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ROLE OF MODELING AND SIMULATION IN EVALUATING THE QTc PROLONGATION POTENTIAL OF DRUGS Shashank Rohatagi, S. Song, N. Sarapa, Daiichi Sankyo Pharma Development, Edison, NJ; T. Khariton, T. Carrothers, J. Kuwabara-Wagg, H. Kastrissios, Pharsight Corporation, Mountain View, CA. Today, the Fridericia method is thought to be superior to the Bazett.[1] However, their common flaw is assuming the same correction for all individuals, which ignores the inter-patient variability in the HR-QT relationship. Under the “individual-correction” method, the exponent is estimated on an individual basis, QT QTc RR given an adequate number and quality of measurements. Following HR correction, the steps taken in modeling corrected QT interval values (QTc) were as follows: (Note: Data analysis was based on non-linear mixed-effects modeling and typically conducted in NONMEM 5 ™ for PK and S-PLUS 6.2 ™ for PD): 1. Build population PK model and obtain predicted drug concentrations for the QT observation times. 2. Build base PD model • Fit nonlinear mixed-effect model to baseline data, with only time of the day as a covariate. This effect of diurnal variation was usually represented in a truncated Fourier series as follows: 2 t 1 2 t 2 2 t 3 f (t ) A1 cos A2 cos A3 cos 24 12 6 where t represents time of the day, and parameters Ai and i are amplitude and acrophase parameters for periods of 24, 12 and 6 hours, to the extent required. The baseline model was then as follows: QTc(base) 0 f t • Add active treatment data and estimate placebo and active treatment effects. Fit nonlinear mixedeffect model to all the data by adding a drug effect, f(Cp), a function of the individually predicted drug concentrations. QTc 0 f t f (Cp) Provided that concentration is associated with prolongation of QT-interval, the functional form of the drug effect term is dependent on the data, with the two most common terms being linear (left) or sigmoid Emax (right). In our experience, the linear model was usually more appropriate. max 1 50 As part of each of the above steps, demographic covariates were tested to determine their significance on baseline and concentration-effect. For covariate inclusion-exclusion, a stepwise forward addition (α = 0.05), backward elimination (α = 0.01) process was used, based on -2*log-likelihood. f Cp Cp • • E * Cp f Cp EC Cp Determination of the error structure is an important aspect of model building. It was assumed that the residual error, e, is normally distributed. Baseline (β0) and drug effect (β1) are the random effects. When the study had a cross-over design, it was assumed both random effects have a more complex nested structure, with the first level measuring variation among periods within an individual and the second level measuring variation between individuals. The structural model for QTc was kept similar to the one described in Methods – Modeling Steps section Conclusions: There was no evidence of potential for QTc prolongation. 1000 1200 460 440 440 QTcF 380 400 420 420 800 1000 1200 Bazett Fridericia 420 400 360 380 400 QTcF 420 440 RR 440 RR 1400 360 360 1400 Slope vs.1400 Cp relationship with CI 1000 for QTc 1200 800 1000 95%1200 800 400 380 QTcF 440 380 400 420 Figure 3. Comparison of correction methods using baseline data. Points represent all individual measurements, lines are the smoothed fit showing an overall trend. In this particular case, neither Bazett nor Fridericia provided an adequate correction. As a result, a linear mixed-effects model was constructed to obtain the most appropriate power for each individual’s QT correction (S-PLUS function lme was employed). 360 360 380 400 QTpred 420 440 Indiv . Correction 6 8 10 12 14 16 0 18 500 1000 1500 2000 2500 3000 Conc CS-917 (ng/mL) Time of the day at baseline (h) Figure 8. QTcF seems to show a slight diurnal pattern at baseline but does not seem to show any significant relationship with predicted drug concentration Case 7 – Anti-platelet Agent AP1 is a novel antiplatelet agent. 