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The Thicket of Challenges in GPCR
Molecular Pharmacology: Paradigm
shifts bring new opportunities and
new hurdles
Ryan T. Strachan, PhD
Goals:
• Convey my enthusiasm about the current and future states of GPCR
Drug Discovery
• Includes 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 methods
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 health
• Draw from ideas across multiple fields of study:
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•
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Biochemistry
Chemistry
Physiology
Computer Science
•
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Clinical Medicine
Mathematics
Engineering
Statistics
GPCRs are key mediators of cell
signaling:
* Rajagopal et al. Nat Rev. Drug Discov. 2010
• ~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)
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 targets
• Cancer
• Obesity
• Cardiac dysfunction
• Inflammation
• Diabetes
• Pain
• CNS disorders
* Courtesy of Tudor Oprea
Classic theories give way to new
paradigms:
• As key drivers of human (patho)physiology GPCRs have been
widely studied
1900’s
- ‘Chemoreceptors’ and mathematical models of signaling added
order to chaos
19701990’s
- Advent of functional assays, radioligand binding assays, and the
cloning of G proteins and GPCRs paved the way for biophysical and
structure-function studies
2000
- Sequencing of the human genome revealed the full complement
of GPCRs, including the identification of orphan GPCRs
1990’spresent
- Advances in assay technology revealed that GPCR agonists
disproportionately activate numerous cellular pathways (ie., biased
agonism) via unique receptor conformations
2007present
- Advances in GPCR crystallography and computational approaches
are ushering in a Golden Age of Molecular Pharmacology,
launching the field of structure-based drug discovery
Not so fast, we have a long way to
go….
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 2015
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-HT1A serotonin)
• RGB-2 (D2 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. 2013
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., Gs,
Gi, Gq, G12 or G13)
• 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?
• Large libraries of diverse chemotypes is preferred
Innovation at the bench: arrestin
translocation
cDNA:
Assay:
* Kroeze et al. Nat. Struct. Mol Biol. 2015
• 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 codonoptimized, synthesized GPCRs. Freely available through Addgene or the
Psychoactive Drug Screening Program (PDSP)
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
compounds
• Part 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?
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 success
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), Ogerin
• GPR65 (Huang et al. Nature. 2016)
• Proton-sensing GPCR with 37% identity to GPR68, understudied
• Identified 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 compound
A second major paradigm shift is ‘biased
agonism’, which is revolutionizing how
we target GPCRs with drugs
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. 2010
Opportunities presented by biased
agonism:
• Biased agonism can be exploited to target therapeutic pathways and spare
those responsible for on target adverse effects
-
GPCRs
μ-opioid receptor
Κ-opioid receptor
PTH1R
GPR109A
AT1R
β1AR
β2AR
β2AR
D2R
Therapeutic Bias
- G protein
- G protein
- Arrestin
- G protein
- Arrestin
- Arrestin
- Arrestin
- G protein
- Arrestin
Indication
- Pain
- Pain
- Osteoporosis
- Lipid homeostasis
- Cardiovascular disease
- Cardiovascular disease
- Cardiovascular disease
- Asthma
- Antipsychotic
Fulfilling the therapeutic promise of
biased agonism:
• Limit case: Gi-biased μ-opioid-receptor agonists (PZM21 and TRV130)
achieve separation of the analgesic properties of opioids from the arrestinmediated side effects of respiratory depression and addiction.
* Manglik et al. Nature.
2016
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/KA) (Kenakin et al. ACS Chem. Neurosci.
2012)
• Emax/EC50 (equiactive comparison) (Figueroa et al. J. Pharmacol. Exp. Ther.
2009)
• Tau values (pharmacologic) (Rajagopal et al. Mol. Pharmacol. 2011)
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 (EC50) and efficacy
(Emax) 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)
Amplification turns antagonists into
partial agonists:
Low amplification
Hi amplification
Buffer
Isoproterenol
Procaterol
Alprenolol
125
100
cAMP (% ISO Max)
cAMP (% ISO Max)
100
Buffer
Isoproterenol
Procaterol
Alprenolol
125
75
50
25
0
75
50
25
0
-14
-12
-10
-8
Log [ligand], M
-6
-4
-14
-12
-10
-8
-6
-4
Log [ligand], M
• At endogenous β2AR receptor expression levels alprenolol is an
antagonist
• Overexpressing the β2AR turns alprenolol into a partial agonist
Exercise caution when using databases:
* Roth and Kroeze JBC 2015
• 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
If cellular assays pose such a problem,
then why don’t we bypass them?
Bypassing the need for cells: quantifying
signaling in vitro
*Accomplished by viewing GPCRs
as allosteric machines
• Intrinsic efficacy (ε) of classic
theory is equal to the energetic
effect that drives formation of
an active ternary complex (α)
* Onaran et al. Trends Pharmacol. Sci. 2014
* Onaran and Costa Nat. Chem. Biol. 2012
*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)
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
*De Lean et al. JBC. 1980
*Kent et al. Mol. Pharmacol. 1980
Screening the ‘transducerome’ with
single transducer resolution:
[ligand]
Transducer 2
T
T
*
100
50
0
no
T
%Bound
unfused
Biased agonism is an intrinsic molecular property of GPCR
150
ligands
fus
ion
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
T
C
curve shift integral
T
T
B
%Bound
Transducer 1
transducer
fused
...
[ligand]
...
Transducer 19
T
T
T
%Bound
A
[ligand]
* Strachan et al. JBC. 2014
Goal: Mine the unexplored
pharmacology of GPCRs for new modes
of signaling bias:
*
T
T
fused
...
...
%Bound
Transducer 19
0
7
8
9
10
11
12
13
14
15
16
17
18
19
Transducer
transducer
• This would require patterns to be extracted
from complex data sets
• e.g., transducerome shifts, clinical endpoints,
gene expression, and behavioral data
Transducer 19
T
T
0
transducer
[ligand]
T
50
50
Unfused
T
100
no
%Bound
unfused
*
100
no
[ligand]
Transducer 2
Transducer 2
150
curves
between
Areacurve
shift integral
C
150
fus
ion
1
2
3
4
5
6
7
8
fus 190
i1o1n
12
131
142
153
164
17
185
196
T
C
...
T
T
B
%Bound
Transducer 1
...
A
B
Transducer 1
curve shift integral
A
[ligand]
• To our knowledge no one is thinking on this
scale
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
discoveries
Opportunities for Deep Learning to
facilitate GPCR drug discovery:
• Identification of novel chemical matter (empirical approaches are too
slow) from virtual screening campaigns
• Tool 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 trials
The call to collaborate: successful
integration of wet bench pharmacology
and computation
Empirical screens
*Collaboration has
been essential
Computation/
prediction
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 Ca2+ release, cAMP, arrestin
recruitment; use the Psychoactive Drug Screening Program )
- Molecular efficacy values from ‘transducerome screening’
Thank you!
Questions?