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Fang-Chu Lin, Chao-Hsiung Hsu, Yung-Ya Lin Department of Chemistry and Biochemistry, University of California, Los Angeles, CA, USA Background: Nano-therapeutic utilizing hyperthermia therapy in combination with chemotherapy, surgery, and radiation is known to treat various types of cancer. These cancer treatments normally focus on reducing tumor burden. Nevertheless, it is still challenging to confine adequate thermal energy in a tumor and obtain a complete tumor ablation to avoid recurrence and metastasis while leaving normal tissues unaffected. Consequently, it is critical to attain an alternative tumor-killing mechanism to circumvent these challenges. Studies have demonstrated that extracellular heat shock proteins (HSPs) activate antitumor immunity during tumor cell necrosis. Such induced immunity was further shown to assist in regressing tumor and reducing recurrence and metastasis. However, only a narrow range of thermal dose is reported to be able to acquire the optimal antitumor immune outcome. Consequently, it is crucial to understand how extracellular HSPs are generated.Materials and methods: In this work, a predictive model integrating HSP synthesis mechanism and cell death model is proposed to elucidate the HSP involvement in hyperthermia cancer immune therapy and its relation with dead tumor cells. This new model aims to provide insights into the thermally released extracellular HSPs by dead tumor cells for a more extensive set of conditions, including various temperatures and heating duration time.Results: Our model is capable of predicting the optimal thermal parameters to generate maximum HSPs for stimulating antitumor immunity, promoting tumor regression, and reducing metastasis. The obtained nonlinear relation between extracellular HSP concentration and increased dead cell number, along with rising temperature, shows that only a narrow range of thermal dose is able to generate the optimal antitumor immune result.Conclusion: Our predictive model is

Fang-Chu Lin, Chao-Hsiung Hsu, Yung-Ya Lin Department of Chemistry and Biochemistry, University of California, Los Angeles, CA, USA Background: Nano-therapeutic utilizing hyperthermia therapy in combination with chemotherapy, surgery, and radiation is known to treat various types of cancer. These cancer treatments normally focus on reducing tumor burden. Nevertheless, it is still challenging to confine adequate thermal energy in a tumor and obtain a complete tumor ablation to avoid recurrence and metastasis while leaving normal tissues unaffected. Consequently, it is critical to attain an alternative tumor-killing mechanism to circumvent these challenges. Studies have demonstrated that extracellular heat shock proteins (HSPs) activate antitumor immunity during tumor cell necrosis. Such induced immunity was further shown to assist in regressing tumor and reducing recurrence and metastasis. However, only a narrow range of thermal dose is reported to be able to acquire the optimal antitumor immune outcome. Consequently, it is crucial to understand how extracellular HSPs are generated.Materials and methods: In this work, a predictive model integrating HSP synthesis mechanism and cell death model is proposed to elucidate the HSP involvement in hyperthermia cancer immune therapy and its relation with dead tumor cells. This new model aims to provide insights into the thermally released extracellular HSPs by dead tumor cells for a more extensive set of conditions, including various temperatures and heating duration time.Results: Our model is capable of predicting the optimal thermal parameters to generate maximum HSPs for stimulating antitumor immunity, promoting tumor regression, and reducing metastasis. The obtained nonlinear relation between extracellular HSP concentration and increased dead cell number, along with rising temperature, shows that only a narrow range of thermal dose is able to generate the optimal antitumor immune result.Conclusion: Our predictive model is

Chencai Wang,1 Chao-Hsiung Hsu,1,2 Zhao Li,1 Lian-Pin Hwang,2 Ying-Chih Lin,2 Pi-Tai Chou,2 Yung-Ya Lin1 1Department of Chemistry and Biochemistry, University of California, Los Angeles, CA, USA; 2Department of Chemistry, National Taiwan University, Taipei, Taiwan Abstract: Magnetic resonance (MR) nano-theranostic hyperthermia uses magnetic nanoparticles to target and accumulate at the lesions and generate heat to kill lesion cells directly through hyperthermia or indirectly through thermal activation and control releasing of drugs. Preclinical and translational applications of MR nano-theranostic hyperthermia are currently limited by a few major theoretical difficulties and experimental challenges in in vivo conditions. For example, conventional models for estimating the heat generated and the optimal magnetic nanoparticle sizes for hyperthermia do not accurately reproduce reported in vivo experimental results. In this work, a revised cluster-based model was proposed to predict the specific loss power (SLP) by explicitly considering magnetic nanoparticle aggregation in in vivo conditions. By comparing with the reported experimental results of magnetite Fe3O4 and cobalt ferrite CoFe2O4 magnetic nanoparticles, it is shown that the revised cluster-based model provides a more accurate prediction of the experimental values than the conventional models that assume magnetic nanoparticles act as single units. It also provides a clear physical picture: the aggregation of magnetic nanoparticles increases the cluster magnetic anisotropy while reducing both the cluster domain magnetization and the average magnetic moment, which, in turn, shift the predicted SLP toward a smaller magnetic nanoparticle diameter with lower peak values. As a result, the heating efficiency and the SLP values are decreased. The improvement in the prediction accuracy in in vivo conditions is particularly pronounced when the magnetic nanoparticle diameter is in the range of ~10–20 nm. This ha

Chencai Wang,1 Chao-Hsiung Hsu,1,2 Zhao Li,1 Lian-Pin Hwang,2 Ying-Chih Lin,2 Pi-Tai Chou,2 Yung-Ya Lin1 1Department of Chemistry and Biochemistry, University of California, Los Angeles, CA, USA; 2Department of Chemistry, National Taiwan University, Taipei, Taiwan Abstract: Magnetic resonance (MR) nano-theranostic hyperthermia uses magnetic nanoparticles to target and accumulate at the lesions and generate heat to kill lesion cells directly through hyperthermia or indirectly through thermal activation and control releasing of drugs. Preclinical and translational applications of MR nano-theranostic hyperthermia are currently limited by a few major theoretical difficulties and experimental challenges in in vivo conditions. For example, conventional models for estimating the heat generated and the optimal magnetic nanoparticle sizes for hyperthermia do not accurately reproduce reported in vivo experimental results. In this work, a revised cluster-based model was proposed to predict the specific loss power (SLP) by explicitly considering magnetic nanoparticle aggregation in in vivo conditions. By comparing with the reported experimental results of magnetite Fe3O4 and cobalt ferrite CoFe2O4 magnetic nanoparticles, it is shown that the revised cluster-based model provides a more accurate prediction of the experimental values than the conventional models that assume magnetic nanoparticles act as single units. It also provides a clear physical picture: the aggregation of magnetic nanoparticles increases the cluster magnetic anisotropy while reducing both the cluster domain magnetization and the average magnetic moment, which, in turn, shift the predicted SLP toward a smaller magnetic nanoparticle diameter with lower peak values. As a result, the heating efficiency and the SLP values are decreased. The improvement in the prediction accuracy in in vivo conditions is particularly pronounced when the magnetic nanoparticle diameter is in the range of ~10–20 nm. This ha