1. Library Design for Leadlike Compounds: A Historical Perspective Tudor I. Oprea EST Lead Informatics

2. Medicinal Chemistry / AstraZeneca Mölnal Tudor I. Oprea What Is A Lead? Many compounds are active, but not all actives are leads Leads have to meet project dependent criteria: biological activity validated, both in primary and secondary screens, against known targets, for a series of compounds (when available) must be patentable, and display good initial DMPK profile

3. Medicinal Chemistry / AstraZeneca Mölnal Tudor I. Oprea Common Sources for Leads in Drug Discovery One needs to distinguish “leadlike” leads from other sources of lead structures, e.g., natural products that are high-affinity compounds (NPY or taxol are leads!) or from “druglike” leads that are marketed structures (e.g., propranolol)

4. Medicinal Chemistry / AstraZeneca Mölnal Tudor I. Oprea Is There A Leadlike Space? There is a general consensus that lead discovery is an essential goal that precludes drug discovery For the time being, the only way to analyse the nature of the “leadlike” space is to examine the structures that, historically, were leads. The problem is: can these structures provide an objective link between lead-space and drug-space? Can we define how these two spaces overlap?

5. Medicinal Chemistry / AstraZeneca Mölnal Tudor I. Oprea Is There A Leadlike Space? (2) Initial goal of the retrospective leadlike analysis: gather as much information as possible about leads, i.e., which drug has been developed from which lead Only a few authors describe the chemical structure of the lead compound that was used to derive a given drug. Lead structures are often disclosed in a series (SAR), making it difficult to pinpoint at a given compound. Furthermore, a drug can have 1 or more leads a lead can be a drug a lead can lead to several drugs.

6. Medicinal Chemistry / AstraZeneca Mölnal Tudor I. Oprea Mifepristone (RU486): Drug with multiple leads: Mifepristone originates from progesterone and RU2323 Progesterone RU2323 RU486

7. Medicinal Chemistry / AstraZeneca Mölnal Tudor I. Oprea Cocaine: Drugs that are leads: Cocaine (local anesthetic) was the lead for procaine (local anesthetic) which was, in turn, the lead for procainamide (antiarrhythmic) Cocaine Procaine Procainamide

8. Medicinal Chemistry / AstraZeneca Mölnal Tudor I. Oprea Diltiazem: Classes of leads that have generate drugs with diverse medical applications: benzodiazepines are a well described class of leadlike structures that resulted in several drugs, ranging from CNS agents (hypnotics, anxiolytics) to calcium channel blockers and ACE inhibitors 26 launched BZDs in MDDR Thiazesim Oxazepam Diltiazem

9. Medicinal Chemistry / AstraZeneca Mölnal Tudor I. Oprea M.W. & CMR Av. 302 Md. 315 Av. 391 Md. 384 Av. 8.3 Md. 8.4 Av. 10.6 Md. 10.2 62 “pure” leads 75 “pure” drugs CMR MW 0 5 10 15 20 156208260312364416468520 572 624 Frequency 0 2 4 6 8 10 12 14 16 129182234286339391444496548 Frequency 0 2 4 6 8 10 12 345678910111213141516 Frequency 0 2 4 6 8 10 12 14 345678910111213141516 Frequency

10. Medicinal Chemistry / AstraZeneca Mölnal Tudor I. Oprea RNG & RTB Av. 2.37 Md. 2 Av. 3.36 Md. 3 Av. 5.48 Md. 4 Av. 7.51 Md. 6 62 “pure” leads 75 “pure” drugs RTB RNG Frequency 0 5 10 15 20 25 0123456 0 1 2 3 4 5 6 7 8 9 10 0123456789 More 0 2 4 6 8 10 12 0123456789101112More 0 2 4 6 8 10 12 14 16 18 20 01234567

