This document discusses the importance of stereochemistry in medicinal chemistry research and drug development. It notes that many approved drugs are chiral molecules where the specific stereochemistry is important. Better rules for medicinal chemistry could help reduce high drug development costs by improving predictions of properties like absorption, distribution, metabolism, and excretion. The document advocates mining large datasets of in vitro pharmacology data to extract actionable knowledge about stereochemistry and its effects on important drug properties and clinical outcomes. This could help medicinal chemists design safer and more effective compounds with lower attrition rates in development.
Automated Extraction of Actionable Knowledge from Large Scale in-vitro pharmacology data
1. 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
2. MedChemica | Jan 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
3. MedChemica | Jan 2017
Chiral Drug Molecules
Atazanavir
Atorvasta+n
Esomeprazole
Escitalopram
Sertraline
Topiramate
Eze+mibe
4. MedChemica | Jan 2017
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
5. MedChemica | Jan 2017
rod
sphere
disc
Commercial fragments
access this space
N
N
H
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
6. MedChemica | Jan 2017
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
7. MedChemica | Jan 2017
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
8. MedChemica | Jan 2017
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
9. MedChemica | Jan 2017
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?
10. MedChemica | Jan 2017
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
11. MedChemica | Jan 2017
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
12. MedChemica | Jan 2017
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
13. MedChemica | Jan 2017
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
14. MedChemica | Jan 2017
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
15. MedChemica | Jan 2017
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
16. MedChemica | Jan 2017
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
17. MedChemica | Jan 2017
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
18. MedChemica | Jan 2017
Grand Rule database
Better medicinal chemistry by sharing knowledge not data & structures
MMP
finder
MCPairs=
19. MedChemica | Jan 2017
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
20. MedChemica | Jan 2017
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
21. MedChemica | Jan 2017
• 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-B1
2
2
3
3
3
4
4
4
12
23
3
34
4
4
A
B
22. MedChemica | Jan 2017
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)
23. MedChemica | Jan 2017
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
24. MedChemica | Jan 2017
More
environment
=
right
detail
HMe Solubility:
• 225 different environments
25. MedChemica | Jan 2017
HF
What
effect
on
Clearance?
• Median
Δlog(Clint)
Human
microsomal
clearance
• 37
different
environments
2
fold
improvement
2
fold
worse
Increase
clearance
decrease
clearance
26. MedChemica | Jan 2017
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
27. MedChemica | Jan 2017
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
28. MedChemica | Jan 2017
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….
29. MedChemica | Jan 2017
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
30. MedChemica | Jan 2017
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
31. MedChemica | Jan 2017
Early successes
From GRDv1 May 2014
31
J.
Med.
Chem.,
2015,
58
(23),
pp
9309–9333
DOI:
10.1021/acs.jmedchem.5b01312
33. MedChemica | Jan 2017
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
34. MedChemica | Jan 2017
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:
35. MedChemica | Jan 2017
The application helped lead optimization in
project
• 193
compounds
• Enumerated
Objective: improve metabolic stability
MMP
Enumeration
Calculated Property
Docking
8 compounds
synthesized
36. MedChemica | Jan 2017
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
38. MedChemica | Jan 2017
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
39. MedChemica | Jan 2017
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.
40. MedChemica | Jan 2017
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
42. MedChemica | Jan 2017
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
43. MedChemica | Jan 2017
Pharmacophores
and
Toxophores
by
extended
analysis
from
the
MMPA
PharmacophoresBigData Stats
Matched
Pairs
Finding
Public and in-
house potency
data
44. MedChemica | Jan 2017
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
45. MedChemica | Jan 2017
Toxophores - Detailed, specific & transparent
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
Predicted: 8.6
Mean with: 8.3
Mean without: 6.7
Odds Ratio: 407
GABA-A
Actual: 9.0
Predicted: 8.7
Mean with: 8.0
Mean without: 6.8
Odds Ratio: 1506
β1 adrenergic receptor
Actual: 7.8
Predicted: 7.7
Mean with: 6.5
Mean without: 5.7
Odds Ratio: 1501
Find Potent
Fragments
Matched
Pairs
Finding
Find
Pharmacophore
Dyads
Public and in-
house potency
data
47. MedChemica | Jan 2017
Novartis Predictions From Our Model
Domain of Applicability….
Actual: 8.4[1]
Predicted: 7.5
47
Actual: 7.6[1]
Predicted: 7.5
1. J MedChem(2016), Bold et al.
2. MedChem Lett (2016), Mainolfi et al.
Actual: 7.7[2]
Predicted: 7.1
Actual: 9.0[2]
Predicted: Out of Domain
50. MedChemica | Jan 2017
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
51. MedChemica | Jan 2017
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
52. MedChemica | Jan 2017
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
54. MedChemica | Jan 2017
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
55. MedChemica | Jan 2017
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.
56. MedChemica | Jan 2017
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