Of Mice and Men Bridging the Translational Disconnect in CNS Drug Discovery
Of Mice and Men Bridging the Translational Disconnect in CNS Drug Discovery
复制标题
DOI：
10.2165/11310890-000000000-00000
复制
发表时间：
2009-01-01
期刊：
影响因子：
6
通讯作者：
Geerts, Hugo
中科院分区：
文献类型：
--
作者：
Geerts, Hugo
转基因动物技术的巨大进步，特别是在阿尔茨海默病领域，并没有导致进入临床开发的药物的成功率明显提高。尽管研究和开发预算大幅增加，但批准的药物数量总体上并没有增加，这导致了所谓的创新差距。虽然动物模型在记录许多中枢神经系统疾病的可能病理机制方面非常有用，但它们在药物开发领域的预测性并不高。本文报道了动物模型和人类患者在药物发现方面的一些未被充分认识的根本差异，特别强调了阿尔茨海默病和精神分裂症。例如相同药物对人与啮齿动物靶亚型的不同亲和力，以及动物模型中缺乏许多功能基因型。我还提供了一些可能的解决方案来弥合翻译脱节并提高临床前模型的可预测性，例如更加重视高质量的翻译研究，更多的竞争前信息共享以及拥抱多靶点药理学策略。是另一个可能的，但破坏性的解决方案，以不断扩大的创新差距。这包括开发混合计算模型，基于记录的临床前生理学和药理学，但使用实际患者的临床数据进行填充和验证。
The tremendous advances in transgene animal technology, especially in the area of Alzheimer's disease, have not resulted in a significantly better success rate for drugs entering clinical development. Despite substantial increases in research and development budgets, the number of approved drugs in general has not increased, leading to the so-called innovation gap. While animal models have been very useful in documenting the possible pathological mechanisms in many CNS diseases, they are not very predictive in the area of drug development.This paper reports on a number of under-appreciated fundamental differences between animal models and human patients in the context of drug discovery with special emphasis on Alzheimer's disease and schizophrenia, such as different affinities of the same drug for human versus rodent target subtypes and the absence of many functional genotypes in animal models. I also offer a number of possible solutions to bridge the translational disconnect and improve the predictability of preclinical models, such as more emphasis on good-quality translational studies, more pre-competitive information sharing and the embracing of multi-target pharmacology strategies.Re-engineering the process for drug discovery and development, in a similar way to other more successful industries, is another possible but disrupting solution to the growing innovation gap. This includes the development of hybrid computational models, based upon documented preclinical physiology and pharmacology, but populated and validated with clinical data from actual patients.