A biomarker strategy aims to answer key clinical questions to support drug development through identifying and testing biomarkers. Developing a robust biomarker strategy can mitigate risks and inform clinical study design by generating testable hypotheses to bridge pre-clinical and clinical research. Effective biomarker strategies consider assay suitability, study design, and sample availability to reliably detect biomarkers and provide statistically meaningful results. Emerging technologies allow deeper interrogation of drugs and disease through multiplexed readouts to enhance biomarker discovery and clinical development.

1. Developing a Biomarker strategy
What can drug discovery translational research do
increase success?
Gayle Marshall
Lead Scientist - Biomarkers
MDC Connects
Webinar Series
Wednesday,
17 June 2020

2. © 2019 Medicines Discovery Catapult. All rights reserved.
Why do we need to develop a biomarker strategy?
• Many drugs fail in the clinic costing >15 years and $2 billion per medicine
• Whilst many compounds and target hypotheses fail before the clinic many drugs still go onto the clinic to fail there.
• Clinical trials are expensive with high attrition rate due to:
• Lack of efficacy
• PK/PD
• Wrong safety profile
• Wrong strategy
• Key is to understand the clinical questions
• Build testable and scientific evidence to transition between pre-clinical and the clinical setting
• Use early studies to test hypothesis
Target
Identification
Target
Validation
Lead
Identification
Lead
Optimisation
Pre-clinical Clinical

3. © 2019 Medicines Discovery Catapult. All rights reserved.
• A biomarker strategy is set out to
answer a range of key clinical questions.
• Biomarkers are to be identified and
tested through pre-clinical and into
clinical testing, mitigating risks and
testing hypothesis.
• This ensures a more informed clinical
study design and project decision-
making – A robust clinical strategy
What is a Biomarker strategy?
Generating
testable
scientific
hypothesis to
support drug
development
What dose
level?
What
schedule?
Which
disease?
Which
patients?
Is this better
than current
standards of
care?
Combination
options?
Acquired
resistance?

4. © 2019 Medicines Discovery Catapult. All rights reserved.
When do you think about introducing Biomarkers?
Biomarker Discovery
Strategy and delivery

5. Key Biomarker questions to support clinical study design
• PharmacoDynamic (PD) / Proof of Mechanism (PoM)
– Does the drug hit its target?
• Proof of Principle (PoP) – Does the morphology
change due to modulation of the target?
• Proof of Concept (PoC) – What are the clinical effects
• Predictive biomarkers – Can you predict you have an
effect
• Patient Selection – Which patients will best respond?
• Safety biomarkers – What safety considerations need
to mitigate?
• Are there potential markers of resistance?
• What are the best combination options?
• How does response compare to current Standard of
Care (SoC)

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• There are many potential impactful applications for the use of clinical biomarkers.
• Diagnostics - Patient selection (treatment decision or recruitment onto a clinical study)
• Translational Science – Support clinical development Go/No Go decision making, defining the right dose
and impact on the disease biology.
• Many biomarkers have been identified yet very few have entered the clinic as a diagnostic.
• Biomarker uptake can be due to the fact identified clinical biomarkers may still lack clinical utility.
• Complex and underestimated (variability of the marker)
• Lack of understanding of the pathology and heterogeneity of the disease
• Use of inappropriate samples for discovery and validation
• Methodology Limitations
In 2019 there were more than 880 000 papers indexed in PubMed directly related to
biomarkers. Despite advances in laboratory technology and an enormous expansion in
relevant literature, we seem quite far from widespread clinical use of biomarkers in
discovery, treatment, or monitoring. In fact, in cancer, for example, today there exists only
a few dozen clinically relevant biomarkers in use.
The Challenge

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For many targeted drugs the presence of the target is usually a good place to start
• Cases where the target amount is predictive:
Herceptin –HER2
Pembrolizumab, nivolumab, durvalumab etc. – PD-L1 (sometimes…)
• Cases where the target is predictive:
Crizotinib–ALK fusions
Vemurafenib –BRAF V600E
Tagrisso–EGFR T790M
• Cases where target levels are not predictive:
Lynparza–PARP levels not predictive, BRCA mutation is
Cetuximab –EGFR levels not predictive, KRAS mutation is
Start with the expression of the target – oncology examples

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• Gefitinib (ZD1839) is an EGFR tyrosine kinase inhibitor which
blocks signalling through the epidermal growth factor
receptor.
• When Gefitinib was launched in Japan 2002.
• It was approved as monotherapy for the treatment of
patients with locally advanced or metastatic NSCLC after
failure of both platinum-based and docetaxel
chemotherapies.
• EGFR mutations first observed in 2004
• Gefitinib is currently approved for the treatment of 1st line
EGFR M+ advanced NSCLC patients in 85 countries.
• Phoenix from the flames but not every drug has a happy
ending so what can we do to ensure we know this
information from the start?
Biomarker Example Drug Development Impact

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• Understand the biology
• Pre-clinical translational studies
• Determine whether to go down the targeted biomarker route as early as possible
• The tissue is the issue – collect as many samples as possible
• No sample no biomarker
• Pathologists are key
• Conflict between push for faster studies and push for targeted healthcare, fast recruited are not often the most
experienced at sample collection
• A targeted drug needs a diagnostic approach
• It matters
• What you measure
• How you measure it
• How you define a positive cut off
Lessons Learned from landmark studies

