New FTY720-docetaxel nanoparticle therapy overcomes FTY720-induced lymphopenia and inhibits metastatic breast tumour growth

Purpose: Combining molecular therapies with chemotherapy may offer an improved clinical outcome for chemoresistant tumours. Sphingosine-1-phosphate (S1P) receptor antagonist and sphingosine kinase 1 (SK1) inhibitor FTY720 (FTY) has promising anticancer properties, however, it causes systemic lymphopenia which impairs its use in cancer patients. In this study, we developed a nanoparticle (NP) combining docetaxel (DTX) and FTY for enhanced anticancer effect, targeted tumour delivery and reduced systemic toxicity. Methods: Docetaxel, FTY and glucosamine were covalently conjugated to poly(lactic-co-glycolic acid) (PLGA). NPs were characterised by dynamic light scattering and electron microscopy. The cellular uptake, cytotoxicity and in vivo antitumor efficacy of CNPs were evaluated. Results: We show for the first time that in triple negative breast cancer cells FTY provides chemosensitisation to DTX, allowing a four-fold reduction in the effective dose. We have encapsulated both drugs in PLGA complex NPs (CNPs), with narrow size distribution of ~ 100 nm and excellent cancer cell uptake providing sequential, sustained release of FTY and DTX. In triple negative breast cancer cells and mouse breast cancer models, CNPs had similar efficacy to systemic free therapies, but allowed an effective drug dose reduction. Application of CNPs has significantly reversed chemotherapy side effects such as weight loss, liver toxicity and, most notably, lymphopenia. Conclusions: We show for the first time the DTX chemosensitising effects of FTY in triple negative breast cancer. We further demonstrate that encapsulation of free drugs in CNPs can improve targeting, provide low off-target toxicity and most importantly reduce FTY-induced lymphopenia, offering potential therapeutic use of FTY in clinical cancer treatment

CATMoS: Collaborative Acute Toxicity Modeling Suite

BACKGROUND: Humans are exposed to tens of thousands of chemical substances that need to he assessed for their potential toxicity. Acute systemic toxicity testing serves as the basis for regulatory hazard classification, labeling, and risk management. However, it is cost- and time-prohibitive to evaluate all new and existing chemicals using traditional rodent acute toxicity tests. In silica models built using existing data facilitate rapid acute toxicity predictions without using animals.OBJECTIVES: The U.S. Interagency Coordinating Committee on the Validation of Alternative Methods (ICCVAM) Acute Toxicity Workgroup organized an international collaboration to develop in silico models for predicting acute oral toxicity based on five different end points: Lethal Dose 50 (LD50 value, U.S. Environmental Protection Agency hazard (four) categories, Globally Harmonized System for Classification and Labeling hazard (live) categories, very toxic chemicals [LD50 (LD50 <= 50 mg/kg)], and nontoxic chemicals (LD50 > 2,000 mg/kg).METHODS: An acute oral toxicity data inventory for 11,992 chemicals was compiled, split into training and evaluation sets, and made available to 35 participating international research groups that submitted a total of 139 predictive models. Predictions that fell within the applicability domains of the submitted models were evaluated using external validation sets. These were then combined into consensus models to leverage strengths of individual approaches.RESULTS: 'the resulting consensus predictions, which leverage the collective strengths of each individual model, form the Collaborative Acute Toxicity Modeling Suite (CATMoS). CATMoS demonstrated high performance in terms of accuracy and robustness when compared with in vivo results.DISCUSSION: CATMoS is being evaluated by regulatory agencies for its utility and applicability as a potential replacement for in vivo rat acute oral toxicity studies. CATMoS predictions for more than 800,000 chemicals have been made available via the National Toxicology Program's Integrated Chemical Environment tools and data sets (ice.ntp.niehs.nih.gov). The models are also implemented in a free, standalone, open-source tool, OPERA, which allows predictions of new: and untested chemicals to be made