Enhancing molecule quality by combining fragments and early pharmacy input
17th RSC/SCI Medicinal Chemistry Symposium
Cambridge, UK. September 2013
Simon Hodgson*
GSK, Stevenage, UK
*Current address – Stevenage Bioscience Catalyst
Collaborative project between GSK and Astex
Simon Hodgson * Dave Clapham
Karen Affleck
Simon Teague
Emma Sherriff
Jon Hutchinson
Linda Russell
Sorif Udin
Joelle Le
Don Somers
Ashley Hancock
Heather Hobbs
Robin Carr
Andrew Leach
Gordon Saxty *
Paul Mortenson
David Norton
Lee Page
Phil Day
Caroline Richardson
Anne Cleasby
Joe Coyle
Rachel McMenamin
David Rees
Chris Murray
Jeff Yon
* Co‐project leaders
Talk structure
• Background
– Attrition
– Importance of Solubility
– DD approaches
• Application of FBDD and early pharm dev intervention ‐ PGDS example
Attrition – we can have an impact!
• Early example of how a major attrition factor of PK/ bioavailability changed 1991‐2000
• Early incorporation of DMPK reason for the improvement
• But attrition challenges shifted further to clinical efficacy
Kola, Landis NRDD 2004
The War on Attrition
Industry Success Rates & Causes of Attrition 2006‐10
Data supplied by Phil Miller, Thomson Reuters
© CMR International, a Thomson Reuters business • Pre‐clinical to Phase III success = 4.3% survival rate; ‘Phase II graveyard’
• 4‐5% Improvement will double output • Caveat: clinical phase attrition is often multi‐factorial: ‘poor exposure’
contributes to efficacy failure in Phase II
Courtesy P.Leeson
• Industry is moving from a ‘quantity’ to a ‘quality’ strategy
Pharma’s problems: Paul et al, Nat. Rev. Drug Disc., 2010, 9, 203; Scannell et al, Nat. Rev. Drug Disc., 2012, 11, 191
Phase II Attrition: Pfizer Data 2005‐2009 (n=44)
Exposure confidence
Levels of Confidence in 3 ‘Pillars’: 1) Exposure at target site of action; 2) Binding to target; 3) Pharmacological response
High
Low
Exposure & Binding
All met
n = 12
•5 tested mechanism
•2 phase III starts
n = 14
• 14 tested mechanism
• 12 achieved positive POC
• 8 phase III starts
None or partially met
Binding & Pharmacology
n = 12
•12 failed to test mechanism
•0 phase III starts
Low
n = 6
• 5 tested mechanism
• 0 phase III starts
Pharmacological confidence
High
• Low confidence in exposure in 18/34 non‐progressing molecules: “cannot conclude mechanism tested adequately in 43% of cases”
• Compound quality issue: formulation; DMPK; dose prediction; safety margin. Should be resolved prior to Phase II?
3 Pillars: Morgan et al, Drug Discovery Today 2012, 17, 419‐424 Courtesy P.Leeson
What Do Medicinal Chemists Actually Make? • A 50‐year retrospective from J.Med. Chem
• Mean properties for compounds appearing in JMC papers during the first 5 years of publication and in the most recent 5‐year period
W Walters et al Vertex
J. Med. Chem. 2011, 54, 6405–6416
Probing the links between in vitro potency, ADMET and physicochemical parameters •
Analysis using the ChEMBL database, which includes more than 500,000 drug discovery and marketed oral drug compounds
•
Key findings
– oral drugs generally don’t have low nanomolar potency (50 nM on average); – many oral drugs have considerable off‐target activity
– in vitro potency does not correlate strongly with the therapeutic dose. •
These findings suggest that the perceived benefit of high in vitro potency may be negated by poorer ADMET properties •
‘Catch22’ – chasing low dose (to obviate DD issues) by high target affinity (and DD methods) made properties worse!
P.Gleeson, A.Hersey, D. Montanari, J.Overington
NRDD 2011
Developability Classification System (DCS): for Formulation Development. Useful for Chemists to focus on!
