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This study investigated different machine learning models to predict the time\u2010to\u2010event of 2\u2010month culture conversion in the REMoxTB trial with phase 2a time\u2010to\u2010positivity biomarker data and the impact of different phase 2a study lengths. Time\u2010to\u2010positivity at baseline and up to 14\u2009days or 28\u2009days after two moxifloxacin\u2010containing regimens and one control regimen were analyzed using nonlinear mixed\u2010effects modeling. The final models were used to predict individual baseline time\u2010to\u2010positivity and time\u2010to\u2010positivity differences between 0 and 14\u2009days or 0 and 28\u2009days. The individual predictions served as features in the following machine learning analysis. Statistical metrics and Kaplan\u2013Meier plots informed model selection. Bi\u2010exponential and exponential decay models described the 14\u2010day and 28\u2010day time\u2010to\u2010positivity data, respectively. In the machine learning analysis, baseline time\u2010to\u2010positivity and\/or time\u2010to\u2010positivity differences were ranked as the most important features in all models. Statistical metrics and Kaplan\u2013Meier plots indicated a good fit to culture conversion at 8\u2009weeks using 4\u2010week phase 2a information with a C\u2010support vector classification model. Four\u2010week time\u2010to\u2010positivity phase 2a data provided more information compared to the 2\u2010week time\u2010to\u2010positivity data for the prediction of culture conversion. The workflow demonstrated the potential of machine learning to predict the time\u2010to\u2010event of phase 2b culture conversion up to 8\u2009weeks using time\u2010to\u2010positivity phase 2a biomarker information in a clinical trial assessed with pharmacometric analysis.<\/jats:p>","DOI":"10.1111\/cts.70694","type":"journal-article","created":{"date-parts":[[2026,8,11]],"date-time":"2026-08-11T03:33:26Z","timestamp":1786419206000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Applying a Pharmacometrics\u2010Enabled Machine Learning Analysis to Predict 2\u2010Month Culture Conversion Using Phase 2a Data in a Tuberculosis Clinical Trial"],"prefix":"10.1111","volume":"19","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1497-9136","authenticated-orcid":false,"given":"Huifang","family":"You","sequence":"first","affiliation":[{"name":"Department of Pharmaceutical Biosciences Uppsala University  Uppsala Sweden"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3424-9686","authenticated-orcid":false,"given":"Ulrika S. 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