MedChemica - Automated Extraction of Actionable Knowledge from Large Scale in-vitro pharmacology data: the importance of stereochemistry in life science research

MedChemica | Jan 2017

Automated Extraction of Actionable Knowledge from Large Scale in-vitro

pharmacology data: the importance of stereochemistry in

life science research

Dr Al Dossetter MedChemica Limited Sheffield Stereochemistry – January 2017


Overview

•  Stereochemistry in real Drugs – Not just chirality but stereochemistry

matters •  What is important in drug designing? •  Effects in chirality in Key in-vitro •  Why do drugs fail in the clinic and the

staggering rise in R&D cost? – Better rules for Medicinal Chemistry

•  How do we reduce the costs? – Mining Actionable knowledge


Chiral Drug Molecules

Atazanavir

Atorvasta+n Esomeprazole Escitalopram

Sertraline Topiramate Eze+mibe


Confirm tumour supression with in-‐Vivo Xenograph model in Nude mouse

Inspira:on – A Modern Drug

Black Box Screening Natural Products

Halichondrin B Marine natural Product Phenotypic screening –

arrested cancer cell growth Eribulin (Halaven) Approved Nov 2010 Metasta:c Breast Cancer Inhibitor of microtubule dynamics

Inspiring Med Chem Inspiring synthesis

Cancer Res. 2001, 61(3), 1013

Addressing EFFICACY

DU-‐145 tumour cell line – growth inhibited


rod sphere

disc

Commercial fragments access this space

N

NH

F

O

H H

More interesting Fragment?

Fragment needs to be useful!

Shape Diversity Analysis of Commercial Fragment Libraries

Represents 14000+ fragments, from 4 vendors includes random selection of compounds from Chemonaut db


Nutlin example – MDM2 binder that disrupts interac:on with P53 The inspira:onal drug discovery program in ‘Protein Protein Interac:on’ world

Directed Screening

Use knowledge to Select compounds

Nutlin-‐3 Ro-‐7112 – 18nM

Structure based design played a key part in compound op:misa:on Vu et al, ACS Med Chem Le_, 2013. This and other compounds have ‘sparked an understanding’ that (Fragment) libraries for PPIs need to be different Morelli, Roche et al MedChemComm, 2013, DOI: 10.1039/c3md00018d


Cathepsin K – Di-‐methoxy surprise – Man and Machine

pIC50 7.95 LogD 0.67 HLM <2.0 Solubility 280μM DTM ~1.0 mg/kg UID Potent Too polar / Renal Cl

PDB -‐ 97% of structures Crawford, J.J.; Dosse_er, A.G J Med Chem. 2012, 55, 8827. Dosse_er, A. G. Bioorg. Med. Chem. 2010, 4405 Lewis et al, J Comput Aided Mol Des, 2009, 23, 97–103

pIC50 8.2 LogD 2.8 HLM <1.0 Solubility >1400μM DTM 0.01 mg/kg UID High F% / stability maximised

Increase in LogP, Properties improved

Solubility ΔpIC50 - 0.1 ΔLogD +1.4 ΔpSol +1.2 ΔHLM + 0.25

No renal Cl low F%

ΔpIC50 +0.1 ΔLogD - 0.7 ΔpSol ~0.0 ΔHLM - 0.25

High F% rat/Dog Electrosta:c poten:al minima between oxygens

Approx like N from 5-‐het, new compound can not form a quinoline

Incr. selec:vity

ΔpIC50 +0.1 ΔLogD - 0.7 ΔpSol ~0.0 ΔHLM - 0.25

High F% rat/Dog


liver

kidneys

bladder Dissolve (SOLUBILITY)

Cross Membranes (PERMEABILITY)

Metabolism (Human Liver Microsomal, Cytochrome P450 oxidation and Inhibition)

Avoid Excretion

Oral Dosing of Drugs

BBB (Blood Brain Barrier)

Brain difficult Target Tissue

Survive pH range 1.5-8

Absorption Distribution Metabolism Excretion


Effect of Chriality on Properties Effecting Drug Design - 1

S -‐ omeprazole R -‐ omeprazole

Find all the known chirally pure enantiomers PAIRS with measured biological ADMET properties. Can we see a biological difference? How does is compare to physical properties like aqueous solubility?


