Transcript Efficient HTS - Stanford Translational Medicine
Key Considerations for Implementation of
Efficient Effective HTS
Steve Young
A Presentation in four parts … 1. General overview of HTS
- overview of the aims and issues of HTS
Options in more detail … 2. Generic screening Technologies 3. Quality control methodology 4. HTS as a process
- overview of management and organisation at Welwyn
Model linking Efficiency, Effectiveness and Economy Objectives
Economy
Input
Efficiency Effectiveness
Output
Key Considerations for Implementation of Efficient Effective HTS HTS: 20k-50k samples/week (per screen) 250-625 96 well plates/week 65-160 384 well plates/week Screening: The Primary Objective: Rapidly identify a tractable chemical series with the requisite biological activity against the target of choice (using the minimum practicable resource)
Factors which Impact Efficiency/Effectiveness
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Integration of the HTS department within the company.
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Integration of the pivotal screening activities
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Compound supply Assay design and execution Data analysis and tracking
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Segregation of peripheral activity (i.e. technology development).
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Automation / computerised data handling
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Reduce random error (be alert to systematic errors)
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Quality Control
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Far better to screen fewer compounds well
Integration of the HTS Department within the Company Thorough involvement with all interested parties (i.e. biologists AND chemists) at an early stage in target evaluation / screen development
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Screen prioritisation.
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Assay to screen transition.
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Reagent requirements
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recombinant material
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external supply limitations (availability, delivery timescales)
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Assay format decisions.
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Compound input (number, type, concentration etc..).
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Secondary / selectivity assays (synergy / resourcing).
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Lead development (resourcing)
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assay support for chemists and natural product teams (if any)
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Rescreening.
Compound Supply : Preparation / Storage The logistics of compound supply (weighing and solubilisation) dictates the use of large pre-prepared liquid sample arrays. These may be generated by a combination of manual and automated labour This forces some compromises :-
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Standardisation of procedure is inflexible
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Repetitive generation of liquid stores may be wasteful
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Liquid storage accelerates decomposition Regular monitoring of sample condition is essential
Compound Supply : Preparation / Storage Considerations
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Solvent : e.g. aqueous vs pure DMSO
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Temperature : 20 o C, 4 o C or lower ?
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Humidity : water uptake and compound stability
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Storage structures : - open/closed plates, densities, volumes etc.
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Shelf life : consider deterioration after 6 months Regular monitoring of sample condition is essential.
Compound Supply : Selection of Sample Source a) Traditional Medicinal Chemistry
Hits tractable.
Discrete entities - ready identification of pharmacophore x Non-renewable resource.
x Decomposition in historic collections.
Compound Supply : Selection of Sample Source b) CombiChem arrays & Bead Libraries
Hits tractable.
- Restricted diversity within libraries
ideal for focussed screening
simple SAR from primary screen x limitations for random screening
Novel formats offer unique possibilities. e.g: - tagged beads “Abbot” HDF protocol ! Pharmacophore identification complicated by - mixture effects - bioactive precursors or side-products
Compound Supply : Selection of Sample Source c) Natural products : plants, bacteria, fungi, marines
Exceptionally diverse.
x Pharmacophore identification difficult (impossible ?) - synergies - mixture effects - toxic contaminants x Often have low chemical tractability.
x Contaminant interference (pigment, surfactant).
x Poor reproducibility.
x Procurement of additional material difficult.
Compound Supply : Determining Compound Input Evaluate the minimum number of compounds which cover the largest possible “diversity space”. Utilise the services of a computational chemist
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If a compound is unacceptable to the chemists DO NOT screen it ! Get expert chemical input early.
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Follow up hits rapidly. Use combichem to generate analogue libraries around potential pharmacophores.
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Re-evaluate for each new target being screened.
Compound Supply : Determining Compound Input Example of sample input:
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Pre-screen to provide comparative evaluation of assay performance - i.e. pilot of 12,800 compounds (always screened)
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60-70% of selected trad. med. chem. compounds - selected by project , HTS and computational chemist
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30-40% combichem templates.
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No natural products ? (discuss !).
Compound Supply : Pooling Can efficiency gains be achieved by pooling compounds e.g. 10 per well ?
