Oxidative stress induced by sustained supraphysiological intrastriatal GDNF delivery is prevented by dose regulation

Despite its established neuroprotective effect on dopaminergic neurons and encouraging phase I results, intraputaminal GDNF administration failed to demonstrate significant clinical benefits in Parkinson's disease patients. Different human GDNF doses were delivered in the striatum of rats with a progressive 6-hydroxydopamine lesion using a sensitive doxycycline-regulated AAV vector. GDNF treatment was applied either continuously or intermittently (2 weeks on/2 weeks off) during 17 weeks. Stable reduction of motor impairments as well as increased number of dopaminergic neurons and striatal innervation were obtained with a GDNF dose equivalent to 3- and 10-fold the rat endogenous level. In contrast, a 20-fold increased GDNF level only temporarily provided motor benefits and neurons were not spared. Strikingly, oxidized DNA in the substantia nigra increased by 50% with 20-fold, but not 3-fold GDNF treatment. In addition, only low-dose GDNF allowed to preserve dopaminergic neuron cell size. Finally, aberrant dopaminergic fiber sprouting was observed with 20-fold GDNF but not at lower doses. Intermittent 20-fold GDNF treatment allowed to avoid toxicity and spare dopaminergic neurons but did not restore their cell size. Our data suggest that maintaining GDNF concentration under a threshold generating oxidative stress is a pre-requisite to obtain significant symptomatic relief and neuroprotection.

Towards a machine-learning assisted diagnosis of psychiatric disorders and their operationalization in preclinical research: Evidence from studies on addiction-like behaviour in individual rats

Over the last few decades, there has been a progressive transition from a categorical to a dimensional approach to psychiatric disorders. Especially in the case of substance use disorders, interest in the individual vulnerability to transition from controlled to compulsive drug taking warrants the development of novel dimension-based objective stratification tools. Here we drew on a multidimensional preclinical model of addiction, namely the 3-criteria model, previously developed to identify the neurobehavioural basis of the individual's vulnerability to switch from controlled to compulsive drug taking, to test a machine-learning assisted classifier objectively to identify individual subjects as vulnerable/resistant to addiction. Datasets from our previous studies on addiction-like behaviour for cocaine or alcohol were fed into a variety of machine-learning algorithms to develop a classifier that identifies resilient and vulnerable rats with high precision and reproducibility irrespective of the cohort to which they belong. A classifier based on K-median or K-mean-clustering (for cocaine or alcohol, respectively) followed by artificial neural networks emerged as a highly reliable and accurate tool to predict if a single rat is vulnerable/resilient to addiction. Thus, each rat previously characterized as displaying 0-criterion (i.e., resilient) or 3-criteria (i.e., vulnerable) in individual cohorts was correctly labelled by this classifier. The present machine-learning-based classifier objectively labels single individuals as resilient or vulnerable to developing addiction-like behaviour in a multisymptomatic preclinical model of addiction-like behaviour in rats. This novel dimension-based classifier increases the heuristic value of these preclinical models while providing proof of principle to deploy similar tools for the future of diagnosis of psychiatric disorders.