Early Cardiac Safety Data in Clinical Trials

Copyright © 2012 Quintiles

Early Clinical InsightsThe Case for Early Cardiac Safety Data & Modeling

J. Rick Turner, PhD

Senior Scientific Director,

Translational Cardiovascular Safety

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Structure of the Webinar

• General Introduction and Opening Remarks> Evolution of cardiac safety

• Presenters> Dhiraj Narula, MD, Medical Director, Cardiac Safety> Jared Schettler, MS, Director, Phase I Biostatistics

• Questions & Answers

Agenda

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Pipeline pressures have intensified the

demand for speed & productivity

Faster decision-making about

which compounds to progress has

become even more critical

Portfolio productivity

requires better early-phase studies

that deliver optimum quality

data & insights to make the correct

decisions

Novel approaches can:

•Better indicate an investigational drug’s viability

•Identify risks •Facilitate better go/no-go decisions

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Early Cardiac Safety Data & ModelingUnderstanding the larger context

>>

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Evolution of Cardiac Safety

US Food and Drug Administration (FDA)

Health Canada

European Medicines Agency (EMA)

Insights into current regulatory thinking

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The Essence of Today’s TopicThe QT Prolongation

Stylized Surface Electrocardiogram (ECG), QT Interval, and QT Prolongation

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Drug-Induced Torsades de Pointes

• Waveform of TdP looks like the ‘twisting of a double pointed sword’

• TdP is associated with inherited Long QT Syndrome, which is of considerable clinical concern

• TdP can also be drug-induced, which is the basis for the field of Cardiac Safety

The Double-Pointed Sword of Drug Development

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Drug-Induced Torsades de Pointes

• Torsades de Pointes (TdP) – a particular form of ventricular tachycardia with three defining characteristics:> QRS morphology twists around an imaginary axis;> The QRS complex takes on many morphologies, leading to the descriptor

‘polymorphic’;> It is associated with QT interval prolongation

• TdP can lead to:> abrupt-onset syncope (loss of consciousness);> seizures;> sudden cardiac death

• TdP is rare, often self-correcting, but it can be fatal

The Double-Pointed Sword of Drug Development

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Drugs Market Withdrawals

Drug Indication Year Withdrawn

Prenylamine Antianginal 1989 (UK)

Terodiline Urinary incontinence 1991 (UK, US)

Sparfloxacin Antibiotic 1996 (US)

Sertindole Antipsychotic 1998 (UK)

Terfenadine Antihistamine 1998 (US)

Astemizole Antihistamine 1999 (US)

Grepafloxacin Antibiotic 1999 (UK, US)

Cisapride Gastro-esophageal reflux 2000 (UK, US)

Droperidol Schizophrenia 2001 (UK, US)

Levacetylmethadol Opiate addiction 2003 (UK)

Examples in the UK and US for Proarrhythmic Safety Concerns

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The History of ICH S7B & ICH E14 Evolution and adoption for clinical and non-clinical studies

EMEA releases Points to Consider document on non-cardiovascular medicinal products

European Society of Cardiology holds conference: The Potential for QT Prolongation & Proarrhythmia by Non-antiarrhythmic Drugs

Health Canada submits Concept Paper to FDA

Health Canada & FDA release joint paper

• ICH issues ICH S7B & ICH E14

• EMEA & FDA adopt ICH S7B & ICH E14

• Health Canada adopts ICH S7B & ICH E14• FDA forms Interdisciplinary Review Team• Health Canada releases regional Q&A

interpretation of ICH E14• ICH E14 Implementation Working Group

release Q&A interpretation document

The Japanese PMDA adopts ICH E14

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Basic Nature of the TQT Study

• A four-arm study:> Active control drug (typically moxifloxacin) to establish assay sensitivity;> Placebo, for comparison of response to two doses of the test drug;> Proposed therapeutic dose of the drug;> Supratherapeutic dose, several multiples of the therapeutic dose, to mimic the

worse case scenario in patients if the drug were to be approved:- Patients who have compromised metabolism or excretion and patients taking

other medications;- Each can lead to greater than intended concentrations of the drug in the body.

• A fully balanced cross-over design is preferable, but parallel designs are necessary in certain cases:> If the drug has a long half-life, making wash-out periods very long;> If the drug has active metabolites.

