Understanding Compound Quality Focus on Molecular Property Design - PowerPoint PPT Presentation

Understanding Compound Quality Focus on Molecular Property Design Paul D Leeson Paul Leeson Consulting Ltd paul.leeson@virgin.net A high level view Oral small molecules Guiding Optimal Compound Design and Development, Boston, 19 th March 2015

Success rates: Preclinical-Phase III 4.3% ; Phase II 23% Evidence for progression of unoptimised compounds • Pfizer: ‘4 Pillars’ for phase II success (44 phase II projects, 2005-9) – Exposure at target; Binding to target; Pharmacological response; Target linked clinically to disease modification – Low confidence in exposure in 18/34 non-progressing molecules: “cannot conclude mechanism tested adequately in 43% of cases” • AstraZeneca: ‘5Rs’ (>114 preclinical to phase II projects, 2005-10) – ‘Right’: Target & Tissue (4Ps); Safety; Patient; Commercial potential – 29% Clinical efficacy failures “dose limited by compound characteristics or tissue exposure not established” – Decision making process: eg, 38% projects advanced to clinic had low confidence in safety & 78% of these eventually failed due to toxicity • GSK: solubility-limited candidates – BCS II/DCS class IIb – Add 2 years to development: “lack of efficacy owing to lack of exposure” • FDA submissions (302 NMEs, 2000-12; 151 (50%) unsuccessful 1 st time) – 29% Unsuccessful 1 st submissions had dose or clinical end point issues Success rates : Thomson Reuters, 2006-10; 4 Pillars: Morgan et al, Drug Discovery Today 2012, 17 , 419; Bunnage, et al Nat. Chem. Biol . 2013, 9 , 195; 5Rs: Cook et al, Nat. Revs. Drug Disc. 2014, 13 , 419; Solubility: Hann & Keserű , Nat. Rev. Drug Disc . 2012, 11 , 355; FDA: Sacks et al, JAMA 2014, 311 , 378; Pharma’s problems: Scannell et al, Nat. Rev. Drug Discov . 2012, 11 , 191

A Significant Body of Evidence links Physical Properties to Probability of ADMET Risk Key properties: lipophilicity + ionisation. Property forecast index (PFI) ≥67% 34 -66% <33% % chance of achieving target in particular bin PFI : Young et al, Drug Disc. Toda y 2011, 16 , 822; Physical property reviews : Meanwell, Chem. Res. Toxicol . 2011, 24 , 1420; Young, Top Med. Chem . 2015, 9 , 1; Gleeson et al, in The Handbook of Medicinal Chemistry: Principles and Practice , eds A.M. Davis and S. Ward, RSC, 2015, p1-31; Hann & Keserű , Nat. Rev. Drug Disc . 2012, 11 , 355; Gleeson et al. Nat. Rev. Drug Disc. 2011, 10 , 197 ; Lipophilicity: Waring, Exp. Op. Drug Disc . 2010, 5 , 235; Ionisation : Charifson & Walters, J. Med. Chem. 2014, 57, 9701; Ar rings review : Ritchie & Macdonald, J. Med. Chem. , 2014, 57 , 7206; Critique - statistics : Kenny & Montanari, J. Comp.-Aid. Mol. Des . 2013, 27 , 1; Critique - toxicity data : Muthas et al, MedChemCommun . 2013, 4 , 1058

Properties of Patented Compounds & Oral Drugs cLogP (1-octanol/water) Mol Wt Patent targets 2000-11 % Drugs or Patent targets Patent targets 2000-11 % Drugs or Patent targets Oral drugs published post 1980 Oral drugs published post 1980 20 30 25 15 20 15 10 10 5 5 0 0 cLogP bin Mol Wt bin • ‘Inflated’ patented compounds are likely to possess increased ADMET risks vs recently marketed drugs  pipeline attrition? • Will the probability of success in a portfolio of drug candidates increase as its balance of biological and physicochemical properties more closely resembles that of successful marketed drugs? • What other viable strategies exist for medicinal chemists to improve productivity? • Compound quality is a medicinal accountability. Fixed at the point of design, controllable in optimisation, must not be the root cause of clinical attrition Drug data: Leeson et al , Med. Chem. Comm . 2011, 2 , 91, updated to 2014 Patent data : Leeson & St-Gallay, Nature Revs. Drug Disc. 2011, 10 , 749