• Importance of solubility, permeability and dose
• Earlier BCS classification to DCS, uses FaSSIF solubility, and sub‐
divides class II further
J.Butler, J.Dressmann
Journal of Pharmaceutical Sciences, Vol. 99, 4940–4954 (2010)
Top 121 oral prescription medicines by DCS
Dose/Intestinal solubility (mL)
DCS 1
N=50‐65
DCS 2A
N=17‐34
DCS 2B
N=8
DCS 4
N=4
DCS 3
N=25‐27
J.Butler J.Dressmann
Journal of Pharmaceutical Sciences, Vol. 99, 4940–4954 (2010)
Despite a wide recognition of the importance of solubility, pipeline drugs are not optimal
• Current portfolio of pipeline development drugs aimed at oral administration in comparison to marketed drugs, shows a strong trend towards drug candidates with low aqueous solubility
• ~90% of pipeline drugs fall into BCS classes II & IV
Ralph Lipp. Amer. Pharmaceutical Review Apr 2013
http://www.americanpharmaceuticalreview.com/Featured‐Articles/135982‐The‐Innovator‐Pipeline‐Bioavailability‐
Challenges‐and‐Advanced‐Oral‐Drug‐Delivery‐Opportunities/
Poor solubility impacts from early to late stage drug discovery
• Early Discovery
– In vitro assays – inaccurate assessment of potency and variability
– Low bioavailability
• Poor pre‐clinical efficacy
• Post Selection of Development Candidate
– Inability to achieve pre‐clinical tox cover to support clinical studies
– Poor clinical efficacy/ insufficient or variable exposure to test the mechanism
– Formulation development cost and time increase
• For early clinical studies
• Late stage attrition! Complex formulation development often delayed until after encouraging phase1/2
Approaches to addressing solubility
• Working with the Candidate
– Salt formation might be enough
– Particle size to increase dissolution rate
– Complex formulation
• E.g Lipidic in capsule
– Stabilised amorphous form
Late
Pass the problem on!
Approaches to addressing solubility
• Working with the Candidate
– Salt formation might be enough
– Particle size to increase dissolution rate
– Complex formulation
Late
Pass the problem on!
• E.g Lipidic in capsule
– Stabilised amorphous form
• Change the chemistry of the lead series
– Pro‐drug the lead candidate
– Structurally modify a poorly soluble series
Quite late
Further LO resource
Complexity
Approaches to addressing solubility
• Working with the Candidate
– Salt formation might be enough
– Particle size to increase dissolution rate
– Complex formulation
Late
Pass the problem on!
• E.g Lipidic in capsule
– Stabilised amorphous form
• Change the chemistry of the lead series
– Pro‐drug the lead candidate
– Structurally modify a poorly soluble series
Quite late
Further LO resource
Complexity
• Start in intrinsically more soluble chemical space !
Property comparison between leads derived from fragments vs. other approaches
Properties taken from patent literature of 18 companies.
Astex (fragments) vs. the other companies shows reduced logP and MW
Astex in‐house analysis comparing their fragment derived leads and hits with literature for same targets
C.Murray, M.Verdonk & D.Rees
TIPS May 2012, Vol. 33, No. 5
P.Leeson, S.St‐Gallay,
NRDD 2011, 10, 749–765
Influence of discovery strategies on properties of drug candidates – not clear on solubility
• Analysis based on general literature, very diverse set • Fragments vs. HTS : Solubility higher for hits, but not leads
G.Keserü, Makara, NRDD 2009
Example with Haematopoietic Prostaglandin D2 Synthase (PGDS)
PGDS in the eicosanaoid pathway
•
PGDS inhibition could impact on a range of lipid mediators & receptors potentially important in allergic and inflammatory diseases Prostaglandin lipid pathways ‐ PGDS
Example PGDS inhibitors
• Typified by lipophilic Biaryl groups in the active site
F
F
N
O
N
N
O
N
NH
O
NH
N
N
O
NH
N
1
H
N
3
2A
2
N
N
N N
HQL79
Matsushita, N. Jpn. J. Pharmacol. 1998,78,,1–10 & 11–22
N
F
F
F
N
F
N
N N
Pfizer
US2008/0146569A1
Sanofi
WO2008/121670A1
PGDS ‐ Med Chem challenge and approach
• PGDS
– binds lipid substrate
– example inhibitors generally quite lipophilic
• Known inhibitors bind in lipophilic enzyme active site (Xray)
• Trial screening set on PGDS showed strong potency/logP
correlation
• Hypothesis
 Structurally driven, fragment approach to increase chances for low MWt leads with good solubility properties
• Early use of pharmacy to measure progress, decision making
The Astex Pyramid Approach
INTEGRATED BIOPHYSICAL SCREENING
FRAGMENT SELECTION
N N
N
N
H2N
S
HN
N
N
Cl
Cl
N
N
N
OH
Cl
N
NH2
N
Cl
NH2
Astex
Proprietary
Fragment Library
Tm
5
4x10
5
3x10
5
2x10
5
1x10
305 310 315 320 325 330 335
Temperature(K)
O
NH2
HN
O
5
5x10
Fluorescence (a.u.)
