Clinical Challenges in Dose Selection for CombinationTherapy 12 - - PowerPoint PPT Presentation

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Clinical Challenges in Dose Selection for CombinationTherapy 12 - - PowerPoint PPT Presentation

Clinical Challenges in Dose Selection for CombinationTherapy 12 May 2017 Mark Pegram, M.D. Susy Yuan-Huey Hung Professor of Oncology Associate Director for Clinical Research Director, Stanford Breast Oncology Program Associate Dean for


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Mark Pegram, M.D. Susy Yuan-Huey Hung Professor of Oncology Associate Director for Clinical Research Director, Stanford Breast Oncology Program Associate Dean for Clinical Research Quality Stanford University School of Medicine

Clinical Challenges in Dose Selection for CombinationTherapy

12 May 2017

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EGF-R VEGF-R Notch

  • ther

receptors

Apoptosis Survival/ Proliferation Angiogenesis Protein turnover Mitosis Immuno- modulation Migration/ invasion DNA repair epigenetics

Notch Hedgehog vismodegib RO4929097

High Priority Targets and Drugs

Surface antigens SGN 35 (CD30) HA 22 (CD22) bevacizumab VEGF Trap VEGF cetuximab erlotinib dasatinib sorafenib tipifarnib dasatinib Saracatinib imatinib MK-2206 tramitinib selumetinib PCI-32765 torc ½, MLN0128 temsirolimus TL32711 AT-101

  • batoclax

navitoclax fenretinide

Ceramide

P13 K Akt BCL-2 XIAP mTOR MEK Btk Ras Raf SRC Bcr Abl

sorafenib sunitinib cediranib pazopanib CD105

TRC105

Angiopoietins

AMG386

CDKs

dinaciclib

Microtubules brentuximab vedotin

SCH 900776 MLN 8237 MK-1775

CHK1 Aurora kinase A Wee1 kinase IGF-1R

AMG479 IMC-A12 linsitinib

c-Kit

imatinib sunitinib sorafenib

HER2

Lapatinib Pertuzumab trastuzumab

PDGFR

sunitinib imatinib pazopanib cediranib

Met

ibrutinib

ERa

z-endoxifen

Flt3,RET

sorafenib

bFGFR

cediranaib

Stem cell signalling

PARP

veliparib

HDAC

belinostat entinostat vorinostat

Topoisomerases

LMP400/776

Alkylating

Dimethane sulfonate

Methylation inh.

FdCyd/THU

CTLA44

ipilimumab ticilimumab

IDO

1-Methyl-[D]- tryptophan

Hsp90

AT 13387 PU-H71

Proteasome

bortezomib thalidomide lenalidomide pomalidomide

BCR

tivantinib

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3 Stanford Cancer Center 3 Stanford Cancer Center 3 Stanford Cancer Center

Methodologies Used to Evaluate Drug Combinations

  • Isobologram methods
  • Steel & Peckham (1979)
  • Berenbaum (1981)
  • 3D Response surface methodology
  • Greco, Bravo & Parsons (1995)
  • Multiple drug effect analysis (Combination Index)
  • Chou & Talalay (1984)
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4 Stanford Cancer Center 4 Stanford Cancer Center 4 Stanford Cancer Center

Dose/Response: Chemotherapy + Trastuzumab

Example: Docetaxel + Trastuzumab Trastuzumab Docetaxel Docetaxel + Trastuzumab

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5 Stanford Cancer Center 5 Stanford Cancer Center 5 Stanford Cancer Center

The Median Effect Principle

fa/fu = (D/Dm)m Taking the log of both sides of the equation yields:

The Median Effect Equation

log(fa/fu) = mlog(D) - mlog(Dm)

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6 Stanford Cancer Center 6 Stanford Cancer Center 6 Stanford Cancer Center

Median Effects Plot: Chemotherapy + Trastuzumab

Docetaxel + Trasuzumab

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7 Stanford Cancer Center 7 Stanford Cancer Center 7 Stanford Cancer Center

Recall that: fa + fu = 1 and fu = (1 - fa) Therefore, fa/(1-fa) = (D/Dm)m When m = 1, fa = [1 + (Dm/D)]-1

Looks familiar?

