Transcript Overview of Drug Testing Methods I: ADME - BIDD

CZ3253: Computer Aided Drug design
Lecture 10: Overview of Drug Testing Methods I:
ADME Test
Prof. Chen Yu Zong
Tel: 6874-6877
Email: [email protected]
http://xin.cz3.nus.edu.sg
Room 07-24, level 7, SOC1,
National University of Singapore
Flow of information in a
drug discovery pipeline
Bioinformatics
Toxicity
Computational and Combinatorial
Chemisty
2
Predictive ADME
Absorption
Distribution
Metabolism
Elimination
Pharmacokinetic
Bioavailability
3
Why is the prediction of ADME parameters
so important ?
reasons that cause the failure of a potential drug candidate
4
Bioavailablity of Drugs (I)
5
Bioavailability of Drugs (II)
Uptake of orally administered drug proceeds after the
stomach passage via the small intestine.
In the liver, a series of metabolic transformation occurs.
6
1. What Is Absorption?
“the drug passing from the lumen into the tissue of the GIT” (Sietsema)
Human Intestinal
Absorption (HIA)
1,2 – Stability + Solubility
3 – Passive + Active Tr.
4 – Pgp efflux + CYP 3A4
5 – 1st Pass in liver
Oral Bioavailability (%F)
Frequently HIA is confused with either “Passive Absorption or “Oral %F”7
Different “Absorption Types”
Passive Absorption (PA)
Passive transport across intestinal membrane in vivo
PA = f (PeIntestine)
Absorbed Fraction (FAbs)
Theoretical
concepts
“Passive Absorption” that depends on solubility (SW):
FAbs = f (PeIntestine, SW)
Human Intestinal Absorption (HIA)
HIA = f (PeIntestine, SW, AT, Pgp efflux, gut 1st pass)
Human Oral Bioavailability (%F)
Exp. values
from in vivo
tests
%F = f (HIA, liver 1st pass)
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HIA is usually measured as the ratio of cumulative
urinary excretion of drug-related material following
oral and intravenous administrations:
%HIA = (ExcrOral / ExcrIV )URINE
Urine Excretion
How They Are Measured?
%HIA
ExcrIV
ExcrOral
Hours
%F is the ratio of cumulative plasma concentrations
after oral and intravenous administrations:
%F = (AUCOral / AUCIV )PLASMA
All in vivo data types are poorly reproducible
(dosing, formulation, physiology)
Plasma Conc.
This method is not always applicable (e.g., when biliary excretion interferes).
Oral %F
AUCIV
AUCOral
Hours
Most HIA and %F values are qualitative
9
How They Are Predicted?
1. “Direct” informatics
Structure  “Absorption = f (PeIntestine in vivo)” -- SARs or QSARs
%F = f (Pe , SW, 1st Pass, etc.) – “knowledge bases”
In vitro tests are not used, SW is frequently ignored
2. “Pe + SW ” simulations
Structure  FAbs using in vitro Pe and SW tests
3. “PB - PK” simulations
Structure  %F using a wide array of in vitro tests:
Kinetic dissolution rates, various types of polarized
transport, metabolic stability tests, PBP, etc.
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How They Are Predicted?
4. Statistical Learning Methods
Structure  Molecular Descriptors
Training of Prediction System  Trained using samples of
absorption and non-absorption compounds
Like any other statistical learning method, prediction accuracy
dependent on the diversity and representativity of training data
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Which Methods to Use and When?
Conventional
approach:
New approach:
Computational
Drug Development
Informatics
Maximum FAbs
Oral %F from CP
“ Pe + S W
“
“PB - PK”
Accuracy & Relevance
In reality various types of simulations can be used during the
earliest development stages
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QSAR-Based Methods
“One-step” models using ANNs or PLS:
f (%HIA or %F) = ao +  ai xi
“One-step” kinetic scheme:
ka
"Constant Dose"
Plasma
Correlations by Jurs, Oprea, and many others:
• Did not clearly verify possible dependences on a
compound’s dose, stability, solubility, AT or 1st pass
• Used incorrect functions of %HIA or %F values
• Used “abstract” descriptors that rely on statistics
(rather than knowledge)
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Example of QSAR Model
1. Considered “passive absorption” only
2. Used good physicochemical descriptors
%HIA
=
Incorrect function
(calc. HIA may
exceed 100%)
92 – 22  – 21 
H-Bonding,
f (TPSA)
+ 11 V
Size,
f (MW)
+3E+4
Polarity –
polarizability
Qualitative agreement
with C-SAR models
+ 0 f (pKa)
~ Zero effect
of ionization
Deserves
attention!