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Cardiac Safety Assessments

• Main purpose of the TQT – Help regulators decide how much ECG monitoring (intensity and extent) to be employed in an investigational drug’s Phase III program

• TQT studies have therefore typically been conducted relatively late in Phase II clinical development

• Minimal (if any) attention has been paid to collecting QT data in early-phase studies. ECGs have been taken for safety reasons, but this is quite different

• Thorough QT prolongation assessment requires considerable methodological rigor:> Digital recording of ECGs;> Strict control of subjects’ activity and environment;> Use of a core ECG lab to read the ECGs

The “traditional” approach

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Improving the Approach

• While the TQT study is still needed, regulators are very open to new approaches to the prospective exclusion of unacceptable cardiac risk> Improved nonclinical assessments;> Improved early-phase assessment;> Combination of above in a more comprehensive translational approach.

• Recent Cardiac Safety Research Consortium focusing on QT assessment in early-phase development held at FDA headquarters, with EMA and Health Canada representatives present:> “Can Predictive Value be Enhanced to be Similar to That of a TQT study?”> www.cardiac-safety.org, and then “Think Tanks and Meetings”

Rigorous early-phase assessments and modeling

http://www.cardiac-safety.org/

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Regulatory View & Thoughts for Sponsors

• Regulators pleased if proarrhythmic liability could be identified (or identified as absent) in early-phase studies

• Require empirical evidence from clinical development programs that have done both rigorous early-phase QT assessment and a TQT study that the early work is indeed predictive of the results of the TQT study

• Clearly, should this happen, there is an immediate benefit to sponsors – less cost and less time to complete a clinical development program

• An intuitive question becomes: “until that point, why should I do both?”> Idealistic answer: Drug safety is a shared responsibility, and we all need to play a

part: participation would advance the Science of Drug Safety;> Additionally – there is an immediate “win-win” benefit of using this early-phase

information in decisions regarding termination of a program earlier if advisable, or designing the subsequent TQT study more efficiently in terms of both expense and time

Early QT Assessment - A Win-Win for Sponsors and Patients


Early Clinical Insights

Can We Replace the TQT Study?

Dhiraj Narula, MD, MRCP UK, DM, FACC, FISE

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The Vortex

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Overview

• Cost-Effectiveness of E14

• How should we design phase I studies to collect ECG data?

• How can assay sensitivity be established without a positive control?

• How can we integrate preclinical and early clinical data to assess arrhythmic risk?

• Can we replace the tQT study?

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Cost-Effective Drug Regulation Example of thorough QT/QTc studies

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Cost-Effective Drug Regulation Example of thorough QT/QTc studies

The cost of tQT studies and ECG monitoring of patients on antipsychoticswas €2.4 million per SCD prevented and €187,000 per QALY gained

JC Bouvy1,2, MA Koopmanschap1, RR Shah3 and H Schellekens2.

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The Challenge

• Society expects the Faster development of Safer therapeutics with Less uncertainty at a Lower cost

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Case Study: Phase I Study

• Pharmaceutical Major• Phase I Dose-Escalation Study of a HIV medicine in healthy volunteers at

one site• Four Dose Levels versus Placebo, n=8 each group• 14 day dosing each group• 40 subjects, about 100 ECGs per subject• ECGs centrally analyzed

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Regulatory ConcernsAddressing Concerns in SAD/MAD studies

Regulatory Concerns

• Regulatory experience with ECG data from SAD/MAD studies is limited

• Small cohorts/low statistical power/high variability

• Unbalanced design in #subjects on placebo versus drug

• Doses tested are sometimes lower than eventual therapeutic dose

• ECG assessment methodology is inconsistent, and serial ECGs are not collected over the dosing interval

• On treatment ECG data not routinely time-matched to baseline ECG data

• No concurrent positive control

Mitigation Strategies

• Submit ECG analyses from SAD/MAD studies

• Make a prospective plan to combine subjects across studies

• Consider a design change to use crossover data for each subject

• Plan to use concentration-QT modeling

• Design Phase I with input from ECG core lab partner, with a good ECG protocol

• Crossover data on placebo or design in a day 0 for time-matching or QTcI

• Alternate assay sensitivity method

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Results

QTcBReplicates

Time-Points 1 2 3 4 5 6Pre -8 0 -2 -1 -1 -1

0.5 HR POD 2 3 4 4 5 5

1 HR POD 12 13 11 12 12 12

1.5 HR POD 10 11 11 11 10 102 HR POD 10 8 8 8 8 9

4 HR POD 12 10 10 11 10 106 HR POD 6 4 4 3 3 3

Placebo-subtracted mean change from time-matched baseline following 400mg Moxifloxacin in 40 subjects

