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BIOINF 4372 Drug Design 2

Oliver Kohlbacher

Winter 2011/2012

12. Toxicity Predic@on

Overview

•  ADME •  Bioavailability •  Metaboliza1on, Elimina1on

•  Toxicity •  Effect and side effects •  Mechanisms of toxicity

•  Models •  Animal models

•  In vitro models

•  Theore1cal models and predic1ons

2

Failure in Late Development

•  90% of all drug candidates fail between discovery and introduc1on to the market

•  The late development phases are the most expensive phases

•  In a study from 1988, Pren1s et

al. found that in more than 60% of the cases, poor

pharmacokine@c (PK) or toxicological proper@es were the cause

Prentis et al., Br. J. Clin. Pharmacol. 1988, 25, 387-396.

6%

22%

41%

31%

Reason for Failure

Market Toxicity PK Efficacy

3

Failure in Late Development

•  More recent studies find that this problem has changed since •  Currently PK and bioavailability play a minor role •  This is mostly due to improved tes1ng, but quite likely also due to

improved computa1onal models for these proper1es •  The problem of toxicity has goRen worse (rela1vely), however, and

good computa1onal models in this area are urgently needed Kola & Landis, Nat Rev Drug Discov. 2004;3(8):711-5.

4

Absorp@on and Elimina@on

Central Compartment Biliary Renal

Gehirn

Blood-Brain Barrier

Enteral

Absorption

Elimination

Membranes of the GI Tract

5

Overview of the Different Areas

Application

Dissolution

Absorption

Distribution

Place of Action (Receptors)

Pharmacological Effect

Clinical Effect Toxic Effect

Storage

Biotransformation

Excretion

After: Mut, p. 5

Pharmaceutical Phase

Pharmacokinetic Phase

Pharmacodynamic Phase

6

Bioavailability

•  Drug has to reach the place of ac1on and achieve a sufficient concentra1on there for the dura1on of ac1on

•  This bioavailability is a key criterion for the effec1veness of a drug

•  Also summarized as ADME (Absorp1on, Distribu1on, Metabolism, Excre1on) and ADMET (ADME-‐Tox) •  Pharmaceu1cal basics: see Lecture Drug Design 1

•  Absorp1on and Distribu1on are pharmacokine1c proper1es •  Metaboliza1on is much harder to predict •  It is not only relevant for the predic1on of elimina1on, but also for toxicity (toxic metabolites!)

7

First Pass Effect

•  Absorbed substances have to pass through the gastro-‐intes1nal wall first, then through the liver (portal vein)

•  First pass effect: metaboliza1on in the liver before the compound reaches systemic circula1on, reduces bioavailability dras1cally

Gastro-intestinal lumen

Gastro-intestinal wall

Liver Blood vessel

Biotransformation Elimination, Biotransformation

8

Biotransforma@on

•  Oxida1on •  Reduc1on •  Hydrolysis •  Decarboxyla1on •  Methyla1on •  Acetyla1on

•  Conjuga1on with •  Ac1vated glucuronic acid

•  Sulfuric acid •  Glycine

•  ...

•  Many enzymes catalyze the transformation of substrate families that also include numerous drugs

•  Particularly active in this regard are liver enzymes, which also happen to have a broad substrate specificity

•  Frequent biotransformations are:

9

Bioavailability

•  Experimental determina1on of ADME parameters is

1me-‐consuming and costly

•  Computa1onal methods could thus have a large impact

•  Bioavailability is not governed by a single property, it is

the sum of all ADME processes

•  Modeling it is thus very difficult and QSPR models s1ll

have limited reliability in this area

10

Models in Use System Models Pros Cons

In silico (Q)SAR, (Q)SPR

High throughput, cheap, easy to use

Require high-quality exp. data, not all biological processes modeled

In vitro Artificial membranes, cell-based assays (Caco2, MDCK)

Medium to high throughput, includes active and passive transport mechanisms

Many phenomena are strongly model-dependent, no active transport (membrane-based), analytically difficult