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Disproportionate increase in ‘hit’ rate.
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Deconvolution complex - mixture effects - possibility of side reactions - logistic problems/informatics challenges
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Concentration restrictions.
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Solubility problems compounded.
Compound Supply : Miniaturisation 1x(96) 4x(384) 9x(864) 16x(1536) 36x(3456)
Reduced reagent consumption.
Increased screening rate.
384 format the current default.
x Increasing technical challenges.
x Stores compatibility - the control problem - reformatting considerations x “off-the-peg” solutions for the higher densities limited/costly.
Assay Development: Important Aspects
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Safety.
Robustness - quenching, stripping, non-specific effects use of “Robustness test kit” - reagent stability signal:noise ratio, Z’ factor etc
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Reagent availability/cost.
Simplicity (easiest workable technique) - minimise assay steps (e.g. liquid handling)
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Validity (e.g. substrates at Km).
Waste disposal (isotopes, scintillant etc.).
Sample concentration.
Appropriate automation.
Hit threshold and performance prediction (“Rep pilot”).
Keep it clean, simple and cheap !
Generic Screening Technologies
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Cell based assays in vitro assays using well characterised reagents
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SPA
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Fluorescent / colourimetric
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hTRF
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Fluorescence polarisation
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Immunoassay
Summary
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Simplicity
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Innovation
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Quality control
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Integration
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Forward planning
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Multidisciplinary teamwork
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Regularly re-evaluate prejudices
Where Next ?
Generic Screening Technologies Quality Control/performance indicators Defining the HTS process
Generic Screening Technologies
including Specific Examples
Generic Screening Technologies: SPA
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Suitable for use with 3 H, 125 I, 33 P Beads precoated with SA, Biotin, WGA, Ab’s Versatile homogenous assay format
Example: SPA Assay for Polymerase
Biotinylated primer Pol dNTP’s 3 H-NTP Template signal Capture on Streptavidin SPA beads
Example: SPA Assay for Glycosyl Transferase
Substrate Peptide Biotin
[ 3 H]
-glycosyl-CoA
Enz [ 3 H]
-glycosyl Peptide Biotin Streptavidin-SPA bead light signal SPA BEAD • • •
versatile proven cost-effective (ish) BUT isotopic [ 3 H]
-glycosyl Peptide Biotin Strepavidin -emission
Generic Screening Technologies: Fluorescent Intensity
• Quenched-fluorescence assay for a viral protease using EDANS (fluorophore) and DABSYL (quencher)
Generic Screening Technologies: Fluorescence Polarisation
• • • •
Fluorescent tracer (small molecule) binds to large molecule (enzyme, nucleic acid or antibody) Tracer is excited with plane-polarised light and tumbles randomly Quick tumbling w.r.t fluorescence lifetime fluorescence depolarises Slow tumbling (molecule bound) - fluorescence remains polarised
Example: Fluorescence Polarisation Kinase Assay
rapidly rotating
plane polarised excitation beam ATP
large Ab P-peptide complex small fluorescent substrate
TYROSINE KINASE ADP 4G10
reduced rotation
depolarised polarised
Generic Screening Technologies: Time Resolved Fluorescence (TRF)
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Similar to fluorescence intensity except that detection is gated Substantially enhanced sensitivity Need long lived fluorescent compounds - lanthanides (Europium, Terbium, Dysprosium Samarium) Lanthanides are held in ‘cages’ (chelates/cryptates) to protect them from solvents and to enhance fluorescence Conventional TRF requires enhancement before detection so is not single step Homogenous TRF (hTRF/LANCE) is preferable
Example: Kinase TRF assay
Anti Rabbit Europium Anti Phospho Substrate
P
Peptide Substrate Kinase + ATP GST Glutathione
Generic Assay Technologies: Homogeneous Time Resolved Fluorescence (hTRF)
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hTRF is based on FRET (Fluorescence Resonance Energy Transfer) FRET relies on energy transfer from a donor to an acceptor fluorophore.
