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Data-driven discovery of innate immunomodulators via machine learning-guided high throughput screening

The innate immune response is vital for the success of prophylactic vaccines and immunotherapies. Control of signaling in innate immune pathways can improve prophylactic vaccines by inhibiting unfavorable systemic inflammation and immunotherapies by enhancing immune stimulation. In this work, we dev...

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Autores principales: Tang, Yifeng, Kim, Jeremiah Y., IP, Carman K. M., Bahmani, Azadeh, Chen, Qing, Rosenberger, Matthew G., Esser-Kahn, Aaron P., Ferguson, Andrew L.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: The Royal Society of Chemistry 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10646978/
https://www.ncbi.nlm.nih.gov/pubmed/38020385
http://dx.doi.org/10.1039/d3sc03613h
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author Tang, Yifeng
Kim, Jeremiah Y.
IP, Carman K. M.
Bahmani, Azadeh
Chen, Qing
Rosenberger, Matthew G.
Esser-Kahn, Aaron P.
Ferguson, Andrew L.
author_facet Tang, Yifeng
Kim, Jeremiah Y.
IP, Carman K. M.
Bahmani, Azadeh
Chen, Qing
Rosenberger, Matthew G.
Esser-Kahn, Aaron P.
Ferguson, Andrew L.
author_sort Tang, Yifeng
collection PubMed
description The innate immune response is vital for the success of prophylactic vaccines and immunotherapies. Control of signaling in innate immune pathways can improve prophylactic vaccines by inhibiting unfavorable systemic inflammation and immunotherapies by enhancing immune stimulation. In this work, we developed a machine learning-enabled active learning pipeline to guide in vitro experimental screening and discovery of small molecule immunomodulators that improve immune responses by altering the signaling activity of innate immune responses stimulated by traditional pattern recognition receptor agonists. Molecules were tested by in vitro high throughput screening (HTS) where we measured modulation of the nuclear factor κ-light-chain-enhancer of activated B-cells (NF-κB) and the interferon regulatory factors (IRF) pathways. These data were used to train data-driven predictive models linking molecular structure to modulation of the NF-κB and IRF responses using deep representational learning, Gaussian process regression, and Bayesian optimization. By interleaving successive rounds of model training and in vitro HTS, we performed an active learning-guided traversal of a 139 998 molecule library. After sampling only ∼2% of the library, we discovered viable molecules with unprecedented immunomodulatory capacity, including those capable of suppressing NF-κB activity by up to 15-fold, elevating NF-κB activity by up to 5-fold, and elevating IRF activity by up to 6-fold. We extracted chemical design rules identifying particular chemical fragments as principal drivers of specific immunomodulation behaviors. We validated the immunomodulatory effect of a subset of our top candidates by measuring cytokine release profiles. Of these, one molecule induced a 3-fold enhancement in IFN-β production when delivered with a cyclic di-nucleotide stimulator of interferon genes (STING) agonist. In sum, our machine learning-enabled screening approach presents an efficient immunomodulator discovery pipeline that has furnished a library of novel small molecules with a strong capacity to enhance or suppress innate immune signaling pathways to shape and improve prophylactic vaccination and immunotherapies.
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spelling pubmed-106469782023-10-18 Data-driven discovery of innate immunomodulators via machine learning-guided high throughput screening Tang, Yifeng Kim, Jeremiah Y. IP, Carman K. M. Bahmani, Azadeh Chen, Qing Rosenberger, Matthew G. Esser-Kahn, Aaron P. Ferguson, Andrew L. Chem Sci Chemistry The innate immune response is vital for the success of prophylactic vaccines and immunotherapies. Control of signaling in innate immune pathways can improve prophylactic vaccines by inhibiting unfavorable systemic inflammation and immunotherapies by enhancing immune stimulation. In this work, we developed a machine learning-enabled active learning pipeline to guide in vitro experimental screening and discovery of small molecule immunomodulators that improve immune responses by altering the signaling activity of innate immune responses stimulated by traditional pattern recognition receptor agonists. Molecules were tested by in vitro high throughput screening (HTS) where we measured modulation of the nuclear factor κ-light-chain-enhancer of activated B-cells (NF-κB) and the interferon regulatory factors (IRF) pathways. These data were used to train data-driven predictive models linking molecular structure to modulation of the NF-κB and IRF responses using deep representational learning, Gaussian process regression, and Bayesian optimization. By interleaving successive rounds of model training and in vitro HTS, we performed an active learning-guided traversal of a 139 998 molecule library. After sampling only ∼2% of the library, we discovered viable molecules with unprecedented immunomodulatory capacity, including those capable of suppressing NF-κB activity by up to 15-fold, elevating NF-κB activity by up to 5-fold, and elevating IRF activity by up to 6-fold. We extracted chemical design rules identifying particular chemical fragments as principal drivers of specific immunomodulation behaviors. We validated the immunomodulatory effect of a subset of our top candidates by measuring cytokine release profiles. Of these, one molecule induced a 3-fold enhancement in IFN-β production when delivered with a cyclic di-nucleotide stimulator of interferon genes (STING) agonist. In sum, our machine learning-enabled screening approach presents an efficient immunomodulator discovery pipeline that has furnished a library of novel small molecules with a strong capacity to enhance or suppress innate immune signaling pathways to shape and improve prophylactic vaccination and immunotherapies. The Royal Society of Chemistry 2023-10-18 /pmc/articles/PMC10646978/ /pubmed/38020385 http://dx.doi.org/10.1039/d3sc03613h Text en This journal is © The Royal Society of Chemistry https://creativecommons.org/licenses/by/3.0/
spellingShingle Chemistry
Tang, Yifeng
Kim, Jeremiah Y.
IP, Carman K. M.
Bahmani, Azadeh
Chen, Qing
Rosenberger, Matthew G.
Esser-Kahn, Aaron P.
Ferguson, Andrew L.
Data-driven discovery of innate immunomodulators via machine learning-guided high throughput screening
title Data-driven discovery of innate immunomodulators via machine learning-guided high throughput screening
title_full Data-driven discovery of innate immunomodulators via machine learning-guided high throughput screening
title_fullStr Data-driven discovery of innate immunomodulators via machine learning-guided high throughput screening
title_full_unstemmed Data-driven discovery of innate immunomodulators via machine learning-guided high throughput screening
title_short Data-driven discovery of innate immunomodulators via machine learning-guided high throughput screening
title_sort data-driven discovery of innate immunomodulators via machine learning-guided high throughput screening
topic Chemistry
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10646978/
https://www.ncbi.nlm.nih.gov/pubmed/38020385
http://dx.doi.org/10.1039/d3sc03613h
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