• Statistically significant results are highlighted in yellow.• ∆∆QTcB was statistically significant at 4 time-points with 1,2,3,4,5,6 replicates

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Model vs. Data

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Design of SAD/MAD Studies for Cardiac Safety• Attention to ECG protocol for quality of recording

> Heart rate stability, rest period before ECGs are recorded> Standardized conditions, ECG before blood draw> Education for clinic staff on ECG recording methods

• Baseline day data> Correction for baseline> Individual QT/RR pattern (QTcI)

• Triplicates at each time point• Central Lab Analysis• Primary target is measure of central tendency

> Upper bound of 90% CI and categorical outlier analysis is of little use in small cohorts and cant really pool groups for this

> Concentration-QT analysis is very useful• Power the analysis to detect changes in the order of 15-20 ms

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Development Strategy After SAD/MAD Data• Early detection of QT liability• Possible no-go for the molecule

> Risk vs. benefit> QT effect size> Competitors> Other side effects

• Help with timing of tQT study• Intensity of ECG monitoring in trials before the tQT study

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Indication Dependence

Low

Low Low

High

High High

Benefit

TolerableRisk Tolerable

Ignorance

From Stockbridge, FDAFrom Stockbridge, FDA

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Alternatives to a Positive Control

• Quality Measures (Malik)> Small intra-subject SD of QTc across all baselines , e.g. <10 ms> Intra-subject SD of mean QTc is less than inter-subject SD

• Autonomic measures, Standing and Food effects

> 74 healthy male subjects> QTcF decreased by 9.2 ms (7.0-11.3) after standing for 5 minutes> Acute autonomic loads like Valsalva had inconsistent effects

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In a study of 39 drugs, incongruity was seen at all levels of hERG safety margin when correlated with QT effect, though the tendency to QTprolongation >5 ms does decrease with greater safety margins

Evaluation of hERG assay performanceTranslating preclinical safety studies to clinical QT prolongation

Gary Gintant*

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Model from 4 Phase I & II studies Results from tQT study AD1, an anti-diabetic drug

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• Simulation Case Study (“Near tQT”)> Compound assumed to have a 5 ms QT effect> Simulated MAD study with 4 arms (placebo, 0.5x, 1x, 2.5x

therapeutic dose)> Assumed 10 participants each arm> Assumed 4 days each dose> PK collections at 9 time points for each arm> Duplicate ECGs at each of these time points> Assumed inter subject variability of 14 ms

• 5000 simulations

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MAD Simulation Studyshows that for a real 5 msaverage effect, the modelgives a positive estimate (LCL>0) 99% of the time

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The Need for a tQT Study

• A tQT study may not be needed if preclinical and early clinical data suggest that:> There is a clear effect on QT prolongation*> There is a QT shortening effect*

• The preclinical data are important because there is no convincing evidence of a clinical QT prolongation when preclinical data have excluded the mechanistic aspects**

• Regulatory experience with ECG data from SAD/MAD studies is limited and there are many concerns; one way to address these might be submission of SAD/MAD ECG data with central tendency and PK/PD analysis***

• A tQT study may not be needed once it is demonstrated that a model that includes preclinical and early clinical data will reliably predict the result of the tQT study*

Can we eliminate the need?

*Robert Temple, **Krishna Prasad, ***Colette Stranadova: speaker comments at CSRC think tank, Feb 3, 2012

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Mountainview Hospital, Las Vegas, 2007


Early Clinical QTc Signal Detection through

Concentration-QTc Modeling

Jared Schettler, Director of Phase I Biostatistics

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Outline

• Motivation for and advantages of CQT assessment early in development

• Key study design features for CQT assessment

• Optimal study designs for assessing CQT

• Overview of CQT Methods

• Discussion

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Key Motivation and Advantages

“For drugs that prolong QT, the approval and labeling decisions (e.g., benefit-risk and dose selection) are based to a large extent on dose- and concentration-QT relationships.” - Garnett 2008

• Gain clearest possible understanding of a drug’s QTc prolongation risk

• Determine optimal timing of TQT study in drug’s development plan

• Avoid surprise TQT results

Concentration QTc Effect (CQT) Modeling in Early Development

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Key Motivation and Advantages, cont.