Oral ‘Druglike’ Properties: Changes over Time Median 291 324 313 308 331 339 371 376 416 409 451 2.30 2.34 2.73 2.74 2.96 2.59 2.37 2.46 3.01 3.15 4.07 n 144 223 302 236 217 164 141 107 78 53 85 144 223 302 236 217 164 141 107 78 53 85 8 600 6 500 Mol Wt cLogP 4 400 2 300 0 -2 200 -4 100 1950 - 54 1955 - 59 1960 - 64 1965 - 69 1970 - 74 1975 - 79 1980 - 84 1985 - 89 1990 - 94 1995 - 99 2000 + 1950 - 54 1955 - 59 1960 - 64 1965 - 69 1970 - 74 1975 - 79 1980 - 84 1985 - 89 1990 - 94 1995 - 99 2000 + Publication Year Bin Publication Year Bin Increasing significantly ~10-20 years No change until 2000 + • Least change: cLogP, HBD, %PSA, Fsp3 & chiral atoms • Most change: Mol Wt, HBA, RotB, PSA & Ar; all increasing Hypothesis: drug properties changing least are more important Global oral drug approvals to end 2014. Property vs time publications : Leeson & Davis, J. Med. Chem 2004 , 47 , 6338; Proudfoot, Bioorg. Med. Chem. Lett. 2005, 15 , 1087; Leeson & Springthorpe, Nat. Rev. Drug Disc. 2007 , 6 , 881; Walters et al, J. Med. Chem . 2011, 54 , 6405; Leeson et al , Med. Chem. Comm . 2011, 2 , 91; Phase I-III data : Wenlock et al, J. Med. Chem . 2003, 46, 1250

Does Size Matter? Mol Wt AZLogD <300 >0.5 AZLogD limits required 300-350 >1.1 to achieve >50% chance 350-400 >1.7 of high permeability for 400-450 >3.1 a given Mol Wt 450-500 >3.4 >500 >4.5 GSK: ADME ‘4/400’ rule AZ: Mol Wt & LogD dependent permeability Gleeson, J. Med. Chem . 2008, 51 , 817 Waring, Bioorg. Med. Chem. Lett., 2009 , 19 , 2844 Mol Wt vs cLogP vs TPSA n= 2138 oral drugs Acid Base Neutral Mol Wt Zwitterion eLogD Pfizer: ‘Golden triangle’ Ro5 QSAR: cLogP = 0.0173 Mol Wt - 0.564 O+N - Johnson et al, Bioorg. Med. Chem. 0.439 OH+NH + 0.246 n=2138, r 2 = 0.616 Lett ., 2009, 19 , 5560

Inflation of ‘Druglike’ Physical Properties Oral Drugs Publication Decade 18 Companies Patents 2000-11 Oral Drugs Publication Decade Orals Phase I-III 2014 Orals Phase I-III 2014 500 500 Mol Wt <400 + cLogP <3 Wy BMS 44% post 1950 drugs BI 475 475 S-a Tak 6.6% Patent targets Nov SP Mrk GSK 450 450 Ro Lly BS AZ Orals Phase I-III 2014 (456) Orals Phase I-III 2014 (456) Median Mol Wt Median Mol Wt 425 425 Pfz Amg 1990s on (216) 1990s on (216) Abt Vtx 400 400 Mean values Chiral C Fsp3 Ar ring 375 375 1980s (375) 1980s (375) Post 1950 oral 1.65 0.43 1.77 350 350 drugs (n=1750) 1970s (381) 1970s (381) Patent targets 1.01 0.32 2.55 325 325 1950s (367) 1960s (538) 1950s (367) 1960s (538) (n=2605) 300 300 2 2 2.25 2.25 2.5 2.5 2.75 2.75 3 3 3.25 3.25 3.5 3.5 3.75 3.75 4 4 4.25 4.25 4.5 4.5 Median cLogP Median cLogP Drug data: Leeson et al , Med. Chem. Comm . 2011, 2 , 91, oral drugs updated to 2014 ; Patent targets 2000-11 from 18 companies: Leeson & St-Gallay, NRDD 2011, 10 , 749; Phase I-III orals : http://www. citeline.com/

