Pharmacometrics: A Business Case May 25, 2010 Pharmacometrics Task Force

Pharmacometrics: A Business Case

May 25, 2010

Pharmacometrics Task Force

2 |

What is Pharmacometrics (PM)?

Pharmacometrics (PM) analyses are:

Quantitative analyses of data pertaining to:▪ Pharmacokinetics▪ Biomarkers▪ Clinical outcomes▪ Disease characteristics▪ Trial characteristics

▪ Can include:

– Mathematical modeling and simulation

– Statistical analysis

▪ Often used to facilitate efficient drug development and approval process

▪ Needs multidisciplinary team consisting of quantitative clinical pharmacologists, statisticians, engineers, data management experts, and clinicians

3 |

Utilization of PM allows for a more efficient drug development process

Propose best doses

Estimate effect size

Rescue discarding good drug

Main advantages

Target patient selection

Maximize value of prior data

Drug approval

Labeling

The cost of PM is marginal compared to the final cost of a trial. In most cases, the level of effort for PM is as low as 1 person for 2-6 months1

1 Depending on the complexity of analysis, the level of effort may be slightly lower or higher

4 |

Common questions about PM

No!

▪ Will it delay my NDA?

▪ Will it take a long time?

▪ Does it take many resources to deliver?

5 |

Example benefits (1/2)Scenario and impact examples

Propose best doses

▪ Exposure – response relationships for organ rejection (effectiveness) and creatinine clearance (safety) were developed. Simulations explored alternative dosing regimens to optimize benefit – risk profile; level of effort was 1 person for less than 2 months. Pharmacometric analyses presented to Cardio-Renal Advisory Committee in 2005 with recommendations to conduct another clinical study using an optimized dosing regimen (pg 9)

1

▪ Highly variable PK of Tacrolimus between ulcerative colitis patients and high trough concentrations in Phase II studies presented challenges to further development. Simulation of dose titration based on exposure-response was effective for identifying target trough concentration, demonstrating effectiveness and justifying Phase III studies (pg 13)

2

▪ The exposure-viral load reduction model predicted the effect at different doses, resulting in a range of possible active oral doses used in the design of phase IIa trials (pg 15)

3


▪ Enhanced the trial success by increasing study duration; trial now suitable for registration (pg 21)4

▪ A new dosing regimen was selected based on pharmacometric analyses and evaluated in an additional clinical trial. Nesiritide was approved by FDA in 2001 (pg 30)


5

6 |

Example benefits (2/2)Scenario and impact examples

▪ Pharmacometric dose – response analysis identified the proportion of mildly diseased non-responders was the primary cause of lack of evidence of effectiveness. FDA’s approvable letter suggested that sponsor conduct a future study including patients with moderate and severe disease (pg 35)


6


▪ Approval of oxcarbazepine monotherapy in pediatrics was based on demonstrating similar exposure –response relationship for seizure frequency in pediatrics and adults using prior data from adjunctive therapy trials. No additional monotherapy pediatric trials were required (pg 38)

7

Drug approval

▪ Confirmatory evidence provided by significant dose–response (chorea score) relationship. Internal consistency of results across one positive, one negative trial and their extensions . (pg 40)

8

▪ Clinical trial simulations a paricalcitol dosage regimen based on iPTH/80 was predicted to significantly lower the rate of hypercalcemia, compared to the iPTH/60 based regimen tested in clinical trials, without significantly impacting efficacy. Oral paricalcitol was approved by the FDA for use in CKD Stage 5 patients at a dose of iPTH/80 TIW without the conduct of further clinical trials in patients (pg 41)

9

Labeling

▪ Cleviprex dosing regimen used in clinical trials resulted in overshooting and oscillations around the target blood pressure. Simulations of the exposure – response relationship were used to optimize the dosing regimen to quickly achieve and maintain target blood pressure (pg 44)

10

7 |

Three big challenges exist

Perception that PM may slow the drug development timeline and raise costs

Lack of awareness of full benefits and usage

Lack of adequate training infrastructure in the US and abroad

Challenges

8 |

Key success factors for enhancing adoption of PM

▪ Educate professionals around cost and speed implications of PM, recognizing that “in some” cases it may take longer time in Phase 2

▪ Increase awareness of PM benefits with clinical development executives and scientists and widely share successes

▪ Align structurally PM in large organizations to reflect multi-disciplinary nature and clinical decision-making implications

