Selective inactivation of enzymes conjugated to nanoparticles using tuned laser illumination

A predictive model for personalization of nanotechnology-based phototherapy in cancer treatment

A major challenge in radiation oncology is the prediction and optimization of clinical responses in a personalized manner. Recently, nanotechnology-based cancer treatments are being combined with photodynamic therapy (PDT) and photothermal therapy (PTT). Predictive models based on machine learning techniques can be used to optimize the clinical setup configuration, including such parameters as laser radiation intensity, treatment duration, and nanoparticle features. In this article we demonstrate a methodology that can be used to identify the optimal treatment parameters for PDT and PTT by collecting data from in vitro cytotoxicity assay of PDT/PTT-induced cell death using a single nanocomplex. We construct three machine learning prediction models, employing regression, interpolation, and low- degree analytical function fitting, to predict the laser radiation intensity and duration settings that maximize the treatment efficiency. To examine the accuracy of these prediction models, we construct a dedicated dataset for PDT, PTT, and a combined treatment; this dataset is based on cell death measurements after light radiation treatment and is divided into training and test sets. The preliminary results show that the performance of all three models is sufficient, with death rate errors of 0.09, 0.15, and 0.12 for the regression, interpolation, and analytical function fitting approaches, respectively. Nevertheless, due to its simple form, the analytical function method has an advantage in clinical application and can be used for further analysis of the sensitivity of performance to the treatment parameters. Overall, the results of this study form a baseline for a future personalized prediction model based on machine learning in the domain of combined nanotechnology- and phototherapy-based cancer treatment.

meso-Tetrahydroxyphenylchlorin-Conjugated Gold Nanoparticles as a Tool To Improve Photodynamic Therapy

Photodynamic therapy (PDT) is a promising therapeutic modality for cancer. However, current protocols using bare drugs suffer from several limitations that impede its beneficial clinical effects. Here, we introduce a new approach for an efficient PDT treatment. It involves conjugating a PDT agent, meso-tetrahydroxyphenylchlorin (mTHPC) photosensitizer, to gold nanoparticles (AuNPs) that serve as carriers for the drug. AuNPs have a number of characteristics that make them highly suitable to function as drug carriers: they are biocompatible, serve as biomarkers, and function as contrast agents in vitro and in vivo. We synthesized AuNPs and covalently conjugated the mTHPC drug molecules through a linker. The resultant functional complex, AuNP-mTHPC, is a stable, soluble compound. SH-SY5Y human neuroblastoma cells were incubated with the complex, showing possible administration of higher doses of drug when conjugated to the AuNPs. Then cells were irradiated with a laser beam at 650 nm to mimic the PDT procedure. Our study shows higher rates of cell death in cells incubated with the AuNP-mTHPC complex compared to the incubation with the free drug. Using the new complex may form the basis for a better PDT strategy for a wide range of cancers.