Evaluation of the drug-drug interaction potential of treosulfan using a physiologically-based pharmacokinetic modelling approach.

AIMS: The aim of this work is the development of a mechanistic physiologically-based pharmacokinetic (PBPK) model using in vitro to in vivo extrapolation to conduct a drug-drug interaction (DDI) assessment of treosulfan against two cytochrome p450 (CYP) isoenzymes and P-glycoprotein (P-gp) substrates. METHODS: A PBPK model for treosulfan was developed de novo based on literature and unpublished clinical data. The PBPK DDI analysis was conducted using the U.S. Food and Drug Administration (FDA) DDI index drugs (probe substrates) midazolam, omeprazole and digoxin for CYP3A4, CYP2C19 and P-gp, respectively. Qualified and documented PBPK models of the probe substrates have been adopted from an open-source online model database. RESULTS: The PBPK model for treosulfan, based on both in vitro and in vivo data, was able to predict the plasma concentration-time profiles and exposure levels of treosulfan applied for a standard conditioning treatment. Medium and low potentials for DDI on CYP3A4 (maximum area under the concentration-time curve ratio (AUCRmax = 2.23) and CYP2C19 (AUCRmax = 1.6) were predicted, respectively, using probe substrates midazolam and omeprazole. Treosulfan was not predicted to cause a DDI on P-gp. CONCLUSION: Medicinal products with a narrow therapeutic index (eg, digoxin) that are substrates for CYP3A4, CYP2C19 or P-gp should not be given during treatment with treosulfan. However, considering the comprehensive treosulfan-based conditioning treatment schedule and the respective pharmacokinetic properties of the concomitantly used drugs (eg, half-life), the potential for interaction on all evaluated mechanisms would be low (AUCR < 1.25), if concomitantly administered drugs are dosed either 2 hours before or 8 hours after the 2-hour intravenous infusion of treosulfan.

Assessing consistency in clinical trials with two subgroups and binary endpoints: A new test within the logistic regression model.

In late stage drug development, the experimental drug is tested in a diverse study population within the relevant indication. In order to receive marketing authorization, robust evidence for the therapeutic efficacy is crucial requiring investigation of treatment effects in well-defined subgroups. Conventionally, consistency analyses in subgroups have been performed by means of interaction tests. However, the interaction test can only reject the null hypothesis of equivalence and not confirm consistency. Simulation studies suggest that the interaction test has low power but can also be oversensitive depending on sample size-leading in combination with the actually ill-posed null hypothesis to findings regardless of clinical relevance. In order to overcome these disadvantages in the setup of binary endpoints, we propose to use a consistency test based on the interval inclusion principle, which is able to reject heterogeneity and confirm consistency of subgroup-specific treatment effects while controlling the type I error. This homogeneity test is based upon the deviation between overall treatment effect and subgroup-specific effects on the odds ratio scale and is compared with an equivalence test based on the ratio of both subgroup-specific effects. Performance of these consistency tests is assessed in a simulation study. In addition, the consistency tests are outlined for the relative risk regression. The proposed homogeneity test reaches sufficient power in realistic scenarios with small interactions. As expected, power decreases for unbalanced subgroups, lower sample sizes, and narrower margins. Severe interactions are covered by the null hypothesis and are more likely to be rejected the stronger they are.

The potential of the estimands framework for clinical pharmacology trials: Some discussion points.

The recently finalised and published guideline ICH E9 (R1) introduced a new framework for the statistical analysis of clinical trials, namely that of "estimands". While the framework was originally developed for the analysis of late-phase trials, it could also provide a rigorous basis for the analysis of clinical pharmacology trials. We illustrate potential applications on two examples: a multiple dose pharmacology trial and the interpretation of confirmatory bioequivalence (BE) trials according to the current FDA and EMA BE guidelines.

Statistical reporting of clinical pharmacology research.

Research in clinical pharmacology covers a wide range of experiments, trials and investigations: clinical trials, systematic reviews and meta-analyses of drug usage after market approval, the investigation of pharmacokinetic-pharmacodynamic relationships, the search for mechanisms of action or for potential signals for efficacy and safety using biomarkers. Often these investigations are exploratory in nature, which has implications for the way the data should be analysed and presented. Here we summarize some of the statistical issues that are of particular importance in clinical pharmacology research.