11. Medicinal Chemistry / AstraZeneca Mölnal Tudor I. Oprea CLogP & LogD7.4 Av. 0.72 Md. 0.72 n=57 Av. 1.69 Md. 1.69 n=71 62 “pure” leads75 “pure” drugs LogD7.4 Av. 1.57 Md. 2.11 n=58 Av. 2.73 Md. 2.54 n=70 CLogP 0 2 4 6 8 10 12 - 7.5- 6.5- 5.5- 4.5- 3.5- 2.5- 1.5- 0.5 0.5 1.5 2.5 3.5 4.5 5.5 Frequency 0 2 4 6 8 10 12 14 - 7.5- 6.5- 5.5- 4.5- 3.5- 2.5- 1.5- 0.5 0.5 1.5 2.5 3.5 4.5 5.5 Frequency 0 2 4 6 8 10 12 -2.3-1.8-1.3-0.8-0.30.30.81.31.82.32.83.33.84.34.85.35.8 Frequency 0 1 2 3 4 5 6 7 -3.8 -2.8 -1.8-0.8 0.31.32.33.34.35.3 Frequency

12. Medicinal Chemistry / AstraZeneca Mölnal Tudor I. Oprea Donors & Acceptors Av. 4.8 Md. 4 Av. 5.8 Md. 5 62 “pure” leads75 “pure” drugs HAC Av. 2.3 Md. 2 Av. 2.1 Md. 2 HDO 0 2 4 6 8 10 12 14 16 01234567891011 Frequency 0 2 4 6 8 10 12 14 012345678 9101112More Frequency 0 5 10 15 20 25 0123456789 Frequency 0 2 4 6 8 10 12 14 0123456789101112More Frequency

13. Medicinal Chemistry / AstraZeneca Mölnal Tudor I. Oprea Druglike Score Av. 0.66 Md. 0.66 Av. 0.81 Md. 0.81 Av. 0.45 Md. 0.54 Av. 0.54 Md. 0.66 62 “pure” leads75 “recent” drugs PPFS DFPS 0 5 10 15 20 0.30.40.50.60.70.80.91.0 Frequency 0 2 4 6 8 10 12 14 0.40.50.60.70.80.91.0 Frequency 0 5 10 15 20 0.40.50.60.70.80.91.0 Frequency 0 5 10 15 20 0.30.40.50.60.70.80.91.0 Frequency

14. Medicinal Chemistry / AstraZeneca Mölnal Tudor I. Oprea Is There A Difference? IndexRNG RTB RGB MWHDO HAC CLP Mean0.55 1.90 3.34 78.97-0.18 0.45 1.25 Std Err.0.13 0.42 0.67 10.13 0.2 0.23 0.31 Median0 2 3 69.88 0 1 0.67 STDEV1.06 3.41 5.45 82.95 1.62 1.88 2.58 Min.-2 -12 -11 -120.1 -5 -5 -5.95 Max.5 12 24 386.3 3 5 9.7 Data from 67 drug-lead unique pairs. For more than 1:1 correspondence, only larger structures were considered.

15. Medicinal Chemistry / AstraZeneca Mölnal Tudor I. Oprea Is There A Real Difference?! IndexRNG RTB RGB MWHDO HAC CLP Mean1.0 2.48 5.86 111-0.21 0.41 1.90 Std Err.0.14 0.61 0.73 16.6 0.38 0.41 0.47 Median1 2 6 96.2 0 1 0.21 STDEV0.78 3.3 3.93 89.2 2.0 2.2 2.54 Min.0 -3.0 -1 -19 -5.0 -5.0 -3.74 Max.3 12 13 386.3 3 4 9.7 Data from 29 recent drug-lead unique pairs. For more than 1:1 correspondence, only larger structures were considered.

