DPP-4 Inhibitors As Antidiabetic Agents

Discovery of DPP-4 Inhibitors as Antidiabetic Agents

Tesfaye Biftu Merck & Co., Inc.

• c. 1552 B.C. – 1st reference to Rx for polyuria by Hesy- Ra, 3rd Dynasty Ebers Papyrus

• c. 100 B.C. – The father of medicine in India, Susruta of the Hindus, diagnosed diabetes

• c. 30 B.C. ~ 50 A.D. – 1st clinical description of diabetes, Aulus Cornelius Celsus

• c. 150 A.D. – The name “diabetes” was first introduced from the Greek word for “siphon” by Aretaeus of Cappadocia

• Discovery of Insulin (1921) – An Instant Hit – First Human Clinical Trial 1922 – Noble Price 1923 Benting & MacLoed – Commercial Production 1923 Eli Lilly – First Total Synthesis 1965 Chinese Team

• Discovery of Glucagon (1923) – Largely Unnoticed – Sequenced in 1957 – Rediscovered in 1970’s for its role in diabetes

• Discovery of Incretins – Gastrointestinal Hormones – 1932 The term “incretin” was first used – 1970 GIP was isolated – 1982 GLP-1 was sequenced

Europe 2000: 33.3 million 2030: 48 million

Middle East 2000: 15.2 million 2030: 42.6 million Africa The Americas 2000: 7 million Asia and Australia 2000: 33 million 2030: 18.2 million 2000: 82.7 million 2030: 66.8 million 2030: 190.5 million

Prevalence (%) in persons 35-64 yrs

<3 3-5 6-8 >8 • 2000: 171 million cases worldwide; 2030: 366 million cases predicted • >90% is Type 2 diabetes: obesity is a major risk factor Copyright © 2010 Merck & Co., Inc., Whitehouse Station, New Jersey, USA All rights Reserved WHO Trends among U.S. Adults: 1994 to 2004

1994 2004

<4% 4-4.9% 5-5.9% 6+% missing data

Ø 20,800,000 People in US (7%) have diabetes (2005) Ø 6th Leading cause of death in US (2002), by death certificate Ø >90% Type 2, associated with metabolic syndrome

Drug Class Side Effects Sulfonylureas Hypoglycemia, weight gain

Meglitinides Hypoglycemia, weight gain

Metformin GI intolerance

Thiazolidinediones Edema, weight gain

α-Glucosidase inhibitors GI intolerance

Insulin Hypoglycemia, weight gain Safer and more effective medications are needed

• Glucagon-Like-Peptide (GLP-1) and Glucose-Dependent- Insulotropic-Polypeptide (GIP) are secreted in response to food intake (glucose-dependent mechanism) • GLP-1 and GIP - (↑)stimulate insulin biosynthesis & secretion • GLP-1 inhibits - (↓) glucagon secretion • GLP-1 and GIP - (↓)lower blood sugar level

Stabilize GLP-1 (Active) HA EGTFTSDVSSYLEGQAAKEFIAWLVKGR-NH 2

Inhibit DPP-4 t½ ~ 1 min

GLP-1 (Inactive) EGTFTSDVSSYLEGQAAKEFIAWLVKGR-NH 2

• First Isolated from rat liver in 1966 • Identical to CD26, a marker for activated T cells • Its role in energy homeostasis was discovered which led to the first patent application in 1996 • Cell surface serine dipeptidase belonging to the prolyl oligopeptidase family • Widely expressed, and has Specificity for P1 Pro >> Ala

Copyright © 2010 Merck & Co., Inc., Whitehouse Station, New Jersey, USA All rights Reserved Selective Inhibitors F O O Me O S N NH2 O Me O N N N N N NH2 S NH F N 2 I CF3 DPP-4 Selective QPP Selective DPP-8/9 Selective

Enzyme IC50, nM DPP-9 > 100,000 11,000 55 DPP-8 69,000 22,000 38 FAP > 100,000 > 100,000 > 100,000 DPP-4 27 1900 30,000 PEP > 100,000 > 100,000 > 100,000 QPP/DPP-2 > 100,000 19 14,000 APP > 100,000 > 100,000 > 100,000 prolidase > 100,000 > 100,000 > 100,000

QPP DPP-8/9 DPP-4 2 wk. Rat Toxicity Selective Selective Selective Alopecia ü Thrombocytopenia ü Reticulocytopenia ü ü