1400 RR QTcB 2.0 Objectives of the Analysis: Quantitatively evaluate whether AP1 exposure results in QTc prolongation in healthy volunteers. Figure4. Slope characterizing QT and FQ2 exposure relationship with 95% CI for different QT correction methods 2.00 1.81 Model Development: Fridericia’s correction proved good enough to correct for heart rate. The structural model was described by the following set of equations: AP1 1.69 1.5 10 12 14 Simulation of QTc Prolongation: The final popPK and QTc models were used to simulate likely QTc interval prolongation as a function of FQ1 dose, day, infusion duration, and subject covariates. For each such combination, a distribution of Cmax values was computed. For each Cmax value, the final QTc model was used to calculate a corresponding ΔΔQTcF (equivalent to the maximum ΔΔQTcF value because FQ1 Cmax values were used). The mean maximum ΔΔQTcF value and the corresponding 90% confidence bound (the upper bound corresponding to a 95% one-sided confidence interval) were then computed. Simulations were completed assuming a single individual or a study population comprised of 16 or 60 individuals. Simulations of maximum ΔΔQTcF values based on populations consisting of 60 individuals were then used to predict the likely QTc outcome for studies based on the FQ1 dosing regimens of interest. QTc outcomes were classified as per the FQ1 QTc Outcome Criteria outlined above. Conclusions: The final QTc model suggested that QTc prolongation increases with Cmax (proportional to dose) in an approximately linear fashion over a dose range of 200 mg IV QD to 2000 mg IV QD. The presence of a term for day in the model may represent the effect of a metabolite. Prolongation effects were predicted to be negligible at doses of 200 mg IV QD. A modest effect was predicted for doses of 400 mg IV QD such that an E-R model predicted an acceptable QTc study outcome for 400 mg IV QD doses with infusion durations of 1 hour or longer irrespective of the dosing duration. A comparable outcome was predicted for 600 mg IV QD provided the infusion duration was 2 hours or greater and the dosing duration did not exceed 7 days. All simulated 800 mg dosing regimens were predicted to result in “Ambiguous” QTc outcomes, while all higher doses and associated regimens were predicted to result in an “Unacceptable” QTc outcome. Case 2 – Pre-clinical to Clinical Fluoroquinolone #2 (FQ2) is a broad-spectrum fluoroquinolone with demonstrated activity against a wide array of medically important Gram positive and Gram negative bacteria. Data: Two pharmacokinetic studies of oral and IV FQ2 administrations in cynomolgus monkeys; 3 monkeys following a single IV dose and 3 monkeys following single oral FQ2 administration. QT study in 4 cynomolgus monkeys administered a single IV dose of FQ1 and QT study in 4 cynomolgus monkeys administered a single IV dose of FQ2. Objective of the Analysis: Characterize the magnitude of QTc prolongation by FQ2 in humans based on preclinical data from FQ1 and FQ2 and clinical data from FQ1. Model Development: Analysis of FQ1 monkey and human QTc prolongation and exposures was used to derive a potency scaling factor for predicting human QTc prolongation from monkey data. Major assumptions: the magnitude of effect on QTcB is the same as on QTcF; FQ2 does not have an active metabolite as was postulated for FQ1, and therefore the day of the study has no effect on QTcF magnitude; baseline QTcF value of 400 msec. 1) Derive FQ2 pharmacokinetic model for monkey data 2) Derive a potency scaling factor for FQ1 based on monkey and human QTc prolongation and exposures 3) Based on FQ2 monkey PK and potency and QTc scaling for FQ1, adjust the slope of FQ1 and QTc relationship accordingly. Assume that FQ2 does not have an active metabolite. Simulations: The FQ2 popPK model was used to simulate PK profiles for an average 70 kg man. PK parameters for humans and monkey were assumed to have comparable uncertainty. This uncertainty was included in the PK simulations. The PK model was linked to the final FQ2 QTc model by simulated Cmax exposures. For generated concentration profiles, maximum ΔΔQTcF effect was simulated. Results: Doses and regimens for FQ2 were classified according to the schema used previously for FQ1 in Case 1. Regions where QTc prolongation would be expected to be dose-limiting were identified prior to any actual ECGs data for FQ2 in clinical trials. Epilogue: After FIM SAD trial was analyzed for FQ2, modeling projections were compared to the E-R model from FIM results. The scaling-based point estimate of 2.5 [msec/(mg/L)] was within 33% of the FIM estimate of 1.7 [90% CI: (1, 2.4)]. 