Single Cytotoxicity Assay Research Articles

The Target Differences of Anti-Tumorigenesis Potential of Curcumin and its Analogues Against HER-2 Positive and Triple-Negative Breast Cancer Cells.

Purpose: The current study aims to evaluate the in vitro cytotoxic and cell migration effects of synthetic curcumin and its analogues on HER2 and nuclear factor kappa B (NFκB) pathways, as well as the in vivo inhibitory effect on cancer growth of metastatic breast cancer. Methods: Cell viability, protein expression, and protein localization were determined in vitro using MTT assay, western blotting, and immunofluorescence, respectively. Meanwhile, scratch wound healing assay and gelatin zymography were conducted to investigate the metastasis inhibitory effect. The in vivo anti-tumor ability was evaluated in xenograft mouse model using triple-negative breast cancer (TNBC) cells. Results: Curcumin, PGV-0, and PGV-1 exhibited cytotoxic effect against HER2-overexpressing breast cancer cells. Although PGV-1 showed the best activity in the single cytotoxic assay, curcumin showed the strongest synergism with doxorubicin. Curcumin and PGV-0 inhibited membrane localization of HER2. In contrast, PGV-1 neither inhibited localization nor decreased the expression of HER2, nonetheless showed the most potent inhibition against nuclear localization of p65 indicating the different mechanisms of curcumin, PGV-0, and PGV-1. Regarding cancer metastasis, curcumin and PGV-1 showed inhibitory activities against cell migration and inhibited MMP-2 and MMP-9 protein expression. Lastly, PGV-1 was more potent compared to curcumin to suppress the tumor formation of metastatic breast cancer xenograft model in nude mice. Conclusion: Overall, our study strengthens the potency of curcumin analogue, PGV-1, for treating several types of cancer, including metastatic breast cancer.

Design, Synthesis, and Evaluation of Antineoplastic Activity of Novel Carbocyclic Nucleosides.

Cancer is the leading cause of death among men and women under age 85. Every year, millions of individuals are diagnosed with cancer. But finding new drugs is a complex, expensive, and very time-consuming task. Over the past decade, the cancer research community has begun to address the in silico modeling approaches, such as Quantitative Structure-Activity Relationships (QSAR), as an important alternative tool for targeting potential anticancer drugs. With the compilation of a large dataset of nucleosides synthesized in our laboratories, or elsewhere, and tested in a single cytotoxic assay under the same experimental conditions, we recognized a unique opportunity to attempt to build predictive QSAR models. Early efforts with 2D classification models built from part of this dataset were very encouraging. Here we report a further detailed evaluation of classification models to flag potential anticancer activities derived from a variety of 3D molecular representations. A quantitative 3D-model model that discriminates anticancer compounds from the inactive ones was attained, which allowed the correct classification of 82 % of compounds in such a large and diverse dataset, with only 5 % of false inactives and 11 % of false actives. The model developed here was then used to select and design a new series of nucleosides, by classifying beforehand them as active/inactive anticancer compounds. From the compounds so designed, 22 were synthesized and evaluated for their inhibitory effects on the proliferation of murine leukemia cells (L1210/0), of which 86 % were well-classified as active or inactive, and only two were false actives, corroborating the good predictive ability of the present discriminant model. The results of this study thus provide a valuable tool for the design of novel potent anticancer nucleoside analogues.

Probing the Anticancer Activity of Nucleoside Analogues: A QSAR Model Approach Using an Internally Consistent Training Set

The cancer research community has begun to address the in silico modeling approaches, such as quantitative structure-activity relationships (QSAR), as an important alternative tool for screening potential anticancer drugs. With the compilation of a large dataset of nucleosides synthesized in our laboratories, or elsewhere, and tested in a single cytotoxic assay under the same experimental conditions, we recognized a unique opportunity to attempt to build predictive QSAR models. Here, we report a systematic evaluation of classification models to probe anticancer activity, based on linear discriminant analysis along with 2D-molecular descriptors. This strategy afforded a final QSAR model with very good overall accuracy and predictability on external data. Finally, we search for similarities between the natural nucleosides, present in RNA/DNA, and the active nucleosides well-predicted by the model. The structural information then gathered and the QSAR model per se shall aid in the future design of novel potent anticancer nucleosides.

Challenges for assessing carbon nanomaterial toxicity to the skin

This manuscript reviews a number of issues that must be dealt with to assess carbon nanomaterial interactions with the skin in the context of potential toxicity. The potential pathway for dermal absorption of carbon nanomaterials is discussed. The few existing studies assessing carbon nanomaterial toxicity to skin are reviewed. This paper addresses potential confounding factors in dealing with the experimental design of nanomaterial toxicity studies and their interpretation. Certain standard cytotoxicity assays that are well suited to assess chemical toxicity may generate conflicting results when carbon materials are assessed. This was demonstrated in an experimental study comparing carbon effects on human keratinocyte cytotoxicity assessed by transmission electron microscopy, neutral red and MTT cell viability assays, as well as irritation assessed by release of the cytokine IL-8. Four sources of carbon black particles were assessed. Conflicting results were obtained across all cytotoxicity endpoints potentially secondary to the adsorbing properties of carbon interfering with viability markers in the assay systems. These data suggest that a single cytotoxicity assay should not be relied upon in assessing carbon nanomaterial toxicity and that carbon black may not be optimal control particles for assessing nanomaterial toxicity in epidermal cell culture systems due to the wide range of responses seen between the carbon black varieties.