Exposure prediction and dose optimization of polymyxin B based on bayesian and machine learning.
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| Title: | Exposure prediction and dose optimization of polymyxin B based on bayesian and machine learning. |
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| Authors: | Xu Q; Xiangya School of Pharmaceutical Sciences, Central South University, Changsha, China., Li X; Xiangya School of Pharmaceutical Sciences, Central South University, Changsha, China., Huang S; Department of Pharmacy, The Third Xiangya Hospital, Central South University, Changsha, China., Ding Q; Department of Pharmacy, The Third Xiangya Hospital, Central South University, Changsha, China., Li Y; Department of Pharmacy, The Third Xiangya Hospital, Central South University, Changsha, China., Yang N; Department of Pharmacy, The Third Xiangya Hospital, Central South University, Changsha, China., Deng C; Linking Truth Technology Co., Ltd., Shanghai, China., Tang B; Changsha Kyan Pharmaceutical Technology Co., Ltd., Changsha, China., Yang G; Xiangya School of Pharmaceutical Sciences, Central South University, Changsha, China.; Department of Pharmacy, The Third Xiangya Hospital, Central South University, Changsha, China.; Center of Clinical Pharmacology, The Third Xiangya Hospital, Central South University, Changsha, China.; National-Local Joint Engineering Laboratory of Drug Clinical Evaluation Technology, Changsha, China., Pei Q; Xiangya School of Pharmaceutical Sciences, Central South University, Changsha, China.; Department of Pharmacy, The Third Xiangya Hospital, Central South University, Changsha, China. |
| Source: | Frontiers in pharmacology [Front Pharmacol] 2026 Jun 26; Vol. 17, pp. 1859709. Date of Electronic Publication: 2026 Jun 26 (Print Publication: 2026). |
| Publication Type: | Journal Article |
| Journal Info: | Publisher: Frontiers Media] Country of Publication: Switzerland NLM ID: 101548923 Publication Model: eCollection Cited Medium: Print ISSN: 1663-9812 (Print) Linking ISSN: 16639812 NLM ISO Abbreviation: Front Pharmacol Subsets: PubMed not MEDLINE |
| Database: | MEDLINE Ultimate |
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| ISSN: | 1663-9812 |
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| DOI: | 10.3389/fphar.2026.1859709 |