A Phase I study to determine the pharmacokinetic profile, safety and tolerability of sildenafil (Revatio® ) in cardiac surgery: the REVAKI-1 study.

AIMS: Acute kidney injury (AKI) is a common and severe complication of cardiac surgery. There is no effective prevention or treatment. Sildenafil citrate (Revatio® , Pfizer Inc.), a phosphodiesterase type 5 inhibitor, prevents post cardiac surgery AKI in pre-clinical studies, however its use is contraindicated in patients with symptomatic cardiovascular disease. The aim of this study is to assess the safety and pharmacokinetics of intravenous sildenafil in cardiac surgery patients. METHODS: We conducted an open label, dose escalation study with six patients per dose level. The six doses were 2.5 mg, 5 mg or 10 mg as a bolus, either alone or followed by an additional 2 h infusion of 2.5 mg sildenafil. RESULTS: Thirty-six patients entered the trial, of which 33 completed it. The mean age was 69.9 years. One patient died during surgery, two others were removed from the trial before dosing (all at dose level 5 mg + 2.5 mg). The pharmacokinetic profile of sildenafil was similar to previously published studies. For a dose of 10 mg administered as a bolus followed by 2.5 mg administered over 2 h the results were AUC∞ 537 ng h ml-1 , Cmax 189.4 ng ml-1 and t1/2 10.5 h. The drug was well tolerated with no serious adverse events related to drug administration. Higher sildenafil doses stabilized post-surgery nitric oxide bioavailability. CONCLUSIONS: Pharmacokinetics of sildenafil during cardiopulmonary bypass were comparable to those of other patient groups. The drug was well tolerated at therapeutic plasma levels. These results support the further evaluation of sildenafil for the prevention of AKI in cardiac surgery.

Infarct size following complete revascularization in patients presenting with STEMI: a comparison of immediate and staged in-hospital non-infarct related artery PCI subgroups in the CvLPRIT study.

BACKGROUND: The CvLPRIT study showed a trend for improved clinical outcomes in the complete revascularisation (CR) group in those treated with an immediate, as opposed to staged in-hospital approach in patients with multivessel coronary disease undergoing primary percutaneous intervention (PPCI). We aimed to assess infarct size and left ventricular function in patients undergoing immediate compared with staged CR for multivessel disease at PPCI. METHODS: The Cardiovascular Magnetic Resonance (CMR) substudy of CvLPRIT was a multicentre, prospective, randomized, open label, blinded endpoint trial in PPCI patients with multivessel disease. These data refer to a post-hoc analysis in 93 patients randomized to the CR arm (63 immediate, 30 staged) who completed a pre-discharge CMR scan (median 2 and 4 days respectively) after PPCI. The decision to stage non-IRA revascularization was at the discretion of the treating interventional cardiologist. RESULTS: Patients treated with a staged approach had more visible thrombus (26/30 vs. 31/62, p = 0.001), higher SYNTAX score in the IRA (9.5, 8-16 vs. 8.0, 5.5-11, p = 0.04) and a greater incidence of no-reflow (23.3 % vs. 1.6 % p < 0.001) than those treated with immediate CR. After adjustment for confounders, staged patients had larger infarct size (19.7 % [11.7-37.6] vs. 11.6 % [6.8-18.2] of LV Mass, p = 0.012) and lower ejection fraction (42.2 ± 10 % vs. 47.4 ± 9 %, p = 0.019) compared with immediate CR. CONCLUSIONS: Of patients randomized to CR in the CMR substudy of CvLPRIT, those in whom the operator chose to stage revascularization had larger infarct size and lower ejection fraction, which persisted after adjusting for important covariates than those who underwent immediate CR. Prospective randomized trials are needed to assess whether immediate CR results in better clinical outcomes than staged CR. TRIAL REGISTRATION: ISRCTN70913605 , Registered 24th February 2011.

Linagliptin fixed-dose combination with metformin is bioequivalent to co-administration of linagliptin and metformin as individual tablets.

