Post on 16-Dec-2015
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A BIT OF ADME FOR EVERYONE:
LogD, SOLUBILITY, PERMEABILITY
AND
THE UNIVERSAL DETECTION SYSTEM
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OUTLINE
• ADMET in modern discovery• A brief refresher:
– LogD, Solubility, Permeability– Issues, methods, tradeoffs
• More interesting ADME assays:– Caco/PAMPA-less BBB permeability
• The Universal Detection System - UDS
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The Miracle of a Drug
1 Inducible2 Active transport increases permeability of some compounds3 Intestinal re-uptake of some compounds4 Drug-drug interaction potential
Stomach Intestine
Stability pH 2 enzymatic
Solubility pH 3-8
Stability pH 3-8 enzymatic
StabilityPhase I4
Organ Cell
Permeability2
Passive Pgp Efflux4
(Organ Specific)
Permeability2
Passive Pgp Efflux4
CellularPartitioning
Distribution(whole body)
Blood
Permeability2
(epithelium) Passive pH 3-8 Pgp Efflux4
StabilityEnzymatic
ProteinBinding4
Stability1
Phase I4
Phase II
Biliary Clearance3
UrinaryClearance
Portal Vein
LiverKidney
After Susan PetuskyWyeth Research
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ADME in Discovery: Many Strategies (1)
Purity
LogD
Solubility
Permeability
Sequential
•Data integrity
• Compound conservation
• Too slow
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ADME in Discovery: Many Strategies (2)
Purity
LogD
Solubility
Permeability
Weighted Score
• Holistic
Metabolism
Protein binding
2.2
10-5
30%
99%
85%
10
0.85
0.9
0.75
0.5
4
1
5
2
3.4
0.9
3.75
1.0 9.05
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LogP/LogD (1)
• Equilibrium partition coefficient between 1-octanol/buffer
• LogP – non-ionized compound• LogD – ionized compound – f(pH)
• Good Absorption characteristics:– LogP ~ 2.5
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Lipophilicity in Absorption
Navia MA, et.al., DDT 1, (5) May 1996
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LogP/LogD (2)
• LogD > 5:– Tough to measure– Promiscuous binders– Poor solubility, oral absorption– Strong CYP450 interaction
• LogD 0-3:– Best balance of solubility, permeability
• LogD < 0:– Good solubility, poor permeability
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Measuring LogD
• The real thing: Shake flask method• RP HPLC:
– See K. Valko, J. Chrom. Sci, 1037 (2004)
• pH-metric• Microemulsion electrokinetic
chromatography• Calculating:
– ACD LogP– CLogP– PrologD
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A Brief Reminder: LogD is Difficult
Buffer Composition pH LogD0.01 M Universal Buffer1 7.4 0.015 0.0090.01 M K-Phosphate Buffer 7.4 0.010 0.0070.01 M Na-Phosphate Buffer + 0.15 M NaCl 7.4 0.632 0.0090.01 M Universal Buffer1 + 0.15 M NaCl 7.4 0.697 0.0110.10 M K-Phosphate Buffer 7.4 0.981 0.0080.01 M Universal Buffer1 11.0 1.147 0.0040.10 M K-Phosphate Buffer 11.0 1.530 0.0020.07 M Na-Bicarbonate Buffer 11.0 2.440 0.010
Compound: Propranolol1 – Universal buffer is composed of a mixture of acetic, phosphoric, and boric acids with NaOH
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ADW:LogD Assay
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Validation: LogD Accuracy
LogDADW = 0.002(± 0.008) + 1.011(± 0.005)*LogDmanual
N = 179; r2 = 0.9960; standard error of estimate = 0.1022
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A Simple Concept
• Solubility = Concentration of a dissolved compound in equilibrium with its solid
• But:– Which solid?
• Equilibrium (most stable form) vs. apparent (other forms)
– Which solvent?• Buffers (intrinsic?) and co-solvents (kinetic)
– Which equilibrium?• Time (kinetic) and temperature
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Key Differentiating Factor
Elemental Concentration:CarbonNitrogen
...
Static Light Scattering
Absorbance
Evaporative LightScattering
No standards / calibration
No standards / calibration
Compound specific
• Are you measuring the actual concentration?
Elemental:Yes, directly
Absorbance:Yes, indirectly
Turbidity:No, solubility is inferred from dilution factor off a standard
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Saturated solutionsPhosphate buffer, pH 111 week incubation
Left to Right:
ChlorpromazineHCl Bendroflumethiazide Clofazimine Bifonazole ThioridazineHCl TriflupromazineHCl Nifedipine Perphenazine PromazineHCl
Can You Tell The Saturated Solution?
