1. CZ3253: Computer Aided Drug design Lecture 6: QSAR part II Prof. Chen Yu Zong Tel: 6874-6877 Email: csccyz@nus.edu.sg http://xin.cz3.nus.edu.sg Room 07-24, level 7, SOC1, National University of Singapore csccyz@nus.edu.sg http://xin.cz3.nus.edu.sgcsccyz@nus.edu.sg http://xin.cz3.nus.edu.sg

2. 2 Examples of QSAR Applications: Application of in silico technology to screen out potentially toxic compounds using expert and QSAR models

3. 3 Commercial Software Commercially available toxicity estimation packages are available to predict a variety of toxic endpoints including mutagenicity, carcinogenicity, teratogenicity, skin and eye irritation and acute toxicity: DEREK (Deductive Estimation of Risk from Existing Knowledge)- www.chem.leeds.ac.uk/luk HazardExpert – www.compudrug.com/hazard CASE (Computer Automated Structure Evaluation) – www.multicase.com TOPKAT (Toxicity Prediction by Computer Assisted Technology) – www.accelrys.com/products/topkat OncoLogic – www.logichem.com

4. 4 Pharma Algorithms N 10,000 22,000 8,000 20,000 5,500 1,000 500 1,000 900 36,000... LogP DMSO Solubility pKa Stability at pH < 2 Aqueous Solubility Permeability (HIA) Active Transport Pgp Transport Oral Bioavailability (Human) LD50 Intraperitoneal... Providers of Databases, Predictors and Development Tools

5. 5 Pharma Algorithms Development Tools Algorithm Builder development platform: Data storage and manipulation Generation of fragmental descriptors Statistical procedures: MLR, PLS, PCA, Recursive Partitioning, HCA Tools for predictive algorithm development

6. 6 Generation of Descriptors... Y Structure 1 2 N... F 1 F 2 F M F 3

7. 7

8. 8 Algorithm Development Graphical Interface provides easy to use tools for programming complex algorithms Combine fragmental, descriptor and similarity based methods Use logical expressions, conditions and equations based on descriptors, sub-fragments, internal interactions or any other chemical criteria Combine multiple sub-algorithms into general algorithms Rapidly develop ‘custom’ filters incorporating ‘expert’ in-house or project specific rules

9. 9 Our focus Tox Effects in Drug Design Tox Effect Acute (LD 50 ) Organ-specific effects Mutagenicity Reproductive effects Carcinogenicity Programs Topkat, AB/LD 50 AB/Tox* (next version) Many programs, AB/Tox Many programs, AB/Tox*

10. 10 Existing Programs QSAR Expert Other DEREK HAZARD TopKatQickProp ADMELD 50 AB/ToxAB/LD 50 AB/Oral %F Mixed Combinations of above “Manually” derived skeletons COMPACT Combined Descriptors C-SAR M-CASE“Statistical” skeletons META Will consider these

11. 11 What Is LD 50 A dose that kills 50% of animals during 24 hrs In drug design, used at pre-clinical stage In early stages, replaced with “reductionist” considerations Some scientists question its utility

12. 12 InformaticsToxicologistsPK Specialists “Reactivity + log P ” Empirical knowledge Empirical knowledge + simulations Complexity of LD 50

13. 13 Is this good enough? Acute Tox in Drug Design Lead Selection No tests performed Reactive groups discarded Lead Optimization Basal cytotoxicity tested Intra-cellular effects considered Pre-clinical Stage Animal tests are required ADME effects considered

14. 14 Acute Tox in Drug Design An LD 50 Model for mouse (intraperitoneal administration) was developed using data from the RTECS database (35,000 compounds)

15. 15 Distribution of Acute Effects Extra-cellular effects - may be “invisible” in cytotoxic assays RTECS DB: mouse, intraperitoneal administration LD 50 < 50 mg/kg (N = 4,099) All compounds (N ~ 35,000)

16. 16 In Vivo vs. In Vitro IC 50 cannot model LD 50 when extra-cellular effects occur

17. 17 How to Predict These Effects? Quality of Predictions = Knowledge of Specific Effects How much knowledge do we get? “Reductionist” QSARs do not work LD 50 involves much more than “log P + reactivity”

