Phase 2b dose selection, leveraging comparator data through - - PowerPoint PPT Presentation

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Phase 2b dose selection, leveraging comparator data through - - PowerPoint PPT Presentation

Dec 25, 2023 109 likes •258 views

Phase 2b dose selection, leveraging comparator data through multidisciplinary modeling & simulation Thomas Kerbusch Thomas Kerbusch* (1), Russ Wada (2), Anthe Zandvliet (1), Kuenhi Tsai (6), Jing Su (6), Joanna Zhuoying Peng (4), Yaming

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SLIDE 1

Phase 2b dose selection, leveraging comparator data through multidisciplinary modeling & simulation

Thomas Kerbusch

Thomas Kerbusch* (1), Russ Wada (2), Anthe Zandvliet (1), Kuenhi Tsai (6), Jing Su (6), Joanna Zhuoying Peng (4), Yaming Hang (6), Christine Xu (3), Richard Shames (5), Ann Horowitz (3), Diane Neiman (4), Mani Lakshminarayanan (6), Usha Barai (3), Ferdous Gheyas (3), Paul Peloso (4), Devan Mehrotra (6), Nancy Zhang (2), Hanbin Li (2), Jaap Mandema (2), Gary Herman (4), Sandy Allerheiligen (6) (1) Merck Research Laboratories, Oss, The Netherlands; (2) Quantitative Solutions, Menlo Park, CA, USA; (3) Merck Research Laboratories, Kenilworth, NJ, USA; (4) Merck Research Laboratories, Rahway, NJ, USA; (5) Merck Research Laboratories, Palo Alto, CA, USA; 6 Merck Research Laboratories, Upper Gwynedd, PA, USA.

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SLIDE 2

Multi-disciplinary collaboration

M&S Clin Pharm Clin Res combine quantitative thinking share workload capture physiology & pharmacol. agree on assumptions define end-points and decision criteria explore opportunities to optimize trial design FIT FOR PURPOSE CRO: Quantitative Solutions clinical PK-PD BARDS

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SLIDE 3

Difficult to establish a dose-response relationship based on Phase 1b data

  • Cohorts Phase 1b study:

– ●1x mg/kg (n=4) IV

– ▲5x mg/kg (n=4) IV

– ♦30x mg/kg (n=8) IV

– ▼100x mg/kg (n=8) IV – ￭ placebo ratio 3:1

  • Strong PoC response: All

active treatments resulted in maximal effect at W28

  • Need to select Phase 2b

dose-range using Phase 1b data

  • Problem:

– Limited Phase 1b data, with n=3-6 patients per dose group. – No robust PKPD model could be established. IV doses at 0, 8, and 12 weeks

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SLIDE 4
  • Initial proposal for Phase 2b dose selection: based on max injectable

SC dose – 25 mg SC at W0, W4, W16 (n=35) – 100 mg SC at W0, W4, W16 (n=70) – 200 mg SC at W0, W4, W16 (n=70) – 200 mg SC every 4 weeks (n=35) – Placebo SC every 4 weeks (n=35)

  • Decision required: Phase 2b dose-selection

– Will the initially proposed dose range (25-200 mg) allow for:

  • estimation of dose-response?
  • determination of lowest maximum effective dose?
  • Actions: Conduct a comparator analysis

– Model-based dose selection

  • Solution to in-house data limitations: borrow strength from

published comparator data – Best-in-Class strategy

  • Explore comparator landscape to understand requirements for

Phase 3 doses – need for maximum learning in Phase 2b

Dose selection for Phase 2b study using limited Phase 1b data

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SLIDE 5

Critical assumptions comparator analysis

  • The maximum efficacy for Merck’s compound is similar to other compounds with

similar mechanism-of-action (MoA)

  • The time-course of the onset of response is similar across compounds
  • The efficacy of Phase 1b dose regimen (wk0, wk8, wk12) of Merck’s compound

is similar to the efficacy of the Phase 2b dose regimen (wk0, wk4, wk16).

  • Phase 1b and Phase 2 patient populations are assumed the same.
  • Mean study-arm level data were combined for over 10,000 patients.

Compound

MoA # Trials # Study arms

  • incl. plac

# pts Adalimumab (Humira) Type 1 4 9 1658 Etanercept (Enbrel) Type 1 9 20 2868 Infliximab (Remicade) Type 1 6 15 1695 Ustekinumab (Stelara) Type 2 5 13 2868 Briakinumab (ABT-874) Type 2 2 6 1585 Merck compound 1 5 24

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SLIDE 6

Confidence in estimating efficacy response can be enhanced by co-modeling through correlation

Data are plotted across all arms and time points in the database. Symbol size is proportional to the square root of the arm size. The fitted line is the cumulative normal distribution with mean of log (disease score / baseline), standard deviation of 0.66, and cutoff at -1.2.

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SLIDE 7

2 4 6 8 10 20 40 60 80 100 SCH900222 20 40 60 80 20 40 60 80 100 adalimumab 200 400 600 800 20 40 60 80 100 briakinumab 10 20 30 40 50 20 40 60 80 100 etanercept 2 4 6 8 10 20 40 60 80 100 infliximab 50 100 150 20 40 60 80 100 ustekinumab

Adjusted Dose % Reduction/Responder

endpoint Mean PASI PASI75

in-house compound

Disease score continuous dichotomous

Dose-response models of in-house compounds and competitors (week 16 , 95% CI)

  • All compounds were

estimated with different potencies

  • Onset of efficacy

faster for mean % change than for responder fraction

  • Limited Ph 1b data

(n=24) resulted in large uncertainty

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SLIDE 8

Dose-response model of in-house compound: increased response over time (80%CI)

Dose-response

  • Near maximum effect is

predicted to be achieved ≥50 mg.

