# Drug target identification using CRISPRi/a screening

> **NIH NIH R41** · BIOIO, LLC · 2020 · $224,455

## Abstract

ABSTRACT
As phenotypic screening makes a comeback in drug discovery, its major issue is drug target identification.
There aren’t good generalizable approaches to do target identification for compounds of interest. Identifying
how a new drug works at the molecular level is critical for improving it, which is often needed to give it the best
chance of ultimately being both safe and effective in patients. In this proposal, we demonstrate a genome-wide
CRISPRi/a screening process in human cells that readily identifies the molecular target(s) for drugs of interest.
We present preliminary evidence it can also identify alternative targets for the disease indication for the drug as
well as potential side effect targets. We demonstrate this screening process can be applied to drugs for diverse
diseases. Thus, it has the potential to improve the number of drugs that make it through drug development
pipelines that currently have high failure rates such as those for Alzheimer’s and diabetes. In the last two
decades companies like Foundation Medicine have used genomics to make an increasing impact on disease
diagnostics in the Healthcare industry. We propose to similarly use genomics to make an impact on the
pharmaceutical industry creating a “drug diagnostics” market in the process.

## Key facts

- **NIH application ID:** 10006378
- **Project number:** 1R41GM137625-01
- **Recipient organization:** BIOIO, LLC
- **Principal Investigator:** Luke Gilbert
- **Activity code:** R41 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $224,455
- **Award type:** 1
- **Project period:** 2020-05-01 → 2021-04-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10006378

## Citation

> US National Institutes of Health, RePORTER application 10006378, Drug target identification using CRISPRi/a screening (1R41GM137625-01). Retrieved via AI Analytics 2026-05-27 from https://api.ai-analytics.org/grant/nih/10006378. Licensed CC0.

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