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Go Native …
Amanda Woodrooffe
03 June, 2020
Characterising therapeutic effect in primary cellular models
2
▪ Target validation – where do cellular models fit ?
▪ Primary cells – advantages, disadvantages, opportunities & positioning
▪ Case Studies
▪ Why go native ?
▪ The importance of assay development, characterisation and appropriate validation
▪ Applications from discovery to clinical – support translation
Overview
Going native…
3
▪ Attrition during clinical development remains an issue
▪ Often attributed to inadequate pre-clinical target validation
and/or suitable clinical translation data
▪ Development and application of relevant preclinical cell
based models can help !
▪ The most important considerations:
▪ What is your question ?
▪ What are the relevant data to enable progress ?
▪ How to characterise the relevant biology ?
▪ What model is most fit-for-purpose ?
▪ Complexity of disease: involvement of different cells ?
Target Validation
Use of cellular models
There Remains A Need To Address
Clinical Attrition and Efficacy
Challenges
• Thorough understanding of complex
disease and target biology
• Better human / clinical predictive models
and biomarkers
4
▪ Advantages
▪ Most “in vivo like”
▪ Native target(s)
▪ Interindividual variation
▪ Plethora of culture formats / media
Going Native : primary cells
When, Why, How ?
▪ Disadvantages
▪ Availability / sourcing
▪ Scale
▪ Interindividual variation
▪ Maintenance of cellular phenotype
▪ Positioning
▪ Tertiary screen / candidate selection
▪ Higher throughput screen (e.g. PBMCs)
▪ Clinical translation / biomarker(s)
▪ Opportunities
▪ High volume isolations
▪ Cryopreservation
▪ Pooling donors
Human bronchial
epithelial cells (2D)
Human bronchial
epithelial cells (3D)
Human stellate
cells (2D)
Human liver
(3D co culture)
5
▪ Increase in intracellular free Ca2+ in response
to acetylcholine in NLEC and HLE-B3 cell line
▪ Consistent in cell line and native cells
Case Study 1 : Be more in vivo like - cell line vs native
Is it the right biology ?
From Collison et al., Invest Ophthalmol Vis Sci. 2000
0
25
50
75
100
125
0.001 0.01 0.1 1 10
Responseamplitude(%)
Antagonist concentration (uM)
0
25
50
75
100
125
0.001 0.01 0.1 1 10
Responseamplitude(%)
Antagonist concentration (uM)
▪ But, responses are antagonised differently
◆ Pirenzipine (M1 antagonist)  AF-DX384 (M3 antagonist)
Native cells - NHLEC Cell line - HLE-B3
0
2000
4000
6000
8000
M1 M2 M3 M4 M5
Muscarinic sub-type
Copynumber
0
500
1000
1500
2000
M1 M2 M3 M4 M5
Muscarinic sub-type
Copynumber
M1 M2 M3 M4 M5 M1 M2 M3 M4 M5
0
0.25
0.5
0.75
1
1.25
0.001 0.01 0.1 1 10 100 1000
RelativePeakAmplitude
Agonist Concentration (uM)
NLEC
HLE-B3
6
▪ Model : Recapitulates native islet morphology in prolonged culture
▪ Target(s) : Maintenance of key pharmacological targets e.g. GPR40
▪ Relevant function(s) : GSIS & responsive to standard secretagogues
▪ Reliable : Robust, reproducible methodology – data from over 80
individual donors
Assay development and characterisation
islet ORGANDOT 3D culture
Pancreatic
islets
in situ
islet
ORGANDOT
cultures
Insulin + ProInsulin Insulin + C peptide Insulin + Glucagon Insulin + Somatostatin
Insulin
GLUT2
glucokinase
GLP1 R
GPR40
GPR119
glucagon
7
▪ A potent and selective GPR40 agonist is theorized to be a safe and effective antidiabetic drug with little or no risk of
hypoglycaemia.
