Advanced Hepatocellular Carcinoma: Perfusion Computed Tomography-Based Kinetic Parameter as a Prognostic Biomarker for Prediction of Patient Survival

Computer Optimization of Stealth Biodegradable Polymeric Dual-loaded Nanoparticles for Cancer Therapy Using Central Composite Face-centered Design

BACKGROUND

Combination chemotherapy capable of overcoming cancer drug resistance can be facilitated by nanotechnology.

OBJECTIVE

Synthesis, characterization, statistical experimental design, analysis and optimization of stealth pH-sensitive polymeric nanoparticles suitable as a platform for simultaneous delivery of paclitaxel and 17-AAG in breast cancer therapy were investigated.

METHODS

An acetal crosslinker and a poly(ɛ)caprolactone macromonomer were synthesized and characterized. The statistical experimental design used was the response surface method (RSM). We used the central composite face-centered design (CCF) in three independent factors and seventeen runs. Nanoparticles were fabricated by dispersion polymerization techniques. Response variables evaluated were: particle size, drug loading, encapsulation efficiency, and in vitro availability.

RESULTS

Scanning electron micrographs showed the formation of spherical nanoparticles. Computer software was used for the analysis of variance with a 95% confidence level and Q2 (goodness of prediction) to select an appropriate model for each of the response variables. Each term in each of the models was tested for the significance of the regression coefficients. The computer software optimizer was used for optimization to select factor combination to minimize particle size, time (h) for maximum release of paclitaxel and 17-AAG, to maximize paclitaxel and 17-AAG loading efficiency and to maximize paclitaxel and 17-AAG encapsulation efficiency.

CONCLUSION

The optimization was successful, as shown by the validation data which lie within the confidence intervals of predicted values of the response variables. The selected factor combination is suitable for the in vivo evaluation of the nanoparticles loaded with paclitaxel and 17-AAG.

Use of patient outcome endpoints to identify the best functional CT imaging parameters in metastatic renal cell carcinoma patients

OBJECTIVE

To use the patient outcome endpoints overall survival and progression-free survival to evaluate functional parameters derived from dynamic contrast-enhanced CT.

METHODS

69 patients with metastatic renal cell carcinoma had dynamic contrast-enhanced CT scans at baseline and after 5 and 10 weeks of treatment. Blood volume, blood flow and standardized perfusion values were calculated using deconvolution (BV_deconv, BF_deconv and SPV_deconv), blood flow and standardized perfusion values using maximum slope (BF_max and SPV_max) and blood volume and permeability surface area product using the Patlak model (BV_patlak and PS). Histogram data for each were extracted and associated to patient outcomes. Correlations and agreements were also assessed.

RESULTS

The strongest associations were observed between patient outcome and medians and modes for BV_deconv, BV_patlak and BF_deconv at baseline and during the early ontreatment period (p < 0.05 for all). For the relative changes in median and mode between baseline and weeks 5 and 10, PS seemed to have opposite associations dependent on treatment. Interobserver correlations were excellent (r ≥ 0.9, p < 0.001) with good agreement for BF_deconv, BF_max, SPV_deconv and SPV_max and moderate to good (0.5 < r < 0.7, p < 0.001) for BV_deconv and BV_patlak. Medians had a better reproducibility than modes.

CONCLUSION

Patient outcome was used to identify the best functional imaging parameters in patients with metastatic renal cell carcinoma. Taking patient outcome and reproducibility into account, BV_deconv, BV_patlak and BF_deconv provide the most clinically meaningful information, whereas PS seems to be treatment dependent. Standardization of acquisition protocols and post-processing software is necessary for future clinical utilization. Advances in knowledge: Taking patient outcome and reproducibility into account, BV_deconv, BV_patlak and BF_deconv provide the most clinically meaningful information. PS seems to be treatment dependent.