In situ Rat intestinal perfusion

Very close to in vivo, includes all key mechanisms except for systemic effects

Labour-intensive, differences between species In vivo Rat portal vein

studies As in situ but also includes presystemic metabolism

Pelkonen et al.; Eur J Clin Pharmacol 57:621-629, 2001

11

Experimental Models in vitro

in situ

in vivo

http://www.transonic.com http://www.uv.es/~mbermejo/projects.htm

http://www.millipore.com 12

QSPR Model

•  Turner et al. published a purely computa1onal study for bioavailability in 2003

•  169 with literature data on bioavailability •  10 compounds randomly selected as a test set •  Training on the remaining 159 •  Model based on eight different descriptors •  Selected from 94 descriptors by stepwise mul1ple linear regression

•  Predic1on: R = 0.72

Turner et al.; Anal Chim Acta 485;89-102, 2003

13

QSPR Model

•  Descriptors used: •  Electron affinity (H-‐bonds) •  Number of aroma1c rings •  Energy of the highest occupied molecular orbital (HOMO energy)

•  Par11on coefficient octanol/water (log P) •  Molar volume •  Ra1o of hydrophilic/lipophilic groups •  Solubility in water •  Contribu1on of H-‐bonds to solubility

Turner et al., Anal Chim Acta (2003), 485, 89-102

14

Predic@on of Bioavailability

Turner et al., Anal Chim Acta (2003), 485, 89-102

15

Toxicity and Side Effects

•  Are there drugs without side effects? •  W. Kuschinski: “If it is claimed that a substance has no side effects, then it is to be assumed that it has no desired effect either.“

•  Required: Es1mate of toxicity

16

Toxicity and Side Effects

•  Paracelsus: „The dose makes the poison“

•  No clear dis1nc1on between ‘medicine’ and ‘poison’

•  Usually, there is not a single ‘cause’ for a toxic effect •  Many mechanisms are involved

•  Toxic effect onen also occurs through metaboliza1on of

the substance

17

Dose-‐Response Rela@onships

•  „The dose makes the poison“ •  Typically, an increase in dose increases

the effect •  Above a certain dose, addi1onal toxic

effects may be observed •  Strength/dura1on of effect depends on

many factors, e.g., genotype, age, body mass, …

•  Difficult to quan1fy strength of the response

) Dose-‐response rela1onships

measured for collec@ves •  ED50: median effec@ve dose •  LD50: median lethal dose

Katzung, Basic and Clinical Pharmacology, p. 30

18

Therapeu@c Index •  Therapeu@c index or therapeu1c ra1o is the ra1o between the

concentra1on causing a toxic effect and the concentra1on causing a therapeu1c effect

•  It is a measure of a drug‘s safety

Mut, S. 81

ED50 LD50

19

Human Toxicity

•  Defini1on of acute LD50 in human not helpful – it is generally not experimentally accessible

•  A lethal dose has to be avoided at all costs •  For a safe drug, we need to achieve less than 1 death per million, i.e. LD0,000001

•  Apart form acute toxicity, long-‐term effects are of great importance •  Mutagenicity

•  Carcinogenicity

•  ...

20

Poison/Drug •  Poisons obviously have a biological ac1vity •  We can thus apply the same principles as for pharmaceu1cal

ac1vity

•  Toxicokine1cs

•  Toxicodynamics

•  In principle, all of the methods described to model biological ac1vity are applicable as well

•  Problem:

•  Toxicity is onen not a single, well defined process (in contrast to binding, ac1va1on, ...)

•  What is the mechanism? Where to start?

21

Types of Toxicity

•  Acute toxicity •  Exposi1on to a single dose or mul1ple doses in a short space of 1me

•  Symptoms occur immediately or briefly aner exposi1on

•  Chronic toxicity •  Prolonged exposi1on to low-‐level doses •  Slow accumula1on of the poison to toxic concentra1ons

•  Onen due to lack of excre1on/elimina1on

22

Classifica@on of Poisons

•  Target organ (liver, kidneys, ...)

•  Applica@on (pes1cide, solvent, food supplement, ...)

•  Source (animal or plant poisons,

synthe1c, ...)

•  Effect (cancer, mutagenesis, liver damage,

kidney failure, ...) 23

Exposi@on to Poisons

•  Environmental exposi1on is a common source

•  Exposi1on to toxic substances is controlled by legisla1on •  Short-‐term exposure limit (STEL) [in Germany: MAK (maximale Arbeitsplatzkonzentra1on)]

•  However: Carcinogenic substances are never harmless!

