Company: BFRG
Filing Date: 2025-03-14
Form Type: 10-K
Source: 0001493152-25-010367
Chunk: 69

Company: BullFrog AI Holdings, Inc.
Filing Date: 2025-03-14
Form: 10-K
Item: Item 1
Chunk 69
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 benefit of creditors, appoints
or suffers appointment of a receiver, trustee, or similar agent over its property.

Summary
for CATIE Schizophrenia Case Study

As
part of the Partnership Agreement, the Company worked with LIBD to analyze data from the landmark Clinical Antipsychotic Trials of Intervention
Effectiveness (“CATIE”) trials. The CATIE trials were the largest trials ever conducted for anti-psychotic medications. The
Company analyzed CATIE data from ~200 schizophrenia patients, with a library of almost 1 million genetic data points for each patient,
more than 200 non-genetic attributes per patient, and 4 different medications used in the trial. For each of the four medications used,
bfLEAP™ analysis revealed new, previously unknown relationships between individual genetic variants and negative patient symptoms.
The genetic loci identified represent potential druggable targets, as well as potential stratifying criteria for future clinical trials
in schizophrenia.

We
performed another analysis on the data using our new advanced clustering algorithms bfLEAP 2.0 but focused on one particular drug named
Olanzapine. Our bfLEAP™ 2.0 analytical results identified previously unknown, multi-dimensional associations among newly identified
genetic variants, drug clearance, clinical trial sites, and clinical outcome variables in schizophrenia patients.

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FIGURE
1 – bfLEAP™ Analytical Map

Each
green node represents a different sampling of the data, and arrows point to attributes (blue nodes) which were found to be key indicators
according to that sampling. Attribute importance is determined by how many samplings identify that attribute as an indicator (i.e., number
of incoming arrows to each blue node).

Identification
of clustered multi-variate associations (e.g., novel genetic variants, drug clearance, substance abuse) could help us (1) identify novel
drug targets, (2) predict which patients are most likely to respond, and (3) identify modifiable factors that could contribute to better
outcomes.

 10 

Summary
for Cardiovascular Case Study

The
Company worked with an international collaborator in cardiovascular devices to analyze data from an ongoing clinical trial for a new
device. Bullfrog analyzed data from ~55 patients, with a library of almost 15,000 unique attributes of data for each patient. The data
also included adverse events, and key demographic information. For this collaborator, bfLEAP™ analysis was able to provide ground
truth for the Company, confirming multiple correlations