Field template-based design and biological evaluation of new sphingosine kinase 1 inhibitors

Purpose: Sphingosine kinase 1 (SK1) is a protooncogenic enzyme expressed in many human tumours and is associated with chemoresistance and poor prognosis. It is a potent therapy target and its inhibition chemosensitises solid tumours. Despite recent advances in SK1 inhibitors synthesis and validation, their clinical safety and chemosensitising options are not well described. In this study, we have designed, synthesised and tested a new specific SK1 inhibitor with a low toxicity profile. Methods: Field template molecular modelling was used for compound design. Lead compounds were tested in cell and mouse cancer models. Results: Field template analysis of three known SK1 inhibitors, SKI-178, 12aa and SK1-I, was performed and compound screening identified six potential new SK1 inhibitors. SK1 activity assays in both cell-free and in vitro settings showed that two compounds were effective SK1 inhibitors. Compound SK-F has potently decreased cancer cell viability in vitro and sensitised mouse breast tumours to docetaxel (DTX) in vivo, without significant whole-body toxicity. Conclusion: Through field template screening, we have identified a new SK1 inhibitor, SK-F, which demonstrated antitumour activity in vitro and in vivo without overt toxicity when combined with DTX

Aberrant expression of the S1P regulating enzymes, SPHK1 and SGPL1, contributes to a migratory phenotype in OSCC mediated through S1PR2.

Oral squamous cell carcinoma (OSCC) is a lethal disease with a 5-year mortality rate of around 50%. Molecular targeted therapies are not in routine use and novel therapeutic targets are required. Our previous microarray data indicated sphingosine 1-phosphate (S1P) metabolism and signalling was deregulated in OSCC. In this study, we have investigated the contribution of S1P signalling to the pathogenesis of OSCC. We show that the expression of the two major enzymes that regulate S1P levels were altered in OSCC: SPHK1 was significantly upregulated in OSCC tissues compared to normal oral mucosa and low levels of SGPL1 mRNA correlated with a worse overall survival. In in vitro studies, S1P enhanced the migration/invasion of OSCC cells and attenuated cisplatin-induced death. We also demonstrate that S1P receptor expression is deregulated in primary OSCCs and that S1PR2 is over-expressed in a subset of tumours, which in part mediates S1P-induced migration of OSCC cells. Lastly, we demonstrate that FTY720 induced significantly more apoptosis in OSCC cells compared to non-malignant cells and that FTY720 acted synergistically with cisplatin to induce cell death. Taken together, our data show that S1P signalling promotes tumour aggressiveness in OSCC and identify S1P signalling as a potential therapeutic target.This article is freely available via Open Access. Click on the 'Additional Link' above to access the full-text via the publisher's site.Published (Open Access

Equation-free mechanistic ecosystem forecasting using empirical dynamic modeling

It is well known that current equilibrium-based models fall short as predictive descriptions of natural ecosystems, and particularly of fisheries systems that exhibit nonlinear dynamics. For example, model parameters assumed to be fixed constants may actually vary in time, models may fit well to existing data but lack out-of-sample predictive skill, and key driving variables may be misidentified due to transient (mirage) correlations that are common in nonlinear systems. With these frailties, it is somewhat surprising that static equilibrium models continue to be widely used. Here, we examine empirical dynamic modeling (EDM) as an alternative to imposed model equations and that accommodates both nonequilibrium dynamics and nonlinearity. Using time series from nine stocks of sockeye salmon (Oncorhynchus nerka) from the Fraser River system in British Columbia, Canada, we perform, for the the first time to our knowledge, real-data comparison of contemporary fisheries models with equivalent EDM formulations that explicitly use spawning stock and environmental variables to forecast recruitment. We find that EDM models produce more accurate and precise forecasts, and unlike extensions of the classic Ricker spawner–recruit equation, they show significant improvements when environmental factors are included. Our analysis demonstrates the strategic utility of EDM for incorporating environmental influences into fisheries forecasts and, more generally, for providing insight into how environmental factors can operate in forecast models, thus paving the way for equation-free mechanistic forecasting to be applied in management contexts

Equation-free mechanistic ecosystem forecasting using empirical dynamic modeling

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Equation-free mechanistic ecosystem forecasting using empirical dynamic modeling

It is well known that current equilibrium-based models fall short as predictive descriptions of natural ecosystems, and particularly of fisheries systems that exhibit nonlinear dynamics. For example, model parameters assumed to be fixed constants may actually vary in time, models may fit well to existing data but lack out-of-sample predictive skill, and key driving variables may be misidentified due to transient (mirage) correlations that are common in nonlinear systems. With these frailties, it is somewhat surprising that static equilibrium models continue to be widely used. Here, we examine empirical dynamic modeling (EDM) as an alternative to imposed model equations and that accommodates both nonequilibrium dynamics and nonlinearity. Using time series from nine stocks of sockeye salmon (Oncorhynchus nerka) from the Fraser River system in British Columbia, Canada, we perform, for the the first time to our knowledge, real-data comparison of contemporary fisheries models with equivalent EDM formulations that explicitly use spawning stock and environmental variables to forecast recruitment. We find that EDM models produce more accurate and precise forecasts, and unlike extensions of the classic Ricker spawner–recruit equation, they show significant improvements when environmental factors are included. Our analysis demonstrates the strategic utility of EDM for incorporating environmental influences into fisheries forecasts and, more generally, for providing insight into how environmental factors can operate in forecast models, thus paving the way for equation-free mechanistic forecasting to be applied in management contexts

Crystal structure of sphingosine kinase 1 with PF-543

The most potent inhibitor of Sphingosine Kinase 1 (SPHK1) so far identified is PF-543. The crystal structure of SPHK1 in complex with inhibitor PF-543 to 1.8 Å resolution reveals the inhibitor bound in a bent conformation analogous to that expected of a bound sphingosine substrate but with a rotated head group. The structural data presented will aid in the design of SPHK1 and SPHK2 inhibitors with improved properties