▪ TAK-875 (Fasiglifam) was effective at potentiating insulin secretion in islet ORGANDOT cultures
▪ TAK-875 was withdrawn from Phase III clinical development due to drug-induced liver injury (DILI)
▪ Retrospective analysis in BioIVT’s C-DILI model demonstrated the hepatic safety issues observed clinically
▪ Effective use of primary cellular models, in combination, to validate target, evaluate drug effect & potential safety liability
Case Study 2 : GPR40 agonist for diabetes
Getting the balance right…
islet ORGANDOT data C-DILI data (courtesy of BioIVT)
8
Case Study 3 : From discovery to clinical
Development and full validation to support Marketing Authorisation Application
▪ Human primary cell-based assay for screening anti-TNFa biosimilars
▪ 2D, synovial fibroblasts, RA donors
▪ Assay development
▪ Reference standard inhibitor
▪ Titration of stimulus
▪ Selection of sentinel marker
▪ Assay validation
▪ Linearity and range
▪ Relative potency
▪ Parallelism
▪ Specificity – formulation effect
▪ Precision – intra / inter assay
▪ Formulation effect
▪ Biosimilar screen
▪ Relative potency versus Humira
Assay being used to screen small molecules in discovery too…
9
✓ Modelling native biology at native receptors in simple or complex cellular systems can be achieved
✓ Positioning of your assay – consider format, complexity, throughput, source materials – suitable for
secondary or tertiary screen ?
✓ Characterise a fit for purpose model – what actionable data are required ? Make sure your model delivers.
✓ Level of validation needed – multiplexed endpoints, clinically relevant, triage to selected clinical markers ?
✓ Consider the bigger picture – efficacy and safety…
Summary
Keep thinking about clinical translation…
THANK YOU!
we look forward to staying in touch
Credit: Patrick Foley, Cheyenne, Wyoming, USA
From: The Pathologist, April 2020
Keep those primary cells smiling… !
amanda.woodrooffe@precisionformedicine.com

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MDC Connects: Go native… Characterising therapeutic effect in primary cellular models

  • 1. Go Native … Amanda Woodrooffe 03 June, 2020 Characterising therapeutic effect in primary cellular models
  • 2. 2 ▪ Target validation – where do cellular models fit ? ▪ Primary cells – advantages, disadvantages, opportunities & positioning ▪ Case Studies ▪ Why go native ? ▪ The importance of assay development, characterisation and appropriate validation ▪ Applications from discovery to clinical – support translation Overview Going native…
  • 3. 3 ▪ Attrition during clinical development remains an issue ▪ Often attributed to inadequate pre-clinical target validation and/or suitable clinical translation data ▪ Development and application of relevant preclinical cell based models can help ! ▪ The most important considerations: ▪ What is your question ? ▪ What are the relevant data to enable progress ? ▪ How to characterise the relevant biology ? ▪ What model is most fit-for-purpose ? ▪ Complexity of disease: involvement of different cells ? Target Validation Use of cellular models There Remains A Need To Address Clinical Attrition and Efficacy Challenges • Thorough understanding of complex disease and target biology • Better human / clinical predictive models and biomarkers
  • 4. 4 ▪ Advantages ▪ Most “in vivo like” ▪ Native target(s) ▪ Interindividual variation ▪ Plethora of culture formats / media Going Native : primary cells When, Why, How ? ▪ Disadvantages ▪ Availability / sourcing ▪ Scale ▪ Interindividual variation ▪ Maintenance of cellular phenotype ▪ Positioning ▪ Tertiary screen / candidate selection ▪ Higher throughput screen (e.g. PBMCs) ▪ Clinical translation / biomarker(s) ▪ Opportunities ▪ High volume isolations ▪ Cryopreservation ▪ Pooling donors Human bronchial epithelial cells (2D) Human bronchial epithelial cells (3D) Human stellate cells (2D) Human liver (3D co culture)
