PK-PD Modeling with the QTc: Is it possible to avoid a TQT Study?

Paul A. Frohna, MD, PhD, PharmD

Biotechnology Consultant

Frohna Biotech Consulting

www.frohnabiotechconsulting.com

Part 2. Approaches to QTc Evaluation During Clinical Development

Approaches to QTc Evaluation During Clinical Development

Role of PK-PD

Recent Examples and Outcomes

The Learn-Confirm Approach for QTc Assessment

Learn: Collect ECG data in Phase I/IIa for

exposure-response analysis with PK and DQTc

– Requires advanced planning

– Helps determine Phase III ECG monitoring frequency

– Risk reduction strategy

Confirm: Design your TQT study based on your

―Learn‖ analysis

– May allow smaller sample sizes

May be acceptable to regulatory authorities

when a TQT study is infeasible or when

significant amounts of QTc data have already

been collected (some examples will be given)

PK-QTc Evaluation of Ranolazine (Ranexa)A Comprehensive Database Planned

Developed by CV Therapeutics (now Gilead) for the symptomatic treatment of angina

– Cardiac drug that has multiple electrophysiologic effects and an uncertain MOA, but not an anti-arrhythmic

All ECGs in CVT-sponsored studies read by a central core ECG laboratory

Population QTc analysis of data from 15 studies

– 15,819 QTc-plasma concentration pairs

– All observations at steady state

CV Therapeutics and FDA agreed that ranolazine prolonged QTc but patient risk was different

Assessment of QTc Effects of Ranolazine Infusion to Intolerability Study

ECG

PK

Vitals

Ranolazine n = 22

Placebo n = 6

31 subjects: 16 male, 15 female5137 ECGs (3355 on ranolazine)

0

5000

10000

15000

-24 0 24 48 72 96 120

Targ

et

[RA

N],

ng

/mL

-24 0 24 48 72 96 120 -24 0 24 48 72 96 120

Ranolazine n = 22

Placebo n = 6

Ranolazine n = 7

Placebo n = 3

Time, hr

Placebo (single-blind, all subjects)

Ranolazine (double-blind)

Max50% 95%

Ranolazine Concentration vs. ∆QTc Infusion to Intolerability Study

0 2000 4000 6000 8000 10000 12000Plasma ranolazine concentration, ng/mL

-100

-80

-60

-40

-20

0

20

40

60

80

100

ΔQ

Tc

vs

ave

rag

ed

ba

se

lin

e, m

se

c

Slope = 2.29 msec per 1000 ng/mL (R2 = 0.20)

The target concentration of

15,000 ng/mL could not be

reached due to dizziness,

nausea, postural hypotension,

diplopia, somnolence, syncope,

and paresthesia.

Individually optimized regressions of mean RR intervals and median QT intervals.

Percentiles are peak concentrations on 1000 mg bid in CVT 3031 and CVT 3033 shown for comparison.

Clinical Events With Potential QTc LinkPhase II/III Controlled Studies

Patients with events, n (%)

Ranolazine, mg bid

Placebo 500 750 1000 1500

Total patients, N 455 181 279 459 187

Preferred term

Dizziness 6 (1.3) 2 (1.1) 10 (3.6) 29 (6.3) 22 (11.8)

Heart arrest 1 (0.2) 0 0 1 (0.2) 0

Palpitation 5 (1.1) 0 2 (0.7) 2 (0.4) 4 (2.1)

Sudden death 2 (0.4) 1 (0.6) 1 (0.4) 1 (0.2) 0

Syncope 0 0 0 5 (1.1) 3 (1.6)

Ventricular fibrillation 0 1 (0.6) 0 0 0

Ventricular tachycardia 0 0 1 (0.4) 0 0

Torsade de pointes 0 0 0 0 0

Summary—Ranolazine and DQTc

The QTc effect of ranolazine is well characterized

– At plasma concentrations exceeding tolerability

– Remains linear at 2.4 msec per 1000 ng/mL

The slope of the ranolazine vs DQTc relationship was

not altered by important covariates:

– Heart rate – Heart failure – Age

– Gender – Diuretics – Anti-anginals

– This is a different profile from drugs known to cause TdP

Intolerability limits exposure to concentrations

associated with larger QTc increases

Approved by FDA and EMA

FDA’s Analysis of YOUR PKQT Data

Excerpted from the FDA Reviewer’s comments for the Ranolazine NDA

Concentration-QTc Modeling as a Tool During Development

Use of a Phase I/II PK-QTc Dataset Shashank Rohatagi et al. ACoP 2008 . ROLE OF MODELING AND SIMULATION IN EVALUATING THE

QTc PROLONGATION POTENTIAL OF DRUGS

Conclusions: 1. Negative TQT study results with the anti-diabetic drug confirmed negative

simulation results from phase I/II C-QT models.