hTRF uses Europium as the donor and APC/XL665 as the acceptor hTRF/FRET is versatile and suitable for enzymic, protein-protein, binding, DNA hybridisation, and immuno assays) Homogeneous assay Many reagents can be labelled with donors and acceptors (antibodies, Streptavidin, biotin)
Example: hTRF Protein-Protein Interaction Assay 337nm
Eu-labelled streptavidin
665nm
Biotinylated anti rat IgG2b
Protein 1: rIgG Fusion Protein 2: hIgG fusion
XL665 anti human IgG1
Key features
• • • •
versatile/modular - Other Ig fusion's, kinases etc.
non-isotopic modest reagent demands amenable to miniaturisation
GST sub x Example: hTRF Kinase Assay ATP GST sub x Ser73-phosphate KINASE ser73 Excitation Eu labelled Ab SIGNAL Ser73-P Ab APC labelled anti GST GST sub x
Where Next ?
Generic Screening Technologies Quality Control/performance indicators Defining the HTS process
Performance Indicators and Quality Control including example data from Pilot screens
Pilot screening typically involves screening 12,800 cmpds in duplicate
Assessing the Robustness of an Assay
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Intraplate variations Interplate variations Day to day variations Standard inhibitor IC 50 Compound ‘spiking’ (r 2 , Hill Coefficient) Reproducibility of a pilot screen
Assessing the Robustness of an Assay
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Routine measurements: Control means (window) standard deviation %CV Z’ factor (measure of assay variability incorporating SD): Z’ = 1 3*S.D.high + 3*S.D.low
mean ( high ) – mean ( low )
Zhang et al. 1999
Poor Assay despite “good” S:N. Z’ Factor a better indicator 170 150 130 110 90 70 50 30 10 S/B=10 Z’=0.1
-10 0 8 16 24 32 40 48 Sample # 56 64 72 80 96
Zhang et al. 1999
Good assay despite lower S:N. Z’ Factor a better indicator 70 60 50 40 30 20 10 0 S/B=5 Z’=0.5
-10 0 8 16 24 32 40 48 Sample # 56 64 72 80 96
Zhang et al. 1999
Pilot Screen - Histogram of simple absorbance assay
• Distribution around origin • 10 hits highlighted • Negative skew on curve due to cmpd absorbance at 340nM Origin
%age inhibition
Hit threshold
Histogram Showing Distribution of full Screen
8000 0 -100 0
%age inhibition
100
Determination of Hit Threshold
160 140 120 100 80 60 40 20 0 90 80 70 60 50 40 30 20 10 0
Readout
Typical Screen Result:Activity Base Analysis
1.2
1 0.8
0.6
0.4
0.2
0.2
0 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 0
Plate
1.2
1 0.8
0.6
-CNTRL +CNTRL STND Z' FACTOR COMP REG 0.4
Example of a Pilot Screen
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A pilot screen can also highlight plate edge effects (Spotfire analysis)
May indicate automation problems
Can predict liability of complete screen
All plates average Blue < 0% Orange > 15% Red > 40%
Impact of Systematic Errors
76% Quadrant for primary screen 1 2 3 4 7% 9% 8% Primary Hits Confirmed hits Retest rate 15 7% 28 10% 24 10% 60 3%
Visualisation of primary screening
Reflected in
retest
rates Diagnosis
96 well pipetting into 384 well plates:
mechanical variation
Pilot Screen: Assessment of reproducibility
40 plates assayed twice independently • Tight correlation (some scatter around origin) • 10 hits (>50% inhibition - total agreement) • Indicates highly reproducible assay
Predicted hit rate: 0.078% Actual hit rate (full screen): 0.083%
Pilot Screen: Assessment of reproducibility
Expanded view of hit correlation Single Outlying point
Numerical Comparison of Independently Determined Hit Values
Compound Cmpd1 Cmpd2 Cmpd3 Cmpd4 Cmpd5 Cmpd6 Cmpd7 Cmpd8 Cmpd9
Outlier
Run 1 95.1
92.8
67.8
82.7
69.4
92.3
52.4
96.4
89.9
62.2
Run 2 95.5
92.5
66.3
82 64.8
91.5
48.5
95.3
89.1
80.3
Average 95.30
92.65
67.05
82.35
67.10
91.90
50.45
95.85
89.50
71.25
s.d
0.28
0.21
1.06
0.49
3.25
0.57
2.76
0.78
0.57
12.80
Plots showing deviations between duplicates (cumulative plot for population (12800 compounds)) Abs_1: 95.94% determinations closer than 10%
Further Work: (Ongoing) Investigate how hit selection/threshold affects value of screen output Gather similar detailed data for future/current screens Use information to refine interpretation of quality control data generated during pilot screen and enhance effectiveness All actives confirmed in biochemical assay using solid compound Analysis and categorisation of hits by project chemists Comprehensive decision data to be recorded This will enable comparison of results and lessons between HTS campaigns for different targets.
