UK QSAR Symposium at Syngenta
'Rapid Physicochemical Profiling'
Derek P. Reynolds
25th April 2001
Physical Chemistry Team
Christopher Bevan, Alan Hill, Klara Valko, Pat McDonough
Chemical and Analytical Technologies Department,
GlaxoWellcome R&D,
Stevenage, UK
Turning Hits and Leads into New Medicines
 GlaxoWellcome has funded a worldwide project to deliver high throughput
screens for Physicochemical, Pharmacokinetic, Metabolic, and
Toxicological Factors
 OBJECTIVES:
– High-throughput to screens support discovery projects
– A large international repository of consistent data which can help us
learn more about fundamental mechanisms regulating kinetics and
toxicology
– Construction of predictive models which aid the design of drugs
Screens Available
 Physicochemical Screens
– Lipophilicity (CHI)
– Solubility
– pKa
 ADME
– In-vitro metabolism (Liver Microsomes)
– Permeation (MDCK)
– In-vivo pharmacokinetics - (Cassette Dosing)
 Genetox
– SOS gene, umuC mutagenicity assay
 Analysis Tools
– Calculated properties
– Modeling
The Physicochemical Properties of a Drug
have an important influence on its
Absorption and Distribution in-vivo
Predictive models aid drug design
however
models are built on real data and novel
compounds often need new rules!
Comparison
Measured logD (x axis) and clogD (y axis)
octanol/water pH 7.4
Measured vs c logD s for 434 compounds
y = 1.0926x - 0.6948
2
R = 0.4075
8
6
4
2
0
-4
-3
-2
-1
0
-2
-4
-6
-8
1
2
3
4
Part 1
Experimental Methods for Measurement of
Lipophilicity, pKa, and Solubility
Part 2
Using Physicochemical Data to Understand
Biological Data. An Example:
Intestinal Absorption of Drugs
What is high-throughput ?
 Is it:
high total numbers?
speed of measurement?
rapid response?
lower total cost?
lower cost per sample?
accurate?
flexible?
‘Toolkit’ of High Throughput Methods for
lipophilicity, solubility, and pKa
 Standardised general methods suitable for libraries and large compound
sets (deployed globally)
 Rapid response ‘open-access’ versions for ‘immediate answers’ and project
specific investigations
 Automated versions of classical determinations e.g. octanol logD
 Over 17,000 accurate determinations of lipophilicity, solubility, and pKa
made by GW in the UK over the last 12 months
 Some methods now developed are suitable for deployment alongside invitro biological screens
Fast Generic Gradient HPLC:
The basis for high throughput characterisation, purification, and property
determination of new compounds and libraries
For details see:
‘Separation Methods in Drug Synthesis and Purification’
Ed. KlaraValko, Elsevier, October 2000
Relevant Chapters:
 Fast generic HPLC methods- I.M.Mutton
 Coupled chromatography-mass spectrometry techniques for the analysis of
combinatorial libraries- S.Lane
 The development and industrial application of automated preparative HPLCT.Underwood, R. Boughtflower and K.A. Brinded
 Measurements of physical properties for drug design in industry- K. Valko
Fast Generic Gradient HPLC as a basis for
Physicochemical Property Measurement
Advantages:
 Fast, accurate and automation friendly
 Can analyse DMSO solutions directly
 Tolerant of impure compounds
 Compatible with MS for identity confirmation
Options for Lipophilicity Measurement
 logD measurement by automation of the classical
partition experiment. Solute concentration measured by
gradient HPLC
 HPLC retention time as a measure of lipophilicity
Octanol/Water LogP Determination
The aqueous phase can be sampled through the octanol phase
without cross-contamination
Analysis:The samples and blanks are analysed using either an
HP1050 or HP1100 HPLC system using a fast generic gradient.
Generic Gradient HPLC ( ‘Five minute CHI method’)
LunaC18(2) 50 x 4.6 mm; 2.00 ml/min; Mobile phase A 50 mM ammonium acetate pH 7.4 and B is 100% acetonitrile. Gradient: 0 - 2.5
min 0 - to 100% B; 2.5 - 2.7 min 100% B.
