Dual Beneficial Effects of (-)-Epigallocatechin-3-Gallate on Levodopa Methylation and Hippocampal Neurodegeneration: In Vitro and In Vivo Studies

A combination of levodopa (L-DOPA) and carbidopa is the most commonly-used treatment for symptom management in Parkinson's disease. Studies have shown that concomitant use of a COMT inhibitor is highly beneficial in controlling the wearing-off phenomenon by improving L-DOPA bioavailability as well as brain entry. The present study sought to determine whether (-)-epigallocatechin-3-gallate (EGCG), a common tea polyphenol, can serve as a naturally-occurring COMT inhibitor that also possesses neuroprotective actions.Using both in vitro and in vivo models, we investigated the modulating effects of EGCG on L-DOPA methylation as well as on chemically induced oxidative neuronal damage and degeneration. EGCG strongly inhibited human liver COMT-mediated O-methylation of L-DOPA in a concentration-dependent manner in vitro, with an average IC50 of 0.36 microM. Oral administration of EGCG moderately lowered the accumulation of 3-O-methyldopa in the plasma and striatum of rats treated with L-DOPA+carbidopa. In addition, EGCG also reduced glutamate-induced oxidative cytotoxicity in cultured HT22 mouse hippocampal neuronal cells through inactivation of the nuclear factor kappaB-signaling pathway. Under in vivo conditions, administration of EGCG exerted a strong protective effect against kainic acid-induced oxidative neuronal death in the hippocampus of rats.These observations suggest that oral administration of EGCG may have significant beneficial effects in Parkinson's patients treated with L-DOPA and carbidopa by exerting a modest inhibition of L-DOPA methylation plus a strong neuroprotection against oxidative damage and degeneration

Crowdsourced analysis of clinical trial data to predict amyotrophic lateral sclerosis progression

Amyotrophic lateral sclerosis (ALS) is a fatal neurodegenerative disease with substantial heterogeneity in its clinical presentation. This makes diagnosis and effective treatment difficult, so better tools for estimating disease progression are needed. Here, we report results from the DREAM-Phil Bowen ALS Prediction Prize4Life challenge. In this crowdsourcing competition, competitors developed algorithms for the prediction of disease progression of 1,822 ALS patients from standardized, anonymized phase 2/3 clinical trials. The two best algorithms outperformed a method designed by the challenge organizers as well as predictions by ALS clinicians. We estimate that using both winning algorithms in future trial designs could reduce the required number of patients by at least 20%. The DREAM-Phil Bowen ALS Prediction Prize4Life challenge also identified several potential nonstandard predictors of disease progression including uric acid, creatinine and surprisingly, blood pressure, shedding light on ALS pathobiology. This analysis reveals the potential of a crowdsourcing competition that uses clinical trial data for accelerating ALS research and development