440 460 420 8 Active treatment group Baseline measurements 600 800 No Correction 1000 1200 1400 Indiv. Correction 440 360 6 Placebo group 40 420 400 340 4 Conclusions: One-sided 95% CI would exceed 10 msec at the higher end of the dose range tested; however, this is several-fold higher than the expected therapeutically active dose. QTc QTCf (msec) 2 The placebo effect component had a statistically significant impact on the overall fit and varied as time passed. Based on the final QTc model, at the maximum observed plasma concentration the QTc prolongation was 20 msec at the highest dose. AD1 is a anti-diabetic compound. Data: Two Phase I and two Phase II studies that comprised the dataset for the existing population PK model were used to develop the E-R model for the QTc effect of AD1. The E-R analysis was dominated by a single Phase I study which had serial triplicate ECGs taken during periods with substantial drug concentrations while the other three studies had ECG measurements taken ≥48 hrs post-dose. Objectives of the Analysis: Characterize the magnitude of QTc prolongation, if any, and provide guidance for dose selection as required by the results of the E-R study. Model Development and Results: 1) Fridericia’s correction did not prove adequate to correct for heart rate, thus an individual correction factor (α=0.24 on the average) was derived based on a mixed-effects linear model (see Figure 5). 2) Error structure was structured to take into account crossover study design. 3) Time-of-day effects on baseline were modeled via a truncated Fourier series . 4) The central estimate of the slope was negative (Figure 6) and well-characterized, indicating no concern for QT prolongation with this compound. 440 -50 6 Conc 420 0 Eff 400 50 Case 4 – Agreement with TQT AP1 380 15 360 10 2 t 3 Base Q1 QTcbase 6 12 PCB 4 5 log TreatmentDay QTCf QTc (msec) 5 Figure 2. Smoothing plots showing the relationship between FQ1 concentrations and day of the study and ΔΔQTcF (baseline and placebo adjusted based on baseline and placebo fit) QTc Base PCB Eff 1.0 0 400 50 QTcF 380 2.5 QTc 20 380 360 delta QTc [msec] RR Model Development: Data: Data from one Phase I study conducted in healthy volunteers was used to conduct the analyses. Day of the study 3 800 100 100 QT QTcF CONC * CONC DAY *log DAY Predicted Cp (mg/L) Figure1. Schematic representation of normal ECG trace. RR 0 Appropriate correction of QT interval for changes in HR is an essential element of the evaluation of druginduced QT prolongation, and the first step in E-R modeling. Since the 1920s, researchers have attempted to find a formula that would make the corrected QT interval independent of HR. Most of the investigators today use either the Bazett (QTcB) or the Fridericia (QTcF) correction[1]: RR FQ1 -50 Methods QT QTcB Eff delta-delta QTcF (msec) Figure 1 shows the interval typically measured on an ECG. QT interval is a measure of the time between the start of the Q-wave and the end of the T wave in the heart's electrical cycle and represents the time required for both ventricular depolarization and repolarization to occur, and therefore it roughly estimates the duration of an average ventricular action potential. This interval can range from 0.2 to 0.5 seconds, depending upon heart rate. The QT interval obviously shortens with a shorter RR interval (equivalently, a faster heart rate), thus RR interval is one of the main sources of QT variability. Other sources of variability affecting QT include the following: genetic differences, food intake (increases of 16-23 msec have been observed during the hour following a meal), circadian rhythm, sex (females tend to have 8-10 msec longer intervals than males at baseline), obesity (more than 5 msec increase per 10 kg increase in fat mass), physical activity, electrolyte disturbances, blood glucose level, blood pressure, alcoholism, age, and presence of U-wave.