Drug response biomcare webinar - jan 2022

Importance of microbiomes
The role of the Gut Microbiome for
Drug Response
Webinar, Jan 25th, 2022

Welcome
BIOMCARE ApS | Inge Lehmanns gade 10 | 8000 Aarhus C | Biomcare.com | 3157 2081
Louise Thingholm
CEO Biomcare
Harward University, US
• In Huttenhower Lab, Microbiome bioinformatics
University Hospital Kiel (UKSH), Germany
• PhD Bioinformatics, Microbiome role in metabolic disease, Ob & T2D
• PostDoc, University Hospital Kiel (UKSH), Microbiome in GI inflammatory
disease
Melbourne university, Australia
• Southey Lab, Breast cancer research
Aarhus University, DK
• M.Sc. Molecular medicine

Welcome
Practicalities
- Q&A
- Chat
- Raise hand
Louise Thingholm,
CEO Biomcare
Senior Bioinformatician
PhD Bioinformatics
Anne Mathiesen
Bioinformatics Scientist
M.Sc. Molecular Biology
Regin Jensen
Digital & IT
M.Sc. in IT

Importance of gut microbiome
for varying drug response

Agenda
- The gut microbiome
- How the microbiome can affect drug
response
- Benefits of microbiome profiling in
clinical studies and trials
- Biomcare’s platform
- Concluding

Welcome to all of you!
- Do you work with microbiome?
- Yes / no
- Do you work with clinical studies / trials?
- Yes / no
- Do you research drug metabolism?
- Yes / no
Q

The human gut
microbiome
Communities of microorganisms
• bacteria
• fungi
• protozoa
• viruses
http://worldmicrobiomeday.com
>10,000
Number different
microbe species so
far found to live
in/on the human
body
150:1
The genes in the
human microbiome
outnumber human
genes by 150 to one
80-90%
How different two
humans are in term
of their microbiome
1:1
Ratio of human and
microbial cells in
human holobiome
90%
Up to 90% of all
diseases is linked to
the gut and gut
microbiome
10-100 trillion
(ca. 2kg)
Number microbial
cells living in the gut
of a normal human
being

The human gut
microbiome

Ways the microbiome influence drug response
Direct effects
• enzymatically transforming the drug’s structure
Indirect effects
• modifying immune status
• modify human gene expression
• drugs effect on microbiome
Altering its bioavailability, bioactivity or
toxicity (pharmacomicrobiomics).

The extend of microbiome drug metabolism
Broad metabolizing capacity of gut bacteria
• Key study by Zimmermann et al., 2019, tested metabolic capacity
of 76 strains on 271 drugs, found that 176 drugs (66%) were
metabolised by a least one kind of stain.
Drug classes affected include:
• cardiovascular drugs, analgesics, chemotherapeutic agents,
antidiabetics, antiviral, antimalarial, PPIs
Specific species and genes can be linked directly to
metabolite levels of active compounds
Recommended reading:
Zimmermann 2019 - Mapping human microbiome drug metabolism by gut bacteria and their genes (figure reference)
Zimmermann 2021 - Towards a mechanistic understanding of reciprocal drug–microbiome interactions
Guthrie and Kelly 2019 - Bringing microbiome-drug interaction research into the clinic

Antiviral drug brivudine
• Metabolised to bromovinyluracil by both the host
and the gut microbiota
• Bromovinyluracil exert hepatic toxicity
• Microbial gene responsible: bt4554 (purine
nucleoside phosphorylase)
• 70% of brivudine toxicity is attributable to gut
microbes
• Effect ascribed particularly to Bacteroides
thetaiotaomicron and Bacteroides ovatus.
Separating host and microbiome contributions to drug
pharmacokinetics and toxicity
Zimmermann et al., 2019 http://dx.doi.org/10.1126/science.aat9931
Recommended video:
“Do Gut Bacteria Contribute To Drug Metabolism?”
https://www.youtube.com/watch?v=LyjiVRRhIzM

Parkinson drug Levodopa
Levodopa metabolized by both human and bacteria
-> bacterial metabolism of L-dopa decreases drug availability
Rekdal et al., 2019 - pubmed/31196984

Parkinson drug Levodopa
Levodopa metabolized by both human and bacteria
-> bacterial metabolism of L-dopa decreases drug
availability
But … it is not so “simple”
-> gut bacterial metabolism of L-dopa also induces
adverse drug response
Rekdal et al., 2019 - pubmed/31196984

