# Functional drug fingerprinting with all-optical electrophysiology

> **NIH NIH R44** · QUIVER BIOSCIENCE INC. · 2024 · $378,127

## Abstract

Project Summary
Phase II: Functional drug fingerprinting with all-optical electrophysiology
Q-State Biosciences has developed a therapeutic discovery platform that uniquely integrates i) scalable human
cellular models of disease states, ii) diverse readouts of neuronal function using proprietary all-optical
electrophysiology called OptopatchTM, and iii) artificial intelligence/machine learning (AI/ML) analytics to establish
unique, disease-relevant phenotypes for assessment of therapeutics. Optopatch simultaneously records >500
electrical activity parameters from hundreds of neurons with 1 millisecond temporal resolution, single-cell spatial
resolution, and a throughput >20,000-fold higher than manual patch clamp. Leveraging this high-throughput,
high-content data, Q-State has developed “drug fingerprinting” (DFP) algorithms to learn the archetypal patterns
of changes induced by disease states and chemical compounds, building a map of the functional
electrophysiology of human neurons. In Phase I, we populated this map with fingerprints of 400 diverse and
selective tool compounds for a wide range of hand-curated molecular targets, which serve as landmarks for
interpreting the functional effects of disease states and novel chemical compounds. We also demonstrated that
this technology could identify a disease phenotype in neurons and predict drug rescue. In Phase II, we will
advance the DFP platform in 3 ways: 1) Build tools to improve the reliability, interpretability, accessibility, and
automation of the technology. Our current platform is capable of extracting data from Optopatch assays and
fingerprinting the impacts of perturbations using representation-learning. To achieve a production-ready pipeline,
“explainable AI” techniques will be used to track how the algorithms make choices, and unit tests will affirm the
correctness of all modules. The pipeline will be automated and deployed for access by non-coders. Finally, a
new set of modules will extend DFP to high-content imaging data for mapping biological states across form and
function of neurons. 2) Expand DFP library to include clinically relevant compounds and additional neuronal
subtypes. To enable therapeutic discovery applications, we will expand our reference database to include a new
custom-assembled library of ~3,000 clinically relevant drugs, including a comprehensive set of approved
compounds, as well as unapproved bioactive molecules with acceptable safety profiles and favorable drug-like
properties. Scaling to a 384-well format assay, we will collect data for all compounds in human iPSC-derived
excitatory and inhibitory neurons, measuring pharmacological impact on both neuronal excitability and cell
morphology in a dose-dependent format to create unique multi-modal fingerprints. 3) Demonstrate
pharmacological rescue of a monogenic epilepsy in vitro and in vivo. As a proof-of-concept demonstration of the
DFP platform, we will use our new database to identify compounds with potentia...

## Key facts

- **NIH application ID:** 10880299
- **Project number:** 5R44MH122052-05
- **Recipient organization:** QUIVER BIOSCIENCE INC.
- **Principal Investigator:** Benjamin Nathaniel Harwood
- **Activity code:** R44 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $378,127
- **Award type:** 5
- **Project period:** 2019-09-20 → 2025-10-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10880299

## Citation

> US National Institutes of Health, RePORTER application 10880299, Functional drug fingerprinting with all-optical electrophysiology (5R44MH122052-05). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/10880299. Licensed CC0.

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