[1] QTcF Base PCB Eff No Correction 3) The effect of FQ1 (see Figure 2) was statistically significant and could be modeled best by a simple linear function. Day of the study had a significant impact on the active treatment as well and was modeled as an additive component. Weight, age, sex, race and time of the day did not have a statistically significant impact on placebo or on drug effect. The final form of the model was an additive combination of baseline, placebo, active treatment and residual variability (ε) components. FQ1 Objectives of the Analysis: Develop E-R models to predict effects on QTc interval of MB2 plasma concentration (Cp). Where ή0(ID) and ή0(PERIOD in ID) are normally distributed first and second-level random effects attributed to random variation in ID and among periods within each individual respectively. Conclusions: Modeling QTcF and QTcB values (as opposed to individually corrected QTc) leads to a seemingly more precise estimate of the slope, but the QTcF and QTcB estimates are biased upwards. Thus, using QTcF or QTcB instead of individual correction would lead to an overestimation of prolongation and, as a result, unnecessary restrictions on dose selection. 340 320 Bazett Fridericia 440 -20 0 400 -60 340 0 1000 1200 1400 50 100 150 200 Figure 6. Negative slope indicates no QTc prolongation Comparison to Completed TQT: A thorough QT study was completed after the studies used to build the PK and E-R models. The TQT study was negative, indicating agreement with the E-R model. Case 5 – Anti-coagulant FXa1 is a direct Factor Xa inhibitor (FXa). Data: Data from seven Phase I and one Phase IIa study, consisting of 307 healthy volunteers (276 males, 31 females) and 603 surgical patients (253 males, 350 females). Objectives of the Analysis: Evaluate whether FXa1 exposure results in clinically significant QTc prolongation in healthy volunteers and surgical patients. Model Development: 1) Fridericia’s correction proved adequate for correcting QT for heart rate 2) The structural model for QTc was kept similar to the one described in Methods section Conclusions: Based on the final QTc model, at the maximum observed plasma concentration the QTc prolongation was 2.5 msec. This observation was in a healthy volunteer, and it corresponds to a steady-state BID dose three-fold higher than the expected therapeutic dose. Thus, the results of this analysis show that FXa1 exposure does not significantly prolong QTc interval neither in healthy volunteers nor in surgical patients. Figure 7. Smoothing plot showing the relationship between FXa1 plasma concentrations and baseline and placebo adjusted QTc (ΔΔQTc). Red line is a smooth. Dashed lines represent ΔΔQTc of 5 msec and 20 msec, respectively. 0 2 4 Predicted log Conc (ng/mL) 6 0 20 40 60 80 100 Time since medication (hr) Pooling of QTc data from multiple studies may increase the power and improve the predictivity of the QTc risk assessment (either at one stage of development or in the NDA). • Conclusions from a compound’s exposure-response model established from the pooled Phases I/II data aligned with conclusions from a completed “negative” thorough-QTc study. • Individual correction, when possible, can provide a more reliable correction of QT for HR. • For an anti-infective drug, the predictions made based on allometric and cross-compound scaling of non-clinical data were similar to results obtained from a subsequent FIM study. • E-R may extrapolate the QTc effect to drug exposures that were not tested (or could not be tested) in the TQT study. This may be relevant for risk assessment in vulnerable subsets of patients with the target indication. References 1. 2. 4. 5. 6. 7. Piotrovsky V. Pharmacokinetic-pharmacodynamic modeling in the data analysis and interpretation of druginduced QT/QTc prolongation. AAPS J. 2005 Oct 24;7(3):E609-24. Food and Drug Administration, HHS. International Conference on Harmonization; guidance on E14 Clinical Evaluation of QT/QTc Interval Prolongation and Proarrhythmic Potential for Non-Antiarrhythmic Drugs; availability. Notice. Fed Regist. 2005 Oct 20;70(202):61134-5. Bonate PL , Russell T . Assessment of QTc prolongation for noncardiac-related drugs from a drug development perspective. J Clin Pharmacol . 1999 ; 39 : 349 - 358 . Bonate PL . Assessment of QTc interval prolongation in a phase 1 study using Monte Carlo simulation. In: Kimko H , Duffull S, eds. Simulation in Clinical Trials . New York, NY : Marcel Dekker ; 2003 :353- 367. Shah RR . Drug-induced QT dispersion: does it predict the risk of torsade de pointes? J Electrocardiol . 