EPIX Pharmaceuticals

clatteringlippsΒιοτεχνολογία

5 Δεκ 2012 (πριν από 4 χρόνια και 8 μήνες)

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Drug Discovery & GPCR Models


Sheila DeWitt, PhD

VP Discovery & Manufacturing


October 25, 2007


2

Outline


Overview of EPIX’ Product Portfolio


Drug Discovery Strategy


Case Study


5HT1A

3

Clinical Portfolio


Internally Discovered

Phase 3

NDA

Approved

Phase 2

Phase I

IND/

GLP Tox

Lead
Optimization

Lead
Discovery

Target

Product

PRX
-
03140

(5
-
HT4)

Alzheimer's Disease (GSK has exclusive option)

PRX
-
00023

(5
-
HT1A)

Depression

PRX
-
08066

(5
-
HT2B)

Pulmonary Hypertension w/ COPD

PRX
-
07034

(5
-
HT6)

Obesity, Cognitive Impairment

Three Drug Candidates in Phase 2 Development

COPD = Chronic Obstructive Pulmonary Disease

4

Proprietary Drug Discovery Technology


GPCRs


Strategic drug development targets


Embedded proteins in surface membrane of all cells


Mediate biological signaling in health/disease


Commercially validated
-

40% of top 100 drugs


Never crystallized


3D Structures Unknown


SAR a “hit
-
or
-
miss” exercise requiring years


Side effect / selectivity issues remain problematic


Opportunity for EPIX


Proprietary modeling / screening technologies


Commitment to discovery triad:


Computational and medicinal chemistry integrated with biology

5

Outline


Overview of EPIX’ Product Portfolio


Drug Discovery Strategy


Case Study


5HT1A

6

EPIX Discovery Strategy

Screening

Hit

Characterization

Lead

Optimization

Preclinical

Development

Drug Discovery

Model

Development


Modeling

Novel GPCR modeling methodology (PREDICT™)


Screening

in silico

screening > 4 Mil commercially available cmpds


Hit Charact

3D Models & Purchased SAR (pSAR) to prioritize scaffolds


Lead Opt

3D Models, Biology, and Med Chem to optimize


7

EPIX Discovery Strategy

Screening

Hit

Characterization

Lead

Optimization

Preclinical

Development

Drug Discovery

Model

Development


Modeling

Novel GPCR modeling methodology (PREDICT™)


Screening

in silico

screening > 4 Mil commercially available cmpds


Hit Charact

3D Models & Purchased SAR (pSAR) to prioritize scaffolds


Lead Opt

3D Models, Biology, and Med Chem to optimize


8

Modeling GPCRs with PREDICT™


Unique de novo GPCR structure prediction algorithm


Based on scientific understanding of GPCR folds


from experiments, simulations and theory


Folds the protein within its membrane environment


Does not rely on rhodopsin x
-
ray structure


Does not use homology modeling


Applicable (in principle) to any GPCR

10

Ser
199

TM
5

Asp
116

TM
3

PREDICT Model

GPCR sequence

Two
-
Tier Modeling:
~5,000 Decoys


Virtual Complex

N
H
O
PREDICT Modeling Process

11

PREDICT
TM
: Step I
-

Build
7
TMs


Represent each helix by a
2
D dial


Generate all closed
2
D configurations of
7
dials


under geometrical constraints


Optimize each
2
D configuration


to maximize hydrophobic moment in the direction of the membrane
(introduce experimental constraints)


7

1

2

5

4

6

3

Binding pocket

12

PREDICT
TM
: Step II


Translate
2
D to
3
D


Extend each optimized
2
D configuration into a
3
D
representation and optimize in
3
D


7

1

2

5

4

6

3

13

EPIX Discovery Strategy

Screening

Hit

Characterization

Lead

Optimization

Preclinical

Development

Drug Discovery

Model

Development


Modeling

Novel GPCR modeling methodology (PREDICT™)


