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Kienle, A.

Publications and source records attributed to Kienle, A..

2 recordsLinked to original sources

A mathematical investigation of chemotherapy-induced peripheral neuropathy

Chemotherapy-induced peripheral neuropathy (CIPN) is a prevalent, painful side effect which arises due to a number of chemotherapy agents. CIPN can have a prolonged effect on quality of life. Chemotherapy treatment is often reduced or stopped altogether because of the severe pain. Currently, there are no FDA-approved treatments for CIPN partially due to its complex pathogenesis in multiple pathways involving a variety of channels, specifically, voltage-gated ion channels. A surrogate of neuropathic pain in an in vitro setting is hyperexcitability in dorsal root ganglia (DRG) peripheral sensory neurons. Our study employs bifurcation theory to investigate the role of voltage-gated ion channels in inducing hyperexcitability as a consequence of spontaneous firing, due to the common chemotherapy agent paclitaxel. Our mathematical investigation suggests that the sodium channel Nav1.8 and the delayed rectifier potassium channel conductances are the most critical for hyperexcitability in normal firing small DRG neurons. Introducing paclitaxel into the model, our bifurcation analysis predicts that hyperexcitability is extreme for a medium dose of paclitaxel, which is validated by multi-electrode array recordings. Our findings using multi-electrode array experiments reveal that the Nav1.8 blocker A-803467 and the delayed rectifier potassium enhancer L-alpha-phosphatidyl-D-myo-inositol 4,5-diphosphate, dioctanoyl (PIP2) have a protective effect on the firing rate of DRG when administered separately together with paclitaxel as suggested by our bifurcation analysis.

neuroscience↗

Using Bifurcation Theory for Exploring Pain

Pain is a common sensation which inescapably arises due to injuries, as well as, various diseases and disorders. However, for the same intensity of disturbance arising due to the forgoing causes, the threshold for pain sensation and perception varies among individuals. Here, we present a computational approach using bifurcation theory to understand how the pain sensation threshold varies and how it can be controlled, the threshold being quantified by the electrical activity of a pain-sensing neuron. To this end, we explored the bifurcations arising from a mathematical model representing the dynamics of this neuron. Our findings indicate that the bifurcation points are sensitive to specific model parameters. This demonstrates that the pain sensation threshold can change as shown in experimental studies found in literature. Further investigation using our bifurcation approach coupled with experimental studies can facilitate rigorous understanding of pain response mechanism and provide strategies to control the pain sensation threshold.

neuroscience↗