Molecular design of radiocopper-labelled Affibody molecules

The use of long-lived positron emitters Cu-64 or Cu-61 for labelling of Affibody molecules may improve breast cancer patients' stratification for HER-targeted therapy. Previous animal studies have shown that the use of triaza chelators for Cu-64 labelling of synthetic Affibody molecules is suboptimal. In this study, we tested a hypothesis that the use of cross-bridged chelator, CB-TE2A, in combination with Gly-Glu-Glu-Glu spacer for labelling of Affibody molecules with radiocopper would improve imaging contrast. CB-TE2A was coupled to the N-terminus of synthetic Affibody molecules extended either with a glycine (designation CB-TE2A-G-ZHER2:342) or Gly-Glu-Glu-Glu spacer (CB-TE2A-GEEE-ZHER2:342). Biodistribution and targeting properties of Cu-64-CB-TE2A-G-ZHER2:342 and Cu-64-CB-TE2A-GEEE-ZHER2:342 were compared in tumor-bearing mice with the properties of Cu-64-NODAGA-ZHER2:S1, which had the best targeting properties in the previous study. Cu-64-CB-TE2A-GEEE-ZHER2:342 provided appreciably lower uptake in normal tissues and higher tumor-to-organ ratios than Cu-64-CB-TE2A-GZHER2:342 and Cu-64-NODAGA-ZHER2:S1. The most pronounced was a several-fold difference in the hepatic uptake. At the optimal time point, 6 h after injection, the tumor uptake of Cu-64-CB-TE2A-GEEE-ZHER2: 342 was 16 +/- 6% ID/g and tumor-to-blood ratio was 181 +/- 52. In conclusion, a combination of the cross-bridged CB-TE2A chelator and Gly-Glu-Glu-Glu spacer is preferable for radiocopper labelling of Affibody molecules and, possibly, other scaffold proteins having high renal re-absorption

Site-selective protein-modification chemistry for basic biology and drug development.

Nature has produced intricate machinery to covalently diversify the structure of proteins after their synthesis in the ribosome. In an attempt to mimic nature, chemists have developed a large set of reactions that enable post-expression modification of proteins at pre-determined sites. These reactions are now used to selectively install particular modifications on proteins for many biological and therapeutic applications. For example, they provide an opportunity to install post-translational modifications on proteins to determine their exact biological roles. Labelling of proteins in live cells with fluorescent dyes allows protein uptake and intracellular trafficking to be tracked and also enables physiological parameters to be measured optically. Through the conjugation of potent cytotoxicants to antibodies, novel anti-cancer drugs with improved efficacy and reduced side effects may be obtained. In this Perspective, we highlight the most exciting current and future applications of chemical site-selective protein modification and consider which hurdles still need to be overcome for more widespread use.We thank FCT Portugal (FCT Investigator to G.J.L.B.), the EU (Marie-Curie CIG to G.J.L.B. and Marie-Curie IEF to O.B.) and the EPSRC for funding. G.J.L.B. is a Royal Society University Research Fellow.This is the author accepted manuscript. The final version is available from NPG via http://dx.doi.org/10.1038/nchem.239

Information Market-Based Decision Fusion

Improved classification performance has practical real-world benefits ranging from improved effectiveness in detecting diseases to increased efficiency in identifying firms that are committing financial fraud. Multiclassifier combination (MCC) aims to improve classification performance by combining the decisions of multiple individual classifiers. In this paper, we present information market-based fusion (IMF), a novel multiclassifier combiner method for decision fusion that is based on information markets. In IMF, the individual classifiers are implemented as participants in an information market where they place bets on different object classes. The reciprocals of the market odds that minimize the difference between the total betting amount and the potential payouts for different classes represent the MCC probability estimates of each class being the true object class. By using a market-based approach, IMF can adjust to changes in base-classifier performance without requiring offline training data or a static ensemble composition. Experimental results show that when the true classes of objects are only revealed for objects classified as positive, for low positive ratios, IMF outperforms three benchmarks combiner methods, majority, average, and weighted average; for high positive ratios, IMF outperforms majority and performs on par with average and weighted average. When the true classes of all objects are revealed, IMF outperforms weighted average and majority and marginally outperforms average.multiclassifier combination, decision fusion, information markets, software agents