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Assay Effects: Particles
• Solid particles are an integral part of the solubility assay– Particles are always present– They must be present for turbidity to work– They are artifacts in absorbance/elemental
assays
• The effects of particles on the data must be considered when examining the data– Subtle to substantial influence on quality of
results
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Permeability: The Real Thing
Lipid bilayer (10,000,000 X)
“The Machinery of Life”David GoodsellCopernicus (Springer-Verlag)
Gap junction (1,000,000 X)
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Two Choices
Measure average
concentrations
Bilayer chemistry Transport physics Experimental details
Calculate permeability
Experimental database
Structural parameters
Establish relationship
Measure structural
parameters
Plug into relationship
Calculate permeability
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PAMPA: Stirring + Acceptor Sink
Drug Scavenger Drug + Scavenger
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BBB Permeability:An Alternative Approach*
• Instead of:– Trying to find the “ultimate” membrane model– Deciphering permeability from a complex
experiment
• Use real-world in vivo data + structural descriptors:– What prediction level is necessary?– Which descriptors are useful?
*Gulyaeva et. al, EJMC 38 (2003)
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Data for BBB Permeation
# CNS Compound
1 - Acyclovir
2 - Acetaminophen
3 - Albendazole
4 - Albuterol
5 - Ampicillin
6 - Antipyrine
7 - Astemizole 2HCl
8 - Atenolol
9 - Cimetidine
10 -* Domperidone HCl
11 - Ebastine
12 - 5-Fluorouracil
13 - Ftorafur
14 - Furosemide
15 - 5-Hydroxytryptophan
e
16 - Iproniazid
17 - Metoclopramide
18 - Metoprolol
19 - Metronidazole
20 - Phenelzine
21 - Pirenzepine HCl
# CNS Compound
23 - Tiapride
24 + Amitriptiline HCl
25 + Chlorpromazine HCl
26 + Clomipramine
27 + Clonidine HCl
28 + Desipramine HCl
29 + Doxepin
30 + Doxylamine succinate
31 + Estrone
32 + Fluoxetine HCl
33 + Flupentixol 2HCl
34 + Fluphenazine
35 + Haloperidole HCl
36 + Homochlorcyclizine
37 + Hydroxyzine 2HCl
38 + Ibuprofen
39 + Imipramine HCl
40 + Indomethacin
41 + Lidocaine HCl
42 +* Loperamide HCl
# CNS Compound
44 + Mequitazine HCl
45 + Minaprine
46 + Mefexamide
47 + Naltrexone HCl
48 + Naloxone
49 + Nortriptyline
50 + Perphenazine 2HCl
51 + Physostigmine
52 + Progesterone
53 + Promazine HCl
54 + Promethazine HCl
55 + Propranolol HCl
56 + Protriptyline
57 + Pyrilamine
58 + Thioridazine HCl
59 + Tranylcypromine
60 + Trazodone
61 + Trifluoperazine
62 + Triflupromazine
63 + Trimipramine
+: logBB > 0.3--: logBB < -1.0
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Structural Descriptors (1)
• Basic Premise:– Only structural differences matter – since
compounds are predominantly different by structure
• Key Methodology:– Ask for minimally acceptable answer:
• Must you know a number or will a classification suffice?
