strategic testing
DESCRIPTION
CZ3253: Computer Aided Drug design Lecture 10: Overview of Drug Testing Methods II: Test of TOX 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. Toxic Effects Pathways. - PowerPoint PPT PresentationTRANSCRIPT
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CZ3253: Computer Aided Drug designCZ3253: Computer Aided Drug design
Lecture 10: Overview of Drug Testing Methods II: Lecture 10: Overview of Drug Testing Methods II: Test of TOXTest of TOX
Prof. Chen Yu ZongProf. Chen Yu Zong
Tel: 6874-6877Tel: 6874-6877Email: Email: [email protected]@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
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StrategicTesting
SelectValidationChemicals
QSARModelsRules
NN,SVM
StructuralRequirementsFor Pathways
RegulatoryAcceptancy
Criteria
QSAR LibrariesRules Collections
NN, SVM ClassifiersModeling Engine
Estimation of Missing Data
Analogue Identification
Prioritization/Ranking
DistributedDatabases
ChemicalInventories
Identify early critical events
High QualityData Sets
Toxic EffectsPathways
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33Library of Toxicological Pathways
Init
iati
ng
Eve
nts
Imp
aire
d R
epro
du
ctio
n/D
evel
op
men
t
Mapping Toxicological Paths to Adverse Outcomes “Estrogen Signaling Pathway”
ER Binding
ER Transactivation
VTG mRNA
Vitellogenin Induction
Sex Steroids
Altered Reproduction/Development
Molecular Cellular Organ Individual
Chemical 3-D
Structure/Properties
Chemical 2-D
Structure
Structure
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Analysis of Commercial Computational Analysis of Commercial Computational Toxicology SoftwareToxicology Software
• QSAR-BASED– Collects Molecular Fragments and
Descriptors– Calculates Values of Chemical
Descriptors– Compares to Known Compounds– Reports Probability of Being a Member
of a Toxic Class Using Multifactorial Statistical Analysis
– Identifies Structural Liabilities– Unvalidated Structural Relationships
• EXPERT RULE-BASED– Inspects Molecules for Known Structural
Liabilities
– Identifies Structural Liabilities
– Prepares Summary Report of Findings
– Validated Structural Relationships with Known Toxic Mechanisms
– Provides References & Predicted Mechanisms
ADAPT/TOPKAT MultiCASE/LeadScope DEREK
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Strengths and Weaknesses of Strengths and Weaknesses of Virtual Toxicology Commercial SoftwareVirtual Toxicology Commercial Software
• QSAR-BASED– Provide Relative Dose and Liability
Prediction– Easy to Determine if Compound is Well
Represented in Training Set via Similarity Search
– Can Be Biased to Minimize False Positives and/or False Negatives
– Challenging to Systematically Improve Model: No Linearity
– Difficult to Train General Model: Excellent Predictiveness for Single Event; Problematic for Multiple Events
Good For Specific Models
• EXPERT RULE-BASED– Chemically Intuitive Results
– Good Initial Filter for Known Liabilities: Lacks Specificity
– Only Predicts Presence of Identified Fragments
– Cannot Discriminate within a Structural Sub-Class
– Retrospective in Nature
– Cannot Extrapolate Prediction to New Chemotypes
Good For General Models
ADAPT/TOPKAT MultiCASE/LeadScope DEREK
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New Toxicology Prediction MethodsNew Toxicology Prediction MethodsNeural Networks– Use of structure descriptors to discriminate between modes of toxic action of
phenols. J Chem Inf Model. 2005 Jan-Feb;45(1):200-8. – Toward an optimal procedure for PC-ANN model building: prediction of the
carcinogenic activity of a large set of drugs. J Chem Inf Model. 2005 Jan-Feb;45(1):190-9.
SVM– Prediction of torsade-causing potential of drugs by support vector machine
approach. Toxicol Sci. 2004 May;79(1):170-7. Epub 2004 Feb 19.– Prediction of Genotoxicity of Chemical Compounds by Statistical Learning
Methods. J Chem Inf Model
Fuzzy Set– Prediction of noninteractive mixture toxicity of organic compounds based on
a fuzzy set method. J Chem Inf Comput Sci. 2004 Sep-Oct;44(5):1763-73.
Ensemble recursive partitioning– In silico models for the prediction of dose-dependent human hepatotoxicity.
J Comput Aided Mol Des. 2003 Dec;17(12):811-23.
