The Thicket of Challenges in GPCR Molecular Pharmacology: P - PowerPoint Presentation

Slide1

The Thicket of Challenges in GPCR Molecular Pharmacology: Paradigm shifts bring new opportunities and new hurdles

Ryan T. Strachan, PhDSlide2

Goals:Convey my enthusiasm about the

current and future states of GPCR Drug DiscoveryIncludes the first presentation of a novel screening strategy aimed at studying the unexplored pharmacology of GPCRs (screening the ‘

transducerome’)Initiate a discussion about how to best apply computational methods to facilitate GPCR drug discovery (if at all

)Establish strategic collaborations to facilitate the generation of robust and predictive Deep Learning methodsSlide3

The role of the Molecular Pharmacologist in GPCR Drug Discovery:

Concerned with the study of drug action on a molecular and chemical level

Seek to discover and validate new therapeutic strategies to improve human healthDraw from ideas across multiple fields of study:

Biochemistry Chemistry PhysiologyComputer Science

Clinical Medicine

Mathematics

Engineering

StatisticsSlide4

GPCRs are key mediators of cell signaling:~800 receptors transduce endogenous and exogenous signals from diverse ligands (photons, odorants,

tastants, hormones, neurotransmitters, lipids, etc…)Large variety of signal transducers (17 different Gα subtypes and 4 arrestins)

*

Rajagopal

et al.

Nat Rev. Drug

Discov

. 2010Slide5

GPCRs as key drivers of human health and disease:GPCRs are

active in just about every organ system and present a wide range of opportunities as therapeutic targetsCancer Cardiac dysfunction

Diabetes CNS disorders

* Courtesy of Tudor

Oprea

Obesity

Inflammation

PainSlide6

Classic theories give way to new paradigms:

As key drivers of human (patho)physiology GPCRs have been widely studied

- ‘Chemoreceptors’ and mathematical models of signaling added order to chaos

- Advent of functional assays, radioligand binding assays, and the cloning of G proteins and GPCRs paved the way for biophysical and structure-function studies

- Advances in assay technology revealed that GPCR agonists disproportionately activate numerous cellular pathways (

ie

., biased

agonism

) via unique receptor conformations

- Advances in GPCR crystallography and computational approaches are ushering in a Golden Age of Molecular Pharmacology, launching the field of structure-based drug discovery

- Sequencing of the human genome revealed the full complement of GPCRs, including the identification of orphan GPCRs

1900’s

1970-

1990’s

2000

1990’s-present

2007-presentSlide7

Not so fast, we have a long way to go….Slide8

Orphan/understudied GPCRs: a treasure trove of drug targets

We

possess a very superficial view of how GPCRs function in normal and disease states~40% of non-olfactory GPCRs are understudied from chemical and biological perspectives (large green circles)

* Roth and

Kroeze

JBC 2015Slide9

Opportunities presented by orphan GPCRs:

Well-characterized GPCRs play key roles

in

(

patho

)physiology,

therefore orphan/understudied GPCRs have untapped therapeutic potential

Small molecule receptors:

G-21 (5-

HT

1A

serotonin)

RGB-2 (

D

2

dopamine)

Neuropeptide receptors:

ORL-1 (

OrphaninFQ

/

Nociceptin

)

HFGAN72 (Orexin1)

GPR10 (Prolactin-releasing peptide)

APJ (

Apelin

)

GHSR (Ghrelin)

GPR54

(

Kisspeptin

/

metastin

)

GPR73a/b (

Prokineticin

)

GPR154 (Neuropeptide S

)

*

Civelli

et al.

Ann. Rev.

Pharmacol

.

Toxicol

. 2013Slide10

The challenges presented by orphan GPCRs:

Finding tool/endogenous molecules is hard!

Interrogating orphan GPCRs

en

masse

in a parallel and simultaneous fashion is currently technologically and economically unfeasible

.

Hurdle 1:

Uncertainty about which signaling pathway to quantify

Functional assays have typically used readouts that depend on the native or forced coupling of GPCRs

with different

G proteins, (e.g.,

G

s

,

G

i

,

G

q

, G

12

or G

13

)

What about the remaining 12 or so G proteins?

Hard to test all in parallel,

run the risk of missing active compounds

Hurdle 2:

Chemical diversity-which

class of compounds to

screen?