C om pound
T heophylline
Phenyltetrazole
B enzim idazole
C olchicine
Phenyltheophylline
A cetophenone
Indole
Propiophenone
B utyrophenone
V alerophenone
C H I 7.4
at pH 7.4
18.4
23.6
34.3
43.9
51.7
64.1
72.1
77.4
87.3
96.4
C H I2
at pH 2
17.9
42.2
6.3
43.9
51.7
64.1
72.1
77.4
87.3
96.4
C H I 10.5
at pH 10.5
5.0
16.0
30.6
43.9
51.7
64.1
72.1
77.4
87.3
96.4
Calibration of CHI at pH 7.4
120.00
y = 54.329x - 71.702
2
R = 0.9972
100.00
80.00
60.00
40.00
20.00
0.00
1.4
1.9
2.4
2.9
3.4
CHI - Chromatographic Hydrophobicity Index
A measurement for the Pragmatist not the Purist !
 CHI is an HPLC retention index derived from retention time in a gradient
HPLC run and scaled using a set of standard compounds
 Provided the same stationary phase and mobile phase are used, then CHI
for a given compound should be a reproducible measure of lipophilicity
(independent of equipment, operator, or laboratory)
 CHI is essentially a solvent strength parameter (scaled to approximate to
the % organic concentration in the mobile phase when logk=0)
CHI = f (logkwater , logkorganic )
Where: logkwater =retention factor extrapolated to pure water
logkorganic = retention factor extrapolated to 100% organic
General Solvation Equation
logSP = Solute Property, i.e., property of a series of solutes
in a given phase system, e.g., logP, logBBB,
logk, CHI, etc
logSP = c + e.E + s.S + a.A + b.B + v.Vx
The coefficients
c, e, s, a, b, and v
are specific to each
Solute Property
Equations are robust and apply
to molecules in their unionized
state. Correlation coeffs R >
0.90 for most processes
Descriptors are specific to each
molecule, where:
E = Excess Molar Refraction
S = Polarisability
A = Hydrogen Bond Acidity
B = Hydrogen Bond Basicity
Vx = McGowan Volume
SOLVATION EQUATIONS FOR CHI
CHI = C + v(e’ E + s’ S + a’ A + b’ B + Vx)
E - excess molar refraction term, normalised to alkanes
S - solute dipolarity/polarisability descriptor
A - solute hydrogen bond acidity descriptor
B - solute hydrogen bond basicity descriptor
Vx - McGowan characteristic volume
Systems
LogPhexadecane
CHIACN
CHIMeOH
LogPoct
CHIIAM
v
4.5
65
50
3.8
50
e
0.15
0.1
0.1
0.15
0.15
s
-0.35
-0.25
-0.2
-0.25
-0.15
a
-0.8
-0.35
-0.15
0.0
0.1
b
-1.1
-1.0
-0.85
-0.9
-1.0
Equations are robust and apply to molecules in their unionised state.
Correlation coeffs R > 0.95
Measurements of molecular descriptors via retention
data from several diverse HPLC systems
 We can set up solvation equations for various reversed-phase HPLC
partition systems.
 Knowing the regression constants for the HPLC systems, the molecular
descriptors can be derived by iterative fitting from the retention data of
the solute.
Selected HPLC systems
 Luna C-18 column with acetonitrile gradient (CHIACN)
CHIACN = 7.1 + 0.41E - 1.06S - 1.59A - 4.88B + 4.8Vx
 Luna C-18 column with trifluoroethanol gradient (CHITFE)
CHITFE = 6.9 + 0.67E - 1.96S - 3.1A - 3.94B + 5.67Vx
 Polymer C-18 column with acetonitrile gradient (CHIPLRP)
CHIPLRP = 8.19 - 0.41E - 0.44S - 2.50A - 5.64B + 4.38Vx
 DevelosilCN column with methanol gradient (CHICN-MeOH)
CHICN-MeOH = 3.93 + 0.79E - 1.05S - 0.72A - 4.5B - 5.42Vx
 DevelosilCN column with acetonitrile gradient (CHICN-AcN)
CHICN-AcN = 5.67 + 0.2E - 0.28S - 0.55A - 4.15B + 3.68Vx
 Fluorooctyl column with trifluoroethanol (CHIFO-TFE)
CHIFO-TFE = 7.45 - 0.12E - 0.57S - 3.67A - 1.89B + 3.11Vx
Lipophilicity and Solubility are pKa-dependent
 Lipophilicity v pH profiles are needed to fully understand partition
behaviour
 pH cannot be properly controlled in the CHI experiment because of the
organic modifier. Ionisation can be suppressed with buffer additives to give
reliable CHIN values (I.e. CHI lipophilicity of the neutral form of the
molecule)
 A rapid method for pKa determination is needed to allow the computation
of lipophilicity v pH profiles
Gradient Titration: a faster way to measure pKa
values
 Prototype instrument developed by Alan Hill at GlaxoWellcome Research
(Stevenage, UK)
 Collaboration with Sirius from 1997 to develop instrument.