Screening

in silico

screening > 4 Mil commercially available cmpds


Hit Charact

3D Models & Purchased SAR (pSAR) to prioritize scaffolds


Lead Opt

3D Models, Biology, and Med Chem to optimize


14

EPIX


in silico Screening Process

Data collection

Target modeling

Library generation

Scoring

Selection of virtual hits

Biological assays

Docking

PREDICT
TM

15

EPIX Screening Libraries


Size:


~
4
million drug
-
like compounds


Source:

Catalogues of ~
30
reputable vendors


Updates:

Continuously (+before new projects)


Criteria:

Availability for immediate purchase


Advantages:


Diverse


Rapid access to newest compounds (
30
% change per year)


Cheap to obtain and to maintain


Quick registration (buy only what is actually needed)


Limitations:


Non
-
standard targets may not be represented well


Need to improve IP properties since hits will be in public domain

16

in silico Screening & Hit Characterization

1.
Datamine collection of >4 Mil commercially available cmpds

2.
Select focused cmpd library for target

(100,000

400,000)

3.
In silico

screening of focused library against target protein

4.
Scoring & selection of prioritized cmpds (200
-
300 ‘virtual hits’)

5.
Purchase and test ‘virtual hits’ in biological assay

1.
Hit criteria Ki/IC50 < 10mM (validated dose response)

6.
Datamine around hits to generate pSAR

7.
Prioritize scaffold for Lead Optimization

8.
Further optimize model for specific scaffold using pSAR

17

EPIX Discovery Strategy

Screening

Hit

Characterization

Lead

Optimization

Preclinical

Development

Drug Discovery

Model

Development


Modeling

Novel GPCR modeling methodology (PREDICT™)


Screening

in silico

screening >
4
Mil commercially available cmpds


Hit Charact

3
D Models & Purchased SAR (pSAR) to prioritize scaffolds


Lead Opt

3
D Models, Biology, and Med Chem to optimize


18

EPIX Paradigm for Lead Optimization


Integrated MedChem

CompChem teams (
2
:
1
ratio)


Extensive use of computational tools (
3
D
structures, predictive ADMET) to navigate the
multiple possible optimization pathways:


Suggest/prioritize what to synthesize


Suggest/prioritize what NOT to synthesize



Efficient process, robust, agnostic to the receptor
class

19

Hits

in silico

screen

4
M compounds

~
6
months

Hits

“wet” assay screen

<
1
M compounds

~
12
months

Lead Optimization

~
1
,
000
compounds

2
-
5
years to clinical candidate

Industry Standards

EPIX

Lead Optimization

100 compounds or less

6
-
12 months to clinical candidate

Efficient and Effective Discovery Engine

21

(
1
) Estimated

#
Target
Indication
Lead (Ki)
Duration of
Lead Optimization
No. Compounds
Evaluated
In Silico
No. Compounds
Synthesized
Development Phase
Drug
1
5-HT1A
GAD, MDD
1 nM
5 mo.
300
(1)
31
(EDC= #23)
Phase II
PRX-00023
2
5-HT4
AD
21 nM
6 mo.
270
53
(EDC=#40)
Phase II
PRX-03140
3
5-HT2B
PAH
13 nM
9 mo.
570
80
(EDC=#66)
Phase II
PRX-08066
4
5-HT6
Obesity, Cog Imp
14 nM
7 mo.
469
44
(EDC=#34)
Phase Ib
PRX-07034
5
S1P1
RA, MS
0.7 uM
10 mo.
460
82
(EDC=#38)


w/Amgen
PRX-13038
6
Kv1.5/4.3
Atrial Fibrilation
0.9 uM
0.7 uM
10 mo.
10 mo.
757
776
66
66
LO (pre-EDC)
PRX-17027
7
CCR2
Atherosclerosis,
RA
0.6 uM
3 mo.
493
32
Hit-to-Lead
8
P2Y2
CF, Constipation
10 nM
2 mo.
354
0
Hit-to-Lead
9
NK1
Depression
56 nM
9 mo.
500
(1)
62
10
ADH-V2
Cirrhosis, CHF
1.5 uM
6 mo.
400
28
LO terminated [@ Ki = 7 nM]