Even smallest amounts can cause gene1c altera1ons.

24

Molecular Toxicology

•  Studies the interac1on of a poison with a biological object

•  Consider the effect on a molecular level

•  Becoming increasingly important as the mechanisms of

toxic ac1on of compounds are becoming known

•  Obviously of the utmost importance in the drug design

process

25

What Causes Toxicity?

Many different mechanisms involved: •  Biotransforma@on (metaboliza1on) •  Interac@ons between several substances which are not toxic by themselves (at the individual doses)

•  Inhibi1on/inac1va1on/denatura1on of proteins (enzymes, receptors, ...)

•  Satura@on of metabolism •  Ac1va1on/blocking of receptors •  ...

26

Paracetamol

•  Very common analgesic and an1pyre1c (Acetaminophen)

•  Normal dose: 500-‐1000 mg

•  Usually well tolerated •  But: high doses can be toxic! •  Doses of 10 g/day or more lead to severe liver

cell necrosis (onen lethal)

•  The effect is caused by a toxic metabolite of paracetamol, N-‐acetyl-‐p-‐benzoquinoneimine

à The dose makes the poison

OH

NH

O

CH3

Paracetamol (Acetaminophen)

27

Paracetamol •  Damage to liver cells is caused by highly reac1ve metabolites

•  Formed by cytochromes, their reac1on with proteins in the liver causes the toxic effect

•  Low doses: metabolites are captured and detoxified by glutathione by forming harmless conjugates

•  Toxic dose: •  Glutathione storage exhausted •  Metabolites cannot be detoxified

O

N

O

CH3

OH

NH

O

CH3

-2H

Glutathione

28

Paracetamol

•  S1mula1on of chytochrome p450 lowers the glutathione level

•  Some therapeu1cally ac1ve compounds ac1vate p450 (strongly)

•  Consequence: even very low (normally non-‐toxic) doses of paracetamol become toxic

•  Toxic doses well below 6 g •  Problema1c for pa1ents with pre-‐exis1ng damage to the liver (alcohol abuse!) ) Interac@on with other substances can cause toxicity!

29

Grapefruit

•  Grapefruits contain high levels of inhibitors of cytochrome P450 (CYP3A4) •  Elimina1on of many drugs (terfenadine, methadone, diazepame, …) is thus dras1cally

reduced •  Plasma levels can increase dras1cally as a consequence if drugs are taken with grapefruit

juice http://www.news-medical.net/images/grapefruit.jpg http://www.pharmacyplusplus.com/details.php?product_id=138&product_type_id=1 30

Drug Interac@ons

•  Example: administra1on of phenytoin (an an1epilep1c) and

salicylic acid at the same 1me results in abnormally high

plasma levels

•  Problem: both drugs are eliminated through the same

enzyme

•  Consequence: satura1on of the enzyme reduces

elimina1on rates

•  This can lead to toxic effects, as the effec1ve plasma

concentra1on is much higher than an1cipated

Drug interac@on by satura@on of an enzyme!

31

Benzene/Toluene

•  Benzene has a high acute toxicity and is also carcinogenic – in contrast to structurally very similar toluene

•  Structures differ only by a single methyl group

•  Benzene is easily absorbed, also across the skin •  It is also easily excreted again, mostly through the

lung

•  About half of the absorbed amount is typically metabolized

•  Toxic effect is most likely due to this biotransforma1on

C H 3

Benzene

Toluene

32

Benzene

Acute toxicity

•  More than 0.5 ml/kg causes

•  Intoxica1on •  Headaches, dizziness

•  Higher doses: •  Convulsions •  Unconsciousness •  Cardiac arrhythmia

•  Eventually death by central respiratory paralysis

33

Benzene

Chronic toxicity or massive single doses: •  Hemotoxicity

•  Inhibi1on of erythropoiesis, leukopoiesis and thrombopoiesis

•  No therapy known

•  Carcinogenicity •  Leukemia

•  Causes irreversible chromosomal aberra1ons in lymphocytes and bone marrow cells