If physical properties drove ADMET then enantiomeric pairs should have equivalent ADMET properties:

Enantiomeric pairs reveal that key medicinal chemistry parameters vary more than simple physical property based models can explain Andrew G. Leach et al, Med. Chem. Commun., 2012,3, 528-540.

1x difference between matched ena:omeric pairs = whole molecule proper:es will be enough.

Some:mes true but not useful enough……

Effect of Chriality on Properties Effecting Drug Design - 2


Company Ticker Number of drugs approved

R&D Spending Per Drug ($Mil)

Total R&D Spending 1997-2011 ($Mil)

AstraZeneca AZN 5 11,790.93 58,955

GlaxoSmithKline GSK 10 8,170.81 81,708

Sanofi SNY 8 7,909.26 63,274

Pfizer Inc. PFE 14 7,727.03 108,178

Roche Holding AG RHHBY 11 7,803.77 85,841

Johnson & Johnson JNJ 15 5,885.65 88,285

Eli Lilly & Co. LLY 11 4,577.04 50,347

Abbott Laboratories ABT 8 4,496.21 35,970

Merck & Co Inc MRK 16 4,209.99 67,360

Bristol-Myers Squibb Co.

BMY 11 4,152.26 45,675

Novartis AG NVS 21 3,983.13 83,646

Amgen Inc. AMGN 9 3,692.14 33,229

Sources: InnoThink Center For Research In Biomedical Innovation; Thomson Reuters Fundamentals via FactSet Research Systems

The Truly Staggering Cost Of Inventing New Drugs Matthew Herper - Forbes

Drug failures later in development are mainly due to EFFICACY and SAFETY


Actual spending / Chemistry everywhere

Paul, S. M. et al How to improve R&D productivity: the pharmaceutical industry’s grand challenge, Nat. Rev. Drug Discovery 2010, 9, 203

Snap-Shot of a medium sized companies R&D spend in one year - $1.7 billion

For a period large pharma set targets at each stage of the process – this was an “attrition model” – this was unsuccessful and very wasteful

Better chemistry Reduce the number of projects

Chemistry influences the success and speed


What Causes Attrition in Development?

PK 7%

Lack of efficacy in

man 46%

Adverse effects in man

17%

Animal toxicity 16%

Commercial reasons

7%

Miscellaneous 7%

• Many compounds fail in development through inadequate pharmacokineCcs / bioavailability and unacceptable toxicological profiles in addi:on to lack of efficacy in man


Big Data - Knowledge Based Design The Life Science industry has woken up to Big Data •  Human Genome •  Biological systems •  Kinome •  Metabolomics •  Proteomics •  3D structural information (CDC /

Protein Data Bank) •  Literature and Patents (GVK Bio,

ChEMBL, Pubmed, PubChem) •  Reaction informatics – what works,

what doesn’t •  Document management •  Regulatory submissions Huge Opportunity in this area


What about research data?

SAFE DRUGS

‘Potency’ Do not sacrifice

The be_er it is the lower the dose

Improved tes+ng in-‐vivo

with fewer animals

Clinical linkage to protein target

Can test In-‐Vivo An: SAR

e.g. hERG, Nav1.5, 5-‐HT2a…

Analysis of In-‐Vivo data Pfizer – rat data

<0.2mg/Kg Dose

Metabolism & Pharmacokine+cs

Be_er design so dose is lower

Grand Rule Database

Hughes et al, Bioorg Med Chem LeK. 2008, 18(17), 4872


Key findings: •  Stereochemistry is important in Drug hunting •  There is a strong need powerful rules to understand med

chem better and reduce compound numbers and costs How? •  Secure sharing of large scale ADMET knowledge between

large Pharma is possible •  The collaboration generated great synergy •  Many findings are highly significant •  Matched Molecular Pair Analysis (MMPA) is a great tool for

idea generation •  The rules have been used in drug-discovery projects and

generated meaningful results •  MMPA methodology can be extended to extract

pharmacophores


Fewer compounds designed from be_er rules from data analysis

•  Improved compounds quicker

•  Applicable ideas

•  Confident design decisions

•  Help when stuck

•  Clearly describable plans

•  Maximizing value from ADMET testing

•  Pursuing dead-end series

•  Pursuing dead-end projects

•  Running out of time or $

Essentials Gains

Pains


Grand Rule database Better medicinal chemistry by sharing knowledge not data & structures

