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GERO.AI validation

Gero were the first, but we are not the only ones. Other AI/ML teams will also make sense out of this data, may be in a few years. The reason why we were able to build GERO.AI two years ahead of the competition is our in-house aging research.
We were looking for a drug targeting senescence, so even the first 50,000 whole-exomes released two years ago were enough for us to get statistical power as soon as every one of those 50,000 people was exposed to aging.

That allowed us to work on this AI for almost two years ahead of competition and refine our methods. When additional 150,000 WES were released in november 2020, we successfully cross-validated our predictions.
2018
AI/ML research and model trainig
50,000 WES released and used by GERO for aging research
2019
2020
2021
2022
2023
2024
2025
Drug Development
200,000 WES released, which enables target ID for “less prevalent” diseases and conditions but still only with dimensionality reduction in place
AI/ML research and model trainig
Other AI/ML teams
Drug Development
In-vivo validation: 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.
"
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
IMPROVED SURVIVAL
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
Reduced frailty index
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
Reduced senescent cell burden
0,5
1
1,5
Control
Rapamycin
Antibody
Relative value of PSD95/ β-actin
1
1,38
1,5
Improved synaptic plasticity
~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
Survival CHF, 2 years window
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
Despite the fact that UK Biobank provides only a few thousands of CHF cases, the pathway aggregation allows us to produce a sufficiently powerful model suggesting a few novel mechanisms of action.
To test the hypothesis fast, we can propose existing drugs for Proof of Concept and then initiate drug discovery.
Despite the fact that UK Biobank provides only a few thousands of CHF cases, the pathway aggregation allows us to produce a sufficiently powerful model suggesting a few novel mechanisms of action.
To test the hypothesis fast, we can propose existing drugs for Proof of Concept and then initiate drug discovery.