•  Benzene is one of the most important environmental poisons

34

Metaboliza@on of Benzene

•  Ini1al step: enzyma1c epoxida1on

•  Epoxides are highly reac1ve •  Can react with hydrogen atoms of biological

macromolecules

•  Carcinogenic and mutagenic effects is (probably) caused by reac1ons with nucleic acids

•  Toluene is metabolized differently (star1ng with

the methyl group) and is thus much less toxic O

H

H

Mono- oxygenase

35

Carcinogens

•  In animal models, symmetric dialkylnitrosamines cause

•  Liver tumors aner chronic exposure to low doses

•  Kidney tumors aner exposure to a single high dose

•  Small amounts of nitrosamines are very common (in par1cular in alcoholic beverages, certain meat products)

•  Also formed endogenously (produc1on of nitrite from saliva and gastric juice)

NNCH3

CH3

O

Dimethylnitrosamine

36

Carcinogens •  Polycyclic aroma1c hydrocarbons (PAHs) are onen carcinogenic

•  Effect is again caused by metabolites

•  Some of these metabolites show acute gene toxicity

•  They have also been shown to react with DNA in vivo

•  Bay region is important for metabolic ac1va1on

Benzo(a)pyren

1,2-5,6 Dibenzanthrazen

37

Metaboliza@on

O

H

H

OH

OH

OH

H OH

OH

OH

NH

NH

N

N

N

R

O

Metabolization of benzo(a)pyrene to a diol epoxide, which then reacts with the exocyclic amino group of guanine

38

Tes@ng Prevents Disasters

•  No or insufficient tes1ng of novel pharmaceu1cal compounds led to several major disasters •  Brain damage and death in small children due to sulfonamides (late 1930s)

•  More than 100 deaths through diethylene glycol used as a solvent for sulfanilamide (this incident led to the founda1on of the Food and Drug Administra1on [FDA] in the USA)

•  Severe birth defects aner the use of thalidomide during pregnancy in about 10,000 children worldwide

•  High standards for drug safety have dras1cally reduced these incidents

•  Nevertheless, drugs are taken off the market again because long-‐term (side) effects have not been recognized early on

39

Models: Animal Models

•  How to test for human toxicity early on?

•  Difficult: there is rather liRle toxicological data available for

humans (systema1c toxicological tes1ng with human

subjects is not considered acceptable!)

•  The vast majority of reliable data thus stems from animal

experiments

•  As we have seen before, these data are onen hard to transfer to humans

•  But: beRer than nothing!

40

Animal Models

•  Animal experiments are... •  Expensive •  Time-‐consuming

•  Raise ethical issues •  Required by law

•  Reduc1on of animal use by •  In vitro models

•  Computa1onal models

http://abclabs.com

41

Animal Use

•  Strong growth of animal use between 1945 and 1968

•  Stagna1on un1l the middle of the 70s, then steady decrease (1978-‐1988 decrease by 60% in West Germany)

•  Reduc1on quite remarkable: more substances tested than ever!

•  Key reason: in vitro tests (Ames test)

•  Theore1cal models s1ll play a very minor role

42

Animal Use 1991-‐1995 (D) Species 1991 1992 1993 1994

Mouse 1,223,741 1,064,883 973,106 868,312

Rat 611,530 558,516 508,769 459,781

... ... ... ... ...

Total 2,402,710 2,082,588 1,924,221 1,758,500

Quelle: Bundesministerium für Verbraucherschutz, Information Nr. 44 v. 30. Oktober 1995 http://www.bmelv.de/cae/servlet/contentblob/765788/publicationFile/43424/2008-TierversuchszahlenGesamt.pdf

•  About half of them are related to medical research

•  Most of them are rodents (mice, rats)

•  Over the last decade the number of animals used in animal experiments has been increasing steadily (2008: 2.6 mio. in Germany)

•  Number for mice are always increasing, most other species going down

•  About 171,000 are currently being used per year for toxicological studies

43

Comparison between Species •  Different species may react very differently to the same drug

•  Example: lysergic acid diethylamide (LSD)

•  Experiment: administer a hallucinogenic, but subtoxic, dose to a male Asia1c elephant

•  Es1mate: dose of 0.3 g

(about 0.06 mg/kg)

•  Result: Death.