▪ Enhance number of trained professionals

▪ Develop standardized analysis and reporting

9 |





Drug approval

Labeling

Propose best doses

Detailed business cases

10 |

Determining appropriate dosing for future trials

Cyclosporine troughng/ml

400300200100

Probability of failure Percent

50

45

40

35

30

25

20

Everolimus 9 ng/ml

Everolimus 6 ng/ml

Everolimus 3 ng/ml

Azathioprine

Everolimus 12 ng/ml

-40

-30

-20

-10

0

10

200100 400

Cyclosporine troughng/ml

Mean CrCL change from baselineml/min

300

PM approach and impact

▪ Sponsor NDA for everolimus tablets (a prophylaxis of organ rejection following heart transplantation) needed to provide safe and effective dosing regimens that would minimize renal toxicity

▪ Key questions included:

– Are the effectiveness and renal toxicity clinical outcomes related to drug exposure?

– What is a rational dosing regimen that would maximize effectiveness (benefit) and minimize nephrotoxicity (risk)?

▪ Analysis conducted by 1 person for <2 months

▪ FDA performed simulations based on heart transplantation study to project range of outcomes of altered dosing schemes

▪ Determined the appropriate dosing to test in future trials

PROPOSE BEST DOSES1

SOURCE: Reprinted by permission from Macmillan Publishers Ltd: Clin Pharmacol Ther, Impact of pharmacometric reviews on new drug approval and labeling decisions--a survey of 31 new drug applications submitted between 2005 and 2006. Bhattaram VA, Bonapace C, Chilukuri DM, Duan JZ, Garnett C, Gobburu JV, Jang SH, Kenna L, Lesko LJ, Madabushi R, Men Y, Powell JR, Qiu W, Ramchandani RP, Tornoe CW, Wang Y and Zheng JJ. (reference citation) 81:213-221, copyright 2007 (year of publication)

11 |

Enhancing trials through prodrug dosing

0

0.2

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1.0

0

Response

70

Area under the curve (AUC)mg.hr/L

50

Toxicity

60

Effectiveness

40302010

0

0.2

0.4

0.6

0.8

1.0

Response

Weightkg

Predicted toxicity – weight-based dosing

Predicted effectiveness – weight-based dosing

Predicted toxicity – fixed dosing

Predicted effectiveness – fixed dosing

100908070605040

SOURCE: Wang Y, Bhattaram AV, Jadhav PR, Lesko LJ, Madabushi R, Powell JR, Qiu W, Sun H, Yim DS, Zheng JJ and Gobburu JV (2008) Leveraging prior quantitative knowledge to guide drug development decisions and regulatory science recommendations: impact of FDA pharmacometrics during 2004-2006. J Clin Pharmacol 48:146-156.


▪ Sponsor developing a prodrug (test) for the treatment of a life threatening disease in which a noninferiority comparison to the parent (reference) drug failed to establish effectiveness

▪ Proposed a fixed dosing strategy for patent drug (as approved) with key question:

– What is the appropriate dosing regimen (fixed vs. per kg dosing) for noninferiority comparison of the prodrug and parent drugs?

▪ Analysis conducted by 1 person for <2-4 weeks

▪ FDA analyzed data from failed test drug trials to develop the parent drug AUC, effectiveness, and toxicity relationships

▪ Proved dosing regimen for prodrug (mg vs. mg/Kg), which enhanced future trial to support an approval

PROPOSE BEST DOSES

12 |

Recommending dosage warning label

0

0.2

0.4

0.6

0.8

Probability of quitting at weeks 9-12 or nausea after treatmentPercent

AUCNg/ml

100 3000 400200


▪ Sponsor conducted five dose-finding and registration clinical trials to determine the appropriate dosing


– What is the optimal dose?

– Is there a need for dose adjustment in subjects with renal impairment? If so, by how much?

▪ Sponsor conducted population pharmokinetics analysis, model simulations and exposure-response analysis to determine basis for discussing the dose selection

▪ Led to recommendation of dosage warning to be included in current label

PROPOSE BEST DOSES


13 |

Clarifying dosing and regulatory expectations

NOTE: Only dose-finding studies shown

0403022001 05 06 2007

Mar 02

CS02/N = 129, 6 mo

CS07/N = 172, SD

CS06/N = 82, SD

Activity

CS21 dose/regimennot finalized

EOP2A meeting

CS14/N = 127, 12 mo

CS12/N = 187, 12 mo

Mar 05Mar 04 Mar 06 Mar 07Mar 03

CS21/N = 610

NDA submission – Feb 29, 2008; approval – Dec 24, 2008

Registration trial


▪ Sponsor needed to determine the dosing for a drug 7 years in development for advanced prostate cancer patients

▪ Key questions were:

– Is a loading dose needed to suppress testosterone, and, if so how much?

