Genetic data is breaking the Eroom's law

Natural selection runs clinical trials some mutations in our genome significantly affect our health

People with different mutation patterns are exposed to stresses and diseases. Some of them are more likely to survive, for example, a Coronary Heart Disease (CHD).

In 2003 a gain-of-function mutation in the PCSK9 gene was found to be associated with Familial Hypercholesterolemia.

By the year 2020, Pharma successfully translated this discovery into a billion-dollar market for PCSK9 inhibitors per year.

PCSK9 gene inhibitors case demonstrates how we can identify a significant to health mutation to treat a disease.

Billion-dollar drugs — suppression of the PCSK9 gene product results in protection against cardiovascular disease — were found in a little drop of the genetic ocean

The discovery was made by looking into COMMON GENE VARIANTS (FREQUENT MUTATIONS or SNPs) — which “cover” a little drop — only 0,5% of the whole genome length and are mostly responsible for hair and eye color, height, ancestry.

How many drugs are there in undiscovered waters?
WHOLE-EXOME (and eventually GENOME) SEQUENCING reveal rare mutations, with our technology we can discover ideas for drugs in the remaining 99.5% of the genetic.

Rare mutations have stronger effects on human health

Suggests drugs imitating (or inhibiting) the effect of the mutation to treat the disease

COMMON GENE VARIANTS (frequent mutations or SNPs) are almost always about your ancestry, eye color, etc. but rarely about your health.

During World War 2 the people from statistical department in the US Navy were asked to look at the airplanes to understand which parts of airframes to be reinforced.

The naive idea would be to look at the places where are many holes to see if airplanes needs to be reinforced there.

Identifies rare mutations significantly affecting a complex disease from real-world data

SOLUTION: GERO.AI

Obviously, this is a mistake because reinforced should be the places there are no holes because if there is a hit there the airplane would go down immediately and not come back from the mission at all. The same happens in our genome

We have a bombardment of mutations and the places where mutations are allowed to happen (common variants) are exactly the places where is no effect on your health (positive or negative) so those places are not that important.

And the opposite is true - there are places where you rarely or never see mutations because these are the places where genes related to function occurs.

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GERO.AI identifies rare mutations that sufficiently affect human health from real-world big clinical and genetic (WES) and molecular data and then suggests druggable targets and drugs (mimicking or inhibiting) the effect of the mutation treat the disease. We can aggregate the effects of multiple rare mutations on the level of biological processes (or pathways) and give you numerous choices of interventions with the required effect.

Gero were the first, but we are not the only ones

For the first time in history, systemic rejuvenation is achieved by the MoA predicted by AI

In the lab of one of the world's most recognized aging researchers Brian Kennedy, we demonstrated outstanding results.

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GERO.AI has successfully identified and validated a human target for treating senescence-associated disorders and aging

GERO.AI proved itself in our in-house drug discovery program in senescence associated diseases in-vivo

We achieved systemic rejuvenation in experiments with 100-weeks old animals by intervention vs. protein predicted by the AI.

Time (days)

20

100

180

60

40

140

60

80

100

GERO.AI

Rapamycin

Control

golden standard for mice life extension

Enhanced longevity came along with functional and histological improvements

We will take a specific binder to the identified target into preclinical studies

4

2

6

8

Control

Rapamycin

Antibody

P=0,006

P=0,008

Frailty at week 120

40

20

60

80

Control

Rapamycin

Antibody

CD3 p16

70

65

30

0,5

1

1,5

Control

Rapamycin

Antibody

Relative value of PSD95/ β-actin

1

1,38

1,5

~10% CHF patients die within 0.5 years
GERO.AI identifies the pathway activation signatures of low risk through the effects of rare mutations in Whole-Exome sequencing data

KM_estimate

Case study: Congestive Heart Failure (CHF)

Drugs scored high by GERO.AI have better chances to show efficacy in clinical trials

0.825

0.850

0.875

0.900

0.925

0.950

0.975

1.000

0.5

1.0

1.5

2.0

Heart failure

Cardiovascular disease

Trial phase I/II

Trial phase III/IV

0.4

0.5

0.6

Enrichment

*p=0.02

*p=0.01