Enlarged spleen ü

Mortality ü

Acute dog toxicity Bloody diarrhea ü

G. Lankas, et al., Diabetes 2005, 54, 2988

Copyright © 2010 Merck & Co., Inc., Whitehouse Station, New Jersey, USA All rights Reserved Potential Importance of Selective Inhibition for the Treatment of Type 2 Diabetes • Conclusion – “Off-target” peptidase inhibition (i.e. inhibition of other DPP family peptidases such as DPP8/9) can produce severe toxicity in preclinical species • Variables that may determine degree of toxicity – Cell penetration

• Unlike DPP-4, DPP8/9 are intracellular DPP-4 proteins – Extent of inhibition of DPP8 and/or DPP9 • Not known if inhibition of both enzymes (or how much) is required to produce toxicities – Intraspecies differences in DPP8/9 inhibition from Demuth et al. Biochim. Biophys. Acta 2005, 1751, 33

Identify a potent and selective DPP-4 inhibitor for the treatment of type 2 diabetes mellitus with the following characteristics:

• >1000-fold selectivity over other proline peptidases, especially DPP-8 and DPP-9 • Half-life suitable for BID or preferably QD dosing • Structure lacking reactive electrophile as a serine trap, e.g., O O B(OH)2 H CN H N N 2 N R N R R' X

• Biological screening of compounds • Substrate or active-structure based design • Enzyme – inhibitor crystal structure

• β-Amino acid proline amides H O N IC = 1.9 µM NH2 O O 50 O N N H

• β-Amino piperazines

H2N IC50 = 11 µM N O N N NHSO2Me H

NH2 O

F NH

Radioactivity Recovered in 24 hrs (% Dose) Collection Period Intravenous Oral Bile 28.3 ± 2.6% 38.2 ± 4.7% Urine 18.1 ± 2.0% 14.0 ± 4.2% Feces 1.0 ± 0.2% 1.4 ± 0.6% Total 47.4 ± 3.9% 54.6 ± 4.9%

PK Rat: • high clearance (>150 mL/min/kg) and • short half-life.

Microsomes: piperazine ring oxidation.

Hepatocytes: Conjugation of the primary amino group (glucuronic acid and glucose). Oxidation of the piperazine, the phenyl and the fluoro-phenyl rings. Glutathione conjugation on the fluoro-phenyl ring; d) amide bond hydrolysis.

Bile of PO treated rats: carbamoyl glucuronide conjugate on the piperazine ring. Glutathione conjugation of the fluoro-phenyl ring, piperazine ring oxidation, and N- glucuronidation of the primary amine were also observed.

Bile of IV treated rats: Glutathione conjugation on the fluoro-phenyl ring, piperazine and phenyl ring oxidation, and N-glucuronidation of the primary amine.

Urine of PO treated rats: Acid formed by the de-ethylation and oxidative de- amination of the piperazine ring. Oxidation of the piperazine ring, glucuronidation of the primary amine, and amide bond hydrolysis were also observed.

Urine of IV treated rats: excreted mainly parent compounds

NH2 O NH2 O N F X = NH, O N F N

N N N N N N N N N N N N R N R N N N N R R R

O N R S N N N N N N R O N R R O N S R' R N

R R R N N N N O N N N R' N R' R R N N N N O O R" R"

R R N N N N N N N N R" N R R R N N N N R' R'

N N N N N N N N N R R R R R N N N N

NH2 O R N N N N

CF3

Rat PK

R DPP-4 IC50 Clp t½ Frat (nM) (mL/min/kg) (h) (%) 3,4-diF 130 51 1.8 44 2,5-diF 27 43 1.6 50 2,4,5-triF 18 60 1.7 76

MK-0431 = 100

JANUVIA™ (sitagliptin phosphate)

D. Kim, et al., J. Med. Chem. 2005, 48, 141

CF3

IC50, (nM) Selectivity Ratio

DPP-9 > 100000 > 5000

DPP-8 48000 2700

DPP-4 FAP > 100000 > 5000 Gene DPP-4 18 1 Family DPP-6 not active not active

PEP > 100000 > 5000

Other Proline QPP/DPP-2 > 100000 > 5000 Specific Enzymes APP > 100000 > 5000 prolidase > 100000 > 5000

CF3

Clp Vd t½ PO AUCnorm PO Cmax F

species (mL/min/kg) (mL/kg) (h) (µM·h/mpk) (µ M ) (% )

