Hit to Lead Michael Rafferty 1

Hit to Lead Michael Rafferty

Hit to Lead

Michael Rafferty Ph.D. Department of Medicinal Chemistry University of Kansas [email protected] 1

Background

• Ph.D. Medicinal Chemistry, University of Kansas • Postdoctoral Fellowship, NIH • 25+ years experience in drug discovery with Parke-Davis, Bristol-Myers, Searle, and Pfizer • Current affiliations: Adjunct Prof., Department of Medicinal Chemistry, University of Kansas

Recommended readings

• Alanine et al. (2003) Lead Generation- Enhancing the Success of Drug Discovery by Investing in the Hit to Lead Process, Combinatorial Chemistry and High Throughput Screening 6: 51-66 • Barelier and Krimm (2011) Ligand Specificity, Privileged Substructures and Protein Druggability from Fragment-Based Screening, Curr. Opin. Chem. Biol. 15: 469-474 • Bleicher et al. (2003) Hit and Lead Generation: Beyond High Throughput Screening, Nat. Rev. Drug Disc. 2: 369-378 • Ferenczy and Keseru (2010) Enthalpic Efficiency of Ligand Binding, J. Chem. Inf. Model. 50: 1536-1541 • Ferenczy and Keseru (2012) Thermodynamics of Fragment Binding, J. Chem. Inf. Model. 52: 1039-1045 • Gleeson et al. (2011) Probing the Links Between in vitro Potency, ADMET, and Physicochemical Parameters, Nat. Rev. Drug. Disc. 10: 197-208 • Hann (2011) Molecular Obesity, Potency, and Other Addictions in Drug Discovery, Med. Chem. Commun. 2: 349-355 • Hann and Keseru (2012) Finding the Sweet Spot: The Role of Nature and Nurture in Medicinal Chemistry, Nat. Rev. Drug Disc. 11: 355-365 •Hannet al. (2001) Molecular Complexity and its Impact on the Probability of Finding Leads for Drug Discovery, J. Chem. Inf. Comput. Sci. 41: 856-864 • Keseru and Makara (2009) The Influence of Lead Discovery Strategies on the Properties 3 of Drug Candidates, Nat. Rev. Drug Disc. 8: 203-212

The screen versions of these slides have full details of copyright and acknowledgements 1 Hit to Lead Michael Rafferty

Presentation outline

• What is “Hit to Lead”? ¾ Definitions ¾ Objectives • Getting from Hit… ¾ Definition of a Hit ¾ Hit sources ¾ Selecting a Hit • …to Lead ¾ Technologies and Resourcing ¾ Hit to Lead Strategies • Factors which Influence HtL Success • Case Studies 4 • Concluding Comments

The drug discovery process

HtL LD LO

Hit Lead Series Candidate Focus on target Focus on identifying Focus on fine tuning potency and selectivity; a novel series with ADMET properties defined in vitro the desired activity to identify one properties profile profile including or more preclinical functional activity development candidates in vitro and in vivo, with refinement of physical properties • Drug Discovery “…often resembles an unpredictable journey on a chaotic surface rather than a quantitative and predictive science.” (Hann (2011), Med. Chem. Commun. 2: 349-255)

Images5 reproduced with permission from Monarch Watch, University of Kansas www.monarchwatch.edu

Hit to Lead evolved in the 1990’s to address a growing problem with drug candidate failures

• In the “old” days— ¾ Focus only on potency; SAR advancement emphasized potency with minimal consideration of other attributes ¾ Result: compounds with poor bioavailability, high clearance, high risk off-target effects, low solubility and low dissolution properties, clinical trial failures ¾ Success rate of drug candidates from candidate nomination to market: 4-6%

The modern day HtL process was developed to identify and eliminate poor quality leads right up front

The screen versions of these slides have full details of copyright and acknowledgements 2 Hit to Lead Michael Rafferty

The Hit to Lead process

• “Hit to Lead” is the first engagement of medicinal chemistry in the Drug Discovery continuum • The objective of “Hit to Lead” is to identify and advance the highest quality chemical starting points for a small molecule drug discovery program

Validated Validated Lead Ta r g e t 1° Hits Hits SAR Series

Literature HTS FBS Confirmation VS Calculated properties Analogue testing Measured properties Preliminary chemistry Prioritization Multidimensional SAR

This process is designed to quickly and efficiently determine whether a quality lead can be found, and identify issues or limitations that will need 7 to be addressed during later stages of the Discovery Process

1: Hit validation and prioritization

Sources of hits

For most early discovery programs, the biggest challenge is not in finding hits, but in finding a few good hits!

