Advances in photoactivated carbon-based nanostructured materials for targeted cancer therapy

In this review, we explore key innovations in photoactivated therapeutic programming of carbon-based nanomaterials (CBNs), focusing on their diverse nanostructural configurations and their exceptional photothermal, photochemical, and photoacoustic properties. These attributes position CBNs as remarkable phototherapeutic agents, capable of addressing critical challenges in targeted cancer therapy through their precision, multifunctionality, and adaptability to specific therapeutic modalities. We will explore their diverse derivatives, and the role of chemical augmentation and site-specific surface functionalisation, which are pivotal in optimising the targeting and efficacy of phototherapeutic interventions. The biological and physical relevance of this ever-growing library of nanomaterials in targeted phototherapy will be thoroughly explored. Dynamic photo-triggering of the underlying molecular mechanisms of action e.g., energy conversion modalities lie at the heart of these therapeutic innovations. We will further discuss the tunability and programming of these carriers and structure-function alterations at specific therapeutic wavelengths. The application space of phototherapies is thoroughly mapped exploring the three primary approaches of photothermal therapy, photodynamic therapy and photochemical internalisation as well as emerging techniques and promising multimodal approaches that combine two or more of these processes. The specificity of the target tissue site and the approach under study forms another critical focus area of this review, with an emphasis on three types of cancer-breast cancer, lung cancer, and gliomas-that have demonstrated some of the most promising outcomes from photomedicine. We also provide a perspective on in vitro and in vivo validation and preclinical testing of CBNs for phototherapeutic applications. Finally, we reflect on the potential of CBNs to revolutionise targeted cancer therapy through data-driven materials design and integration with computational tools for biophysical performance optimisation. The exciting integration of machine learning into nanoparticle research and phototherapy has potential to fundamentally transform the landscape of nanomedicine. These techniques ranging from supervised learning algorithms such as random forests and support vector machines to more advanced neural networks and deep learning, can enable unprecedented precision in predicting, optimising, and tailoring the properties of nanoparticles for targeted applications. The transformative impact of photoactivated CBNs in advancing cancer treatment, paves the way for their clinical application and widespread adoption in personalised photomedicine. We conclude with a section on the current challenges facing the reproducibility, manufacturing throughput, and biocompatibility of these nanostructured materials including their long-term effects in trials and degradation profiles in biological systems as evaluated in vitro and in vivo.