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Academic Editor: Sabrina Venditti Received: 22 May 2025 Revised: 5 July 2025 N6-methyladenosine (m6A) represents the most common and thoroughly investigated RNA modification and exerts essential functions in regulating gene expression through influencing the RNA stability, the translation efficiency, alternative splicing, and nuclear export processes. The rapid development of high-throughput sequencing approaches, including miCLIP and MeRIP-seq, has profoundly transformed epitranscriptomics research. Show less

Reactive molecules, including oxygen and nitrogen species, serve dual roles in human physiology. While they function as essential signaling molecules under normal physiological conditions, they contribute to cellular dysfunction and damage when produced in excess by normal metabolism or in response to stressors. Oxidative/nitrosative stress is a pathological state, resulting from the overproduction of reactive species exceeding the antioxidant capacity of the body, which is implicated in several chronic human diseases. Antioxidant Show less

One of the major challenges in precision oncology is the identification of pathogenic, actionable variants and the selection of personalized treatments. We present Onkopus, a variant interpretation framework based on a modular architecture, for interpreting and prioritizing genetic alterations in cancer patients. A multitude of tools and databases are integrated into Onkopus to provide a comprehensive overview about the consequences of a variant, each with its own semantic, including pathogenicity predictions, allele frequency, biochemical and protein features, Show less

Background

Patients with colorectal cancer (CRC) harboring BRAF mutation have a poor prognosis. The median survival time for patients with advanced BRAF^V600E-mutant CRC is only approximately one year. Owing to the insensitivity to standard chemotherapy, there are still no effective and highly specific treatment strategies available in clinical practice for CRC patients with BRAF mutation. Therefore, targeting the BRAF^V600E mutation site, researching and exploring novel targeted therapies are essential to improve the survival rate of patients with this CRC subtype.

Aim

This study aims to develop a precise therapeutic system for BRAF^V600E CRC, based on the carrier properties of extracellular vesicles (EVs) and gene therapy targeting BRAF^V600E.

Method

We first obtained engineered cells capable of stably producing EVs loaded with BRAF^V600E nucleic acid drugs (siBRAF^V600E). Next, BRAF^V600E-mutant and wild-type CRC cell lines, as well as corresponding subcutaneous and metastasis models, were used to evaluate the therapeutic efficacy of EVs-siBRAF^V600E and explored the mechanism. Notably, patient-derived xenograft (PDX) models, which share the same molecular characteristics, pathological features, and heterogeneity as patients do, were utilized to further explore the therapeutic efficacy and mechanisms.

Result

EVs-siBRAF^V600E specifically inhibited BRAF^V600E CRC but didn't affect BRAF wild-type CRC in vitro and vivo. EVs-siBRAF^V600E exerts its therapeutic effect by regulating the MEK1/2-ERK1/2 pathway, and it has demonstrated excellent therapeutic efficacy in PDX models.

Conclusion

The therapeutic EVs we constructed are effective and specific for the BRAF^V600E-mutant CRC. This study provides a novel strategy for the treatment of CRC patients with BRAF^V600E mutation. Show less

Drug discovery aims at finding new compounds with specific chemical properties for the treatment of diseases. In the last years, the approach used in this search presents an important component in computer science with the skyrocketing of machine learning techniques due to its democratization. With the objectives set by the Precision Medicine initiative and the new challenges generated, it is necessary to establish robust, standard and reproducible computational methodologies to achieve the objectives set. Currently, predictive models based on Machine Learning have gained great importance in the step prior to preclinical studies. This stage manages to drastically reduce costs and research times in the discovery of new drugs. This review article focuses on how these new methodologies are being used in recent years of research. Analyzing the state of the art in this field will give us an idea of where cheminformatics will be developed in the short term, the limitations it presents and the positive results it has achieved. This review will focus mainly on the methods used to model the molecular data, as well as the biological problems addressed and the Machine Learning algorithms used for drug discovery in recent years. Show less