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Prediction of clinical irrelevance of PK differences in atorvastatin using PK/PD models derived from literature-based meta-analyses.

ABSTRACT: To support the development of a fixed-dose combination (FDC) of ezetimibe and atorvastatin for the treatment of dyslipidemia, bioequivalence (BE) studies were conducted across a combined dose range (10/10, 10/20, 10/40, and 10/80?mg of ezetimibe/atorvastatin). In the BE trials, all parameters met traditional BE bounds except for atorvastatin peak plasma concentration (Cmax) at two intermediate doses. Literature-based metadata analysis predicted that the observed difference in Cmax between an ezetimibe+atorvastatin FDC and coadministration of these agents translates directly into a non-clinically significant change of <1.2% absolute difference in the percentage lowering of low-density-lipoprotein cholesterol . Both FDC doses were confirmed to be clinically equivalent to coadministration in the subsequent clinical equivalence trials. These data suggest that modeling of dose-response relationships may be useful in predicting clinical equivalence, lowering cost/timelines through effective powering of studies, and predicting the effectiveness of new dosage formulations without the need for additional clinical efficacy trials in regulatory settings.

SUBMITTER: Vargo R

PROVIDER: S-EPMC4062834 | biostudies-other | 2014 Jul

REPOSITORIES: biostudies-other

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Prediction of clinical irrelevance of PK differences in atorvastatin using PK/PD models derived from literature-based meta-analyses.

Vargo R R Adewale A A Behm M O MO Mandema J J Kerbusch T T

Clinical pharmacology and therapeutics 20140328 1

To support the development of a fixed-dose combination (FDC) of ezetimibe and atorvastatin for the treatment of dyslipidemia, bioequivalence (BE) studies were conducted across a combined dose range (10/10, 10/20, 10/40, and 10/80 mg of ezetimibe/atorvastatin). In the BE trials, all parameters met traditional BE bounds except for atorvastatin peak plasma concentration (Cmax) at two intermediate doses. Literature-based metadata analysis predicted that the observed difference in Cmax between an eze ...[more]

PMID: 24682029

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Project description:Statins can cause muscle symptoms resulting in poor adherence to therapy and increased cardiovascular risk. We hypothesize that combinations of potentially functional SNPs (pfSNPs), rather than individual SNPs, better predict myalgia in patients on atorvastatin. This study assesses the value of potentially functional single nucleotide polymorphisms (pfSNPs) and employs six machine learning algorithms to identify the combination of SNPs that best predict myalgia. Methods: Whole genome sequencing of 183 Chinese, Malay and Indian patients from Singapore was conducted to identify genetic variants associated with atorvastatin induced myalgia. To adjust for confounding factors, demographic and clinical characteristics were also examined for their association with myalgia. The top factor, sex, was then used as a covariate in the whole genome association analyses. Variants that were highly associated with myalgia from this and previous studies were extracted, assessed for potential functionality (pfSNPs) and incorporated into six machine learning models. Predictive performance of a combination of different models and inputs were compared using the average cross validation area under ROC curve (AUC). The minimum combination of SNPs to achieve maximum sensitivity and specificity as determined by AUC, that predict atorvastatin-induced myalgia in most, if not all the six machine learning models was determined. Results: Through whole genome association analyses using sex as a covariate, a larger proportion of pfSNPs compared to non-pf SNPs were found to be highly associated with myalgia. Although none of the individual SNPs achieved genome wide significance in univariate analyses, machine learning models identified a combination of 15 SNPs that predict myalgia with good predictive performance (AUC >0.9). SNPs within genes identified in this study significantly outperformed SNPs within genes previously reported to be associated with myalgia. pfSNPs were found to be more robust in predicting myalgia, outperforming non-pf SNPs in the majority of machine learning models tested. Conclusion: Combinations of pfSNPs that were consistently identified by different machine learning models to have high predictive performance have good potential to be clinically useful for predicting atorvastatin-induced myalgia once validated against an independent cohort of patients.

Dataset Information

Prediction of clinical irrelevance of PK differences in atorvastatin using PK/PD models derived from literature-based meta-analyses.

Publications

Prediction of clinical irrelevance of PK differences in atorvastatin using PK/PD models derived from literature-based meta-analyses.

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