Liquid Handling Processes Impact Computational Modeling in Drug Discovery

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The introduction of new pharmaceutical drugs has slowed while money and effort expended by the industry has dramatically increased. We suggest that some of that effort may be inadvertently wasted in …

The introduction of new pharmaceutical drugs has slowed while money and effort expended by the industry has dramatically increased. We suggest that some of that effort may be inadvertently wasted in drug screening and quantitative structure-activity relationship studies where results can be strongly skewed by the method of liquid handling and the protocol used.

Recent work has demonstrated that dispensing processes have a profound influence on estimates of IC50. What appear to be minor modifications in the design of concentration gradients coupled with long-standing liquid handling procedures have generated a 1.5 to 1,000-fold difference in IC50 showing no correlation or ranking between competing processes. Importantly when such data are used for computational modeling, the computed pharmacophores for each dataset are different and lead to the development of compounds with significantly different structures and chemico-physical properties. Dispensing processes are therefore an important source of error that impacts computational and statistical results. At the same time, commonly-used protocols can generate data can introduce errors independent of the dispensing technology. This paper is an overview of some of the experiences of the authors based on using online chemical compound databases, and publically available data.

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  • 1. Liquid Handling Processes ImpactComputational Modeling in DrugDiscoveryJoe Olechno1, Sean Ekins2, Antony Williams3, Rich Ellson1Pittcon 2013Session 26703:55 PM, March 21, 20131. Labcyte Inc.2. Collaboration in Chemistry3. Royal Society of Chemistry
  • 2. Agenda• What is Acoustic Liquid Handling?• Serial Dilutions vs. Direct Dilutions• Lead Optimization and Pharmacophores• The Impact of Serial Dilutions on Drug Discovery• Conclusions 2
  • 3. Acoustic Droplet Ejection (ADE) Comley J, Nanolitre Dispensing, Drug Discovery World, Summer 2004, 43-54 3
  • 4. Acoustic Droplet Ejection (ADE)Acoustic energy expels droplets without physical contact 15.0• Extremely precise 12.5• Extremely accurate 10.0• Rapid %CV 7.5• Auto-calibrating 5.0• Completely touchless 2.5 – No cross-contamination 0 0.1 1 10 100 1000 10000 – No leachates Volume (nL) Comley J, Nanolitre Dispensing, Drug Discovery – No binding World, Summer 2004, 43-54 4
  • 5. Agenda• What is Acoustic Liquid Handling?• Serial Dilutions vs. Direct Dilutions• Lead Optimization and Pharmacophores• The Impact of Serial Dilutions on Drug Discovery• Conclusions 5
  • 6. Conventional Dose-Response Set-up by Serial Dilution Source Plate Assay Plate Intermediate Buffer Dilution Plate
  • 7. Serial Dilution vs. Direct DilutionSerial with Tips Direct with Acoustics• Equal volumes of changing • Changing volumes of equal concentrations concentrations• Compounds are sequentially • Maximum of one dilution step diluted. Each new dilution is the source for the next step.• Many ―touches‖ with tips (or • Touchless—no carry- significant potential for carry-over over, leachates or binding or leachates) No solute lost• Errors are compounded Serial Dilution • Reduced error• Low-volume assays with high • Low-volume Direct Dilution assays with low solvent concentration (or solvent concentration compound loss) 7
  • 8. Direct Dilution Process Third Step Transfer 75, 25, 7.5 and 2.5 nL of each hit to four consecutive 12-point wells curves (30, 10, 3 and one droplets, respectively) Source Plate Assay Plate Fourth Step First Step Transfer 75, 25, 7.5 and Transfer 252.5 2.5 nL of each diluted and 2.5 nL to sample to four consecutive two wells in an wells of the assay plate intermediate plate (30, 10, 3 and one droplets, respectively) Second Step Dilute intermediate plate with 25 L DMSO in each well Intermediate Plate Intermediate Plate
  • 9. Agenda• What is Acoustic Liquid Handling?• Serial Dilutions vs. Direct Dilutions• Lead Optimization and Pharmacophores• The Impact of Serial Dilutions on Drug Discovery• Conclusions 9
  • 10. Traditional Scaffold ModificationsFibrinogen Receptor Inhibitor Poor stability, poor IC50 = 29 µM bioavailability, non- patentable Poor stability, poor IC50 = 3 µM bioavailability IC50 = 0.15 µM Poor oral availability Excellent oral IC50 = 0.067 µM availability, good stability 10
  • 11. But what to do if the structures are dissimilar? Both compounds bind strongly to the GABAA receptor. Diazepam CGS-9896 These compounds are extremely different in structure but both have the same effect. Is there a way to reconcile this and generate information to make new drugs?
  • 12. Pharmacophores• Describes the optimal binding of a protein to a ligand.• Shows how different structures bind to same site.• Designed from screening data. 12
  • 13. GABAA Receptor Pharmacophore Hydrogen bond acceptor Hydrogen bond donor Hydrophobic pocket
  • 14. GABAA Receptor Pharmacophore Hydrogen bond acceptor Hydrogen bond donor Hydrophobic pocket