MMP finder MCPairs =


Barriers Broken to Sharing Knowledge

Data Integrity and

curation Knowledge extraction algorithms

Consortium building to

share knowledge Into the minds of

chemists

✓ ✓

✓ ✓

Grand Rule Database

MCPairs


MCPairs Plarorm

•  Extract rules using Advanced Matched Molecular Pair Analysis •  Knowledge is captured as transforma:ons

–  divorced from structures => sharable

Measured Data

rule finder Exploitable

Knowledge

MC Expert Enumerator

System

Problem molecule

Solution molecules

Pharmacophores& toxophores

SMARTS matching

Alerts Virtual screening Library design

Protect the IP jewels

MCPairs


•  Matched Molecular Pairs – Molecules that differ only by a particular, well-defined structural transformation

•  Transformation with environment capture –MMPs can be recorded as transformations from A B

•  Environment is essential to understand chemistry

Statistical analysis •  Learn what effect the transformation has had on ADMET properties in

the past

Griffen, E. et al. Matched Molecular Pairs as a Medicinal Chemistry Tool. Journal of Medicinal Chemistry. 2011, 54(22), pp.7739-‐7750.

Advanced MMPA

Δ Data A-B 1

2

2

3

3

3

4

4

4

1 2

2 3

3

3 4

4

4 A B


Magic Methyl – Big Potency and Property improvements

Example from Leung, C.S.; Leung, S.S.F.; Tirado-‐Rives, J.; Jorgensen, W.L. J. Med. Chem. 2012, 55, 4489

Changing H to CH3 can bring big improvement even through this increases lipophilicity

Methyl group changes the shape of the molecule (often bringing ‘twists’ to rings)


Environment really ma_ers

HMe: •  Median Δlog(Solubility) •  225 different environments

2.5log

1.5log

HMe: •  Median Δlog(Clint)

Human microsomal clearance

•  278 different environments

We can see in the context the shape changes that bring about

improved proper:es


More environment = right detail HMe Solubility: •  225 different environments


HF What effect on Clearance? •  Median Δlog(Clint) Human microsomal clearance •  37 different environments

2 fold improvement 2 fold worse

Increase clearance

decrease clearance


Rule Example 1

Endpoint mean±SD count LogD7.4 Solubility –log(μM) Cyp3A4 pIC50

- 0.880±0.542 n = 19 - 0.003±0.861 n = 14 - 0.111±0.431 n = 14


Rule Example 2

Endpoint mean±SD count LogD7.4 Human Liver Microsomal Clint

0.1±0.65 n = 14 - 0.39±0.12 n = 14


Rule Example 3

Endpoint mean±SD count Human Liver Microsomal Clint Hepatocyte Cells Clint

- 0.35±0.25 n = 12 - 1.0 ±0.3 n = 9

MMPA can tell us occasions to make our molecules chiral and times not to….


Pharma 1 100k rules

Pharma 2 92k rules

Pharma 3 37k rules

5.8k rules in common (pre-merge) ~ 2%

New Rules 88k ~26% of total

Merge

Combining data yields brand new rules Gains: 300 -‐ 900%

Merging knowledge – GRDv1







pharmacophores


Early successes From GRDv1 May 2014

31

J. Med. Chem., 2015, 58 (23), pp 9309–9333 DOI: 10.1021/acs.jmedchem.5b01312


- Fix hERG problem whilst maintaining potency

Waring et al, Med. Chem. Commun., (2011), 2, 775

Glucokinase Activators

MMPA ∆pEC50: -0.1 ∆logD: -0.6 ∆hERG pIC50 :-0.5

n=33 n=32 n=22

MMPA ∆pEC50: +0.3 ∆logD: +0.3 ∆hERG pIC50 :-0.3

n=20 n=23 n=19

MMPA ∆pEC50: -0.1 ∆logD: -0.6 ∆hERG pIC50 :-0.5

n=27 n=27 n=7


Knowledge Based Design – MPO –  Novel more efficient core required, improve hERG for CD –  CNS penetration, good potency and deliver tool for in vivo testing

