首页|University Hospital Dupuytren Reports Findings in Antiinfectives (A machine lear ning approach to predict daptomycin exposure from two concentrations based on Mo nte Carlo simulations)
University Hospital Dupuytren Reports Findings in Antiinfectives (A machine lear ning approach to predict daptomycin exposure from two concentrations based on Mo nte Carlo simulations)
扫码查看
点击上方二维码区域，可以放大扫码查看
原文链接
NETL
NSTL
By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-New research on Drugs and Therapies - Antiinfectives is the subject of a report. According to news reporting from Limo ges, France, by NewsRx journalists, research stated, "Daptomycin is a concentrat ion-dependent lipopeptide antibiotic for which exposure/effect relationships hav e been shown. Machine learning (ML) algorithms, developed to predict the individ ual exposure to drugs, have shown very good performances in comparison to maximu m a posteriori Bayesian estimation (MAP-BE)." The news correspondents obtained a quote from the research from University Hospi tal Dupuytren, "The aim of this work was to predict the area under the blood con centration curve (AUC) of daptomycin from two samples and a few covariates using XGBoost ML algorithm trained on Monte Carlo simulations. Five thousand one hund red fifty patients were simulated from two literature population pharmacokinetic s models. Data from the first model were split into a training set (75% ) and a testing set (25%). Four ML algorithms were built to learn A UC based on daptomycin blood concentration samples at pre-dose and 1 h post-dose . The XGBoost model (best ML algorithm) with the lowest root mean square error ( RMSE) in a 10-fold cross-validation experiment was evaluated in both the test se t and the simulations from the second population pharmacokinetic model (validati on). The ML model based on the two concentrations, the differences between these concentrations, and five other covariates (sex, weight, daptomycin dose, creati nine clearance, and body temperature) yielded very good AUC estimation in the te st (relative bias/RMSE = 0.43/7.69%) and validation sets (relative bias/RMSE = 4.61/6.63%). The XGBoost ML model developed allowed acc urate estimation of daptomycin AUC using C0, C1h, and a few covariates and could be used for exposure estimation and dose adjustment."
LimogesFranceEuropeAntibioticsAn tiinfectivesCyborgsCyclic PeptidesDaptomycin TherapyDrugs and TherapiesEmerging TechnologiesHealth and MedicineLipopeptidesMachine LearningPep tidesPharmaceuticals
NETL
NSTL
