Hidden hearing loss (HHL) is an auditory neuropathy characterized by normal hearing thresholds but reduced amplitudes of the sound-evoked auditory nerve compound action potential (CAP). In animal models, HHL can be caused by moderate noise exposure or aging, which induces loss of inner hair cell (IHC) synapses. In contrast, recent evidence has shown that transient loss of cochlear Schwann cells also causes permanent auditory deficits in mice with similarities to HHL. Histological analysis of the cochlea after auditory nerve remyelination showed a permanent disruption of the myelination patterns at the heminode of type I spiral ganglion neuron (SGN) peripheral terminals, suggesting that this defect could be contributing to HHL. To shed light on the mechanisms of different HHL scenarios observed in animals and to test their impact on type I SGN activity, we constructed a reduced biophysical model for a population of SGN peripheral axons whose activity is driven by a well-accepted model of cochlear sound processing. We found that the amplitudes of simulated sound-evoked SGN CAPs are lower and have greater latencies when heminodes are disorganized, i.e. they occur at different distances from the hair cell rather than at the same distance as in the normal cochlea. These results confirm that disruption of heminode positions causes desynchronization of SGN spikes leading to a loss of temporal resolution and reduction of the sound-evoked SGN CAP. Another mechanism resulting in HHL is loss of IHC synapses, i.e., synaptopathy. For comparison, we simulated synaptopathy by removing high threshold IHC-SGN synapses and found that the amplitude of simulated sound-evoked SGN CAPs decreases while latencies remain unchanged, as has been observed in noise exposed animals. Thus, model results illuminate diverse disruptions caused by synaptopathy and demyelination on neural activity in auditory processing that contribute to HHL as observed in animal models and that can contribute to perceptual deficits induced by nerve damage in humans.

Tuberculosis (TB) continues to be one of the deadliest infectious diseases in the world, causing ~1.5 million deaths every year. The World Health Organization initiated an End TB Strategy that aims to reduce TB-related deaths in 2035 by 95%. Recent research goals have focused on discovering more effective and more patient-friendly antibiotic drug regimens to increase patient compliance and decrease emergence of resistant TB. Moxifloxacin is one promising antibiotic that may improve the current standard regimen by shortening treatment time. Clinical trials and in vivo mouse studies suggest that regimens containing moxifloxacin have better bactericidal activity. However, testing every possible combination regimen with moxifloxacin either in vivo or clinically is not feasible due to experimental and clinical limitations. To identify better regimens more systematically, we simulated pharmacokinetics/pharmacodynamics of various regimens (with and without moxifloxacin) to evaluate efficacies, and then compared our predictions to both clinical trials and nonhuman primate studies performed herein. We used GranSim, our well-established hybrid agent-based model that simulates granuloma formation and antibiotic treatment, for this task. In addition, we established a multiple-objective optimization pipeline using GranSim to discover optimized regimens based on treatment objectives of interest, i.e., minimizing total drug dosage and lowering time needed to sterilize granulomas. Our approach can efficiently test many regimens and successfully identify optimal regimens to inform pre-clinical studies or clinical trials and ultimately accelerate the TB regimen discovery process.

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