Fosse Vibeke, Oldoni Emanuela, Gerardi Chiara, Banzi Rita, Fratelli Maddalena, Bietrix Florence, Ussi Anton, Andreu Antonio L, McCormack Emmet
Centre for Cancer Biomarkers, Department of Clinical Science, University of Bergen, 5021 Bergen, Norway.
EATRIS ERIC, European Infrastructure for Translational Medicine, 1081 HZ Amsterdam, The Netherlands.
J Pers Med. 2022 Jul 19;12(7):1177. doi: 10.3390/jpm12071177.
The introduction of personalized medicine, through the increasing multi-omics characterization of disease, brings new challenges to disease modeling. The scope of this review was a broad evaluation of the relevance, validity, and predictive value of the current preclinical methodologies applied in stratified medicine approaches. Two case models were chosen: oncology and brain disorders. We conducted a scoping review, following the Joanna Briggs Institute guidelines, and searched PubMed, EMBASE, and relevant databases for reports describing preclinical models applied in personalized medicine approaches. A total of 1292 and 1516 records were identified from the oncology and brain disorders search, respectively. Quantitative and qualitative synthesis was performed on a final total of 63 oncology and 94 brain disorder studies. The complexity of personalized approaches highlights the need for more sophisticated biological systems to assess the integrated mechanisms of response. Despite the progress in developing innovative and complex preclinical model systems, the currently available methods need to be further developed and validated before their potential in personalized medicine endeavors can be realized. More importantly, we identified underlying gaps in preclinical research relating to the relevance of experimental models, quality assessment practices, reporting, regulation, and a gap between preclinical and clinical research. To achieve a broad implementation of predictive translational models in personalized medicine, these fundamental deficits must be addressed.
随着疾病多组学特征描述的不断增加,个性化医疗的引入给疾病建模带来了新的挑战。本综述的范围是对分层医学方法中应用的当前临床前方法的相关性、有效性和预测价值进行广泛评估。选择了两个案例模型:肿瘤学和脑部疾病。我们按照乔安娜·布里格斯研究所的指南进行了一项范围综述,并在PubMed、EMBASE和相关数据库中搜索描述个性化医疗方法中应用的临床前模型的报告。分别从肿瘤学和脑部疾病搜索中识别出1292条和1516条记录。对总共63项肿瘤学研究和94项脑部疾病研究进行了定量和定性综合分析。个性化方法的复杂性凸显了需要更复杂的生物系统来评估反应的综合机制。尽管在开发创新和复杂的临床前模型系统方面取得了进展,但目前可用的方法在其在个性化医疗努力中的潜力得以实现之前,还需要进一步开发和验证。更重要的是,我们发现临床前研究在实验模型的相关性、质量评估实践、报告、监管以及临床前和临床研究之间的差距等方面存在潜在不足。为了在个性化医疗中广泛实施预测性转化模型,必须解决这些基本缺陷。