Mechanistic Research for the Student or Educator (Part I of II).
Mechanistic Research for the Student or Educator (Part I of II).
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DOI：
10.3389/fphar.2022.775632
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发表时间：
2022
影响因子：
5.6
通讯作者：
Schreiber, James B.
中科院分区：
文献类型：
--
作者：
Leak, Rehana K.;Schreiber, James B.
关键词：
生物科学中的许多发现都来自观察研究，但学生研究人员还需要学习如何设计区分相关性和因果关系的实验。例如，确定具有治疗潜力的药物的生理作用机制需要建立因果联系。只有通过专门干扰药物的作用机制，研究人员才能确定药物如何引起其生理效应。通常，采用药理学或遗传学方法来修饰生物药物靶标或下游途径的表达和/或活性，以测试药物的有益特性是否因此被消除。然而，实验技术有警告，往往被低估，特别是对较新的方法。此外，统计效应并不能保证其生物学重要性或跨模型和物种的可翻译性。在这个由两部分组成的系列中，简要描述了机械临床前研究的注意事项和优势，使用人类疾病实验模型中药物测试的直观例子。第一部分着重于技术的实用性和常见的陷阱的细胞和动物模型设计的药物测试，第二部分简单地描述了如何利用全因子方差分析，以测试之间的因果关系的药物诱导的激活（或抑制）的生物靶点和治疗结果。完成本系列课程后，学生将对机械研究中的技术和理论注意事项有深入的了解，并理解“模型只是模型”。这些见解可以帮助新学生欣赏科学研究的优势和局限性。
Many discoveries in the biological sciences have emerged from observational studies, but student researchers also need to learn how to design experiments that distinguish correlation from causation. For example, identifying the physiological mechanism of action of drugs with therapeutic potential requires the establishment of causal links. Only by specifically interfering with the purported mechanisms of action of a drug can the researcher determine how the drug causes its physiological effects. Typically, pharmacological or genetic approaches are employed to modify the expression and/or activity of the biological drug target or downstream pathways, to test if the salutary properties of the drug are thereby abolished. However, experimental techniques have caveats that tend to be underappreciated, particularly for newer methods. Furthermore, statistical effects are no guarantor of their biological importance or translatability across models and species. In this two-part series, the caveats and strengths of mechanistic preclinical research are briefly described, using the intuitive example of pharmaceutical drug testing in experimental models of human diseases. Part I focuses on technical practicalities and common pitfalls of cellular and animal models designed for drug testing, and Part II describes in simple terms how to leverage a full-factorial ANOVA, to test for causality in the link between drug-induced activation (or inhibition) of a biological target and therapeutic outcomes. Upon completion of this series, students will have forehand knowledge of technical and theoretical caveats in mechanistic research, and comprehend that “a model is just a model.” These insights can help the new student appreciate the strengths and limitations of scientific research.
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影响因子：
7.7
作者：
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