Eye is a distinctive organ with protective anatomy and physiology. Several pharmacokinetics compartment model of ocular drug delivery has been developed for describing the absorption, distribution and elimination of ocular drugs in the eye. Determining pharmacokinetics parameters in ocular tissues is a major challenge because of the complex anatomy and dynamic physiological barrier of the eye. In this review, pharmacokinetics of these compartments exploring different drugs, delivery systems and routes of administration are discussed including factors affecting intraocular bioavailability. Factors such as pre-corneal fluid drainage, drug binding to tear proteins, systemic drug absorption, corneal factors, melanin binding, drug metabolism renders ocular delivery challenging and elaborated in this manuscript. Several compartment models are discussed those are developed in ocular drug delivery to study the pharmacokinetics parameters. There are several transporters present in both anterior and posterior segments of the eye which play a significant role in ocular pharmacokinetics and summarized briefly. Moreover, several ocular pharmacokinetics animal models and relevant studies are reviewed and discussed in addition to the pharmacokinetics of various ocular formulations.

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Purpose: These studies were designed to determine whether ritonavir inhibits breast cancer in vitro and in vivo and, if so, how. Experimental Design: Ritonavir effects on breast cancer cell growth were studied in the estrogen receptor (ER)^positive lines MCF7 and T47D and in the ER-negative lines MDA-MB-436 and MDA-MB-231. Effects of ritonavir on Rb-regulated and Akt-mediated cell proliferation were studied. Ritonavir was tested for inhibition of a mammary carcinoma xenograft. Results: ER-positive estradiol-dependent lines (IC 50 , 12-24 Amol/L) and ER-negative (IC 50 , 45 Amol/L) lines exhibit ritonavir sensitivity. Ritonavir depletes ER-a levels notably in ER-positive lines. Ritonavir causes G 1 arrest, depletes cyclin-dependent kinases 2, 4, and 6 and cyclin D 1 but not cyclin E, and depletes phosphorylated Rb and Ser 473 Akt. Ritonavir induces apoptosis independent of G 1 arrest, inhibiting growth of cells that have passed the G 1 checkpoint. Myristoyl-Akt, but not activated K-Ras, rescues ritonavir inhibition. Ritonavir inhibited a MDA-MB-231 xenograft and intratumoral Akt activity at a clinically attainable serum C max of 22 F 8 Amol/L. Because heat shock protein 90 (Hsp90) substrates are depleted by ritonavir, ritonavir effects on Hsp90 were tested. Ritonavir binds Hsp90 (K D , 7.8 Amol/L) and partially inhibits its chaperone function. Ritonavir blocks association of Hsp90 with Akt and, with sustained exposure, notably depletes Hsp90. Stably expressed Hsp90a short hairpin RNA also depletes Hsp90, inhibiting proliferation and sensitizing breast cancer cells to low ritonavir concentrations. Conclusions: Ritonavir inhibits breast cancer growth in part by inhibiting Hsp90 substrates, including Akt. Ritonavir may be of interest for breast cancer therapeutics and its efficacy may be increased by sustained exposure or Hsp90 RNA interference.

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BackgroundNeoplastic cells proliferate rapidly and obtain requisite building blocks by reprogramming metabolic pathways that favor growth. Previously, we observed that prostate cancer cells uptake and store lipids in the form of lipid droplets, providing building blocks for membrane synthesis, to facilitate proliferation and growth. Mechanisms of lipid uptake, lipid droplet dynamics and their contribution to cancer growth have yet to be defined. This work is focused on elucidating the prostate cancer-specific modifications in lipid storage pathways so that these modified gene products can be identified and therapeutically targeted.MethodsTo identify genes that promote lipid droplet formation and storage, the expression profiles of candidate genes were assessed and compared between peripheral blood mononuclear cells and prostate cancer cells. Subsequently, differentially expressed genes were inhibited and growth assays performed to elucidate their role in the growth of the cancer cells. Cell cycle, apoptosis and autophagy assays were performed to ascertain the mechanism of growth inhibition.ResultsOur results indicate that DGAT1, ABHD5, ACAT1 and ATGL are overexpressed in prostate cancer cells compared to PBMCs and of these overexpressed genes, DGAT1 and ABHD5 aid in the growth of the prostate cancer cells. Blocking the expression of both DGAT1 and ABHD5 results in inhibition of growth, cell cycle block and cell death. DGAT1 siRNA treatment inhibits lipid droplet formation and leads to autophagy where as ABHD5 siRNA treatment promotes accumulation of lipid droplets and leads to apoptosis. Both the siRNA treatments reduce AMPK phosphorylation, a key regulator of lipid metabolism. While DGAT1 siRNA reduces phosphorylation of ACC, the rate limiting enzyme in de novo fat synthesis and triggers phosphorylation of raptor and ULK-1 inducing autophagy and cell death, ABHD5 siRNA decreases P70S6 phosphorylation, leading to PARP cleavage, apoptosis and cell death. Interestingly, DGAT-1 is involved in the synthesis of triacylglycerol where as ABHD5 is a hydrolase and participates in the fatty acid oxidation process, yet inhibition of both enzymes similarly promotes prostate cancer cell death.ConclusionInhibition of either DGAT1 or ABHD5 leads to prostate cancer cell death. Both DGAT1 and ABHD5 can be selectively targeted to block prostate cancer cell growth.

