Time-resolved interaction analysis on living cells: Comprehensive data generates new knowledge and better decisions Karl Andersson CEO karl@ridgeview.eu
Karl Andersson, PhD Ridgeview Instruments AB Molecular interactions on living cells Founder, inventor, CEO karl@ridgeview.eu Uppsala University Associate professor & senior lecturer Dpt of Immunology, Genetics and Pathology In the past Biacore R&D (1997-2007) PhD bioinformatics MSc scientific computing
The first contact between drug and cell
The first contact between drug and cell Number of drugs bound to target
Technology background 3 simulated binding traces Same affinity 1 nm Different interaction dynamics First 2 h incubation @ 10 nm Then 7 h wash-out 100 0 % occupied target 0 2 4 6 8 h
Technology background Fast on - fast off requires continuous supply 100 % occupied target Slow on - slow off will occupy fraction of targets for a long time 0 0 2 4 6 8 h
Technology background Frequent administration of low concentration. 100 % occupied target Rare administration of high concentration. 0 0 2 4 6 8 h Affinity alone cannot provide this information
Technology background Curve shape may be indicative of mode-of-action 100 % occupied target Relative signal may reflect receptor abundance 0 0 2 4 6 8 h Affinity alone cannot provide this information
Binding kinetics in context Drug discovery: effort to relate in vitro binding kinetics to in vivo effects Local concentration of drug Ligand + Target k on k off LT complex Downstream biological processes Pharmacokinetics Binding Kinetics Pharmacodynamics
Our solution: LigandTracer Measure interactions Often: Protein cell Truly: Any Any Label based detection Fluorescence or radioactivity
LigandTracer cell based assay Cells in regular Petri dish Rotating, sloping support Detection at drained higher end
Signal Detector Time Medium + Ligand Petri dish with cells (T) and background (B) position LigandTracer Output: Background corrected real-time binding curve
The first contact between drug and cell Number of drugs bound to target
The first contact between drug and cell What can you learn about your molecules? Examples from: Rituximab - CD20 EGF EGFR
Rituximab CD20 CD20 Drug target since late 1990 s Challenges for real-time cell-based assays B-cells in suspension = need to anchor them CD20 suggested to form clusters / oligomers = complex framework mab treatment is often conveyed through CDC or ADCC Not too much literature on the CD20 biology
Challenge 1: Anchoring Anchoring protocol developed: BAM Verified on a range of different cells and targets Daudi / CD20 A431 / EGFR Jurkat / CD3 BAM Target cells in PBS Cultivate in cell-culture medium Bondza et al, Front. Immunol., 24 April 2017
Challenge 2: Clustering Interactions often thought as one-to-one Ligand Glycosphingolipid Monomer Homodimer Heterodimer Oligomer Clustering in microdomains Cholesterol
Challenge 2: Clustering Interactions often thought as one-to-one Poor match with the reality on a cell surface... Ligand Glycosphingolipid Monomer Homodimer Heterodimer Oligomer Clustering in microdomains Cholesterol
Challenge 2: Clustering - quantification Develop proximity assay to follow cluster (or dimer) formation over time: Bondza et al, Anal Chem. 2017 Dec 19
Challenge 2: Clustering - results Rituximab induces clustering of CD20 CD20 and B-cell receptor co-localize????
Challenge 3: Antibody effect issues
Challenge 3: Antibody effect issues
Challenge 3: Antibody effect issues Is there a relation between anti- CD20 mab interaction and CDC??? C1q binding to Rituximab on a B-cell line 20 nm Fitc- C1q + 150 nm unl Rituximab
Rituximab CD20 conclusions Confirmed non-1:1 interaction = biology to discover Activities in our lab Clustering and co-localization confirmed and in process of quantification Interaction dependent CDC mechanisms in pipeline to be monitored, on-line Next step: Expand antibody library with plethora of CD20 binders Share findings to enrich literature on the subject
Examples from the EGFR family Overexpressed in variety of cancers Used to establish our toolbox of methods Common trait: Comparison of similar states Culturing conditions / cell lines / labels / molecules
EGF( 125 I) EGFR multiple conditions and cell lines A431 cells Conditions Red: Regular Blue: Starving Green: Gefitinib Black: Gefitinib + starvation 3 nm 9 nm 0 nm 3 nm 9 nm 0 nm 3 nm 9 nm 0 nm 3 nm 9 nm 0 nm Björkelund H, et al, PLoS One. 2011;6(9):e24739.
Comparing cell lines EGFR HER2 A431 +++ + SKOV3 ++ +++ Conditions Red: Regular conditions Blue: Starvation Green: Gefitinib (Iressa) Black: Gefitinib + starvation Björkelund H, et al, PLoS One. 2011;6(9):e24739.
Unlocking results with Interaction Map
Quantify drug-induced dimerization Quenching effect increased by 50% with Gefitinib treatment Kinetic parameters did not change significantly Increased quenching due to increase in EGFR-HER2 dimers
EGFR finding: Complex dimer biology Dimerization depends on drug and on cell line Altered dimerization pattern affects affinity Functional evaluation of dimerization mechanisms Björkelund H et al, Mol Clin Onc. 2013. 1: 343-352
Extending toolbox beyond EGFR and CD20 Nanoparticles Tissue samples Non-human cell models Virus Bacteria Incubate spot with 6ug/mL of Ab against E.coli membrane compound, 3h. Then wash. Incubate E.coli, OD=1.0, 1h at 37ºC, then block with BSA 1%, 30 min Ready to go.
All in all We work with time-resolved molecular interaction analysis on living cells Brings new light on biology Precise & repeatable Ideal for comparisons Advanced assays and data evaluation Simple technology with low running costs Available in all aspects; hardware, software
But one challenge remains Exploring Biophysical principles in Cell biology: Different way of thinking Different constraints New types of results
But one challenge remains Exploring Biophysical principles in Cell biology: Different way of thinking Different constraints New types of results So: Educate Arrange courses (new) Embed learning package in software
Courses Courses arranged by us Advanced level; WHY, not how Small group, genuine experts as teachers SPR-focus May 2018 Cell-based focus Fall 2018 On-site training using us Multiple universities embed our courses Dedicated training on-site at pharma companies (SPR and/or cell-based)
Learning package embedded in software More basic type education 1. HOW 2. WHY 3. WHAT 4. TEST Additional lessons in pipeline.
All in all We work with time-resolved molecular interaction analysis on living cells Brings new light on biology Precise & repeatable Ideal for comparisons Advanced assays and data evaluation Simple technology with low running costs Available in all aspects; hardware, software Digital learning module included Courses of various types available
Acknowledging Fantastic colleagues and collaborators, former and current, far too many to list Karl Andersson, PhD CEO karl@ridgeview.eu
Thank you Karl Andersson, PhD CEO karl@ridgeview.eu