OBJECTIVE: To demonstrate bioequivalence of linagliptin/metformin fixed-dose combination (FDC) tablets and the corresponding combination of individual tablets taken together, i.e., free-pill (FP) treatment. METHODS: Three dosing combinations were evaluated in three separate randomized studies: linagliptin 2.5 mg with 500 mg, 850 mg, or 1,000 mg metformin. These studies used a prospective, open-label, randomized, two-way crossover design to evaluate bioequivalence in healthy volunteers (n = 287). After an overnight fast, participants received an FDC tablet once, and on a separate visit received the corresponding FP treatment. The two possible treatment sequences (FDC/FP and FP/FDC) were randomly allocated to the participants. A washout period of 35 days separated the two study treatments. The primary endpoints were maximum plasma concentration (Cmax) of linagliptin and metformin, area under the plasma concentration time curve from 0 to 72 hours (AUC0-72) for linagliptin, and from 0 to infinity (AUC0-inf) for metformin. RESULTS: The 90% confidence intervals of the adjusted geometric mean ratios of Cmax and AUC (calculated as FDC/ FP) were within the bioequivalence acceptance limits of 80 - 125%. The number of participants reporting at least one adverse event following FDC treatments was comparable to, or less than, that following FP treatments. Evaluation of vital signs and clinical laboratory tests revealed no safety issues. CONCLUSIONS: FDC tablets of linagliptin and metformin are bioequivalent to individual tablets of respective dose strengths taken together. Both treatments were well tolerated.

Effect of food and tablet-dissolution characteristics on the bioavailability of linagliptin fixed-dose combination with metformin: evidence from two randomized trials.

OBJECTIVE: The objectives of the studies reported here were to determine the relative bioavailability of linagliptin and metformin when administered in a fixed-dose combination (FDC) tablet with and without food, and to investigate the relative bioavailability of linagliptin and metformin FDC tablets from two treatment batches with different dissolution behavior. METHODS: These studies were open-label, single-dose, randomized, two-way crossover trials. After an overnight fast, healthy volunteers received an FDC tablet once (with/without food in the food-effect study; or from one of two batches with differing dissolution behavior in the tablet-dissolution study). On a separate visit, following a washout period of 35 days, participants received the alternative treatment. In the food-effect study the primary endpoints were maximum measured concentration in plasma (C(max)) for linagliptin and metformin, area under the plasma concentration-time curve from 0 to 72 hours (AUC(0-72)) for linagliptin and from 0 to infinity (AUC(0-inf)) for metformin. In the tablet-dissolution study the primary endpoints were Cmax for both analytes, AUC(0-72) for linagliptin, and from 0 to the time of the last quantifiable data point (AUC(0-t)) for metformin. RESULTS: The administration of the FDC tablet with food had no influence on the relative bioavailability of linagliptin and metformin with regard to the extent of exposure as determined by AUC(0-72) (linagliptin) and AUC(0-inf) (metformin) compared with FDC tablet administration while fasting. After food intake, peak plasma concentrations of linagliptin were slightly lowered (from 4.99 to 4.56 nmol L⁻¹), but the 90% confidence interval (CI) of the geometric mean test/reference ratio was still located within the generally applied bioequivalence acceptance limits of 80 - 125%. The median time from dosing to the maximum concentration of linagliptin in plasma (t(max)) was similar under both conditions. Administration with food reduced the rate of absorption of metformin indicated by a prolongation in median tmax (from 2 to 4 hours) and a decrease in Cmax by ~ 18%. There were no notable differences between the two treatment groups with respect to safety and tolerability. In the tablet-dissolution study, bioequivalence was demonstrated between linagliptin/metformin FDC tablets with normal and slower dissolution characteristics. For both linagliptin and metformin, the 90% CI of all pharmacokinetic (PK) parameters were well within the bioequivalence acceptance limits of 80 - 125%. Tablets from both batches were well tolerated with no unexpected adverse events. CONCLUSIONS: Food did not have a relevant impact on the bioavailability of linagliptin from the FDC tablet. The effect of food on the metformin component was comparable to that previously demonstrated. Furthermore, differences in tablet-dissolution characteristics did not have an impact on the bioavailability of linagliptin or metformin from the FDC tablet.

Group-by-Treatment Interaction Effects in Comparative Bioavailability Studies.

