Select the right drugs for each patient based on the
No more trial and error, one-size-fits-all approach… Drug-PIN is available for Doctor use in
Hospitals,
Clinics and Labs as well as Doctors in Private Practice.
The Drug PIN technology can best be described as Functional Biochemistry based on Genomic Profile.
Developed by university researchers over the last decade, Drug-PIN presents a multidimensional
approach
to personalised drug therapies. Through a deep learning algorithm, Drug PIN is able to infer
patterns
and make predictions by analysing a patient’s unique data against multiple, updated medical datasets
that catalogue:
- the roughly 4 million known associations between side effects, drugs, and proteins;
- more than 19,000 proteins in the human body that interact with each other and with medications.
By considering a Patient’s unique characteristics including:
Functional Biochemistry
Lab Analysis
Genome
Lifestyles / Behaviours
Clinical History
Drug-Drug Interactions
Functional
Biochemistry
Lifestyles /
Behaviours
Lab
Analysis
Clinical
History
Genome
Drug-Drug Interactions
The efficiency of a
drug can be measured
for that Patient, while
at the same time
analysing the
Drug Selection by Patient’s functional biochemistry
Drug-Drug Interaction based on regulation and metabolic profile
1
Drug Selection by Patient’s functional biochemistry
2
Drug-Drug Interaction based on regulation and metabolic profile
Drug-PIN’s functionalities are aimed at analysing how effective each drug will be for the Patient and
how
the drugs interact for a specific Patient, highlighting any likely risks of adverse side effects for
each
combination.
In addition to detecting dangerous or ineffective drug cocktails, Drug-PIN may present possible
alternatives
to high-risk drugs, or elimination of certain drugs altogether, allowing for fine tuning of the
Patient’s
therapy and reducing the overall risk of undesirable interactions.
Key Characteristics
Certified External Data and Open Data
Drug PIN uses a base of official data from the European Agency of Medicines, FDA, CPIC / Pharm
GKB
and Open Data.
Artificial Intelligence
We use semantic analysis algorithms and machine learning to analyse, correlate and compare
medicines, symptoms, interactions and related information.