Dendrimer-Peptide Conjugates for Effective Blockade of the Interactions between SARS-CoV-2 Spike Protein and Human ACE2 Receptor

The coronavirus disease 2019 (COVID-19) pandemic has threatened the stability of global healthcare, which is becoming an endemic issue. Despite the development of various treatment strategies to fight COVID-19, the currently available treatment options have shown varied efficacy. Herein, we have developed an avidity-based SARS-CoV-2 antagonist using dendrimer-peptide conjugates (DPCs) for effective COVID-19 treatment. Two different peptide fragments obtained from angiotensin-converting enzyme 2 (ACE2) were integrated into a single sequence, followed by the conjugation to poly(amidoamine) (PAMAM) dendrimers. We hypothesized that the strong multivalent binding avidity endowed by dendrimers would help peptides effectively block the interaction between SARS-CoV-2 and ACE2, and this antagonist effect would be dependent upon the generation (size) of the dendrimers. To assess this, binding kinetics of the DPCs prepared from generation 4 (G4) and G7 PAMAM dendrimers to spike protein of SARS-CoV-2 were quantitatively measured using surface plasmon resonance. The larger dendrimer-based DPCs exhibited significantly enhanced binding strength by 3 orders of magnitude compared to the free peptides, whereas the smaller one showed a 12.8-fold increase only. An in vitro assay using SARS-CoV-2-mimicking microbeads also showed the improved SARS-CoV-2 blockade efficiency of the G7-peptide conjugates compared to G4. In addition, the interaction between the DPCs and SARS-CoV-2 was analyzed using molecular dynamics (MD) simulation, providing an insight into how the dendrimer-mediated multivalent binding effect can enhance the SARS-CoV-2 blockade. Our findings demonstrate that the DPCs having strong binding to SARS-CoV-2 effectively block the interaction between ACE2 and SARS-CoV-2, providing a potential as a high-affinity drug delivery system to direct anti-COVID payloads to the virus.

Bimodal liquid biopsy for cancer immunotherapy based on peptide engineering and nanoscale analysis

Despite its high potential, PD-L1 expressed by tumors has not been successfully utilized as a biomarker for estimating treatment responses to immunotherapy. Circulating tumor cells (CTCs) and tumor-derived exosomes that express PD-L1 can potentially be used as biomarkers; however, currently available assays lack clinically significant sensitivity and specificity. Here, a novel peptide-based capture surface is developed to effectively isolate PD-L1-expressing CTCs and exosomes from human blood. For the effective targeting of PD-L1, this study integrates peptide engineering strategies to enhance the binding strength and specificity of a β-hairpin peptide derived from PD-1 (pPD-1). Specifically, this study examines the effect of poly(ethylene glycol) spacers, the secondary peptide structure, and modification of peptide sequences (e.g., removal of biologically redundant amino acid residues) on capture efficiency. The optimized pPD-1 configuration captures PD-L1-expressing tumor cells and tumor-derived exosomes with 1.5-fold (p = 0.016) and 1.2-fold (p = 0.037) higher efficiencies, respectively, than their whole antibody counterpart (aPD-L1). This enhanced efficiency is translated into more clinically significant detection of CTCs (1.9-fold increase; p = 0.035) and exosomes (1.5-fold increase; p = 0.047) from patients' baseline samples, demonstrating stronger correlation with patients' treatment responses. Additionally, we confirmed that the clinical accuracy of our system can be further improved by co-analyzing the two biomarkers (bimodal CTC/exosome analysis). These data demonstrate that pPD-1-based capture is a promising approach for capturing PD-L1-expressing CTCs and exosomes, which can be used as a reliable biomarker for cancer immunotherapy.

Machine-Learning-Based Clinical Biomarker Using Cell-Free DNA for Hepatocellular Carcinoma (HCC)

Simple Summary

Circulating cell-free DNA (cfDNA) has attracted a great deal of scientific interest as a predictive biomarker for the diagnosis and prognosis of hepatocellular carcinoma (HCC). HCC result in high mortality due to the absence of blood biomarkers for early diagnosis and prognosis. We established cfD_HCC as a new scoring system by applying a machine learning algorithm that integrates the expression profiles of cfDNA. Based on this, it was possible to accurately predict the clinico-pathological characteristics of patients with HCC as well as improve their survival.

