of cancer treatment. The transportation of nanoparticles in a tumor is affected by many factors such as the sizes of the tumor and necrotic region, vascular density and its distribution in the tumor, and the characteristics of nanoparticles. We developed a mathematical tumor model based on the governing equations for the fluid flow to investigate the drug transportation in a tumor and computed the resulting accumulative concentration. Moreover, the cell survival rate, which was calculated with the help of the accumulative concentration, was evaluated to quantify the therapeutic effect. The survival rates after multiple treatments are helpful to evaluate the efficiency of the chemotherapy plan.
The model was applied to both an isolated tumor and a subcutaneous tumor with heterogeneous vascular distribution, and various dextrans were chosen as the nanodrug carrier to study the impact of the sizes of tumor and necrotic region and vascular surface area per unit tumor volume ( ) on the average accumulative concentration. Furthermore, doxorubicin was chosen as the traditional chemotherapeutic agent and the treatment effect was compared with that of dextrans. The results showed that: 1) large nanoparticles produced a large accumulative concentration in the well-vascular region, but low dose in the necrotic region; 2) small nanoparticles can penetrate into the necrotic region; however, its accumulative concentration was low and was more toxic to normal tissues; 3) the influence of the tumor size on the average accumulative concentration was much more pronounced for small nanoparticles, while the effect of was relatively more significant when employing large nanoparticles; 4) the treatment effect of nanoparticles on tumor is better than traditional chemotherapeutic agent and the damage on normal tissue is much smaller as well. The results indicated that the effectiveness of the anti-tumor drug delivery was determined by the interplay of the vascular density and nanoparticle size, and using nanoparticles as anti-tumor drug is better because its high treatment efficiency on tumors and less damage to normal tissues.


TABLE OF CONTENTS
TABLE OF CONTENTS v
LIST OF FIGURES vii
LIST OF TABLES x
CHAPTER 1 INTRODUCTION 1
1.1 Background 1
1.2 Purpose 3
1.3 Frameworks 4
CHAPTER 2 LITERATURE REVIEW 5
2.1 Nanoparticle Delivery 5
2.1.1 Abnormal Blood Vessel Network 6
2.1.2 Abnormal Lymphatic Network 7
2.2 Tumor Cell Survival Rate 8
2.3 Numerical Modeling of Fluid Flow in Tumors 9
CHAPETR 3 MATERIAL AND METHODS 11
3.1 Instruments 11
3.1.1 Geometric Configuration 11
3.1.2 Parameter Values 13
3.2 Mathematical Model 17
3.2.1 Interstitial Fluid Transportation 17
3.2.2 Interstitial Solute Transportation 19
3.2.3 Tumor Cell Survival Rate 20
CHAPTER 4 RESULTS AND DISSCUSSION 23
4.1 Spatial Concentration Distribution 23
4.2 Tumor Cell Survival Rate 31
CHAPTER 5 CONCLUSIONS 40
BIBLIOGRAPHY 43









LIST OF FIGURES
Figure 2.1. Longitudinal fluorescence imaging of vascular structure of normal (top) and tumor (bottom) colon tissue in a floxed Apc mouse. (Jain and Stylianopoulos, 2010) 7
Figure 3.1 The schematic of tumor vascular distribution. Two types of distribution can be computed by this model — (a) linear distribution and (b) sine distribution. The S/V is highest at the tumor boundary and decays with increasing depth into tumor. 12
Figure 3.2. Cross-sectional schematics of an isolated (left) and a subcutaneous tumor (right). 13
Figure 4.1. The spatial interstitial distribution for (a) an isolated tumor and (b) a subcutaneous tumor. R is the tumor radius, Rn is the radius of the necrotic region, Tn is the thickness of the surrounding normal tissue, is the dimensionless position from center relative to tumor radius. 24
Figure 4.2. The spatial AUC distribution for (a) an isolated tumor and (b) a subcutaneous tumor. R is the tumor radius, Rn is the radius of the necrotic region, Tn is the thickness of the surrounding normal tissue, is the dimensionless position from center relative to tumor radius. 24
Figure 4.3. (a) The spatial AUC distribution for different tumor sizes, and (b) different vascular densities ( ). R is the tumor radius, is the surface area of blood vessel per tumor volume, is dimensionless position from center. 27
Figure 4.4. The tornado diagrams display the impact of tumor characteristics on the average AUC of subcutaneous tumor in (a) the vascular region, (b) necrotic region, and (c) the surrounding normal tissue. R is the tumor radius, is the surface area of blood vessel per tumor volume, is the ratio of necrotic region to tumor size. 28
Figure 4.5. The spatial distribution of accumulative concentration and the cell survival rate of (a) 10 kDa dextran and (b) doxorubicin. Blue line indicates AUC distribution and green line represents cell survival rate. is the dimensionless position from center relative to tumor radius. The AUC of dextran is higher than doxorubicin at tumor region and is much lower at normal tissues. 32
Figure 4.6. The overall tumor (a) and normal tissue (b) cell survival rate after multiple 10 kDa dextran treatments. X-axis represents the number of treatments and the time interval between each treatment is assumed as ten days. Cmax is the maximum concentration in the blood vessel and is assumed to be 5314.4 nM. 34
Figure 4.7. The overall tumor (a) and normal tissue (b) cell survival rate after multiple doxorubicin treatments. X-axis represents the number of treatments and the time interval between each treatment is assumed as ten days. Cmax is the maximum concentration in the blood vessel and is assumed to be 5314.4 nM. 34
Figure 4.8. The overall tumor cell survival rate after 10 kDa dextran treatments. (a) The maximum concentration (Cmax) in the blood vessel is assumed to be 5314.4 nM and the number of treatments is four times. The time interval between each treatment of is assumed as ten days. (b) The maximum concentration (Cmax) in the blood vessel is assumed to be 2657.2 nM and the number of treatments is eight times. The time interval between each treatment of is assumed as five days. 36
Figure 4.9. The overall normal tissue cell survival rate after 10 kDa dextran treatments. (a) The maximum concentration (Cmax) in the blood vessel is assumed to be 5314.4 nM and the number of treatments is four times. The time interval between each treatment of is assumed as ten days. (b) The maximum concentration (Cmax) in the blood vessel is assumed to be 2657.2 nM and the number of treatments is eight times. The time interval between each treatment of is assumed as five days. 37








