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 Clinical Research Quality Stanford University School of Medicine

High Priority Targets and Drugs IGF-1R AMG479 Surface antigens IMC-A12 SGN 35 (CD30) linsitinib bevacizumab HA 22 (CD22) HER2 cetuximab VEGF Trap Lapatinib VEGF Notch Pertuzumab RO4929097 trastuzumab other c-Kit Hedgehog EGF-R VEGF-R receptors Notch imatinib vismodegib sunitinib sorafenib sorafenib Met Raf Bcr tivantinib sunitinib Ras SRC Abl ERa cediranib z-endoxifen pazopanib erlotinib Stem cell dasatinib PDGFR sunitinib CDKs tipifarnib sorafenib signalling dasatinib imatinib dinaciclib P13 K Saracatinib pazopanib Microtubules imatinib cediranib brentuximab PARP Flt3,RET MK-2206 Akt vedotin MEK Btk veliparib sorafenib CHK1 HDAC bFGFR SCH 900776 cediranaib belinostat tramitinib thalidomide Aurora kinase A entinostat BCR PCI-32765 selumetinib lenalidomide MLN 8237 vorinostat ibrutinib AT-101 pomalidomide CD105 Wee1 kinase Topoisomerases obatoclax torc ½, MLN0128 BCL-2 TRC105 MK-1775 CTLA44 LMP400/776 navitoclax temsirolimus Hsp90 ipilimumab Angiopoietins AT 13387 Alkylating ticilimumab AMG386 Dimethane PU-H71 XIAP mTOR TL32711 sulfonate IDO Proteasome 1-Methyl-[D]- Methylation inh. bortezomib tryptophan FdCyd/THU fenretinide Ceramide Survival/ Protein Immuno- Apoptosis Angiogenesis Proliferation turnover modulation DNA repair Migration/ Mitosis invasion epigenetics

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

Dose/Response: Chemotherapy + Trastuzumab Example: Docetaxel + Trastuzumab Docetaxel + Trastuzumab Docetaxel Trastuzumab Stanford Cancer Center Stanford Cancer Center Stanford Cancer Center 4 4 4

The Median Effect Principle fa/fu = (D/D m ) m Taking the log of both sides of the equation yields: The Median Effect Equation log(fa/fu) = mlog(D) - mlog(D m ) Stanford Cancer Center Stanford Cancer Center Stanford Cancer Center 5 5 5

Median Effects Plot: Chemotherapy + Trastuzumab Docetaxel + Trasuzumab Stanford Cancer Center Stanford Cancer Center Stanford Cancer Center 6 6 6

The Median Effect Principle Recall that: fa + fu = 1 and fu = (1 - fa) Therefore, fa/(1-fa) = (D/D m ) m When m = 1, fa = [1 + (Dm/D)] -1 Looks familiar? Stanford Cancer Center Stanford Cancer Center Stanford Cancer Center 7 7 7

The Median Effect Principle Looks familiar? Michaelis Menton Equation fa = [1 + (Dm/D)] -1 v/Vmax = [1 + (Km/S)] -1 Stanford Cancer Center Stanford Cancer Center Stanford Cancer Center 8 8 8

COMBINATION INDEX (CI): CI = (D) 1 + (D) 2 + α (D) 1 (D) 2 (D x ) 1 (D x ) 2 (D x ) 1 (D x ) 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. Stanford Cancer Center Stanford Cancer Center Stanford Cancer Center 9 9 9

Multiple Drug Effect Analysis: EXAMPLE Curve Fit: 4-Parameter Corr. Coeff: 0.978 Curve Fit: 4-Parameter Corr. Coeff: 0.978 y = (A-D)/(1 + (x/C)^B ) + D y = (A-D)/(1 + (x/C)^B ) + D A = 1.70 B = 0.985 C = 0.0546 D = -0.0250 A = 2.14 B = 0.640 C = 1.86 D = -3.72 2 3 OD OD 0 0 0.0001 10 0.0001 10 Log scale of ARBITRARY Log scale of ARBITRARY Curve Fit: 4-Parameter Corr. Coeff: 0.998 y = (A-D)/(1 + (x/C)^B ) + D A = 1.24 B = 1.70 C = 0.122 D = 0.0752 2 OD 0 0.0001 10 Log scale of ARBITRARY