Not everything is “perfect”, but the results are much
more useful than from any other QSAR works
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Rule-Based Methods
Simple rules using “data mining”, PCA, or recursive partitioning:
Lipinski
> 2,000 compounds that passed 2nd phase of clinical trials
Absorbable if MW  500, log P  5, (OH + NH)  5, (O + N)  10
Veber
> 1,000 compounds with rat %F
Absorbable if PSA  140 A2, (O+N+OH+NH)  12,
Rot-Bonds  10
Deserve
criticism
AB/ADME Boxes
> 800 compounds with exp %HIA (passive absorption only)
(independently – SW, AT, Pgp, 1st Pass, and Oral %F)
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Example Of Rule-Based Predictions
Three types of passive absorption considered:
Large natural compounds
Traditional leads
“Very small”
molecules
A very rough approximation:
IF (MW < 250 OR MW < 580 AND TPSA < 150) THEN “POSITIVE”
IF (MW < 580 AND TPSA > 150 OR TPSA > 290) THEN “NEGATIVE”
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Generalization of the Rules:
The “Rule of Five” Formulation for Drug-Like Molecules
Poor absorption or permeation are
more likely when:
•
•
•
•
There are more than 5 H-bond donors.
The molecular weight is over 500.
The LogP is over 5.
There are more than 10 H-bond
acceptors.
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Exception to the rule of five
Compound classes that are substrates
for biological transporters:
• Antibiotics
• Fungicides-Protozoacides antiseptics
• Vitamins
• Cardiac glycosides.
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Computational calculations for new chemical entities
• Applied to entities introduced between 1990-1993
• Average values:
– MlogP=1.80
– H-bond donor sum=2.53
– Molecular weight =408
– H-bond acceptor sum=6.95
• Alerts for possible poor absorption-12%
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Intrinsic Limitations
No matter how good our
rules are, “marginal”
compounds will create
false predictions
Qualitative rules cannot
accurately model
continuous processes
We must also know probabilities that our rules will be obeyed
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SVM Prediction System for HIA
J. Chem. Inf. Comput. Sci. 44,1630-1638 (2004)
Molecular Descriptors Important for HIA
Descriptor Class
Simple molecular connectivity Chi indices for cycle of 5 atoms
Connectivity
valence molecular connectivity Chi indices for cycle of 5 atoms
Connectivity
Atom-type H Estate sum for CH n (unsaturated)
Electro-topological state
Atom-type Estate sum for -CH 3
Electro-topological state
Atom-type Estate sum for =C<
Electro-topological state
Polarizability index
Quantum chemical properties
Number of H-bond donors
Simple molecular properties
Atom-type H Estate sum for -OH
Electro-topological state
Atom-type Estate sum for =CH-
Electro-topological state
Valence molecular connectivity Chi indices for cluster
Connectivity
Simple molecular connectivity Chi indices for cycle of 6 atoms
Connectivity
Atom-type H Estate sum for > NH
Electro-topological state
Atom-type H Estate sum for :CH: (sp2, aromatic)
Electro-topological state
Atom-type Estate sum for : C:-
Electro-topological state
Atom-type Estate sum for >NH
Electro-topological state
Atom-type Estate sum for :N:
Electro-topological state
Sum of solvent accessible surface areas of negatively charged atoms
Geometrical properties
Sum of charge weighted solvent accessible surface areas of negatively charged atoms
Geometrical properties
Length vectors (longest distance of 4th atom)
Geometrical properties
Simple molecular connectivity Chi index for path order 2
Connectivity
Simple molecular connectivity Chi indices for cluster
Connectivity
valence molecular connectivity Chi indices for cycle of 6 atoms
Connectivity
Atom-type Estate sum for =N-
Electro-topological state
Atom-type Estate sum for -OH
Electro-topological state
Atom-type Estate sum for =O
Electro-topological state
Hydrogen bond donor acidity (covalent HBDA)
Quantum chemical properties
Electron affinity
Quantum chemical properties
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Prediction Accuracy Measurement
• Common measure (other measures also exist)
• Sensitivity SE=TP/(TP+FN)
• Specificity SP=TN/(TN+FP)
•
•
•
•
•
•
For example, prediction of binding peptides to a particular receptor
Experimental
Predicted
Class
Example 1 Binder
Binder
True positive (TP)
Example 2 Non-binder
Non-binder
True negative (TN)
Example 3 Binder
Non-binder
False negative (FN)
Example 4 Non-binder
Binder
False positive (FP)
• Prediction system that has SE=0.8 and SP=0.9 will correctly predict
8 of 10 experimental positives, and for each 10 experimental
negatives it will make one false prediction. This prediction accuracy
may be very good for prediction of peptide binding, but is not very
good for some other predictions, for example gene prediction.
