CZ3253: Computer Aided Drug designCZ3253: Computer Aided Drug design

Lecture 10: Overview of Drug Testing Methods I: Lecture 10: Overview of Drug Testing Methods I: ADME Test ADME Test

Prof. Chen Yu ZongProf. Chen Yu Zong

Tel: 6874-6877Tel: 6874-6877Email: Email: csccyz@nus.edu.sgcsccyz@nus.edu.sghttp://xin.cz3.nus.edu.sghttp://xin.cz3.nus.edu.sg

Room 07-24, level 7, SOC1, Room 07-24, level 7, SOC1, National University of SingaporeNational University of Singapore

22

Flow of information in aFlow of information in a drug discovery pipelinedrug discovery pipeline

Bioinformatics

Computational and Combinatorial Chemisty

Toxicity

33

Predictive ADMEPredictive ADME

Absorption

Distribution

Metabolism

Elimination

Pharmacokinetic

Bioavailability

44

Why is the prediction of ADME parameters Why is the prediction of ADME parameters so important ?so important ?

reasons that cause the failure of a potential drug candidate

55

Bioavailablity of Drugs (I)Bioavailablity of Drugs (I)

66

Bioavailability of Drugs (II)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.

77

1. What Is Absorption?1. What Is Absorption?

1,2 – Stability + Solubility

3 – Passive + Active Tr.

4 – Pgp efflux + CYP 3A4

“the drug passing from the lumen into the tissue of the GIT” (Sietsema)

Frequently HIA is confused with either “Passive Absorption or “Oral %F”

Human Intestinal Absorption (HIA)

5 – 1st Pass in liver

Oral Bioavailability (%F)

88

Exp. values from in vivo

tests

Theoreticalconcepts

Different “Absorption Types”Different “Absorption Types”

Passive Absorption (PA)

Human Intestinal Absorption (HIA)

Human Oral Bioavailability (%F)

Absorbed Fraction (FAbs)

Passive transport across intestinal membrane in vivo

PA = f (PeIntestine)

HIA = f (PeIntestine, SW, AT, Pgp efflux, gut 1st pass)

%F = f (HIA, liver 1st pass)

“Passive Absorption” that depends on solubility (SW):

FAbs = f (PeIntestine, SW)

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How They Are Measured?How They Are Measured?

Most HIA and %F values are qualitative

All in vivo data types are poorly reproducible (dosing, formulation, physiology)

%F is the ratio of cumulative plasma concentrations after oral and intravenous administrations:

%F = (AUCOral / AUCIV )PLASMA

AUC Oral

AUC IV

Pla

sm

a C

on

c.

Hours

Oral %F

This method is not always applicable (e.g., when biliary excretion interferes).

HIA is usually measured as the ratio of cumulative urinary excretion of drug-related material following oral and intravenous administrations:

%HIA = (ExcrOral / ExcrIV )URINE H ours

%HIAExcr IV

ExcrO ral

Uri

ne

Exc

reti

on

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2. “Pe + SW ” simulations

3. “PB - PK” simulations

How They Are Predicted?How They Are Predicted?1. “Direct” informatics

Structure “Absorption = f (PeIntestine in vivo)” -- SARs or QSARs

Structure FAbs using in vitro Pe and SW tests

Structure %F using a wide array of in vitro tests:

Kinetic dissolution rates, various types of polarized transport, metabolic stability tests, PBP, etc.

In vitro tests are not used, SW is frequently ignored

%F = f (Pe , SW, 1st Pass, etc.) – “knowledge bases”

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How They Are Predicted?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?Which Methods to Use and When?

Oral %F from CPMaximum FAbs

Conventional approach:

Informatics

“Pe + SW “

“PB - PK”

Dru

g D

eve

lop

me

nt

Accuracy & Relevance

In reality various types of simulations can be used during the earliest development stages

Computational

New approach:

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QSAR-Based MethodsQSAR-Based Methods

• 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)

Correlations by Jurs, Oprea, and many others:

f (%HIA or %F) = ao + ai xi

“One-step” models using ANNs or PLS: “One-step” kinetic scheme:

Plasmaka

"Constant Dose"

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Example of QSAR ModelExample of QSAR Model

1. Considered “passive absorption” only

2. Used good physicochemical descriptors

Not everything is “perfect”, but the results are much more useful than from any other QSAR works

%HIA = 92 – 22 – 21 + 11 V + 3 E + 4 + 0 f (pKa)

H-Bonding, f (TPSA)

Qualitative agreement with C-SAR models

Polarity –polarizability

Size, f (MW)

~ Zero effect of ionization

Deserves attention!

