SCAP siRNA-LNP Reduces the Dyslipidemia of Spontaneously Dysmetabolic Rhesus

Monkeys

Beth Ann Murphy, Marija Tadin-Strapps, Robin Mogg, Shirly Pinto, David McLaren, Stephen Previs and Kristian Jensen. Merck & Co., U.S.A.

Presented at the “Crossroads in Lipid Metabolism and Diabetes”

-Keystone Conference (Copenhagen, Denmark; April 2015).

Links to Presentation Sections Below:• Introduction• Materials and Methods• Summary of Baseline Characteristics by Treatment Group• Results• Summary and Conclusions

• SREBP cleavage-activating protein (SCAP) is a cholesterol binding ER membrane protein required to activate the two membrane bound transcription factors SREBP-1c and -2.

Introduction

Transcription Factor Genes Regulated Downstream Effect

SREBP-1c Fatty Acid Synthesis Circulating [triglyceride}

SREBP-2Cholesterol Synthesis Circulating [cholesterol]

PCSK9 expression and LDL-R regulation

LDL Clearance

Inhibit SCAP

Decrease transcriptional activity of SREBP’s

↓ Hepatic de novocholesterol and TG

synthesis

↑ LDL-c Clearance ↓[Chol] & [TG]

Predict that inhibiting

SCAP should reduce [LDL-c]

↓ PCSK9↑ LDLR

• Inhibiting SCAP using a siRNA-LNP decreases circulating PCSK9, LDL-c and TG in normo-lipidemic rhesus monkeys 1,2.

• The Spontaneous Dysmetabolic Rhesus Monkey Model (DysMet NHP) recapitulates the pathophysiological lipid profile seen in Type 2 diabetics with dyslipidemia (i.e. elevated TG with reduced HDL and moderately elevates LDL-c.).

• Therefore the DysMet NHP is an attractive tool to evaluate the therapeutic potential of inhibiting SCAP.

Goal: To determine the effect that inhibiting SCAP using a siRNA-LNP has on the lipid profile of Dysmetabolic Rhesus monkeys.

Parameter Healthy DysMet

Weight (kg) 12.8 ± 0.52 15.4 ± 0.55 (*)

Cholesterol (mg/dl) 169 ± 5.7 168 ± 4.7

HDL-c (mg/dl 87 ± 3.4 63 ± 2.4 (**)

LDL-c (mg/dl) 46 ± 2.2 58 ± 2.3 (**)

TG (mg/dl) 30 ± 2.5 131 ± 12.9 (**)

PCSK9 (ng/ml) 107 ± 9.8 78 ± 3.9 (**)

Glucose (mg/dl) 66 ± 1.3 66 ± 1.2

Insulin 12 ± 2.3 38 ± 7.4 (**)

Characteristics of Healthy and DysMet Rhesus Monkeys

• ↓HDL-c• ↑LDLc • ↑↑ TG

DysMet vs. Healthy

• Test Subjects– Male (n=14) and Female (n=10) rhesus monkeys – Dysmetabolic disease state (insulin resistant ; elevated TG and ; reduced HDL-c.)– In good health based upon veterinary assessment (medical history ,physical examination , clinical chemistry and

hematology).– LDL-c >45 mg/dl.

• Test Articles – SiRNA-LNP’s specifically designed to target and disrupt the translation of hepatic SCAP or PCSK9. A non-

targeting siRNA control included as a reference.– All 3 reagents were show to knock down their specific hepatic mRNA by > 85% at Day 21 post-injection in

healthy Rhesus monkeys. The role of each reagent in the study design (shown below)

– The LNP formulation is well-tolerated by and does not to alter the lipid profile or change body weight in DysMet monkeys when compared to saline (Data not shown).

