Use of Predictive ADME in Library Profiling and Lead Optimization

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1 Use of Predictive ADME in Library Profiling and Lead Optimization Osman F. Güner and Robert D. Brown 223 rd ACS National Meeting April 2002, Orlando Florida

2 Why Predictive ADME in Early Discovery? The average cost of a new drug approval is $ million and requires years Discovery phase 1/3 cost Development phase 2/3 cost Only 3 in10 drugs achieve revenues greater than their development costs If use of predictive ADME tools in early discovery can improve the quality of the drug candidates and reduce the number of compounds entering to clinical trials by 20% Cost savings - $30-50M per drug!! Source:Pharma. Exec. Jan 2000; Windhover Information; Prentis Grabowski, 1994 Journal of Health Economics, Vol. 13

3 Bottlenecks in Drug Discovery Process Mechanistic Formulation of biochemical hypotheses In vitro assays Establishment of mechanistic assays Target availability Molecular biology, cloned receptors and enzymes Screening capacity Establishment of HTS and robotics Compound availability Combinatorial chemistry Screening capacity uhts Quality of leads Parallel optimization Candidate evaluation Predictive in silico models

4 Causes of Candidate Failures in Man , in the UK 10.1% 10.1% 11.1% 29.3% 39.4% Pharmacokinetics Efficacy Animal Toxicity Adverse Effects Business/Other 50% of failures due to ADME/Tox! Source: Prentis, et al., Br. J. Clin. Pharmac. 1988, 25,

5 Drugs Are More Than Binders to the Targets The ideal lead is a balance of potency, selectivity, pharmacokinetics, and toxicity profile Understanding and predicting drug response requires understanding ADME parameters Understanding a drug s potential side effects requires understanding its toxicity profiles Appropriate ADME/Tox properties are major determinants of good leads becoming good drugs

6 How Can We Improve the Success Rate? Evaluation of candidates MedChem leads are typically selected for potency and then optimized for pharmacokinetics later Develop models of ADME properties to use when selecting MedChem candidates Quality of screening hits Combinatorial libraries have tended to produce screening hits that are poor MedChem leads Develop computational tools for populating libraries with pharmaceutically relevant molecules. This should increase the probability that any screening hit is a good development candidate

7 Predictive ADME/Tox Models Could Facilitate Parallel Optimization Current process: sequential optimization of properties with backtracking Optimize potency Optimize selectivity Optimize bioavailability Minimize any toxicity Ensure chemical stability Process using computational models: design compounds with multiply optimized properties Drug Candidate

8 Constrained Diversity Naïve diversity (or similarity) selection assumes all subset libraries of equal diversity are equally desirable Some subset libraries may be more favorable than others Libraries that obey the combinatorial constraint are more synthetically efficient Some libraries may make deconvolution/decoding easier than others Some library molecules may be more desirable than others as hits in screens Good ADME properties Amenable to development in medicinal chemistry Some reagents may be more desirable than other Cost Availability (in-house, preferred vendor) Constrained by chemist

9 Library Optimization Protocol Stochastic Optimization of Rgroup Fragments using wholemolecule (products) properties Select Initial Fragments Identify corresponding products Evaluate Objective Function Yes Undo Mutation No Accept Mutation? Mutate one fragment in one Rgroup Repeat loop until convergence Evaluate Objective Function Identify corresponding products

10 Library Design Example 186x186 Dipeptide library Select 20x20 library Maximize diversity as measured by cell-based density Obey Lipinski-like rules Mw <500 Alogp98 <5 HBA <10 HBD <5 Optimize Diversity only Penalty only Diversity-penalty

11 Score Components Diversity Score Diversity Only* Penalty Score Penalty Only * with combinatorial constraint - cherry pick optimum = 0.99

12 Score Components Diversity Score Diversity Only* Diversity Penalty (x1) Diversity Penalty (x10) Penalty Score Penalty Only * with combinatorial constraint - cherry pick optimum = 0.99

13 Requirements for Lead Identification and Optimization Chemistry coverage Should cover a broad spectrum of chemistry High throughput Should run very fast Applicable to virtual libraries of 100,000s of compounds High quality Provide adequately reliable results C2.ADME C2.Absorption Human intestinal absorption model C2.BBB Blood-brain barrier penetration C2.Solubility Aqueous solubility

14 Absorption Model Dataset C2.Absorption 199 well absorbed molecules (>90%) 35 compounds <30% human gut absorption 181 were drugs or drug-like Validation dataset Physician s desk reference (PDR) Comprehensive medicinal chemistry database (CMC) Pharmacopeia libraries (PCOP) Factors controlling absorption Lipophilicity Hydrophilicity Size Hydrogen bonding

15 Absorption Model compounds >90% absorbed compounds <30% absorbed actively transported compounds AlogP PSA

16 Validation Studies Physician s desk reference 438 orally delivered compounds (Tablets, capsules, liquid suspensions) 77.6% are predicted to have 90% absorption, (95% confidence) (81.4% excluding actively transported compounds) 87.4% are predicted to have 90% absorption, (99% confidence) (90.1% excluding actively transported compounds) Comprehensive Medicinal Chemistry Database 7,577 compounds 5,836 drug-like compounds by listed class 75.0% drug-like are predicted to have 90% absorption (95% confidence) 83.5% drug-like are predicted to have 90% absorption (99% confidence)

17 Caco-2 Permeability Predictions for PCOP compounds P app (nm/s) 99% confidence ellipse 95% confidence ellipse AlogP98 PSA