McCoull, Dossetter et al, Med. Chem. Commun., (2013), 4, 456

ΔpIC50 -0.4 ΔlogD -1.8 ΔhERG pIC50 +0.4

Ghrelin Inverse agonists

~

MMPA Cores

pIC50 9.9 logD 5.0 hERG pIC5 5.0 LLE 4.9 very potent very lipophilic

ΔpIC50 +0.9 ΔlogD +0.2 ΔhERG pIC50 -0.3

pIC50 8.2 logD 1.3 hERG pIC50 4.4 LLE 6.9

ΔpIC50 -2.2 ΔlogD -2.2 ΔhERG pIC50 -0.7

100 compounds

made

LLE = lipophilic ligand efficiency: LLE=pIC50-logD

LLE 6.4

LLE 6.9


A Less Simple Example Increase logD and gain solubility

Property Number of Observa+ons

Direc+on Mean Change Probability

logD 8 Increase 1.2 100%

Log(Solubility) 14 Increase 1.4 92%

What is the effect on lipophilicity and solubility? Roche data is inconclusive! (2 pairs for logD, 1 pair for solubility)

logD = 2.65 Kine:c solubility = 84 µg/ml IC50 SST5 = 0.8 µM

logD = 3.63 Kine:c solubility = >452 µg/ml IC50 SST5 = 0.19 µM

Ques+on:

Available Sta+s+cs:

Roche Example:


The application helped lead optimization in project

•  193 compounds •  Enumerated

Objective: improve metabolic stability

MMP Enumeration

Calculated Property Docking

8 compounds synthesized


Solving a tBu metabolism issue

Benchmark compound

Predicted to offer most improvement in microsomal stability (in at least 1 species / assay)

R2 R1

tBu Me Et iPr

99 392

16 64

78 410

53 550

99 288

78 515

41 35

98 327

92 372

24 247

35 128

24 62

60 395

39 445

3 21

20 27

57 89

54 89

•  Data shown are Clint for HLM and MLM (top and bottom, respectively)

R1 R2 R1 tBu Roger Butlin Rebecca Newton Allan Jordan


…so what are you going to make

next…?


Comparison of Merck in-‐house MMPA with SALTMinerTM

Structure:

ADMET Issue: hERG Lead A2A receptor antagonist compound in Merck Parkinson's project

138 suggestion molecules with predicted improvement in hERG

binding

How many match the results of Merck?

•  Also shows potent binding to the hERG ion channel

•  Deng et al performed in-house MMPA on hERG binding compound data and have published 18 resulting fluorobenzene transformations, which they have synthesized and tested for hERG activity

Deng et al, Bioorg. & Med Chem Let (2015), doi: h_p://dx.doi.org/10.1016/j.bmcl.2015.05.036


R group:

Measured hERG pIC50 change

-‐1.187 -‐1.149 -‐1.038 -‐1.215 -‐1.157 -‐0.149 -‐1.487 -‐1.133

GRD median historic pIC50 change

0 -‐0.171 -‐0.1 -‐0.283 -‐0.219 -‐0.318 -‐0.159 -‐0.103

Results: 8 out of the 18 fluorobenzene transformations produced by Merck were also suggested by MCExpert to decrease hERG binding:

Searching the GRD for transformations that increase hERG there were none that matched the remaining 10 of 18 transformations in the paper.

MCExpert also suggested an additional 50 fluorobenzene replacements to decrease hERG binding NOT mentioned in the publication.


Fast building block access from CRO collaboration

40

MCExpert suggests

improved building blocks

Specialist synthesis CROs access unique

chemistries

Rapid access to building blocks that address

metabolism and solubility issues

Mono & disubstituted chiral piperidines and pyrollidines

Chiral α methyl aryl amines and alcohols


Collaborators and Users -‐ experience







pharmacophores


Pharmacophores and Toxophores by extended analysis from the MMPA

Pharmacophores BigData Stats Matched

Pairs Finding

Public and in-house potency

data


Mining transform sets to find influen:al fragments

Identify the ‘Z’ fragments associated with a significant number of potency increasing changes – irrespective of what they are replaced with ‘Z’ is ‘worse than anything you replace it with’

Fragment A Fragment B Change in binding

measurement

Public Data

Find Matched

Pairs

Find Potent Fragments

+2.7

+3.2

+0.6

+0.6

Identify the ‘A’ fragments associated with a significant number of potency decreasing changes – irrespective of what they are replaced with ‘A’ is ‘better than anything you replace it with’