 First Sirius commercial instrument now in routine use at Stevenage
 The Team:
GlaxoWellcome: Alan Hill*, Chris Bevan*, Derek Reynolds*
Sirius: John Comer, Brett Hughes, Karl Box, Kin Tam, Roger Allen, Simon
Thomson, Paul Hosking
*GT inventors; Patent applied for (WO99/13328)
A faster way to measure pKa values
 The goal:
– >96 samples per day
– pKa measurement between pH3 and pH11
– automatic dilution: samples in DMSO solution in microtitre plates
– Easy to use and suitable for ‘open-access’ operation
 A new instrument
– Sirius Gradient Titrator for pKa measurement
– Spectroscopic measurement technique
– Commercial instrument launched 1Q 2000
Sirius pKa Profiler
Calibrating GT with standard compounds
Calibration Curve for Standard pKa Values
12
y = -0.0653x + 12.394
abs x10+2
R2 = 0.9959
0.02581
10
0.01595
0.00609
8
pH
-0.00377
-0.01363
6
-0.02349
0
-0.03336
-0.04322
4
-0.05308
30.0
41.5
53.0
64.5
76.0
87.5
99.0
110.5
100
110
122.0
133.5
2
20
30
40
50
60
70
80
90
120
130
145.0
P oints
140
150
Time (secs/2)
Benzoic acid
Phthalic acid
Nitrophenol
Chlorophenol
Phenol
pKa 3.978
pKa 4.843
pKa 6.973
pKa 9.240
pKa 9.796
Five standards. First derivative peak
maxima correlated with pH-metric
pKa values (25°C, I = 0.15M).
Standards can be mixed for rapid
calibration. Time (seconds) is
proportional to pH.
What are suitable measurements for
physicochemical screening?
 Lipophilicity and pKa are valuable for compound selection- but there are not
usually any absolute pass/fail criteria
 Lipophilicity is essentially a composite parameter which reflects the
properties of both the polar surface and the hydrophobic surface of a
molecule. Descriptors which are derived from several partition systems will
be more likely to yield general QSAR relationships
 Aqueous solubility depends on specific packing and intermolecular
interactions in the solid as well as on the properties and ionisation state of
the molecule in solution- For some drug targets (e.g. related to arachidonic
acid cascade or fatty acid metabolism) then low solubility of leads may be a
general issue that may require a solubility screen
Options for Solubility Measurement
 Solubility measurement by equilibration of solid sample with buffer.
After filtration the solute concentration is measured by gradient
HPLC. Sample preparation is rate limiting (20 per day)
 Precipitation by dilution of concentrated DMSO solution. After
filtration the solute concentration is measured by generic gradient
HPLC. Can be automated (96 well plate per day)
 Precipitation by dilution of concentrated DMSO solution. Detect
appearance/disappearance of precipitate by nephelometry. The
introduction of microtitre plate nephelometers makes this suitable
for use by biochemical screening groups (Many plates per day)
Solubility by Laser Nephelometry
The laser nephelometer used is the NEPHELOstar (BMG LabTechnologies Offenburg, Germany). This
instrument is a forward scattering Laser-Nephelometer employing a polarised laser diode that lases in
the red at 635 nm. The Laser beam is passed through the well in a vertical and concentric path as
shown below: Forward scattered light is measured beneath the well.
References:
1. C. D. Bevan, R. S. Lloyd, Anal. Chem. 72 (2000) 1781.
Solubility by Laser Nephelometry
Procedure:
Compounds are supplied as 10 mM solutions in DMSO in 96 well microtitre plates. These are initially
diluted 20 times with aqueous buffer to give a 5% DMSO/aqueous buffer solution. Then stepwise serial
dilutions are made with 5% DMSO/aqueous buffer until precipitated compounds just redissolved. These
dilutions are then monitored nephelometrically.