• Insufficient power to exclude a 10 ms prolongation: 41% power for 12 subjects per arm for a single ECG (assumptions below)

• Under parallel design (as in typical SAD/MAD study), 85% power requires approximately 85 completing subjects per arm when 4 ms prolongation present and STD is 12 ms (Zhang 2011)

• Between-subject variability in ECGs is quite high (12 ms is a low estimate)• In contrast to parallel design, a within-subject design (eg. Balanced

Crossover or Randomized Incomplete Block designs) would require 39 completing subjects (under same assumptions) due to greater-precision for within-subject testing

• Only those subjects from each dose level contribute to estimate of ddQTc for that dose, in a simple comparison of means

• Intersection-Union Test on Comparison of Active and Placebo is biased toward false-positive findings (Hutmacher 2008)

Shortcomings in Standard Assessment of Central Tendency per ICH E14

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• More directly links the prolongation to its cause (drug concentrations)> Essential for therapies with high PK variability

• Uses the entire study sample to estimate placebo-corrected, change-from-baseline QTc (ΔΔQTc): all dose levels and corresponding concentrations contribute → Greater precision

• Can account for lag between concentration and effect• Can easily combine multiple studies, even of differing designs• Allows prediction of QTc effect at unstudied intermediate dose levels

• Rohatagi 2009 > Demonstrated accuracy and reliability estimating ΔΔQTc using from early studies> May aid in therapeutic dose selection> Can be used to identify subpopulations or risk factors

Advantages of CQT Modeling in Early Development

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• Avoids need for adjusting/powering for multiple statistical comparisons • Required sample size can be 1/3 to 1/2 that for E14 analysis for central

tendency (Garnett 2008)• Can be used to simulate a future TQT trial• More leeway for innovative methods of correcting QT interval for RR/HR

Advantages of CQT Modeling in Early Development

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SAD/MAD Study Design

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Key Study Design Features

• Replicate, centrally-processed ECGs at each time point as a simple way to reduce variability

• PK samples matched closely in time to ECG assessments• Series of ECGs over range of possible individual Tmax plus up to 2 hours

afterwards (to account for lag in effect

• Placebo arm with corresponding ECGs (always present in SAD/MAD); OR• Time-matched baseline day of ECGs

> Allows simpler modeling using within-subject correction for diurnal effects> Allows estimation of individually corrected QT (QTcI)

• But preferably both (especially for parallel designs, as for typical SAD/MAD)> Without baseline day or placebo data, QTc effect is confounded with diurnal

patterns> Standard approach for parallel design TQT studies

SAD/MAD studies as mini-TQT trials with respect to ECG collection

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Optimal SAD/MAD Designs forCQT Modeling

• Within-subject dose escalation designs are statistically preferable (when clinically possible) due to the high correlation of ECGs within a subject (0.5 to 0.9+ correlation) and the resulting lower variability in ΔQTc> Example: Partially balanced incomplete block (PBIB) design

> For more dose than 3 or 4 dose levels, multiple cohorts can be planned so that dose levels continuously increase (‘leap-frog’)- For example, add a similar cohort of subjects for dose levels: 15, 30, 60, 90

Treatment PeriodSequence 1 2 3 41 Placebo 20 mg 40 mg 80 mg2 10 mg Placebo 40 mg 80 mg3 10 mg 20 mg Placebo 80 mg4 10 mg 20 mg 40 mg Placebo

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Optimal SAD/MAD Designs forCQT Modeling

• Another possibility: adding placebo periods to some dose levels within a standard SAD/MAD design (ideally in randomized crossover manner)

Treatment PeriodCohort Sequence 1 21 1 Placebo 5 mg

2 5 mg Placebo4 1 Placebo 30 mg

2 30 mg Placebo8 1 Placebo 200 mg

2 200 mg Placebo

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CQT Modeling Methods

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Overview of CQT Modeling Methods

Increased accuracy and precision of CQT models cannot overcome a biased QTc metric

• Complexity ranges from simple linear regression to relatively simple linear mixed models to quite complex nonlinear mixed models

• Key estimate: mean prolongation attributable to the therapy at Cmax and corresponding 90% CI for all dose levels of interest> Same estimate requested by FDA in reviews of TQT protocols

• Must be based on an appropriate and sufficiently well fitting model

• Adequacy of QT correction cannot be overlooked while focusing on the complexity of the model

Models estimate QT effect as a function of drug concentration

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‘First-pass’ Linear Model

• Proposed by Garnett et al (2008)• Essentially a linear regression for ΔΔQTc on concentration

> Adapted to repeated measurements per subject to allow individualdeviations from the population slope for the CQT relationship

• Best suited for crossover designs so corrections to placebo can be used to remove diurnal patterns

• Per Garnett, does not include a baseline QTc covariate which is a very strong predictor for either on-treatment observed QTc and change in QTc


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‘First-pass’ Linear Model, cont.

