computational predictive admet: guiding...
TRANSCRIPT
Computational predictive ADMET:Guiding Medicinal Chemistry
Neil Berry
University of Liverpool
9th February 2016
Outline• Why do we need predictive ADMET?
• ADMET compound quality – physicochemical and structural
• Quantitative Structure Activity Relationship (QSAR) – qualifying ADMET predictions
• Metabolism
• Case studies from University of Liverpool and Liverpool School of Tropical Medicine
Malaria
Chronic pain
Filariasis
• Summary
• Future work
• Acknowledgements
2
Introduction - NeedWhy do we need predictive ADMET?
• Chances of drug candidate reaching Phase II ~37%
• Probability of success in Phase II ~34%
• Physicochemical properties have key role in success
• ADMET characteristics - huge impact on
EfficacySelectivity
⇒ ADMET key factor in drug discovery
⇒ Robust ADMET prediction expedites success
Nature Reviews Drug Discovery, 2013, 12, 948Nature Reviews Drug Discovery, 2003, 2, 192 3
• ADMET Compound quality - no universal definition
Physicochemical Quality
• Permeability/solubility
Pfizer analysis of drugs and candidates. 90% oral drugs: MW<500, LogP<5, #(OH+NH)<5, #(O+N)<10
• Receptor promiscuity
AZ analysis of >2000 compounds in Cerep assayscLogP<3 – decrease riskcLogP>4 – increase riskLLE > 5 decrease risk (Lipophilic ligand efficiency = pIC50 – cLogP)
• ADMET
GSK analysis of ~30k compoundsMW<400 and cLogP<4 reduces ADMET risk
Introduction – Compound Quality4
Advanced Drug Delivery Reviews, 1997, 23, 3Nature Reviews Drug Discovery, 2007, 6, 881Journal of Medicinal Chemistry, 2008, 51, 817
Introduction – Compound QualitySubstructural Quality - Filters and flagging
• Identify compounds with undesirable chemical features at early stage
ToxicophoresMetabolically labileHTS false positives (e.g. pan-assay interference compounds)Interfere with biochemical assays (e.g. fluorescent, coloured, aggregators)
• Usually exclusion – removal/flagging of unwanted compounds
• Can be inclusion – at least one polar atom, at least one rotable bond etc.
• Usually hard cutoffs – more recent overall desirability measures
QED – Quantitative estimate of drug likenessQED outperforms
Lipinski4/400Veber (≤10 rotable bonds & PSA<140Å2)etc.
5
Drug Discovery Today 2003, 8, 86Nature Reviews Drug Discovery, 2013, 12, 948
Nature, 2014, 513, 481Journal of Medicinal Chemistry, 2015, 58, 7076
Quantitative Structure Activity Relationships - QSAR
6
Quantification of ADMET predictions
• QSAR - empirical models giving quantitative prediction via algorithm
• ∆ (Measured end point) = f (∆Structure)
End point – e.g. potency, solubility, ADMET readout etc.
• Relate molecular properties with measured end point
• Several thousand properties can be calculated
Accounts of Chemical Research, 1993, 26, 147Expert Opinion in Drug Discovery, 2010 5: 633
Molecular Informatics, 2010, 29, 476
Molecular property Interaction Example descriptors
Lipophilicity Hydrophobic logP
Polarisability van-der-Waals Molar refractivity
Electron density Ionic, dipole-dipole, hydrogen bonds, charge transfer
HOMO, LUMO
Topology Steric hindrance, geometrical fit Distances, volumes
• Machine learning methods used to relate measurements with calculated properties
Challenging
• Fraught with difficulties, including:
Errors in dataErrors in compound structureErrors in calculated molecular propertiesMolecular properties not capturing key molecular informaitonErrors in machine learningExtrapolation of models beyond “domain of applicability”
• Domain of applicability
No one universal definition
QSAR
7
Nature Reviews Drug Discovery, 2013, 12, 948http://www.oecd.org/chemicalsafety/testing/oecdquantitativestru
cture-activityrelationshipsprojectqsars.htm
Examples
• Multiple examples of successful QSAR
Approved drugs and lead optimisation programmes
Automated QSAR
• Use of information technology to build and update QSAR with little intervention
• Example of successful automated QSARs in drug discovery
AZ – manual logD model not as good in predictive power over time
QSAR
8Reviews in Computational Chemistry, 1990, 1, 335
Nature Reviews Drug Discovery, 2013, 12, 948
Introduction
• Metabolism is one of major clearance pathways for ~75% of drugs
• Biotransformation can give metabolites with substantially altered compound profile
PhysicochemicalPhysiologicalPharmacologicalToxicological
• Metabolism main factor in mediating (de)activation, (de)toxification
• Metabolic systems highly complex
• Expression and substrate specificity vary greatly
• Inter- and intra-individual factors
Gender, Genetic polymorphisms, intestinal flora, lifestyle, medication etc.