This technique is able to reproducibly detect turbidity in suspensions and distinguishes them from true
solutions.
The method is non-destructive and simple and uses procedures very similar to those used for
determination of dose response curves in biochemical assays. It is easy to integrate in a high
throughput drug screening process.
AUTOMATING THE DETERMINATION OF AQUEOUS DRUG
SOLUBILITY USING LASER NEPHELOMETRY IN MICROTITRE
PLATES
David Proudlock*, Malcolm Willson, Barbara Carey, Glaxo Wellcome R&D Medicines Research Centre, Stevenage UK
Three pieces of equipment were required for plate handling, reagent addition and measurement.
They were:Zymark Twister, Labsystem Multidrop, BMG Nephelostar
Summary: Measurements that characterise the properties
of molecules are now readily available
 Conventional measurements (octanol partition and solubility) can be
automated to some degree
 Rapid gradient HPLC retention times can be converted into a reliable index
of lipophilicity (CHI)
 HPLC at extremes of pH provide a convenient way to determine the
lipophilicity of the unionised form of acids and bases (CHIN)
 CHIN values from HPLC systems with different selectivity characteristics
can be combined to determine molecular parameters that define solute
polarity and H-bonding (S, A, B)
 A new type of titration (gradient titration) provides rapid pKa measurement
 Solubility can be rapidly estimated alongside biological screening by using
a microtitre plate based nephelometer
 Measured pKa values can be combined with single point solubility or
lipophilicity determinations to calculate pH profiles
Part 2
Using Physicochemical Data to Understand
Biological Data. An Example:
Intestinal Absorption of Drugs
What should we use physicochemical profiles for?
 Comparison with calculated properties
 Derivation of both general and project specific QSAR models
 Selection of physico-chemically diverse molecules for biological
investigation (in vitro and in-vivo)
 To provide insight into the mechanisms of biological partition and in-vivo
transport processes
What about ‘Biomimetic’ measurements?
(e.g. Membrane affinity, Serum albumin binding,
Cell Permeability)
Do they predict in-vivo properties better than
‘classical’ measurements?
(e.g. logP, solubility, pKa)
Provide additional rather than alternative
information
High-throughput permeability screens?
 CACO2 (e.g Artursson et. al.)
 MDCK
 PAMPA (Kansy et. al., Hoffman-La Roche)
 Alkane/Water membranes (Wohnsland and Faller, Novartis)
Simplistic interpretation of data can be misleading. All are potentially
valuable when used systematically to help in the understanding of
biologically relevant mechanisms of action.
Affymax MDCK permeability screen (Lori Takahashi)
COS: Components & Assembly
Top Block
Base Block
Seeded Transwell
Figure 1. The COS system is an in-vitro assay apparatus utilizing a single
sheet of cultured epithelial cells sandwiched between an array of loading
wells on the apical side and a complementary array of receiving wells on the
basolateral side. The construct allows for the collection of in-vitro Papp data
with greater throughput, consistency, and reproducibility over the traditional
Transwell™ apparatus.
Predicting Human Oral Absorption
(Plot of Human Intestinal Absorption v MDCK Cell Permeability)
120
100
HIA %
80
60
40
20
0
1
10
50
100
MDCK Papp (nm/sec)
1000
Model for MDCK cells
based on CHI values
logP app MDCK = 0.0372CHI(MeOH) - 0.227 cMR -0.78Ind (acid) + 1.659
Predicting Human Oral Absorption
(Model includes measured lipophilicity and calculated molecular size)
% Human Oral Absorption = 1.31 CHI(MeOH) -10.93cMR + 88.6
n=52 r=0.81 s=19.7 F=15.9
% absorbed drug
140
120
P re d ic t e d
100
80
60
40
20
0
5
25
45
65
M e as u r e d
85
105
Solvation equation for oral absorption
% Abs = 92 + 2.9E + 4.1S - 21.7A - 21.1B + 10.5Vx
n=170 r2=0.74
sd=14%
 Note that the relative size of the v coefficient is smaller than for
water/solvent partitions.
