uct oct 2014

Molecular design for drug discovery

Peter W Kenny

http://fbdd-lit.blogspot.com

Outline of presentation

• Some thoughts on molecular design

• Design of compound libraries for screening

• Relationships between structures as framework for

analysing biological activity and physicochemical

properties

Some things that make drug design difficult

• Having to exploit targets that are weakly-linked to

human disease

• Poor understanding and prediction of 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

Molecular Design

• Control of behavior of compounds and materials by

manipulation of molecular properties

• Hypothesis-driven or prediction-driven

• Sampling of chemical space

– For example, does fragment-based screening allow better

control of sampling resolution?

Kenny, Montanari, Propopczyk, Sala, Sartori (2013) JCAMD 27:655-664 DOI

Kenny JCIM 2009 49:1234-1244 DOI

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

Kenny, Leitão & Montanari JCAMD 2014 28:699-710 DOI

Property-based design as search for ‘sweet spot’

Green and red lines represent probability of achieving ‘satisfactory’ affinity and

‘satisfactory’ ADMET characteristics respectively. The blue line shows the product of

these probabilities and characterizes the ‘sweet spot’. This way of thinking about the

‘sweet spot’ has similarities with Hann molecular complexity model

Kenny & Montanari, JCAMD 2013 27:1-13 DOI

Hypothesis-driven Molecular Design

• Ask good questions with informative compounds and

relevant assays

• Framework for establishing structure-activity

relationships (SARs) as efficiently as possible

• Molecular interactions provide natural framework in

which to pose design hypotheses

Kenny JCIM 2009 49:1234-1244 DOI Hypothesis-driven design versus prediction driven molecular design

Linusson et al JMC 2000 43:1320-1328 DOI Statistical molecular design

Bissantz, Kuhn & Stahl JMC 2010 53:5061-5084 DOI Medicinal chemist’s guide to molecular interactions

Do1 Do2

Ac1

Kenny JCIM 2009 49:1234-1244 DOI

Illustrating hypothesis-driven design

Adenine bioisosteres

Ac2

Watson-Crick Donor & Acceptor Electrostatic Potentials for

Adenine Isosteres

Vm

in(A

c1)

Va (Do1)

Kenny JCIM 2009 49:1234-1244 DOI

PTP1B (Diabetes/Obesity): Fragment elaboration

Literature SAR was mapped onto intial fragment hit (green). Note overlay of

aromatic rings of elaborated fragment (blue) and difluorophosphonate (red).

Black et al BMCL 2005 15:2503-2507 DOI

Inactive at 200mM

15 mM

3000 mM3 mM

150 mM

(Conformational lock)

130 mM

(3-Phenyl substituent)

“Why can’t we pray for something good, like a tighter bombing pattern, for example? Couldn’t we pray for a tighter bombing pattern?” , Heller, Catch 22, 1961

Design of compound libraries for screening

(a view from Hanoi with additional insight from Heller)

Measures of Diversity & Coverage

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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

Coverage, Diversity & Library Design

••

• ••

•• •• •• •

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Acceptable diversity

And coverage?

Assemble library in

soluble form

Add layer to core

Incorporate layer

Yes

No

Select core

Core and layer library design

Compounds in a layer are selected to be diverse with respect to core compounds. The ‘outer’ layers

typically contain compounds that are less attractive than the ‘inner’ layers. This approach to library

design can be applied with Flush or BigPicker programs (Dave Cosgrove, AstraZeneca, Alderley

Park) using molecular similarity measures calculated from molecular fingerprints.

Blomberg et al JCAMD 2009 23:513-525 DOI

Sample

AvailabilityMolecular

Connectivity

Physical

Properties

screening samples Close analogs Ease of synthetic

elaboration

Molecular

complexity

Ionisation Lipophilicity

Solubility

Molecular

recognition

elementsMolecular shape

3D Pharmacophore

Privileged

substructures

Undesirable

substructures

Molecular

size

3D Molecular

Structure

Fragment selection criteria

Why I don’t use the rule of 3: http://fbdd-lit.blogspot.com/2011/01/rule-of-three-considered-harmful.html

Library design for phenotypic screening

•• •

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•

•

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•

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Chemical fingerprints typically used to calculate molecular similarity while biological fingerprints

can be used directly (sampling of actives from different assays)

Bio

log

y (

assa

y r

esu

lts)

Chemistry (structures)

Another way to look at structure-activity relationships?

Leatherface molecule 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

MUDO Molecule Editor

• SMIRKS-based re-write of Leatherface

using OEChem

• Can also process 3D structures (e.g.

form covalent bond between protein and

ligand)

• Identification of matched molecular pairs

much simpler than with Leatherface

Kenny, Montanari, Propopczyk, Sala, Sartori JCAMD 2013 27:655-664 DOI

K777 docked (green) covalently to

Cruzain with crystallographic ligand

Examples of relationships between structures

Tanimoto coefficient (foyfi) for structures is 0.90

Ester is methylated acid Amides are ‘reversed’

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

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

-0.316

-0.315

-0.296

-0.295

Bioisosterism: Carboxylate & tetrazole

-0.262

-0.261

-0.268

-0.268

Kenny JCIM 2009 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 BMCL 2009 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 and efficient?

• Screening library design as optimization of

bombing patterns

• Even molecules can have meaningful relationships

Stuff to think about

Spare slides follow…

(Descriptor-based) QSAR/QSPR:

Some questions

• 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 just ensembles of

local models?

• How well do the methods handle ‘activity cliffs’?

• How should we account for sizes of descriptor pools

when comparing model performance?

Fragment-based lead discovery: Generalised workflow

Target-based compound selection

Analogues of known binders

Generic screening library

Measure

Kd or IC50

Screen

Fragments

Synthetic elaboration

of hits

SARProtein

Structures

Milestone achieved!Proceed to next

project

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

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

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