Synthesis and biological evaluation of PSMA-targeting paclitaxel conjugates

Prostate cancer (PC) is the second most commonly occurring cancer in men. Conventional chemotherapy has wide variety of disadvantages such as high systemic toxicity and low selectivity. Targeted drug delivery is a promising approach to decrease side effects of therapy. Prostate specific membrane antigen (PSMA) is overexpressed in prostate cancer cells while low level of expression is observed in normal cells. In this study we describe the development of Glu-urea-Lys based PSMA-targeting conjugates with paclitaxel. A series of new PSMA targeting conjugates with paclitaxel was designed and synthesized. The cytotoxicity of conjugates was evaluated against prostate (LNCaP, 22Rv1 and PC-3) and non-prostate (Hek293T, VA13, A549 and MCF-7) cell lines. The most promising conjugate 21 was examined in vivo using 22Rv1 xenograft mice model. It demonstrated good efficiency comparable with paclitaxel, while reduced toxicity. 3D molecular docking study was also performed to understand underlying mechanism of binding and further optimization of the linker substructure and conjugates structure for improving the target affinity. These conjugates may be useful for further design of novel PSMA targeting delivery systems for PC.     

Monitoring and prediction of an epidemic outbreak using syndromic observations

The paper presents a method for syndromic surveillance of an epidemic outbreak due to an emerging disease, formulated in the context of stochastic nonlinear filtering. The dynamics of the epidemic is modeled using a stochastic compartmental epidemiological model with inhomogeneous mixing. The syndromic (typically non-medical) observations of the number of infected people (e.g. visits to pharmacies, sale of certain products, absenteeism from work/study, etc.) are assumed available for monitoring and prediction of the epidemic. The state of the epidemic, including the number of infected people and the unknown parameters of the model, are estimated via a particle filter. The numerical results indicate that the proposed framework can provide useful early prediction of the epidemic peak if the uncertainty in prior knowledge of model parameters is not excessive.     

L<Subscript>2</Subscript>-norm multiple kernel learning and its application to biomedical data fusion

Background  

This paper introduces the notion of optimizing different norms in the dual problem of support vector machines with multiple kernels. The selection of norms yields different extensions of multiple kernel learning (MKL) such as L _∞, L ₁, and L ₂ MKL. In particular, L ₂ MKL is a novel method that leads to non-sparse optimal kernel coefficients, which is different from the sparse kernel coefficients optimized by the existing L _∞ MKL method. In real biomedical applications, L ₂ MKL may have more advantages over sparse integration method for thoroughly combining complementary information in heterogeneous data sources.