A

+2.1 +2.2 +1.4

+0.4

+1.8

Z

pKi/ pIC50

Compounds with destructive fragment

Compounds with constructive fragments

Generate Pharmacophore dyads by permuta:ng all the fragments with the shortest path between them


Toxophores - Detailed, specific & transparent

45

Dopamine D2 receptor human Actual: 9.5

Predicted: 9.1 Mean with: 8.0 Mean without: 6.6 Odds Ratio: 340

Dopamine Transporter Actual: 9.1


GABA-A Actual: 9.0


β1 adrenergic receptor Actual: 7.8 Predicted: 7.7 Mean with: 6.5 Mean without: 5.7 Odds Ratio: 1501


Matched Pairs

Finding

Find Pharmacophore

Dyads

Public and in-house potency

data


Prediction of unseen new molecules The acid test…

•  Vascular endothelial growth factor receptor 2 tyrosine kinase (KDR)

•  Inhibitors have oncology and ophthalmic indications

•  Large dataset in CHEMBL

•  10 fold cross validated PLS model

•  Selected model by minimised RMSEP

46

Compounds 4466 Matched Pairs 288100 Fragments 678 Pharmacophore dyads 787 RMSEP 0.8 R2 0.64 Y-scrambled R2 0.0 ROC 0.95 Geomean odds ratio 80

●

●

●

●

●

●

●

●

●

●

●

●

●●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●●●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●●

