Transcript Rapid Physicochemical Profiling as an Aid to Drug
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
-4 -3 Measured vs c logD s for 434 compounds y = 1.0926x - 0.6948
R 2 = 0.4075
8 6 4 2 -2 -1 0 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 in vitro 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 HPLC T.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 I 2 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
100.00
80.00
60.00
40.00
20.00
0.00
1.4
1.9
y = 54.329x - 71.702
2.4
R 2 = 0.9972
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 = E C + v(e’ E + s’ S + a’ A + b’ B + V x ) - excess molar refraction term, normalised to alkanes S - solute dipolarity/polarisability descriptor A - solute hydrogen bond acidity descriptor B V x - solute hydrogen bond basicity descriptor - McGowan characteristic volume
Systems LogP hexadecane CHI ACN CHI MeOH LogP oct CHI IAM 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 (CHI ACN ) CHI ACN
=
7.1
+
0.41
E -
1.06
S -
1.59
A -
4.88
B +
4.8
Vx
Luna C-18 column with trifluoroethanol gradient (CHI TFE ) CHI TFE
=
6.9
+
0.67
E - 1.96S -
3.1
A - 3.94B +
5.67
Vx
Polymer C-18 column with acetonitrile gradient (CHI PLRP ) CHI PLRP
=
8.19
- 0.41E -
0.44
S -
2.50
A -
5.64
B +
4.38
Vx
DevelosilCN column with methanol gradient (CHI CN-MeOH )
CHI CN-MeOH
=
3.93
+
0.79
E -
1.05
S -
0.72
A -
4.5
B -
5.42
Vx
DevelosilCN column with acetonitrile gradient (CHI CN-AcN ) CHI CN-AcN
=
5.67
+
0.2
E - 0
.28
S -
0.55
A -
4.15
B +
3.68
Vx
Fluorooctyl column with trifluoroethanol (CHI FO-TFE ) CHI FO-TFE
=
7.45
- 0.12E -
0.57
S -
3.67
A -
1.89
B +
3.11
Vx
Lipophilicity and Solubility are pK a -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 pK a 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 pK a values
The goal:
–
>96 samples per day
– – –
pK a 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 pK a measurement
–
Spectroscopic measurement technique
–
Commercial instrument launched 1Q 2000
Sirius pKa Profiler
Calibrating GT with standard compounds Calibration Curve for Standard pK a Values 12
abs x10+2
10
0.02581
0.01595
0.00609
8
-0.00377
-0.01363
6
0
-0.02349
-0.03336
4
-0.04322
2 20
-0.05308
30.0
30
y = -0.0653x + 12.394
R 2 = 0.9959
40
Benzoic acid Phthalic acid Nitrophenol Chlorophenol Phenol 41.5
50
53.0
60
64.5
pK a 3.978
pK a 4.843
pK a 6.973
pK a 9.240
pK a 9.796
70
76.0
87.5
80 90 Time (secs/2)
99.0
100
110.5
110
122.0
120 130
133.5
140
145.0
P oints
150
Five standards. First derivative peak maxima correlated with pH-metric pK a 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 80 60 40 20 0 1 10 50 100 MDCK P app (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
1 4 0 1 2 0 1 0 0 8 0 6 0 4 0 2 0 0 5 2 5 4 5
M e a s u r e d
6 5 8 5 1 0 5
Solvation equation for oral absorption % Abs = 92 + 2.9
E + 4.1
S 21.7
A 21.1
B + 10.5
V x
n=170 r 2 =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 + 2.9
E + 4.1
S 21.7
A 21.1
B + 10.5
V x
n=170 r 2 =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 + 2.9
E + 4.1
S 21.7
A 21.1
B + 10.5
V x
100 80 60 40 20 0 -20 0 20 40
Observed
60 80 100 Training set
Drugs 229-241 Low solubility Dose dependant
Solvation equation for oral absorption % Abs = 92 + 2.9
E + 4.1
S 21.7
A 21.1
B + 10.5
V x
n=170 r 2 =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 + 0.20
V x n = 127, r 2 = 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 + 0.20
V x
n = 127 r 2 = 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) logk oct = 0.088
+ 0.562 E – 1.054
S + 0.034
A 3.46
B + 3.814
V x 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 + 0.20
V x
n = 127 r 2 = 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 + 0.20
V x
n = 127 r 2 = 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 logk up = -5.34
+ 0.08 E + 0.20 S 0.08 A 0.28 B + 0.33 V x
n=21 r 2 =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 + 0.20
V x
n = 127 r 2 = 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 logP app (CaCo2) = - 4.4
0.20
E + 0.26
S 1.27
A 0.24 B + 0.09
V x logP app (MDCK) = 4.3
+ 0.10
E + 0.19
S 1.73 A 0.79
B 0 .
17 V x 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 + 0.20
V x
n = 127 r 2 = 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 p
K
a (b) desipramine (basic p
K
a = 10.6), and determination of their permeabilities through the = 4.0), 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 P e permeability P o (I.e. Intrinsic membrane and permeability through an unstirred water layer P ul ) Relative contributions from P o and P ul 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 300m m and they quote estimates of 1500 m m in the CACO2 model and 50m m 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