Mouse 64 3.8 0.8 0.41 0.51 61

Rat 60 6.4 1.7 0.52 0.33 76

Dog 6.0 9.0 4.9 8.3 2.2 100

Monkey 28 2.3 3.7 1.0 0.33 68

100 100 Glucose AUC DPP-4DPP-4 InhibitionInhibition 80 80 (uncorrected) 60 60

40 40 (uncorrected) 20 20 Glucose AUC, % vehicle AUC, % Glucose Plasma DP-IVPlasma inhibition % 0 0 0 0.1 0.3 1 3 0 0.1 0.3 1 3 Sitagliptin,L-224715, mg/kg mg/kg Sitagliptin,L-224715, mg/kgmg/kg 8 Active GLP-1 Dose, mg/kg [sitagliptin], nM

, pM 6 0.1 19 intact 0.3 52 4 1 190 2 3 600

Plasma [GLP-1] 0 Mouse DPP-4: IC = 69 nM 0 0.1 0.3 1 3 50 L-224715, mg/kg Sitagliptin,Copyright © 2010mg/kg Merck & Co., Inc., Whitehouse Station, New Jersey, USA All rights Reserved Chronic Efficacy with a Sitagliptin Analog in Mice Restoration of Pancreatic Islet β Cells

Islets from HFD/STZ mice +/- 10 weeks of treatment with sitagliptin analog

Diabetic Mice Diabetic Mice + Sitagliptin analog Lean Control Mice

Green: Insulin-producing β cell Red: Glucagon-producing α cell

• Selective inhibition of DPP-4, in particular with respect to DPP-8 and/or DPP-9, provides an improved safety profile in preclinical species. • Sitagliptin is a potent and selective DPP-4 inhibitor, very well tolerated in pre-clinical toxicity studies and in human clinical trials. • In patients with type 2 diabetes, once daily administration of sitagliptin stabilizes active GLP-1 and GIP, reduces glucose excursion, enhances insulin levels, suppresses glucagon levels, and improves glycemic control. • JANUVIA™ (sitagliptin) was approved by the FDA as a new treatment for type 2 diabetes.

For more info: www.januvia.com

Identify a specific DPP-4 inhibitor as a back-up to sitagliptin • Potency equivalent to or better than sitagliptin • Increased half-life suitable for QD dosing • Structural diversity

• Allo isomer is ~10-fold more toxic than threo • Incorporate “threo” bias into α-amino acid

series? Me O Me O Me Me N N NH2 S NH2 S threo Ile-thiazolidide allo Ile-thiazoldide

Enzyme IC50 (nM) IC50 (nM) DPP-4 440 460 QPP 14,000 18,000 DPP-8 2,200 220 DPP-9 1,600 320

Enzyme IC50 (nM) Enzyme IC50 (nM) DPP-4 420 DPP-4 64 QPP 14,000 QPP 2,700 DPP-8 2,200 DPP-8 88,000 DPP-9 1,600 DPP-9 86,000

Rat PK

Clp 8.6

t1/2 (h) 2.2 F (%) 85

hERG IC50 = 1.1 µM

J. Xu et al. Bioorg. Chem. Med. Lett. 2005, 15, 2533; S. D. Edmondson et al., J. Med. Chem. 2006, 49, 3614 Copyright © 2010 Merck & Co., Inc., Whitehouse Station, New Jersey, USA All rights Reserved β-Substituted Phenylalanine Derivatives

Me O Me O Me N N NH2 NH 2 S F F

Enzyme IC50 (nM) Enzyme IC50 (nM) • Optimization Goals DPP-4 420 DPP-4 64 – Increase potency QPP 14,000 QPP 2,700 and selectivity DPP-8 2,200 DPP-8 88,000 DPP-9 1,600 DPP-9 86,000 – Decrease ion

channel binding Rat PK – Maintain good oral Cl 8.6 p bioavailability t1/2 (h) 2.2 F (%) 85

hERG IC50 = 1.1 µM

Me O Me Me O N O Me N Me O N NH2 NH N 2 S F N NH2 F N F F N

Enzyme IC50 (nM) Enzyme IC50 (nM) Enzyme IC50 (nM) DPP-4 420 DPP-4 64 DPP-4 8.8 QPP 14,000 QPP 2,700 QPP ~100,000 DPP-8 2,200 DPP-8 88,000 DPP-8 >100,000 DPP-9 1,600 DPP-9 86,000 DPP-9 >100,000