BackgroundCirculating tumour cells (CTC) are an important indicator of metastasis and associated with a poor prognosis. Detection sensitivity and specificity of CTC in the peripheral blood of metastatic cancer patient remain a technical challenge.MethodsCoherent anti-Stokes Raman scattering (CARS) microscopy was employed to examine the lipid content of CTC isolated from the peripheral blood of metastatic prostate cancer patients. CARS microscopy was also employed to evaluate lipid uptake and mobilization kinetics of a metastatic human prostate cancer cell line.ResultsOne hundred CTC from eight metastatic prostate cancer patients exhibited strong CARS signal which arose from intracellular lipid. In contrast, leukocytes exhibited weak CARS signal which arose mostly from cellular membrane. On average, CARS signal intensity of prostate CTC was 7-fold higher than that of leukocytes (P<0.0000001). When incubated with human plasma, C4-2 metastatic human prostate cancer cells exhibited rapid lipid uptake kinetics and slow lipid mobilization kinetics. Higher expression of lipid transport proteins in C4-2 cells compared to non-transformed RWPE-1 and non-malignant BPH-1 prostate epithelial cells further indicated strong affinity for lipid of metastatic prostate cancer cells.ConclusionsIntracellular lipid could serve as a biomarker for prostate CTC which could be sensitively detected with CARS microscopy in a label-free manner. Strong affinity for lipid by metastatic prostate cancer cells could be used to improve detection sensitivity and therapeutic targeting of prostate CTC.

Purpose: This study was performed to discover prognostic genomic markers associated with postoperative outcome of stage I to III non-small cell lung cancer (NSCLC) that are reproducible between geographically distant and demographically distinct patient populations.Experimental Design: American patients (n ¼ 27) were stratified on the basis of recurrence and microarray profiling of their tumors was performed to derive a training set of 44 genes. A larger Korean patient validation cohort (n ¼ 138) was also stratified by recurrence and screened for these genes. Four reproducible genes were identified and used to construct genomic and clinicogenomic Cox models for both cohorts.Results: Four genomic markers, DBN1 (drebrin 1), CACNB3 (calcium channel beta 3), FLAD1 (PP591; flavin adenine dinucleotide synthetase), and CCND2 (cyclin D2), exhibited highly significant differential expression in recurrent tumors in the training set (P < 0.001). In the validation set, DBN1, FLAD1 (PP591), and CACNB3 were significant by Cox univariate analysis (P 0.035), whereas only DBN1 was significant by multivariate analysis. Genomic and clinicogenomic models for recurrence-free survival (RFS) were equally effective for risk stratification of stage I to II or I to III patients (all models P < 0.0001). For stage I to II or I to III patients, 5-year RFS of the low-and high-risk patients was approximately 70% versus 30% for both models. The genomic model for overall survival of stage I to III patients was improved by addition of pT and pN stage (P < 0.0013 vs. 0.010).Conclusion: A 4-gene prognostic model incorporating the multivariate marker DBN1 exhibits potential clinical utility for risk stratification of stage I to III NSCLC patients. Clin Cancer Res; 17(9); 2934-46. Ó2011 AACR.