Company: LNAI
Filing Date: 2025-11-14
Form Type: 10-Q
Source: 0001731122-25-001544
Chunk: 113

Company: Lunai Bioworks Inc.
Filing Date: 2025-11-14
Form: 10-Q
Item: Part I, Item 2
Chunk 113
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 (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 observations that are critical for this application. The result is both an experimental platform and coupled
computational model that can identify both neurotherapeutics and neurotoxins, the former through direct experimental screening, and the
latter through machine-learning based prediction based on chemical structure.

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We next examined the ability of
one of these compounds (BioS_831) to resolve seizures in a murine model using a previously established electroshock seizure assay. Briefly,
after dosing each mouse with either BioS_831, negative control (vehicle), or positive control (Sodium Val