– Use smallest number of descriptors:• Principle of parsimony
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Structural Descriptors (2)
• LogD(7.4):– Measure of relative affinity between polar
and non-polar media– Obtained from experiments or calculations
• N(CH2):– Measure of relative affinity between two
aqueous solvents of different structures– Obtained using aqueous two-phase
partitioning experiments (Dex-PEG systems)
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• Predict what?– Probability of a compound being CNS+
• Predict how?– Logistic regression model using penalized maximum
likelihood (nested models) or information criteria (non-nested models)
• Predictive accuracy:– 96.6% using cross validation procedures (one point
out of the model for all 63 data points)
Predictive Equation
215.04 12.56*log 0.4* * ( )
( ) exp( ) /(1 exp( ))
A D LogD N CH
P CNS A A
=- + -
+ = +
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P(CNS+)
-20
0
20
40
60
80
100
120
0
1
2
3
4
5
05
1015
2025
P(C
NS
+)
LogD
(7.4
)
N(CH2)
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Typical Big Pharma - ANALIZA Collaboration
• LogD (pH =7.4)• Solubility (pH = 6.5)• Thermodynamic solubility (dry)
• Over 21,000 data points in 2005
• Real data: high throughput, miniaturized shake flask methods
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1 Year Precision Data: Solubility Controls
0
100
200
300
400
500
600
12/29/2004 2/17/2005 4/8/2005 5/28/2005 7/17/2005 9/5/2005 10/25/2005 12/14/2005
Assay Date
uM
harmine imipramine sulfamethizole
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1 Year Precision: LogD Controls
-3
-2
-1
0
1
2
3
4
1/3/2005 2/22/2005 4/13/2005 6/2/2005 7/22/2005 9/10/2005 10/30/2005 12/19/2005
log
D
harmine imipramine sulfamethizole
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1 Year Statistics
LogD Solubility
Plate TotalAggreg. rate, %
Total failure rate, %
Net failure rate, %
Net failure rate, %
A 3,811 5.8 6.1 0.3 0.7
B 3,781 6.4 6.9 0.5 0.7
C 2,758 2.5 3.0 0.5
D 2,833 5.6 6.3 0.7
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Universal Detection System – UDS
Overview
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The Need
• We have automated ID solutions: MS, etc.• Pharmaceutical compounds in
discovery/development are impure:– Combichem libraries are 90-95% pure– Compounds synthesized during LD are 90+% pure– Serious implications for:
• Early ADME evaluation (e.g., solubility)• Late selection and lead optimization• Process development and FDA submissions
• Impurity quantification is a vexing problem:– Impurity has to be separated– Standards must be prepared– Very difficult in practice!
• Highly desirable:– Automated quantification method for all impurities in
a sample w/o standards
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Unique Advantages
• Orthogonal detection methodology to Ultra Violet (UV) or Mass Spectrometry (MS)
• Equimolar detection for instrument universal calibration curve
• Wide applicability to most pharmaceutical compounds (93% +)
• Large intrinsic dynamic range (ca. 0.1-50,000 ppm nitrogen)
• Complete automation:– On-board separation capabilities with individual peak
concentrations– Peak zoom capability to focus on assaying of minor
peaks– Automated optimization with variable instrument
gain and injection volume
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UDS Architecture
Agilent 1100PDA
Antek 8060
AgilentChemStation
AnalizaNDCS
Agilent 35900EADC
Sample
N Gain, AZ
N s
igna
l(a
nalo
g)
N ra
w
data
PD
A
data Analiza CTRL
PC
Other Agilent1100 modules
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Development System – Hardware
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Development System – SW
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UDS Operating Modes
• Calibration– Construct universal piece-wise linear curve
• Quantification– Noise A(gressive)– Noice C(onservative)– P(arent) P(eak) G(ain)– P(arent) P(eak) A(rea)– O(ptimize) V(olume)
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UDS Sequence Table – After Run
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Universal Calibration Example
Area Under The Curve
# mg/ml ppmN Inj. #1 Inj. #2 Inj. #3 Avg.
1 0.0005 0.144 113 114 96 108
2 0.001 0.289 198 163 171 177
3 0.0025 0.721 389 399 393 394
4 0.005 1.443 802 803 786 797
5 0.01 2.886 1617 1629 1608 1618
6 0.025 7.214 4284 4283 4255 4274
7 0.05 14.428 8871 8818 8866 8852
8 0.1 28.856 18549 18502 18340 18463
9 0.25 72.141 47908 47484 47484 47625
10 0.5 144.281 97254 96869 95795 96639
11 1 288.563 193570 193361 193109 193347
12 2.5 721.407 460138 462725 466960 463274
13 5 1442.814 849204 841916 852793 847971
14 10 2885.627 1494800 1486750 1476160 1485903
ppm Nitrogen
0.1 1 10 100 1000 10000
Are
a U
nder
the
Cur
ve
1e+1
1e+2
1e+3
1e+4
1e+5
1e+6
1e+7
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Universal Calibration – UDS Screen
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UDS Performance Tests
Sample S mg/ml ppmN Inj. #1
Inj. #2
Inj. #3
Inj. #4
Avg. %
AC 0.025:0.0
5
A 0.025 2.3163 2.269 2.267 2.31 2.284 2.28 -1.5
C 0.05 14.4281
14.79 14.78 14.89 14.77 14.81 2.6
AC 0.01:1 A 0.0083 0.7721 0.723 0.668 0.712 0.703 0.70 -9.1
C 0.8333 240.47 240.7 238.25 239.32 238 239.07 -0.6
AC 0.1:10 A 0.09804
9.0835 9.362 9.358 9.265 9.26 9.31 2.5
C 9.8039 2829.04
2881.7 2892.7 2889.8 2869.6 2883.4 1.9
A – Acetaminophene C - Caffeine
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UDS Report Screen
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Thank You