2005 ; 38 : 10 - 18 . Bazett HC. An analysis of the time relations of electrocardiograms. Heart . 1920 ; 7 : 353 - 370 . Fridericia LS. Die systolendauer im elektrokardiogramm bei normalen Menchen und bei Herzkranken. Acta Med Scand . 1920 ; 53 : 469 – 486. 8. Bhattaram VA, Bonapace C, Chilukuri DM, et al.: Impact of pharmacometric reviews on new drug approval and labeling decisions – a survey of 31 New Drug Applications submitted between 2005 and 2006. Clin Pharmacol Ther (2007) 81:213-221. 9. Powell JR, Gobburu JVS. Pharmacometrics at FDA: Evolution and Impact on Decisions Clin Pharm Ther (2007) 82, 97 - 102 -100 0 -20 • 3. 100 100 Pharmacokinetic and exposure-response models were able to characterize the relationship between drug exposure in plasma and the potential for QTc prolongation across a variety of compounds and therapeutic areas. Predicted AD1 Concentrations [ng/mL] 200 80 • 250 RR [msec] Figure 5. Individual QT correction proved superior. 60 Conclusions 360 800 40 -20 380 600 20 Figure 9. Time dependent decline in QTcF -40 320 0 Time since medication (hr) 420 delta-delta QTcF (msec) Background PCB PCB0 PCBSTUDY Data: Data from four Phase I and two Phase II studies in 144 diabetic subjects (79 male, 65 female) receiving DB1 or placebo were used to conduct the E-R analysis. CONC 1 ID 1 PERIOD in ID * CONC 380 The evidence that multiple classes of noncardiac drugs significantly prolong the QT interval of the surface ECG and have cardiotoxic potential, leading to life-threatening arrhythmias has been accumulating since the 1980s. In general, there is a qualitative relationship between QT prolongation and the risk of torsades de pointes (TdP), especially for drugs that cause substantial prolongation of the QT interval. Most drugs that have caused TdP clearly increase the QT interval as well. Documented cases of TdP (both fatal and nonfatal) associated with the use of a drug have resulted in the withdrawal from the market of several drugs and relegation of other drugs to second-line status. Because prolongation of the QT interval is the ECG finding associated with the increased susceptibility to these arrhythmias, an adequate nonclinical and clinical investigation of the safety of a new pharmaceutical agent should include rigorous characterization of its effects on the QT interval. Once proarrhythmic safety of a new drug has been established in early development, large Phase III studies and post marketing surveillance can be limited to less intensive and laborious designs (e.g., a smaller number of ECGs involved in patient monitoring, less emphasis on the QT correction for heart rate). Eff FQ 2 360 Introduction 2) Once the baseline model was established, baseline visit data was grouped with the data from the active treatment visit. The placebo effect (i.e., post-baseline versus baseline) had a statistically significant impact on the overall fit and varied in its magnitude from one study to another. DB1 is a prodrug intended to control and reduce hyperglycemia in type 2 diabetes. QTc(base) 0 0 ID 0 PERIOD in ID f t QT [msec] • Characterize the relationship between drug exposure and potential for prolongation of the QTc interval. • Quantitatively characterize the sources of variability in the QTc interval, including subject demographics, twenty-four hour time-of-day, study, occasion, individual, and measurement. • Compare the conclusions from a compound’s exposure-response model for QTc to conclusions from a completed thorough-QTc study. • Determine the potential impact of using different methods to correct QT interval for differences in heart rate. • Analyze the adequacy of first-in-man QTc predictions made based on allometric and cross-compound scaling methods using nonclinical in vitro/in vivo exposure and in vivo QTc data. QTc(base) 0 f t AGE AGE 29 FEMALES STUDY Data: FIM Phase I study to investigate safety, tolerability and pharmacokinetics of FQ2 in healthy adult volunteers. The study consisted of two cohorts of 12 healthy volunteers that each received FQ2 and placebo. Objectives of the Analysis: Characterize the magnitude of QTc prolongation and provide dose recommendation for Phase II/III studies. Model Development: 1) Fridericia’s correction did not prove adequate to correct for heart rate, thus an individual correction factor (α=0.2 on the average) was derived based on mixed-effects linear model (see Figure 3). 