Yang Song

Nucleus-Localizing Coacervates Synergize with Chemotherapy for the Treatment of Drug-Resistant Ovarian Tumors

Tumor-targeting intracellular chemotherapy represents a precision therapy to overcome multidrug resistance (MDR) in ovarian cancers, yet efficient drug enrichment in resistant cells is difficult. Peptide-based coacervates have emerged as an intracellular reservoir for drug delivery; however, enhancing their antitumor efficacy requires precise control over the spatiotemporal distribution of drugs within tumor cells. To address this, we developed a nucleus-localizing coacervate system by complexing a cell-penetrating peptide with sodium alginate (SA), which enables efficient delivery of the DNA-binding drug doxorubicin (DOX) into the cell nucleus. Remarkably, the fluorescence partition coefficient of DOX in the nucleus of ovarian cancer cells increased by 4 ± 0.5-fold compared to coacervate-free controls, while nuclear drug retention was extended from approximately 4 to 36 h. This nucleus-localized drug delivery and sustained retention enhanced the killing efficacy of DNA-targeting medicine against MDR cells by 60 ± 5% at clinical doses, offering a promising therapeutic strategy for treating drug-resistant ovarian cancers. Keywords: complexed coacervates, intracellular drug delivery, ovarian cancer, multi-drug resistance, cell-penetrating peptide.

Gel-to-Coacervate Transition in Peptide/HA Complexes for MMP-9-Activated Penetration into Tumor Spheroids

Short phase-separating peptides serve as liquid-based vehicles due to their remarkable fluidity and cell permeability, holding great promise in diffusion-limited applications such as intracellular drug delivery or penetration into deep-seated tumors. However, tuning the phase stability and the phase-transition sensitivity of these coacervates in response to specific pathological signals remains a significant challenge. To tackle this challenge, this study presents a phase-separating peptide/hyaluronic acid (HA) complex coacervate system, which undergoes a solid-to-coacervate transition upon exposure to matrix metalloproteinase 9 (MMP-9). By harnessing this disease-relevant enzyme, overexpressed in the ovarian tumor microenvironment, we further demonstrate the improved infiltration of the coacervates into Hey cells and tumor spheroids. These observations highlight the feasibility of modulating phase behaviors and advanced functions of coacervates through sequence-specific monomer design, offering a practical strategy for the on-target delivery of coacervates and medicine into tumors.

Histogram Analysis Comparison of Monoexponential, Advanced Diffusion‐Weighted Imaging, and Dynamic Contrast‐Enhanced MRI for Differentiating Borderline From Malignant Epithelial Ovarian Tumors

BackgroundThe accurate preoperative differentiation between borderline and malignant epithelial ovarian tumors (BEOTs vs. MEOTs) is crucial for determining the proper surgical strategy and improving the patient's postoperative quality of life. Several diffusion and perfusion MRI technologies are valuable for the differentiation; however, which is the best remains unclear.PurposeTo compare the whole solid‐tumor volume histogram analysis of diffusion‐weighted imaging (DWI), diffusion kurtosis imaging (DKI), intravoxel incoherent motion (IVIM), and dynamic contrast‐enhanced MRI (DCE‐MRI) in the differentiation of BEOTs vs. MEOTs and to identify the correlations between the perfusion parameters from IVIM and DCE‐MRI.Study TypeRetrospective.PopulationTwenty patients with BEOTs and 42 patients with MEOTs.Field Strength/Sequence1.5T/DWI, DKI, and IVIM models fitting from 13 different b factors and 40 phases DCE‐MRI.AssessmentHistogram metrics were derived from the apparent diffusion coefficient (ADC), diffusion kurtosis (K), diffusion coefficient (Dk), pure diffusion coefficient (D), pseudodiffusion coefficient (D*), perfusion fraction (f), volume transfer constant (Ktrans), rate constant (kep), and extravascular extracellular volume fraction (ve).Statistical TestsThe Mann–Whitney U‐test and receiver operating characteristic curve were used to determine the best histogram metrics and parameters. Multivariate logistic regression analysis was used to determine the best combined model for each two from the four technologies. Spearman's rank correlation was used to analyze the correlations between the IVIM and DCE‐MRI parameters.ResultsADC, D, Dk, and D* were significantly higher in BEOTs than in MEOTs (P < 0.05). K, Ktrans, kep, and ve were significantly lower in BEOTs than in MEOTs (P < 0.05). The 10th percentile of Dk was the most reliable single metric, with an area under the curve (AUC) of 0.921. Dk combined with Ktrans yielded the highest AUC of 0.950. A weak inverse correlation was found between D and Ktrans (r = −0.320, P = 0.025) and between D and kep (r = −0.267, P = 0.037).Data ConclusionThe 10th percentile of Dk was the most valuable metric and Dk combined with Ktrans had the best performance for differentiating BEOTs from MEOTs. There was no evident link between perfusion‐related parameters derived from IVIM and DCE‐MRI.Level of Evidence: 4Technical Efficacy Stage: 2J. Magn. Reson. Imaging 2020;52:257–268.