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4 March 2019 A Theoretical Model for Predicting and Optimizing In Vitro Screening of Potential Targeted Alpha-Particle Therapy Drugs
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Abstract

One highly promising approach to cancer treatment, especially for tumors that have undergone micrometastasis, is targeted alpha-particle therapy (TAT). However, the development of a TAT drug has been impeded due to numerous unsuccessful attempts to establish effective in vitro screening methods. The goal of this study was to construct a model to predict and optimize in vitro screening of potential TAT drugs. Based on mean field hypothesis, microdosimetry and the classic linear-quadratic equation, a novel model was built, which can predict our own in vitro experiments and replicate published data from others. Interestingly, this model can also be used to quickly optimize several key parameters in in vitro screening of potential TAT drugs, instructing the optimal combinations of the expression level of antigen, the binding affinity of antibody and drug antibody ratio, as well as others. In addition, to conveniently evaluate the therapeutic benefit of different drugs, a simple but universal parameter, the death ratio, is proposed. To our knowledge, this is the first model that can predict and guide the optimization of in vitro potential targeted alpha-particle therapy drug screening, which may then accelerate the development of potential targeted alpha-particle therapy drugs dramatically.

Ma, W., Wang, X., Liu, W., Ma, H., Su, Y., Yang, Y., Liu, N., Wang, Y. and Yang, G. A Theoretical Model for Predicting and Optimizing In Vitro Screening of Potential Targeted Alpha-Particle Therapy Drugs. Radiat. Res. 191, 475–482 (2019).

© 2019 by Radiation Research Society All rights of reproduction in any form reserved.
Wenzong Ma, Xudong Wang, Weihao Liu, Huan Ma, Yue Su, Yuanyou Yang, Ning Liu, Yugang Wang, and Gen Yang "A Theoretical Model for Predicting and Optimizing In Vitro Screening of Potential Targeted Alpha-Particle Therapy Drugs," Radiation Research 191(5), 475-482, (4 March 2019). https://doi.org/10.1667/RR15297.1
Received: 26 November 2018; Accepted: 31 January 2019; Published: 4 March 2019
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