• Study Design– Animals were assigned to receive either NT control, (n=10) SCAP (n=10) or PCSK9 (n=4) siRNA-LNP so that pre-

study LDL-c levels were similar across the treatment groups.– The test article was administered intravenously at 26.7 mg/m2 (which approximates 2 mg/kg).– Weekly fasted blood samples were collected over the 28-day study to assess circulating metabolic- and

toxicological- related biomarkers. – One-week after LNP dosing, the animals were administered Deuterated water (2H20). Palmitate and cholesterol

synthesis was determined by using MS-LCMS to measure the incorporation of the 2H20 of label into palmitate and cholesterol from plasma samples obtained on study days 8 and 10.

– All animal procedures were performed with the approval of the Merck IACUC

siRNA LNP Reagent Role in Study Design

Non-targeting (NT) control The NT control is not associated with any physiological changes

SCAP Target of interest

PCSK9 Positive control (PCSK9 inhibition is known to decrease LDL-c)

Materials and Methods

Study Schematic

Data Analysis and Statistical Plan• LDL-C , PCSK9 and TG concentrations were log-transformed and analyzed based on a constrained longitudinal

data analysis (cLDA) model containing fixed effects for treatment (NT control siRNA, SCAP siRNA, and PCSK9 siRNA) and day (Days -14, -7, 1, 8 and 15, 22 and 29), and a random effect for subject. This model assumed a common mean across treatment groups prior to injection (at Days -14, -7, and 1), and a different mean for each treatment group at Days 8 ,15, 22 and 29 and (post-injection).

• The treatment group differences at Days 8,15, 22,and 29 (SCAP siRNA vs NT control siRNA and PCSK9 siRNA vs NT control siRNA) were estimated from this model.

• The Kenward-Roger adjustment was used with restricted (or residual) maximum likelihood (REML). Posterior distributions of the model estimates were generated using non-informative priors, under an assumption of normality. The study hypotheses was met if there is at least 50% posterior probability that the true mean reduction in LDL-C is at least 20%, in PCSK9 is at least 35% and in TG is at least 20% after injection with SCAP siRNA relative to NT control siRNA.

• Changes HDL-c , ApoB, ApoA1, insulin and glucose levels of siRNA-LNP injected animals were subjected to the same analysis, but the criteria were set after the study was completed.

D(-14) D(-7) D1 D8 D10 D15 D22 D29

LNP D20

Fasted blood sample=

Materials and Methods-cont’d

Table 1:Summary of Baseline Characteristics by Treatment Group

Parameter NT Control SCAP PCSK9

n 10 10 4

BW 17 ± 1.4 14 ± 1.3 16 ± 1.3

LDL-c (mg/dl) 62 ± 6.0 70 ± 5.2 68 ± 9.4

PCSK9 (mg/dl) 128 ± 11.2 151 ± 26.5 136 ± 29.7

TG (mg/dl) 186 ± 24.2 293 ± 61.5 156 ± 33.5

HDL-c (mg/dl) 54 ± 4.4 66 ± 8.4 50 ± 8.9

Glucose (mg/dl) 74 ± 4.4 78 ± 7.5 78 ± 3.5

Insulin 73 ± 15.6 55 ± 13.2 36 ± 9.1

HbA1C (%) 5.5 ± 0.25 5.3 ± 0.48 4.6 ± 0.30

Baseline parameters for each test subject were an average of 3 readings obtained from blood samples obtained on study days -14, -7, and 1. The data for each

treatment group is expressed as the Average + SEM.

