Company: LNAI
Filing Date: 2025-09-29
Form Type: 10-K
Source: 0001731122-25-001316
Chunk: 1115

Company: Lunai Bioworks Inc.
Filing Date: 2025-09-29
Form: 10-K
Item: Item 1
Chunk 1115
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rafish genes are mapped
to phenotypic terms using over 300 individually-trained machine learning models, that fill in the gaps of known gene-phenotype associations.
Leveraging this platform, we can prioritize human genes on the basis of predicted experimental phenotype, reducing the number of required
experiments to produce human-informed in vivo disease models.

11

Finally, one important component of the BioSymetrics platform is that it couples AI-based prediction
with experimental validation. BioSymetrics has designed deep learning-based computer vision software that automatically identifies and
characterizes organ systems relevant to neurological, cardiovascular, and muscle development (Figure below). Additionally, BioSymetrics
has developed a proprietary light stimulus battery and characterized a small number of known CNS therapeutics and unknown compounds, providing
an initial basis for evaluation of chemical effects.

A large-scale behavioral profiling platform for identification of neurotherapeutics. (A) 96-well plate containing 8 zebrafish larvae per well, as used for behavioral profiling. (B-C) Line plots showing activity over time for 48 replicate wells focusing in on a small (5 min) part of the behavioral profile. Unlike control wells (B), drug-treated wells (C) show much higher activity levels. (D-H) Example profiles for 5 different reference compounds showing the average behavioral profiles for control and compound-treated wells (n= 4 wells; orange and blue lines, respectively). Note that each of the 5 compounds causes a distinct behavioral profile. (I) Examples of morphological segmentation. Colored micrographs of laterally oriented zebrafish larvae show computer-vision-based organ segmentation using trained ML classifiers.  

In prior work, we leveraged our
motion profiling capabilities to identify novel neuroactive compounds capable of resolving epilepsy-related phenotypes. Specifically,
we screened 1400 compounds from a proprietary small molecule library both with and without addition of a chemical convulsant. The experimental
component of this screen was completed by one technician in 10 days, giving us an approximate pace of 1,000 compounds screened per technician
per week. This study resulted in identification of a hit compound that later showed efficacy in a mouse seizure model, now being further
progressed as a potential therapeutic by BioSymetrics. Additionally, during the course of this study we screened approximately 100 known
neuroactives, including clinical anticonvulsants, antidepressants, antipsychotics, and dopaminergic signaling activators and inhibitors.
This study produced two key