N N
H
X‐Ray
O
ITC
NMR
N
H
0
20
Time (min)
40
60
80
100
0.1
0.0
-0.1
µcal/sec
H2N
STRUCTURE‐LED OPTIMISATION
-0.2
-0.3
-0.4
-0.5
Astex Rule of 3™
-0.6
0
kcal/mole of injectant
Targeted & Virtual Screening
Fragment
Sets
-2
-4
-6
-0.5 0.0
0.5
1.0
1.5
2.0
2.5
3.0
3.5
Molar Ratio
Astex PYRAMID™
Screening
Fragment‐to‐
Candidate Chemistry
Pharm Dev work to support selection of the Development Candidate
• Typically conducted on candidate/pre candidate
– Full pH/solubility profile
– Full pH/solution state stability profile
– Solid state form
• XRPD, DSC, TGA, GVS
– Solid state stability – If required version and form assessment
– Formulation recommendations for formal toxicology studies and early clinical studies
Introduction of earlier Property Intervention for PGDS (after CLND)
• CLND solubility to triage compounds
– High Throughput (1,000’s per week)
– Screen out insoluble compounds to aid SAR
– Precipitation from DMSO solution
• Early Pharm Dev intervention
– Low throughput <10 per week
– Solubility from solid in Physiologically relevant fluids
• Simulated Gastric Fluid (pH=1.6), Fed State Simulated Intestinal Fluid (pH=6.5), Fasted State Simulated Intestinal Fluid (pH=6.5), Water
– Solution state stability assessment
• Chemical and Photostability
– Investigated Pharm Dev properties of early compounds
• Identify solubility or stability challenge in series allowing chemistry effort to be redirected to more promising areas
• Inadequate PhysChem properties in the light of predicted dose could lead to recommendation not to progress PGDS Progression Profiles
Validated
Hits
Confirmed Binding Mode (Xray)
LE >0.3
Fragment Opt
H2L
Enzyme/Cell 6‐ 7
LE/LLE 0.4
Tractable
ppb
CLND Sol
(p450; X‐screen)
‘N’ clusters
Multiple chemical series
Commit to LO
LEAD OPT
Novel series
Enzyme/Cell 7‐8
LE/LLE >0.3
Stability/Solubility Profile
Oral activity
Small number series
for LO
Development
Candidate
Fragment screen results
• Biophysical screening
• 76 validated hits
– Confirmed by X‐ray
– Measurable activity in fluorescence polarisation binding assay
• IC50 >1000uM ‐ 0.5uM
• LE 0.26 – 0.52 kcal/mol/HAC
• LLEAT*0.23 – 0.50 kcal/mol/HAC
• => grouped into 10 clusters
* LLE
AT, P.Mortenson, C.Murray, J. Comp‐Aided Mol Design, 2011, 25, 663
Cluster 1 – noteworthy example
•
•
•
•
•
4 is weak but efficient inhibitor
Small perturbation of protein
Induces disruption of intramol H‐bond of Tyr152 phenol and Asp96 acid
Polar interactions in active site pocket
Fragment optimisation to 5, gave >10‐fold potency increase
>10 fold
4
IC50 =8.5uM
LE 0.46; LLEAT 0.36
5
IC50 = 0.5uM
LE 0.57; LLEAT 0.44
X‐ray Fragment Hit
Fragment Optimisation
Cluster 2 – noteworthy example
Fragment 6 also shows similar Asp96 & Try152 shift
• Polar interactions in active site pocket again
• Xray overlays show different binding of Biaryl in 6 compared with published Biaryl ligand 2
• CN overlays with carbonyl in 2
•
6
52% inhib @30uM
LE ~0.44; LLEAT ~0.39
X‐ray Fragment Hit
Cluster 2 – fragment optimisation
• 7 has lower MWt, higher LE/LLE. Pyridyl‐N H‐bond to water
• Filling pocket more optimally with c‐propyl
• 8 occupies same binding as 6, but potency increased, high efficiency
6
52% inhib @30uM
LE ~0.44; LLEAT ~0.39
X‐ray Fragment Hit
7
58% inhib @100uM
LE ~0.50; LLEAT ~0.50
Analogue Screening
8
IC50 = 17uM
LE 0.46; LLEAT 0.45
Fragment Optimisation
Cluster 2 – fragment growth and optimisation to orally active inhibitor
X‐ray structural analysis suggested better growth vector opportunities with fragment 8. Target between 5‐ and 6‐pyridyl position
• 400 fold potency increase with 9. High LE/LLE maintained.