2) The structural model for QTc and FQ2 was kept similar to the one described in Case 1 (sans day effect). As suspected, FQ2 did not have a day effect. 3) Different error structure was utilized to take into account the crossover study design. QT Objectives Fluoroquinolone #1 (FQ1) is a new antibacterial agent that exhibits a marked activity against multidrugresistant staphylococci, streptococci, and enterococci, including strains resistant to other marketed quinolones. Data: The data for exposure-QTc response analysis came from five placebo-controlled Phase I studies. 192 healthy subjects (148 males, 44 females) received an IV infusion every 24h for 10 to 15 days. In three studies serial ECG readings were performed at approximately the same time of day across different visits in order to minimize any effects of a diurnal rhythm in QT interval when comparing on-drug QT measurements to baseline measurements. To minimize the intrinsic variability of QT interval measurement, a triplicate ECG (three readings not more than two minutes apart) was taken in two out of five studies. For the QTc E-R analysis, only baseline and active period visit data were utilized. Objectives of the Analysis: Provide dose recommendation for Phase II/III studies based on FQ1 exposure-QTc response analysis in conjunction with the exposure-efficacy response analysis. QTc Outcome Criteria for FQ1: QTc Outcome Criteria for FQ1 were defined prior to completion of the exposure-QTc prolongation analysis. Outcome criteria were based on the upper bound of the 95% 1-sided confidence interval (estimate of largest mean placebo-subtracted QTc effect for a study population) and were defined as follows: ≤ 8 msec, >8 msec but ≤ 15 msec, and >20 msec constituted “Negative”, “Acceptable”, and “Unacceptable” outcomes, respectively, while 15-20 msec represented an “Ambiguous” outcome. Model Development: 1) Age, gender, time of the day and study effect were found to be significant modifiers of baseline QTc. Case 3 – Individual QT Correction Case 6 – Anti-diabetic Agent QTcB Concentration/Exposure-response (E-R) modeling of QTc prolongation has been conducted for multiple compounds currently in clinical development in different therapeutic areas. Data from available single and multiple ascending dose (SAD/MAD) studies were pooled to construct a population E-R model, with post-hoc predictions of concentration provided by a pharmacokinetic model. All SAD and MAD studies employed a customized robust QTc assessment with time-matched triplicate ECGs and centralized manual QTc reading. Sources of variability were characterized, and the relationship between covariates and model parameters was explored, with a particular emphasis on correcting QT interval for heart rate and modeling the diurnal variation using a truncated Fourier series. Where applicable, all detectable metabolites of the administered compound were examined for a potential relationship with QTc interval. The results of population prediction of QTc prolongation were compared to available thorough QTc (TQT) study results, and the E-R model was evaluated to determine whether it could establish the QTc prolongation relationship without the TQT results. Several benefits of the modeling approach were found. (1) Extrapolation of pre-clinical models in cynomolgus monkeys to humans using allometric scaling and potency scaling between two novel fluoroquinolones provided predictions for the QTc results of the first-in-human SAD study which compared well with the later observations. (2) Negative TQT study results confirmed the negative simulation results from a Phase 1/2 E-R model. (3) The superiority of individual correction of QT for heart rate was shown where typical population correction methods would have substantially overstated the E-R relationship and could have led to unnecessary restrictions on dose selection. E-R modeling should be implemented as a standard part of modeling and simulation at different phases of drug development and used in conjunction with other data that influence the need and/or the timing of a TQT study. Case 1 – Phase II Dose Selection delta-delta QTcF (msec) Abstract