Identification of Glucose Transport Modulators In Vitro and Method for Their Deep Learning Neural Network Behavioral Evaluation in Glucose Transporter 1-Deficient MiceS

Metabolic flux augmentation via glucose transport activation may be desirable in glucose transporter 1 (Glut1) deficiency syndrome (G1D) and dementia, whereas suppression might prove useful in cancer. Using lung adenocarcinoma cells that predominantly express Glut1 relative to other glucose transporters, we screened 9,646 compounds for effects on the accumulation of an extracellularly applied fluorescent glucose analog. Five drugs currently prescribed for unrelated indications or preclinically characterized robustly enhanced intracellular fluorescence. Additionally identified were 37 novel activating and nine inhibitory compounds lacking previous biologic characterization. Because few glucose-related mechanistic or pharmacological studies were available for these compounds, we developed a method to quantify G1D mouse behavior to infer potential therapeutic value. To this end, we designed a five-track apparatus to record and evaluate spontaneous locomotion videos. We applied this to a G1D mouse model that replicates the ataxia and other manifestations cardinal to the human disorder. Because the first two drugs that we examined in this manner (baclofen and acetazolamide) exerted various impacts on several gait aspects, we used deep learning neural networks to more comprehensively assess drug effects. Using this method, 49 locomotor parameters differentiated G1D from control mice. Thus, we used parameter modifiability to quantify efficacy on gait. We tested this by measuring the effects of saline as control and glucose as G1D therapy. The results indicate that this in vivo approach can estimate preclinical suitability from the perspective of G1D locomotion. This justifies the use of this method to evaluate our drugs or other interventions and sort candidates for further investigation.