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 IC 30 IC 40 IC 50 IC 60 IC 70 Dm m r Alkylator 66.2uM 0.81 0.99 MAb HER2 675.0nM 0.15 0.96 0.37 0.41 0.49 0.60 0.52 27.1uM 0.59 0.99 Alk + MAb HER2 Combined effect Synergy Synergy Synergy Synergy Synergy CI = 1, Interaction is SUMMATION CI = (D) 1 + (D) 2 + α (D) 1 (D) 2 CI < 1, Interaction is SYNERGY (D x ) 1 (D x ) 2 (D x ) 1 (D x ) 2 CI > 1, Interaction is ANTAGONISM T.C. Chou and P. Talalay (1984) Pegram, et al., Oncogene 18: 2241-2251,1999 Adv. Enz. Regul . 22, 27-55. Stanford Cancer Center Stanford Cancer Center Stanford Cancer Center 11 11 11

Combination Index Values for Chemotherapy/Trastuzumab Drug Combinations in vitro Drug Combination Index P-value Interaction Synergy Cisplatin 0.56 ± 0.15 0.001 Synergy Etoposide 0.54 ± 0.15 0.0003 Thiotepa 0.67 ± 0.12 0.0008 Synergy Addition Doxorubicin 1.16 ± 0.18 0.13 Addition Paclitaxel 0.91 ± 0.23 0.21 Addition Methotrexate 1.36 ± 0.17 0.21 Vinblastine 1.09 ± 0.19 0.26 Addition Antagonism 5-Fluorouracil 2.87 ± 0.51 0.0001 Pegram, et al., Oncogene 18: 2241-2251,1999 Stanford Cancer Center Stanford Cancer Center Stanford Cancer Center 12 12 12

Examples of Antagonism, Addition, and Synergy 100 90 80 Hypothetical Treatment Effect 70 Drug A Drug B 60 Drug A + B (antagonistic) Drug A + B (additive) 50 Drug A + B (synergistic) 40 30 20 10 0 Stanford Cancer Center Stanford Cancer Center Stanford Cancer Center 13 13 13

Treatment of MCF7/HER2 xenografts with trastuzumab in combination with (A) VP-16, (B) vinblastine, (C) methotrexate, and (D) 5-fluorouracil. Pegram, et al., Oncogene, 18: 2241-2251, 1999 Stanford Cancer Center Stanford Cancer Center Stanford Cancer Center 16 16 16

Distance measures (observed vs expected) Stanford Cancer Center Stanford Cancer Center Stanford Cancer Center 20 20 20

Effect of Order and Timing of Chemotherapy and Trastuzumab Administration on Xenograft Volume Treatment Hour of Experimental Agent 120 Hour s 120 72 Hours 72 24 Hours 24 8 Hours 0 Hours 8 CDDP ⇒ MAb MAb ⇒ CDDP 0 0 50 100 150 200 250 Xenograft Volume (mm 3 ) Lopez, Pegram, Slamon, Landaw Proc Natl Acad sci USA 96: 13023-8, 1999. Pietras RJ, Fendly BM, Chazin VR, Pegram MD, Howell SB, and Slamon DJ. Oncogene 9: 1829-1838, (1994).

M77001 Estimated Survival Trastuzumab + Taxotere (n=92) Taxotere Alone/Crossover (n=41) Taxotere Alone (n=53) 1.0 0.8 Estimated Probability 0.6 0.4 0.2 15.3 21.9 27.7 0 0 3 6 9 12 15 18 21 24 27 30 Months Marty, M et al., J Clin Oncol. 2005 Jul 1;23(19):4265-74.

Phenotypic Analysis of erbB2 Conditional Knock-out Mouse Myocardium erbB2-floxed erbB2-CKO Trichrome staining Transmission EM Crone SA, et al., m = ↑ mitochondria Arrows = ↑ vacuoles Nature Medicine 8: 459-465 (2002)

ROLE OF CYP ENZYMES IN HEPATIC DRUG METABOLISM RELATIVE HEPATIC CONTENT % DRUGS METABOLIZED OF CYP ENZYMES BY CYP ENZYMES CYP2E1 CYP2D6 7% 2% CYP 2C19 11% CYP 2C9 14% CYP2D6 CYP 2C 23% 17% OTHER CYP 1A2 36% 14% CYP 1A2 12% CYP2E CYP 3A4-5 5% 33% CYP 3A4-5 26% Consider substrates, inhibition, induction and polymorphisms

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

Combination Therapies: Opportunities and Pitfalls Opportunities Pitfalls Validate novel biological hypotheses Unreliable pre-clinical models Synergize anti-tumor effect without Optimal selection of drugs and targets synergizing toxicity to study in combination Increase therapeutic index/window Optimal sequence and dose of combination therapy Synthetic lethality: optimize Risk overlapping toxicity combination use of single agents with limited single agent activity Counteract primary and secondary Lack of standard design for phase 1 / 2 resistance for combination therapies Develop novel indications for existing Competing interests of researchers, and approved drugs corporations and / or institutions to combine treatments Yap, Omlin & de Bono, JCO 2013

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