{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,20]],"date-time":"2026-10-20T07:23:00Z","timestamp":1792480980798,"version":"4.5.0"},"reference-count":114,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2025,10,17]],"date-time":"2025-10-17T00:00:00Z","timestamp":1760659200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Pharmacol."],"abstract":"<jats:sec><jats:title>Introduction<\/jats:title><jats:p>Tuberculosis (TB) remains the primary cause of death due to infectious disease in the world. TB, while treatable, requires an extended course of multiple antibiotics, taking 6\u20139\u00a0months, and many antibiotic regimens have deleterious side effects. Treatment is complicated by co-infection, emerging drug resistance, and compliance issues; accordingly, the identification of new and optimal regimens has been a recent focus. Rodent models of TB (e.g., mouse, rabbit) do not mimic some severe pathologies well, while nonhuman primate models are costly. Several computational and <jats:italic>in vitro<\/jats:italic> tools have been developed to explore drug regimen design and efficacy for TB, each providing insight into human disease dynamics.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>Here we briefly review existing tools and introduce a novel, integrated approach combining <jats:italic>in vitro<\/jats:italic> predictions of drug pharmacokinetics, pharmacodynamics and drug-drug interactions with a granuloma-scale computational model (<jats:italic>GranSim<\/jats:italic>). Our method captures <jats:italic>in vivo<\/jats:italic> dynamics to test how well systematic <jats:italic>in vitro<\/jats:italic> data predict granuloma-scale outcomes such as CFU burden and sterilization time. To evaluate <jats:italic>in vitro<\/jats:italic> measurements under various growth conditions and to compare to clinical and experimental datasets, we simulated five well-known regimens in our pipeline: HRZM, BPaMZ, RMZE, BPaL and HRZE.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>We find that <jats:italic>in vitro<\/jats:italic> measurements of antibiotic regimen pharmacodynamics under specific growth conditions can be used to simulate virtual granulomas consistent with low-burden human and primate granulomas.<\/jats:p><\/jats:sec><jats:sec><jats:title>Discussion<\/jats:title><jats:p>This work provides a novel tool that can be used to quickly and efficiently evaluate drug regimens for TB.<\/jats:p><\/jats:sec>","DOI":"10.3389\/fphar.2025.1639673","type":"journal-article","created":{"date-parts":[[2025,10,17]],"date-time":"2025-10-17T05:30:36Z","timestamp":1760679036000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":4,"title":["GEODE: an in silico tool that translates in vitro to in vivo predictions of tuberculosis antibiotic combination efficacy"],"prefix":"10.3389","volume":"16","author":[{"given":"Maral","family":"Budak","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mariana Pereira","family":"Moraes","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Talia","family":"Greenstein","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Pauline","family":"Maiello","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"H. 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