 The e and s coefficients are insignificant
 Absorption is generally high (90%) unless several H-bond donor/acceptor
groups on a molecule decrease absorption. The equation is not affected by
whether a compound is acidic or basic
 The equation is consistent with other models e.g.
– polar surface area (Palm and Clark)
– CHI - CMR
– logD v CMR
Advantages of Abraham QSAR Models
% Abs = 92 + 2.9E + 4.1S - 21.7A - 21.1B + 10.5Vx
n=170 r2=0.74
sd=14%
 Solute parameters can be estimated from molecular structure fragments or
derived from experimental partition measurements
– Allows prediction drug behaviour prior to synthesis and a test of the
model after synthesis by accurate physicochemical property
measurement
 The same parameters are always used so that different systems can be
directly compared
– Can be used to investigate molecular mechanisms
Prediction of Human Intestinal Absorption from the Solvation Equation
% Abs = 92 + 2.9E + 4.1S - 21.7A - 21.1B + 10.5Vx
100
80
Training set
Predicted
60
Drugs 229-241
40
Low solubility
20
Dose dependant
0
-20
0
20
40
Observed
60
80
100
Solvation equation for oral absorption
% Abs = 92 + 2.9E + 4.1S - 21.7A - 21.1B + 10.5Vx
n=170 r2=0.74
sd=14%
Comparison with other processes
A pseudo-rate equation can be derived from the equation for %of Absorbed Dose
log{ln[100/(100-%Abs.)]} = 0.54 - 0.025 E + 0.14 S - 0.41 A - 0.51 B + 0.20Vx
n = 127, r2 = 0.80, SD = 0.29, F = 94
Zhao YH, Le J, Abraham MH, Hersey A, Eddershaw PJ, Luscombe
CN, Butina D, Beck G, Sherborne B, Cooper I, Platts,J.A.. J Pharm Sci.,
submitted
A very different equation when compared to:
A pseudo-rate equation can be derived from the equation for %of Absorbed Dose
log{ln[100/(100-%Abs.)]} = 0.54 - 0.025 E + 0.14 S - 0.41 A - 0.51 B + 0.20Vx
n = 127 r2 = 0.80 SD = 0.29 F = 94
Zhao YH, Le J, Abraham MH, Hersey A, Eddershaw PJ, Luscombe
CN, Butina D, Beck G, Sherborne B, Cooper I, Platts,J.A.. J Pharm Sci.,
submitted
This does not fit a partition model of membrane
transport (e.g. octanol/water)
logkoct = 0.088 + 0.562 E – 1.054 S + 0.034 A - 3.46 B + 3.814 Vx
A very different equation when compared to:
A pseudo-rate equation can be derived from the equation for %of Absorbed Dose
log{ln[100/(100-%Abs.)]} = 0.54 - 0.025 E + 0.14 S - 0.41 A - 0.51 B + 0.20Vx
n = 127 r2 = 0.80 SD = 0.29 F = 94
Zhao YH, Le J, Abraham MH, Hersey A, Eddershaw PJ, Luscombe
CN, Butina D, Beck G, Sherborne B, Cooper I, Platts,J.A.. J Pharm Sci.,
submitted
A very different equation when compared to:
A pseudo-rate equation can be derived from the equation for %of Absorbed Dose
log{ln[100/(100-%Abs.)]} = 0.54 - 0.025 E + 0.14 S - 0.41 A - 0.51 B + 0.20Vx
n = 127 r2 = 0.80 SD = 0.29 F = 94
Zhao YH, Le J, Abraham MH, Hersey A, Eddershaw PJ, Luscombe
CN, Butina D, Beck G, Sherborne B, Cooper I, Platts,J.A.. J Pharm Sci.,
submitted
Solvation equation for rate of uptake into C18 extraction disc
logkup = -5.34 + 0.08 E + 0.20 S - 0.08 A - 0.28 B + 0.33 Vx
n=21 r2=0.95
sd=0.08 F=30
A very similar equation to:
A pseudo-rate equation can be derived from the equation for %of Absorbed Dose
log{ln[100/(100-%Abs.)]} = 0.54 - 0.025 E + 0.14 S - 0.41 A - 0.51 B + 0.20Vx
n = 127 r2 = 0.80 SD = 0.29 F = 94
Zhao YH, Le J, Abraham MH, Hersey A, Eddershaw PJ, Luscombe
CN, Butina D, Beck G, Sherborne B, Cooper I, Platts,J.A.. J Pharm Sci.,
submitted
Cell Permeability Models
logPapp (CaCo2) = - 4.4 - 0.20 E + 0.26 S - 1.27 A - 0.24 B + 0.09Vx
logPapp (MDCK) = 4.3 + 0.10 E + 0.19 S - 1.73 A - 0.79 B - 0.17Vx
Similar but not identical to:
A pseudo-rate equation can be derived from the equation for %of Absorbed Dose