Summary of Baseline Characteristics by Treatment Group

Results1. LDL-c, PCSK9 and Triglycerides are Reduced in

DysMet Rhesus Monkeys Administered SCAP siRNA-LNP

2. SCAP siRNA-LNP does not change Palmitate or Cholesterol Synthesis of DysMet Rhesus Monkeys.

3. Additional Findings4. The magnitude of PCSK9 Reduction Suggests < 60%

Knockdown of SCAP mRNA by siRNA-LNP in DysMetRhesus Monkeys

Summary and Conclusions

LDL-c

-20

0

40

60

80BL 8 15 22 29

20

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A

Study Day

% R

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2 0

4 0

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* * * *

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S tu d y D a y % R

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Study Day

% R

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H-9

Figure 1: LDL-c (A) PCSK9 (B) and Triglycerides (C) of DysMet Rhesus Monkeys injected with siRNA-LNPs. Each test subject received a single IV bolus (2 mg/kg) of NT Control (n=10, Black), SCAP (n=10, Red) or PCSK9 (n=4, Blue) on Study Day 1. LDL-c, PCSK9 and Triglyceride levels were determined from fasted blood samples. The data is expressed as % reduction relative to the mean of the NT Control test group . (**P <0.01 vs. NT Control ; *P<0.05 vs. NT Control ).

GroupStudy

DayGM 90% CI

1-sided p-

value

PP(True

GM Red.

20%)

8 46.2 (32.6, 57) < 0.001 99.7

15 46.7 (36.0, 55.7) < 0.001 > 99.9

22 42.1 (29.5, 52.5) < 0.001 99.5

29 36.8 (24.9, 46.8) < 0.001 98.6

8 4.4 (-13.3, 19.4) 0.326 4.3

15 20.4 (8.6, 30.8) 0.005 52.6

22 21 (8.3, 32.0) 0.006 55.8

29 17 (5.5, 27.1) 0.011 31.6

PCSK9

SCAP

GroupStudy

DayGM 90% CI

1-sided p-

value

PP(True

GM Red.

20%)

8 87.3 (82.2, 90.9) < 0.001 > 99.9

15 83.7 (77.5, 88.2) < 0.001 > 99.9

22 76.5 (66.6, 83.5) < 0.001 99.9

29 70.4 (58.9, 78.7) < 0.001 99.4

8 40.2 (22.7, 53.7) 0.001 12.2

15 43.4 (27.8, 55.6) < 0.001 19.5

22 36.1 (16.7, 51) 0.004 6.4

29 31 (11.5, 46.1) 0.009 1.8

PCSK9

SCAP

GroupStudy

DayGM 90% CI

1-sided

p-value

PP(True

GM Red.

20%)

8 7.7 (-31.6, 35.2) 0.352 24.7

15 16.2 (-15.4, 39.1) 0.177 40.3

22 4.4 (-35.5, 32.5) 0.414 19.5

29 -7.2 (-36.5, 15.8) 0.686 2.5

8 28.8 (7.0, 45.5) 0.02 76.9

15 26.4 (6.3, 42.2) 0.02 72.1

22 27.3 (5.4, 44.2) 0.025 73.1

29 20.2 (4.2, 33.5) 0.023 50.8

PCSK9

SCAP

LDL-c, PCSK9 and Triglycerides are Reduced in DysMet Rhesus Monkeys Administered SCAP siRNA-LNP

CH3-C-CoA

[2H]cholesterol

[2H]palmitate

NADPH2H2O

O=

Incorporation 2H20 of label into palmitate and cholesterol used to

measure new synthesis

N T C o n tr o l S C A P P C S K 9

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1 5 0

2 0 0

2 5 0

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(ug

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A .

N T C o n tr o l S C A P P C S K 9

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Ch

ole

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g/m

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B .

SCAP siRNA-LNP does not change Palmitate or Cholesterol Synthesis of DysMet Rhesus Monkeys.

Figure 2: Palmitate (A) and Cholesterol (B) synthesis of DysMet Rhesus Monkeys injected with siRNA-LNP.’s Each test subject received a single IV bolus (2 mg/kg) of NT Control (n=10,), SCAP (n=10) or PCSK9 (n=4) on Study Day 1. The concentration of newly synthesized palmitate or cholesterol was determined from plasma samples obtained on study days 8 and 10.