– Is a maintenance dose and suppression regimen needed?

▪ Sponsor developed a mechanistic data model to explore dosing strategies via trial simulations

▪ Identified alternative dosing strategies and clarified regulatory expectations that led to approval

SOURCE: FDA, Drug Approval Package, Degeralix Injection, Ferring Pharmaceuticals, December 24, 2008

PROPOSE BEST DOSES

14 |

Simulating dose to justify Phase 3 design


▪ Tacrolimus, a potent approved immunosuppressant, had a promising expansion as an oral therapy for ulcerative colitis (UC)

▪ Highly variable PK between patients and high trough concentrations in Phase II studies presented challenges to further development

▪ Logistic analysis of PK/response in late Phase II demonstrated trough concentration is a good predictor for response

▪ Simulation of dose titration based on PK/response was effective for attaining target trough concentration, demonstrating efficacy and justifying Phase III studies

SOURCE: Presented by Atsunori Kaibara (Astellas Pharma, Inc) at the “PK/PD Internal Symposium on Modeling and Simulation in Drug Development and Clinical Applications", Yonsei University Medical Center, Seoul, Korea (2006)

High trough concentrations in Phase II studies

Simulated dose titration shows eliminated high trough

PROPOSE BEST DOSES2

15 |

Promoting innovative trial designs to determineeffective and safe dosing


74

53

29

84

64

39

62

44

27

73

54

33

05 10 20 10 20 40

Biomarker

Genotype


▪ Sponsor developing a new compound to treat type 2 diabetes

▪ Key questions include:

– Is this a once-a-day drug?

– Is dosing adjusted to match exposures reasonable to characterize the effectiveness and safety of the drug?

▪ Built a semimechanistic model, using parameters derived from FDA’s prior experience with 26-wk trials, to describe the time course and drug concentrations

▪ Provided the FDA team with the ability to discuss innovative trial designs and the sponsor with a model to apply to other similar drugs

Response ratePercent

BID QD

PROPOSE BEST DOSES

16 |

Accelerating drug development through a model-based approach to understanding dosing


▪ Sponsor needed to select a range of active oral doses for clinical phase IIa trials of a novel anti-HIV drug, maraviroc

▪ PK/PD model to link plasma concentration to inhibition of viral replication was adapted for short-term treatment based on disease model parameters from literature and preclinical data

▪ The model predicted the effect on viral load of different doses, resulting in a range of possible active oral doses used in the design of phase IIa trials

SOURCE: Reprinted by permission from Macmillan Publishers Ltd: Clin Pharmacol Ther, Rosario MC, Jacqmin P, Dorr P, van der Ryst E, Hitchcock C. A pharmacokinetic-pharmacodynamic disease model to predict in vivo antiviral activity of maraviroc, (reference citation), 2005 Nov;78(5):508-19, copyright year of publication)

Solid lines: Measured Dotted lines: Simulated Bold: 100 mg 2x / day Narrow: 25 mg 1x / day

Time, days

Diff

log

(B

SL

)-lo

g (

Da

y 6

), c

op

ies/

ml

IC50 = 5.75 ng/ml

IC50 = 0.64 ng/ml

Simulated and observed viral load for 2 dosage regimens

PROPOSE BEST DOSES3

17 |

Promoting collaboration and more informed decisions to determine dosing


PM approach and impact Chance of being the preferred dosePercent

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30

20

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100

0

100%

4844403632282420161284

TimeWeek

10mg BID

20 mg BID

40 mg QD

▪ Sponsor seeking appropriate dosing and trial design for a new class of antiviral compound in conjunction with another approved drug


– Are the proposed dose and dosing regimen reasonable for the phase 2b trial?

– Is dose selection based on the first 1-mo data reliable?