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SVM Prediction Results
Cross
validation
HIA+
HIA-
TP
FN
SE (%)
TN
FP
SP (%)
1
22
5
81.5
10
2
83.3
2
20
1
95.2
11
0
100.0
3
35
5
87.5
8
4
66.7
4
18
2
90.0
10
5
66.7
5
22
1
95.7
13
2
86.7
Average
90.0
80.7
J. Chem. Inf. Comput. Sci. 44,1630-1638 (2004)
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Cytochrome P450
The super-family of cytochrome P450 enzymes has a
crucial role in the metabolism of drugs.
Almost every drug is processed by some of these enzymes.
This causes a reduced bioavailability.
Cytochrome P450 enzymes show extensive structural
polymorphism (differences in the coding region).
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Cytochrome P450 metabolisms (I)
During first liver passage: First pass effect
extensive chemical transformation of lipophilic or heavy
(MW >500) compounds. They become more hydrophilic
(increased water solubility) and are therefore easier to
excreat.
H
O
CH3
COOH
phase I
N
COOH
phase II
Predominantly cytochrome P450 (CYP) enzymes are
responsible for the reactions belonging to phase I.
Usually, the reaction is a monooxygenation.
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Cytochrome P450 Metabolisms (II)
The substrates are monooxygenated in a catalytic cycle.
Drug-R + O2
CYP
NADPH
Drug-OR + H2O
NADP
The iron is part of a HEM moiety
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Cytochrome P450 Metabolisms (III)
The cytochromes involved in the metabolism are mainly
monooxygenases that evolved from the steroid and fatty
acid biosynthesis.
So far, 17 families of CYPs with about 50 isoforms have
been characterized in the human genome.
classification:
CYP 3 A 4 *15 A-B
family
isoenzyme allel
>40% sequencesub-family
homology
>55% sequencehomology
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Cytochrome P450 gene families
Human 14+
Molluscs 1
CYP450
Plants 22
Insects 3
Fungi 11
Bacteria 18
Yeasts 2
Nematodes 3
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Human cytochrome P450 family
Of the super-family of all cytochromes, the following families
were confirmed in humans:
CYP 1-5, 7, 8, 11, 17, 19, 21, 24, 26, 27, 39, 46, 51
Function:
CYP 1, 2A, 2B, 2C, 2D, 2E, 3
metabolism of xenobiotics
CYP 2G1, 7, 8B1, 11, 17, 19, 21, 27A1, 46, 51 steroid
metabolism
CYP 2J2, 4, 5, 8A1
fatty acid metabolism
CYP 24 (vitamine D), 26 (retinoic acid), 27B1 (vitamine D), ...
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Cytochrome P450 enzymes (I)
Flavin Monooxygenase Isoenzyme
Alkohol Dehydrogenase
Aldehyd Oxidase
Monoamin Dehydrogenase (MAO)
Drug-R + O2
CYP
NADPH
The redox activity is
mediated by an iron
porphyrin in the active
center
Drug-OR + H2O
NADP
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Cytochrome P450 enzymes (II)
Despite the low sequence identity between CYPs from
different organisms, the tertiary structure is highy
conserved.
Superposition of
hCYP 2C9 (1OG5.pdb) and
CYP 450 BM3 (2BMH.pdb)
Bacillus megaterium
In contrast to bacterial CYPs, the microsomal mammalian CYPs
possess an additional transmembrane helix that serves as an
anchor in the membrane
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Cytochrome P450 enzymes (III)
The structures of several mammalian CYPs have now
been determined in atomistic detail and are available
from the Brookhaven Database:
http://www.pdb.mdc-berlin.de/pdb/
1DT6.pdb CYP 2C5 rabbit
Sep 2000
1OG5.pdb CYP 2C9 human
Jul 2003
1PO5.pdb CYP 2B4 rabbit
Oct 2003
1PQ2.pdb CYP 2C8 human
Jan 2004
They are suitable templates for deriving homology
models of further CYPs
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Cytochrome P450 enzymes (IV)
The majority of CYPs is found in the liver, but certain CYPs
are also present in the wall cells of the inestine
The mammalian CYPs are bound to the endoplasmic reticulum,
and are therefore membrane bound.