●

●

●

●

●

●

●

●

●

●

●

●●●

●

●

●

●

●

●

●●

●

●

●

●

●

●

●

●

●●

●

●●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●●●

●

●

●

●

●●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●●

●●

●●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●●

●

●

●

●

●

●

●

●

●●

●

●

●●

●

●

●

●●

●●

●

●

●

●

●

●

●

● ●

●

●●

●

●

●

●

●

●

●

●

●

●

●

●

●

●●

●

●

●

●

●

●

●

● ●

●

●

●

●

●

●●

●

●

●

●

● ●

●

●

●

●

●

●

●

●

●

●

●

●●

●

●

●

●

●

●

●

●

●

●

●

●

●

●●

●

●

●

●

●

●

●

●

●

●

●

●

● ●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●●●

●

●

●

●

●

●

●●●

●

●

●

●

●

●

●

●

●

●

●

●●

●

●

●

●

●●

●

●

●

●

●

●

●

●●

●

●

●

●

●

●●

●

●●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●●

●

●

●

●

● ●

●

●

●

●

●

●

●

●

●

●●

●

●

●

●

●

●

●

●

●

●

●

●

●●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●●

●

● ●

●●

●

●●

●

●

●●

●

●

●

● ●●●

●

●

●

●

●

●

●

●

●●

●

●

●●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●●

●

●

●

●

●

●●

●

●

●●

●

●

●

●●

●

●

●

● ●

●

●

●

●

●

●

●

●

●

●

●●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●●

●

●

●

●

●

●

●

●●●

●●

●

●

●

●

● ●●

●●●

●

●●

●

●

●

●

●

●

●

●

●

●

●●

●

●

●●

●

●●

●

●

●

●

●

●●

●

●●

●

●

●

●

●

●

●

●

●

●

●

●

●

●●

●

●

●

●

●

●

●

●

●●

●

●

●

●

●

●

●

●

●●

●●

●

●

●

●

●

●

●

●

●

●

●

●

●●

●

●●●●

●

●●

●●●●

●

●

●●

●

●

●●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●●

●

●

●

●

●

●

●

●

●

●

●●●

●

●

●

●●

●

●

●

●

●

●

●

●

●

●●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●●

●

●

●

●●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

● ●

●

●

●

●●

●

●

●●

●

●

●

●

●

●

●

●

●

●

●

●

●

●●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●●

●

●

●

●

●

●

●

●

●

●

●●

●

●

●

●

●

●

●

●

●

●

●

●

●●

●

●●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●●

●

●

●

●

●

●●

●●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●●

●●●

●

●●

●●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●●

●

●

●

●

●

●●

●

●

●●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●●

●

●

●

●

●

●

●

●

●●

●

●

●

●

●●

●

●

●●

●●

●

●

●

●

●

●●

●

●

●

●

●

●

●

●

●

●

●

●

●

●●

●

●

●

●

●●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●●

●

●●

●

●

●

●

●●

●●

●

●

●

●

●

●

●●

●

●

●

●

●

●

●

●

●

●

●

●

●

●●

●

●

●●

●

●

●

●

●●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●●

●

●

●

●●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●●

●

●

●

●

●

●

●

●●

●

●

●

●

●

●

●●

●

●

●

●

●

●

●●

●●

●

●

●

●

●

●●

●●

●●

●

●●

●

●

●

●

●

●

●

●

●

●

●

●

●●

●

4

6

8

10

5 7 9pIC50_pred

pIC50


Novartis Predictions From Our Model Domain of Applicability….

Actual: 8.4[1] Predicted: 7.5

47


1. J MedChem(2016), Bold et al. 2. MedChem Lett (2016), Mainolfi et al.


Actual: 9.0[2] Predicted: Out of Domain


Target Number of compounds

Number of compound

pairs

Number of Fragments

Number of Pharmacophore dyads a|er filtering

R2 RMSEP ROC odds_ra:o (geomean)

Acetylcholine esterase - human 383 27755 44 10 0.43 1.57 0.80 4 β 1 adrenergic receptor 505 145447 276 313 0.64 0.70 0.96 833 Androgen receptor 1064 113163 186 46 0.47 0.77 0.86 140 CB1 canabinnoid receptor 1104 88091 165 90 0.61 1.02 0.87 96 CB2 canabinnoid receptor 1112 82130 194 158 0.19 0.85 0.64 5.7 Dopamine D2 receptor - human 3873 230962 483 602 0.42 0.88 0.69 110 Dopamine D2 receptor - rat 1807 118736 267 377 0.29 0.85 0.78 125 Dopamine Transporter 1470 106969 282 336 0.58 0.73 0.88 141 GABA A receptor 848 39494 106 167 0.70 0.76 0.97 560 hERG ion channel 4189 242261 392 76 0.61 0.96 0.92 55 5HT2a receptor 642 50870 197 267 0.61 0.59 0.83 600 Monoamine oxidase 264 15439 44 11 0.12 1.25 0.48 181 Muscarinic acetylcholine receptor M1 628 48200 97 510 0.62 0.94 0.89 48

µ opioid receptor 1128 37184 33 11 0.69 1.30 0.87 81

Critical safety target analysis

•  Build models using 10-fold cross validated PLS •  Assess using ROC / BEDROC, R2 vs 100 fold y-scrambled R2 and geomean odds ratio

48

Public Data

Find Matched Pairs

Pharmacophores Find

Pharmacophore dyads



MCBiophore GUI screenshot

Assay Image Mean_with Mean_without PLS_coeff Path SMARTS1 SMARTS2 n_examples odds_ratio2

VEGFR 8.3 6.4 0.71 [c]c[c][c] Cc1ccc[c]c1[n] [c]/C(=N/O)/C 18 259.83

VEGFR 8.2 6.4 0.17 [c] [c]c1cc(cc[c]1)/C(=N/OC)/CCc1ccc[c]c1[n] 17 257.60

VEGFR 8.1 6.4 -0.01 [CH3] [cH]c([cH])/C(=N/OC)/C[c]/C(=N/OCC)/C 8 20.21

VEGFR 8.1 6.4 -0.01 [CH3] [c]c1cc(cc[c]1)/C(=N/OC)/C[c]/C(=N/OCC)/C 8 151.2

Detailed results in

excel


Matched Molecular Pair

data A data B

data C data D

data E data F

Chemical Transformations

Δ data A B

Δ data C D

Δ data E F

Chemical Transformations

Δ data A B

Δ data C D

Δ data E F

Δ data G H

Δ data I J

Δ data K L

Matched Molecular Pair Analysis (MMPA) enables SAR sharing

Without sharing underlying structures and data

Grand Rule

Database

Enumeration

Rate-My-Idea

GRD-Browser

ChEMBL Tox database Toxophores MC-

Biophore

MCPairs







pharmacophores


A Collaboration of the willing

Craig Bruce OE John Cumming Roche David Cosgrove C4XD Andy Grant★

Martin Harrison Elixir Huw Jones Base360 Al Rabow Consulting David Riley AZ Graeme Robb AZ Attilla Ting AZ Howard Tucker retired Dan Warner Myjar Steve St-Galley Syngenta David Wood JDR Lauren Reid MedChemica Shane Monague MedChemica Jessica Stacey MedChemica