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Better clinical study
design
Cell models that better
reflect the patient
Multiplex readouts for a deeper
understanding of drugs and disease
Back
translation
• PharmacoDynamic (PD) / Proof
of Mechanism (PoM)
• Proof of Principle (PoP)
• Proof of Concept (PoC)
• Predictive biomarkers
• Patient Selection
• Safety biomarkers
• Additional identification of
markers of resistance along with
combination options
Genomics Analysis
• Mutation analysis
• Genomic loss / gain
• Copy number changes
RNA Analysis
• Transcriptomics
• Gene expression profiling
• Gene Fusion expression analysis
Protein Analysis
• Proteomics
• comparisons to IHC
Metabolite Analysis
• Metabolomics
• (unique chemical Fingerprint)
Lipidomics
Imaging
Biomics
Protein analysis
• Clinical Tissue - Frozen and FFPE (mRNA, DNA
mutation, protein, gene fusions, miRNA, CNV)
• Blood (mRNA, circulating tumour cells,
circulating tumour DNA, PBMCs)
MDC Biomarker Translational Science Approach

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Emerging Technologies for A Deeper Understanding of
Drug and Disease Biomarkers
- Advancing Biomarker Discovery through emerging technologies, multiplexed readouts can be interrogated for a deeper
understanding of drug and disease
- Hypothesis free and hypothesis driven biomarker analysis workflows are being established to compare gold standard methods
to emerging multiplexing platforms
Phosphocholine (m/z 772) in rat brain Phospholipids in rat brain, displaying
differential localisation.
m/z
810
m/z
772
m/z
844
- Focus on
Transcriptomics,
Lipidomics,
Metabolomics;
Proteomics
- Building spatial biomarker
analysis into our offering
- Mass Spectrometry
Imaging
- Nanostring GeoMx
Digital Spatial Profiler
Nanostring
GeoMx
Nanostring
Pathways
Leica Bond
IHC & ISH
MSI
DESI/MALDI
SIMOA/
Luminex
Confocal/Super
Res Microscopy

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• Is the underlying biology adequately characterised?
• What is the prevalence and levels within patient tissue
• How heterogeneous?
Scientifically relevant
biomarker(s)
• Can the assay reliably detect the biomarker (e.g.
pharmacodynamics) change we expect to see clinically?
• Go/No Go thresholds defined
Assay Suitability
• Do we know when to take a clinical sample, how to ensure
stability of the marker and how many we will need to
provide a statistically robust decision?
Study design
• Can we deliver this assay in the real world?Sample
Assay Development - Clinical Utility Considerations

13. © 2019 Medicines Discovery Catapult. All rights reserved.
D538GMutsignalD538GWTsignal
Cycle 1 Day 1 Cycle 1 Day 2 Cycle 1 Day 15
Predictive hypothesis testing in early clinical development
Retrospective testing
• Where all comers are enrolled, biomarker testing is done at end of
study to determine outcomes in different groups
• This can be pre-defined (more statistical power) or more usually post-
hoc “hypothesis free” (potentially no statistical power)
- Subsequent prospective testing can validate findings
• Only informative for prevalent marker phenotypes
• Example - Phase I exploratory analysis
• Safety and tolerability
• Opportunity to test pre-clinical hypothesis
• Breast cancer example – oestrogen receptor (ER) down-regulator
• Detection of circulating tumour DNA by ddPCR
• Monitoring response with treatment
Early Clinical Biomarker opportunities
Longitudinal testing
ER(WT)ER(Mutation)

14. In depth, quantitative, pathways analysis,
supporting PoM and PoP in preclinical and clinical
samples.
Example - in vivo study for SME investigating IO
drug combination, data supporting clinical trial
strategy.
Investigated signatures of tumour, micro-
environment and immune response and drill deeper
into the key pathways involved.
Tumour infiltrating lymphocytes
Identification of key pathways and cell types in drug response from large
disease-relevant pre-defined or custom panels
Pathway Biomarkers
– Support Clinical combination Approach
Combo Drug 1 PBS Drug 2

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Clinically ready assays - key information needs to be collated – Biomarker Strategy
• Is the underlying biology adequately characterised?
• Identify Right clinical questions - Bridge the gap between pre-clinical and clinical drug development.
• Develop the Right Biomarker Strategy - Through highly multiplex analysis understand the heterogeneity of the disease
to identify the Right biomarkers. Understand the prevalence and levels within patient tissue.
• Assay Suitability
• Right assays - Design and deliver robust assays to generate preclinical evidence that will translate across into the clinic.
Key to understand the decision-making cut offs
• Can the assay reliably detect the biomarker change we expect to see clinically
• Study design
• when to take a clinical sample, how to ensure stability of the marker and how many we will need to provide a
statistically robust decision
• Sample availability
• Right Samples – understand the variability of the biomarkers identified, collect the best samples to support the
hypothesis through pre-clinical models through to clinical testing.
Turning Molecules into Medicines

16. © 2019 Medicines Discovery Catapult. All rights reserved.
Acknowledgements
Pre-clinical Imaging – Juliana Maynard
Mass spectrometry Imaging – Philippa Hart/Ekta Patel
Acoustic Mist – Martin Bachman
Nanostring/GeoMx – Gayle Marshall/Michael Eyres
Simoa/ddPCR - Andrzej Rutkowski
Histology – Kevin Randall