Rat PK Rat PK

Clp 8.6 Clp 2.8

t1/2 (h) 2.2 t1/2 (h) 1.6 F (%) 85 F (%) 100

hERG IC50 = 1.1 µM hERG IC50 = >100 µM

N NH2 N F F • Criteria N ✔ Potency equivalent to or better than sitagliptin

– Increased t1/2 ✔ Potential for balanced renal/hepatic clearance ✔ Structural diversity

• Potential Liabilities – Modest QTc prolongation in CV dog

• Strategy – Position as an insurance back-up, should unexpected safety issues arise with sitagliptin – Bulk drug prepared for 14-wk safety studies

Ø Identify an additional back up compound lacking QTc liability

• Sitagliptin is a triazolopyrazine F F NH2 O

N N N F N CF • Triazolopyrazines are reported as bioisosters3 of amides

N N TL, 41, (2000) 4533 N N & Ann Rep Med Chem

N O

F F NH O R 2 NH2 O R N N O N N F N F NH

CF3

R = H, IC50 = 27 nM R = H, IC50 = 140 nM R = Me, IC50 = 7.2 nM R = Me, IC50 = 89 nM

O R R O R N N N O N DPP-4 N N

IC50, nM 160 89 14

R = Di-Fluoro phenyl β-AA series

F F F F F F NH O NH O 2 2 (R) NH2 O (S) O O O N N * N (R) (R) (R) * F NH F NH F NH

DPP-4 IC50 = 140 nM DPP-4 IC50 = 6.6 nM DPP-4 IC50 = 150nM

F F F F F F NH O NH O NH O 2 2 (R) 2 (S) O O O N N N (S) (S) F NH F NH F NH

DPP-4 IC50 = 403 nM DPP-4 IC50 = 67,000 nM DPP-4 IC50 = 2,300 nM

Activity: R,R > R,S > S,S > S,R

R DPP-4 QPP DPP8 R F (nM) (nM) (nM) F NH O 2 O H 0.91 17,000 21,000 N NH F 2-Me 0.26 7,600 21,000

2-F 0.33 21,000 25,000

2-CF3 0.35 5,100 36,000

[2-Pyridine] 0.49 89,000 53,000

[2-Pyrrazole] 0.29 75,000 80,000

R DPP-4 (nM) QPP (nM) DPP8 (nM) F F NH O R 2 O H 140 >100,000 82,000 N NH F Me 6.6 59,000 46,000

Et 16 47,000 13,000

CH2CF3 2.6 59,000 28,000 iBu* 25 35,000 79,000

CH2(cPr) 4.8 27,000 11,000

* N-methyl diazepanone

R DPP-4 QPP DPP8 (nM) (nM) (nM) F F NH O 2 O H 6.6 59,000 46,000 N N F R Me 6.6 >100,000 21,000

CH2CH2OH 16 78,000 53,000

CH2CF3 19 56,000 43,000 iPr 44 18,000 >100,000 cPr 8 46,000 22,000

R DPP-4 (nM) QPP (nM) DPP8 (nM) F F NH O 2 O 5α-Me/5β-Me 765/1050 N NH F 6α-Me/6β-Me 5 13/33 56,000 70,000 6 7

6,6-di Me 140 17,000 26,000

7α –Me/7β-Me 15/400 73,000 79,000

7-CF3 140 >100,000 76,000

7-(2-F-Bn) 130 8,700 19,000

CF3 NH O 2 O N R NH

R DPP-4 (nM) QPP (nM) DPP8 (nM)

2-F 84 >100,000 27,000

2,5-Di F 7.4 >100,000 22,000

2,4,5-Tri F 2.6 59,000 28,000

R = 2,4,5 tri-F R-isomer more potent than S-isomer Best potency and PK Substituted benzyl/aryl most potent

R NH2 O R O Less potent N Short half-life N R' Loss of potency Not promising Less potent, Less potent Good selectivity α isomer more Improved PK potent than β α isomer more potent than β

Cl T F nAUC Rat PK p 1/2 oral (mL/min/kg) (h) (%) (µM h/mpk)

O O N NH 88 1 41% 0.18

CF3 O O N NH 94 2 94% 0.16

O O N 93 4.3 45% 0.39 N

O O N 51 1.5 49% 0.25 NH

*PK parameters were obtained following a iv (1 mg/kg) or po (2 mg/kg) dose

Potency and selectivity: F IC50 (nM) F CF3 BU NH O 2 O DPP-4 2.6 QPP 59,000 N NH DPP8 28,000 F DPP9 41,000 PEP >100,000 APP >100,000 Prolidase >100,000 FAP 23,000

