Monday, June 23, 2025 6/23/2025

Functional drug fingerprinting with all-optical electrophysiology

Award Number: R44MH122052
ORGANIZATION: NATIONAL INSTITUTE OF MENTAL HEALTH
OPDIV: NIH
AWARD CLASS: DISCRETIONARY
AWARD ACTIVITY TYPE: SCIENTIFIC/HEALTH RESEARCH (INCLUDES SURVEYS)
PERIOD OF PERFORMANCE START DATE: 09/20/2019
PERIOD OF PERFORMANCE END DATE: 06/30/2025

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Functional drug fingerprinting with all-optical electrophysiology - 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 potential to rescue effects of epilepsy causing mutations. Putative in silico hits will be experimentally tested and pharmacological rescue of phenotypes will be quantified. The best compounds will be advanced to in vivo testing in a rodent model as an initial demonstration of the ability of the platform to rapidly identify new, clinically viable treatments for epilepsy.


Issue Date FY	Funding FY	Legal Entity Name	Legal Entity Address	Legal Entity City	Legal Entity State	Legal Entity Zip Code	Legal Entity COUNTY	Legal Entity COUNTRY	Assistance Listing	Award Code	Budget Year	Action Date	Action Type	Action Amount

Issue Date FY: 2025 ( Subtotal = $0 )
2025	2024	QUELLTX INC	179 SIDNEY ST	CAMBRIDGE	MA	02139	MIDDLESEX	USA	Mental Health Research Grants	000	5	10/23/2024	NON-COMPETING CONTINUATION	$0
														Subtotal = $0

Issue Date FY: 2024 ( Subtotal = $378,127 )
2024	2024	QUELLTX INC	179 SIDNEY ST	CAMBRIDGE	MA	02139	MIDDLESEX	USA	Mental Health Research Grants	004	5	7/29/2024	NON-COMPETING CONTINUATION	$378,127
2024	2023	QUELLTX INC	179 SIDNEY ST	CAMBRIDGE	MA	02139	MIDDLESEX	USA	Mental Health Research Grants	002	4	11/28/2023	CHANGE OF GRANTEE / TRAINING INSTITUTION / AWARDING INSTITUTION	$718,700
2024	2023	QUELLTX INC	179 SIDNEY ST	CAMBRIDGE	MA	02139	MIDDLESEX	USA	Mental Health Research Grants	003	4	1/23/2024	CHANGE OF GRANTEE / TRAINING INSTITUTION / AWARDING INSTITUTION	$0
2024	2022	Q-STATE BIOSCIENCES, INC	179 SIDNEY ST	CAMBRIDGE	MA	02139	MIDDLESEX	USA	Mental Health Research Grants	001	3	11/28/2023	COMPETING CONTINUATION	-$718,700
2024	2022	Q-STATE BIOSCIENCES	179 SIDNEY ST	CAMBRIDGE	MA	02139	MIDDLESEX	USA	Mental Health Research Grants	000	3	11/15/2023	COMPETING CONTINUATION	$0
														Subtotal = $378,127

Issue Date FY: 2023 ( Subtotal = $793,383 )
2023	2023	QUELLTX INC	179 SIDNEY ST	CAMBRIDGE	MA	02139	MIDDLESEX	USA	Mental Health Research Grants	000	4	7/1/2023	CHANGE OF GRANTEE / TRAINING INSTITUTION / AWARDING INSTITUTION	$793,383
														Subtotal = $793,383

Issue Date FY: 2022 ( Subtotal = $1,296,058 )
2022	2022	Q-STATE BIOSCIENCES, INC.	179 SIDNEY ST	CAMBRIDGE	MA	02139	MIDDLESEX	USA	Mental Health Research Grants	000	3	7/19/2022	COMPETING CONTINUATION	$1,296,058
														Subtotal = $1,296,058

Grand Total All Awards = $2,467,568

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Functional drug fingerprinting with all-optical electrophysiology

Award Number: R44MH122052

ORGANIZATION: NATIONAL INSTITUTE OF MENTAL HEALTH

OPDIV: NIH

AWARD CLASS: DISCRETIONARY

AWARD ACTIVITY TYPE: SCIENTIFIC/HEALTH RESEARCH (INCLUDES SURVEYS)

PERIOD OF PERFORMANCE START DATE: 09/20/2019

PERIOD OF PERFORMANCE END DATE: 06/30/2025

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