# Predictive Models for Small-Molecule Accumulation in Gram-Negative Bacteria

> **NIH NIH R01** · SLOAN-KETTERING INST CAN RESEARCH · 2021 · $1,239,304

## Abstract

PROJECT SUMMARY
Predictive Models for Small-Molecule Accumulation in Gram-Negative Bacteria.
Antibiotic-resistant Gram-negative bacterial infections are increasing in incidence and novel antibiotics are
urgently needed to combat this growing threat to public health. A major roadblock to the development of novel
antibiotics is our poor understanding of the structural features of small molecules that correlate with bacterial
penetration and efflux. As a result, while potent biochemical inhibitors can often be identified for new targets,
developing them into compounds with whole-cell antibacterial activity has proven challenging.
To address this critical problem, we propose herein a comprehensive, multidisciplinary approach to develop
quantitative models to predict small-molecule penetration and efflux in Gram-negative bacteria. We have
pioneered a general platform for systematic, quantitative evaluation of small-molecule accumulation in bacteria,
using label-free LC-MS/MS detection and multivariate cheminformatic analysis. We have also developed
unique isogenic strain sets of wild-type, hyperporinated, efflux-knockout, and doubly-compromised E. coli,
P. aeruginosa, and A. baumannii that allow us to dissect the individual contributions of outer/inner membrane
penetration and active efflux to net accumulation, using a kinetic model that accurately recapitulates available
experimental data. Moreover, we have developed machine learning and neural network approaches to QSAR
(quantitative structure–activity relationship) modeling of pharmacological properties that will now be used to
develop predictive cheminformatic models for Gram-negative accumulation, penetration, and efflux.
This project will be carried out by a multidisciplinary SPEAR-GN Project Team (Small-molecule Penetration &
Efflux in Antibiotic-Resistant Gram-Negatives, “speargun”) involving the labs of Derek Tan (MSK, PI), Helen
Zgurskaya (OU, PI), Bradley Sherborne (Merck, Lead Collaborator), Valentin Rybenkov (OU, Co-I), Adam
Duerfeldt (OU, Co-I), Carl Balibar (Merck, Collaborator), and David McLaren (Merck, Collaborator), comprising
extensive combined expertise in organic and diversity-oriented synthesis, biochemistry, microbiology, high-
throughput screening, mass spectrometry, biophysical modeling, cheminformatics, and medicinal chemistry.
Herein, we will design and synthesize chemical libraries with diverse structural and physicochemical
properties; analyze their accumulation in the isogenic strain sets in both high-throughput and high-density
assay formats; extract kinetic parameters for penetration and efflux from the resulting experimental datasets;
develop and validate robust QSAR models for accumulation, penetration, and efflux; and demonstrate the utility
of these models in medicinal chemistry campaigns to develop novel Gram-negative antibiotics against three
targets. This project will provide a major advance in the field of antibacterial drug discovery, providing powerful
enabli...

## Key facts

- **NIH application ID:** 10226047
- **Project number:** 5R01AI136795-04
- **Recipient organization:** SLOAN-KETTERING INST CAN RESEARCH
- **Principal Investigator:** DEREK S TAN
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $1,239,304
- **Award type:** 5
- **Project period:** 2018-08-10 → 2023-07-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10226047

## Citation

> US National Institutes of Health, RePORTER application 10226047, Predictive Models for Small-Molecule Accumulation in Gram-Negative Bacteria (5R01AI136795-04). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10226047. Licensed CC0.

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