In vitro and in vivo antitumor properties of 7-epidocetaxel, a major impurity of docetaxel

Purpose: To investigate the antitumor properties and toxicity of 7-epi docetaxel (7-epi DTX) as an active pharmaceutical ingredient, and in formulations.Methods: Docetaxel-loaded albumin nanoparticles (DTX NPs) were prepared by freeze-drying, while 7- epi DTX was detected and isolated by high performance liquid chromatography (HPLC). Their antitumor properties were evaluated in vitro in CT26 cells and in vivo in BALB/c sk-ov-3 xenograft nude mice model. The tissues were histological examined.Results: The in vivo antitumor effects of DTX NPs at different doses of 7-epi DTX were similar. Moreover, the in vitro anti-cancer effect of 7-epi DTX was comparable to that of DTX. However, the in vivo antitumor effectiveness of 7-epi DTX was inferior to that of DTX. In toxicity studies, 7-epi DTX did not elicit any acute toxic effects both as active pharmaceutical ingredients, and as a component of formulations.Conclusion: The results indicate that 7-epi DTX does not elicit acute toxic effects both as an active pharmaceutical ingredient and in bulk formulations. The antitumor property of 7-epi DTX is less than that of DTX.Keywords: 7-Epidocetaxel, Impurity, Antitumor properties, Toxicit

Dendrimer conjugates for light-activated delivery of antisense oligonucleotides

PAMAM dendrimer conjugates are used to co-deliver oligonucleotides and photosensitizers to cancer cells. After photo-irradiation, substantial reporter eGFP expression is produced by functional delivery of a model oligonucleotide

Better to Ask in English: Cross-Lingual Evaluation of Large Language Models for Healthcare Queries

Large language models (LLMs) are transforming the ways the general public accesses and consumes information. Their influence is particularly pronounced in pivotal sectors like healthcare, where lay individuals are increasingly appropriating LLMs as conversational agents for everyday queries. While LLMs demonstrate impressive language understanding and generation proficiencies, concerns regarding their safety remain paramount in these high-stake domains. Moreover, the development of LLMs is disproportionately focused on English. It remains unclear how these LLMs perform in the context of non-English languages, a gap that is critical for ensuring equity in the real-world use of these systems.This paper provides a framework to investigate the effectiveness of LLMs as multi-lingual dialogue systems for healthcare queries. Our empirically-derived framework XlingEval focuses on three fundamental criteria for evaluating LLM responses to naturalistic human-authored health-related questions: correctness, consistency, and verifiability. Through extensive experiments on four major global languages, including English, Spanish, Chinese, and Hindi, spanning three expert-annotated large health Q&A datasets, and through an amalgamation of algorithmic and human-evaluation strategies, we found a pronounced disparity in LLM responses across these languages, indicating a need for enhanced cross-lingual capabilities. We further propose XlingHealth, a cross-lingual benchmark for examining the multilingual capabilities of LLMs in the healthcare context. Our findings underscore the pressing need to bolster the cross-lingual capacities of these models, and to provide an equitable information ecosystem accessible to all.Comment: 18 pages, 7 figure

Lifelong Embedding Learning and Transfer for Growing Knowledge Graphs

Existing knowledge graph (KG) embedding models have primarily focused on static KGs. However, real-world KGs do not remain static, but rather evolve and grow in tandem with the development of KG applications. Consequently, new facts and previously unseen entities and relations continually emerge, necessitating an embedding model that can quickly learn and transfer new knowledge through growth. Motivated by this, we delve into an expanding field of KG embedding in this paper, i.e., lifelong KG embedding. We consider knowledge transfer and retention of the learning on growing snapshots of a KG without having to learn embeddings from scratch. The proposed model includes a masked KG autoencoder for embedding learning and update, with an embedding transfer strategy to inject the learned knowledge into the new entity and relation embeddings, and an embedding regularization method to avoid catastrophic forgetting. To investigate the impacts of different aspects of KG growth, we construct four datasets to evaluate the performance of lifelong KG embedding. Experimental results show that the proposed model outperforms the state-of-the-art inductive and lifelong embedding baselines.Comment: Accepted in the 37th AAAI Conference on Artificial Intelligence (AAAI 2023

Direct oligonucleotide–photosensitizer conjugates for photochemical delivery of antisense oligonucleotides

Direct conjugation of photosensitizer to oligonucleotides allows spatial and temporal co-localization of the two modalities in the target cells and thus leads to superior photochemical delivery of oligonucleotides

Dendritic nanoconjugates of photosensitizer for targeted photodynamic therapy

Abstract Application of photodynamic therapy for treating cancers has been restrained by suboptimal delivery of photosensitizers to cancer cells. Nanoparticle (NP)-based delivery has become an important strategy to improve tumor delivery of photosensitizers; however, the success is still limited. One problem for many NPs is poor penetration into tumors, and thus the photokilling is not complete. We aimed to use chemical conjugation method to engineer small NPs for superior cancer cell uptake and tumor penetration. Thus, Chlorin e6 (Ce6) was covalently conjugated to PAMAM dendrimer (generation 7.0) that was also modified by tumor-targeting RGD peptide. With multiple Ce6 molecules in a single nanoconjugate molecule, the resultant targeted nanoconjugates showed uniform and monodispersed size distribution with a diameter of 28 nm. The singlet oxygen generation efficiency and fluorescence intensity of the nanoconjugates in aqueous media were significantly higher than free Ce6. Targeted nanoconjugates demonstrated approximately 16-fold enhancement in receptor-specific cellular delivery of Ce6 into integrin-expressing A375 cells compared to free Ce6 and thus were able to cause massive cell killing at low nanomolar concentrations under photo-irradiation. In contrast, they did not cause significant toxicity up to 2 μM in dark. Due to their small size, the targeted nanoconjugates could penetrate deeply into tumor spheroids and produced strong photo-toxicity in this 3-D tumor model. As a result of their great cellular delivery, small size, and lack of dark cytotoxicity, the nanoconjugates may provide an effective tool for targeted photodynamic therapy of solid tumors. Graphical abstrac

Changes in the Expression of miR-381 and miR-495 Are Inversely Associated with the Expression of the MDR1 Gene and Development of Multi-Drug Resistance

Multidrug resistance (MDR) frequently develops in cancer patients exposed to chemotherapeutic agents and is usually brought about by over-expression of P-glycoprotein (P-gp) which acts as a drug efflux pump to reduce the intracellular concentration of the drug(s). Thus, inhibiting P-gp expression might assist in overcoming MDR in cancer chemotherapy. MiRNAome profiling using next-generation sequencing identified differentially expressed microRNAs (miRs) between parental K562 cells and MDR K562 cells (K562/ADM) induced by adriamycin treatment. Two miRs, miR-381 and miR-495, that were strongly down-regulated in K562/ADM cells, are validated to target the 3'-UTR of the MDR1 gene. These miRs are located within a miR cluster located at chromosome region 14q32.31, and all miRs in this cluster appear to be down-regulated in K562/ADM cells. Functional analysis indicated that restoring expression of miR-381 or miR-495 in K562/ADM cells was correlated with reduced expression of the MDR1 gene and its protein product, P-gp, and increased drug uptake by the cells. Thus, we have demonstrated that changing the levels of certain miR species modulates the MDR phenotype in leukemia cells, and propose further exploration of the use of miR-based therapies to overcome MDR.The authors would like to declare that we received funding from a commercial source, i.e. Bioplatforms Australia. This does not alter the authors' adherence to all PLOS ONE policies on sharing data and materials