Aspects of pharmaceutical molecular design (Fidelta version)

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

• Poor understanding and predictability of toxicity

• Can’t measure free (unbound) physiological

concentrations of drug for remote targets in vivo

– Intracellular

– On far side of blood brain barrier

Dans la merde, FBDD & Molecular Design blog

Pharmaceutical 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

Hypothesis-Driven

• Framework in which to assemble

SAR/SPR as efficiently as

possible

• Understand your molecules and

ask good questions

Prediction-Driven

• Assume that we can build

predictive models with required

degree of accuracy

Molecular Design

TEP = log10([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 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 designframework has similarities with molecular complexity model proposed by Hann et al.

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

Beware correlation inflation

Data-driven design decision-making

• Predictivity of trend determined by its strength rather

than its significance

• Strength of trend determines how rigidly design

guidelines should be adhered to

• Search for strong local correlations rather than for

ways to inflate weak global correlations

Preparation of synthetic data sets(drug-likeness ‘experts’ are reluctant to 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 variationSee 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

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

Beyond octanol/water

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

DlogP = 0.5

PSA/ Å2 = 48

Polar Surface Area is not predictive of hydrogen bond strength

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

Relationships between structures

as framework for SAR

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; 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

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?

A quick look at ligand efficiency

Scale activity/affinity by risk factor

LE = ΔG/HA

Offset activity/affinity by risk factor

LipE = pIC50 ClogP

Ligand efficiency metrics

There is no reason that normalization of activity with respect to risk factor should be restricted to either of these functional forms.

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

There’s a reason why we say standard free energy

of binding

DG = DH TDS = RTln(Kd/C0)

• Adoption of 1 M as standard concentration is

arbitrary

• A view of a chemical system that changes with

the choice of standard concentration is

thermodynamically invalid (and, with apologies to

Pauli, is ‘not even wrong’)

Kenny, Leitão & Montanari (2014) JCAMD 28:699-701 DOIEfficient voodoo thermodynamics, FBDD & Molecular design blog

Use trend actually observed in data for normalization

rather than some arbitrarily assumed trend

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

Can we accurately claim to have normalized a data set if we have

made no attempt to analyse it?

Green: line of fitRed: constant LEBlue: constant LipE

NHA Kd/M C/M (1/NHA) log10(Kd/C)

10 10-3 1 0.30

20 10-6 1 0.30

30 10-9 1 0.30

10 10-3 0.1 0.20

20 10-6 0.1 0.25

30 10-9 0.1 0.27

10 10-3 10 0.40

20 10-6 10 0.35

30 10-9 10 0.33

Effect on LE of changing standard concentration

Analysis from Kenny, Leitão & Montanari (2014) JCAMD 28:699-701 DOINote that our article overlooked similar observations 5 years earlier by

Zhou & Gilson (2009) Chem Rev 109:4092-4107 DOI

Water

Octanol

pIC50

LipE

What we try to capture when we use lipophilic efficiency

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

There are two problems with this approach. Firstly octanol, is not ideal non-polar reference statebecause it can form hydrogen bonds with solutes (and is also wet). Secondly, logP does notmodel cost of transfer from water to octanol for ligands that bind as ionized forms

logP

Linear fit of ΔG to HA for published PKB ligands

Data from Verdonk & Rees (2008) ChemMedChem 3:1179-1180 DOI

HA

Δ

G/

kcal

mo

l-1ΔG/kcalmol-1 0.87 (0.44 HA)

R2 0.98 ; RMSE 0.43 kcalmol-1

-ΔGrigid

Ligand efficiency, group efficiency and residuals plotted for PKB binding data

Res

id|

GE

GE/kcalmol-1HA-1

Resid/kcalmol-1

LE/kcalmol-1HA-1

Residuals and group efficiency values show similar trends with pyrazole (HA = 5) appearing

as outlier (GE is calculated using ΔGrigid ). Using residuals to compare activity eliminates

need to use ΔGrigid estimate (see Murray & Verdonk 2002 JCAMD 16:741-753 DOI) which is

subject to uncertainty.

• Data can be massaged and correlations can be

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

• How can we make hypothesis-driven design more

systematic and more efficient?

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

centered charges) if we choose to look for it

• Even molecules can have meaningful relationships

• Ligand efficiency: nice concept, shame about the metrics

Stuff to think about

Spare slides

In tissues

Free in

plasma

Bound to

plasma

protein

Dose of drug

Eliminated drug

A simplified view of what happens to drugs after dosing

Do1 Do2

Ac1

Kenny (2009) JCIM 49:1234-1244 DOI

Illustrating hypothesis-driven design withDNA base isosteres: H bond acceptor & donor definitions

Watson-Crick Donor & Acceptor Electrostatic Potentials for Adenine Isosteres

Vm

in(A

c1)

Va (Do1)

DlogP

(corrected)

Vmin/(Hartree/electron)

DlogP

(corrected)

Vmin/(Hartree/electron)

N or ether OCarbonyl O

logPalk as perturbation of logPoct

Prediction of contribution of acceptors to DlogP

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

DlogP = DlogP0 x exp(-kVmin)

logPoct = 0.89predicted logPalk = -4.2PSA/Å2 = 53

logPoct = 1.58predicted logPalk = -1.4PSA/Å2 = 65

Lipophilicity/polarity of Morphine & Heroin

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

Basis for ClogPalk model

logP

alk

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

logP

alk

Clo

gPal

k

AtropinePropanolol

Papavarlne

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

Measures of Diversity & Coverage

•• •

•

••

•

•

•

••

•

••

•

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

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 or just locating neighbors?

• To what extent are ‘global’ models just 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 recognize ‘activity cliffs’?