Leak Rehana K, Schreiber James B
Graduate School of Pharmaceutical Sciences, Duquesne University, Pittsburgh, PA, United States.
School of Nursing, Duquesne University, Pittsburgh, PA, United States.
Front Pharmacol. 2022 Jul 1;13:775632. doi: 10.3389/fphar.2022.775632. eCollection 2022.
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.
生物科学中的许多发现都来自观察性研究,但学生研究人员也需要学习如何设计实验,以区分相关性和因果关系。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。只有通过特异性干扰药物的假定作用机制,研究人员才能确定药物是如何产生其生理效应的。通常,采用药理学或遗传学方法来改变生物药物靶点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而消除。然而,实验技术存在一些往往未被充分认识的问题,尤其是对于较新的方法。此外,统计效应并不能保证其生物学重要性或在不同模型和物种之间的可转化性。在这个两部分系列中,我们将以人类疾病实验模型中药物测试这个直观的例子,简要描述机制性临床前研究的注意事项和优势。第一部分重点介绍用于药物测试的细胞和动物模型的技术实用性和常见陷阱,第二部分简单描述如何利用全因子方差分析来测试生物靶点的药物诱导激活(或抑制)与治疗结果之间联系的因果关系。完成本系列学习后,学生将预先了解机制性研究中的技术和理论注意事项,并理解“模型只是模型”。这些见解有助于新生认识到科学研究的优势和局限性。