) Toxic dose for an

elephant about 1000

lower than for a mouse!

West LJ, Pierce CM, Thomas WD. Lysergic Acid Diethylamide: Its Effects on a Male Asiatic Elephant. Science. 1962 Dec 7;138(3545):1100-1103. 44

Transferability of the Data

Toxicity of LSD

Species LD50 [mg/kg]

Mouse 50-60

Rat 16.5

Rabbit 0.3

Elephant << 0.06

Human >> 0.003

45

Transferability of Data

•  Example: 2,3,7,8-‐tetrachlorodibenzo-‐p-‐dioxin (TCDD, “Seveso poison”)

•  Even in the closely related species hamster and guinea pig toxicity differs by three orders of magnitude

•  Extrapola1on is thus very, very dangerous!

•  Even data from close rela1ves can be misleading: apes are rather insensi1ve to TCDD

46

Models: in vitro Tests

•  Example: Ames test iden1fies cpds with mutagenic (and carcinogenic) poten@al

•  Carefully engineered strain of Salmonella typhimurium

•  Lacks the ability to synthesize His •  Incubated together with the compound and a few other things (e.g., liver extract to check for mutagenicity of possible metabolites)

•  Mutagenic agent can cause backmuta1ons that can grow into larger colonies

•  Colony count is a measure for mutagenic poten1al Ames, B., F. Lee, and W. Durston; Proc. Natl. Acad. Sci. USA 70:782-786, 1973

47

Problems Predic@ng Toxicity

•  Wide range of biochemical processes involved

•  Very similar structures have very different toxicological proper1es (structure-‐toxicity landscape is very rough)

•  Different mechanisms can result in the same toxicological outcome

•  Very different structure-‐ac1vity rela1onships between different classes of compounds

•  Onen caused by metabolites, so not a property directly related to a compound‘s structure!

48

Theore@cal Models

Which approaches are there?

•  Knowledge-‐based (“expert systems”)

•  Store expert knowledge as individual rules

•  Applying these rules to a given structure results in a classifica1on

•  Sta@s@cal models ((Q)SAR)

•  Automated sta1s1cal analysis of large-‐scale data sets using sta1s1cal methods

•  No experts required, but strongly dependent on data quality, inclusion of all relevant processes

49

Top-‐down/Bocom-‐up

Richard, Toxicol Lett 102-103:611-616, 1998 50

What is being modeled? •  There is not an single toxicity model, but numerous different toxicological proper1es are being modeled independently: •  Liver toxicity •  Kidney toxicity •  Carcinogenicity •  Mutagenicity

•  Reproduc1ve toxicity •  Acute LD50 (rat)

•  Transdermal absorp1on

•  ... 51

Approaches

Sta@s@cal Approaches •  CASE/Mul@CASE

Klopman; J. Am. Chem. Soc 106:7315-‐7321, 1984

Klopman; QSAR 11:176-‐184, 1992

•  TOPKAT

Enslein et al.; Mutat. Res. 305:47-‐61, 1992

Expert Systems

•  DEREK

Sanderson, Earnshaw; Human Exp. Toxicol. 10:261-‐273, 1991

•  ONCOLOGIC

Woo et al.; Toxicol. LeR. 79:219-‐228, 1995

52

Predic@on Models

Richard, Toxicol Lett 102-103:611-616, 1998

53

Example: DEREK

•  DEREK is a knowledge-‐based system (Deduc1ve Es1ma1on of Risk from Exis1ng Knowledge)

•  Based on a program for organic synthesis planning (LHASA)

•  Rather old (1980/1985)

•  Developed for VAX

•  600 modules in Fortran, C and Macro

•  Rule-‐descrip1on language CHMTRN

•  Extends LHASAs rule language for elements required in toxicology (àDERTRN)

•  Ini1ally about 50 different rules

„...based on a combina<on of over 30 years experience of toxicological work...“

54

DEREK •  Simple rules of the structure IF structural chemical property THEN specific outcome possible

•  Results are purely qualita1ve (!) •  In addi1on, it contains the FDA‘s rule set for carcinogenicity