• Shown to be inadequate in many instances (Tsong 2008)> Linear CQT characterization may be adequate overall but biased in

prediction at Cmax> CQT relationship may be time-dependent – Eg. QTc effect is

different at the same concentration for distribution vs. elimination> In practice, underestimates ΔΔQTc relative to E14 analysis even

though theoretically should exceed it (by capturing the true peak effect)

> More complex models needed- Eg. accounting for hysteresis:

» Lag from peak concentration to peak ΔΔQTc of 0.5 to 1.5 hour not uncommon

> Other time-dependence of CQT relationship • Often little concordance between estimates from this model and

more complex models


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Simple Mean Diurnal Effects Model

• Estimate model assuming SAME underlying QTc mean profile in time for subjects on BOTH placebo and active treatments

• Attribute all deviations between placebo and active to concentration effect

• The CQT relationship can be estimated as linear in simplest case, or as more a more complex function (ideally monotonic and asymptotic)

• Similar to the ‘standard’ longitudinal or E14 approach except that treatment effects are attributed to concentration in a continuous manner rather than to treatment (active vs placebo) in a categorical manner

• Applicable to parallel studies where subject-level placebo correction is not possible

• Likely to have same drawbacks detailed in Tsong 2008


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Cmax/Emax Model

• Simplest approach statistically for SAD/MAD (one datapoint/subject)

• Model individual maximum ΔΔQTc vs Cmax (linear or nonlinear)

• Directly links maximum exposure to maximum QT effect on a subject level. > Accounts for possible hysteresis/lag effects on individual level

• Consider restricting subject ΔΔQTc to ECGs concurrent with or after nominal Tmax

• For parallel designs Placebo correction must be performed after the model estimation

• May overestimate ΔΔQTc (needs investigation)> Well documented that statistical comparisons of mean maximum ΔΔQTc overestimates the true effect and has a high false-positive rate


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Nonlinear Mixed Effects Modeling

• Often referred to as a ‘population modeling’ approach• Typical approach consists of modeling the following steps:> Correcting QT for heart rate using baseline data only

- If a standard QTc is not adequate, then a model-based correction can be used

- Assessment of QTc dependence on RR necessary on treatment also

> Modeling diurnal patterns as a function of clock time (potentially including covariates) using baseline data only

- Often estimated using the sum of cosine functions (partial Fourier series)

- Can allow subjects to each have their own diurnal pattern- Selection of time scale (treating study days as consecutive vs.

concurrent ) can have large impact on model selected- Simple baseline covariate can predict in itself as well as diurnal

models


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Nonlinear Mixed Effects Modeling cont.

• Modeling concentration effect (potentially including covariates)using all data, under the assumed diurnal patterns from Step 2

> Linear or log-linear often used but alternatives must be assessed> Hysteresis (lag) must be considered or even included by default> Concentration data may be observed or predicted from population PK

Model

• AFTER final model is selected, ΔΔQTc is estimated at representative Cmax

> Otherwise there is risk for selecting model that yields most favorable result

> Need for research on individual Cmax effect as compared to mean effect at mean Cmax


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Nonlinear Mixed Effects Modeling cont.

• Can be very lengthy process to determine the ‘best’ model (weeks if not months) but this can be shortened considerably by pre-specifying reasonable model effects rather than trying the great multitude of possibilities:

> Periods of diurnal effects and random effects thereof> Large battery of diagnostics assessments possible > Covariates> Shape of concentration effect curve and random effects thereof

• Repeated hypothesis testing to select the final model ‘spends’the nominal significance level (alpha)

> Net result: 90% CI for final estimate of ΔΔQTc yields far less than 90% confidence

• Models developed early in drug cycle may have to be simpler than those from a TQT study or meta-analysis of many studies

> Not statistically ideal to develop a model with 10+ parameters from 30 subjects


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Conclusions on Early CQT Modeling

• CQT modeling can yield valuable and reliable early cardiac safety information, but only if an appropriate model is used.

• Alterations to standard SAD/MAD designs should be considered• Research needed to determine the ‘best’ CQT modeling approach for small

datasets> ‘Planning is under way to standardize data set formats, C-QT analysis methods, and

modeling results to facilitate meta-analyses across studies to gain insights into various design and risk-related issues.’ (Garnett 2008)

> Initiative by CQT working group to develop standardized CQT analysis plan (2012)

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Question and Answers

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Early Cardiac Safety Data in Clinical Trials