• Pharma services >10,000 nM Off-target Activity: except Ca channel (Ki=5.5 µM) IC50 (nM) BU IKr >90,000 Ca 11,000 Na ~50,000

Prot.Binding Clp Vd t½ PO AUCnorm PO Cmax F Species (% bound) (mL/min/kg) (L/kg) (h) (µM·h/mpk) (µM) (%) Rat 89 94 12 1.9 0.157 0.085 36 Dog 91 18 11 8.9 2.19 0.522 95 Rhesus 90 53 18 5.4 0.15 0.162 20** Human 91 [6-23]*

* Estimated ** Low %F due to first pass effect

Liver Microsomes Hepatocytes at 1 µM, 1 mg/mL protein at 1 µM, 1 M cells/mL

100.0 100.0

80.0 80.0

60.0 60.0

40.0 RLM 40.0 RLM DLM DLM MLM MLM %Parent Remaining 20.0 HLM %Parent Remaining 20.0 HLM

0.0 0.0 0 10 20 30 40 50 60 70 0 20 40 60 80 100 120 Time (min) Time (min)

100 100 Glucose AUC DP-IV inhibition 80 (uncorrected) 80 60 60

40 40 (uncorrected) 20 20 Glucose vehicle AUC, % Plasma inhibition DP-IV % 0 0 0 0.1 0.3 1 3 0 0.1 0.3 1 3 L-001169872,Dose, mg/kg mg/kg L-001169872,Dose, mg/kg mg/kg 12

10 Active GLP-1 Dose, mg/kg Plasma Level, nM , pM 8 0.1 7.5 intact

6 0.3 24.8

4 1 84.2

2 3 266 Plasma [GLP-1]

• Structurally diverse – unique right hand side • Potent • Selective • Efficacious in OGTT model • Excellent PK profile • Metabolically stable in human microsome preparation • 14 weeks rat and dog safety OK

Kept on hold as an insurance back-up to sitagliptin

• Biological screening of compounds • Substrate or active-structure based design • Enzyme – inhibitor crystal structure Crystal structure of DPP-4 reported in 2002

NH2 N Me

O Me Val-pyr

IC50 = 1600 nM

F F NH2 O

N N N F N

CF3 Sitagliptin

IC50 = 18 nM

H-Bonding Salt Bridge H-Bonding Salt Bridge

F F O NH2 O NH2

N N N N N F

F3C

IC50 = 4.4 nM

Plenty of No Room!! Room The Same New H-Bonding Salt Bridge Salt Bridge H-Bonds

F F F F NH O O NH2 2 N N N N N N N F F N CF F3C 3

IC = 0.6 nM 50 Rotate

F H2N

F O N F A F A F C B NH2 O N Best Fit F

B F H2N

F C N

R125 F F NH 2 O N710 S209 N N N F N

E205 CF3 Sitagliptin E206 IC50 = 18 nM

F F NH2 R358 F N N N Y666 N Y547 CF3 F357

NH2 CF3 R O H O N NH

Tri-F pheny results in better H-bond and enhances potency

R DPP-4 (nM) QPP (nM) DPP8 (nM) IKr (nM)

2-F 84 >100,000 27,000 -

2,5-Di F 7.4 >100,000 22,000 63,000

2,4,5-Tri F 2.6 59,000 28,000 90,000

F Enzyme IC50 (nM) F NH2 DPP-4 21 QPP >100,000 DPP-8 >100,000 F DPP-9 >100,000 N N N IKr 40,000 N Ca 12,000 CF3 Na >50,000

Clp t½ PO AUCnorm PO Cmax F Species (mL/min/kg) (h) (µM·h/mpk) (µM) (%) Rat 7.6 6.6 6.8 0.14 130 Dog 3.9 18 10.1 0.93 95 Rhesus 9.5 14 3.9 0.56 94

OGTT – maximum efficacious dose 1 mpk

F F NH2

F N N N N

CF3 IC50 = 21 nM

F F NH2

F N

CF3

F F NH2 • Increasing selectivity: 3D QSAR Model F N H-bond acceptor H-bond donor R

R DPP-4 QPP DPP-8 (nM) (nM) (nM)