Additional Findings• All other metabolically-relevant endpoints remained unchanged by the SCAP

siRNA-LNP intervention (i.e. ApoA1, ApoB, glucose, insulin, HbA1c).a

• The transient and recoverable changes in toxicologically-related biomarkers after SCAP siRNA-LNP injection were considered of minimal toxicological significance.

a The selection criteria for the study population and statistical powering was focused on LDL-c reduction. Consequently, the other parameters (glucose, insulin, HbA1c) were too variable to detect a change. Furthermore, by definition the dysmetabolic monkeys are not overtly diabetic ( HbA1c and glucose levels not elevated and a wide spectrum of insulin levels. Individuals in the dysmetabolic population may be very different with regards to insulin resistance.

The magnitude of PCSK9 Reduction Suggests < 60%

Knockdown of SCAP mRNA by siRNA-LNP in DysMet

Rhesus Monkeys

• Data across 3 studies in which lean healthy monkeys were injected with SCAP siRNA-LNP was used to generate the % SCAP mRNA Knockdown vs. % Reduction in [PCSK9 ] relationship.

• The degree of SCAP mRNA knockdown of the SCAP siRNA-LNP treatment of this current study was extrapolated to be 56% using this model.

Relationship Between SCAP mRNA Expression and Δ in

PCSK9 Levels in Lean/Healthy Rhesus Monkeys

% PCSK9 Reduction of SCAP siRNA-LNP-injected DySMet Rhesus @ 2 weeks post-dose.

95 90 80 70 50 30 99.956

Extrapolated % SCAP mRNA KD of SCAP siRNA-LNP-injected

DySMet Rhesus

1. The reduction in LDL-c, TG and PCSK9 in SCAP siRNA-LNP treated monkeys is consistent with inhibition of SCAP activity.

2. Although statistically significant, the magnitude of both LDL-c and PCK9 lowering observed in the SCAP siRNA-LNP group is less than that of the PCSK9 siRNA-LNP group. This observation suggests that:

1. The degree of SCAP mRNA knockdown may not have reached the level to achieve maximal LDL-c lowering in the DysMet monkeys or

2. SCAP inhibition is less potent to reduce LDL-c compared to PCSK9 inhibition in Dysmet monkeys or

3. The SCAP siRNA-LNP silenced less of its target gene than the PCSK9 siRNA-LNP.

**The measurement of hepatic target mRNA expression (via liver biopsy) would enable a more definitive conclusion.

3. The finding that the SCAP siRNA-LNP did not change the amount of new fatty acid or cholesterol synthesized is contrary to the expected effect of inhibiting SCAP. This observation suggests that:

1. The degree of SCAP mRNA knockdown may not have reached the level to achieve measurable changes in fatty acid and cholesterol synthesis or

2. Changes in palmitate/cholesterol synthesis may not be a major contributor to the [LDL-c] elevation seen in the monkeys.

**The measurement of hepatic target mRNA expression (via liver biopsy) and/or understanding the underlying cause of elevated [LDL-c] would enable a more definitive conclusion.

Summary and Conclusions

References1 Jensen et.al., Abstract 18950: PCSK9 and LDL-C Lowering Effect of SCAP Targeting siRNA in Mouse and Non-Human Primate Models

Circulation. 2014; 130: A18950 .

2 Pinto, S., “Therapeutic Approaches for Lipid Modulation.”, to be presented at “ The Crossroads of Lipid Metabolism and Diabetes (Keystone Conference)”, Copenhagen Denmark, April 23, 2015.

AcknowledgementsThe authors would like to thank Duncan Brown for siRNA design, Merck Process Chemistry and Pharmaceutical Sciences teams for siRNA synthesis and LNP formulation. We would also like to thank Stephanie Williams for help with siRNA qualification studies , Julja Burchard for assistance with statistical analysis , Daniel Metzger, Rachel Ortiga and Stacey Conarello for logistical in-life study support, Rupesh Amin for toxicological consultation and Laura Sepp-Lorenzino, Joseph Metzger Mark Erion and Deborah Slipetz for their support of the work.