A pharmacokinetic-pharmacodynamic model for the quantitative prediction of dofetilide clinical QT prolongation from human ether-a-go-go-related gene current inhibition data.

BACKGROUND: QT prolongation is an important biomarker of the arrhythmia torsades de pointes and appears to be related mainly to blockade of delayed inward cardiac rectifier potassium currents. The aim of this study was to quantify the relationship between in vitro human ether-a-go-go-related gene (hERG) potassium channel blockade and the magnitude of QT prolongation in humans for the class III antiarrhythmic dofetilide. METHODS: The in vitro affinity and activity of dofetilide were determined in recombinant cell cultures expressing the hERG channel, and the QT-prolonging effect of dofetilide was assessed in 5 clinical studies (80 healthy volunteers and 17 patients with ischemic heart disease). A population pharmacokinetic-pharmacodynamic analysis of the in vitro and in vivo data was performed in NONMEM by use of the operational model of pharmacologic agonism to estimate the efficiency of transduction from ion channel binding to Fridericia-corrected QT response. RESULTS: A 3-compartment pharmacokinetic model with first-order absorption characterized the time course of dofetilide concentrations. On the basis of an in vitro potency of 5.13 ng/mL for potassium current inhibition and predicted unbound dofetilide concentrations, the estimated transducer ratio (tau) of 6.2 suggests that the QT response plateaus before currents are fully blocked. In our study population, 10% hERG blockade corresponds to a QT prolongation of 20 ms (95% confidence interval, 12-32 ms). With long-term dofetilide administration, tolerance develops with a half-life of 4.7 days. CONCLUSIONS: The current mechanism-based pharmacokinetic-pharmacodynamic model quantified the relationship between in vitro hERG channel blockade and clinical QT prolongation for dofetilide. This model may prove valuable for assessing the risk of QT prolongation in humans for other drugs that selectively block the hERG channel on the basis of in vitro assays and pharmacokinetic properties.

CYP-mediated therapeutic protein-drug interactions: clinical findings, proposed mechanisms and regulatory implications.

Therapeutic proteins (TPs) may affect the disposition of drugs that are metabolized by cytochrome P450 (CYP) enzymes, as is evident from a review of data in recently published literature and approved Biologic License Applications. Many TPs belonging to the cytokine class appear to differentially affect CYP activities. Cytokine modulators may affect CYP enzyme activities by altering cytokine effects on CYP enzymes. The alteration in CYP enzyme activities seems to result from changes in transcription factor activity for CYP enzyme expression or changes in CYP enzyme stability, which have been observed during altered immunological states such as infection and inflammation. Human growth hormone also appears to differentially affect CYP activities through unknown mechanisms. Because TP-drug interaction research is an evolving area, limited information is available during drug development on TP-drug interactions mediated by CYP inhibition or induction. The authors of this review suggest that effort be made to understand TP-drug interactions for the safe and effective use of TPs in combination with small-molecule drugs.

Modeling and simulation of adherence: approaches and applications in therapeutics.

Partial adherence with a prescribed or randomly assigned dose gives rise to unintended variability in actual drug exposure in clinical practice and during clinical trials. There are tremendous costs associated with incomplete and/or improper drug intake-to both individual patients and society as a whole. Methodology for quantifying the relation between adherence, exposure and drug response is an area of active research. Modeling and statistical approaches have been useful in evaluating the impact of adherence on therapeutics and in addressing the challenges of confounding and measurement error which arise in this context. This paper reviews quantitative approaches to using adherence information in improving therapeutics. It draws heavily on applications in the area of HIV pharmacology.

Estimating treatment effect in the presence of non-compliance measured with error: precision and robustness of data analysis methods.

Non-compliance with the nominal prescribed dosage causes unintended variability in actual drug exposure during clinical trials. In the ideal case that compliance is not a confounder, and it is known--hence actual dosage is known--true dose-response can be validly estimated. Measuring compliance presents a challenge, however. A simulation study of the case that dosage history questionnaires (C(Q)--usually over-optimistic estimates of actual compliance) are available in all subjects enrolled in a clinical trial, but accurate compliance measurements (C--e.g. from electronic medication event monitors), are only available in a (random) fraction of subjects is reported. It reveals that a 'Maximum Penalized Marginal Likelihood' (MPML) method which uses all compliance data, effectively calibrating C(Q) to C, is superior to other methods which use only one compliance measure, or both, or neither (neither = ITT, intention to treat, which assumes actual dosage equals nominal dosage), but do not calibrate. MPML yields the most precise estimates of dose-response over widely varying clinical trial designs, extremes in quality and quantity of compliance information, and a range of drug effect sizes. It is most beneficial when compliance data are sparse and maintains good performance even when its key assumptions are somewhat violated.