Metabolism
9Nature Reviews Drug Discovery, 2015, 14, 387
Towards computational prediction
• Knowing metabolic properties of molecule – help optimise compound stability
⇒ In vivo half life
⇒ Risk/benefit ratio as a therapy
• Experimental approaches demanding
EquipmentExpertiseCostTime
• Computational methods – especially very early stage discovery
Higher throughputLower cost
Metabolism
10Nature Reviews Drug Discovery, 2015, 14, 387
Computational approaches
• Scope includes prediction – for a small molecule
Sites of metabolismMetabolitesMetabolic ratesInteraction with metabolising enzymesToxicological effects of metabolites
• Utilise a variety of underlying technologies
QSARDockingData miningMachine learningetc.
• Vast number of software available
Free, commercial, download, online etc.
Metabolism
11Nature Reviews Drug Discovery, 2015, 14, 387
Strategy → Replace Me with CN
Reduce chain length
Replace CN with
Sulfonamide
Example
• Bayer - Non-steroidal mineralocorticoid receptor antagonist
• Metabolite identification – limited throughput
• Optimisation of metabolic properties in vitro/vivo with predictions sites of metabolism
Fmax - % compound remaining CL – blood clearance in rats
Metabolism12
Nature Reviews Drug Discovery, 2015, 14, 387
Case Study 1 - Malaria
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Why do we need a drug against malaria?
• Global health problem
• Best estimates put the number of clinical episodes of malaria at 0.5 billion
• ~650,000 deaths and >400 million drug treatments/year
• Huge human and socioeconomic burden
• Current therapies are failing
• Parasite resistance remains a major threat
• Very few attractive drug targets
PfNDH2
World Malaria Report 2012, World Health Organisation, Geneva, Switzerland
Case Study 1 - Malaria
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PfNDH2
• The mitochondria is proven drug target - e.g. Atovaquone in MalaroneTM
• Provides intermediates for pyrimidine biosynthesis
• Inhibition leads to mitochondiral dysfunction and parasite death
Methods in Enzymology; Allison, W. S., Scheffler, I. E., Eds.; AcademicPress: New York, 2009; Vol. 456, Chapter 17 , pp 303−320.
• Enzyme “choke point” in electron transport chain
• Pf type II NADH:quinone oxidoreductase (PfNDH2) outstanding therapeutic target
• Only one selective inhibitor known (HDQ)
• HDQ – poor pharmacokinetics and non drug-like
Case Study 1 - Malaria
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Hit Identification
• HTS has been developed, validated and scaled up
Journal of Medicinal Chemistry, 2012, 55, 3144
Goal
• Identify several novel molecular scaffolds which inhibit PfNDH2
• Identification achieved via HTS with compounds selected using chemoinformatics
1) Explore the chemical space around the active hits (similarity searching)
2) Identify new active chemotypes (scaffold-hopping)
3) Favour drug/lead like compounds
⇒ Select 16000 compounds for HTS from 750000 library
• Scaffolds identified
⇒ Medicinal chemistry programme
Case Study 1 - Malaria
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Chemoinformatics Methods
Similarity searches
Diversity selection
Scoring compounds - Lead and Drug likeness bias
Lipinski and Veber filters
⇒ 16000 "leadlike“, diverse compounds selected via chemoinformatic approach for HTS
Compound Score = 4 * (Similarity) + 1*f(LogS) + 1*f(LogP) + 2*f(MW)
Journal of Medicinal Chemistry, 2012, 55, 3144
Case Study 1 - Malaria
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PNAS, 2012, 109 8298Journal of Medicinal Chemistry, 2012, 55, 1844Journal of Medicinal Chemistry, 2012, 55, 1831Journal of Medicinal Chemistry, 2012, 55, 3144
Case Study 1 - Summary
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Summary
• Potent inhibitors active against blood stages of malaria
• nM inhibition against enzyme and parasite (sensitive and resistant strains)
• Rapid selective depolarisation of mitachondria => parsite death
• Potent oral acitivty in mouse model, favourable PK aligned with single-dose treatment
• Ease of synthesis low cost of goods
• Fulfil target product profile for potent, safe, inexpensive drug for clinical evaulation
PNAS, 2012, 109 8298Journal of Medicinal Chemistry, 2012, 55, 1844Journal of Medicinal Chemistry, 2012, 55, 1831Journal of Medicinal Chemistry, 2012, 55, 3144
Case Study 2 – Chronic Pain
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Why do we need a drug for chronic pain?
• Longstanding unmet need
• Chronic pain affects ~20% adults in USA and Europe
• Wrecks lives
• Huge economic impact – lost working days
• Current medication only effective on ~40% sufferers
• Pain market >$6 billion by 2017
O
OH
H2N
Pregabalin
O OH
NH2
Gabapentin
European Journal of Pain, 2006, 10, 287The Oncologist, 2010, 15, 24
Nature Medicine, 2010, 16, 1241
Strategy → Modulate pKa
EC50 α1 Gly (µM) 4.8 ± 1.2 0.0067 ± 0.003
Case Study 2 – Chronic Pain
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Rationale
4-Chloropropofol Enhances Glycinergic Chloride Curre nts
• Breakthrough in vitro proof-of-concept
• Strychnine-sensitive glycine receptors
• >1000 fold more potent than propofol0.0%
100.0%
200.0%
300.0%
400.0%
0.1 1.0 10.0 100.0 1000.0 10000.0
4-chloropropofol [nM]
% Potentiation of the glycine [10 µM] response
EC50 = 6.7 ± 0.3 nM
Case Study 2 – Chronic Pain
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• CNS penetration required
• Guidelines suggested - different to Lipinski’s
• MPO score - probability of CNS penetration
• 74% CNS drugs have MPO score > 4
• Six key physicochemical parameters
• Traffic light system
• Design and drive medicinal chemistry
Property Raw Trans .