Comparative bioavailability studies often involve multiple groups of subjects for a variety of reasons, such as clinical capacity limitations. This raises questions about the validity of pooling data from these groups in the statistical analysis and whether a group-by-treatment interaction should be evaluated. We investigated the presence or absence of group-by-treatment interactions through both simulation techniques and a meta-study of well-controlled trials. Our findings reveal that the test falsely detects an interaction when no true group-by-treatment interaction exists. Conversely, when a true group-by-treatment interaction does exist, it often goes undetected. In our meta-study, the detected group-by-treatment interactions were observed at approximately the level of the test and, thus, can be considered false positives. Testing for a group-by-treatment interaction is both misleading and uninformative. It often falsely identifies an interaction when none exists and fails to detect a real one. This occurs because the test is performed between subjects in crossover designs, and studies are powered to compare treatments within subjects. This work demonstrates a lack of utility for including a group-by-treatment interaction in the model when assessing single-site comparative bioavailability studies, and the clinical trial study structure is divided into groups.

Rationale, design, and organization of a randomized, controlled Trial Evaluating Cardiovascular Outcomes with Sitagliptin (TECOS) in patients with type 2 diabetes and established cardiovascular disease.

Sitagliptin, an oral dipeptidyl peptidase-4 inhibitor, lowers blood glucose when administered as monotherapy or in combination with other antihyperglycemic agents. TECOS will evaluate the effects of adding sitagliptin to usual diabetes care on cardiovascular outcomes and clinical safety. TECOS is a pragmatic, academically run, multinational, randomized, double-blind, placebo-controlled, event-driven trial recruiting approximately 14,000 patients in 38 countries who have type 2 diabetes (T2DM), are at least 50 years old, have cardiovascular disease, and have an hemoglobin A1c value between 6.5% and 8.0%. Eligible participants will be receiving stable mono- or dual therapy with metformin, sulfonylurea, or pioglitazone, or insulin alone or in combination with metformin. Randomization is 1:1 to double-blind sitagliptin or matching placebo, in addition to existing therapy in a usual care setting. Follow-up occurs at 4-month intervals in year 1 and then twice yearly until 1300 confirmed primary end points have occurred. Glycemic equipoise between randomized groups is a desired aim. The primary composite cardiovascular endpoint is time to the first occurrence of cardiovascular death, nonfatal myocardial infarction, nonfatal stroke, or hospitalization for unstable angina, with cardiovascular events adjudicated by an independent committee blinded to study therapy. TECOS is a pragmatic-design cardiovascular outcome trial assessing the cardiovascular effects of sitagliptin when added to usual T2DM management.

The sodium glucose cotransporter 2 inhibitor empagliflozin does not prolong QT interval in a thorough QT (TQT) study.

BACKGROUND: Empagliflozin is a potent, selective sodium glucose cotransporter 2 (SGLT2) inhibitor in development as an oral antidiabetic treatment. This QT interval study assessed potential effects of empagliflozin on ventricular repolarisation and other electrocardiogram (ECG) parameters. METHODS: A randomised, placebo-controlled, single-dose, double-blind, five-period crossover study incorporating a novel double-placebo period design to reduce sample size, while maintaining full statistical power. TREATMENTS: single empagliflozin doses of 25 mg (therapeutic) and 200 mg (supratherapeutic), matching placebo and open-label moxifloxacin 400 mg (positive control). Triplicate 12-lead ECGs of 10 second duration were recorded at baseline and during the first 24 hours after dosing. The primary endpoint was mean change from baseline (MCfB) in the population heart rate-corrected QT interval (QTcN) between 1-4 hours after dosing. RESULTS: Thirty volunteers (16 male, 14 female, mean [range] age: 34.5 [18-52] years) were randomised. The placebo-corrected MCfB in QTcN 1-4 hours after dosing was 0.6 (90% CI: -0.7, 1.9) ms and -0.2 (-1.4, 0.9) ms for empagliflozin 25 mg and 200 mg, respectively, below the ICH E14 defined threshold of regulatory concern 10 ms. Assay sensitivity was confirmed by a placebo-corrected MCfB in QTcN 2-4 hours post-dose of 12.4 (10.7, 14.1) ms with moxifloxacin 400 mg. Empagliflozin tolerability was good for all volunteers; 23.3% experienced adverse events (AEs) with empagliflozin and 27.6% with placebo. The most frequent AE was nasopharyngitis. CONCLUSIONS/INTERPRETATION: Single doses of empagliflozin 25 mg and 200 mg were not associated with QTcN prolongation and were well tolerated in healthy volunteers. TRIAL REGISTRATION: ClinicalTrials.gov: NCT01195675.

