machine learning methods to predict antibiotic resistance ... · machine learning software to...
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Machine learning software to predict antibiotic resistance and
pathogen typesDr. Sebastian Dümcke
Clemedi AG, Schlieren, Switzerland
Advancing Data Technologies to corner AMR June 5th, Amsterdam
Image credits: Microrao; https://commons.wikimedia.org/wiki/File:Antibiotic_resistant_bacteria.jpgImage credits: Netha Hussain; https://en.wikipedia.org/wiki/File:Infiltrating_ductal_carcinoma_20X.jpg
Image credits: Microrao; https://commons.wikimedia.org/wiki/File:Antibiotic_resistant_bacteria.jpgImage credits: Netha Hussain; https://en.wikipedia.org/wiki/File:Infiltrating_ductal_carcinoma_20X.jpg
Diagnostic Platform
0 h 6-24 h
IVD Kit IVD Software
(1)
DNA/RNA
(2) (3)
Amplification Sequencing
(4)
Machine-learning
(5)
Diagnostic report
OPTIMAL INITIAL ANTIMICROBIAL THERAPY
This translates to
better patient care
reduced hospitalization costs
control the spread of antimicrobial resistance
A new standard for in vitro diagnostics
FAST COSTAUTOMATEDCOMPREHENSIVE
100s of targets
analyzed in a
single test, down to
the single
nucleotide
Turn around time
between 8-24
hours, and handle
upto 384 samples
per run
The solution works on
any current NGS
platforms.
All the steps are
optimized on liquid
handling systems to
enable minimal
hands-on time
Novel approach to
harness the power of
sequencing and keep
the costs competitive
PLUG AND PLAY
Urinary tract infections
Sexually transmitted infections
Tuberculosis
Post-operative infections
Meningitis
Infectious Diseases
Applications
In 2018
10,400,000 cases
1,600,000 deaths
~2 billion people carry the disease
Increasing drug resistance
480,000 cases
230,000 deaths
Drug resistant tuberculosis – a global challenge
Rifampicin
Isoniazid
Pyrazinamide
Streptomycin
Ethionamide
Kanamycin
Capreomycin
Amikacin
Rifabutin
Prothionamide
Levofloxicin
Gatifloxicin
Ciprofloxicin
Ofloxicin
Moxifloxicin
PAS Cycloserine
Delamanid
Clofazamide
Bedaquiline
Carbapenems
PA-824
Linezoid
β-lactams
SQ-109
BTZ
Trimethoprim
Macrolide
NAS-21/NAS-91
M. tuberculosis
Lineage 2
Lineage 3
Lineage 4
Lineage 5
Lineage 6
Lineage7
Lineage 1
M. avium
M. bovis
M. africanum
M. canetti
M. caprae
Which tuberculosis?
Which antibiotic therapy?
Future antibiotics
Targeted DNA Sequencing
Machine learning
+
Aim: choose the correct initial therapy
Aim: choose the correct initial therapy
Advisors
• Algorithms• Financing• Leadership
CEO CSO BoardBoard
• Chemistry• Regulatory• R&D
• Infrastructure• Network• Supervision
• Network• Infrastructure• TB expert
100% 100% 10%10%
Dr. PrajwalDr. Sebastian
DümckeProf. Thorsten
BuchDr. Peter Keller
8 years bioinformatics1 year cancer
diagnostics
11 years molecular biology
Quality management
Role
Tasks
Experience
Time committed
10 years medical doctor6 years diagnostics
17 years genetics8 years leadership
Team
R&D
Business Development
Suppliers & distributors
Regulatory
BioEntrepreneurship
& Innovation
Fund raising
Market access &policy drivers
Partners