---
title: "Predicting TB relapse from 28-day CFU and RS ratio in the relapsing mouse model"
id: "biorxiv-4-predicting-tuberculosis-relapse-based-on-28-day-cfu-rs-ratio-and-or-drug"
canonical_url: "https://medichelpline.com/clinical-feed/biorxiv-4-predicting-tuberculosis-relapse-based-on-28-day-cfu-rs-ratio-and-or-drug"
content_type: "clinical_feed_article"
specialty: "Infectious Disease"
source_name: "bioRxiv (Biomedical Preprints)"
source_url: "https://www.biorxiv.org/content/10.64898/2026.07.27.740024v1?rss=1"
published_at: "2026-07-30T12:00:00.000Z"
evidence_level: "Verified Feed"
license: "CC-BY-NC-4.0 / Informational Use"
---
# Predicting TB relapse from 28-day CFU and RS ratio in the relapsing mouse model
## Provenance & Clinical Metadata
- **Canonical URL:** https://medichelpline.com/clinical-feed/biorxiv-4-predicting-tuberculosis-relapse-based-on-28-day-cfu-rs-ratio-and-or-drug
- **Specialty:** [Infectious Disease](https://medichelpline.com/clinical-feed/infectious-disease.md)
- **Primary Source:** bioRxiv (Biomedical Preprints)
- **Source URL:** [Original Journal Publication](https://www.biorxiv.org/content/10.64898/2026.07.27.740024v1?rss=1)
- **Published At:** 2026-07-30T12:00:00.000Z
- **Evidence Rating:** Verified Feed
## Executive GIST (TL;DR)
- The study developed a computational model to predict long-term **tuberculosis relapse** in the **relapsing mouse model** using short-term biomarkers measured after 28 days (4 weeks) of treatment. - Investigators combined the conventional lung **colony forming units (CFU)** with the innovative **ribosomal RNA synthesis (RS ratio)** as measures of Mycobacterium tuberculosis activity and drug effect. - Model development used nine datasets covering 58 unique regimens, with 843 short-term biomarker observations and 2,239 long-term relapse outcomes. - Development proceeded in three iterative phases with external validations; the final model corrected for experimental conditions to enable unbiased regimen ranking across experiments. - The final external validation produced an area under the receiver operator curve of **0.90**, indicating strong discriminatory performance. - Removing either biomarker was tested: **CFU-only** performance approximated combined CFU and RS ratio performance when the model incorporated the sterilizing contribution of individual drugs to regimens. - For novel drugs whose individual sterilizing contributions are unknown, measuring the **RS ratio** may provide additional predictive value for relapse risk. - The model differentiated regimens with 2-, 3-, and 4-month sterilizing efficacy using only 4-week data, supporting prioritization and acceleration of treatment-shortening regimen development. - The work emphasizes leveraging short-term biomarkers to expand the number of regimens screened and to prioritize candidates for resource-intensive long-term relapsing mouse studies. - Competing interests: two authors are co-inventors on a US patent related to the **RS ratio**; other authors declared no competing interests. Funding sources were reported (NIH, Gates Foundation, Gates Medical Research Institute, CDC subcontract, Veterans Affairs).
