Acute and sub-chronic functional neurotoxicity of methylphenidate on neural networks in vitro

Abstract

Methylphenidate (MPH) is the drug of choice in the treatment of attention deficit and hyperactivity disorders. Although a popular drug, concentration-dependent electrophysiological alteration or impairment (functional toxicity) and reversibility, have not been quantified. This study used spontaneously active neuronal networks growing on microelectrode arrays (MEA) to investigate functional neurotoxicity of MPH by assessing its acute and sub-chronic electrophysiologic effects on auditory cortex networks (ACN) and frontal cortex networks (FCN) at concentrations that reflect clinical doses and overdoses. Acute exposure to 1-300 microM MPH induced concentration-dependent inhibition of spontaneous activity with spike rate IC(50) values (concentration inducing 50% inhibition) of 112.9 +/- 18.6 and 108.0 +/- 18.9 microM for ACNs and FCNs respectively. Sub-chronic exposure to 1 mM MPH for 24 h blocked all activity followed by partial spontaneous recovery after 15 h. Tyrosine hydroxylase immunocytochemistry analysis indicated positive staining of neurons, confirming the presence of catecholaminergic neurons in cultured cortex networks.

Effects of chronic treatment with methylphenidate on oxidative stress and inflammation in hippocampus of adult rats

“…As mentioned already, MPH in all doses cause increase of GSSG and decrease of GSH significantly and this data is consistent with results of MPH effects on GPx [7]. According to previous findings, chronic administration of MPH can cause degeneration of dopaminergic neurons, with unidentified mechanism of action [2,12].…”

Assessment of Styrene Oxide Neurotoxicity Using In Vitro Auditory Cortex Networks

“…The cell culture techniques using ACNs have been published earlier [29–31]. This study was approved by the University of North Texas Institutional Animal Care and Use Committee.…”

An in vitro model for testing drugs to treat tinnitus

“…Each digital signal processor can discriminate up to four different action potential waveforms or "active" units. Multiple unit data were analyzed off-line using custom programs for burst recognition and analyses (Gopal et al, 2007). Burst patterns derived from spike integration (τ = 100 ms) provided a high signal-to-noise feature extraction that has been shown previously to reveal several modes of neuronal network activity in the form of action potentials (spike activity) of high frequency clusters (burst activity).…”