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