ClogP 3.9 0.550
ClogD 3.7 0.150
TPSA 20.23 0.012
MW 178 1.000
HBD 1 0.833
pKa 11 0.000
MPO 2.5
MPO – Multiparameter Optimisation
ACS Chemical Neuroscience, 2010, 1, 435
Case Study 2 – Chronic Pain
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OH
N
O
O
OH
N
O
OH
Cl
F
Strategy → Metabolic blockade
Reduce cLogP Increase water solubility
Increase metabolic stability
EC50 α1 Gly (µM) 0.0007 ± 0.003 0.001 >100 0.00035 ±0.00002
IC50 GABA A (µM) 5% @ at 100 µM Modulation @ 0.12 Modulation @ 30 >30
MPO 1.8 2.9 3.4 4.4
Progression
Case Study 2 – Summary
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PharmacokineticsOral bioavailability (rat) 86% ����
Stability in rat liver microsomes 740 mins ����
Stability in human hepatocytes 56 mins ����
Brain:CSF levels >10 times EC50 at 2h (oral dose 3mg/kg) ����
Plasma half life >180 mins ����
In vitro plasma protein binding (rat) 62% ����
Cytochrome P450 inhibition >10 µM (CYP3A4, 2D6, 2C9, 2C19,1A2) ����
Cytochrome P450 induction >10 µM (CYP3A4, 2D6, 2C9, 2C19,1A2) ����
SafetyFunctional hERG assay >30 µM ����
Cytotoxicity in HepG2 cells >100 µM ����
Genotoxicity: Ames Negative (at 250 µg/mL) ����
Absence of metabolic alerts None present ����
7-day preliminary tox study in rodents No organ toxicity observed (x100 therapeutic dose) ����
• Proceed towards optimised candidate selection
• Supported by optimised chemically distinct backups
Case Study 3 - Filariasis
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Introduction
• Filariasis - parasitic disease caused by an infection with roundworms
• Serious health problem
• Leading cause of global morbidity
Lymphatic filariasis Onchoceriasis
Elephantiasis River blindness
120 million 37 million
Symbiosis, 2010, 51, 55
Case Study 3 - Filariasis
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Wolbachia
• Bacteria lives in gut of filarial worms
• Wolbachia essential for filarial worms
• Drugs which target wolbachia should be effective
• Improved clinical case management - pathology and inflammatory
Current Therapy
• 4-6 week – antibiotic doxycycline
Issues
• Relatively long treatment – compliance
• Contraindicated in pregnancy and children
Symbiosis, 2010, 51, 55
Case Study 3 - Filariasis
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Astra-Zeneca Collaboration
• Provides
Measured and predicted PK paramaters
logD7.4
Aqueous solubility
Human PPB
Human microsome clearance
Rat heptocytes clearance
Hit Identification
• HTS has been developed, validated and scaled up
• Screening of multiple libraries (>2M compounds) – informed by chemoinformatics
• Several hit series identified
⇒ Medicinal chemistry optimisation
Journal of Biomolecular Screening, 2014, 1
Correlation coefficient = 0.80 Correlation coefficient = 0.87
Case Study 3 - Filariasis
Measured vs. Predicted Aqueous Solubility Measured vs. Predicted LogD7.4
How do we use the predicted data?
• Help prioritise proposed synthetic targets in each series in the lab
• Used alongside QSAR models for potency
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Added value
• Predicted PK is
Extremely good and incredibly useful
• Enables us to place our effort in the molecules with the best chance of success
• Warns us away from misleading literature
• Two current lead compounds – from distinct chemotypes
• Very good ADMET profile
• Further optimisation ongoing towards candidate selection
Case Study 3 - Summary
Series 1 Series 2
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Summary
29
• Physicochemical and structural properties have key role in success
• ADMET characteristics - huge impact on
EfficacySelectivity
• Compound quality – physicochemical and structural
• Computational predictive
QSAR
ADMET
in harness with in vitro/vivo data
⇒ Enables rational, rapid, realisation of quality compounds for development
Liverpool School of Tropical Medicine
Steve Ward
Giancarlo Biagini
Mark Taylor
30
Acknowledgements*
University of Liverpool
Paul O’Neill
Martin Leuwer
Funding
CROs
AWOL work was supported by a grant from the Bill and Melinda Gates Foundation awarded to the Liverpool School of Tropical Medicine as part of the Anti-Wolbachia consortium
AstraZenecaPeter Webborn
Mark Wenlock
* PIs