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Predictive GenotoxicityPredictive Genotoxicity
• Goal: Improve current predictive toxicology capabilities for mutagenicity and carcinogenicity through customizing and augmenting current predictive software
• 1. Modeling & Informatics:– Enhancing current predictive software.
• Bias model to minimize false negatives (and indeterminants).
– Provide support to discovery groups to eliminate mutagenic liabilities.– Create a central data repository and populate it with literature data as well as institutional data.– Deliver a predictive mutagenicity package in a format that can be supported as a standard
system.– Allow for novel models to be added as they are developed
• 2. Use:– Prioritization of synthesis & testing candidates.– Identification of substructures responsible for an observed mutagenic liability and suggested
synthetic alternatives.– Regulatory and due diligence support (what will the FDA see?).
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Requirements for a Requirements for a QualityQuality Filter Filter
• Identify ALL compounds having mutagenic liability– Identify strengths & weaknesses of models– Identify strategy for maintaining & improving the model– User friendly & intuitive– Provide support information for model
• Chemists’ and toxicologists’ needs are not always equivalent– Chemistry:
• Suggest synthetic alternatives; do not limit chemical space• Repository of prior knowledge (both institutional and external)
– Toxicology:• Prioritization of synthesis and in vitro testing candidates• Regulatory and due diligence support; overprediction is acceptable
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Annotation of Substructural AlertsAnnotation of Substructural Alerts 95 mutagenicity alerts annotated
76 Native DEREK mutagenicity alerts 6 reclassified carcinogenicity alerts (genotoxic mechanism) 13 alerts Implemented by BMS ~300 DEREK Literature References Extracted, Archived and Summarized Probable mechanism(s), including reactive intermediates, described Additional SARs & mechanisms derived using publicly available data (TOXNET, RTECS, NTP) Updated literature archived, integrated and summarized
300+ additional references Lessons learned from QSARs included Validation Statistics included
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2002 Validation 2002 Validation Expanded Data SetExpanded Data Set
Class % Sample # CompoundsBMS 23% 416 (65 / 351)
Drugs 29% 534 (107 / 427)
Other 48% 875 (398 / 477)
Ames Pos: 31% 570
Ames Neg: 69% 1255
•~5% of BMS space covered by validation compounds.•~10% of drug space covered by validation compounds.
All Data:
1825 Compounds
Drugs:
534 Compounds
BMS:
416 Compounds
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1111
0 100 200 300 400 500 600 700 800
False (+)
False (-)
Indeterminate
Concordance
DEREK (DK) TOPKAT (TPK) MultiCASE (MC)
Parallel DK/MC Parallel DK/TPK Parallel MC/TPK
Parallel DK/MC/TPK Sequential D/MC Sequential MC/TPK
Sequential DK/MC & DK/TPK Sequential DK/MC/TPK
Which program works best?Which program works best?A combination of twoA combination of two
Random
Random
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Improving the System:Improving the System:Correction of the 2Correction of the 2oo Amine Alert Amine Alert
• 20/51 (39%) compounds triggering DR005 were predicted positive by an Ames assay (S9)
• Derek Rule 005 Addendum– Exclude Secondary Amides– Exclude Secondary
Sulfonamides
• Modified DR 005 Correctly Predicts 20/35 Compounds (57% concordance).
• Reduced False Positives from 31 to 15.
• Additional Rules and QSARs can be Developed to Improve the Accuracy of this Rule Even Further.
Substructure Name # Pos # Occurrences
R'
N O
R
H
SecondaryAmides
0 10
R'
N
S
O
H
OR
SecondarySulfonamide
0 8
N
R
H
Ar
SecondaryAniline
Derivatives19 30
N
R
H
R'
SecondaryAmines
1 5
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Improving the System:Improving the System:Substructures Identified by BMSSubstructures Identified by BMS
Alert NameAmes
PositiveBMS DEREK
PositivePositive
AccuracySubstructure 1 42 77 54.5%
Substructure 2 41 167 24.6%
Substructure 3 16 70 22.9%
Substructure 4 42 89 47.2%
Substructure 5 3 3 100.0%
Substructure 6 54 110 49.1%
Substructure 7 16 18 88.9%
Substructure 8 4 6 66.7%
Substructure 9 5 7 71.4%
Substructure 10 52 84 61.9%
Substructure 11 86 128 67.2%
Substructure 12 1 2 50.0%
Substructure 13 4 10 40.0%
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Predictive ToxicologyPredictive ToxicologyComparing Apples to ApplesComparing Apples to Apples
Secondary and Aromatic Amines: The Data Set: 334 Compounds
Selected for drug-likeness (expanded Lipinski filter) Clustered for diversity Commercially available from Aldrich at over 96% purity
Assayed in the SOS Chromotest assay for genotoxicity Induction of lacZ reporter gene under transcriptional control of SOS DNA
damage repair pathway 90% concordance with the Ames Assay High Reproducibility (± 0.05 fold) 193 compounds considered non-toxic 72 compounds considered weakly toxic 69 compounds considered strongly toxic
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Comparing Bad ApplesComparing Bad ApplesMethod % Concordance % Sensitivity % Specificity
ADAPT 72 69 74
TopKat (v5.0) 60 54 63
MultiCASE (A2I) 59 61 57
MultiCASE (SOS) 64 64 64
Leadscope† 74 65 83
DEREK (v5.0) 41 100 0
† Selected Leadscope fingerprints were combined with scaffolds and 8 properties.Logistic PLS method (50 factors) was used after selecting features – Preliminary Data.