Pharmacokinetics of linagliptin in subjects with hepatic impairment.

WHAT IS ALREADY KNOWN ABOUT THIS SUBJECT: • Linagliptin is an oral, highly selective dipeptidyl peptidase-4 (DPP-4) inhibitor that was approved in the United States, Europe and elsewhere in 2011 for the treatment of type 2 diabetes mellitus. • The elimination of linagliptin is primarily non-renal. Therefore, a potential effect of hepatic impairment on the elimination of linagliptin may have important implications for dosing recommendations. WHAT THIS STUDY ADDS: • This study shows that mild, moderate or severe hepatic impairment did not result in an increase in linagliptin exposure after single and multiple dosing as compared with normal hepatic function. • No linagliptin dose adjustment is required in patients with any degree of hepatic impairment. AIM: To investigate whether hepatic impairment affects linagliptin pharmacokinetics, pharmacodynamics and tolerability. METHOD: This open label, parallel group, single centre study enrolled patients with mild (n= 8), moderate (n= 9) or severe (n= 8) hepatic impairment and healthy subjects (n= 8). Groups were matched with regard to age, weight and gender. Primary endpoints were linagliptin exposure following 5 mg linagliptin once daily for 7 days in patients with mild and moderate hepatic impairment vs. healthy subjects or after a single 5 mg dose for patients with severe hepatic impairment vs. healthy subjects. RESULTS: In mild hepatic impairment, steady-state linagliptin exposure was slightly lower than in healthy subjects [AUC(τ,ss) geometric mean ratio (GMR) 75.5%, 90% confidence interval (CI) 61.6%, 92.5%, and C(max,ss) GMR 64.4%, 90% CI 43.2%, 96.0%]. Exposure also tended to be lower in moderate hepatic impairment (AUC(τ,ss) GMR 85.5%, 90% CI 70.2%, 104.2% and C(max,ss) GMR 92.3%, 90% CI 62.8%, 135.6%). After a single dose, AUC(0,24 h) in patients with severe hepatic impairment was similar to that in healthy subjects (GMR 100.4%, 90% CI 75.0%, 134.3%) and C(max) was lower (GMR 77.0%, 90% CI 44.9%, 132.3%). Accumulation based on AUC or C(max) and renal excretion of unchanged linagliptin (≤ 7%) were comparable across groups. Median plasma DPP-4 inhibition was similar in healthy subjects (91%), and patients with mild (90%) and moderate (89%) hepatic impairment at steady-state trough concentrations, and in patients with severe hepatic impairment 24 h after a single dose (84%). Linagliptin was well tolerated. CONCLUSION: Mild, moderate or severe hepatic impairment did not result in an increase in linagliptin exposure after single and multiple dosing compared with normal hepatic function. Dose adjustment with linagliptin is not required in patients with hepatic impairment.

Pharmacokinetics of single and multiple oral doses of 5 mg linagliptin in healthy Chinese volunteers.

OBJECTIVE: To investigate the pharmacokinetic and safety profile of linagliptin after single and multiple doses in healthy Chinese volunteers. METHODS: Men and women (n = 12) aged 18 - 45 years with body mass index 19 - 24 kg/m2 received a single 5-mg dose of linagliptin on Day 1, followed by 7-day washout and 6 consecutive days of once-daily administration. Vital signs, electrocardiogram, routine laboratory tests, urinalysis, and adverse events were recorded. Blood and urine analytes were measured by HPLC/MS/MS. RESULTS: Linagliptin was rapidly absorbed; median time to maximum concentration was 1.75 h for single dose and 1.5 h at steady-state. Maximum plasma drug concentration (Cmax) after single dose was 4.9 ng/ml (10.4 nM), with a geometric coefficient of variation (gCV) 46%. The corresponding geometric mean area under the plasma concentrationtime curve (AUC) was 71 ng×h ml-1 (150 nmol×h l-1, gCV 25%). At steady-state, Cmax and AUC were 6.7 ng/ml (14.1 nM, gCV 49%) and 96 nmol×h l-1 (204 nmol×h l-1, gCV 25%). An accumulation half-life of ~ 11.5 h (gCV 46.9%) was calculated. Renal excretion of linagliptin was low and < 8% of administered dose at steady-state (< 2% at Day 1). Single and multiple daily oral doses of 5 mg linagliptin were safe and well tolerated. No adverse events or clinically significant changes in laboratory tests, vital signs, or physical examination were reported. CONCLUSIONS: Linagliptin demonstrated a favorable safety profile in healthy Chinese volunteers, with a pharmacokinetic profile that was similar to that observed previously in subjects of Japanese, Caucasian, or African American origin.