16. Medicinal Chemistry / AstraZeneca Mölnal Tudor I. Oprea What Have We Learned? There is a difference between “leads” and “drugs” this difference is apparent mostly in pairwise comparisons However, this difference is apparent mostly in pairwise comparisons, as the “leadspace” and “drugspace” appear to overlap 1 ring, 2 rotatable bonds, 100 daltons, 1 acceptor and 0.5-1 LogP unit Average difference: 1 ring, 2 rotatable bonds, 100 daltons, 1 acceptor and 0.5-1 LogP unit Scoring leads yields 0.1-0.2 less units than drugs… The optimal way to use this information is to provide “leadlike” profiles for combichem libraries, as well as guidelines for HTS “hit” analysis in medicinal chemistry efforts

17. Medicinal Chemistry / AstraZeneca Mölnal Tudor I. Oprea How to Reach a Leadlike Profile? There is no universal answer for a leadlike library depending on the final goal (e.g., CNS vs. urinary antiinflammatory drugs) However, depending on the final goal (e.g., CNS vs. urinary antiinflammatory drugs), one can generate criteria to focus combichem or HTS libraries (go into “cherry-picking” mode right from the start) 1-5 rings, 2-15 rotatable bonds, up to 400 daltons, 0-2 donors, 1-8 acceptors and 0-3 LogP units For example: 1-5 rings, 2-15 rotatable bonds, up to 400 daltons, 0-2 donors, 1-8 acceptors and 0-3 LogP units would cover MDDR-like space (75%) Additional criteria provided by druglike scoring schemes (e.g., DFPS > 0.4, PPFS > 0.4) PSA < 60 Å 2 for CNS; PSA < 140 Å 2 for oral activity...

18. Medicinal Chemistry / AstraZeneca Mölnal Tudor I. Oprea Lipinski’s Rule of 5: Just How Good Is It?! 0% 10% 20% 70% 80% PASSFAILSKIPPED ACD MDDR PDR HB-acceptors clogP 80 70 60 50 40 30 20 10 Oral absorption 2 3 4 5 6 7 8 9 More reagents than drugs pass the “Rule of 5” test Towards a computational model for oral absorption

19. Medicinal Chemistry / AstraZeneca Mölnal Tudor I. Oprea Filtering HTS hits & Combichem Filtering Step 1: avoid any unwanted chemical structures from the library (including isotopes, salts, etc.) Step 2: remove compounds with undesired property values, e.g. extreme CLogP values (lower than -2, higher than 4), or more than 3 H-bond donors more than 4 rings / 2 chiral centers etc.

20. Medicinal Chemistry / AstraZeneca Mölnal Tudor I. Oprea Focus on Oral Availability: Lipinski Rules and Beyond QSAR QSAR models are currently being used to highlight properties important for permeability: hydrophylic vs. lipophylic surface areas, H-bond donors, LogD 7.4, etc. Lipinski rules can be extended to include chemical information (e.g., number of rings or rotatable bonds), and to focus on leadlike space Achievable goal:probability orally Achievable goal: increase probability of compounds being orally active by providing additional filters

21. Medicinal Chemistry / AstraZeneca Mölnal Tudor I. Oprea drug-like lead-like Library 1 10000 Chemical Library (10000) "chemical space" Library 2 1000 Library 3 1000 ChemGPS Score 1 (DFPS, PPFS) Score 2 (Oral/BBB/Sol) "Filters" MW<400 CLOGP < 4.2 etc. Drug- likeness Spotfire, Excel, Simca The Future Today Score 1 Score 2

22. Medicinal Chemistry / AstraZeneca Mölnal Tudor I. Oprea There Is A Leadlike Space The bottom line is that we can direct discovery towards leadlike profiling For the time being, we are limited by the number of lead-drug pairs in our historycal analysis An ISIS database is available for those interested. We are currently working on expanding this for including validated HTS hits (is there a “HTS-hits” space? Is it different from the leadlike space?)

23. Medicinal Chemistry / AstraZeneca Mölnal Tudor I. Oprea Thanks to... Andy Davis, Simon Teague & Paul Leeson Bertil Samuelsson Mark Divers & Lennart Svensson Johan Gottfries, Ismael Zamora Thomas Kühler & Bob Carter