The Median Effect Principle

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8 Stanford Cancer Center 8 Stanford Cancer Center 8 Stanford Cancer Center

fa = [1 + (Dm/D)]-1

The Median Effect Principle

Looks familiar? v/Vmax = [1 + (Km/S)]-1 Michaelis Menton Equation

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9 Stanford Cancer Center 9 Stanford Cancer Center 9 Stanford Cancer Center

COMBINATION INDEX (CI): CI = (D)1 + (D)2 + α(D) 1(D)2

(Dx)1 (Dx)2 (Dx)1(Dx)2 T.C. Chou and P. Talalay (1984) Adv. Enz. Regul. 22, 27-55.

CI = 1, Interaction is SUMMATION CI < 1, Interaction is SYNERGY CI > 1, Interaction is ANTAGONISM

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Curve Fit:

  • Corr. Coeff:

4-Parameter

A = 2.14 B = 0.640 C = 1.86 D = -3.72

y = (A-D)/(1 + (x/C)^B ) + D 0.978

0.0001 10 Log scale of ARBITRARY 3 OD

Curve Fit:

  • Corr. Coeff:

4-Parameter

A = 1.70 B = 0.985 C = 0.0546 D = -0.0250

y = (A-D)/(1 + (x/C)^B ) + D 0.978

0.0001 10 Log scale of ARBITRARY 2 OD

Curve Fit:

  • Corr. Coeff:

4-Parameter

A = 1.24 B = 1.70 C = 0.122 D = 0.0752

y = (A-D)/(1 + (x/C)^B ) + D 0.998

0.0001 10 Log scale of ARBITRARY 2 OD

Multiple Drug Effect Analysis: EXAMPLE

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11 Stanford Cancer Center 11 Stanford Cancer Center 11 Stanford Cancer Center

Calculated values for the Combination Index: Fractional inhibition of SK-BR-3 cell proliferation by a mixture of alkylating agent and trastuzumab

Combination Index Values at: Parameters: Drug IC30 IC40 IC50 IC60 IC70 Dm m r

Alkylator 66.2uM 0.81 0.99 MAb HER2 675.0nM 0.15 0.96 Alk + MAb HER2

0.52 0.37 0.41 0.49 0.60

27.1uM 0.59 0.99

Combined effect

Synergy Synergy Synergy Synergy Synergy

Pegram, et al., Oncogene 18: 2241-2251,1999

CI = (D)1 + (D)2 + α(D) 1(D)2

(Dx)1 (Dx)2 (Dx)1(Dx)2

CI = 1, Interaction is SUMMATION CI < 1, Interaction is SYNERGY CI > 1, Interaction is ANTAGONISM

T.C. Chou and P. Talalay (1984)

  • Adv. Enz. Regul. 22, 27-55.
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12 Stanford Cancer Center 12 Stanford Cancer Center 12 Stanford Cancer Center

Drug Combination Index P-value Interaction Cisplatin Etoposide Thiotepa Doxorubicin Paclitaxel Methotrexate Vinblastine 5-Fluorouracil 0.56 ± 0.15 0.54 ± 0.15 0.67 ± 0.12 1.16 ± 0.18 0.91 ± 0.23 1.36 ± 0.17 1.09 ± 0.19 2.87 ± 0.51 0.001 0.0003 0.0008 0.13 0.21 0.21 0.26 0.0001 Synergy Synergy Synergy Addition Addition Addition Addition Antagonism Pegram, et al., Oncogene 18: 2241-2251,1999

Combination Index Values for Chemotherapy/Trastuzumab Drug Combinations in vitro

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13 Stanford Cancer Center 13 Stanford Cancer Center 13 Stanford Cancer Center

10 20 30 40 50 60 70 80 90 100

Drug A Drug B Drug A + B (antagonistic) Drug A + B (additive) Drug A + B (synergistic)

Examples of Antagonism, Addition, and Synergy

Hypothetical Treatment Effect

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16 Stanford Cancer Center 16 Stanford Cancer Center 16 Stanford Cancer Center

Pegram, et al., Oncogene, 18: 2241-2251, 1999

Treatment of MCF7/HER2 xenografts with trastuzumab in combination with (A) VP-16, (B) vinblastine, (C) methotrexate, and (D) 5-fluorouracil.

