Hit to Lead Michael Rafferty

Transcription

1 it to Lead 1 Ph.D. Department of Medicinal Chemistry University of Kansas raffe01@ku.edu Background Ph.D. Medicinal Chemistry, University of Kansas Postdoctoral Fellowship, I 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 2 3 Recommended readings Alanine et al. (2003) Lead Generation- Enhancing the Success of Drug Discovery by Investing in the it to Lead Process, Combinatorial Chemistry and igh Throughput Screening 6: Barelier and Krimm (2011) Ligand Specificity, Privileged Substructures and Protein Druggability from Fragment-Based Screening, Curr. pin. Chem. Biol. 15: Bleicher et al. (2003) it and Lead Generation: Beyond igh Throughput Screening, at. Rev. Drug Disc. 2: Ferenczy and Keseru (2010) Enthalpic Efficiency of Ligand Binding, J. Chem. Inf. Model. 50: Ferenczy and Keseru (2012) Thermodynamics of Fragment Binding, J. Chem. Inf. Model. 52: Gleeson et al. (2011) Probing the Links Between in vitro Potency, ADMET, and Physicochemical Parameters, at. Rev. Drug. Disc. 10: ann (2011) Molecular besity, Potency, and ther Addictions in Drug Discovery, Med. Chem. Commun. 2: ann and Keseru (2012) Finding the Sweet Spot: The Role of ature and urture in Medicinal Chemistry, at. Rev. Drug Disc. 11: ann et al. (2001) Molecular Complexity and its Impact on the Probability of Finding Leads for Drug Discovery, J. Chem. Inf. Comput. Sci. 41: Keseru and Makara (2009) The Influence of Lead Discovery Strategies on the Properties of Drug Candidates, at. Rev. Drug Disc. 8:

2 4 Presentation outline What is it to Lead? Definitions bjectives Getting from it Definition of a it it sources Selecting a it to Lead Technologies and Resourcing it to Lead Strategies Factors which Influence tl Success Case Studies Concluding Comments The drug discovery process tl LD L 5 it Lead Series Candidate Focus on target potency and selectivity; defined in vitro properties profile Focus on identifying a novel series with the desired activity profile including functional activity in vitro and in vivo, with refinement of physical properties Focus on fine tuning ADMET properties to identify one or more preclinical development candidates Drug Discovery often resembles an unpredictable journey on a chaotic surface rather than a quantitative and predictive science. (ann (2011), Med. Chem. Commun. 2: ) Images reproduced with permission from Monarch Watch, University of Kansas 6 it to Lead evolved in the 1990 s to address a growing problem with drug candidate failures In the old days Focus only on potency; SAR advancement emphasized potency with minimal consideration of other attributes Result: compounds with poor bioavailability, high clearance, high risk off-target effects, low solubility and low dissolution properties, clinical trial failures Success rate of drug candidates from candidate nomination to market: 4-6% The modern day tl process was developed to identify and eliminate poor quality leads right up front 2

3 The it to Lead process it to Lead is the first engagement of medicinal chemistry in the Drug Discovery continuum The objective of it to Lead is to identify and advance the highest quality chemical starting points for a small molecule drug discovery program Target 1 its Validated its Validated SAR Lead Series Literature TS FBS VS Confirmation Calculated properties Analogue testing Measured properties Preliminary chemistry Prioritization Multidimensional SAR 7 This process is designed to quickly and efficiently determine whether a quality lead can be found, and identify issues or limitations that will need to be addressed during later stages of the Discovery Process 1: it validation and prioritization 8 Sources of hits For most early discovery programs, the biggest challenge is not in finding hits, but in finding a few good hits! Literature atural Products/Ligands igh Throughput Screening (TS) Fragment Screening (FBS) Docking and Scoring (Virtual Screening, VS) 9 3

4 it selection has evolved into an extensive filtering process reliant on a great deal of information 1 its (>500) In silico filters Filtered list retest tl candidates Refined 2 testing Confirmed 10 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 for activity and ADMET properties 11 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 ydrogen bond donors and acceptors Ionization constant (pka calc ) logd (partition coefficient at p 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 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) istorical 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 12 4