Delayed effects in the exposure-response analysis of clinical QTc trials.

In clinical development, thorough QT interval/corrected QT interval (QT/QTc) studies are performed to demonstrate that new investigational drugs do not change cardiac repolarization. In addition to the analysis recommended by ICH E14, exposure-response modeling has recently gained increased scientific attention. A direct concentration-QTc relationship is usually assumed with this pharmacokinetics (PK) QT analysis. Consequently, unconsidered effect delays might lead to severe bias in estimation. Although literature and the Food and Drug Administration (FDA) recommend checking for hysteresis, little guidance is given for evaluating the presence of lagged effects. Based on simulated delay scenarios, we derive a metric that allows for the detection of relevant effect delays. With this, the necessity for refined modeling of lag times can be easily recognized.

The DPP-4 inhibitor linagliptin does not prolong the QT interval at therapeutic and supratherapeutic doses.

AIM: To evaluate the potential effects of therapeutic and supratherapeutic doses of linagliptin (BI 1356) on the QT/QT(c) interval in healthy subjects. METHODS: The study was a randomized, double-blind, placebo-controlled, four-period crossover study using single oral doses of linagliptin (5 mg and 100 mg), moxifloxacin (400 mg) and placebo. Electrocardiogram (ECG) profiles using triplicates of 12-lead 10-s ECGs were digitally recorded pre-dose and after drug administration. The mean change from baseline (MCfB) of the individually heart rate corrected QT interval (QT(c) I) between 1 and 4 h postdrug administration was the primary end point. Blood samples to measure plasma concentrations of linagliptin and its main metabolite were also obtained. RESULTS: Forty-four Caucasian subjects (26 male) entered the study and 43 subjects completed the study as planned in the protocol. Linagliptin was not associated with an increase in the baseline-adjusted mean QT(c) I, at any time point. The placebo-corrected MCfB of QT(c) I was -1.1 (90% CI -2.7, 0.5) ms and -2.5 (-4.1, -0.9) ms for linagliptin 5 mg and 100 mg, respectively, thus within the non-inferiority margin of 10 ms according to ICH E14. Linagliptin was well tolerated; the assessment of ECGs and other safety parameters gave no clinically relevant findings at either dose tested. Maximum plasma concentrations after administration of 100-mg linagliptin were ∼24-fold higher than those observed previously for chronic treatment with the therapeutic 5-mg dose. Assay sensitivity was confirmed by a placebo-corrected MCfB of QT(c) I with moxifloxacin of 6.9 (90% CI 5.4, 8.5) ms. CONCLUSIONS: Therapeutic and significantly supratherapeutic exposure to linagliptin is not associated with QT interval prolongation.

Mixed models for data from thorough QT studies: part 1. assessment of marginal QT prolongation.

We investigate mixed models for repeated measures data from cross-over studies in general, but in particular for data from thorough QT studies. We extend both the conventional random effects model and the saturated covariance model for univariate cross-over data to repeated measures cross-over (RMC) data; the resulting models we call the RMC model and Saturated model, respectively. Furthermore, we consider a random effects model for repeated measures cross-over data previously proposed in the literature. We assess the standard errors of point estimates and the coverage properties of confidence intervals for treatment contrasts under the various models. Our findings suggest: (i) Point estimates of treatment contrasts from all models considered are similar; (ii) Confidence intervals for treatment contrasts under the random effects model previously proposed in the literature do not have adequate coverage properties; the model therefore cannot be recommended for analysis of marginal QT prolongation; (iii) The RMC model and the Saturated model have similar precision and coverage properties; both models are suitable for assessment of marginal QT prolongation; and (iv) The Akaike Information Criterion (AIC) is not a reliable criterion for selecting a covariance model for RMC data in the following sense: the model with the smallest AIC is not necessarily associated with the highest precision for the treatment contrasts, even if the model with the smallest AIC value is also the most parsimonious model.