CYP 2D6
2%
CYP 2A6
4%
CYP distribution
other
7%
CYP 3
31%
CYP 1A2
13%
CYP 1A6
8%
CYP 2C6
6%
CYP 2E1
13%
CYP 2C11
16%
CYP 3
CYP 2C11
CYP 2E1
CYP 2C6
CYP 1A6
CYP 1A2
CYP 2A6
CYP 2D6
other
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Cytochrome P450 enzymes (V)
Especially CYP 3A4, CYP 2D6, and CYP 2C9 are involved in
the metabolism of xenobiotics and drugs.
Metabolic Contribution
hepatic only
CYP 2C9
10%
CYP 1A2 other
2%
3%
CYP 3A4
CYP 2D6
CYP 2C9
CYP 1A2
other
CYP 3A4
55%
CYP 2D6
30%
also small intestine
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Substrate specificity of CYPs (I)
specific substrates of particular human CYPs
CYP 1A2
verapamil, imipramine, amitryptiline,
caffeine (arylamine N-oxidation)
CYP 2A6
nicotine
CYP 2B6
cyclophosphamid
CYP 2C9
diclofenac, naproxen, piroxicam, warfarin
CYP 2C19
diazepam, omeprazole, propanolol
CYP 2D6
amitryptiline, captopril, codeine,
mianserin, chlorpromazine
CYP 2E1
dapsone, ethanol, halothane, paracetamol
CYP 3A4
alprazolam, cisapride, terfenadine, ...
see also http://medicine.iupui.edu/flockhart/
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Substrate specificity of CYPs (II)
Decision tree for human P450 substrates
CYP 1A2, CYP 2A-E, CYP 3A4
CYP 2E1
CYP 2C9
low
Volume
high
medium
acidic
basic
pK
a
CYP 3A4
CYP 2D6
neutral
CYP 1A2, CYP 2A, 2B
CYP 2B6
low
planarity
high
CYP 1A2
medium
CYP 2A6
Lit: D.F.V. Lewis Biochem. Pharmacol. 60 (2000) 293
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Cytochrome P450 polymorphisms
„Every human differs (more or less) “
The phenotype can be distinguished by the actual
activity or the amount of the expressed CYP enzyme.
The genotype, however, is determined by the individual
DNA sequence. Human: two sets of chromosomes
That means: The same genotype enables different
phenotypes
Depending on the metabolic activity, three major catagories of
metabolizers are separated: extensive metabolizer (normal),
poor metabolizer, and ultra-rapid metabolizer (increased
metabolism of xenobiotics)
Lit: K. Nagata et al. Drug Metabol. Pharmacokin 3 (2002) 167
37
CYP 2D6 Polymorphism (I)
The polymorphisms of CYP 2D6 has been studied
in great detail, as metabolic differences have first
been described for certain antipsychotics
Localized on chromosome 22
Of the 75 allels, 26 are associated with adverse effects
see http://www.imm.ki.se/CYPalleles/cyp2d6.htm
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CYP 2D6 Polymorphism (II)
Lit: J. van der Weide et al. Ann. Clin. Biochem 36 (1999) 722
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CYP 2D6 Polymorphism (III)
MGLEALVPLAVIVAIFLLLVDLMHRRQRWAARYPPGPLPLPGLGNLLHVDFQNTPYCFDQ
poor debrisoquine metabolism S
R impaired mechanism of sparteine
LRRRFGDVFSLQLAWTPVVVLNGLAAVREALVTHGEDTADRPPVPITQILGFGPRSQGVF
poor debrisoquine metabolism I
LARYGPAWREQRRFSVSTLRNLGLGKKSLEQWVTEEAACLCAAFANHSGRPFRPNGLLDK
poor debrisoquine metabolism R
AVSNVIASLTCGRRFEYDDPRFLRLLDLAQEGLKEESGFLREVLNAVPVLLHIPALAGKV
LRFQKAFLTQLDELLTEHRMTWDPAQPPRDLTEAFLAEMEKAKGNPESSFNDENLRIVVA