Abstract

(1) Background: Hepatocellular carcinoma (HCC) is one of the leading causes of cancer-related death worldwide. Although various serum enzymes have been utilized for the diagnosis and prognosis of HCC, the currently available biomarkers lack the sensitivity needed to detect HCC at early stages and accurately predict treatment responses. (2) Methods: We utilized our highly sensitive cell-free DNA (cfDNA) detection system, in combination with a machine learning algorithm, to provide a platform for improved diagnosis and prognosis of HCC. (3) Results: cfDNA, specifically alpha-fetoprotein (AFP) expression in captured cfDNA, demonstrated the highest accuracy for diagnosing malignancies among the serum/plasma biomarkers used in this study, including AFP, aspartate aminotransferase, alanine aminotransferase, albumin, alkaline phosphatase, and bilirubin. The diagnostic/prognostic capability of cfDNA was further improved by establishing a cfDNA score (cfD_HCC), which integrated the total plasma cfDNA levels and cfAFP-DNA expression into a single score using machine learning algorithms. (4) Conclusion: The cfD_HCC score demonstrated significantly improved accuracy in determining the pathological features of HCC and predicting patients’ survival outcomes compared to the other biomarkers. The results presented herein reveal that our cfDNA capture/analysis platform is a promising approach to effectively utilize cfDNA as a biomarker for the diagnosis and prognosis of HCC.

Dendrimers for cancer immunotherapy: Avidity-based drug delivery vehicles for effective anti-tumor immune response

Cancer immunotherapy, or the utilization of a patient's own immune system to treat cancer, has shifted the paradigm of cancer treatment. Despite meaningful responses being observed in multiple studies, currently available immunotherapy platforms have only proven effective to a small subset of patients. To address this, nanoparticles have been utilized as a novel carrier for immunotherapeutic drugs, achieving robust anti-tumor effects with increased adaptive and durable responses. Specifically, dendrimer nanoparticles have attracted a great deal of scientific interest due to their versatility in various therapeutic applications, resulting from their unique physicochemical properties and chemically well-defined architecture. This review offers a comprehensive overview of dendrimer-based immunotherapy technologies, including their formulations, biological functionalities, and therapeutic applications. Common formulations include: (1) modulators of cytokine secretion of immune cells (adjuvants); (2) facilitators of the recognition of tumorous antigens (vaccines); (3) stimulators of immune effectors to selectively attack cells expressing specific antigens (antibodies); and (4) inhibitors of immune-suppressive responses (immune checkpoint inhibitors). On-going works and prospects of dendrimer-based immunotherapies are also discussed. Overall, this review provides a critical overview on rapidly growing dendrimer-based immunotherapy technologies and serves as a guideline for researchers and clinicians who are interested in this field. This article is categorized under: Nanotechnology Approaches to Biology > Nanoscale Systems in Biology Therapeutic Approaches and Drug Discovery > Nanomedicine for Oncologic Disease Therapeutic Approaches and Drug Discovery > Emerging Technologies.

Hierarchically Multivalent Peptide-Nanoparticle Architectures: A Systematic Approach to Engineer Surface Adhesion

The multivalent binding effect has been the subject of extensive studies to modulate adhesion behaviors of various biological and engineered systems. However, precise control over the strong avidity-based binding remains a significant challenge. Here, a set of engineering strategies are developed and tested to systematically enhance the multivalent binding of peptides in a stepwise manner. Poly(amidoamine) (PAMAM) dendrimers are employed to increase local peptide densities on a substrate, resulting in hierarchically multivalent architectures (HMAs) that display multivalent dendrimer-peptide conjugates (DPCs) with various configurations. To control binding behaviors, effects of the three major components of the HMAs are investigated: i) poly(ethylene glycol) (PEG) linkers as spacers between conjugated peptides; ii) multiple peptides on the DPCs; and iii) various surface arrangements of HMAs (i.e., a mixture of DPCs each containing different peptides vs DPCs cofunctionalized with multiple peptides). The optimized HMA configuration enables significantly enhanced target cell binding with high selectivity compared to the control surfaces directly conjugated with peptides. The engineering approaches presented herein can be applied individually or in combination, providing guidelines for the effective utilization of biomolecular multivalent interactions using DPC-based HMAs.

Size-Dependent Drug Loading, Gene Complexation, Cell Uptake, and Transfection of a Novel Dendron-Lipid Nanoparticle for Drug/Gene Co-delivery

Dendron micelles have shown promising results as a multifunctional delivery system, owing to their unique molecular architecture. Herein, we have prepared a novel poly(amidoamine) (PAMAM) dendron-lipid hybrid nanoparticle (DLNP) as a nanocarrier for drug/gene co-delivery and examined how the dendron generation of DLNPs impacts their cargo-carrying capabilities. DLNPs, formed by a thin-layer hydration method, were internally loaded with chemo-drugs and externally complexed with plasmids. Compared to generation 2 dendron DLNP (D2LNPs), D3LNPs demonstrated a higher drug encapsulation efficiency (31% vs 87%) and better gene complexation (minimal N/P ratio of 20:1 vs 5:1 for complexation) due to their smaller micellar aggregation number and higher charge density, respectively. Furthermore, D3LNPs were able to avoid endocytosis and subsequent lysosomal degradation and demonstrated a higher cellular uptake than D2LNPs. As a result, D3LNPs exhibited significantly enhanced antitumor and gene transfection efficacy in comparison to D2LNPs. These findings provide design cues for engineering multifunctional dendron-based nanotherapeutic systems for effective combination cancer treatment.