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Revolutionizing Stem Cell Research

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123Genetix - Artificially Induced Stem Cells for Drug Discovery

When bringing a new drug to the market, cost becomes a very important concern for pharmaceutical companies. This is especially true for rare diseases where the cost involved in drug discovery and manufacturing plays a very critical role in launching the drug into the market.

Current wet-lab research being used for drug discovery is inefficient when it comes to cost control. Utilization of genetic relationship data for drug discovery can prove more efficient and accurate but this requires big data solutions such as an Artificial Intelligence (AI) Platform like Machine Learning (ML).

123Genetix has taken a step forward to develop a solution to this emerging problem where biomedical researchers can use simulated stem cell models for rare disease modeling and drug discovery which saves time, money and empowers the researchers with state-of-the-art technology.

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The stem cell market

According to Grand View Research, 2016, the global stem cell market has been estimated at USD 7 billion. As per Investing News, 2018, the market is anticipated to expand at a CAGR of 36% between 2016 – 2021. Over the last few years, 1% of this spend has shifted to simulation amounting to a total of USD 70 million. Going forward, the future of stem cell biology is entailed in research-oriented technological advances.

MARKET VALIDATION

What do the numbers say

8-10%

8-10%

OF US POPULATION AFFECTED BY A RARE DISEASE

Approximately 30 Million in the US

50%

50%

OF THOSE AFFECTED BY A RARE DISEASE ARE CHILDREN 

95%

95%

AFFECTED BY A RARE DISEASE DO NOT HAVE AN FDA APPROVED DRUG TREATMENT

Rare Disease market

There are many factors that drive the current rare disease market.

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    Technology

    Researchers are more open for technological adoption due to limited resources.

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    Pharmaceuticals

    Pharma companies are focused on opportunities to reduce costs and make rare disease drug development profitable.

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    Legislation

    Rare disease drug legislation allows pharmacy companies to have extended periods of patent control and profitability.

The DeepNEU Platform

DeepNEU is our big data simulation solution specifically engineered to empower rare disease and other researchers.

AI-2 (4)

This highly novel deep learning computational platform combines (1) extensive and publicly available rare diseases gene network data with (2) modified Neutrosophic*/Fuzzy Logic, and (3) features of deep ANNs, Support Vector Machines and evolutionary systems.

The current validated version (v4.6) of DeepNEU continues to undergo development and has been used to generate published versions of several rare disease stem cell models for which the underlying genetics and clinical features are reasonably well characterized. A few examples of such well characterized diseases are Cystic Fibrosis, Duchenne Muscular Dystrophy, Huntington’s Disease and Sickle Cell Disease. These system biology simulations are easy to customize. We also continue to reach out to key opinion leaders and rare disease researchers for suggested improvements. As part of the ongoing development and validation of this new technology we are making the published models available free of charge to select not-for-profit researchers.

The purpose of these advanced stem cell models is to empower rare disease researchers to: (1) develop new disease hypotheses; (2) design and run experimental simulations to test those hypotheses; (3) write better research grants and (4) identify potential new disease specific biomarkers for drug discovery.

While the original DeepNEU platform (prior to Version 4.2) was not created to be a drug discovery engine and focused on disease modelling and the identification of novel biomarkers, the newer versions are now able to identify and validate disease specific biomarkers for the purpose of targeted drug discovery. Our first drug discovery project is already underway and focused on cellular Calcium metabolism in Infantile Onset Pompei Disease (IOPD).

*Neutrosophic logic was originally created by Florentin Smarandache in 1995 and is an extension/combination of fuzzy logic, intuitionistic logic, paraconsistent logic, and the three-valued logics that include an indeterminate value. In the original neutrosophic logic (NL), every logical variable X is described by an ordered triple, X = (T, I, F) where T is the degree of truth, F is the degree of false and I is the degree of indeterminacy. In DeepNEU logic (dNL), X can be any relationship, degrees become probabilities and we add a new component to X that includes the probability that a relationship does not actually exist. Restated dNL(X) = (p(T(x)), p(I(x)), p(F(x)), p(0(x))) and all four probabilities are estimated by DeepNEU models.

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