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CZ 3253: Computer Aided Drug design Lecture 6: QSAR part II Prof. Chen Yu CZ 3253: Computer Aided Drug design Lecture 6: QSAR part II Prof. Chen Yu Zong Tel: 6874 -6877 Email: [email protected] edu. sg http: //xin. cz 3. nus. edu. sg Room 07 -24, level 7, SOC 1, National University of Singapore

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

Commercial Software Commercially available toxicity estimation packages are available to predict a variety of 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 • Hazard. Expert – www. compudrug. com/hazard • CASE (Computer Automated Structure Evaluation) – www. multicase. com • TOPKAT (Toxicity Prediction by Computer Assisted Technology) – www. accelrys. com/products/topkat • Onco. Logic – www. logichem. com 3

Pharma Algorithms Providers of Databases, Predictors and Development Tools N Log. P 10, 000 Pharma Algorithms Providers of Databases, Predictors and Development Tools N Log. P 10, 000 DMSO Solubility 22, 000 p. Ka 8, 000 Stability at p. H < 220, 000 Aqueous Solubility 5, 500 Permeability (HIA) 1, 000 Active Transport 500 Pgp Transport 1, 000 Oral Bioavailability (Human) 900 LD 50 Intraperitoneal 36, 000. . . 4

Pharma Algorithms Development Tools Algorithm Builder development platform: • Data storage and manipulation • 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 5

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

“Causal” Descriptors Atom chains One-atom ( “Causal” Descriptors Atom chains One-atom ("topological") Examples Activity effects HO O Non-specific (size, PSA) O HO COOH, CONH N HO Specificity Fragment Size Three-atom HO Ionization, H-bonding N Cl HO Five-atom HO O Reactivity, internal interactions N Larger chains, Ring scaffolds O O HO HO N O O N N H O Similarity to natural compounds 7

Algorithm Development • Graphical Interface provides easy to use tools for programming complex algorithms 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 8

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

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

What Is LD 50 A dose that kills 50% of animals during 24 hrs 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 11

Complexity of LD 50 Empirical knowledge + simulations “Reactivity + log P ” Informatics Complexity of LD 50 Empirical knowledge + simulations “Reactivity + log P ” Informatics Empirical knowledge Toxicologists PK Specialists 12

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

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

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

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

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

How Much Knowledge? QSAR Model Knowledge Log 1/LD 50 = ai xi Expert Deduction How Much Knowledge? QSAR Model Knowledge Log 1/LD 50 = ai xi Expert Deduction Active C-SAR + Deduction Active Inactive Little Knowledge Inactive More Knowledge Active Inactive Struct. Space 18

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

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

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

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

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

What is novel? The novel features of the Pharma Algorithms approach are: • Combination 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. 2. 3. 4. 5. Chain fragmentation and PLS with multiple bootstrapping Selection of best fragments with ‘stable’ increments Derivation of multiple models from subsets of the training set to produce ranges of predictions Selection of the best model to use for a particular compound by comprison of the different ranges Calculation of the confidence interval from the range of predictions produced by the most appropriate model 24

Screening the Specs DB SPECS are a supplier of diverse compound screening collections A 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 30 min 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) 25

Toxic Skeletons Most significant 26 Toxic Skeletons Most significant 26

What We Have Learned So Far Screening for basal cytotoxicity is not enough The 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? 27

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

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

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

Cost/Benefit Considerations § In silico Bioavailability and Toxicity predictions for compound collections are inexpensive 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 31

Drug Design General Principles § Aim for low log. P § Aim for low Drug Design General Principles § Aim for low log. P § 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 32

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