missing in CYP2D6*9 allele
DLFSAGMVTTSTTLAWGLLLMILHPDVQRRVQQEIDDVIGQVRRPEMGDQAHMPYTTAVI
P loss of activity in CYP2D6*7
HEVQRFGDIVPLGMTHMTSRDIEVQGFRIPKGTTLITNLSSVLKDEAVWEKPFRFHPEHF
LDAQGHFVKPEAFLPFSAGRRACLGEPLARMELFLFFTSLLQHFSFSVPTGQPRPSHHGV
FAFLVSPSPYELCAVPR
T impaired metabolism of sparteine in alleles 2, 10, 12, 14 and 17 of CYP2D6
see http://www.expasy.org/cgi-bin/niceprot.pl?P10635
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CYP 2D6 Polymorphism (III)
Variability of debrisoquine-4-hydroxylation
HO
H
CYP2D6
N
NH2
NH
NH2
N
NH
= number of individuals (european population)
Homocygote
extensive
metabolizers
Homocygote
poor
metabolizers
= metabolic rate
heterocygote extensive metabolizers
Lit: T. Winkler Deutsche Apothekerzeitung 140 (2000) 38
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Polymorphisms of Other CYPs
• CYP 1A2 individual: fast, medium, and slow turnover of caffeine
• CYP 2B6 missing in 3-4 % of the caucasian population
• CYP 2C9 deficit in 1-3 % of the caucasian population
• CYP 2C19 individuals with inactive enzyme (3-6 % of the caucasian
and 15-20 % of the asian population)
• CYP 2D6 poor metabolizers in 5-8 % of the european,
10 % of the caucasian, and <1% of the japanese population. Over
expression (gene duplication) among parts of the african and oriental
population.
• CYP 3A4 only few mutations
42
Typical inhibitors of various CYPs
CYP 1A2
cimetidine, ciprofloxacine, enoxacine...
grapefruit juice (naringin, 6‘,7‘-dihydroxybergamottin)
CYP 2C9
chloramphenicol, amiodarone,
omeprazole,...
CYP 2C19
fluoxetine, fluvastatin, sertraline,...
CYP 2D6
fluoxetine, paroxetine, quinidine,
haloperidol, ritonavir,...
CYP 2E1
disulfiram, cimetidine,...
CYP 3A4
cannabinoids, erythromycin, ritonavir,
ketokonazole, grapefruit juice
see also http://medicine.iupui.edu/flockhart/
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SVM Prediction of Cytochrome P450 3A4,
2D6, 2C9 Inhibitors and Substrates
Dataset Statistics
Dataset
Inhibitors / noninhibitors
Substrates / nonsubstrates
CYP
Training set
Validation set
Modeling
training set
Modeling
testing set
P+
P-
P+
P-
P+
P-
P+
P-
3A4
216
386
25
75
196
306
20
80
2D6
160
442
20
80
143
359
17
83
2C9
149
453
18
82
134
368
15
85
3A4
312
290
56
44
256
246
56
44
2D6
169
433
29
71
149
353
20
80
2C9
130
472
14
86
121
381
9
91
44
SVM Prediction of Cytochrome P450 3A4,
2D6, 2C9 Inhibitors and Substrates
Distribution of types of forces involved in ligand-enzyme interactions
Dataset
CYP
Inhibitors / noninhibitors
3A4
Electrostatic
(%)
HAcca
(%)
HDona
(%)
Hydrophobic
(%)
56.4
10.1
9.2
24.4
57.7
7.3
6.9
28.0
59.3
6.2
8.4
26.0
59.6
8.0
5.3
27.2
55.3
9.1
10.6
25.0
54.7
10.2
8.5
26.6
2D6
2C9
Substrates / nonsubstrates
3A4
2D6
2C9
45
SVM Prediction of Cytochrome P450 3A4,
2D6, 2C9 Inhibitors and Substrates
Prediction Results
Dataset
Inhibitors / non-inhibitors
CYP
Sensitivity (%)
Specificity (%)
96.0
100.0
90.0
96.3
94.4
98.8
98.2
95.5
96.6
97.2
100.0
98.8
3A4
2D6
2C9
Substrates / non-substrates
3A4
2D6
2C9
46