Other key example drug-microbiome interactions
• Metformin changes the gut microbiome composition,
increase SCFA production, in turn ameliorating drug efficacy
• PPIs are metabolised by microbiome AND changes the gut
microbiome, with Induced microbiome changes influence
health outcomes
• CRC drug gemcitabine metabolized by bacteria into its
inactive form via cytidine deaminase enzyme activity
• Immune checkpoint inhibitors
Want to see more?
Wisit the PharmacoMicrobiomics web portal
http://pharmacomicrobiomics.com/
In-vivo animal and in-vitro studies
Microbial regulation of cancer treatment efficacy
Table 1 lists taxa and effects.
Pryor et al., 2020, PMID: 31386593 Review.
List of example drug-microbe interactions with
well characterized mechanisms.
Table 1, Sharma et al., 2019, PMID: 30937887

Immune checkpoint inhibitors
• Immune checkpoint inhibitors (ICIs) targeting the
PD-1/PD-L1 axis
• Induce sustained clinical responses in a sizable
minority of cancer patients.

Antibiotics disrupt clinical benefit of ICIs
Clinical observations
• Antibiotics inhibited the clinical benefit of ICIs
• A. muciniphila abundance associated with
response
Functional evidence
• FMT from responding patients into germ-free mice
ameliorated the effects of PD-1 blockade, while
FMT from non-responding patients did not.
• Oral supplementation with A. muciniphila after
FMT from non-responders restored the efficacy of
PD-1 blockade
Routy et al., 2018, PMID: 29097494
N=249 cancer patients, different types.

Antibiotics disrupt clinical benefit of ICIs
Meta analysis
• 23 studies
• 2208 patients for PFS and 5560 for OS.
• The pooled hazard ratio was 1.47 for PFS and 1.69
for OS revealing a significantly reduced survival in
patients with NSCLC exposed to antibiotics
• The median OS was reduced on average by 6.7
months
Lurienne et al., 2020, PMID: 32173463

Microbiome profiling in clinical studies
Goal
• Estimate effect of microbiome
• Identify microbial biomarkers
Pre-treatment
stool sample
Treatment
start
Evaluation of
treatment
outcome

Responders (R) & Non-responders (NR)
Single taxa but not beta diversity
associate with R
10 species DA in R versus NR
(across 3 data-types). Differences
in beta-diversity.
Higher alpha diversity in R.
Differences in beta-diversity and
single taxa. (no diff. in oral)
Single taxa and diversity
associate with progression-free
survival.
Studies Microbiome Validation
FMT transfer from
responding patients to GF
mice.
FMT transfer from
mice.
Microbiome role for ICI effect in melanoma treatment
Gopalakrishnan et al., 2018
Matson et al., 2018
Frankel et al., 2017
Peters et al., 2019
NR=13
R=30
NR=26
R=16
NR=16
R=23
NR=12
R=15

Gopalakrishnan et al., 2018
Matson et al., 2018
Frankel et al., 2017
Peters et al., 2019
NR=13
R=30
NR=26
R=16
NR=16
R=23
NR=12
R=15
Meta-analysis
NR=47
R=83
Single taxa but not beta diversity
associate with R
10 species DA in R versus NR
(across 3 data-types). Differences
in beta-diversity.
Higher alpha diversity in R.
Differences in beta-diversity and
single taxa. (no diff. in oral)
Single taxa and diversity
associate with progression-free
survival.
Studies Microbiome Validation Companies
FMT transfer from
mice.
FMT transfer from
mice.
Pierrard and Seront 2019 – A meta-analysis of AB application and baseline microbiome
Limeta et al., 2020 – A meta-analysis of treatment response
Minot et al., 2021 - identifies genome islands associated with immunotherapy response
Robinson et al., 2021 – used MELRESIST trial cohort (69 patients) to build model with 93% accuracy, which could predict
published cohorts patients response well (82-100%).
Microbiotica,
AstraZeneca
4D Pharma, Merck
Enterome
Synlogic, Roche
Vedanta Biosciences,
BMS
And more.
Microbiome role for ICI effect in melanoma treatment

Why do microbiome profiling in clinical studies?
P1
P2
P3
P4
Clinical study
Power
Aim
Method

Why do microbiome profiling in clinical studies?
• Increased power and reduced failure rate of
clinical trials, by supporting stratification of
participants in current and following phase
studies
• Promote mechanistic understanding e.g. of
toxicity, off-targets, complimentary to
metabolomics analysis.
• Candidates for probiotics or selection of
intervention for active improvement of drug
response.
• Biomarkers and targets for further optimization
of treatment, reimbursement opportunities and
ad-on products (companion diagnostics)
Microbiome effects
0
10
Findings
!
STANDARD TREATMENT
STANDARD DOSE
STANDARD TREATMENT
ALTERED DOSE
PROCEED WITH
CAUTION
CONSIDER ALTERNATE TREATMENT
OR
Stratification of participants

