Laneshia K Tague,Hephzibah Anthony,Noha N Salama,Ramsey R Hachem,Brian F Gage,Andrew E Gelman
{"title":"肺移植受者治疗性霉酚酸监测的综合采样策略","authors":"Laneshia K Tague,Hephzibah Anthony,Noha N Salama,Ramsey R Hachem,Brian F Gage,Andrew E Gelman","doi":"10.1016/j.healun.2024.09.007","DOIUrl":null,"url":null,"abstract":"BACKGROUND\r\nMycophenolic Acid (MPA) is the most used anti-proliferative in lung transplantation, but its pharmacokinetic (PK) variability has precluded therapeutic drug monitoring. Both genetic and clinical factors have been implicated in MPA variability. This study aimed to integrate genetic and clinical factors with PK measurements to quantify MPA exposure.\r\n\r\nMETHODS\r\nWe performed 12-hour pharmacokinetic analysis on 60 adult lung transplant recipients maintained on MPA for immunosuppression. We genotyped a SLCO1B3 polymorphisms previously associated MPA metabolism and collected relevant clinical data. We calculated area under the curve (AUC0-12) and performed univariate linear regression analysis to evaluate its association with genetic, clinical, and pharmacokinetic variables. We performed lasso regression analysis to create final AUC estimation tools.\r\n\r\nRESULTS\r\nPK-only measurements obtained 2, 3, and 8 hours after MPA administration (C2, C3, and C8) were strongly associated with MPA AUC0-12 (R267%, 67% and 68% respectively). Clinical and genetic factors associated with MPA AUC0-12 included the MPA dose (p = 0.001), transplant diagnosis (p =0.015), SLCO1B3 genotype (p = 0.049), and body surface area (p = 0.050) The best integrated single-sampling strategy included C2 and achieved an R2 value of 80%. The best integrated limited-sampling strategy included C0, C0.25, and C2 and achieved an R2 value of 90%.\r\n\r\nCONCLUSIONS\r\nAn integrated LSS for MPA allows increased accuracy in prediction of MPA AUC0-12 compared to PK-only modelling. Validation of this model will allow for clinically feasible MPA therapeutic drug monitoring and help advance precision management of MPA.","PeriodicalId":22654,"journal":{"name":"The Journal of Heart and Lung Transplantation","volume":"97 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2024-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"An Integrated Sampling Strategy for Therapeutic Mycophenolic Acid Monitoring in Lung Transplant Recipients.\",\"authors\":\"Laneshia K Tague,Hephzibah Anthony,Noha N Salama,Ramsey R Hachem,Brian F Gage,Andrew E Gelman\",\"doi\":\"10.1016/j.healun.2024.09.007\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"BACKGROUND\\r\\nMycophenolic Acid (MPA) is the most used anti-proliferative in lung transplantation, but its pharmacokinetic (PK) variability has precluded therapeutic drug monitoring. Both genetic and clinical factors have been implicated in MPA variability. This study aimed to integrate genetic and clinical factors with PK measurements to quantify MPA exposure.\\r\\n\\r\\nMETHODS\\r\\nWe performed 12-hour pharmacokinetic analysis on 60 adult lung transplant recipients maintained on MPA for immunosuppression. We genotyped a SLCO1B3 polymorphisms previously associated MPA metabolism and collected relevant clinical data. We calculated area under the curve (AUC0-12) and performed univariate linear regression analysis to evaluate its association with genetic, clinical, and pharmacokinetic variables. We performed lasso regression analysis to create final AUC estimation tools.\\r\\n\\r\\nRESULTS\\r\\nPK-only measurements obtained 2, 3, and 8 hours after MPA administration (C2, C3, and C8) were strongly associated with MPA AUC0-12 (R267%, 67% and 68% respectively). Clinical and genetic factors associated with MPA AUC0-12 included the MPA dose (p = 0.001), transplant diagnosis (p =0.015), SLCO1B3 genotype (p = 0.049), and body surface area (p = 0.050) The best integrated single-sampling strategy included C2 and achieved an R2 value of 80%. The best integrated limited-sampling strategy included C0, C0.25, and C2 and achieved an R2 value of 90%.\\r\\n\\r\\nCONCLUSIONS\\r\\nAn integrated LSS for MPA allows increased accuracy in prediction of MPA AUC0-12 compared to PK-only modelling. Validation of this model will allow for clinically feasible MPA therapeutic drug monitoring and help advance precision management of MPA.\",\"PeriodicalId\":22654,\"journal\":{\"name\":\"The Journal of Heart and Lung Transplantation\",\"volume\":\"97 1\",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2024-09-16\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"The Journal of Heart and Lung Transplantation\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1016/j.healun.2024.09.007\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"The Journal of Heart and Lung Transplantation","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1016/j.healun.2024.09.007","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
An Integrated Sampling Strategy for Therapeutic Mycophenolic Acid Monitoring in Lung Transplant Recipients.
BACKGROUND
Mycophenolic Acid (MPA) is the most used anti-proliferative in lung transplantation, but its pharmacokinetic (PK) variability has precluded therapeutic drug monitoring. Both genetic and clinical factors have been implicated in MPA variability. This study aimed to integrate genetic and clinical factors with PK measurements to quantify MPA exposure.
METHODS
We performed 12-hour pharmacokinetic analysis on 60 adult lung transplant recipients maintained on MPA for immunosuppression. We genotyped a SLCO1B3 polymorphisms previously associated MPA metabolism and collected relevant clinical data. We calculated area under the curve (AUC0-12) and performed univariate linear regression analysis to evaluate its association with genetic, clinical, and pharmacokinetic variables. We performed lasso regression analysis to create final AUC estimation tools.
RESULTS
PK-only measurements obtained 2, 3, and 8 hours after MPA administration (C2, C3, and C8) were strongly associated with MPA AUC0-12 (R267%, 67% and 68% respectively). Clinical and genetic factors associated with MPA AUC0-12 included the MPA dose (p = 0.001), transplant diagnosis (p =0.015), SLCO1B3 genotype (p = 0.049), and body surface area (p = 0.050) The best integrated single-sampling strategy included C2 and achieved an R2 value of 80%. The best integrated limited-sampling strategy included C0, C0.25, and C2 and achieved an R2 value of 90%.
CONCLUSIONS
An integrated LSS for MPA allows increased accuracy in prediction of MPA AUC0-12 compared to PK-only modelling. Validation of this model will allow for clinically feasible MPA therapeutic drug monitoring and help advance precision management of MPA.