Property-based molecular design: where next?

Peter W Kenny http://fbdd-lit.blogspot.com | http://www.slideshare.net/pwkenny

Some things that make drug discovery difficult

• Having to exploit targets that are weakly-linked to

human disease

• Toxicity is poorly understood and unpredictable

• Can’t measure free (unbound) physiological

concentrations of drug for remote (e.g. intracellular)

targets in live humans

Dans la merde, FBDD & Molecular Design blog

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 = log10([𝐷𝑟𝑢𝑔 𝑿,𝑡 ]𝑓𝑟𝑒𝑒

𝐾𝑑)

Target engagement potential (TEP) (or why we can’t design drugs like we design planes link)

Design objectives• Low Kd for target(s)• High (hopefully undetectable) Kd for anti-targets• 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 ofthese probabilities and characterizes the ‘sweet spot’. This molecular design frameworkhas similarities with the molecular complexity model proposed by Hann et al.

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

Data is getting bigger and more open but

this will not prove to be a panacea

Data-driven design decision-making

• Predictivity of a trend determined by its strength

rather than its significance

• Strength of a trend determines how rigidly design

guidelines based on that trend should be adhered to

• Search for strong local correlations rather than for

new ways to inflate weak global correlation

Preparation of synthetic data sets(Property-based design ‘experts’ don’t usually share their data)

Add Gaussian noise (SD=10) to Y

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

An equal number of data points are placed at equally spaced intervals on the line of equality (Y = X) and Normally-distributed noise is added to the values of Y.

Correlation inflation by hiding variationFor examples see Hopkins, Mason & Overington (2006) Curr Opin Struct Biol 16:127-136 DOI | Leeson &

Springthorpe (2007) NRDD 6:881-890 DOI | Lovering, Bikker & Humblet (2009) JMC 52:6752-6756 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

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

N = 11 10 = 110 N = 11 100 = 1100 N = 11 1000 = 11000

Ligand efficiency: nice concept, shame about the metrics

Kenny, Leitão & Montanari (2014) JCAMD 28:699-701 DOI

Can we accurately claim to have normalized Y with respect to X if

we have made no attempt to analyse data?

Green: line of fitPurple: constant LEBlue: constant LipE

Octanol/water is just one of a number of

partitioning systems

There is also the question of which of logP or logD is the more appropriatelipophilicity measure. If interested in how octanol/water came to be the partitioningsystem of choice then take a look at our ClogPalk article.

Kenny, Montanari & Propopczyk 2013 JCAMD 27:389-402 DOI

Polarity

NClogP ≤ 5 Acc ≤ 10; Don ≤5

An alternative view of the Rule of 5

Why is upper polarity limit defined in terms of hydrogen bonding when lower polarity limit is defined using ClogP? What is origin of the hydrogen bond donor/acceptor asymmetry in Ro5?

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

Does octanol/water ‘see’ hydrogen bond donors?

--0.06 -0.23 -0.24

--1.01 -0.66--1.05

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

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

DlogP = 0.5

PSA/ Å2 = 48

Polar Surface Area is not predictive of hydrogen bonding potential

DlogP = 4.3

PSA/ Å2 = 22

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

-0.054

-0.086-0.091

-0.072

-0.104 -0.093

Connection between lipophilicity and hydrogen bonding

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

DlogP = 0.5

DlogP = 1.3Minimized electrostatic potential (Vmin) values (atomic units) are predictive of hydrogen bond basicity

logPoct = 0.97

logPalk = 1.48

logPoct = 2.17

logPalk = −0.31

logPoct = 2.23

logPalk = 0.97

logPoct = 2.42

logPalk = 0.26

logPoct = 1.66

logPalk = 1.38

logPoct = 1.35

logPalk = 2.29

logP as probe of steric and conformational effects

Dearden & Bresnen (2005) Int J Mol Sci 6:119-129 DOI

Structural relationships between compounds as a framework for design

Hypothesis-driven molecular design, matched molecular pair analysis, neighborhood analysis, free energy perturbation…

Prediction-driven design (and descriptor-

based QSAR/QSPR)

• How valid is methodology (especially for validation) when distribution of compounds in training/test space is non-uniform?

• Are models predicting activity/properties or just locating neighbors?

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

• How should we account for number of degrees of freedom when comparing model performance?

• How should we account for sizes of descriptor pools when comparing model performance?

• How does sampling affect correlations between descriptors?

• How well do methods handle ‘activity cliffs’?

Examples of structural relationships between compounds

Tanimoto coefficient (foyfi) for structures is 0.90

Ester is methylated acid Amides are ‘reversed’

Glycogen Phosphorylase inhibitors:Series comparison analysis

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

Hypothesis-driven molecular design and relationships between structures as framework for analysing activity and properties

?

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 wouldlead to decrease in Fu . Tetrazoles were not synthesised even though their logP values are expected tobe 0.3 to 0.4 units lower than for corresponding carboxylic acids.

Birch et al (2009) BMCL19:850-853 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

Structural relationships between compounds

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?

• How can we make hypothesis-driven design more

systematic and more efficient?

• Data can be massaged and correlations can be

inflated but it won’t extract us from ‘la merde’

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

centered charges) if we choose to look for it

• Even molecules can have meaningful relationships

Stuff to think about