• Literature • Natural Products/Ligands • High Throughput Screening (HTS) • Fragment Screening (FBS) • Docking and Scoring (Virtual Screening, VS)

The screen versions of these slides have full details of copyright and acknowledgements 3 Hit to Lead Michael Rafferty

Hit selection has evolved into an extensive filtering process reliant on a great deal of information

1° Hits In silico filters (>500) Filtered list retest

2° testing Refined Confirmed HtL candidates

Advances in computational (“in silico”) methods to calculate properties and rank hits have enriched hit prioritization and decision-making enormously, along with development of an array of plate-based, miniaturized screens 10 for activity and ADMET properties

Readily calculated properties of hits • Molecular weight (MW) • Molecular volume and dimensions • Calculated partition coefficient (clogP) • # rotatable bonds • Total polar surface area (tPSA) • # aromatic rings • Hydrogen bond donors and acceptors

• Ionization constant (pKacalc) • logD (partition coefficient at pH X)

• Physical properties influence the behavior of a compound in the tissue and the effectiveness of the treatment • Properties for most marketed drugs fall within a well-defined range of values 11• Lipinski’s ‘Rule of 5’ used to define potential drug candidates

In silico structural property “filters”

• “Structural Alerts”- structural features which have known associations with toxicity, metabolic lability, poor physicochemical attributes • Toxicity predictors (“Derek”) • Similarity-based clustering (a hit “series” is preferred over singletons) • Historical database mining - evidence of promiscuity, off target risk; can also serve to confirm activity at the desired target for a hit that has previously been found to be active in a closely related target family member

The screen versions of these slides have full details of copyright and acknowledgements 4 Hit to Lead Michael Rafferty

Additional testing during the hits selection process

• Confirmation of structure, purity (HTS hits) ¾ DMSO solvated libraries may degrade over time ¾ Assumed concentrations may be off by 10X due to several factors • Confirmation of on-target activity; potency, kinetics ¾ Demonstration of reversible kinetics, determination of potency, specificity (“promiscuous aggregators”*) • Selectivity vs. target family members and vs. antitargets ¾ Microsomal clearance, hERG channel binding, P450 inhibition • In vitro determinations of solubility, permeability ¾ Automated plate based methods • For a select few of the most interesting candidates: in vivo clearance, oral dose exposure, plasma protein binding

*Jadhav et al., Quantitative analyses of aggregation, autofluorescence, and reactivity artifacts in a screen for inhibitors of a thiol protease, J. Med. Chem. (2010) 53 (1), 37-51 and earlier papers; 13 See also Schoichet Lab website, http://shoichetlab.compbio.ucsf.edu/take-away.php

Some HTS hits examples

O H NH H N NH O O N N N N N H N H2N N N NH N S 2. 3. 4. 1. N COOH N Targets: 5. Me FabI O NHMe Bcr-Abl N MeO H N PTPase OMe O 8. COOH MeO N N H Me H N N CCR2b 6. OMe O NH N O COOH γ-secretase 7. H COOH 9. NPY Y5 H H H N N N N O H 5-HT2c Cl N NH N O Br N S Lp-PLA2 10. Me Me Cl 11. N 12. MTP

O ORL1 O O N N NH H N

S N N 14. H 14 13.