  • Doses of 50-200 mg are

predicted to have little separation in time to reach maximum effect. Therefore, 200 mg is not predicted to have a faster maximum effect.

  • 5 mg and 25 mg will allow for

doses near ED50 (~8.4 mg) and are predicted to allow for establishing dose-response

1 5 10 50 100 20 40 60 80

4 week

1 5 10 50 100 20 40 60 80

8 week

1 5 10 50 100 20 40 60 80

12 week

1 5 10 50 100 20 40 60 80

16 week SCH900222 D ( )

% response, difference from placebo Dose in-house compound (mg)

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SLIDE 9

Positioning of in-house compound in the competitive landscape: a sneak preview

  • Doses > 50 mg (0, 4 16w) predicted to be superior (positive difference in

plot) to etanercept, adalimumab and ustekinumab.

  • Similar potency and onset of action → no major competitive advantage
  • ver ustekinumab
  • After Phase 2b a much

more accurate positioning within the competitor landscape can be determined

1 5 10 50

  • 80
  • 40

20

adalimumab 40 mg q2w

1 5 10 50

  • 80
  • 40

20

briakinumab 200 mg wk 0,4, 100 mg wk 8

1 5 10 50

  • 80
  • 40

20

etanercept 50 mg biw

1 5 10 50

  • 80
  • 40

20

infliximab 5 mg/kg wk 0,2,6, q8w

1 5 10 50

  • 80
  • 40

20

ustekinumab 45 mg wk 0,4, q12w

% response, difference from comparator Dose in-house compound (mg)

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SLIDE 10

Clinical trial simulations

  • Why

– Including doses around ED50 in the Phase 2b trial will allow for identification of the lowest dose reaching maximum effect (“cusp

  • f Emax”): best dose for Phase 3.
  • How

– Limitations and uncertainty in the available data are a fact. – Clinical trial simulations should incorporate these and still allow for a robust dose selection decision for Phase 2b. – Dose-range should bracket (predicted) maximum response and ED50. – Doses for Phase 2b were evaluated for being “near” placebo, “near” maximum effect or in between (near ED50) by simulating 200,000 Phase 2b trials.

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SLIDE 11

Making the decision

0% 20% 40% 60% 80% 100% 5 mg (n=35) 25 mg (n=35) 100 mg (n=70) 200 mg (n=70) 200 mg (n=35) at plateau (difference from maximal response <25%) between placebo and plateau similar to placebo (difference from placebo response <25%)

  • ---------- 0, 4, 16W -----------

Q4W

0% 20% 40% 60% 80% 100% 5 mg (n=35) 25 mg (n=35) 100 mg (n=70) 200 mg (n=70) 200 mg (n=35) at plateau (difference from maximal response <25%) between placebo and plateau similar to placebo (difference from placebo response <25%)

  • ---------- 0, 4, 16W -----------

Q4W

0% 20% 40% 60% 80% 100% 5 mg (n=35) 25 mg (n=35) 100 mg (n=70) 200 mg (n=70) 200 mg (n=35) at plateau (difference from maximal response <25%) between placebo and plateau similar to placebo (difference from placebo response <25%)

  • ---------- 0, 4, 16W -----------

Q4W

  • riginal design

(empirical dose selection) new design (model-based dose selection)

add 5 mg

  • mit 200 mg Q4W
  • 100 and 200 mg

were predicted to be at the plateau of the dose-response relationship.

  • Monthly 200 mg

(max. feasible exposure) is not informative: drop arm

  • Establishing dose-

response requires a dose level between placebo and plateau: reasonable probability of 5 mg not being near plateau or placebo (yellow bar).

Probability (%)

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SLIDE 12

Conclusions

Value addition of M&S Comparator Data Analysis

  • Probability of success of establishing dose-response:

– was low for the original design:

  • 200 mg arm with monthly injections (max. feasible dose-

intensity) is not informative, because 200 mg W0, W4, W16 already predicted above Emax – is high for the new design:

  • A 40-fold dose-range 5-200mg brackets the predicted ED50 (8.4

mg) and maximum effect (≥50 mg).

  • Model-based dose-response in phase 2b will allow for optimal dose-

selection for phase 3.

  • Re-evaluating Competitive Landscape will allow for optimal Best-in-

Class strategy

25 mg W0, W4, W16 (n=35) 100 mg W0, W4, W16 (n=70) 200 mg W0, W4, W16 (n=70) 200 mg every 4 weeks (n=35) Placebo every 4 weeks (n=35) 5 mg W0, W4, W16 (n=35) 25 mg W0, W4, W16 (n=70) 100 mg W0, W4, W16 (n=70) 200 mg W0, W4, W16 (n=70) Placebo W0, W4, W16 (n=35)

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SLIDE 13

BACKUP

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SLIDE 14

Comparative efficacy model

14

5 10 15 20 40 60 80 100

Time (week) PASI Reduction (%)

5 10 15 20 40 60 80 100

Time (week) PASI75 Responders (%)

Emax Placebo Low Dose High Dose Placebo Low Dose High Dose Emax

response rate (%) mean disease score / baseline (%)

  • The model has a maximum effect that gradually increases over time to a

steady-state value.

  • There is a dose-response relationship at each point in time.
  • Key assumptions:
  • The maximum efficacy for in-house compound is similar to competitors

with same MoA

  • The time-course of the onset of response is similar across compounds