• 9 too polar (clogP 0.7) !
• LogP increased to attain good cell potency in 10
•
8
IC50 = 17uM
LE 0.46; LLEAT 0.45
Fragment Optimisation
400 fold
10: AT24111/
GSK296124A
66% Inhibn. @10nM
LE ~0.46; LLEAT ~0.45
Orally Active Lead
clogP 1.8; MW 337
FBDD lead 10 has good solubility in a range of physiologically relevant media
10: AT24111/
GSK296124A
pIC50
7.9
*8.3
7.7
8.8
LE ; LLE
0.43; 0.45
0.37; 0.38
0.44; 0.39
MW ; clogP
337; 1.8
381; 2.2
361; 3.3
Water (ug/ml)
52 (=150uM)
17
4
*1.2
SGF (ug/ml)
885
49
203
FeSSIF (ug/ml)
136
44
<1
FaSSIF
109
31
5
(ug/ml)
SGF = Simulated gastric fluid (pH 1.6)
FeSSIF = Fed simulated intestinal fluid (pH 6.5)
FasSSIF = Fasted simulated intestinal fluid (pH 6.5)
* C. Carron et al, ACS Med. Chem. Lett. 2010, 1, 59–63
Top 121 oral prescription medicines by DCS
Dose/Intestinal solubility (mL)
DCS 1
N=50‐65
DCS 2A
N=17‐34
DCS 2B
N=8
DCS 4
N=4
DCS 3
N=25‐27
J.Butler J.Dressmann
Journal of Pharmaceutical Sciences, Vol. 99, 4940–4954 (2010)
FBDD lead 10 is orally bioavailable using a simple aqueous formulation, and inhibits PGDS in vivo
Inhibition of PGD2 in peritoneal cavity after oral dosing at 30mg/kg to mice
Drug conc in blood
Rat PK, from oral/IV dosing in aqueous formulation
PK Parameter
Compound 10
CLb (mL/min/kg)
33
T½ (h)
2.2
Vss (L/kg)
2.8
Tmax (p.o.) (h)
0.5
Cmax (p.o.) (ng/mL)
196
AUC0‐inf (ng.h/mL)
295
F (%)
19
fub
5.5%
Dosing: 1mg/kg IV; 3mg/kg po
All animal studies were ethically reviewed and carried out in accordance with Animals (Scientific Procedures) Act 1986 and the GSK Policy on the Care, Welfare and Treatment of Animals
FBDD approach identified several series with favourable properties
• With FBDD approach, strong focus on LE/LLE and upper limit of MWt
• Potencies </= 10nM achieved with MWt 300‐400
• Additional solubility dimension => highlights further series strengths
MW vs potency
Solubility vs potency
Conclusions
•
Solubility is a key parameter in drug development
– Impact on bioavailability, dose, complexity of formulation, Drug Development cost, time, attrition (late failure!)
•
Good solubility property has ‘suffered’ due to focus on high target affinity, compound library properties, and DD approach. Pipeline drugs carry risk.
•
Fragment based drug discovery approach successfully generated multiple series with good properties in lipophilic target – PGDS
•
Identification of novel protein movement/H‐bonding disruption, & novel fragment polar binding interactions in lipophilic active site
•
X‐ray guided fragment optimisation, optimal vector growth, and early pharmacy input combined to give a PGDS inhibitor series with favourable properties (LE/LLE, MWt, logP, Solubility and DCS class )
Conclusions
•
Solubility is a key parameter in drug development
– Impact on bioavailability, dose, complexity of formulation, Drug Development cost, time, attrition (late failure!)
•
Good solubility property has ‘suffered’ due to focus on high target affinity, compound library properties, and DD approach
•
Fragment based drug discovery approach successfully generated multiple series with good properties in lipophilic target – PGDS
•
Identification of novel protein movement/H‐bonding disruption, & novel fragment polar binding interactions in lipophilic active site
•
X‐ray guided fragment optimisation, optimal vector growth, and early pharmacy input combined to give a PGDS inhibitor series with favourable properties (LE/LLE, sol, MWt, logP)
•
Orally active from simple aqueous formulation – ‘proof of the pudding’
Thank You
Contact Details
Dr Simon Hodgson Hodgson Pharma Consulting
[email protected]
www.hodgsonpharma.co.uk