Recent developments in monoclonal antibody radiolabeling techniques

Monoclonal antibodies (MAbs) have shown the potential to serve as selective carriers of radionuclides to specific in vivo antigens. Accordingly, there has been an intense surge of research activity in an effort to develop and evaluate MAb-based radiopharmaceuticals for tumor imaging (radioimmunoscintigraphy) and therapy (radioimmunotherapy), as well as for diagnosing nonmalignant diseases. A number of problems have recently been identified, related to the MAbs themselves and to radiolabeling techniques, that comprise both the selectivity and the specificity of the in vivo distribution of radiolabeled MAbs. This paper will address some of these issues and primarily discuss recent developments in the techniques for radiolabeling monoclonal antibodies that may help resolve problems related to the poor in vivo stability of the radiolabel and may thus produce improved biodistribution. Even though many issues are identical with therapeutic radionuclides, the discussion will focus mainly on radioimmunoscintigraphic labels. 78 refs., 6 tabs

Criteria for the selection of radionuclides for tumor radioimmunotherapy

The potential of utilizing monoclonal antibodies as carriers of radionuclides for the selective destruction of tumors (radioimmunotherapy, RIT) has stimulated much research activity. From dosimetric and other considerations, the choice of radiolabel is an important factor that needs to be optimized for maximum effectiveness of RIT. This paper reviews and assesses a number of present and future radionuclides that are particularly suitable for RIT based on the various physical, chemical, and biological considerations. Intermediate to high-energy beta emitters' (with and without gamma photons in their emission) are emphasized since they possess a number of advantages over alpha and Auger emitters. Factors relating to the production and availability of candidate radiometals as well as their stable chemical attachment to monoclonal antibodies are discussed. 34 refs., 4 tabs

Imbalanced Class Learning in Epigenetics

In machine learning, one of the important criteria for higher classification accuracy is a balanced dataset. Datasets with a large ratio between minority and majority classes face hindrance in learning using any classifier. Datasets having a magnitude difference in number of instances between the target concept result in an imbalanced class distribution. Such datasets can range from biological data, sensor data, medical diagnostics, or any other domain where labeling any instances of the minority class can be time-consuming or costly or the data may not be easily available. The current study investigates a number of imbalanced class algorithms for solving the imbalanced class distribution present in epigenetic datasets. Epigenetic (DNA methylation) datasets inherently come with few differentially DNA methylated regions (DMR) and with a higher number of non-DMR sites. For this class imbalance problem, a number of algorithms are compared, including the TAN+AdaBoost algorithm. Experiments performed on four epigenetic datasets and several known datasets show that an imbalanced dataset can have similar accuracy as a regular learner on a balanced dataset