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Improving Bad ApplesImproving Bad Apples
•You have a positive assessment, now what?
– Correct Molecular Context?• Supporting data?
– Interpolating or Extrapolating?• Is compound within model’s scope?
– Mechanistic Support?• Does the biochemistry make sense?
– Confirmatory Assay• Positive
– Develop with caution• Negative
– Feed data back into model(s)
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Exploration of New MethodsExploration of New MethodsStudy Method No. of
CompoundsGT+
Accuracy (%)GT-
Accuracy(%)Q
Overall Accuracy (%)
Snyder RD MCASEDEREKTOPKAT
394394394
48.151.943.4
95.175.188.1
89.673.681.7
Philip D. Mosier k-NN 140 66.7 92.9 85.0
Linnan He Consensus model developed with k-NN, LDA, and PNN classifiers
227 73.8 84.3 81.2
Brian E. Mattioni k-NN 334 69.3 74.1 72.2
BIDD group at NUS
C4.5PNNk-NNSVM
860860860860
55.674.170.477.8
75.080.286.592.7
70.778.982.989.4
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Another Example of Toxicity PredictionAnother Example of Toxicity Prediction
• Torsades de Point (TdP): A dangerous side effect of drugs which commonly act at ion channels in the heart to cause arrhythmia
• A common feature of many compounds is activity at the HERG channel (K+)
• Commonly, this is observed as an elongation of the so-called QT interval in an electrocardiogram of the heart (LQT)
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Example of SertindoleExample of Sertindole
atypical antipsychotic drug for schizophrenia
- licensed in UK May 1996
– prolonged QTc interval
– cardiac arrhythmias
– by November 1998, MCA/CSM received reports of 36 death and 13 serious but non-fatal arrhythmias
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LQTS (long QT Syndrome) LQTS (long QT Syndrome) Data SetData Set
• 122 Compounds, classified into four classes– Class 1: Drugs with Risk of Torsades de Pointes– Class 2: Drugs with possible Risk of Torsades de
Pointes– Class 3: Drugs to be Avoided by Congenital Long QT
Patients– Class 4: Drugs Unlikely to cause Torsades de Pointes
• Using subsets 1 and 4, double cross validation
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LQTS Prediction Results LQTS Prediction Results
by by Naïve Bayesian ClassifierNaïve Bayesian Classifier
• Best: ~80% correct predictions• Database not “finalized”• Confusion matrix
Predicted positive
Predicted negative
Compounds from positive set …
30 4
Compounds from negative set …
8 33
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Over-Predictions (1)Over-Predictions (1)
Fexofenadine
(“Allegra”, IC50 13m)
Example of Fexofenadine.It’s a modification of terfenadine which has a tertiary butyl group instead of a carboxylic acid. This lowers logD, and therefore it falls below the range of logD necessary for activity.
OH
N
OH
X
NH
N
N
H
H
X=CH3, SeldaneX=COOH Allegra(more hydrophilic)
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Over-Predictions (2)Over-Predictions (2)
Example: Sildenafil (Viagra). Positive in Analysis, but negative for Torsades.
Therapeutic ratio is high, 3.5nM at PDE5,But 100m at HERG.
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Under-PredictionsUnder-Predictions
Erythromycin A (membrane disrupting)
Loradatine (hydrolysed)
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SVM Prediction of TdP SVM Prediction of TdP
Training Testing Independent validation
TdP+ TdP- TdP+ TdP-
TP FN Accuracy %
TN FP Accuracy
%TP FN Accuracy
%TN FP Accuracy
%
64 103 11 0 100.0 29 1 96.7 9 1 90.0 28 1 96.6