  • 15. Agenda• What is Acoustic Liquid Handling?• Serial Dilutions vs. Direct Dilutions• Lead Optimization and Pharmacophores• The Impact of Serial Dilutions on Drug Discovery• Conclusions 15
  • 16. Real World Data – EphB4 ReceptorCompound # IC50 Acoustic (µM) IC50 Tips (µM) Ratio IC50Tip/IC50ADE 5 0.002 0.553 276.5 4 0.003 0.146 48.7 7 0.003 0.778 259.3 W7b 0.004 0.152 42.5 8 0.004 0.445 111.3 W5 0.006 0.087 13.7 6 0.007 0.973 139.0 W3 0.012 0.049 4.2 W1 0.014 0.112 8.2 9 0.052 0.170 3.3 10 0.064 0.817 12.8 W12 0.158 0.250 1.6 W11 0.207 14.400 69.6 11 0.486 3.030 6.2 14 compounds with structures and IC50 data. Barlaam et al., WO2009/010794 Barlaam et al., US 7,718,653
  • 17. Real World Data – EphB4 Receptor 2 The acoustic 1 technique always provided a Log IC50-tips more potent 0 IC50 value. -3 -2 -1 0 1 2 The greater -1 the distance from the red line, the greater the -2 difference in IC50 values. -3 Log IC50-acoustic 17
  • 18. Experimental Process Flow Acoustic Model Generate14 Structures pharmacophore modelswith Data for EphB4 receptor Tip-based ModelInitial data set of 14WO2009/010794, US 7,718,653 18
  • 19. AZ PharmacophoresPharmacophore Hydrophobic Hydrogen Hydrogen Observed vs features bond bond donors predicted acceptors IC50Tip-based 0 2 1 0.80Acoustic based 2 1 1 0.92 Tip-based pharmacophore Acoustic-based pharmacophore
  • 20. Experimental Process Flow Results Acoustic Acoustic Model Model Generate Test models14 Structures pharmacophore models against newwith Data for EphB4 receptor data Tip-based Tip-based Model Model Results Initial data set of 14 Independent data set of 12 WO2009/010794, US 7,718,653 WO2008/132505 20
  • 21. Compounds Tested with Tip-based Pharmacophore Tip-based IC50 Tip-based IC50 Name Prediction (mM) Actual (mM) W084.1 0.3488 0.297 W084.2 0.3806 0.456 W084.4 0.6994 0.374 W082.2 0.8392 0.808 W082.4 1.4989 6.270 W083 2.8229 0.198 W084.3 2.9119 0.473 W082.1 3.3829 1.120 WO81 NOT RETRIEVED 38.300 WO82.3 NOT RETRIEVED 1.780 Barlaam wo2008/132505
  • 22. Tip-Based Pharmacophore – Predicted vs. Measured 10.000 8 7 R² = 0.000Measured Tip-based IC50 Measured Rank Order R² = 0.183 6 5 1.000 0.1 1 10 4 3 2 0.100 1 1 2 3 4 5 6 7 8 Predicted Tip-based IC50 Predicted Rank Order The pharmacophore developed from tip-based data is an extremely poor predictor of measured activity. 23
  • 23. Results of Testing PharmacophoresAcoustic Pharmacophore Tip-based Pharmacophore Poor correlation (R2<0.0002) between predicted and measured The model was inadequate to predict activity of 20% ofCorrectly predicted rank of the compoundsmost potent compounds Compound with highest measured activity was predicted to have poor binding Compound predicted to be most active actually had poor activity
  • 24. Experimental Process Flow Results Acoustic Acoustic Acoustic Model Model Model Generate Test models Test models against14 Structures pharmacophore models against new X-ray crystal structurewith Data for EphB4 receptor data pharmacophores Tip-based Tip-based Tip-based Model Model Model Results Initial data set of 14 Independent data set of 12 Independent crystallography data WO2009/010794, US 7,718,653 WO2008/132505 Bioorg Med Chem Lett 18:2776; 25 18:5717; 20:6242; 21:2207
  • 25. Final Nail in the Coffin – X-Ray Crystallography• All pharmacophores created from X-ray structures had both hydrophobic and hydrogen bonding features.• The EphB4-ligand crystal pharmacophore most closely reflects the acoustic pharmacophore.Pharmacophore Hydrophobic Hydrogen bond Hydrogen features acceptors bond donorsTip-based 0 2 1Acoustic based 2 1 1Crystal based 2 1 1(consensus)
  • 26. Agenda• What is Acoustic Liquid Handling?• Serial Dilutions vs. Direct Dilutions• Lead Optimization and Pharmacophores• The Impact of Serial Dilutions on Drug Discovery• Conclusions 27
  • 27. Reasons to Worry• This case strongly suggests that aqueous, serial dilutions transferred with tip- based techniques lead researchers away from the most potent drugs.• How universal is this phenomenon?
  • 28. Acoustic vs. Tip-based Transfers -40 -20 0 20 40 60 80 100 Adapted from Spicer et al., Presentation at Drug10 20 30 40 50 Acoustic % InhibitionSerial dilution IC50 μM Discovery Technology, Boston, MA, August 2005 Adapted from Wingfield. Presentation at ELRIG2012, Manchester, UK 0 0 10 20 30 40 50 -40 -20 0 20 40 60 80 100 Acoustic IC50 μM Aqueous % Inhibition 104 Adapted from Wingfield et al., 103 Amer. Drug Disco. 2007,Serial dilution IC50 μM 3(3):24 Log IC50 tips 102 10 1 Data in this presentation 10-1 10-2 10-3 10-3 10-2 10-1 1 10 102 103 104 Acoustic IC50 μM Log IC50 acoustic
  • 29. Reasons to Worry• This case strongly suggests that aqueous, serial dilutions transferred with tip- based techniques lead researchers away from the most potent drugs.• How universal is this phenomenon?• Sticky surfaces – Many solutes stick to walls and tips at low concentrations – Dose-response experiments require precision solute-handling over many logs.
  • 30. Conclusions• Tip-based aqueous serial dilutions• Databases, public and private, should annotate this meta-data along with biological data.• We encourage researchers to make their data available to expand this study.