PF-05089771

Electrical impulse transmission along the nerve fiber in the form of the action potential is possible due to fast conformational changes of voltage-gated sodium channels (Na_v) that control the sodium ions flow into the cell. The transition between functional states, called the gating mechanism, can be modulated by natural toxins and drugs. Here, we propose to use steered molecular dynamics (SMD) to investigate the ability of various ligands to impact the gating of P. americana cockroach Na_v. By calculating mechanical forces required to relocate the inactivation particle to its binding pocket or to dislocate it, we assessed ligands' efficacy in trapping a channel in a given state (open or fast inactivated). Importantly, we showed that sulfonamide PF-05089771 and phospholipid PIP2 act as insect Na_v channels inhibitors. We confirmed the ligands' action by electrophysiological measurements of their ability to modulate the neural activity. Our approach, applied here on a cockroach channel, can be used in any other Na_v, i.g, as a step in computational-based drug screening to evaluate new drug candidates.

Objective. Chronic pain affects over 20% of the adult population in the United States, posing a substantial personal as well as economic burden and contributing to the ongoing opioid crisis. Effective, non-addictive chronic pain treatments are urgently needed. Traditional drug discovery methods have failed to identify novel, non-addictive compounds, highlighting the need for alternative approaches such as phenotypic screening. Our lab developed a phenotypic screening assay using extracellular electrophysiological recordings from co-cultures of human induced pluripotent stem cell sensory neurons and glia. This study aimed to identify responsive neuronal subtypes within these presumptively heterogeneous cultures. Approach. We induced an inflammation-like state using tumor necrosis factor alpha and evaluated acute responses to nociceptor agonist capsaicin, which targets transient receptor potential vanilloid-1. By employing unsupervised learning, we labeled responsive cells based on changes in mean firing rates (MFR). We then used the labeled cells’ baseline activity to train and validate five classifiers . Main results. None of the classifiers outperformed the others in regards to accuracy. Nonetheless, an RUS-boosted ensemble of decision trees achieved an AUC-ROC of 0.877 classifying nociceptors in an unseen labeled culture. Significance . The notable accuracy suggests that machine learning techniques could be employed to enhance microelectrode array-based neuronal phenotypic assays as readouts (e.g. MFR) can be weighted based on target cell type (e.g. nociceptors).