RESUMO
BACKGROUND: Tyrosine kinase inhibitors (TKIs) are currently the standard therapy for patients with non-small cell lung cancer (NSCLC) bearing mutations in epidermal growth factor receptor (EGFR). Unfortunately, drug-acquired resistance is inevitable due to the emergence of new mutations in EGFR. Moreover, the TKI treatment is associated with severe toxicities due to the unspecific inhibition of wild-type (WT) EGFR. Thus, treatment that is customised to an individual's genetic alterations in EGFR may offer greater therapeutic benefits for patients with NSCLC. METHODS: In this study, we demonstrate a new therapeutic strategy utilising customised antisense oligonucleotides (ASOs) to selectively target activating mutations in the EGFR gene in an individualised manner that can overcome drug-resistant mutations. We use extracellular vesicles (EVs) as a vehicle to deliver ASOs to NSCLC cells. FINDINGS: Specifically guided by the mutational profile identified in NSCLC patients, we have successfully developed ASOs that selectively inhibit point mutations in the EGFR gene, including L858R and T790M, while sparing the WT EGFR. Delivery of the EGFR-targeting ASOs by EVs significantly reduced tumour growth in xenograft models of EGFR-L858R/T790M-driven NSCLC. Importantly, we have also shown that EGFR-targeting ASOs exhibit more potent anti-cancer effect than TKIs in NSCLC with EGFR mutations, effectively suppressing a patient-derived TKI-resistant NSCLC tumour. INTERPRETATION: Overall, by harnessing the specificity and efficacy of ASOs, we present an effective and adaptable therapeutic platform for NSCLC treatment. FUNDING: This study was funded by Singapore's Ministry of Health (NMRC/OFIRG/MOH-000643-00, OFIRG21nov-0068, NMRC/OFLCG/002-2018, OFYIRG22jul-0034), National Research Foundation (NRF-NRFI08-2022, NRF-CRP22-2019-0003, NRF-CRP23-2019-0004), A∗STAR, and Ministry of Education.
RESUMO
Nanoparticles (NPs) hold great potential as therapeutics, particularly in the realm of drug delivery. They are effective at functional cargo delivery and offer a great degree of amenability that can be used to offset toxic side effects or to target drugs to specific regions in the body. However, there are many challenges associated with the development of NP-based drug formulations that hamper their successful clinical translation. Arguably, the most significant barrier in the way of efficacious NP-based drug delivery systems is the tedious and time-consuming nature of NP formulation-a process that needs to account for downstream effects, such as the onset of potential toxicity or immunogenicity, in vivo biodistribution and overall pharmacokinetic profiles, all while maintaining desirable therapeutic outcomes. Computational and AI-based approaches have shown promise in alleviating some of these restrictions. Via predictive modeling and deep learning, in silico approaches have shown the ability to accurately model NP-membrane interactions and cellular uptake based on minimal data, such as the physicochemical characteristics of a given NP. More importantly, machine learning allows computational models to predict how specific changes could be made to the physicochemical characteristics of a NP to improve functional aspects, such as drug retention or endocytosis. On a larger scale, they are also able to predict the in vivo pharmacokinetics of NP-encapsulated drugs, predicting aspects such as circulatory half-life, toxicity, and biodistribution. However, the convergence of nanomedicine and computational approaches is still in its infancy and limited in its applicability. The interactions between NPs, the encapsulated drug and the body form an intricate network of interactions that cannot be modeled with absolute certainty. Despite this, rapid advancements in the area promise to deliver increasingly powerful tools capable of accelerating the development of advanced nanoscale therapeutics. Here, we describe computational approaches that have been utilized in the field of nanomedicine, focusing on approaches for NP design and engineering.