2. C-QT 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.

Model based on Phase I/IIa Data Results from TQT Study

First-in-Human Near Thorough QT StudyMalik M. et al. J Clin Pharm. Aug 29, 2008.

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 C-QT model.

First-in-Human Near Thorough QT StudyMalik M. et al. J Clin Pharm. Aug 29, 2008.

The linear regression model predicts a mean 0.6-millisecond QTc interval prolongation per every 1000-ng/mL increase in drug concentration

Regulatory Acceptability of the Study?Malik M. et al. J Clin Pharm. Aug 29, 2008.

FIH Study ConclusionsMalik M. et al. J Clin Pharm. Aug 29, 2008.

When 2 cohorts of the lowest, middle, and

highest doses were pooled (12 subjects per

active Tx group), the spreads of placebo-

corrected ΔΔQTc values were within the

regulatory requirements (single-sided 95%

confidence interval <10 milliseconds) at all time

points.

The ECG design of the FIH study provided data of

regulatory acceptable accuracy at a small fraction

of the cost of a full thorough QT study.

No disclosure if the data were accepted by FDA

Using PK-PD Modeling when a TQT Study is Not Feasible or Ethical

Most biologics, due to dosing considerations

(max dose, half-life, etc…), MOA and potential

side effect profile

When you can’t use healthy subjects

– Toxicity : Droperidol—small molecule anti-emetic

– Most anti-cancer agents, particularly cytotoxics

• Example—Erlotinib (Tarceva®, Genentech/OSI)

– Started TQT but first 6 subjects developed severe facial

rash at clinical dose so stopped the study

– Designed PK-QT sub-study within the Phase 3 program at a

couple of academic, high enrolling sites with capabilities of

doing ―intensive PK‖ and QTc recording—FDA accepted

Droperidol Study #1: PK and QTc Assessment of Single IV

Concentration-QT Study of 3 IV bolus doses (0.625 mg, 2.5 mg,

and 5 mg) of droperidol were studied in a 4 period, single-blind,

placebo-controlled, crossover trial in healthy subjects.

8 subjects were enrolled and exposed to one or more doses for a

total of 15 exposures

Study was stopped because of moderate to severe

neuropsychiatric side effects experienced by the volunteers.

Trend toward a dose dependent increase in the mean maximal

QTc interval change from baseline (placebo subtracted) of 1, 13,

and 30 milliseconds on the 3 doses respectively.

Outlier QTc changes of 77 and 79 msec on 2.5 & 5 mg

M.Desai1, A.Pinto2, A.Adigun2, J.Hilligoss2, S.H.Haidar1, N.Chang1, B.Rappaport1, S.M.Huang1,

J.C.Gorski2, S.D.Hall2, 1CDER, FDA, Rockville, MD, 2Indiana University, Indianapolis, IN

Droperidol Study #2: PK-QTc of Single IV Doses of 1 mg Droperidol and 4 mg OndansetronCharbit B. et al. Anesthesiology 2008; 109:206–12

A crossover study of 16 healthy volunteers. The linear regression was significant with

droperidol (r =0.34, P=0.005) but not with ondansetron (r =0.16, P=0.26). Continuous

lines represent the linear regression with the 95% prediction band.

Final Thoughts

Conc-QTc Modeling is an important tool in the clinical development plan within ICH E14

More examples of Conc-QTc modeling of Phase I and II data accurately predicting the results from TQT studies will lead to greater regulatory acceptance of these efforts

Conc-QTc data collection requires careful planning early in clinical development to make the most of your clinical trials and to understand your ECG risk early

Ultimate goal is to not have to do the TQT study, which IS possible but you need to be prepared