A Machine Learning-optimized system for on demand, pulsatile, photo- and chemo-therapeutic treatment using near-infrared responsive MoS2-based microparticles in a breast cancer model

Abstract

ABSTRACTCancer therapy research is of high interest because of the persistence and mortality of the disease and the side effects of traditional therapeutic methods, while often multimodal treatments are necessary based on the patient’s needs. The development of less invasive modalities for recurring treatment cycles is thus of critical significance. Herein, a light-activatable microparticle system was developed for localized, pulsatile delivery of anticancer drugs with simultaneous thermal ablation, by applying controlled ON-OFF thermal cycles using near-infrared laser irradiation. The system is composed of poly(caprolactone) microparticles of 200 μm size with incorporated molybdenum disulfide (MoS2) nanosheets as the photothermal agent and hydrophilic doxorubicin or hydrophobic violacein, as model drugs. Upon irradiation the nanosheets heat up to ≥50 °C leading to polymer matrix melting and release of the drug. MoS2nanosheets exhibit high photothermal conversion efficiency and allow for application of low power laser irradiation for the system activation. A Machine Learning algorithm was applied to acquire optimal laser operation conditions; 0.4 W/cm2laser power at 808 nm, 3-cycle irradiation, for 3 cumulative minutes. In a mouse subcutaneous model of 4T1 triple-negative breast cancer, 25 microparticles were intratumorally administered and after 3-cycle laser treatment the system conferred synergistic phototherapeutic and chemotherapeutic effect. Our on-demand, pulsatile synergistic treatment resulted in increased median survival up to 40 days post start of treatment compared to untreated mice, with complete eradication of the tumors at the primary site. Such a system could have potential for patients in need of recurring cycles of treatment on subcutaneous tumors.GRAPHICAL ABSTRACT