Cancer is a leading cause of global mortality, with projections indicating a significant increase in deaths in the coming decades. Current chemotherapeutic agents suffer from limitations such as toxicity, nonspecific distribution, and uncontrolled release. Nanotechnology offers a novel paradigm in cancer treatment through nanomedicines, which can be used for diagnosis, treatment, and molecular-level interventions. Theranostics, a combination of therapy and diagnostics, uses imaging agents for molecular targeting. Nanoconstructs, composed of nanoparticles and ligands, are promising theranostic agents for cancer treatment, designed to overcome the limitations of conventional therapies. They offer benefits like improved specificity, targeted drug delivery, and reduced toxicity, but challenges remain concerning biocompatibility, uneven distribution, and lack of precision. The review focuses on the attributes, applications, and recent advancements of nanoconstructs in cancer diagnosis and therapy, also exploring the role of computational modeling and artificial intelligence in addressing the challenges associated with their delivery.

The review extensively cites literature on various aspects of cancer, nanotechnology, and theranostics. It covers the global cancer burden, genetic factors contributing to cancer development, the role of the tumor microenvironment (TME) in drug resistance and relapse, the limitations of conventional cancer therapies (including immunotherapy), and the advantages of using nanoparticles for drug delivery. Existing literature on various nanoparticulate platforms (polymeric, lipid-based, inorganic) and their use in targeted drug delivery (passive, active, stimuli-responsive, magnetic targeting) is reviewed. The literature also addresses the use of different ligands (e.g., hyaluronic acid, folic acid, peptides, antibodies) for targeting specific receptors on cancer cells, computational modeling methods used in nanoconstruct design, and the applications of various types of nanoconstructs (organic, inorganic, hybrid) for cancer theranosis.

This is a review article. The authors systematically reviewed and synthesized existing scientific literature relevant to nanoconstructs and their use in cancer theranostics. They explored the different types of nanoparticles and ligands used in nanoconstruct design, the various targeting mechanisms employed, and strategies to overcome the limitations associated with their delivery and efficacy. The review discusses computational modeling and artificial intelligence approaches that can aid in the design and optimization of nanoconstructs. The authors presented a summary of existing literature to offer a comprehensive overview of the field, highlighting key advancements and challenges.

The review highlights that nanoconstructs, combining nanoparticles and ligands, offer improved cancer therapy by enabling targeted drug delivery and reduced toxicity compared to conventional methods. Different types of nanoparticles are discussed, including polymeric materials (PLGA, PCL, PLL, etc.), lipid-based platforms, and inorganic materials (iron oxide, gold, silver, black phosphorus). The review categorizes targeting mechanisms into passive, active, stimuli-responsive, and magnetic targeting. Passive targeting utilizes the EPR effect to concentrate nanoconstructs within the tumor. Active targeting uses ligands that bind to overexpressed receptors on tumor cells. Stimuli-responsive targeting uses external or internal stimuli (pH, light, magnetic fields) to control drug release. Ligand selection is critical for effective targeting and various ligands (hyaluronic acid, folic acid, peptides, antibodies) are used based on receptor expression in target cells. The review emphasizes the importance of computational modeling and AI/machine learning for optimizing nanoconstruct design, predicting drug release profiles, and assessing biocompatibility and cytotoxicity. Autonomous and nonautonomous drug delivery systems are introduced as improved classification systems, considering factors like blood flow and the TME. The review showcases examples of inorganic and polymer-based nanoconstructs with theranostic applications, including real-time monitoring of drug release using fluorescence resonance energy transfer (FRET). The role of image-guided drug delivery systems, utilizing various imaging modalities (MRI, optical imaging), for cancer therapy is also highlighted. Specific examples of nanoconstructs for various cancers are discussed, incorporating details on their composition, targeting moieties, therapeutic agents, and imaging capabilities.

The review effectively addresses the research question by presenting a comprehensive overview of nanoconstructs for cancer theranostics. The findings demonstrate the potential of nanoconstructs to enhance cancer treatment by improving drug targeting and reducing toxicity. The discussion of limitations in nanoconstruct delivery, such as the EPR effect's variability and challenges in reaching adequate therapeutic concentrations in human tumors, emphasizes the need for ongoing research. The integration of computational modeling and AI offers promising avenues for overcoming these limitations. The exploration of autonomous delivery systems, using biomimetic approaches or biohybrid bacteria, offers exciting new possibilities for targeted drug delivery. The review's breadth and depth contribute significantly to the field by highlighting both advancements and challenges in this rapidly evolving area.

Nanoconstructs show promise in cancer theranostics, offering improved detection, targeted drug delivery, and reduced toxicity. Computational modeling and AI are key for optimization. While many nanoconstructs are preclinical, future research should focus on advanced materials and multidisciplinary studies for improved clinical translation.

As a review article, the findings are limited by the available literature. The review focuses on preclinical studies; the clinical translation of these nanoconstructs may face additional challenges.