Fragment screening vs. HTS

• Fragment hits are generally low MW, highly efficient enthalpically driven ligands

Hopkins, et al., Drug Discovery Today, 2004, 9: 430-431 15Rees et al., Nature Reviews Drug Disc. 2004 3: 660-672

The screen versions of these slides have full details of copyright and acknowledgements 5 Hit to Lead Michael Rafferty

Examples of fragments identified vs. various protein targets

Taken from 2009 AACR presentation (David Rees)

16Hartshorn et al., J Med Chem 2005, 48(2): 403-13

Factors which influence hit quality

1. The source of the hit ¾Natural product leads tend to be structurally complex 2. The nature of the screening library ¾Historical compound collections are a legacy of past discovery programs; poorly managed DMSO solvated libraries may show considerable degradation 3. The quality and precision of the screening method ¾Noisy or low resolution screens introduce high false positives 4. The nature of the molecular target* ¾Molecular targets designed to interact with lipoidal ligands/substrates tend to favor lipophilic hits; protein-protein interfaces favor structurally complex (high MW) ligands

*Morphy (2006) J. Med. Chem. 49: 2969-2978; 17 Viethand Sutherland (2006) J. Med. Chem. 49: 3451-3453

Simple metrics for hit selection & prioritization

• Lipinski “Rule of 3” for “Lead-Like” Hits ¾ MW < 300 ¾ H-bond donors ≤ 3 ¾ H-bond acceptors ≤ 6 ¾ cLogP < 3 Additional considerations: ¾ tPSA < 60 ang2 ¾ LE > 4

Lipinski (2000) J. Pharmacol. Toxicol. Methods 44: 235-249 18 Congreve et al. (2003) Drug Discovery Today 8: 876-877

The screen versions of these slides have full details of copyright and acknowledgements 6 Hit to Lead Michael Rafferty

2. Hit to Lead process and strategies

HtL resourcing and technologies

• Hit to Lead chemistry is typically a short term (≤ 6 months) investigation of structure-activity relationships • Many Pharmas now employ dedicated Hit to Lead specialist groups which specialize in rapid SAR investigation technologies

20Alanine et al. (2003) Comb. Chem. High Throughput Screening6: 51-66

Technologies for Hit to Lead chemistry

• Automated Parallel Chemistry ¾ Discrete synthesis on milligram scale ¾ Mini-libraries: 20-50 compounds ¾ Examples of PMC platforms in HtL:

MT AutochemMiniblock Chemspeed SLT100 6-48 individual reactions Fully integrated automated chemistry platform 21 Resin-based chemistry techniques

The screen versions of these slides have full details of copyright and acknowledgements 7 Hit to Lead Michael Rafferty

Purification and characterization

• Multichannel prep LC systems with UV detection and automated peak collection ¾ Biotage (e.g., Quad3; 12x) ¾ EpichemFlashmaster II (10x) • Structural confirmation by HPLC/MS and/or flow NMR • Solvent evaporation in vacuo in pre-tared barcoded vials • Reconstitute in DMSO to stock solution concentration and transfer to stock plates for screening

Plate-based testing

• 10-point IC50 determinations vs. primary target • Secondary screens for selectivity, anti-targets (e.g., 3H-dofetilide binding for potential hERG activity) • Kinetic solubility screen (turbidometric or nephelometric) • LogP/logD determination by HPLC or capillary zone Electrophoresis • Permeability screening (PAMPA)

Typical workflow

• Typical cycle time: 3-5 weeks depending on nature of the chemistry and availability of building blocks 24

The screen versions of these slides have full details of copyright and acknowledgements 8 Hit to Lead Michael Rafferty

3. Factors which determine success of Hit to Lead

Hit to Lead was designed to improve both productivity and candidate quality

• The belief: better quality hits result in better quality programs, which lead to better quality candidates and more R&D success • The reality: R&D productivity steadily declined over the past 15-20 years, with no improvement in candidate survival to market So why hasn’t this worked?