•  Mainly used to select compounds in a campaign, remove those with obvious problems

•  Also used as an indicator where addi1onal experiments are required

55

DEREK Rules

Four sec1ons

1.  Descrip1ons

2.  Usage informa1on

3.  Structural paRern

triggering the rule

4.  DERTRN query to refine the structural paRern

further

56

DEREK: Pros/Cons

+ Development of rules is overseen by the users and transparent to them – there is always an explana1on for a rule

+ New rules can be integrated rather simply + Simple user interface -‐  Rule syntax rather limited, no 3D defini1ons -‐  Rules are rather coarse and capture only a few key metabolic mechanisms

-‐  Inclusion of toxicological databases and the knowledge therein might improve predic1ons

57

Example: (Mul@)CASE

•  CASE -‐ Computer Automated Structure Evalua1on

•  Has been extended into Mul1-‐CASE

•  Relies on sta1s1cal analysis of a training data set combining compound structure and their biological ac1vity

•  Training data set must contain a broad range of different structures and toxicological endpoints

58

CASE: Training Data

•  Training data set: relates structures to biological/toxicological ac1vity

•  Ac1vity is given in ‘CASE units’ 10-‐19: inac1ve 20-‐29: weak ac1vity 30-‐99: ac1ve

•  Structures are given as SMILES, KLN, or as a MOL file

•  Analysis is based on heavy atoms alone

59

CASE: Training Data •  Training data set has to be examined thoroughly to ensure

even coverage of chemspace

•  Check for overrepresented structures

•  Iden1fy important missing structures/mechanisms

•  As many data points as possible should be included

•  Data of very similar cpds. with iden1cal mechanisms can be pooled

•  Problem: each change in the data set leads to a different predic1ve model

60

CASE: Model Construc@on

•  Decompose all structures into fragments of size 2 – 10

•  Classify fragments as

•  Biophores/toxicophores (sta1s1cally ac1ve)

•  Biophobes (sta1s1cally inac1ve)

•  Compute physicochemical descriptors and 2D descriptors

for a QSAR analysis

61

CASE: Classifica@on

•  Many fragments are not by themselves determining factors for toxicological ac1vity

•  Assump1on: binomial distribu1on of all fragments within a class

•  Sta1s1cally significant devia1on from this distribu1on à fragment is relevant for ac1vity

62

CASE: Predic@on

•  Two steps: •  Ac1vity predic1on •  Es1ma1on of toxicity

•  Input structure is decomposed into fragments •  Comparison of fragments to biophores/biophobes •  Predic1on of ac1vity likelihood based on these matches

63

CASE: Predic@on

•  Toxicity es1ma1on uses QSAR •  Model based on mul1variate analysis •  Forward selec1on of descriptors •  Ini1al descriptors

•  Biophores •  Biophobes •  Predicted log P

•  Descriptors are added un1l the model does not improve •  All standard caveats discussed earlier in the lectures on QSAR modeling apply!

64

Mul@-‐CASE

•  Aims at reducing problems caused by highly correlated descriptors

•  Solu1on: several stepwise CASE predic1ons •  Predict strongest biophore •  Remove molecules containing this biophore from the training data

•  Repeat un1l the training data set is empty or no significant improvement can be reached

65

Mul@-‐CASE

•  Dis1nguishes between ac@vity and modula@on of ac@vity •  Split training data set into different classes, based on presence or absence of a biophore

•  Conduct a QSAR analysis in each of the classes to determine whether related biophores lead to an increase/decrease in ac1vity

•  Uses a larger number of descriptors than CASE •  Predic1on:

•  Search for biophores in the input structure •  For each biophore iden1fied, search for modula1ng biophores

66

Mul@-‐CASE: Pros/Cons

+ Predic1ve models do not require prior (expert)

knowledge

+ Quan1ta1ve predic1on

-‐  Predic1on accuracy cri1cally dependent on the quality

(and manual cura1on) of the training data set

-‐  Output onen ambiguous à expert needed!