18 Solubility Model Data Set 784 molecules data set 28 Alkanes 20 Alkenes and 9 alkynes 144 Halogen derivatives 70 Aromatic and cyclic 60 N-containing compounds Nitros, Nitriles, Amides 11 Amines 58 Alcohols 20 Ketones 9 aldehydes 27 Esters 14 Ethers 20 Acids 6 Sulfur-containing 48 Drugs and drug-like molecules 258 cmpds with multiple functional groups

19 Solubility Prediction for Combinatorial Libraries Rank = 5 1% Ran k = 1 3% Throughput 26 to 70 compounds/second 1 to 3 days for 7 million compounds Solubility ranking Rank = 4 18% Rank = 3 4% Rank = 2 33% Assigned rank LogS (mol/l) Comparison of Solubility with Drug 1 < Can not be a drug - Lower than 95% of drugs 2-8 to May or may not be a drug - Lower than 95% of drugs, borderline 3-6 to -4 - Can be a drug - At the lower end of 95% drugs 4-2 to - 4 Slightly soluble to soluble 5-2 to 0 Soluble 6 > 0 - Very soluble - Not many drug in this range

20 Solubility Model Training Set (784 Compounds) Test Set (34 Compounds) 3 3 R 2 = Predicted -5 Predicted Experimental Experimental R 2 = 0.84 and RMS = 0.87 R 2 = 0.88, RMSE = 0.79

21 Library Design Experiment UGI library = 10x10x10x10 Select 4x4x4x4 subset Optimize Diversity: Cell-based fraction Absorption: Good or moderate Solubility: Good or optimal

22 Optimizing Diversity vs Penalty Diversity Score Penalty Score Diversity Penalty Diversity = Cell based fraction Penalty = Absorption = {Good, Moderate} and Solubility = {Good, Optimal}

23 Optimizing Diversity vs Penalty Diversity Score Penalty Score Diversity Diversity(5): Penalty(1) Diversity(1): Penalty(1) Penalty Diversity = Cell based fraction Penalty = Absorption = {Good, Moderate} and Solubility = {Good, Optimal}

24 Absorption Profile - Full Virtual Library

25 Absorption Profile- Diversity only subset

26 Absorption Profile - Penalty Only Subset

27 Absorption Profile - Diversity:Penalty 5:1

28 Absorption % of total molecules Virtual Lib Diversity Diversity (5):Penalty (1) Diversity (1):Penalty (1) Penalty 0 Good Moderate Poor Very Poor

29 Solubility % of total molecules Very Low Low Good Optimal Very Sol Virtual Lib Diversity Diversity (5):Penalty (1) Diversity (1):Penalty (1) Penalty

30 401 Ki (nm) <1 nm 1-5 nm 5-50 nm nm nm um 1-5 um 5-10 um [1] [11] [3] [1] [8] [4] [2] [2] [1] [6] [5] [2] [1] [6] [1] [9] [5] [2] [6] [5] [9] [38] [18] [4] [5] [2] [5] [35] [51] [37] [2] [13] [8] [4] [2] [3] [4] [18] [32] [12] [12] [13] [16] [2] [7] [10] [37] [51] [11] [3] [3] [7] Predictive ADME in Lead Optimization An actual lead optimization process at Pharmacopeia Labs. Focused libraries have been generated to increase potency until Nov 99. Then the predictive absorption model is also incorporated into the lead optimization process. The pie charts list Absorption characteristics for each batch um [4] [5] [32] [45] [17] [4] [1] [2] >50 um [1] [7] [28] [67] [17] [2] [7] [3] [4] [1] May99 Jun99 Jul99 Aug99 Sep99 Oct99 Nov99 Dec99 Jan00 Feb00 Mar00 Binned Date Received

31 401 Ki (nm) <1 nm 1-5 nm 5-50 nm nm nm um [1] [11] [3] [1] [8] [4] [2] [2] [1] [6] [5] [2] [1] [6] [1] [9] [5] [2] [6] [5] [9] [38] [18] [4] [5] [2] [5] [35] [51] [37] [2] [13] [8] [4] [2] Predictive ADME in Lead Optimization New libraries are optimized based on combined properties of binding affinity as well as predicted absorption characteristics. 1-5 um [3] [4] [18] [32] [12] [12] [13] [16] [2] 5-10 um [7] [10] [37] [51] [11] [3] [3] [7] um [4] [5] [32] [45] [17] [4] [1] [2] >50 um [1] [7] [28] [67] [17] [2] [7] [3] [4] [1] May99 Jun99 Jul99 Aug99 Sep99 Oct99 Nov99 Dec99 Jan00 Feb00 Mar00 Binned Date Received

32 Conclusions ADME models allow high throughput prediction of Absorption Aqueous solubility Blood-brain barrier presentation These can be used in a stand-alone manner or in a library design encompassing Diversity or similarity ADME profile Reagent properties For smaller synthetic libraries each molecule must contribute maximum information content Pharmaceutically relevant leads will produce more efficient medicinal chemistry projects

33 Acknowledgements and References Cerius2 Diversity & LibProfile Dr Marvin Waldman Dr Moises Hassan Dr Zahra Parandoosh ADME Robert D. Brown, Moises Hassan and Marvin Waldman, "Combinatorial Library Design for Diversity, Cost Efficiency, and Drug-like Character", J. Mol Graphics Mod. 2000, 18, Professor Kenneth Merz Dr Bill Egan Dr Ailan Chang Egan, W. J.; Merz Jr., K. M.; Baldwin, J. J. "Prediction of Drug Absorption Using Multivariate Statistics," J. Med. Chem. 2000, 43, Mr. Giorgio Lauri