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Rob C van Wijk 1 University of California, San Francisco; * [Find this author on Google Scholar](https://www.biorxiv.org/lookup/google-scholar?link_type=googlescholar&gs_type=author&author%5B0%5D=Rob%2BC%2Bvan%2BWijk%2B "Open in new tab") * [Find this author on PubMed](https://www.biorxiv.org/lookup/external-ref?access_num=van%20Wijk%20RC&link_type=AUTHORSEARCH "Open in new tab") * [Search for this author on this site](https://www.biorxiv.org/search/author1%3ARob%2BC%2Bvan%2BWijk%2B) * [ORCID record for Rob C van Wijk](http://orcid.org/0000-0001-7247-1360 "Open in new tab") Belen P Solans 1 University of California, San Francisco; * [Find this author on Google Scholar](https://www.biorxiv.org/lookup/google-scholar?link_type=googlescholar&gs_type=author&author%5B0%5D=Belen%2BP%2BSolans%2B "Open in new tab") * [Find this author on PubMed](https://www.biorxiv.org/lookup/external-ref?access_num=Solans%20BP&link_type=AUTHORSEARCH "Open in new tab") * [Search for this author on this site](https://www.biorxiv.org/search/author1%3ABelen%2BP%2BSolans%2B) * [ORCID record for Belen P Solans](http://orcid.org/0000-0003-4621-5480 "Open in new tab") Linda Chaba 1 University of California, San Francisco; 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* [Find this author on Google Scholar](https://www.biorxiv.org/lookup/google-scholar?link_type=googlescholar&gs_type=author&author%5B0%5D=Radojka%2BM%2BSavic%2B "Open in new tab") * [Find this author on PubMed](https://www.biorxiv.org/lookup/external-ref?access_num=Savic%20RM&link_type=AUTHORSEARCH "Open in new tab") * [Search for this author on this site](https://www.biorxiv.org/search/author1%3ARadojka%2BM%2BSavic%2B) * [ORCID record for Radojka M Savic](http://orcid.org/0000-0003-3143-5579 "Open in new tab") * For correspondence: rada.savic@ucsf.edu * [Abstract](https://www.biorxiv.org/content/10.64898/2026.07.27.740024v1)[](https://www.biorxiv.org/panels_ajax_tab/biorxiv_tab_art/node:5666934/1) * [Info/History](https://www.biorxiv.org/content/10.64898/2026.07.27.740024v1.article-info)[](https://www.biorxiv.org/panels_ajax_tab/biorxiv_tab_info/node:5666934/1) * [Metrics](https://www.biorxiv.org/content/10.64898/2026.07.27.740024v1.article-metrics)[](https://www.biorxiv.org/panels_ajax_tab/article_tab_metrics/node:5666934/1) * [Supplementary material](https://www.biorxiv.org/content/10.64898/2026.07.27.740024v1.supplementary-material)[](https://www.biorxiv.org/panels_ajax_tab/biorxiv_tab_data/node:5666934/1) * [ Preview PDF](https://www.biorxiv.org/content/10.64898/2026.07.27.740024v1.full.pdf+html)[](https://www.biorxiv.org/panels_ajax_tab/biorxiv_tab_pdf/node:5666934/1) ![Loading](https://www.biorxiv.org/sites/all/modules/contrib/panels_ajax_tab/images/loading.gif) ## Abstract Treatment shortening in tuberculosis therapy is needed, but testing all novel antibiotic combinations is unfeasible. Especially the tuberculosis relapsing mouse model is time- and resource demanding. Therefore, our objective is to develop a computational model predictive of long-term relapse prevention in mice based on short-term biomarkers, increasing the number of regimens that can be tested and prioritize regimens for further development. The innovative ribosomal RNA synthesis (RS) ratio is utilized to characterize drug effect on Mycobacterium tuberculosis health and activity, together with colony forming units (CFU) in murine lungs. Nine datasets of 58 unique regimens with 843 short-term biomarker and 2,239 long-term relapse observations were leveraged for model development in 3 iterations with external validations. The final model included therapeutic predictors, such as CFU and RS ratio change from baseline, and corrected for experimental conditions, to enable unbiased ranking of regimens between experiments. Model performance was optimal without model structure change despite fully separate model development at each iteration. Final external validation had an area under the receiver operator curve of 0.90. Challenging the model by assessing removal of either biomarker showed that performance of CFU only was similar to CFU and RS ratio once the sterilizing contribution of individual drugs to the regimens was accounted for. New drugs without this contribution quantified could benefit from RS ratio determination to predict relapse. Our predictive model can successfully differentiate between 2-, 3-, and 4-month regimens in the relapsing mouse model based on 4-week data only, supporting acceleration of treatment-shortening regimen development. ### Competing Interest Statement N.W.D. and G.T.R. are listed as co-inventors on US patent No. 16/632,310 that pertains to the RS ratio. All other authors declare that they have no competing interests. ## Funder Information Declared National Institutes of Health, UM1AI179699 Gates Foundation, INV-009105, INV-009549, INV-009105, INV-008993 Gates Medical Research Institute, 56315, 57159 Centers for Disease Control and Prevention, Westat subcontract 8758-S01 Veterans Affairs, 1I01BX004527-01A1 Copyright The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a [CC-BY 4.0 International license](http://creativecommons.org/licenses/by/4.0/). bioRxiv and medRxiv thank the following for their generous financial support: > The Chan Zuckerberg Initiative, Cold Spring Harbor Laboratory, the Sergey Brin Family Foundation, California Institute of Technology, Centre National de la Recherche Scientifique, Fred Hutchinson Cancer Center, Imperial College London, Massachusetts Institute of Technology, Stanford University, The University of Edinburgh, University of Washington, and Vrije Universiteit Amsterdam. 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