5 Additional testing during the hits selection process Confirmation of structure, purity (TS hits) DMS solvated libraries may degrade over time Assumed concentrations may be off by 10X due to several factors Confirmation of on-target activity; potency, kinetics Demonstration of reversible kinetics, determination of potency, specificity ( promiscuous aggregators *) Selectivity vs. target family members and vs. antitargets Microsomal clearance, herg channel binding, P450 inhibition In vitro determinations of solubility, permeability Automated plate based methods For a select few of the most interesting candidates: in vivo clearance, oral dose exposure, plasma protein binding 13 *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), and earlier papers; See also SchoichetLab website, Some TS hits examples Br 2 1. Me 6. Me Me Me 10. Me Me Me S C 5. S Me C Cl Cl Me C C 9. Targets: FabI Bcr-Abl PTPase CCR2b γ-secretase PY Y5 5-T2c Lp-PLA2 MTP RL1 14 S Fragment screening vs. TS 15 Fragment hits are generally low MW, highly efficient enthalpically driven ligands opkins, et al., Drug Discovery Today, 2004, 9: Rees et al., ature Reviews Drug Disc :

6 Examples of fragments identified vs. various protein targets 16 artshorn et al., J Med Chem 2005, 48(2): Taken from 2009 AACR presentation (David Rees) 17 Factors which influence hit quality 1. The source of the hit atural product leads tend to be structurally complex 2. The nature of the screening library istorical compound collections are a legacy of past discovery programs; poorly managed DMS solvated libraries may show considerable degradation 3. The quality and precision of the screening method oisy or low resolution screens introduce high false positives 4. The nature of the molecular target* Molecular 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: ; Viethand Sutherland (2006) J. Med. Chem. 49: Simple metrics for hit selection & prioritization Lipinski Rule of 3 for Lead-Like its MW < 300 -bond donors 3 -bond acceptors 6 clogp < 3 Additional considerations: tpsa < 60 ang 2 LE > 4 18 Lipinski (2000) J. Pharmacol. Toxicol. Methods 44: Congreve et al. (2003) Drug Discovery Today 8:

7 2. it to Lead process and strategies 19 tl resourcing and technologies it to Lead chemistry is typically a short term ( 6 months) investigation of structure-activity relationships Many Pharmas now employ dedicated it to Lead specialist groups which specialize in rapid SAR investigation technologies 20 Alanine et al. (2003) Comb. Chem. igh Throughput Screening6: Technologies for it to Lead chemistry Automated Parallel Chemistry Discrete synthesis on milligram scale Mini-libraries: compounds Examples of PMC platforms in tl: 21 MT AutochemMiniblock 6-48 individual reactions Resin-based chemistry techniques Chemspeed SLT100 Fully integrated automated chemistry platform 7

8 Purification and characterization Multichannel prep LC systems with UV detection and automated peak collection Biotage (e.g., Quad3; 12x) EpichemFlashmaster II (10x) Structural confirmation by PLC/MS and/or flow MR Solvent evaporation in vacuo in pre-tared barcoded vials Reconstitute in DMS to stock solution concentration and transfer to stock plates for screening 22 Plate-based testing 10-point IC 50 determinations vs. primary target Secondary screens for selectivity, anti-targets (e.g., 3 -dofetilide binding for potential herg activity) Kinetic solubility screen (turbidometric or nephelometric) LogP/logD determination by PLC or capillary zone Electrophoresis Permeability screening (PAMPA) 23 Typical workflow 24 Typical cycle time: 3-5 weeks depending on nature of the chemistry and availability of building blocks 8

9 3. Factors which determine success of it to Lead 25 it 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 years, with no improvement in candidate survival to market So why hasn t this worked? 26 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 TS hits on average tend to be more potent than hits found by other methods such as fragment screening; owever, 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 27 Keseru and Makara (2009) at. Rev. Drug Disc. 8: ann and Keseru (2012) at. Rev. Drug Disc. 11: ann (2011) Med. Chem. Commun. 2:

10 Potency vs. MW during lead optimization Plot of MW vs. potency (as pk D) 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 ajduk and Greer (2007) at. Rev. Drug Disc. 6: 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 TS libraries have resulted in bigger collections, but the average quality of the compounds dropped significantly owever, the properties of drug candidates in development all trend toward more lipophilicity, molecular size, complexity 29 Leeson and St.-Gallay (2011) at. Rev. Drug Disc. 10: Binding energy: contributions from enthalpy and entropy ΔG = Δ TΔS Where: ΔG is the Gibbs free energy of binding Δ 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 30 10