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20 Stanford Cancer Center 20 Stanford Cancer Center 20 Stanford Cancer Center

Distance measures (observed vs expected)

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50 100 150 200 250 120 Hours 72 Hours 24 Hours 8 Hours 0 Hours

Xenograft Volume (mm3)

Treatment Hour of Experimental Agent

Effect of Order and Timing of Chemotherapy and Trastuzumab Administration on Xenograft Volume

120 72 24 8

CDDP ⇒ MAb MAb ⇒ CDDP

Pietras RJ, Fendly BM, Chazin VR, Pegram MD, Howell SB, and Slamon DJ. Oncogene 9: 1829-1838, (1994).

Lopez, Pegram, Slamon, Landaw Proc Natl Acad sci USA 96: 13023-8, 1999.

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Estimated Survival

1.0 0.8 0.6 0.4 0.2 Estimated Probability 3 6 9 12 15 18 21 24 27 30 Months 15.3 27.7 21.9 Trastuzumab + Taxotere (n=92) Taxotere Alone/Crossover (n=41) Taxotere Alone (n=53)

M77001

Marty, M et al., J Clin Oncol. 2005 Jul 1;23(19):4265-74.

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Phenotypic Analysis of erbB2 Conditional Knock-out Mouse Myocardium

erbB2-floxed erbB2-CKO

m = ↑mitochondria

Transmission EM

Trichrome staining

Crone SA, et al., Nature Medicine 8: 459-465 (2002)

Arrows = ↑vacuoles

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OTHER 36% CYP2D6 2% CYP2E1 7% CYP 2C 17% CYP 1A2 12% CYP 3A4-5 26%

RELATIVE HEPATIC CONTENT OF CYP ENZYMES % DRUGS METABOLIZED BY CYP ENZYMES

ROLE OF CYP ENZYMES IN HEPATIC DRUG METABOLISM

CYP 1A2 14% CYP 2C9 14% CYP 2C19 11% CYP2D6 23% CYP2E 5% CYP 3A4-5 33%

Consider substrates, inhibition, induction and polymorphisms

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Clin Cancer Res. 2014 August 15; 20(16): 4210–4217. doi:10.1158/1078-0432.CCR-14-0521.

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Combination Therapies: Opportunities and Pitfalls

Opportunities Pitfalls Validate novel biological hypotheses Unreliable pre-clinical models Synergize anti-tumor effect without synergizing toxicity Optimal selection of drugs and targets to study in combination Increase therapeutic index/window Optimal sequence and dose of combination therapy Synthetic lethality: optimize combination use of single agents with limited single agent activity Risk overlapping toxicity Counteract primary and secondary resistance Lack of standard design for phase 1 / 2 for combination therapies Develop novel indications for existing and approved drugs Competing interests of researchers, corporations and / or institutions to combine treatments

Yap, Omlin & de Bono, JCO 2013

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Developing Combination Therapies: Messages

  • Developing drug combinations is arguably the most important

major challenge in cancer drug development today

  • Major hurdles

– Establishing a strong hypothesis and selecting the right combinations – Understanding functional biology: Feedback loops, redundancies – Intra-tumor heterogeneity – Inter-patient PK-PD variability and optimizing target blockade to abrogate narrow therapeutic indices; multiple schedules? – Drug-Drug interactions – Providing early proof of concept to support Phase 3 investment – Combining agents from different sponsors

  • But with clear thinking we can find solutions to best serve our

patients

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Questions/Comments Discussion

James H. Clark Center Stanford University Stanford Bio-X Program: Biology, Medicine,Chemistry, Physics and Engineering

  • Dennis Slamon
  • Richard Pietras
  • Gottfried Konecny
  • Angela Lopez
  • Nathalie Chorn
  • Richard Finn
  • Toby Ward
  • Kazuhiro Araki
  • Anna Jegg
  • Ralf Landgraf
  • Michelle Gallas
  • Xiaofei Liu
  • Rebecca Olson
  • Jessica Bockhorn
  • Alex Lindqwister
  • Xiaosong Chen
  • Greg Vidal
  • Amy Zong

Pegram Lab Grant Support: Susan G Komen for the Cure BCRF US Dept of Defense NIH – R01 Expedition Inspiration Gateway Foundation TNBC Foundation Jiv Daya Foundation Susy Yuan-Huey Hung Jill and John Freidenrich