Aspects of pharmaceutical molecular design (Fidelta version)
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Transcript of Aspects of pharmaceutical molecular design (Fidelta version)
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’?