Reason #1: fixation on target potency for SAR development

• Careful analysis of several hit-to-lead campaigns show consistent focus on achieving low nM target potency, which invariably can only be achieved by increased lipophilicity and structural complexity, often exceeding the limits of drug-like property space • HTS hits on average tend to be more potent than hits found by other methods such as fragment screening; However, the higher potency often correlates with higher MW and lipophilicity; So the impulse to select the most potent hit for pursuit often places the project at a disadvantage because such leads offer little leeway for modification to improve upon other necessary drug-like properties; Researchers are also often reluctant to give up potency even when a less potent analogue may have a better overall profile

Keseru and Makara (2009) Nat. Rev. Drug Disc. 8: 203-212 Hann and Keseru (2012) Nat. Rev. Drug Disc. 11: 355-365 27 Hann (2011) Med. Chem. Commun. 2: 349-355

The screen versions of these slides have full details of copyright and acknowledgements 9 Hit to Lead Michael Rafferty

Potency vs. MW during lead optimization

•Plot of MW vs. potency (as pKD) for 5 fragment lead optimization programs showed remarkably consistent trend indicating that potency gain of 1 log requires an increase in MW of approximately 64 amu 28 Hajduk and Greer (2007) Nat. Rev. Drug Disc. 6: 211-219

Reason #2: the influence of organizational culture on decision-making

• Physicochemical properties of approved drugs have changed very little over time • Significant differences exist in practices and standards among discovery organizations that ultimately have a significant impact on the quality of development candidates • Investments made to expand HTS libraries have resulted in bigger collections, but the average quality of the compounds dropped significantly • However, the properties of drug candidates in development all trend toward more lipophilicity, molecular size, complexity

29 Leeson and St.-Gallay (2011) Nat. Rev. Drug Disc. 10: 749-765

Binding energy: contributions from enthalpy and entropy

ΔG = ΔH – TΔS Where: ΔG is the Gibbs free energy of binding ΔH is the enthalpy of binding ΔS is the energy required to organize ligand and target system • T is in °K •-TΔS is the temperature-dependent entropy term

The screen versions of these slides have full details of copyright and acknowledgements 10 Hit to Lead Michael Rafferty

Structural influences on enthalpy and entropy to binding potency

• Enthalpic contributions depend primarily on high quality, geometrically restricted bonding interactions (e.g., H-bonds, ionic pairing) • In contrast, Entropic contributions are driven primarily by increased non-directional lipophilic/hydrophobic interactions • The binding energy (ΔG) of ligands with MW < 400

and Nheavy<30 depend primarily on enthalpy

• As MW and Nheavy increases, entropy becomes an increasingly significant contributor to binding energy

31 Hann and Keseru (2012) Nat. Rev. Drug Disc. 11: 355-365

Calculating binding energy

• ΔG = RTlnK =1.372logK (or XC50 as an approximation); (assume T = 300° Kelvin)

K/IC50 LogK ΔG 0.001 -3 -4.116 0.0001 -4 -5.448 0.00001 -5 -6.860 0.000001 -6 -8.232 0.0000001 -7 -9.604 0.00000001 -8 -10.976 0.000000001 -9 -12.348 0.0000000001 -10 -13.720 • 10X increase in observed potency requires a net increase in binding energy of 1.372 kcal/mol (or 1.372 X 4.184 = 5.74 kJ/mol) 32

Efficiency metrics useful in Hit to Lead (and beyond)

Ligand Efficiency LE LE = -RTlnK/Nheavy

Binding Efficiency Index BEI BEI = pK/MW

Lipophilic Ligand Efficiency LLE LLE = pK-logP (or LogD)

LELP LELP = logP/LE

0.3 Size-Independent Ligand Efficiency SILE LE = -RTlnK/(Nheavy)

Enthalpic Efficiency EE EE = DH/Nheavy

K = equals equilibrium affinity constant; may use IC50 value;

Nheavy = number of non-hydrogen atoms in molecule 33

The screen versions of these slides have full details of copyright and acknowledgements 11 Hit to Lead Michael Rafferty

How efficiency metrics are used

• Ligand Efficiency (LE) is a useful indicator of the degree of structural complexity required for target interaction • LE invariably decreases with increasing # heavy (non-hydrogen) atom count, consistent with increasing entropic contributions • LE does not account for either MW or lipophilicity; However, a 10 nM potency lead with MW <500 generally requires a LE value of 0.3 or higher

Nissink (2009) J. Chem. Inf. Model. 49: 1617-1622 Ferenczy and Keseru (2010) J. Chem. Inf. Model. 50: 1536-1541 34 Tarcsay et al. (2012) J. Med. Chem. 55: 1252-1260

Lipophilic efficiency terms (LLE and LELP)