LIST OF TABLES
Table 3.1 Parameter values for tumor tissue of isolated and subcutaneous tumors and the values for normal tissue of subcutaneous tumor. 15
Table 3.2 Parameters for dextrans of different molecular weights in tumors 16
Table 3.3 Parameters for dextrans of different molecular weights in normal tissue 16
Table 3.4 Parameters of doxorubicin used in model. 17
Table 4.1. The average AUCs for tumor using various sized dextrans and doxorubicin. 25



BIBLIOGRAPHY
Baxter L.T., Jain R.K. 1989. Transport of fluid and macromolecules in tumors. (I) role of interstitial pressure and convection. Microvascular Research. 37: 77–104.

Baxter L.T., Jain R.K. 1990. Transport of fluid and macromolecules in tumors. (II) role of heterogeneous perfusion and lymphatics. Microvascular Research. 40: 246–263.

Baxter L.T., Jain R.K. 1991. Transport of fluid and macromolecules in tumors. (III) role of binding and metabolism. Microvascular Research. 41: 5–23.

Baxter L.T., Jain R.K. 1991. Transport of fluid and macromolecules in tumors. (IV) A Microscopic Model of the Perivascular Distribution. Microvascular Research. 41: 252–272.

Boucher Y., Baxter L.T., Jain R.K. 1990. Interstitial pressure gradients in tissue-isolated and subcutaneous tumors: Implication for therapy. Cancer Research. 50:4478–4484.

Chou C.Y., Huang C.K., Lu K.W., Horng T.L., Lin W.L. 2013. Investigation of the Spatiotemporal Responses of Nanoparticles in Tumor Tissues with a Small-Scale Mathematical Model. PLoS ONE. 8(4): e59135.

Dreher M.R., et al. 2006. Tumor Vascular Permeability, Accumulation, and Penetration of Macromolecular Drug Carriers. Journal of the National Cancer Institute. 98(5):335-344.

Eikenberry S. 2009. A tumor cord model for Doxorubicin delivery and dose optimization in solid tumors. Theoretical Biology and Medical Modeling. 6:16.

El-Kareh A.W., Secomb T.W. 2000. A Mathematical Model for Comparison of Bolus Injection, Continuous Infusion, and Liposomal Delivery of Doxorubicin to Tumor Cells. Neoplasia. 2:325-338.

Fleischer A.C., et al. 2004. Sonographic Depiction of Microvessel Perfusion Principles and Potential. Journal of Ultrasound in Medicine. 23:1499–1506
Greene R. F., et al. 1983. Plasma Pharmacokinetics of Adriamycin and Adriamycinol: Implications for the Design of in Vitro Experiments and Treatment Protocols. Cancer Research. 43: 3417-3421.

Jain R.K. 1988. Determinants of tumor blood flow: A review. Cancer Research. 48:2641–2658.

Jain R.K., Stylianopoulos T. 2010. Delivering nanomedicine to solid tumors. Nature Reviews Clinical Oncology. 7(11): 653–664.

Jusko W.J. 1971. Pharmacodynamics of Chemotherapeutic Effects: Dose-Time-Response Relationships for Phase-Nonspecific Agents. J. Pharm. Sci. 60: 892-895

Leu A.J., et al. 2000. Absence of Functional Lymphatics within Amurine Sarcoma: A Molecular and Functional Evaluation. Cancer Res. 60:4324–4327.

Padera T.P., et al. 2002. Lymphatic metastasis in the absence of functional intratumor lymphatics. Science. 296:1883–1886.

Shubik P. 1982. Vascularization of Tumors: a Review. Journal of Cancer Research and Clinical Oncology. 103(3):211-226.

Soltani M., Chen P. 2011. Numerical Modeling of Fluid Flow in Solid Tumors. PLoS ONE. 6(6): e20344.

van Putten L.M., Lelieveld P. 1970. Factors determining cell killing by chemotherapeutic agents in vivo – I.: Cyclophosphamide. European J. Cancer. 6: 313-321.

Welter M. and Rieger H. 2010. Physical determinants of vascular network remodeling during tumor growth. The European Physical Journal E. 33(2):149–163.

Yorke E.D., et al. 1993. Modeling the development of metastases from primary and locally recurrent tumors: comparison with clinical database for prostate cancer. Cancer Res. 53:2987-2993.


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