Disease Risk/Benefit & Property Inflation 36% 2012-14 FDA approvals are orphan drugs post-1990 Orals (n=216) Median cLogP Median Mol Wt ≥2 Ro5 unmet Kinase, HIV prot., HCV (n=45) 4.64 556 40% (18) Others (n=171) 3.07 420 12% (20) Pre-90: 6.5% Telaprevir: HCV NS3 protease Lapatinib: EGFR & ErbB2 kinases F N O Cl N H O N O O NH O NH O O S cLogP 5.8 H cLogP 5.4 NH HN N H O N Mol Wt 581 Mol Wt 680 O N O H N Dose 750mg tid , high fat food; sol. Dose 1500mg uid , 1hr before or after 4.7 μ g/ml, ‘less than marble;’ SDD meal; sol. 7 μ g/ml; hERG inhibitor; formulation; Black Box: serious skin Black Box: hepatotoxic; slow off-rate; reactions; efficacious, superceded standard treatment for breast cancer Medical need & efficacy can overcome risk & dosing inconvenience Telaprevir: Kwong et al, Nat. Biotech. 2011, 29 , 993; Lapatinib: Lackey & Cockerell in Kinase Inhibitor Drugs, Wiley, 2009, p41; Cancer drugs & food interaction: Weitschies, Clin. Pharm. & Therapeutics 2013, 94 , 441

Physical Properties Tend to Increase in Optimisation: the ‘Leadlike’ Hypothesis Leadlike Oral Typical early library drugs combinatorial 25 Library % Compounds 20 15 10 5 0 100 200 300 400 500 600 700 Molecular Weight ‘Leadlike’ lead: Affinity >0.1 μ M; Mol Wt 100-350; cLogP 1-3 Leadlikeness: Teague, Davis, Leeson & Oprea, Angew. Chem. Int . Ed . 1999, 38 , 3743; Oprea et al, J. Chem. Inf. Comput. Sci . 2001, 41 , 1308; Hann et al, J. Chem. Inf. Comput. Sci. 2001, 41 , 856; Synthetic challenges: Doveston et al., Org. Biomol. Chem . 2015, 13 , 859

Property Inflation in Optimisation Leadlike hypothesis : Teague et al, Angew. Chem. Int . Ed . 1999, 38 , 3743 n 1. Lead to drug - historical 469 Lead to Drug 6 6 Mol Wt ↑ 79% 5 5 2. Lead to drug - historical 62 8 8 cLogP ↑ 58% 3. Lead to drug, post 1990 60 7 7 Median cLogP Median cLogP Median cLogP 4. 1 st Drug to follow-on 74 10 5. Lit 2000s optimisation 1680 3 3 3 6. Lit 2000s HTS, hit-to-lead 335 1 1 1 2 2 2 7,8. HTS file/lead/patents 4 companies 9 9 4 4 4 9. Fragment optimisation 145 10. Lit 2000s LLE opt’n 57 LLE = p(Activity) – LogP/D Median Mol Wt Median Mol Wt Median Mol Wt 1. Hann, J.Chem. Inf. Comput. Sci. 2001, 41 , 856; 2. Oprea, J. Chem. Inf. Comput. Sci . 2001, 41 , 1308; 3. Perola, J. Med. Chem . 2010, 53 , 2986; 4 . Giordanetto, DDT 2011 ,16 , 722 ; 5. Morphy, J. Med. Chem. 2006, 49 , 2969; 6. Keseru, NRDD 2009, 8 , 203; 7. Macarron, NRDD 2011, 10 , 188; 8. Leeson, NRDD 2011, 10 , 749; 9. Ferenczy J. Med. Chem. 2013, 56 , 2478; 10. Hopkins, NRDD, 2014, 13 , 105