L

arge

libraries of diverse

chemotypes

is preferredSlide11

Innovation at the bench: arrestin translocation

Our universal platform (PRESTO-Tango) facilitates the parallel interrogation of orphan GPCRs via

arrestin

recruitment (

Barnea

et al.

2008 PNAS)

Open Source Resource:

GPCRome

panel

permits screening of 328

codon-optimized, synthesized GPCRs. Freely

available through

Addgene

or the

Psychoactive Drug Screening

Program (PDSP)

cDNA

:

Assay:

*

Kroeze

et al.

Nat.

Struct

.

Mol

Biol. 2015Slide12

Integrating physical/computational methods to overcome the hurdles of orphan GPCR screening:

Addressed the issue of chemical diversity by integrating physical/computational methods to facilitate tool molecule identification from tens of millions of virtual compoundsPart of a larger discovery effort by the NIH to ‘Illuminate the

Druggable Genome (IDG) (https://commonfund.nih.gov/idg/

index) Develop novel, scalable technologies to shed light on the ‘dark matter’ of the human genome in an effort to identify new biology and new

therapies

Ion channels

Nuclear receptors

Kinases

* Opportunities to mine these datasets via Deep Learning?Slide13

Integrated workflow:

* Using validated screening data to inform the modeling and then cycling between computational prediction and experimental validation has been a key component to successSlide14

Integrating physical and computational approaches identifies novel chemical matter to reveal new biology:

GPR68 (Huang et al. Nature. 2016)Proton-sensing GPCR, understudied and lacks tool molecules

Identified a small molecule positive allosteric modulator (PAM), OgerinGPR65 (Huang

et al. Nature. 2016)Proton-sensing GPCR with 37% identity to GPR68, understudiedIdentified an allosteric agonist and a negative allosteric modulator (NAM)

MRGPRX2 (

Lansu

et al.

Nat. Chem. Biol

.

in review

)

Understudied primate-exclusive GPCR associated with pain and itch

Identified a selective

submicromolar agonist tool compoundSlide15

A second major paradigm shift is ‘biased agonism’, which is revolutionizing how we target GPCRs with drugsSlide16

Biased agonism supplants classic concepts of efficacy:

The “two-state” model postulates an

inactive (R) conformation of the receptor in equilibrium with an active (R*) conformation

The “multi-state” models posits that receptors exist in multiple ligand-specific active conformations, each of which possesses

varying abilities to activate

downstream signaling pathways

*

Rajagopal

et al.

Nat Rev. Drug

Discov

. 2010Slide17

Opportunities presented by biased agonism:

Biased

agonism

can be exploited to target therapeutic pathways and spare those responsible for

on target

adverse effects

GPCRs

Therapeutic Bias

Indication

μ-opioid receptor - G protein - Pain

Κ

-opioid receptor - G protein - Pain

PTH1R -

Arrestin

- Osteoporosis

GPR109A - G protein - Lipid homeostasis

AT

1

R -

A

rrestin

- Cardiovascular disease

β

1

AR -

Arrestin

- Cardiovascular disease

β

2

AR -

Arrestin

- Cardiovascular disease

β

2

AR - G protein - Asthma

D

2

R -

Arrestin

- AntipsychoticSlide18

Fulfilling the therapeutic promise of biased agonism:

Limit case:

G

i

-biased μ-opioid-receptor

agonists

(

PZM21 and TRV130) achieve

separation of the analgesic properties of

opioids

from

the

arrestin

-mediated side

effects

of respiratory depression and addiction.

*

Manglik

et al.

Nature. 2016Slide19

The challenges presented by biased agonism:

Hurdle

:

Screening for biased agonists is not straightforward and requires a reference agonist

It

is difficult to extracting meaningful information about agonist efficacy from complex cellular

assays with varying

degrees of signal

amplification

Analytical efforts to address this have been hotly debated, yet effective

Transduction coefficients (tau

/

K

A

) (

Kenakin

et al.

ACS

Chem.

Neurosci

.

2012)

E

max

/

EC

50

(

equiactive

comparison) (Figueroa

et al.

J.

Pharmacol

. Exp.

Ther

. 2009)

T

au values (pharmacologic) (

Rajagopal

et al.

Mol. Pharmacol

. 2011)Slide20

Signal amplification changes the location of CRCs:

Scenario where two agonists (e.g., full agonist in red and partial agonist in blue) are tested under varying degrees

of

signal transduction efficiency (amplification)

Hi amplification

Low amplification

Potency (EC

50

) and efficacy (

E

max

) values change drastically depending on amplification

Very misleading for detecting bias across assays with disparate amplification

Potentially misleading when used in training sets

(

Rajagopal

et al.

