Cancer nanomedicine: from PDGF targeted drug delivery

In vivo delivery of nuclear targeted drugs for lung cancer using novel synthesis and functionalization of iron oxide nanocrystals

Iron nanoparticles are typically made from inorganic precursors, but for the first time, we synthesized-Fe2O3-NCs from goat blood (a bio-precursor) employing the RBC lysis method (a molecular level approach).

Label-Free Electrochemical Detection of the Cancer Biomarker Platelet-Derived Growth Factor Receptor in Human Serum and Cancer Cells

Overexpression of the platelet-derived growth factor receptor (PDGFR) was already associated with the loss of p53 function as cancer progresses in lung, breast, and cervical cancers. Cancer biomarker detection has faced challenges and barriers due to various limitations, including a high limit of detection, low sensitivity, time-consuming techniques, and expensive equipment. Hence, the present investigation is designed to develop a cost-effective novel biosensor based on a charge-based affinity bait molecule to detect the PDGFR, thus overcoming the limitations and challenges with an immune technique based on antigen-antibody interactions. We employed EDC-NHS coupling between poly (diallyl dimethylammonium chloride) and poly(acrylic acid) to attach the multiwall carbon nanotube surface. As a result, we performed electrochemical PDGFR conversion sensing with a dynamic range of 1-10,000 ng/mL and a detection limit of 1.5 pg/mL, which is comparable to the best current results. The biosensor also displayed good selectivity, 2.51% repeatability (RSD, n = 5), and 30 days of stability. Our study provides a pathway for the design of diagnostic interfaces in biosystems, as well as the emergence of new sensor types based on ligand-receptor interactions.

An Amphiphilic Micromolecule Self-Assembles into Vesicles for Visualized and Targeted Drug Delivery

Described here is the first example of the construction of multifunctional drug delivery systems by employing an amphiphilic micromolecule. The intrinsic aggregation-induced emissive and tumor-targeting amphiphilic conjugate of β-d-galactose with tetraphenylethene (TPE-Gal), in which the hydrophobic TPE moiety spontaneously acts as the imaging chromophore and the hydrophilic Gal moiety spontaneously acts as the targeting ligand and galactosidase trigger, can self-assemble into fluorescent vesicles that can efficiently load both water-soluble and -insoluble anticancer drugs. In vitro and in vivo evaluations revealed that the pH/β-d-galactosidase dual-responsive doxorubicin (DOX)-loaded vesicles TPE-Gal@DOX exhibited good targeting effect and higher antitumor efficacy than free DOX. H&E staining analysis displayed remarkable necroses and weak cell proliferation in the tumor area and no toxicity to major organs, indicating the superior targeting antitumor therapeutic efficacy of TPE-Gal@DOX.

Janus particles: design, preparation, and biomedical applications

Janus particles with an anisotropic structure have emerged as a focus of intensive research due to their diverse composition and surface chemistry, which show excellent performance in various fields, especially in biomedical applications. In this review, we briefly introduce the structures, composition, and properties of Janus particles, followed by a summary of their biomedical applications. Then we review several design strategies including morphology, particle size, composition, and surface modification, that will affect the performance of Janus particles. Subsequently, we explore the synthetic methodologies of Janus particles, with an emphasis on the most prevalent synthetic method (surface nucleation and seeded growth). Following this, we highlight Janus particles in biomedical applications, especially in drug delivery, bio-imaging, and bio-sensing. Finally, we will consider the current challenges the materials face with perspectives in the future directions.

Design of Hierarchical Beads for Efficient Label-Free Cell Capture

Defined hierarchical materials promise cell analysis and call for application-driven design in practical use. The further issue is to develop advanced materials and devices for efficient label-free cell capture with minimum instrumentation. Herein, the design of hierarchical beads is reported for efficient label-free cell capture. Silica nanoparticles (size of ≈15 nm) are coated onto silica spheres (size of ≈200 nm) to achieve nanoscale surface roughness, and then the rough silica spheres are combined with microbeads (≈150-1000 µm in diameter) to assemble hierarchical structures. These hierarchical beads are built via electrostatic interaction, covalent bonding, and nanoparticle adherence. Further, after functionalization by hyaluronic acid (HA), the hierarchical beads display desirable surface hydrophilicity, biocompatibility, and chemical/structural stability. Due to the controlled surface topology and chemistry, HA-functionalized hierarchical beads afford high cell capture efficiency up to 98.7% in a facile label-free manner. This work guides the development of label-free cell capture techniques and contributes to the construction of smart interfaces in bio-systems.

Artificial intelligence in nanomedicine

The field of nanomedicine has made substantial strides in the areas of therapeutic and diagnostic development. For example, nanoparticle-modified drug compounds and imaging agents have resulted in markedly enhanced treatment outcomes and contrast efficiency. In recent years, investigational nanomedicine platforms have also been taken into the clinic, with regulatory approval for Abraxane® and other products being awarded. As the nanomedicine field has continued to evolve, multifunctional approaches have been explored to simultaneously integrate therapeutic and diagnostic agents onto a single particle, or deliver multiple nanomedicine-functionalized therapies in unison. Similar to the objectives of conventional combination therapy, these strategies may further improve treatment outcomes through targeted, multi-agent delivery that preserves drug synergy. Also, similar to conventional/unmodified combination therapy, nanomedicine-based drug delivery is often explored at fixed doses. A persistent challenge in all forms of drug administration is that drug synergy is time-dependent, dose-dependent and patient-specific at any given point of treatment. To overcome this challenge, the evolution towards nanomedicine-mediated co-delivery of multiple therapies has made the potential of interfacing artificial intelligence (AI) with nanomedicine to sustain optimization in combinatorial nanotherapy a reality. Specifically, optimizing drug and dose parameters in combinatorial nanomedicine administration is a specific area where AI can actionably realize the full potential of nanomedicine. To this end, this review will examine the role that AI can have in substantially improving nanomedicine-based treatment outcomes, particularly in the context of combination nanotherapy for both N-of-1 and population-optimized treatment.