▪ Applied a mechanistic viral–dynamic model, including prior knowledge of the approved drug combination, to describe the time course of viral load reduction driven by concentrations

▪ Incorporating prior knowledge to build a model that leads to more informed decisions and FDA/sponsor collaboration

PROPOSE BEST DOSES

18 |

Developing models that accurately identify dosing


▪ Sponsor needed to determine which dose of a pain compound would be needed to achieve superiority to 400 mg ibuprofen in a post-oral surgery model

▪ PK/PD models were developed relating plasma concentrations to pain relief scores

▪ Clinical trial simulations conducted to recommend a dose of 360 mg

▪ Dose of 360 mg was corroborated with placebo- and positive-controlled study evaluating various doses and 400 mg ibuprofen

SOURCE: Reprinted by permission from Macmillan Publishers Ltd: Clinical Pharmacology & Therapeutics, Kowlaski, KG, Olson S, Remmers AE, Hutmacher MM; Modeling and Simulation to Support Dose Selection and Clinical Development of SC-75416, a Selective COX-2 Inhibitor for the Treatment of Acute and Chronic Pain, (reference citation), June 2008 copyright (year of publication)

Workflow for the development of the pain relief and dropout models

Oral solution predictions and clinical trial simulations to support the design of the post–oral surgery pain study

Model IAPain relief

Model IBDropout

Model IAPain relief

Model IBDropout

Oral solution PK

CTS results

Study results

Model IIA/IIBTOTPAR6predictions

Model IA/IBPR prediction

Model IIA/IIB Goodnessof fit

ModelIA/IB Goodnessof fit

SC-75416 capsule

Valdecoxib

SC-75416Oral solution

PROPOSE BEST DOSES

19 |

Determining best dosing to avoid failure


▪ Sponsor seeking approval for an oral suspension product

▪ Key question was: What is the optimal dosing strategy to avoid clinical failure in the majority of patients?

▪ FDA conducted exposure-response analysis to compliment the sponsor’s pre-specified statistical analysis

▪ Enhanced the label and decided to conduct a Phase IV study to evaluate therapeutic advantages of monitoring and adjusting the dosing

PROPOSE BEST DOSES

0

20

40

60

80

0

Steady-state concentrationNg/ml

Patients with clinical failurePercent

302010


20 |

Determining age-appropriate dosage for labeling

SOURCE: Bhattaram VA, Booth BP, Ramchandani RP, Beasley BN, Wang Y, Tandon V, Duan JZ, Baweja RK, Marroum PJ, Uppoor RS, Rahman NA, Sahajwalla CG, Powell JR, Mehta MU and Gobburu JV (2005) Impact of pharmacometrics on drug approval and labeling decisions: a survey of 42 new drug applications. AAPS J 7:E503-E512.

0

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1.0

AgeMonths

Age factor

Age factor = for age >24 months

0.1 0.2 0.3 1 2 3 10 20 30 100


▪ Sponsor seeking approval to use an adult tachycardia drug (already on the market) for pediatrics

▪ Key question was: is the pediatric dosing regimen proposed by the sponsor acceptable?

▪ FDA used data from two clinical trials and modified the sponsor’s exposure-response model to determine dosing regimen for neonates and infants, the subset of pediatrics in question

▪ Approved dosage based on outcome of age analysis and incorporated pediatrics-specific dosing into the label

PROPOSE BEST DOSES

21 |




Drug approval

Labeling



Propose best doses

22 |

Advancing decisions based on dual dose range

-55

-50

-45

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-35

-30

-65 -60 -55 -50 -45 -40 -35 -30 -25 -20 -15 -10 -5 0

Insomnia patients LPS% change from mean placebo response

Healthy volunteers LPS,% change from mean placebo response

-90 -80 -70 -60 -50 -40 -30 -20 -10 0 10 20 30 40 50

Insomnia patients WASO% change from mean placebo response

-5

-30

-25

-20

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Healthy volunteers WASO% change from mean placebo response

Y = 0.31x -32.5r2 = 0.66


▪ Sponsor developing a drug to treat insomnia held an end-of-phase 2a meeting (EOP2A)

▪ Key questions discussed were:

– Is the dose range selected for the Phase 2b studies in insomnia patients reasonable?

– What should be the duration of the Phase 2b studies?