18. 18 How Much Knowledge? QSAR Model Log 1/LD 50 =  a i x i Knowledge Expert DeductionLittle Knowledge ActiveInactive More Knowledge C-SAR + Deduction ActiveInactiveActiveInactive Struct. Space

19. 19 C-SAR + Deduction LD 50 values are split into groups using fragmental descriptors from AB The most significant skeletons are “potential toxicophores”

20. 20 Specific Effects in AB/LD 50 > 33,000 Compounds with LD 50 from RTECS DB

21. 21 Low-Specific Effects Small non-bases are least toxic. Hydrophobic amines are most toxic Arrows denote increasing toxicity

22. 22 C-SAR + Deduction To get new knowledge, statistics must help deduction. To use QSAR models, they must work in narrow structural spaces. Efficacy Comparison Knowledge Struct. Diversity Effort Expert Deduction QSAR Model

23. 23 QSAR Models in AB/LD 50 1. Narrow struct. spaces 2. Dynamic fragmentation 3. “Causal” parameters

24. 24 What is novel? The novel features of the Pharma Algorithms approach are: Combination of approaches used separately in earlier software i.e. Expert Rues (e.g. DEREK), C-SAR (e.g. CASE) and QSAR (e.g. TOPKAT) Reliable Confidence Intervals are generated from QSAR models (class specific and global) that are derived using an automated multi-step process: 1.Chain fragmentation and PLS with multiple bootstrapping 2.Selection of best fragments with ‘stable’ increments 3.Derivation of multiple models from subsets of the training set to produce ranges of predictions 4.Selection of the best model to use for a particular compound by comprison of the different ranges 5.Calculation of the confidence interval from the range of predictions produced by the most appropriate model

25. 25 Screening the Specs DB SPECS are a supplier of diverse compound screening collections A set (N = 14,902) was randomly selected (from > 200,000) and screened using the AB/LD 50 toxicity predictor. Calculation of LD 50 for the set takes about 30min on a standard Windows laptop Compounds were deemed “Toxic” if LD 50 < 50 mg/kg Results: Overall only 2.7% were “toxic” (i.e. 310 of 14,902) As expected a higher proportion (3.9%) of the bases (i.e alkylamines) were toxic (i.e. 92 of 2,351)

26. 26 Most significant Toxic Skeletons

27. 27 What We Have Learned So Far Screening for basal cytotoxicity is not enough The “C-SAR + Deductive” method opens new possibilities The extra-cellular effects can be estimated in silico Can we model in vivo toxicity?

28. 28 Administration vs. ADME ORIVScIP ADME Effects OR – Oral Sc – Subcutaneous IP – Intraperitoneal IV – Intravenous Stomach Intestine Vein Liver Toxic action Dissolution, permeation, hydrolysis, metabolism IV OR Tissue, organs

29. 29 InformaticsADME Specialists “Simple descriptors”“Simulations” Complexity of ADME “Simple descriptors” disregard many factors. Can we simulate them in HT mode?

30. 30 Oral %F Prediction in HT Mode Reliability validated by the consistency of independent predictions Non-Batch Interface:

31. 31 Cost/Benefit Considerations  In silico Bioavailability and Toxicity predictions for compound collections are inexpensive to perform  The value of predictions is variable- Decisions still need to be made by expert scientists in a project context  In silico tools can assist the expert in a detailed evaluation of ‘hits’, ‘leads’ and ‘candidates’ but there is a need for: 1. Predictions for a range of toxicity types:  LD 50 (oral, i.v.,s.c.)  Genotoxicity and Carcinogenicity  Organ specific Effects (e.g. hepatotoxicity) 2.Integration of the prediction software with databases containing the training data so that the availability and behaviour of similar compounds can be checked

32. 32 Drug Design General Principles  Aim for low logP  Aim for low M.Wt. C. Hansch et. al. ‘ The Principle of Minimal Hydrophobicity in Drug Design’ J. Pharm. Sci., 1987, 76, 663 M.C. Wenlock et. Al. ‘Comparison of Physicochemical Property Profiles of Development and Marketed Oral Drugs’ J. Med. Chem., 2003, 46, 1250

33. 33 Simulations in HT Screening Activity Tox %F%F “Reductionist” Methods: High Activity = Low %F + High Tox HT Simulations aim at: High Activity = High %F + Low Tox Activity Tox %F%F Very rough estimations, assuming that activity increases with increasing log P and MWt