• Both terms are designed to reflect the relationship between lipophilicity increases and affinity, in order to determine whether increasing potency corresponds to increased hydrophobic binding contributions ¾ LLE term is simply the difference between the – log values for potency and lipophilicity, will increase with optimization of enthalpic binding interactions, and will decrease with increased hydrophobic interactions; This term ignores molecular size and complexity; Drug-like LLE values are >5 ¾ LELP, which is the ratio of logP and LE, decreases with increasing quality of enthalpic binding; LELP takes into account both lipophilicity and molecular complexity through the LE term; Drug-like LELP values are <10

35 Tarcsay et al. (2012) J. Med. Chem. 55: 1252-1260

4. Examples from literature

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FMS kinase inhibitors as anti-inflammatory agents Patch et al., (2007) BMCL 17: 6070-6074

37 Note: HAC = Heavy Atom Count

Identification and modification of a metabolic “hot spot” and additional SAR lead to excellent lead candidate

HLM clearance, HLM clearance, 10 min: 59% 10 min: 0%

Excellent selectivity vs. 24 other kinases X-ray co-crystal structure determined

In silico Hit to Lead: inhibitors of Pseudomonas thymidylate kinase Choi et al. (2012) J. Med. Chem. 55: 852-870

• Thymidylate kinase is a critical enzyme in the biosynthesis of DNA • Heavy reliance on computational methods and technologies at every stage of the project, Thymidinemonophosphate beginning with hit identification 1

The screen versions of these slides have full details of copyright and acknowledgements 13 Hit to Lead Michael Rafferty

In silico Hit to Lead: inhibitors of Pseudomonas thymidylate kinase (2) Choi et al. (2012) J. Med. Chem. 55: 852-870

1 Virtual library design and synthesis: Thymidinemonophosphate 5000 compounds 2 VS Inhibitor library: 2000 compounds Visual review and selection Synthesis of selected hits: 20 compounds 3

Fragment-based Hit to Lead: CDK1/2 inhibitors Wyatt et al., (2008) J. Med. Chem. 51: 4986-4999

• 1 classical H-bond with backbone NH • 2 electrostatic interactions with aromatic C-H at positions C3 and C5 • 1 H-bond through a bridging water (red dot) • Hydrophobic interactions with surrounding side chains

• 2 classical H-bonds with backbone residues • Hydrophobic interactions as above

• 1 H-bond with backbone NH • Electrostatic C-H interaction with backbone carbonyl • π-π interaction with Phe80; bridged H-bond 41

Fragment-based Hit to Lead: CDK1/2 inhibitors (2) Wyatt et al., (2008) J. Med. Chem. 51: 4986-4999

CDK CDK LE LE IC50/% I (μM) IC50/% I (μM)

6a 6d

6b 6e

6c 6f 42

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Fragment-based Hit to Lead: CDK1/2 inhibitors (3) Wyatt et al., (2008) J. Med. Chem. 51: 4986-4999

The measured logP value was in excess of 4

Fragment-based Hit to Lead: CDK1/2 inhibitors (4) Wyatt et al., (2008) J. Med. Chem. 51: 4986-4999

Fragment-based Hit to Lead: CDK1/2 inhibitors (5) Wyatt et al., (2008) J. Med. Chem. 51: 4986-4999

• Protein surface presentation of compound 33 bound to the CDK2 active site illustrating the excellent fit of the 2,6-dichlorophenyl moiety - LE = 0.40 - LLE = 6.99 - LELP = 0.82

For an excellent review of fragment-based drug discovery, see Murray and Rees 45 (2009) Nature Chemistry 1: 187-192

The screen versions of these slides have full details of copyright and acknowledgements 15 Hit to Lead Michael Rafferty

Conclusions, observations, take-home message

• Hit to Lead Success depends on identification of high quality, low MW enthalpically dependent ligands • Sustained focus on early optimization of properties using efficiency indicators is the best approach to ensuring that the expensive and time-consuming Lead Optimization stage will ultimately yield a high quality development candidate • And finally, new and emerging technologies (particularly computational) will continue to make the process of Hit to Lead more comprehensive and efficient, as well as more successful

Thank you

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