Statistical models for heart rate correction of the QT interval.

The analysis of QT interval data is now an essential part of the assessment of drug safety. As the QT interval is inversely associated with heart rate, an appropriate correction must be applied in order to evaluate QT data in clinical trials. The aim is to characterize changes in QT interval at a standard heart rate, taking into account the correlation between these two variables to adjust for heart rate changes during the course of the trial. It has been shown that the relationship between the RR interval (=1/heart rate) and the QT interval is highly variable between individuals but stable over time within each individual.Many mathematical models have been developed to describe the QT-RR relationship. However, there has been less emphasis on the derivation of suitable statistical models that account for the multilevel structure of the ECG data.An important example is the interpretation of the so-called population-specific heart rate corrections, which are based on data pooled from different subjects. Often, simple regression techniques are used to quantify the population correction, disregarding the subject level and leading to biased parameter estimates. Instead, population-based corrections that account for individual intercepts should be used, in order to distinguish within-subject-effects from between-subject effects. Therefore, population-specific corrections cannot be derived solely from the cross-sectional data. The impact of the different statistical models is illustrated by data from the baseline periods of six clinical QT studies.

Statistical characterization of QT prolongation.

The appropriate assessment of QT prolongation remains controversial. We suggest that before the relative merits of various methods can be evaluated, we must state what we assume an assessment of QT prolongation should be about. As a general framework for the assessment of QT prolongation we propose that an assessment of "absolute" or "uncorrected" QT prolongation is properly carried out through a between-treatment (active versus placebo) comparison of the marginal distributions of QT data; an assessment of "heart rate corrected" QT prolongation is carried out through a between-treatment comparison of the conditional distributions of QT data (conditional on RR interval or heart rate). Under this general framework, conditional QT prolongation is, in general, a function of RR interval, and we discuss three possible summary characteristics for that function. We show how current procedures for the assessment of QT prolongation relate to the general approach (that is, to between-treatment contrasts of the marginal and conditional expectation of the QT interval), and to each other. It transpires that only the so-called "one-step procedure" can provide a complete characterization of conditional QT prolongation. We show that the "two-step procedure" with data-driven correction provides an unbiased estimate of expected conditional QT prolongation, which may, from a clinical point of view, be a more satisfactory characteristic than the conventional characteristic, QT prolongation at the reference RR interval. We strongly suggest that two-step procedures with fixed correction be abandoned in the analysis of thorough QT/QTc studies: Fixed correction is either redundant (when a drug has no effect on average RR interval), or systematically biased (when a drug does affect average RR interval).

Residuals and outliers in replicate design crossover studies.

Outliers in bioequivalence trials may arise through various mechanisms, requiring different interpretation and handling of such data points. For example, regulatory authorities might permit exclusion from analysis of outliers caused by product or process failure, while exclusion of outliers caused by subject-by-treatment interaction generally is not acceptable. In standard 2 x 2 crossover studies it is not possible to distinguish between relevant types of outliers based on statistical criteria alone. However, in replicate design (2-treatment, 4-period) crossover studies three types of outliers can be distinguished: (i) Subject outliers are usually unproblematic, at least regarding the analysis of bioequivalence, and may require no further action; (ii) Subject-by-formulation outliers may affect the outcome of the bioequivalence test but generally cannot simply be removed from analysis; and (iii) Removal of single-data-point outliers from analysis may be justified in certain cases. As a very simple but effective diagnostic tool for the identification and classification of outliers in replicate design crossover studies we propose to calculate and plot three types of residual corresponding to the three different types of outliers that can be distinguished. The residuals are obtained from four mutually orthogonal linear contrasts of the four data points associated with each subject. If preferred, outlier tests can be applied to the resulting sets of residuals after suitable standardization.