Levofloxacin pharmacokinetics/pharmacodynamics, dosing, susceptibility breakpoints, and artificial intelligence in the treatment of multidrug-resistant tuberculosis
<p><strong>Background</strong> Levofloxacin is used for the treatment of multidrug-resistant tuberculosis; however the optimal dose is unknown.</p> <p><strong>Methods</strong> We used the hollow fiber system model of tuberculosis (HFS-TB) to identify 0–24 h...
| Príomhchruthaitheoirí: | , , , , , , , , , , , |
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| Formáid: | Journal article |
| Teanga: | English |
| Foilsithe / Cruthaithe: |
Oxford University Press
2018
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| Ábhair: |
| _version_ | 1826299102238867456 |
|---|---|
| author | Deshpande, D Pasipanodya, JG Mpagama, SG Bendet, P Srivastava, S Koeuth, T Lee, PS Bhavnani, SM Ambrose, PG Thwaites, G Heysell, SK Gumbo, T |
| author_facet | Deshpande, D Pasipanodya, JG Mpagama, SG Bendet, P Srivastava, S Koeuth, T Lee, PS Bhavnani, SM Ambrose, PG Thwaites, G Heysell, SK Gumbo, T |
| author_sort | Deshpande, D |
| collection | OXFORD |
| description | <p><strong>Background</strong>
Levofloxacin is used for the treatment of multidrug-resistant tuberculosis; however the optimal dose is unknown.</p>
<p><strong>Methods</strong>
We used the hollow fiber system model of tuberculosis (HFS-TB) to identify 0–24 hour area under the concentration-time curve (AUC0-24) to minimum inhibitory concentration (MIC) ratios associated with maximal microbial kill and suppression of acquired drug resistance (ADR) of Mycobacterium tuberculosis (Mtb). Levofloxacin-resistant isolates underwent whole-genome sequencing. Ten thousands patient Monte Carlo experiments (MCEs) were used to identify doses best able to achieve the HFS-TB–derived target exposures in cavitary tuberculosis and tuberculous meningitis. Next, we used an ensemble of artificial intelligence (AI) algorithms to identify the most important predictors of sputum conversion, ADR, and death in Tanzanian patients with pulmonary multidrug-resistant tuberculosis treated with a levofloxacin-containing regimen. We also performed probit regression to identify optimal levofloxacin doses in Vietnamese tuberculous meningitis patients.</p>
<p><strong>Results</strong>
In the HFS-TB, the AUC0-24/MIC associated with maximal Mtb kill was 146, while that associated with suppression of resistance was 360. The most common gyrA mutations in resistant Mtb were Asp94Gly, Asp94Asn, and Asp94Tyr. The minimum dose to achieve target exposures in MCEs was 1500 mg/day. AI algorithms identified an AUC0-24/MIC of 160 as predictive of microbiologic cure, followed by levofloxacin 2-hour peak concentration and body weight. Probit regression identified an optimal dose of 25 mg/kg as associated with >90% favorable response in adults with pulmonary tuberculosis.</p>
<p><strong>Conclusions</strong>
The levofloxacin dose of 25 mg/kg or 1500 mg/day was adequate for replacement of high-dose moxifloxacin in treatment of multidrug-resistant tuberculosis.</p> |
| first_indexed | 2024-03-07T04:56:48Z |
| format | Journal article |
| id | oxford-uuid:d6dfdd7a-f1e4-48b5-81fe-b49fd3b38101 |
| institution | University of Oxford |
| language | English |
| last_indexed | 2024-03-07T04:56:48Z |
| publishDate | 2018 |
| publisher | Oxford University Press |
| record_format | dspace |
| spelling | oxford-uuid:d6dfdd7a-f1e4-48b5-81fe-b49fd3b381012022-03-27T08:36:57ZLevofloxacin pharmacokinetics/pharmacodynamics, dosing, susceptibility breakpoints, and artificial intelligence in the treatment of multidrug-resistant tuberculosisJournal articlehttp://purl.org/coar/resource_type/c_dcae04bcuuid:d6dfdd7a-f1e4-48b5-81fe-b49fd3b38101lungartificial intelligencemutationminimum inhibitory concentration resultminimum inhibitory concentration measurementpharmacokineticspharmacodynamicsexposurepulmonary tuberculosistuberculosislevofloxacinmoxifloxacintuberculous meningitismultidrug-resistant tuberculosisprobit trialEnglishSymplectic ElementsOxford University Press 2018Deshpande, DPasipanodya, JGMpagama, SGBendet, PSrivastava, SKoeuth, TLee, PSBhavnani, SMAmbrose, PGThwaites, GHeysell, SKGumbo, T<p><strong>Background</strong> Levofloxacin is used for the treatment of multidrug-resistant tuberculosis; however the optimal dose is unknown.