Incorrect function (calc. HIA may exceed 100%)

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Rule-Based MethodsRule-Based Methods

Lipinski

> 2,000 compounds that passed 2nd phase of clinical trialsAbsorbable if MW 500, log P 5, (OH + NH) 5, (O + N) 10

Veber

> 1,000 compounds with rat %FAbsorbable if PSA 140 A2, (O+N+OH+NH) 12,

Rot-Bonds 10

AB/ADME Boxes

> 800 compounds with exp %HIA (passive absorption only)

(independently – SW, AT, Pgp, 1st Pass, and Oral %F)

Simple rules using “data mining”, PCA, or recursive partitioning:

Deservecriticism

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Example Of Rule-Based PredictionsExample Of Rule-Based PredictionsThree types of passive absorption considered:

250

290

Parace llu la r

150

580

"Restricted"

N on- perm eable

MW

Trans-ce llu la r

TPSA , A 2

Large natural compounds

Traditional leads

“Very small” molecules

IF (MW < 250 OR MW < 580 AND TPSA < 150) THEN “POSITIVE”

IF (MW < 580 AND TPSA > 150 OR TPSA > 290) THEN “NEGATIVE”

A very rough approximation:

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Generalization of the Rules:Generalization of the Rules: The “Rule of Five” Formulation for Drug-Like MoleculesThe “Rule of Five” Formulation for Drug-Like Molecules

• 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.

Poor absorption or permeation are more likely when:

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Exception to the rule of fiveException 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 entitiesComputational 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%

2020

Intrinsic LimitationsIntrinsic Limitations

Qualitative rules cannot accurately model continuous processes

We must also know probabilities that our rules will be obeyed

PositivesNegatives M arginal

Probabilities

Re

lia

bil

itie

s

False results

No matter how good our rules are, “marginal” compounds will create false predictions

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SVM Prediction System for HIA SVM Prediction System for HIA J. Chem. Inf. Comput. Sci. 44,1630-1638 (2004)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 ResultsSVM 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 P450Cytochrome 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)Cytochrome P450 metabolisms (I)During first liver passage: First pass effectextensive chemical transformation of lipophilic or heavy (MW >500) compounds. They become more hydrophilic (increased water solubility) and are therefore easier to excreat.

CH3 COOHO N

H

COOH

phase I 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)Cytochrome P450 Metabolisms (II)The substrates are monooxygenated in a catalytic cycle.

Drug-RCYP

+ O2Drug-OR + H2O

NADPH NADP

The iron is part of a HEM moiety

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Cytochrome P450 Metabolisms (III)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

family>40% sequence-homology sub-family

>55% sequence-homology

isoenzyme

*15 A-B

allel

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Cytochrome P450 gene familiesCytochrome P450 gene families

CYP450

Human 14+

Plants 22

Insects 3

Fungi 11Yeasts 2 Nematodes 3

Bacteria 18

Molluscs 1

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Human cytochrome P450 familyHuman 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)Cytochrome P450 enzymes (I)

Flavin Monooxygenase Isoenzyme

Alkohol Dehydrogenase

Aldehyd Oxidase

Monoamin Dehydrogenase (MAO)

The redox activity is mediated by an iron porphyrin in the active center

Drug-RCYP

+ O2Drug-OR + H2O

NADPH NADP

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Cytochrome P450 enzymes (II)Cytochrome P450 enzymes (II)Despite the low sequence identity between CYPs from different organisms, the tertiary structure is highy conserved.

In contrast to bacterial CYPs, the microsomal mammalian CYPs possess an additional transmembrane helix that serves as an anchor in the membrane

Superposition ofhCYP 2C9 (1OG5.pdb) andCYP 450 BM3 (2BMH.pdb) Bacillus megaterium

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Cytochrome P450 enzymes (III)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)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 distribution

CYP 2C1116%CYP 2E1

13%

CYP 2C66%

CYP 1A68%

CYP 1A213%

CYP 2A64%

CYP 2D62% other

7%

CYP 331%

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)Cytochrome P450 enzymes (V)Especially CYP 3A4, CYP 2D6, and CYP 2C9 are involved in the metabolism of xenobiotics and drugs.

Metabolic Contribution

CYP 2D630%

CYP 1A22%CYP 2C9

10%

other3%

CYP 3A455%

CYP 3A4

CYP 2D6

CYP 2C9

CYP 1A2

other

hepatic only

also small intestine

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Substrate specificity of CYPs (I) Substrate specificity of CYPs (I)

specific substrates of particular human CYPs

CYP 1A2 verapamil, imipramine, amitryptiline,caffeine (arylamine N-oxidation)

see also http://medicine.iupui.edu/flockhart/

CYP 2A6 nicotine

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 2B6 cyclophosphamid

CYP 3A4 alprazolam, cisapride, terfenadine, ...