H 6.6 67,000 1700

CF3 7.6 88,000 5800 heteroatoms and/or polar groups preferred

Y. Gao, et al., Bioorg. Med. Chem. Lett. 2007, 17, 3877

F F F F NH2 NH2

F F N N N R R N

R DPP-4 QPP DPP-8 R DPP-4 QPP DPP-8 (nM) (nM) (nM) (nM) (nM) (nM)

H 6.6 67,000 1700 H 0.67 >100,000 5000

CF3 7.6 88,000 5800 CF3 0.53 >100,000 4600

F • RF = H – Poor PK (highNH2 Clp, low %F)

– hERG IC50 = 37 µm F N • R = CF3 N – Good PK properties R N – hERG IC50 = 4.8 µM – ↑QTc in CV dog

R DPP-4 QPP DPP-8 (nM) (nM) (nM)

H 0.67 >100,000 5000

CF3 0.53 >100,000 4600

Compound Ar DPP-4 DPP8/9 Cav1.2 Cyp2D6 t1/2 F (nM) (nM) (nM) (nM) (h) (%)

N L-001331552 37 >59,000 27,000 950 0.46 98 N

N L-001346262 31 >10,000 240 1,700 1.2 32 N N

N N L-001427229 N 5.3 >100,000 960 110 4.9 87 CF3 N Me L-001464016 N N 1.9 >56,000 88 4,400 1.6 37

F N N N N N N

Target IC50 (nM) IC50 (nM) IC50 (nM) IC50 (nM) DPP-4 330 51 8.3 15 QPP 23,000 >100,000 >100,000 19,000 DPP-8 >100,000 72,000 >100,000 >100,000 DPP-9 >100,000 8,400 >100,000 >100,000

Cav1.2 n.d. >100,000 >100,000 >100,000 Cyp2D6 n.d. 19,000 13,000 5,200

N N

• Potent, selective DPP-4 inhibitor • Novel structure, distinct from other development candidates • Multiple routes of clearance • Human PK projections suggest QD dosing

• Structurally diverse middle ring designed by collaboration between MedChem, Structural biology and modeling groups • Potent • Selective • Efficacious in OGTT model • Excellent PK profile

Promising novel lead for development of a new generation of DPP-4 inhibitors

Copyright © 2010 Merck & Co., Inc., Whitehouse Station, New Jersey, USA All rights Reserved Acknowledgements Medicinal Chemistry Metabolic Disorders Drug Metabolism In Vivo Pharmacology Wally Ashton Savita Jagpal Maria Beconi Ed Brady Tes Biftu Joanne Jiang Adria Colletti George Eiermann Linda Brockunier David Kuo Varsha Didolkar Judy Gorski Chuck Caldwell Barbara Leiting David Evans Gerry Hickey Ping Chen Zhihua Li Chris Kochansky Peggy McCann Hong Dong Frank Marsilio Sanjeev Kumar Joe Metzger Scott Edmondson David Moller David Liu Alex Petrov Dennis Feng Reshma Patel Tony Pereira Dick Saperstein Mike Fisher Kelly Ann Pryor Ralph Stearns Alison Strack Ed Gonzalez Ranabir Sinha Roy Yohannes Teffera Comparative Medicine Jiafang He Nancy Thornberry Regina Wang Irene Capodanno Dooseop Kim Kenny Wong Sherrie Xu Paul Cunningham Gui-Bai Liang Joe Wu Wenji Yin Marcie Donnelly Tony Mastracchio Bei Zhang Immunology/Rheumatology William Feeney Bob Mathvink Lan Zhu Jerry Di Salvo Judy Fenyk-Melody Hyun Ok BCAS Ruth Kilburn James Hausamann You Jung Park Larry Colwell Greg Koch Donald Hora Emma Parmee Huaibing He Elizabeth Nichols Christian Nunes Xiaoxia Qian ’ Dorothy Levorse Eugene O Neill Allison Parlapiano Leah Reigle Linda Wicker Kathy Lyons Gina Seeburger Rosemary Sisco Karen Owens Synthetic Service Xiaolan Shen Liping Wang X-Ray & Modeling Phil Eskola John Strauss Lan Wei Giovanna Scapin Mark Levorse Kenneth Valerich Ann Weber Sangita Patel Bob Frankshun Zuliang Yao Matt Wyvratt Suresh Singh Amanda Jinyou Xu Ying-duo Gao Makarewicz