67

Mul@-‐CASE vs. DEREK

Prediction accuracy [%]

DEREK 59

Multi-CASE 49

COMPACT 54

TOPKAT 57

Greene; ADDR 54:417-431, 2002

Prediction of carcinogenicity of 44 compounds

68

Mul@-‐CASE vs. DEREK

DEREK 4.01 Multi-CASE 3.45

Sensitivity 45% 30%

Specificity 62% 84%

Concordance 60% 79%

Greene; ADDR 54:417-431, 2002

Prediction of Ames test results for 974 cpds.

69

State of the Art – 1990

•  In 1990 Tennant et al. asked several experts in toxicology to predict carcinogenicity for 44 compounds

•  Computa1onal methods were applied in parallel

•  Aner experimental tes1ng, the following results were

obtained in 1993:

•  Best result: expert (80% correct)

•  Theore1cal approaches: 45-‐65% correct

•  Results hardly beRer than random!

•  Not good enough to replace animal models!

Tennant et al.; Mutagenesis 5:3-14, 1990

Ashley, Tennant; Mutagenesis 9:7-15, 1994 70


•  Predic@ve Toxicology Challenge: Compe11on to assess the quality of modern in silico methods

•  Machine learning

•  Limited to the predic1on of carcinogenicity

•  Fourteen teams contributed predic1ons

•  111 models

•  Result: five(!) models performed beRer than random

Tuivonen et al.; Bioinformatics 19(10):1183-1193, 2003

71


•  Valerio et al. (FDA) examined two popular state-‐of-‐the-‐art sonware packages

•  LMA (Leadscope Model Applier)

QSAR/data mining approach based on structural features

•  MC4PC (a Mul@Case descendant)

Rule-‐based and QSAR approach

•  An external dataset of 43 phytochemicals with known rodent carcinogenicity was used to validate the predic1ons

Valerio et al., Mol. Nutr. Food Res., 54:1-9 (2010) Yang et al., Toxicol. Mech. Methods, 18:277-295 (2008)

Matthews et al., Toxicol. Mech. Methods, 18:189-206 (2008) 72


•  Results: comparable for both programs

•  High specificity, low sensi1vity

•  S1ll not very convincing

•  Combina1on of both codes into a consensus leads to even worse predic1ons

Valerio et al., Mol. Nutr. Food Res., 54:1-9 (2010)

MC4PC LMA

Specificity 94% 59%

Sensitivity 47% 50%

False positives 6% 41%

False negatives 53% 50%

73

Are predic@ons possible? •  At the moment predic1ons are not sufficiently reliable

•  Even modern sta1s1cal learning methods fail to capture the full complexity of toxicology

•  Without human experts and experimental tes1ng, no reliable statement on a compound’s toxicity is possible

•  More complex in vitro model are quickly gaining acceptance (1ssues, ar1ficial organs) and replacing many animal models; they

are also producing an increasing amount of new (training) data

•  Nevertheless: •  Good tool for the expert to guide toxicological studies

•  Can yield important hints for an early selec1on of candidates

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Summary •  Bioavailability is an essen1al property for a drug •  In silico predic1ons are possible, although difficult using QSAR

approaches •  All drugs have toxic side effects •  Important is the therapeu1c index •  Predic1on is very difficult due to the complexity of

toxicological mechanisms •  In vitro and in silico approaches s1ll cannot replace animal

models •  In silico toxicity predic1ons: knowledge/rule-‐based and

sta1s1cal approaches are currently in use •  Predic1ons are s1ll not reliable enough, although they are

being used to guide decisions

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References Books •  [BKK] Böhm, Klebe, Kubinyi: Wirkstoffdesign, Spektrum 2002 •  Mutschler: Drug ac1ons. Basic Principles and Therapeu1c Aspects, Medpharm

Scien1fic Publishers; Auflage: 6Rev Ed (1994) •  Klaassen: CasareR and Doull's Toxicology: The Basic Science of Poisons,

Mcgraw-‐Hill Professional; 7th revised ed. (2008)

Papers •  Valerio LG Jr, Arvidson KB, Busta E, Minnier BL, Kruhlak NL, Benz RD. Tes1ng

computa1onal toxicology models with phytochemicals. Mol Nutr Food Res. 2009 (Epub ahead of print), PMID: 20024931

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dd2 ws11 12 toxicity - uni-tuebingen.de · 12. toxicity&predicon & overview& ... •...

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