Behind the screen: drug discovery using the big data of phenotypic analysis

TL;DR

This paper introduces an educational case study that teaches students about drug discovery using phenotypic screening and big data analysis.

Contribution

The novel contribution is an educational case study that introduces phenotypic screening methods and statistical analysis for drug discovery in undergraduate and graduate courses.

Findings

The case study was tested across 70 students at three universities and improved student confidence in discussing drug discovery topics.

Students learned to identify hit compounds and understand the biological significance of their results using practical statistical procedures.

The case study is adaptable for a wide range of courses and successfully engages students in real-world drug discovery strategies.

Abstract

Technological advances in drug discovery are exciting to students, but it is challenging for faculty to maintain the pace with these developments, particularly within undergraduate courses. In recent years, a High-throughput Discovery Science and Inquiry-based Case Studies for Today’s Students (HITS) Research Coordination Network has been assembled to address the mechanism of how faculty can, on-pace, introduce these advancements. As a part of HITS, our team has developed “Behind the Screen: Drug Discovery using the Big Data of Phenotypic Analysis” to introduce students and faculty to phenotypic screening as a tool to identify inhibitors of diseases that do not have known cellular targets. This case guides faculty and students though current screening methods using statistics and can be applied at undergraduate and graduate levels. Tested across 70 students at three universities and a variety of courses, our case utilizes datasets modeled on a real phenotypic screening method as an accessible way to teach students about current methods in drug discovery. Students will learn how to identify hit compounds from a dataset they have analyzed and understand the biological significance of the results they generate. They are guided through practical statistical procedures, like those of researchers engaging in a novel drug discovery strategy. Student survey data demonstrated that the case was successful in improving student attitudes in their ability to discuss key topics, with both undergraduate and graduate students having a significant increase in confidence. Together, we present a case that uses big data to examine the utility of a novel phenotypic screening strategy, a pedagogical tool that can be customized for a wide variety of courses.

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