Aspects of pharmaceutical molecular design

Peter Kenny

Outline of presentation

• General aspects of molecular design and introducing

pharmaceutical molecular design

• How to enhance your correlations in one easy lesson

• Introduction to partition coefficients

• Using relationships between structures to mine

activity and properties of compounds

Some things that make drug discovery difficult

• Having to exploit targets that are weakly-linked to

human disease

• Inability to predict idiosyncratic toxicity

• Inability to measure free (unbound) physiological

concentrations of drug for remote targets (e.g.

intracellular or on far side of blood brain barrier)

Dans la merde : http://fbdd-lit.blogspot.com/2011/09/dans-la-merde.html

In tissues

Free in

plasma

Bound to

plasma

protein

Dose of drug

Eliminated drug

A simplified view of what happens to drugs after dosing

TEP = [𝐷𝑟𝑢𝑔 𝑿,𝑡 ]𝑓𝑟𝑒𝑒

𝐾𝑑

Target engagement potential (TEP)

A basis for pharmaceutical molecular design?

Design objectives• Low Kd for target(s)

• High (hopefully undetectable) Kd for antitargets

• Ability to control [Drug(X,t)]free

Pharmaceutical molecular design

• Control of behavior of compounds and materials by

manipulation of molecular properties

• Sampling of chemical space

– Does fragment-based screening allow better control of

sampling resolution?

Hypothesis-Driven

Framework in which to

assemble SAR/SPR as

efficiently as possible

Prediction-Driven

Assume that we can build

predictive models with

required degree of

accuracy

Molecular Design

Do1 Do2

Ac1

Kenny (2009) JCIM 49:1234-1244 DOI

Illustrating hypothesis-driven design with

DNA base isosteres: H bond acceptor & donor

definitions

Watson-Crick Donor & Acceptor Electrostatic Potentials for

Adenine IsosteresV

min

(Ac1)

Va (Do1)

The lurking menace of correlation inflation

Kenny & Montanari (2013) JCAMD 27:1-13 DOI

Preparation of synthetic data for correlation

inflation study

Add Gaussian

noise (SD=10) to Y

Kenny & Montanari (2013) JCAMD 27:1-13 DOI

Correlation inflation by hiding variationSee Hopkins, Mason & Overington (2006) Curr Opin Struct Biol 16:127-136 DOI

Leeson & Springthorpe (2007) NRDD 6:881-890 DOI

Data is naturally binned (X is an integer) and mean value of Y is calculated for

each value of X. In some studies, averaged data is only presented graphically

and it is left to the reader to judge the strength of the correlation.

R = 0.34 R = 0.30 R = 0.31

R = 0.67 R = 0.93 R = 0.996

r

N 1202

R 0.247 ( 95% CI: 0.193 | 0.299)

N 8

R 0.972 ( 95% CI: 0.846 | 0.995)

Correlation Inflation in FlatlandSee Lovering, Bikker & Humblet (2009) JMC 52:6752-6756 DOI

Kenny & Montanari (2013) JCAMD 27:1-13 DOI

Choosing octanol was the first mistake...

Why are we interested in partition coefficients?

• Definition and quantification of lipophilicity

• For modelling permeability (e.g. blood brain barrier)

• Insight into hydrophobic effect (molecular recognition)

• ‘Compound quality’ (bad stuff like poor solubility and

metabolic instability associated with high lipophilicity)

Lipophilic & half ionised Hydrophilic & neutral

Introduction to partition coefficients

Polarity

NClogP ≤ 5 Acc ≤ 10; Don ≤5

An alternative view of the Rule of 5

Does octanol/water ‘see’ hydrogen bond donors?

--0.06 -0.23 -0.24

--1.01 -0.66

Sangster lab database of octanol/water partition coefficients: http://logkow.cisti.nrc.ca/logkow/index.jsp

--1.05

Octanol/Water Alkane/Water

Octanol/water is not the only partitioning system

logPoct = 2.1

logPalk = 1.9

DlogP = 0.2

logPoct = 1.5

logPalk = -0.8

DlogP = 2.3

logPoct = 2.5

logPalk = -1.8

DlogP = 4.3

Differences in octanol/water and alkane/water logP values

reflect hydrogen bonding between solute and octanol

Toulmin et al (2008) J Med Chem 51:3720-3730 DOI

-0.054

-0.086-0.091

-0.072

-0.104 -0.093

Hydrogen bonding of esters

Toulmin et al (2008) J Med Chem 51:3720-3730 DOI

Basis for ClogPalk model

log

Pa

lk

MSA/Å2

Kenny, Montanari & Propopczyk et al (2013) JCAMD 27:389-402 DOIKenny, Montanari & Propopczyk et al (2013) JCAMD 27:389-402 DOI