▪ FDA used data from 14 studies of internal FDA submissions and insomnia drug literature

▪ Proved:

– Effective use of dual dose range (correlation between healthy subjects and insomnia patients)

– Shorter trials could produce results


ESTIMATE EFFECT SIZE4

23 |

Simulating clinical trials for successful design


▪ Sponsor needed to determine if a new treatment was effective for Alzheimer’s disease (AD)

▪ A crossover design was evaluated to establish proof-of-concept using smaller and shorter duration trials

▪ Clinical trial simulation model was built from Phase I data and literature reports including PK/PD and disease progression of other AD treatments

▪ Eight alternative trial designs were simulated to determine trial design

▪ Trial resulted in more efficient trial design and a conclusive decision for further development

SOURCE: Lockwood, Peter; Ewy, Wayne; Hermann, David; and Holford Nick; Application of Clinical Trial Simulation to Compare Proof-of-Concept Study Designs for Drugs with a Slow Onset of Effect; An Example in Alzheimer’s Disease, Pharmaceutical Research, 2006

ESTIMATE EFFECT SIZE

24 |

Determining precision in dose prediction


▪ Sponsor evaluated pregabalin for pain treatment by determining how precisely pain reduction could be predicted and demonstrated

▪ PK/PD model relating pain relief to gabapentin plasma concentrations was derived from a phase 3 study and further modified to reflect pregabalin preclinical data

▪ Simulation data suggested that doses that identify predefined response may be imprecisely estimated

▪ Quantification of imprecision will drive phase 2 dose and trial design

SOURCE: Lockwood, Peter A; Cook, Jack A; Ewy, Wayne E; and Mandema, Jaap W;The Use of Clinical Trial Simulation to Support Dose Selection: Application to Development of a New Treatment for Chronic Neuropathic Pain, Pharmaceutical Research, November 2003

-0.5

-0.4

-0.3

-0.2

-0.1

0

0.1

Model 3

Drug effect (fraction of baseline value)

Model 2

Model 1

10-1.0 10-0.0 10-2.010-1.0

Drug effect models based only on data within this concen-tration range

Gabapentin concentrationUg/ml

Concentration–response profiles for efficacy


25 |

3.0

3.5

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Mean pain score

TimeDays

3.0

3.5

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0 5 10 15 20 25 30 35 40 45 50

Mean pain score

TimeDays

Placebo (predicted)

2,400 mg daily (predicted)


600 mg daily (predicted)

2,400 mg daily (observed)


600 mg daily (observed)

Placebo (observed)

Conducting exposure-response analysis to prevent the need for additional trials



3.0

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Mean pain score

TimeDays

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Mean pain score

TimeDays

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0 5 10 15 20 25 30 35 40 45 50 55

TimeDays

Mean pain score




▪ Clinical trial data of gabapetin for postherpetic neuralgia was comprised of single replicates of various doses

▪ Exposure-response analysis was used to avoid additional replicate trials for the representative doses

▪ A random-effects model was applied to the submitted studies, demonstrating predictability and establishing exposure-dependent decrease in pain scores and cross-confirmation of trials

▪ Regulatory approval was granted without the need for additional clinical trials

SOURCE: Reprinted by permission from Macmillan Publishers Ltd: Clinical Pharmacology & Therapeutics, Lalonde RL, Kowalski KG, Hutmacher MM, Ew W, Nichols DJ, Milligan PA, Corrigan BW, Lockwood PA, Marshall SA, Benincosa LJ, Tensfeldt TG, Parivar K, Amantea M, Glue P, Koide H and Miller R; Model-based Drug Development, (reference citation), July 2007 copyright (year of publication)


26 |

Using quantitative decision criteria to understand when to terminate development of drug


▪ Gemcabene, a cholesterol-lowering drug, was evaluated for market potential versus existing therapy using quantitative decision criteria based on estimated effect and distribution of trial results

▪ A lower reference value (LRV) of competitive efficacy and target value (TV) of estimated commercial viability were used for risk criteria

▪ PK / PD and disease model data were used to simulate trials using the uncertainty in treatment effect

▪ Both probability of success (a go decision) and probability of a correct decision were used to evaluate trial performance metrics, leading to a decision to terminate development

Go

Pause

Stop

Data-driven decision

Total

Stop (fail)PCT20≤LRV or PCT90≤TV

Go (success)PCT20>LRV and PCT90>TV

Truth (desireddecision)

Prob(∆>TV)Prob(Stop and ∆>TV)

Prob(Go and∆>TV)

∆>TV (Go)

Prob(∆≤TV)Prob(Stop and ∆≤TV)