This should highlight further areas for improvement
Advances in Data Reporting New format web page enables version control on screen updates to projects Spreadsheet format single view consideration of spectrum of data: Primary (n=1) Retest (n=2) IC50 (10pt determination in duplicate) curve Curve fit parameters: r squared hill coefficient IC50 LCMS (compound purity/integrity) data solid availability library flags There is also an opportunity to use the automation to carry out selectivity experiments: (e.g. with resistance mutants) This process is now standard (although still being tweaked!)
Screen shot of example HTS output
Where Next ?
Generic Screening Technologies Quality Control/performance indicators Defining the HTS process
HTS as a Process
Overview of management and organisation at Welwyn
THE HTS PROCESS
Opportunity to include ca. 4K compounds selected by Vscr methods in pilot screen and thus provide data feedback to chemoinformatics
RUN HTS SCREEN OBTAIN HITS FROM RCD CONFIRM HITS KEY INFORMATION TRANSFER INTERACTION WITH OTHER DEPTS WORK WITHIN HTS
RoNo Structure Mr 1 o %inhib (n=1) 2 o %inhib (n=2) s.d. (2 o ) IC50 Hill coefficient r 2 (curve fit) Curve (graph) (Mutant IC50’s ?) Purity data (LCMS) solid availability
INTERIM DATATO PROJECT GROUPS Project Chemists REMOVE UNDESIRABLE COMPOUNDS AUTOMATED SPECIFICITY ASSAY AUTOMATED IC50 DATA PACKAGE TO PROJECT GROUPS RUN PILOT SCREEN
All residual material to Adam for LCMS evaluation Purity data
BIOLOGY LT ASSAY DEVELOP HTS ASSAY ASSEMBLE ROCHE LIBRARY Global library management LCMS PROJECT GP/TAG REQUEST HTS SCREEN
Adam communicates all purity data to global library management
Fig1:Current Situation (Optimal example of one screener being fed by one assay developer) Completion
1 o Retest
Screen 1
mutant wt IC50 Decision to screen Maximum efficiency high flux screening Information generation to increase effectiveness Assay development
Screen 2
Pilot Primary screen Retest mutant wt IC50 Decision to screen Assay development Pilot
Screen 3
0 1 2 3 4 5 6 7 8 Time in months (nominal average for typical screen in past two years) 9 10
Fig 2:Greater MTS Activity within HTSu could enable and accelerate generation of large numbers of IC50 data to facilitate decision making within Projects
Maximum efficiency high flux screening Information generation to increase effectiveness
100+ IC50’s at each step
Pilot Vscr Pil.set.
Primary screen Retest mutant wt IC50 mutant wt IC50 mutant wt IC50 Hits from Vscr compounds Confirmator y assays SAR Ca. 4K Vscr hits Chemkill Feedback data: single conc.
n=2 Prioritised cmpds Project specific combichem libs.
100-1000’s compounds Virtual screening
GAP:
No capacity for iteration Smart RCD analogues for IC50 0 1 2 3 4 5 6 7 8 Time in months (nominal average for typical screen in past two years) 9 10
Fig 3: Impact of Increasing MTS work on HTSu Function/timelines (example only - exact overlap of phases will vary)
Yellow stars indicate disruption to high efficiency primary screening mutant wt Analogue mutant wt CombiC
Previous screen
Vscr Pil.set.
Pilot Primary screen Retest mutant wt IC50 mutant wt Analogue
Current screen
mutant wt CombiC
Next screen
Assay development Vscr Pil.set.
Pilot 0 1 2 3 4 5 6 7 8 Time in months (est) Primary screening 9 10 11