log{ln[100/(100-%Abs.)]} = 0.54 - 0.025 E + 0.14 S - 0.41 A - 0.51 B + 0.20Vx
n = 127 r2 = 0.80 SD = 0.29 F = 94
Zhao YH, Le J, Abraham MH, Hersey A, Eddershaw PJ, Luscombe
CN, Butina D, Beck G, Sherborne B, Cooper I, Platts,J.A.. J Pharm Sci.,
submitted
Wohnsland and Faller, J. Med. Chem. 2001, 44, 923 - 930
Artificial Alkane/Water Membranes
Figure 4 pH-dependent permeability of ionizable compounds: (a) diclofenac (acidic pKa = 4.0),
(b) desipramine (basic pKa = 10.6), and determination of their permeabilities through the
unstirred water layer: (c) diclofenac; (d) desipramine.
Wohnsland and Faller, J. Med. Chem. 2001, 44, 923 - 930
Artificial Alkane/Water Membranes
 They analyse their data based on two transport processes that contribute to
effective measured membrane permeability Pe (I.e. Intrinsic membrane
permeability Po and permeability through an unstirred water layer Pul)
 Relative contributions from Po and Pul were deduced from pH Permeability profiles
and using literature values for aqueous diffusion coefficients, they estimate the
thickness of the unstirred layer
 They demonstrate that intrinsic permeabilities are directly proportional to the
alkane/water partition coefficients
 The estimated thickness of the unstirred layer in their model was 300mm and they
quote estimates of 1500mm in the CACO2 model and 50mm in-vivo in the GI tract
 Are their assumptions correct? They ignore diffusion across the interface and
assume that diffusion rates are the same for all molecules
Mechanistic Inferences from the Different Data
Types
 The different types of information (measured properties, experimental
permeability models, and calculated Abraham parameters) are consistent
with the idea that human intestinal absorption and permeability models
involve similar processes
 Diffusion across the membrane interface (across the unstirred water layer?)
is often the step that controls the overall permeability
 Molecular diffusion rates and interfacial transfer rates are significantly
slowed by the presence of polar functionality and hydrogen bonding
interactions but appear to be relatively insensitive to ionisaton of acidic and
basic groups
 General empirical QSAR models for intestinal absorption are possible based
on a diffusion controlled process. They will produce high estimates when
other mechanisms become rate limiting (e.g. solubility and dissolution,
active efflux, low intrinsic membrane affinity)
Where to next? What should we measure?
 Direct measurement of diffusion rates of molecules (in free solution and at
interfaces)?
– What are the QSAR relationships (e.g. Abraham Solvation Equations)?
 Overall bioavailability (I.e.not just intestinal absorption) is the key parameter
in candidate selection. In general increasing lipophilicity of drugs tends to
increase their susceptibility to metabolism
– What are the specific QSAR relationships for partition and rate of uptake
into liver? What would this tell us about the mechanisms of uptake and
penetration to the sites of metabolism?
Collaborators and Co-workers
 University College London
– Mike Abraham, Chau My Du, James Platts, Yuan Zhao, Joelle Le
 BMG LabTechnologies
– Derek Patton, Monika Siggelkow
 Sirius
– John Comer, Brett Hughes, Karl Box, Kirsty Powell, Kin Tam,
Paul Hosking, Roger Allen, Lynne Trowbridge, Colin Peake
 GlaxoWellcome
– CLOP- Mike Tarbit, Om Dhingra, Mark Patrick, Lori Takahashi
– Rachel Thornley, Anne Hersey, Darko Butina, John Hollerton,
Keith Brinded, Ian Mutton
– Chris Bevan, Alan Hill, Klara Valko, Pat McDonough