Prob(Go and∆≤TV)

∆≤TV (Stop)

1.0Prob(Stop)Prob(Go)Total

Example of a decision rule based on dual criteria

Example of a design performance summary



27 |

Demonstrating drug dosages to allow early trial discontinuation


▪ Proof-of-concept trial of acute stroke therapy used an early termination rule to end trial at the earliest point of establishing futility or efficacy

▪ An adaptive dose allocation and sample size scheme was continually updated to allocate patients to placebo or adaptively to best model the minimal dose providing maximal treatment effect

▪ Termination was based on standard deviation of response

▪ Dose-response relationship was flat, showing that only 3 of the 15 doses were needed to demonstrate efficacy over placebo, allowing early trial discontinuation

-6

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E

1201089684766752 591610 450 22 383327

F

Evaluable population

Eff

ect

over

pl

aceb

o

Dose of UK-279,276 (mg)

The horizontal line at 0 indicates the line of no change, at 1 the futility threshold (F), and at 2 the efficacy threshold (E)

Dose–effect curve of effect over placebo



28 |

Using quantitative data to understand when to discontinue development


▪ Sponsor wanted to compare two potential insomnia compounds

▪ Clinical Utility Index (CUI) was developed using various measures of residual sedation and efficacy

▪ Dose-response analyses were conducted on each end point, using sensitivity analysis to determine the degree of clinical meaning in CUI

▪ Peak CUI values were observed at doses not considered viable, leading to an expedited and quantitative decision to discontinue development

SOURCE: Reprinted by permission from Macmillan Publishers Ltd: Clinical Pharmacology & Therapeutics, Ouellet D, Werth J, Parekh N, Feltner D, McCarthy B and Lalonde, RL; The Use of a Clinical Utility Index to Compare Insomnia Compounds: A Quantitative Basis for Benefit–Risk Assessment (reference citation), March 2009 copyright (year of publication)

Line: median CUIShaded: 80% conf. Symbols: observed

Probability of observing a difference at peak clinical utility index (CUI) value

Difference in CUI

Clinical utility index for lead and backup compounds.

CU

I

Lead compound Back-up compound

Dose, mg Dose, mg


29 |

Evaluating competitive potential of a drug to decide development


▪ Sponsor needed to evaluate the efficacy of a gemcabene, novel cholesterol-lowering drug

▪ Dose response model was developed from publicly available data and proprietary patient data, using the Pharsight Drug Model Explorer visualization technology

▪ Unlike competitor ezitimibe, gemcabene was found to have little additional LDL-C lowering in combination with high statin doses

▪ Use of the model after the first phase II trial facilitated a quick decision to stop development

SOURCE: Mandema, Jaap W; Hermann, David; Wang, Wenping; Sheiner, Tim; Milad, Mark; Bakker-Arkema, Rebecca; and Hartman, Daniel; Model-based development of Gemcabene, AAPS Journal, 2005

Dose, mgLDL

% c

hang

e fr

om b

asel

ine

With placebo (A 0)10 mg atorvastatin (A10)40 mg atorvastatin (A 40)80 mg atrovastatin (A 80)

Atorvastatin dose, mg

LDL

% c

hang

e fr

om b

asel

ine

Gemcabene EzitimibeAtorvastatin alone (top)With agent (bottom)

Dose-response relationship for gemcabene whencombined with placebo

Dose-response relationship of atorvastatin monotherapy and in combination with gemcabene and ezitimibe


30 |

Propose best doses





Drug approval

Labeling



31 |

Revising dosing strategy for quicker approval

▪ Sponsor seeking approval for a drug to treat acute decompensated congestive heart failure (CHF)

▪ Key question: what is the optimal dosing regimen of nesiritide to achieve faster benefits and minimize risk (i.e., undesired hypotension)?

▪ Developed model based on exposure and rate data from original submission to explore alternative dosing strategies

▪ Sponsor revised dosing strategy based on PM results and received quicker approval



RESCUE DISCARDING GOOD DRUG5

0

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15

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25

30

-4

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-5

Nesiritide plasma concentrationsug/l

1.0 3.02.50.50 2.01.5

TimeHours

Placebo-corrected hemodynamics

mmHg

-2

-1

-3

Systolic BP

PCWP

Time course of nesiritide plasmaconcentrationsIndicates predicted

Indicates observed`Typical time course of nesiritide plasma concentrations and the effects on the pulmonary capillary wedge pressure after a 2 μg/kg bolus followed by a fixed-dose infusion of 0.01 μg/kg per minute (data for the initial 3 hours)

32 |

Incorporating dosing strategies into labeling

▪ Sponsor seeking approval for a drug to treat hypercalcemia of malignancy and osteolytic bone metastases


– Is there a need to adjust dosing in patients with renal impairment?