Normal
Response
Toxic
Response
Cautionary
Response
No
Response
Altered
Response
PATIENT
GROUP
DISEASE
DIAGNOSIS
PERSONALIZED TREATMENT:
Same Prescription and
Standard Dose
u Genetic Variation
u Liver Function
u Metabolomics
u Microbiome composition and function
Figure modified from genesisgenome.com
TRADITIONAL TREATMENT:
Personalized treatment

Reduce undesired
drug effects on
the microbiome
Figure from Zimmermann 2021
Administration of pro- and pre-
biotics and FMT has been used to
improve patient responses.

Your hurdle for implementing
microbiome analysis
- What do you see as the biggest hurdle to get
started with microbiome analysis?
o Project planning and sample collection
o Data generation (sequencing)
o Data analysis (bioinformatics & statistics)
o Cost versus value ratio
- Microbiome analysis will add value to my clinical
study?
o Yes / no / I don’t do clinical studies
Q

Biomcare – your microbiome department on demand

CONSULTING, PLANNING & SAMPLING
DATA PROCESSING AND MICROBIOME
PROFILING
BIOSTATISTICAL ANALYSIS
REPORTING
Microbiome projects A-Z

Project design
and planning
Sample
collection &
shipment
DNA extraction, library
preparation and
sequencing
Raw data
available for
download
Sample colelction and microbiome sequencing
Data quality
evaluation, data
filtering, and
microbiome profiling
Project reports,
and all supportive
files on server for
download
Biostatistial analysis and reporting
Classification & Biomarker discovery
Project flow

Planning and Microbiome profiling
Taxonomic profiles
• Conservative pipelines for known taxa
• De-novo discovery by short reads
• Assembly-and-binning for de-novo MAGS
• Strain-level insight
Functional profiles
• Conservative pipelines for pathways and gene families
• “Co-abundant gene groups” and direct mapping
pipelines for organism-independent functional insight
Data quality
evaluation, data
filtering, and
microbiome profiling
Project design and
planning

Classification & biomarker discovery
Classification & Biomarker
discovery

Classification & biomarker discovery
Solution for robust discovery
of microbial biomarkers
Parallel detection
of potential
biomarkers.
Building on
multiple carefully
selected models
and permutations
to increase
robustness.
Incorporating
prior knowledge
Construction of
consensus list of
ranked potential
biomarkers.
Evaluation of
classification
strength,
sensitivity,
specificity etc. of
classification and
biomarkers.
Reporting and
consulting on
results.
M3
M2
M1 M4
INPUT:
Microbiome
profiles

Classification & biomarker
discovery
Classification & Biomarker
discovery
P1
P2
P3
P4
Clinical study
Power
Aim
Method
M3
M2
M1 M4
Key information provided
• How much is the microbiome
involved in mediating response to the
given drug.
• What features of the microbiome
played a role
• Does the microbiome relate to other
recoded features such as adverse
events.

Nordic Sun trial
Study Type : Interventional (Clinical Trial)
Estimated Enrollment : 400 participants
Allocation: Randomized
Primary Purpose: Treatment
Key features:
Multicenter Randomized Phase III Trial
Metastatic Renal Cell Carcinoma
Receiving Checkpoint Inhibitors: Nivolumab & Ipilimumab
Main endpoint Evaluating the Impact of Surgery or No Surgery
Data/samples
Extensive patient and sample/tissue collections.
Tumor biopsies, blood, and stool specimens for translational biomarker research will be sampled
at baseline and after 3 months.
Lead Skejby University hospital Oncology department
Pre-treatment
stool sample
Treatment
start
Evaluation of
treatment
outcome

Potential collaboration?
Solutions
Quality
Innovation You?
Biomcare

30 min. free consulting session for all of you
Schedule a meeting using link we send
around in e-mails after todays webinar.
Louise B. Thingholm,
CEO Biomcare
Senior Bioinformatician
PhD Bioinformatics
Mikkel K. Normark
Head of business
development

Biomcare
Navitas,
Inge Lehmanns Gade 10,
8000 Aarhus C
BiomCare.com
+45 31 57 20 81
info@biomcare.com
Next step?

Consulting
• Study design and planning
• Results interpretation
• Workshops
CONSULTING AND REPORTING

Drug response biomcare webinar - jan 2022

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