Andy Barker Consulting Pat Barton AZ Andy Davis AZ Andrew Griffin Elixir Phil Jewsbury AZ Mike Snowden AZ Peter Sjo AZ Martin Packer AZ Manos Perros Entasis Therapeutics Nick Tomkinson AZ Martin Stahl Roche Jerome Hert Roche Martin Blapp Roche Torsten Schindler Roche Paula Petrone Roche Christian Kramer Roche Jeff Blaney Genentech Hao Zheng Genentech Slaton Lipscomb Genentech Alberto Gobbi Genentech


Appendix


References on Lean in R&D Sewing, A Drug Disco. Techno, 2009, DOI, 10.1016/j.ddtec,2008,12.002 Andersson S et al, Making medicinal chemistry more effective--application of Lean Sigma to improve processes, speed and quality. Drug Discov Today. 2009 Jun;14(11-12):598-604. Johnstone, C.; Pairaudeau, G.;Pettersson, J. A.; Creativity, innovation and lean sigma: a controversial combination? Drug Discov Today. 2011 Jan;16(1-2):50-7 Robb, G.R.; McKerrecher, D.;Newcombe, N.J.;Waring, M.J. A chemistry wiki to facilitate and enhance compound design in drug discovery. Drug Discov Today. 2013 Feb;18(3-4):141-7. Plowright, A.T.; Johnstone, C.; Kihlberg, J.; Pettersson, J.; Robb, G.; Thompson, R.A.; Hypothesis driven drug design: improving quality and effectiveness of the design-make-test-analyse cycle. Drug Discov Today. 2012 Jan;17(1-2):56-62 Baldwin, E.T., Metrics and the effective computational scientist: process, quality and communication. Drug Discov Today. 2012 Sep;17(17-18):935-41. Cumming, J.G.; Winter, J.P.; Poirrette, A. Better compounds faster: the development and exploitation of a desktop predictive chemistry toolkit. Drug Discov Today. 2012 Sep;17(17-18):923-7. Baede, E.J.; Bekker, E.J.W.; Cronin, D.;Integrated project views: decision support platform for drug discovery project teams. J Chem Inf Model. 2012 Jun 25;52(6):1438-49. Contrast to:- MacDonald, J. F.; Smith, P. W. Lead Optimization in 12 months? True confession of a chemistry Team Drug Discovery Today, 2001, 6, 18, 947

•  Parallel Screening was an important outcome of the application of Lean Manufacturing

•  Reducing the work in progress to avoid spreading chemistry effort was important

•  The best results were achieved by encouraging team work and increasing CLARITY through effective COMMUNICATION


Human Element -‐ Chemists like their own ideas…….

They like the look of it

•  Asked 19 chemists to look through a set of fragments and choose what they considered the ‘best ones’ to follow up •  When asked how they choose them they self report that it was mul:-‐factorial •  Analysis shows they were chosen on Ring topology and Func:onal groups (not really on size or lipophilicity)

Kutchukian, P.S. et al ‘Inside the mind of the Medicinal Chemist’ PLOS one 2012, doi: 10.1371/journal.pone.0048476 See also Cheshire, D. R. ‘How well do Medicinal Chemists learn from Experience, Drug Discov. Today, 2011, 16, (17/18), 817. Leeson, P.D.; Springthorpe, B. The influence of drug-‐like concepts on decision-‐making in med. Chem.

Nat. Rev. Drug Discov. 2007, 6, 881.


But the literature says it’s lipophilicity Does it?

‘The focus on Ro5 is oral absorp:on and the rule neither quan:fies the risk of failure associated with non-‐compliance nor provides guidance as to how sub-‐op:mal characteris:cs of compliant compounds might be improved’ Kenny, P. W.; Montanari, C. A. J. Comput Aided Mol Des, 2013, 27, 1-‐13. See also: Carlson, H. A. J. Chem.Inf.Model, 2013, dx.doi.org/10.1021/ci4004249

Data & Analytics

MedChemica - Automated Extraction of Actionable Knowledge from Large Scale in-vitro pharmacology data: the importance of stereochemistry in life science research