𝐶𝑙𝑜𝑔𝑃𝑎𝑙𝑘 = 𝑙𝑜𝑔𝑃0 + 𝑠 ×𝑀𝑆𝐴 −

𝑖

∆𝑙𝑜𝑔𝑃𝐹𝐺,𝑖 −

𝑗

∆𝑙𝑜𝑔𝑃𝐼𝑛𝑡,𝑗

ClogPalk from perturbation of saturated hydrocarbon

logPalk predicted

for saturated

hydrocarbonPerturbation by

functional groups

Perturbation by

interactions

between

functional groups

Kenny, Montanari & Propopczyk et al (2013) JCAMD 27:389-402 DOI

Performance of ClogPalk model

Hydrocortisone

Cortisone

(logPalk ClogPalk)/2

log

Pa

lk

Clo

gP

alk

AtropinePropanolol

Papavarlne

Kenny, Montanari & Propopczyk et al (2013) JCAMD 27:389-402 DOI

Another way to look at Structure Activity Relationships?

(Descriptor-based) QSAR/QSPR

• How likely is it that we will have sufficient data for useful activity

model before project delivers?

• How valid is methodology (especially for validation) when

distribution of compounds in training/test space is highly non-

uniform?

• Are models predicting activity or locating neighbours?

• To what extent are ‘global’ models ensembles of local models?

• How well do methods handle ‘activity cliffs’?

• How should we account for sizes of descriptor pools when

comparing model performance?

Measures of Diversity & Coverage

•• •

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••

•

•

•

••

•

••

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2-Dimensional representation of chemical space is used here to illustrate concepts of diversity

and coverage. Stars indicate compounds selected to sample this region of chemical space.

In this representation, similar compounds are close together

Neighborhoods and library design

Examples of relationships between structures

Tanimoto coefficient (foyfi) for structures is 0.90

Ester is methylated acid Amides are ‘reversed’

Leatherface molecular editor

From chain saw to Matched Molecular Pairs

c-[A;!R]

bnd 1 2

c-Br

cul 2

hyd 1 1

[nX2]1c([OH])cccc1

hyd 1 1

hyd 3 -1

bnd 2 3 2

Kenny & Sadowski Structure modification in chemical databases, Methods and Principles in Medicinal

Chemistry (Chemoinformatics in Drug Discovery 2005, 23, 271-285 DOI

Glycogen Phosphorylase inhibitors:

Series comparison

DpIC50

DlogFu

DlogS

0.38 (0.06)

-0.30 (0.06)

-0.29 (0.13)

DpIC50

DlogFu

DlogS

0.21 (0.06)

0.13 (0.04)

0.20 (0.09)

DpIC50

DlogFu

DlogS

0.29 (0.07)

-0.42 (0.08)

-0.62 (0.13)

Standard errors in mean values in parenthesis; see Birch et al (2009) BMCL 19:850-853 DOI

Effect of bioisosteric replacement

on plasma protein binding

?

Date of Analysis N DlogFu SE SD %increase

2003 7 -0.64 0.09 0.23 0

2008 12 -0.60 0.06 0.20 0

Mining PPB database for carboxylate/tetrazole pairs suggested that bioisosteric

replacement would lead to decrease in Fu so tetrazoles were not synthesised.

Birch et al (2009) BMCL 19:850-853 DOI

-0.316

-0.315

-0.296

-0.295

Bioisosterism: Carboxylate & tetrazole

-0.262

-0.261

-0.268

-0.268

Kenny (2009) JCIM 49:1234-1244 DOI

Amide N DlogS SE SD %Increase

Acyclic (aliphatic amine) 109 0.59 0.07 0.71 76

Cyclic 9 0.18 0.15 0.47 44

Benzanilides 9 1.49 0.25 0.76 100

Effect of amide N-methylation on aqueous solubility

is dependent on substructural context

Birch et al (2009) BMCL 19:850-853 DOI

Relationships between structures

Discover new

bioisosteres &

scaffolds

Prediction of activity &

properties

Recognise

extreme data

Direct

prediction

(e.g. look up

substituent

effects)

Indirect

prediction

(e.g. apply

correction to

existing model)

Bad

measurement

or interesting

effect?

• Molecular design is not just about prediction so

how can we make hypothesis-driven design more

systematic?

• Inflated correlations will not extract us from ‘la

merde’ even if we can get a journal to print them

• There is life beyond octanol/water (and atom-

centered charges) if we only choose to look for it

• Even molecules can have meaningful relationships

Some stuff to think about