Nanoparticle radiosensitization: from extended local effect modeling to a survival modification framework of compound Poisson additive killing and its carbon dots validation

Abstract

Abstract Objective. To construct an analytical model instead of local effect modeling for the prediction of the biological effectiveness of nanoparticle radiosensitization. Approach. An extended local effects model is first proposed with a more comprehensive description of the nanoparticles mediated local killing enhancements, but meanwhile puts forward challenging issues that remain difficult and need to be further studied. As a novel method instead of local effect modeling, a survival modification framework of compound Poisson additive killing is proposed, as the consequence of an independent additive killing by the assumed equivalent uniform doses of individual nanoparticles per cell under the LQ model. A compound Poisson killing (CPK) model based on the framework is thus derived, giving a general expression of nanoparticle mediated LQ parameter modification. For practical use, a simplified form of the model is also derived, as a concentration dependent correction only to the α parameter, with the relative correction (α″/α) dominated by the mean number, and affected by the agglomeration of nanoparticles per cell. For different agglomeration state, a monodispersion model of the dispersity factor η = 1, and an agglomeration model of 2/3 < η < 1, are provided for practical prediction of (α″/α) value respectively. Main results. Initial validation by the radiosensitization of HepG2 cells by carbon dots showed a high accuracy of the CPK model. In a safe range of concentration (0.003–0.03 μg μl−1) of the carbon dots, the prediction errors of the monodispersion and agglomeration models were both within 2%, relative to the clonogenic survival data of the sensitized HepG2 cells. Significance. The compound Poisson killing model provides a novel approach for analytical prediction of the biological effectiveness of nanoparticle radiosensitization, instead of local effect modeling.