# Next Generation Specificity Screening for Biotherapeutics using an Extracellular Proteome Array

> **NIH NIH R44** · INTEGRAL MOLECULAR · 2021 · $502,372

## Abstract

ABSTRACT
Detailed specificity analysis is critical for drugs, as even minimal off-target binding can cause serious adverse
events. As a result, specificity profiling has become an FDA requirement for monoclonal antibodies (MAbs) as
well as other emerging biotherapeutic categories such as CAR-T cell therapy. Current methods for profiling the
specificity of biotherapeutics, primarily tissue cross-reactivity studies and spotted protein arrays, are poorly
predictive of cross-reactivity against the native human proteome, have low sensitivity, and are difficult to
interpret. A novel approach for specificity profiling is needed to better predict off-target binding of MAbs and de-
risk biotherapeutic discovery programs. Here we propose to develop a technology that has the predictive
validity to de-risk biotherapeutics entering preclinical development. This product would have a large impact on
the clinical pipelines of nearly every biopharmaceutical company, has significant commercial potential, and can
be implemented with very low risk. The resulting product will be the first major innovation in in vitro toxicology
testing for biotherapeutics since tissue cross-reactivity studies were adopted 35+ years ago.

## Key facts

- **NIH application ID:** 10206171
- **Project number:** 5R44GM113556-06
- **Recipient organization:** INTEGRAL MOLECULAR
- **Principal Investigator:** Benjamin J Doranz
- **Activity code:** R44 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $502,372
- **Award type:** 5
- **Project period:** 2015-02-01 → 2022-12-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10206171

## Citation

> US National Institutes of Health, RePORTER application 10206171, Next Generation Specificity Screening for Biotherapeutics using an Extracellular Proteome Array (5R44GM113556-06). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10206171. Licensed CC0.

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