Strategic Testing

1. CZ3253: Computer Aided Drug designLecture 10: Overview of Drug Testing Methods II: Test of TOXProf. Chen Yu ZongTel: 6874-6877Email: csccyz@nus.edu.sghttp://xin.cz3.nus.edu.sgRoom 07-24, level 7, SOC1, National University of Singapore

2. Toxic Effects Pathways Identify early critical events Distributed Databases High Quality Data Sets Strategic Testing Structural Requirements For Pathways Select Validation Chemicals Chemical Inventories Regulatory Acceptancy Criteria Estimation of Missing Data QSAR Libraries Rules Collections NN, SVM Classifiers Modeling Engine Analogue Identification Prioritization/Ranking QSAR Models Rules NN,SVM

3. Structure Individual Organ Cellular Molecular Chemical 2-D Structure Altered Reproduction/ Development ER Transactivation VTG mRNA Vitellogenin Induction Sex Steroids ER Binding Chemical 3-D Structure/ Properties Mapping Toxicological Paths to Adverse Outcomes “Estrogen Signaling Pathway” Initiating Events Impaired Reproduction/Development Library of Toxicological Pathways

4. 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 Analysis of Commercial Computational Toxicology Software • 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

5. 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 Strengths and Weaknesses of Virtual Toxicology Commercial Software • 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

6. Neural 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. New Toxicology Prediction Methods

7. Predictive 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. • Biasmodel 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?).

8. Requirements for a Quality 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

9. Annotation 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

10. 2002 Validation Expanded Data Set Drugs: 534 Compounds All Data: 1825 Compounds BMS: 416 Compounds • ~5% of BMS space covered by validation compounds. • ~10% of drug space covered by validation compounds.

11. Which program works best?A combination of two Random Concordance Indeterminate False (-) False (+) 0 100 200 300 400 500 600 700 800 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 Random Sequential DK/MC & DK/TPK Sequential DK/MC/TPK

12. Improving the System:Correction of the 2o 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.

13. Improving the System:Substructures Identified by BMS

14. Predictive ToxicologyComparing 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

15. Comparing Bad Apples † Selected Leadscope fingerprints were combined with scaffolds and 8 properties. Logistic PLS method (50 factors) was used after selecting features – Preliminary Data.

16. Improving 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)

17. Exploration of New Methods

18. Another 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)

19. Example 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

20. LQTS (long QT Syndrome) Data 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

21. LQTS Prediction Results by Naïve Bayesian Classifier • Best: ~80% correct predictions • Database not “finalized” • Confusion matrix

22. Over-Predictions (1) 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. Fexofenadine (“Allegra”, IC50 13m) X=CH3, Seldane X=COOH Allegra (more hydrophilic)

23. Over-Predictions (2) Example: Sildenafil (Viagra). Positive in Analysis, but negative for Torsades. Therapeutic ratio is high, 3.5nM at PDE5, But 100m at HERG.

24. Under-Predictions

25. SVM Prediction of TdP