# Model Driven Construction of Dual-switch Selection Gene Drives to Combat Drug Resistance

> **NIH NIH R21** · PENNSYLVANIA STATE UNIVERSITY, THE · 2020 · $274,274

## Abstract

Project Summary/Abstract
Evolution underlies both the development of humankind as well as the greatest challenges to human health.
Across the tree of life, cancer and infectious viruses, prokaryotes, and eukaryotes exist within complex
competitive landscapes that can promote or inhibit disease progression and therapeutic resistance. The
amazing diversity of heterogenous cell populations raises existential questions about how to combat drug
resistance evolution. The convential approach to this problem is to attempt to reverse engineer evolving
biological systems. I.e., after a selection has occurred, we isolate resistant cells, attempt to determine what
caused drug resistance and treat the resistant state. This strategy results in a “resistance treadmill” whereby
resistance evolution occurs, new drugs combat drug resistance and then resistance re-emerges – a process
that occurs until we run out of effective agents. We believe that instead of combatting evolution, we should
make use of it. We propose to employ a “forward engineering” approach that seeks to create new paradigms to
control and understand evolution. By creating a dual switch gene drive, we posit that we can use engineering
design to build populations whose evolution can be guided by model driven therapeutic interventions. In
essence, we will drive evolution in heterogenous cell populations towards eradicatable outcomes. This would
be paradigm shifting in the clinic, but, by building these cellular systems, manipulating them with chemistry and
biology, and quantiatively modeling their dynamics, we can also “build to understand” evolution as we take
giant strides towards controlling it.

## Key facts

- **NIH application ID:** 9973217
- **Project number:** 5R21EB026617-02
- **Recipient organization:** PENNSYLVANIA STATE UNIVERSITY, THE
- **Principal Investigator:** Justin Pritchard
- **Activity code:** R21 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $274,274
- **Award type:** 5
- **Project period:** 2019-08-01 → 2022-04-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9973217

## Citation

> US National Institutes of Health, RePORTER application 9973217, Model Driven Construction of Dual-switch Selection Gene Drives to Combat Drug Resistance (5R21EB026617-02). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/9973217. Licensed CC0.

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