  • 5. 5 ▪ Increase in intracellular free Ca2+ in response to acetylcholine in NLEC and HLE-B3 cell line ▪ Consistent in cell line and native cells Case Study 1 : Be more in vivo like - cell line vs native Is it the right biology ? From Collison et al., Invest Ophthalmol Vis Sci. 2000 0 25 50 75 100 125 0.001 0.01 0.1 1 10 Responseamplitude(%) Antagonist concentration (uM) 0 25 50 75 100 125 0.001 0.01 0.1 1 10 Responseamplitude(%) Antagonist concentration (uM) ▪ But, responses are antagonised differently ◆ Pirenzipine (M1 antagonist)  AF-DX384 (M3 antagonist) Native cells - NHLEC Cell line - HLE-B3 0 2000 4000 6000 8000 M1 M2 M3 M4 M5 Muscarinic sub-type Copynumber 0 500 1000 1500 2000 M1 M2 M3 M4 M5 Muscarinic sub-type Copynumber M1 M2 M3 M4 M5 M1 M2 M3 M4 M5 0 0.25 0.5 0.75 1 1.25 0.001 0.01 0.1 1 10 100 1000 RelativePeakAmplitude Agonist Concentration (uM) NLEC HLE-B3
  • 6. 6 ▪ Model : Recapitulates native islet morphology in prolonged culture ▪ Target(s) : Maintenance of key pharmacological targets e.g. GPR40 ▪ Relevant function(s) : GSIS & responsive to standard secretagogues ▪ Reliable : Robust, reproducible methodology – data from over 80 individual donors Assay development and characterisation islet ORGANDOT 3D culture Pancreatic islets in situ islet ORGANDOT cultures Insulin + ProInsulin Insulin + C peptide Insulin + Glucagon Insulin + Somatostatin Insulin GLUT2 glucokinase GLP1 R GPR40 GPR119 glucagon
  • 7. 7 ▪ A potent and selective GPR40 agonist is theorized to be a safe and effective antidiabetic drug with little or no risk of hypoglycaemia. ▪ TAK-875 (Fasiglifam) was effective at potentiating insulin secretion in islet ORGANDOT cultures ▪ TAK-875 was withdrawn from Phase III clinical development due to drug-induced liver injury (DILI) ▪ Retrospective analysis in BioIVT’s C-DILI model demonstrated the hepatic safety issues observed clinically ▪ Effective use of primary cellular models, in combination, to validate target, evaluate drug effect & potential safety liability Case Study 2 : GPR40 agonist for diabetes Getting the balance right… islet ORGANDOT data C-DILI data (courtesy of BioIVT)
  • 8. 8 Case Study 3 : From discovery to clinical Development and full validation to support Marketing Authorisation Application ▪ Human primary cell-based assay for screening anti-TNFa biosimilars ▪ 2D, synovial fibroblasts, RA donors ▪ Assay development ▪ Reference standard inhibitor ▪ Titration of stimulus ▪ Selection of sentinel marker ▪ Assay validation ▪ Linearity and range ▪ Relative potency ▪ Parallelism ▪ Specificity – formulation effect ▪ Precision – intra / inter assay ▪ Formulation effect ▪ Biosimilar screen ▪ Relative potency versus Humira Assay being used to screen small molecules in discovery too…
  • 9. 9 ✓ Modelling native biology at native receptors in simple or complex cellular systems can be achieved ✓ Positioning of your assay – consider format, complexity, throughput, source materials – suitable for secondary or tertiary screen ? ✓ Characterise a fit for purpose model – what actionable data are required ? Make sure your model delivers. ✓ Level of validation needed – multiplexed endpoints, clinically relevant, triage to selected clinical markers ? ✓ Consider the bigger picture – efficacy and safety… Summary Keep thinking about clinical translation…
  • 10. THANK YOU! we look forward to staying in touch Credit: Patrick Foley, Cheyenne, Wyoming, USA From: The Pathologist, April 2020 Keep those primary cells smiling… ! amanda.woodrooffe@precisionformedicine.com