Evidence map›Paper›PMID 42413502›Full record

ArticleCell reports methods2026

TACTIC: A transfer learning framework to predict drug interactions in emerging pathogens.

Carolina H Chung, David C Chang, Nicole M Rhoads, Prajna Lalitha, Gunasekaran Rameshkumar, Rajarathinam Karpagam, Manojkumar Vimaladevi, Madeline R Shay, Karthik Srinivasan, Mercy A Okezue and 3 more

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Article in Cell reports methods, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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1 · What the graph read from it

What it found

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

13 authors.

Carolina H ChungDepartment of Biomedical Engineering, University of Michigan, Ann Arbor, MI 48109, USA.
David C ChangDepartment of Biomedical Engineering, University of Michigan, Ann Arbor, MI 48109, USA.
Nicole M RhoadsDepartment of Biomedical Engineering, University of Michigan, Ann Arbor, MI 48109, USA; Department of Pharmacology, University of Michigan, Ann Arbor, MI 48109, USA.
Prajna LalithaDepartment of Ocular Microbiology, Aravind Eye Hospital & PG Institute of Ophthalmology, Madurai, Tamil Nadu 625020, India.
Gunasekaran RameshkumarDepartment of Ocular Microbiology, Aravind Eye Hospital & PG Institute of Ophthalmology, Madurai, Tamil Nadu 625020, India.
Rajarathinam KarpagamDepartment of Ocular Microbiology, Aravind Eye Hospital & PG Institute of Ophthalmology, Madurai, Tamil Nadu 625020, India.
Manojkumar VimaladeviDepartment of Ocular Microbiology, Aravind Eye Hospital & PG Institute of Ophthalmology, Madurai, Tamil Nadu 625020, India.
Madeline R ShayCellular and Molecular Biology Program, University of Michigan Medical School, Ann Arbor, MI 48109, USA.
Karthik SrinivasanDepartment of Ophthalmology and Visual Sciences, University of Michigan Medical School, Ann Arbor, MI 48109, USA.
Mercy A OkezueDepartment of Pharmaceutical Sciences, University of Michigan College of Pharmacy, Ann Arbor, MI 48109, USA.
Bhanuz DechayontDepartment of Pharmaceutical Sciences, University of Michigan College of Pharmacy, Ann Arbor, MI 48109, USA.
Ashlee D BrunaughDepartment of Pharmaceutical Sciences, University of Michigan College of Pharmacy, Ann Arbor, MI 48109, USA.
Sriram ChandrasekaranDepartment of Biomedical Engineering, University of Michigan, Ann Arbor, MI 48109, USA; Cellular and Molecular Biology Program, University of Michigan Medical School, Ann Arbor, MI 48109, USA; Program in Chemical Biology, University of Michigan, Ann Arbor, MI 48109, USA; Center for Bioinformatics and Computational Medicine, Ann Arbor, MI 48109, USA; Rogel Cancer Center, University of Michigan Medical School, Ann Arbor, MI 48109, USA. Electronic address: csriram@umich.edu.

Funding

A multifactorial pipeline to dissect combinatorial drug efficacy in TuberculosisR01AI150826 · NIAID · UNIVERSITY OF WASHINGTON · PI SHERMAN, DAVID R · 2021 to 2024
$2.9M
Linking metabolic activity with drug sensitivity using metabolic influence networksR35GM137795 · NIGMS · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI Sriram Chandrasekaran · 2020 to 2026
$2.6M
Building Non-Communicable Eye Disease Research Capacity in IndiaD43TW012027 · FIC · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI EHRLICH, JOSHUA ROBERT · 2021 to 2025
$1.2M
A multifactorial pipeline to dissect combinatorial drug efficacy in TuberculosisR56AI150826 · NIAID · UNIVERSITY OF WASHINGTON · PI SHERMAN, DAVID R · 2020 to 2020
$733k
A pipeline for prioritizing and evaluating multidrug regimens for Mycobacterium abscessusR21AI144536 · NIAID · SEATTLE CHILDREN'S HOSPITAL · PI HERNANDEZ, RAFAEL E · 2019 to 2020
$494k
FIC NIH HHS D43 TW012027NIAID NIH HHS R01 AI150826NIAID NIH HHS R21 AI144536NIAID NIH HHS R56 AI150826NIGMS NIH HHS R35 GM137795
6 · The paper itself

Abstract

Machine learning (ML) is necessary to efficiently identify potent drug combinations within a large candidate space to combat drug resistance. However, existing ML approaches cannot be applied to emerging and understudied pathogens with limited training data. Hence, we developed a computational framework (TACTIC [transfer-learning and cross-species training to infer combination therapies]) to train ML models on data from multiple model bacteria. TACTIC was built using 2,965 drug interactions from 12 bacterial strains and outperformed traditional ML models in predicting drug interaction outcomes for species with limited training data. Upon analyzing ∼600,000 predicted drug interactions across 18 pathogenic and commensal strains, we identified interactions that are selectively synergistic against Gram-negative and mycobacterial pathogens. We experimentally validated synergistic drug combinations containing clarithromycin, ampicillin, and mecillinam, against clinical strains of M. abscessus, an emerging drug-resistant pathogen. We also confirmed 11 synergistic combinations against Pseudomonas aeruginosa and Staphylococcus aureus strains that cause endophthalmitis.

Indexed as

Anti-Bacterial AgentsDrug InteractionsDrug SynergismHumansMachine LearningMicrobial Sensitivity TestsMycobacterium abscessusPrediction AlgorithmsPredictive Learning ModelsPseudomonas aeruginosaStaphylococcus aureusAnti-Bacterial Agentsantibiotic resistancebroad-spectrum synergycombination therapiesCP: microbiologyCP: systems biologydrug interactionsendophthalmitismachine learningnarrow-spectrum therapiesnon-tuberculous mycobacteria

Identifiers

PMID42413502
PMCPMC13615487

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.