– If so, what doses should be recommended?

▪ Early phase PK studies were used to develop a population model leading to FDA recommendation for dose adjustment in mild and moderate renal impairment patients

▪ Incorporated FDA’s dosing strategies into label


0

5

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15

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25

30

8060 90

Creatinine clearanceml/min

Risk of renal deteriorationPercent

10070504030

Zoledronic acid

Placebo

Observed`Predicted


RESCUE DISCARDING GOOD DRUG

Risk of renal deterioration increases with decreasing renal function (assessed based on baseline creatinine clearance) following 4 mg infusion of zoledronic acid over 15 min and placebo in solid tumor and prostate cancer patients

33 |

Eliminating need for additional trials

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-20 0 20 40 60 80 100 120

Week

Symptomscore PM approach and impact

▪ Sponsor had inconclusive results from two registration trials in patients with a debilitating neurological disorder without approved treatments

▪ Key question was: is there adequate evidence of effectiveness in the current clinical trial database?


▪ FDA analyzed data across studies to investigate whether there was a consistent effectiveness signal

▪ Based on results and need to supply treatment for this disease, additional clinical trials were alleviated



34 |

Specifying additional clinical trial needs to avoid trial failure


▪ Sponsor seeking approval for a drug for patients with an unmet life threatening rheumatologic disorder was unable to demonstrate additional evidence of effectiveness after two trials


– Is the laboratory concentration predictive of the clinical outcome?

– What dose should be approved?

▪ Expanded analysis by simulating the estimated reduction required to achieve a clinical benefit resulting in a dose-response relationship

▪ Recommended to explore the maximally tolerated dose or a dose selected to achieve a greater reduction in this laboratory value


0

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Series

Change in biomarkerPercent

Relative risk of primary end point

0-100 -50 50 100


Relationship between the relative risk of the clinical event and thepercent change in the biomarker (laboratory concentrations).

95% confidence limitsBest fit

35 |




Drug approval

Labeling

Propose best doses



36 |

Selecting patient population

-40

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1.51.00.50.0

Placebo-subtracted change in score A at Week 12

Dosemg

3.02.52.0

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Placebo-subtracted change in score A at Week 12

Dosemg

3.02.52.01.51.00.50.0


▪ Sponsor proposed a drug to treat patients with mild, moderate or severe life-threatening disease with inconsistent results during three trials


– What is the reason for the inconsistent results across the three registration trials?

– How can the success rate of future trials be improved?


▪ FDA developed a new exposure-response model based on analysis across the three studies

▪ Led to suggestion to conduct future study in patient segmentation (moderate and severe disease patients only)

TARGET PATIENT SELECTION6


37 |

Saving time and allowing rapid development decisions by targeting patients


▪ Early development decisions were needed for Alzheimer’s Disease compound to evaluate efficacy and dose-response versus competitor

▪ Original 12-week trial design had 6 parallel groups, 5 dose levels, and 60-80 patients per group

▪ PK/PD and disease data were simulated in various dose-response relationships, using the slowest time patterns to evaluate crossover

▪ The most robust design demonstrated that while hundreds of patients were still needed, shorter trials were sufficient, resulting in time savings

▪ Negative results allowed rapid termination of development

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Effect size at 25mg = -3Effect/dose slope = - 0.12Effect/conc slope = - 0.047

0 3 6 9 12 15 18 21 24 27 30 33 360 3 6 9 12 15 18 21 24 27 30 33 36

ED50 ~ 8 mgEC50 ~ 21 ng/mLHill coefficient = 4

Linear dose response modelCI-1017 tid dose (mg)

Hyperbolic (Emax) dose response modelCI-1017 tid dose (mg)

Sigmoidal (Smax) dose response modelCI-1017 tid dose (mg)

U-shaped dose response modelCI-1017 tid dose (mg)

CI-1017 average steady state plasma concentration (ng/mL)