</p> <p><strong>Methods</strong> We used the hollow fiber system model of tuberculosis (HFS-TB) to identify 0–24 hour area under the concentration-time curve (AUC0-24) to minimum inhibitory concentration (MIC) ratios associated with maximal microbial kill and suppression of acquired drug resistance (ADR) of Mycobacterium tuberculosis (Mtb). Levofloxacin-resistant isolates underwent whole-genome sequencing. Ten thousands patient Monte Carlo experiments (MCEs) were used to identify doses best able to achieve the HFS-TB–derived target exposures in cavitary tuberculosis and tuberculous meningitis. Next, we used an ensemble of artificial intelligence (AI) algorithms to identify the most important predictors of sputum conversion, ADR, and death in Tanzanian patients with pulmonary multidrug-resistant tuberculosis treated with a levofloxacin-containing regimen. We also performed probit regression to identify optimal levofloxacin doses in Vietnamese tuberculous meningitis patients.</p> <p><strong>Results</strong> In the HFS-TB, the AUC0-24/MIC associated with maximal Mtb kill was 146, while that associated with suppression of resistance was 360. The most common gyrA mutations in resistant Mtb were Asp94Gly, Asp94Asn, and Asp94Tyr. The minimum dose to achieve target exposures in MCEs was 1500 mg/day. AI algorithms identified an AUC0-24/MIC of 160 as predictive of microbiologic cure, followed by levofloxacin 2-hour peak concentration and body weight. Probit regression identified an optimal dose of 25 mg/kg as associated with >90% favorable response in adults with pulmonary tuberculosis.</p> <p><strong>Conclusions</strong> The levofloxacin dose of 25 mg/kg or 1500 mg/day was adequate for replacement of high-dose moxifloxacin in treatment of multidrug-resistant tuberculosis.</p> |
| spellingShingle | lung artificial intelligence mutation minimum inhibitory concentration result minimum inhibitory concentration measurement pharmacokinetics pharmacodynamics exposure pulmonary tuberculosis tuberculosis levofloxacin moxifloxacin tuberculous meningitis multidrug-resistant tuberculosis probit trial Deshpande, D Pasipanodya, JG Mpagama, SG Bendet, P Srivastava, S Koeuth, T Lee, PS Bhavnani, SM Ambrose, PG Thwaites, G Heysell, SK Gumbo, T Levofloxacin pharmacokinetics/pharmacodynamics, dosing, susceptibility breakpoints, and artificial intelligence in the treatment of multidrug-resistant tuberculosis |
| title | Levofloxacin pharmacokinetics/pharmacodynamics, dosing, susceptibility breakpoints, and artificial intelligence in the treatment of multidrug-resistant tuberculosis |
| title_full | Levofloxacin pharmacokinetics/pharmacodynamics, dosing, susceptibility breakpoints, and artificial intelligence in the treatment of multidrug-resistant tuberculosis |
| title_fullStr | Levofloxacin pharmacokinetics/pharmacodynamics, dosing, susceptibility breakpoints, and artificial intelligence in the treatment of multidrug-resistant tuberculosis |
| title_full_unstemmed | Levofloxacin pharmacokinetics/pharmacodynamics, dosing, susceptibility breakpoints, and artificial intelligence in the treatment of multidrug-resistant tuberculosis |
| title_short | Levofloxacin pharmacokinetics/pharmacodynamics, dosing, susceptibility breakpoints, and artificial intelligence in the treatment of multidrug-resistant tuberculosis |
| title_sort | levofloxacin pharmacokinetics pharmacodynamics dosing susceptibility breakpoints and artificial intelligence in the treatment of multidrug resistant tuberculosis |
| topic | lung artificial intelligence mutation minimum inhibitory concentration result minimum inhibitory concentration measurement pharmacokinetics pharmacodynamics exposure pulmonary tuberculosis tuberculosis levofloxacin moxifloxacin tuberculous meningitis multidrug-resistant tuberculosis probit trial |
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