SLAS2014 Screen Design and Assay Technology SIG Presentation

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The Screen Design and Assay Technology SIG meeting at SLAS2014, January 22 in San Diego, featured a presentation by Wayne J. Levin of Predictum titled Design of Experiment Challenges & Opportunities. The slides and supplementary material are provided here.

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SLAS2014 Screen Design and Assay Technology SIG Presentation

  1. 1. DOE challenges & opportunities agenda WAYNE J LEVIN PREDICTUM INC. LEVIN@PREDICTUM.COM ! WWW.PREDICTUM.COM About Predictum Use the right methods (and there are more of them) Build a system Assume nothing PREDICTUM INC. WWW.PREDICTUM.COM ©2014 1 About Predictum we increase productivity more exploitable insights in less time, effort, cost less frustration 3 2 DOE purpose look at numerous effects comprehensively yet isolate each effect’s influence independently of the other effects in less time, effort and cost 4
  2. 2. use latest & greatest methods Definitive Screening Designs latest in computer-generated optimal designs Split-plot designs hard to change factors definitive screening more independence of effects 5 Optimal Design -20 runs 6 Definitive screening - 21 runs Optimal Design Definitive screening Sacrifice some D-efficiency, but not much Zero correlation among main effects and 2-way interactions Some correlation among main effects and 2-way interactions 7 8
  3. 3. split-plot designs The concept of hard to change is broader than you might think. ! Here multiple pipetting constitutes hard to change where materials are the same across wells it’s a multivariate universe 9 10 11 12 Scenario The tool is typical of HTS, in that there is limited ways a chemical can be varied within a plate.
  4. 4. generating errors Under REML highlighted items show type I errors if analyzed traditionally generating errors DOE can be a lot of work take the time, make the effort longer more complex experiment designed right often yields more correct insights than a series of small experiments if not done correctly, DOEs will generate type I and type II errors confusion and frustration ! Under Traditional highlighted items show type II errors if analyzed traditionally 13 14 R&D and Improvement Initiatives are launched and completed in isolation. build a system time Copyright © 2013 Predictum Inc. 15 Confidential 16
  5. 5. This is typically what happens. Insufficient institutional memory. Each project is isolated. What if they were connected? What if they started with all relevant information previously acquired and paid for? ! Slippage on retaining acquired, prior insights. Must pay to re-acquire what was previously known. time Copyright © 2013 Predictum Inc. Confidential 17 assume nothing be methodical 19 time Copyright © 2013 Predictum Inc. Confidential 18
  6. 6. Utilizing Design of Experiment Statistical Models to Improve Assay Development in High Throughput Biology Diana Ballard of Predictum Inc., Samuel Hasson of NIH, Wayne Levin of Predictum Inc. Overview Step 1 All 6 plates Nozzle Buffer Type 1 HEPES 2 Tris 3 Tris 4 HEPES 5 Tris 6 HEPES 7 Tris 8 HEPES similar tests assume that the entire experiment pH PK Enzyme concentration 8 100 uM 7 10 uM 8 10 uM 7 10 uM 8 100 uM 8 10 uM 7 100 uM 7 100 uM Nozzle 1 2 3 4 5 6 7 8 REML ANOVA Buffer Type <.0001 <.0001 PK Enzyme concentration*Buffer Type*Nested Packaging of assay <.0001 <.0001 pH Buffer Type*pH *PK Substrate [Nested Packaging of assay] Buffer Type*PK Enzyme concentration pH*PK Substrate[Nested Packaging of assay] PK Enzyme concentration PK Enzyme concentration*Timing*Nested Packaging of assay Magnesium ion concentration*pH pH*Buffer Type pH*Timing*Nested Packaging of assay Buffer Type*Magnesium ion concentration*PK Substrate [Nested Packaging of assay <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 0.0004 0.0006 <.0001 0.0186 0.0111 0.2577 0.0377 0.0432 0.0651 0.0669 0.1765 0.2379 Factor / Effect PK Enzyme concentration Step 2 Error Term in the model PK Enzyme concentration * Buffer Type * pH * Iteration Source (Partial List of 85) Buffer Type Column within plate Experimental unit (EU) The wells on all plates that had unique bottle setups for PK Enzyme. Step 1 had 8 unique bottles, repeating step 1 in iteration 2. Across both iterations, there are 16 EU. The wells on all plates that were treated by one of the 16 bottles made for step 1. PK Enzyme concentration * Buffer Type * pH * Iteration Magnesium concentration * Iteration Magnesium ion concentration Nested packaging of assay Magnesium solution 5 uM water 1 2 3 4 5 6 7 8 Introduction Step 3 Changed on a per plate basis. Any plate could have gotten either setting. Across both iterations, there are 12 EU. Plate * Iteration PK Enzyme concentration * Magnesium ion concentration Buffer Type * Magnesium ion concentration The intersection of the EU for PK Enzyme and the EU for Magnesium ion concentration. 16 x 4 = 64 EU across both iterations. Buffer Type * Nested packaging of assay All 6 plates The wells on all plates that had unique bottle setups for Step 2 and Magnesium concentration. Across both iterations, there are 4 EU. The intersection of the EU for Buffer Type and the EU for Nested packaging of assay. 16 EU x 12 EU = 192 EU across both iterations. PK Enzyme concentration * Buffer Type * pH * Magnesium concentration * Iteration PK Enzyme concentration * Buffer Type * pH * Magnesium concentration * Iteration PK Enzyme concentration * Buffer Type * pH * plate * Iteration The intersection of the EU for Buffer Type and the EU for Magnesium ion concentration. 16 x 4 = 64 EU across both iterations The inclusion of two iterations is critical Prob > F FIG 1 Visualize Buffer Type * Magnesium * PK Substrate [Nested Packaging of Assay] All 6 plates handled in classical full and fractional factorial designs leading them to provide BSA 0.2% concentration water solution 1 2 3 4 5 6 7 8 Prob > F instead of the experimental unit to Plate Methods OBJECTIVES: Step 4 1 2 3 the experimental unit for this model FIG 2 2800 PK Substrate 400nM 40nM PK Substrate 400nM combined with 40nM Kinase Glo 4 5 6 2750 2700 PK Enzyme 2650 10 100 2600 2550 FACTORS: All eight of the following factors are difficult to vary on the Thermo Multi Drop Plate Step 5 1 2800 2 3 Tris 2750 2700 Buffer Type 2650 Timing 5 min 10 min 4 5 6 Conclusions 2600 HEPES 2550 10 20 30 40 50 60 70 80 90 100 110 Tris HEPES Plate Step 6 RESPONSE: Kinase Glo 4 uL nothing 1 2 3 4 5 6 DESIGN NOTES: Step 7 Read all 6 plates Step 8 Wait Several Days Do steps 1-7 again All 6 plates Copyright © 2013 Predictum Inc. experimental design and we found a straightforward method to implement its use in DOE
  7. 7. Copyright © 2008 Predictum Inc.
  8. 8. ACT •what was learned, that if proven valid, can be implemented? •new questions/ insights sought? •what next? STUDY •what happened that was expected? •what did not happen that was expected? •what happened that was not expected? PLAN •insights sought (specify model: main effects, interactions, quadratics) •responses & goals (maximize, minimize, match target) •factors & levels •identify difficult to change factors •constraints on factors & levels •design experiment (evaluate properties) •detail expectations •operational definitions of run changes and response measurement •list all assumptions
  9. 9. knowledge time Copyright © 2008 Predictum Inc.

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