How We Develop Disease Models
Model is created by extracting digital data from urine
The Luventix platform will make the process of creating models for different diseases repeatable and mechanistic.
To develop each specific disease model, we will conduct stringent clinical trials.
We will begin each trial for a specific disease with urine from multiple types of patients to replicate real-world circumstances:
patients diagnosed with the specific disease (known or un-blind sample);
healthy patients
symptomatic but either negative for the disease and/or have conditions that present similar symptoms
with diseases other than the disease for which we are modeling
For example, if we are developing a disease model for colon cancer, we would collect samples from patients diagnosed with colon cancer, healthy patients with no cancers, patients with gastrointestinal conditions that have symptoms similar to those of colon cancer, as well as from patients diagnosed with cancers other than colon cancer.
Signal detection using Artificial Intelligence (AI) and Machine Learning (ML)
We will then analyze urine via Gas Chromatography, delivering a readable digital file representing the metabolic state of the patient.
We leverage AI, ML, and deep learning algorithms to analyze that data and detect intricate patterns within it. This process involves comparing patterns of all kinds of patient types with and without the disease.
Very similar to the use of AI for face, speech or image recognition, our platform delves into the vast amount of data, meticulously classifying patterns and identifying the hidden connections, correlations, and characteristics that may indicate the presence or absence of certain diseases.
Validating a disease model for a specific disease
After we have detected a signal, we create a classification system around the signal and “train” our disease model. Training is conducted using known positive and negative samples.
During each clinical trial, we will determine specificity and sensitivity of the disease model, by performing a controlled blinded study.
We will exercise the trained model, by collecting blinded urine samples from patients with an unknown diagnosis.
We use the same gas chromatography process, for the blinded samples, to create a digital metabolic profile, or “Digital Twin”, of each patient’s metabolic state at a point in time.
A critical step to offering a Luventix test to patients is to demonstrate its accuracy and satisfy clinical trial selectivity and sensitivity target criteria.
What is a Digital Twin?
Using the Test Commercially to Screen and Diagnose Diseases
Once the disease model has been developed and trained, and the test validated, and approved for commercial release, the process of testing patient samples will be identical.
Urine -> gas chromatography -> processing of the digital twin -> resultant output to be shared with physician
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