(due to Merck's aprepitant failure)
H2L terminated - failed to reach
target profile (FTO issues)
EPIX’ Lead Optimization Track Record

22

Outline


Overview of EPIX’ Product Portfolio


Drug Discovery Strategy


Case Study


5HT1A

23


Source: National Institute of Mental Health, 2003 National Comorbidity Study, Sponsored by the National Institutes of Health

PRX
-
00023
~ Depression


5
-
HT
1
A partial agonist, proven mechanism of action


Estimated world market for treatments $
20
billion*


35
M in US (more than
16
% of the population) suffer from depression severe
enough to warrant treatment at some time in their lives


Substantial commercial opportunity for a selective, better
tolerated alternative:


No withdrawal symptoms, sexual dysfunction, weight changes or sleep
disturbances as observed with SSRIs


Lacks the addictive and sedative effects of the benzodiazepines


Does not have side effects of azapirones


Initiated Phase
2
b trial March
2007
~ results expected
1
H
08


Achieved significant results on depression in Phase
3
anxiety clinical trial

24

SSRI / SNRI


Mechanism results in increased levels of serotonin
(5
-
HT), norepinephrine (NE)


Affects 5
-
HT (14), NE (>6) receptors


Affects sleep, sexual function, appetite


Withdrawal symptoms


Black box warning

Azapirones


5
-
HT
1
A agonists


Affinity for “off
-
target” GPCRs


Dopamine D
2
, alpha
-
1
, alpha
-
2


Nausea, lightheadedness, headache, restlessness


Slow dose escalation requirements

Mechanisms of Other Drug Classes

25

Potential advantages


Highly selective for
5
-
HT
1
A


No sexual dysfunction


No effects on sleep or appetite


No withdrawal symptoms


Do not expect black box warning


Well tolerated compared to
azapirones, with minimal dose
escalation required

PRX
-
00023

Mechanism of Action


PRX
-
00023

26

PRX
-
00023
Superior to other
5
-
HT
1
A Agonists


Azapirones & other 5
-
HT1A agonists have selectivity issues and
metabolic liabilities


PRX
-
00023


Very high affinity for 5
-
HT1A (Ki = 5nM)


Better selectivity


minimal binding to alpha
-
1 (Ki = 1600nM), alpha
-
2 (> 3000nM) and dopamine D2
(Ki > 2000nM) receptors compared to Azapirones


Not metabolized to 1
-
(2
-
pyrimidimyl)
-
piperazine, a potent alpha2
-
adrenergic modulator


Better selectivity results in superior tolerability and no need to a few weeks of multi
-
step dose titration


Once daily dosing


No significant inhibition of CYP450 or hERG


Well tolerated in three Phase I and two Phase II clinical trials


No significant nausea / lightheadedness vs. azapirones

27

PRX
-
00023
Phase
2
b in Depression in
Progress ~ Data in
1
H
08


Double
-
blind, randomized, placebo
-
controlled dose clinical
trial of PRX
-
00023
in major depressive disorder (MDD)


8
-
week study with
120
mg
twice daily

flexible dosing


Approximately
330
MDD patients


Randomized
1
:
1
drug vs. placebo


Primary endpoint


Change from baseline in MADRS compared to placebo


Secondary endpoints


Changes in the Hamilton Anxiety Score (HAM
-
A)


Clinical Global Impressions Improvement Scale (CGI
-
I)


Clinical Global Severity of Illness Scale (CGI
-
S)

28

Outline


Overview of EPIX’ Product Portfolio


in silico

Modeling Strategy


Discovery Case Study


5HT1A