From screening to molecular interactions: A short tour

Peter W Kenny (pwk.pub.2008@gmail.com)

Some things that are hurting Pharma

• 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 within blood brain barrier)

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

Screening and Chemical Space

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

The neighborhood concept

Achtung!Spitfire!

Hitting the target: The old way…

Stuka on wikipedia

“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

…and the new

B52 on wikipedia

HTS is so glamorous…

So, Maria, why do you think it is that the

Russians are so much better than the

Germans at tennis these days?

Actually we startedto beat them at their national sport almost

70 years ago and...

.... as Uncle Joe was so fond of saying, quantity

has a quality all of its own.

… that even the stars of tennis have heard of it

• One measure of the power of an assay the weakness of

the binding that can be detected and quantified

Screening Assays

Looking for leads: An overview of screening

Chemical Space

Leads

High throughput

screeningVirtual (directed)

screening

Hit to lead

Fragment

screening

A model for molecular complexity

This model is equally relevant to conventional and fragment-based screening. See Hann, Leach

& Harper J. Chem. Inf. Comput. Sci., 2001, 41, 856-864 | http://dx.doi.org/10.1021/ci000403i

Success landscape

Degree of substitution as measure of molecular complexity

The prototypical benzoic acid can be accommodated at both sites and, provided that binding can be

observed, will deliver a hit against both targets See Blomberg et al JCAMD 2009, 23, 513-525 |

http://dx.doi.org/10.1007/s10822-009-9264-5 | This way of thinking about molecular complexity is

similar to the ‘needle’ concept introduced by Roche researchers. See Boehm et al J. Med. Chem.

2000, 43, 2664-2774 | http://dx.doi.org/10.1021/jm000017s

Hopkins, Groom & Alex, DDT 2004, 9, 430-431

Ligand Lipophilicity Efficiency

LLE = pIC50 - ClogP

Leeson & Springthorpe , NRDD 2007, 6, 881-890.

Measured binding is scaled Measured binding is offset

Binding Efficiency

Measures

Ligand Efficiency

LE= DGº/NonHyd

FBDD Essentials

Screen fragments

Synthetic

Elaboration

Target

Target & fragment hit

Target & lead

Link

Fragment Elaboration Tactics

Merge

Grow

PO

O

O

FF

PO

O

O

FF

15M

Inactive at 200MN

S

N

OO

O

NS

N

OO

O

OMe

NS

N

OO

O

NS

N

OO

O

OMe

AZ103366763 mM

conformational lock

150 M

hydrophobic m-subst

130 M

AZ11548766

3 M

PTP1B: Fragment elaboration

Elaboration by Hybridisation: Literature SAR was mappedonto the fragment AZ10336676 (green). Note overlay ofaromatic rings of elaborated fragment AZ11548766 (blue)and difluorophosphonate (red). See Bioorg Med Chem Lett,15, 2503-2507 (2005)

Overview of fragment based lead discovery

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

Why fragments?

• Access to larger chemical space

• Counter the advantage of competitors’ large

compound collections

• Ligands are assembled from proven molecular

recognition elements

• Just a smart way to do Structure-Based Design

• Control resolution at which chemical space is

sampled

• Control of properties of compounds and materials by

manipulation of molecular properties

• Prediction-Driven or Hypothesis-Driven

Molecular Design

(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?

• Do models predict activity or just locate neighbours?

• Are ‘global’ models ensembles of local models?

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

• How should we account for sizes of descriptor pools

when comparing models?

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 not synthesised.

Birch et al, BMCL 2009, 19, 850-853

Molecular Interactions and Drug Action

Molecular Recognition

• Functional behavior of molecules is determined by the

interactions of its molecules with the different

environments in which they exist

• Mutual presentation of molecular surfaces

• For association in water we need to match interaction

potential to maximise affinity.

Direct interactions Indirect interactions

‘Non-classical’

e.g. heavy

halogen

Electrostatic

e.g. hydrogen

bonding

Dispersion

forcesSteric clash Hydrophobic

Conformational

strain & entropy

Non-covalent interactions

A taxonomy of non-covalent interactions

Hydrogen

Bonding

Interactions between drug

molecules in crystal lattice

(Solubility, melting point

polymorphism, crystallinity)

Interactions between drug and

water molecules

(Solubility, distribution,

permeability, potency, toxicity,

efflux, metabolism)

Interactions between drug

molecules & (anti)target(s)

(Potency, toxicity, efflux ,

metabolism, distribution)

Hydrogen Bonding in Drug Discovery & Development

Interactions between water

molecules

(Hydrophobic effect)

Cartoon representation of hydrophobic effect

Polar Surface

Binding Pocket

Cartoon representation of hydrophobic forces

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

Minimised electrostatic potential has been shown to be an effective predictor of hydrogen bond basicity

Plot of V/kJmol-1 against r/Å for pyridine on lone pair axis showing electrostatic potential minimum 1.2Å from nitrogen

-300

-200

-100

0V

0 1 2 3 4 5

r

Electrostatic potential as function of position for acceptor

V/k

Jm

ol-1

r/År/Å

r

Fluorine: A weak hydrogen bond acceptor

-0.122 -0.113 -0.071

-0.038

-0.054

-0.086-0.091

-0.072

-0.104 -0.093

Hydrogen bonding of esters

Toulmin et al, J. Med. Chem. 2008, 51, 3720-3730

-0.316

-0.315

-0.296

-0.295

Bioisosteric relationship between carboxylic

acids and tetrazoles

Kenny, JCIM, 2009, 49, 1234-1244

-0.262

-0.261

-0.268

-0.268

HO

H HO

H HO

H

H

OH

N

H

O

Effect of complex formation on predicted

hydrogen bond acidity of water

1.2

(~ Alcohol)

2.0

(~ Phenol)

2.8

(~ 4-CF3Phenol)

Kenny, JCIM, 2009, 49, 1234-1244

Summary

• Screening as sampling chemical space

– Fragments are thought to allow better sampling

• Molecular design as a process of tuning

interaction potential

– Design can be hypothesis-driven or prediction-

driven