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Substrate specificity of CYPs (II) Substrate specificity of CYPs (II) Decision tree for human P450 substrates

CYP 1A2, CYP 2A-E, CYP 3A4

Lit: D.F.V. Lewis Biochem. Pharmacol. 60 (2000) 293

Volumelow high

CYP 3A4CYP 2E1

medium

pKaacidic

CYP 2C9basic

CYP 2D6

neutral

CYP 1A2, CYP 2A, 2B

planaritylow

CYP 2B6high

CYP 1A2

medium

CYP 2A6

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Cytochrome P450 polymorphismsCytochrome P450 polymorphisms

„Every human differs (more or less) “

That means: The same genotype enables different phenotypes

The genotype, however, is determined by the individual DNA sequence. Human: two sets of chromosomes

The phenotype can be distinguished by the actual activity or the amount of the expressed CYP enzyme.

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

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CYP 2D6 Polymorphism (I)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 22Of the 75 allels, 26 are associated with adverse effectssee http://www.imm.ki.se/CYPalleles/cyp2d6.htm

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CYP 2D6 Polymorphism (II) CYP 2D6 Polymorphism (II)

Lit: J. van der Weide et al. Ann. Clin. Biochem 36 (1999) 722

4040

MGLEALVPLAVIVAIFLLLVDLMHRRQRWAARYPPGPLPLPGLGNLLHVDFQNTPYCFDQ

LRRRFGDVFSLQLAWTPVVVLNGLAAVREALVTHGEDTADRPPVPITQILGFGPRSQGVF

LARYGPAWREQRRFSVSTLRNLGLGKKSLEQWVTEEAACLCAAFANHSGRPFRPNGLLDK

AVSNVIASLTCGRRFEYDDPRFLRLLDLAQEGLKEESGFLREVLNAVPVLLHIPALAGKV

LRFQKAFLTQLDELLTEHRMTWDPAQPPRDLTEAFLAEMEKAKGNPESSFNDENLRIVVA

DLFSAGMVTTSTTLAWGLLLMILHPDVQRRVQQEIDDVIGQVRRPEMGDQAHMPYTTAVI

HEVQRFGDIVPLGMTHMTSRDIEVQGFRIPKGTTLITNLSSVLKDEAVWEKPFRFHPEHF

LDAQGHFVKPEAFLPFSAGRRACLGEPLARMELFLFFTSLLQHFSFSVPTGQPRPSHHGV

FAFLVSPSPYELCAVPR

CYP 2D6 Polymorphism (III) CYP 2D6 Polymorphism (III)

see http://www.expasy.org/cgi-bin/niceprot.pl?P10635

poor debrisoquine metabolism S R impaired mechanism of sparteine

poor debrisoquine metabolism I

poor debrisoquine metabolism R

missing in CYP2D6*9 allele

P loss of activity in CYP2D6*7

T impaired metabolism of sparteine in alleles 2, 10, 12, 14 and 17 of CYP2D6

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CYP 2D6 Polymorphism (III) CYP 2D6 Polymorphism (III) Variability of debrisoquine-4-hydroxylation

N

NH

NH2 N

NH

NH2

HOH

CYP2D6

Homocygote extensive metabolizers

= metabolic rate

= number of individuals (european population)

heterocygote extensive metabolizers

Homocygote poor metabolizers

Lit: T. Winkler Deutsche Apothekerzeitung 140 (2000) 38

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Polymorphisms of Other CYPs 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

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Typical inhibitors of various CYPsTypical inhibitors of various CYPs

CYP 1A2 cimetidine, ciprofloxacine, enoxacine...grapefruit juice (naringin, 6‘,7‘-dihydroxy-bergamottin)

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, SVM Prediction of Cytochrome P450 3A4, 2D6, 2C9 Inhibitors and Substrates2D6, 2C9 Inhibitors and Substrates

Dataset Statistics

Dataset CYP Training set Validation set Modeling training set

Modeling testing set

P+ P- P+ P- P+ P- P+ P-

Inhibitors / non-inhibitors

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

Substrates / non-substrates

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

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SVM Prediction of Cytochrome P450 3A4, SVM Prediction of Cytochrome P450 3A4, 2D6, 2C9 Inhibitors and Substrates2D6, 2C9 Inhibitors and Substrates

Distribution of types of forces involved in ligand-enzyme interactions

Dataset CYP Electrostatic (%)

HAcca (%)

HDona (%)

Hydrophobic (%)

Inhibitors / non-inhibitors

3A456.4 10.1 9.2 24.4

2D657.7 7.3 6.9 28.0

2C959.3 6.2 8.4 26.0

Substrates / non-substrates

3A459.6 8.0 5.3 27.2

2D655.3 9.1 10.6 25.0

2C954.7 10.2 8.5 26.6

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SVM Prediction of Cytochrome P450 3A4, SVM Prediction of Cytochrome P450 3A4, 2D6, 2C9 Inhibitors and Substrates2D6, 2C9 Inhibitors and Substrates

Prediction Results

Dataset CYP Sensitivity (%) Specificity (%)

Inhibitors / non-inhibitors 3A496.0 100.0

2D690.0 96.3

2C994.4 98.8

Substrates / non-substrates 3A498.2 95.5

2D696.6 97.2

2C9100.0 98.8