Mol.

Pharmacol

. 2011)Slide21

Amplification turns antagonists into partial agonists:

At endogenous

β

2

AR

receptor expression levels

alprenolol

is an antagonist

Overexpressing the

β

2

AR

turns

alprenolol

into a partial agonist

Hi amplification

Low amplificationSlide22

Exercise caution when using databases:

In

silico

approaches that take

advantage of large

databases employing any number of different assays

Despite these issues, we

and

our collaborators

have successfully predicted novel GPCR

targets for

known

drugs and

have designed novel drugs

targeting GPCRs entirely

in

silico

* Roth and

Kroeze

JBC 2015Slide23

If cellular assays pose such a problem, then why don’t we bypass them?Slide24

Bypassing the need for cells: quantifying signaling in vitro

Intrinsic efficacy (ε) of classic theory is equal to the energetic effect that drives formation of an active ternary complex (α)

*Suggests that we can quantify signaling through different transducers

in vitro by measuring cooperativity between the ligand and transducer (i.e., by shifts in agonist affinity)

*Accomplished by viewing GPCRs as allosteric machines

*

Onaran

and Costa Nat. Chem. Biol. 2012

*

Onaran

et al.

Trends

Pharmacol

. Sci. 2014Slide25

Quantifying signaling in vitro is nothing new:

Coincident with development of the Ternary Complex Model (TCM) it was shown that shifts in agonist affinity (molecular efficacy) correlate intrinsic efficacy in cells

*Kent

et al.

Mol.

Pharmacol

. 1980

*De Lean

et al.

JBC. 1980Slide26

Screening the ‘transducerome’ with single transducer resolution:

unfused

fused

T

T

T

T

T

T

T

T

T

[ligand]

[ligand]

[ligand]

%Bound

%Bound

%Bound

Biased

agonism

is an intrinsic molecular property of GPCR ligands

* Strachan

et al.

JBC. 2014Slide27

Goal: Mine the unexplored pharmacology of GPCRs for new modes of signaling bias:

This would require patterns to be extracted from complex data sets e.g., transducerome shifts, clinical endpoints, gene expression, and behavioral data

To our knowledge no one is thinking on this scale

unfused

fused

T

T

T

T

T

T

T

T

T

[ligand]

[ligand]

[ligand]

%Bound

%Bound

%Bound

Transducer

Area between curves

UnfusedSlide28

Summary:

The field of GPCR Molecular Pharmacology is rapidly changing, reinvigorated by paradigm shifts related to the notions that:A

large fraction of receptors are understudied or ‘orphaned’Biased agonism is a property of GPCR ligands

Paradigm shifts afford both numerous opportunities AND challenges

We

have a long way to go in order to fully exploit this

current

Golden Age

of Molecular Pharmacology

I am confident that advances

in crystallography and computational medicinal chemistry will

help to accelerate discoveriesSlide29

Opportunities for Deep Learning to facilitate GPCR drug discovery:

Identification of novel chemical matter (empirical approaches are too slow) from virtual screening campaignsTool molecules for illuminating understudied/orphan GPCRs

Biased agonists (facilitated by biased GPCR structures, e.g., bound by different agonists, different ternary complexes, Nbs,

etc…)Mine complex clinical, transcriptomic, proteomic, and ‘

transducerome

’ datasets at high levels of abstraction to uncover novel modes of therapeutic bias

Step closer to the

NIH notion of ‘Experimental

Medicine

’

as it relates to

fully

characterizing

drug actions before they advance to

large clinical

trialsSlide30

The call to collaborate: successful integration of wet bench pharmacology and computation

Goal: Establish a project devoted to generating the optimal AI training set for GPCR ligand discovery

Target: Well-characterized GPCR family with multiple crystal structures (e.g., opiate receptors)

Ligands: Large library containing multiple chemotypes, with substantial SAR within each

chemotype

Data

(raw and corrected):

Binding affinities (Ki’s through the Psychoactive Drug Screening Program)

Efficacy values (tau for standard assays such as Ca

2+

release,

cAMP

,

arrestin

recruitment; use the Psychoactive Drug Screening Program )Molecular efficacy values from ‘transducerome screening’

Empirical screens

Computation/prediction

*Collaboration has been essentialSlide31

Thank you!Questions?