Effect size at 25 mg = 100% Emax = -3

ED50 ~8.5 mgEC50 ~21 ng/ml½ maximal effect = -2

Effect size at 25 mg tid = -3 = 75% Emax

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Overall effect size at 10mg tid = -3

ED50 Emax ~ 7 mg

ED50 Imax ~ 15 mg

Combined agonist-antagonist dose/responseAgonist (Emax) dose/responseAntagonist (Imax) dose/response

TARGET PATIENT SELECTION


38 |




Propose best doses


Drug approval

Labeling


39 |

Using data to enhance trial design when expanding drug usage


▪ Sponsor seeking approval to use an adult seizure drug (already on the market) for pediatrics

▪ Key questions included:– Is there adequate evidence for approving drug in pediatrics without the

need for additional controlled clinical trials?– What are the appropriate dosing instructions for this indication?

▪ FDA used originally submitted data to build an exposure-response model for quantitative analysis to:– Test whether placebo responses in adult and pediatric patients were

similar– Test whether exposure-response relationship in the two populations were

similar – Derive reasonable dosing recommendations in pediatrics

▪ Proved dosing recommendations in pediatrics matched adults providing an approval without additional trials


MAXIMIZE VALUE OF PRIOR DATA7

40 |



Propose best doses



Labeling


Drug approval

41 |

Identifying treatment dosage and potential harmful effects


0

20

40

60

80

100

Number of patients, alkaline phosphatase

3x ULN

8

4

20

16

12

0

Proportion of patients with favorableendoscopic responsePercent

15010050

Dosemg

Series

Series

▪ Sponsor needed further analysis for an antifungal drug

▪ Key question is: what is the appropriate dose of micafungin for the treatment?

▪ FDA reviewer used data from two studies to model the relationship between dose and effectiveness

▪ Dose–response analysis recommended the treatment dose needed for approval as well as a package insert indicating harmful effects with greater dosage

DRUG APPROVAL 8


42 |

Determining dosing for drug approval


▪ Sponsor conducted 3 trials in chronic kidney disease Stage 5 patients with hyperparathyroidism for an oral paricalcitol drug

▪ Used a validated exposure-response model to define new dosing regimen that reduces the rate of hypercalcemia while maintaining acceptable therapeutic response

▪ Results of trial confirmed predicted results from exposure-response modeling and simulations, maintaining a pre-defined efficacy rate of over 80% and limiting observed rate of hypercalcemia

▪ Drug approved by FDA without need for further clinical trials in patients

SOURCE: Zemplar Capsules United States Package Information, June 2009. Accessed at http://www.accessdata.fda.gov/drugsatfda_docs/label/2009/021606s004lbl.pdf, 18March 2010.

ParameterModel simulated Observed

Efficacy (2 consecutive serum iPTH measurements decreased by 30% from baseline)

81 87.9

Safety (2 consecutive serum calcium measurements over 11 mg/dL; hypercalcemia)

1.5 1.6

% of subjects

DRUG APPROVAL 9

43 |



Propose best doses



Drug approval


Labeling

44 |

Incorporating dosing recommendations into labeling


▪ Sponsor sought approval for Parkinsons’ disease drug for acute use in patients


– Is the maximum recommended dose and the titration strategy proposed by the sponsor appropriate?

– Is there a need for adjusting dose in the renally impaired?

▪ Used data from the dose-finding strategy for simulations using an exposure-response model

▪ Dosing recommendations suggested by the exposure-response analysis were incorporated in the labeling after discussions with the sponsor


LABELING

45 |

Improving dosing regimen to incorporate into label


▪ Drug evaluated in the pre-, during and post- surgical settings for patients with mild to moderate hypertension

▪ Cleviprex dosing regimen used in clinical trials resulted in overshooting and oscillations around the target blood pressure

▪ PK/PD modelling used to simulate the exposure–response relationship to optimize the dosing regimen to quickly achieve and maintain target blood pressure

▪ Resulted in a safer and more effective dosing regimen than employed in clinical trials, which was favored to be incorporated into the label

SOURCE: Drug Approval Package. Cleviprex® (clevidipine butyrate) Application No. 022156. www.accessdata.fda.gov/drugsatfda_docs/nda/2008/022156_cleviprex_toc.cfm . 8-1-2008b.

10 LABELING

Documents

Pharmacometrics: A Business Case May 25, 2010 Pharmacometrics Task Force