11 Structural influences on enthalpy and entropy to binding potency Enthalpic contributions depend primarily on high quality, geometrically restricted bonding interactions (e.g., -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 heavy <30 depend primarily on enthalpy As MW and heavy increases, entropy becomes an increasingly significant contributor to binding energy 31 ann and Keseru (2012) at. Rev. Drug Disc. 11: Calculating binding energy 32 ΔG = RTlnK =1.372logK (or XC 50 as an approximation); (assume T = 300 Kelvin) K/IC 50 LogK ΔG X increase in observed potency requires a net increase in binding energy of kcal/mol (or X = 5.74 kj/mol) Efficiency metrics useful in it to Lead (and beyond) Ligand Efficiency LE LE = -RTlnK/ heavy Binding Efficiency Index BEI BEI = pk/mw Lipophilic Ligand Efficiency LLE LLE = pk-logp (or LogD) LELP LELP = logp/le Size-Independent Ligand Efficiency SILE LE = -RTlnK/( heavy ) 0.3 Enthalpic Efficiency EE EE = D/ heavy K = equals equilibrium affinity constant; may use IC 50 value; heavy = number of non-hydrogen atoms in molecule 33 11

12 ow 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; owever, a 10 nm potency lead with MW <500 generally requires a LE value of 0.3 or higher 34 issink (2009) J. Chem. Inf. Model. 49: Ferenczy and Keseru (2010) J. Chem. Inf. Model. 50: Tarcsay et al. (2012) J. Med. Chem. 55: 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 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 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: Examples from literature 36 12

13 FMS kinase inhibitors as anti-inflammatory agents Patch et al., (2007) BMCL 17: ote: AC = eavy Atom Count Identification and modification of a metabolic hot spot and additional SAR lead to excellent lead candidate LM clearance, 10 min: 59% LM clearance, 10 min: 0% Excellent selectivity vs. 24 other kinases X-ray co-crystal structure determined 38 In silico it to Lead: inhibitors of Pseudomonas thymidylate kinase Choi et al. (2012) J. Med. Chem. 55: Thymidylate kinase is a critical enzyme in the biosynthesis of DA eavy reliance on computational methods and technologies at every stage of the project, beginning with hit identification Thymidinemonophosphate

14 In silico it to Lead: inhibitors of Pseudomonas thymidylate kinase (2) Choi et al. (2012) J. Med. Chem. 55: Virtual library design and synthesis: 5000 compounds VS Inhibitor library: 2000 compounds Visual review and selection Synthesis of selected hits: 20 compounds 3 Thymidinemonophosphate 2 40 Fragment-based it to Lead: CDK1/2 inhibitors Wyatt et al., (2008) J. Med. Chem. 51: classical -bond with backbone 2 electrostatic interactions with aromatic C- at positions C3 and C5 1 -bond through a bridging water (red dot) ydrophobic interactions with surrounding side chains 2 classical -bonds with backbone residues ydrophobic interactions as above bond with backbone Electrostatic C- interaction with backbone carbonyl π-π interaction with Phe80; bridged -bond Fragment-based it to Lead: CDK1/2 inhibitors (2) Wyatt et al., (2008) J. Med. Chem. 51: CDK IC 50/% I (μm) LE CDK IC 50/% I (μm) LE 6a 6d 6b 6e 6c 42 6f 14

15 Fragment-based it to Lead: CDK1/2 inhibitors (3) Wyatt et al., (2008) J. Med. Chem. 51: The measured logp value was in excess of 4 43 Fragment-based it to Lead: CDK1/2 inhibitors (4) Wyatt et al., (2008) J. Med. Chem. 51: Fragment-based it to Lead: CDK1/2 inhibitors (5) Wyatt et al., (2008) J. Med. Chem. 51: Protein surface presentation of compound 33 bound to the CDK2 active site illustrating the excellent fit of the 2,6-dichlorophenyl moiety - LE = LLE = LELP = For an excellent review of fragment-based drug discovery, see Murray and Rees (2009) ature Chemistry 1:

16 Conclusions, observations, take-home message it 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 ptimization stage will ultimately yield a high quality development candidate And finally, new and emerging technologies (particularly computational) will continue to make the process of it to Lead more comprehensive and efficient, as well as more successful 46 Thank you