Fluphenazine-induced parkinsonism in the baboon: pharmacological and metabolic studies

Phenotypic drug screen uncovers the metabolic GCH1/BH4 pathway as key regulator of EGFR/KRAS-mediated neuropathic pain and lung cancer

Increased tetrahydrobiopterin (BH4) generated in injured sensory neurons contributes to increased pain sensitivity and its persistence. GTP cyclohydrolase 1 (GCH1) is the rate-limiting enzyme in the de novo BH4 synthetic pathway, and human single-nucleotide polymorphism studies, together with mouse genetic modeling, have demonstrated that decreased GCH1 leads to both reduced BH4 and pain. However, little is known about the regulation of Gch1 expression upon nerve injury and whether this could be modulated as an analgesic therapeutic intervention. We performed a phenotypic screen using about 1000 bioactive compounds, many of which are target-annotated FDA-approved drugs, for their effect on regulating Gch1 expression in rodent injured dorsal root ganglion neurons. From this approach, we uncovered relevant pathways that regulate Gch1 expression in sensory neurons. We report that EGFR/KRAS signaling triggers increased Gch1 expression and contributes to neuropathic pain; conversely, inhibiting EGFR suppressed GCH1 and BH4 and exerted analgesic effects, suggesting a molecular link between EGFR/KRAS and pain perception. We also show that GCH1/BH4 acts downstream of KRAS to drive lung cancer, identifying a potentially druggable pathway. Our screen shows that pharmacologic modulation of GCH1 expression and BH4 could be used to develop pharmacological treatments to alleviate pain and identified a critical role for EGFR-regulated GCH1/BH4 expression in neuropathic pain and cancer in rodents.

Comorbidity Analysis between Alzheimer's Disease and Type 2 Diabetes Mellitus (T2DM) Based on Shared Pathways and the Role of T2DM Drugs

Background:

Various studies suggest a comorbid association between Alzheimer’s disease (AD) and type 2 diabetes mellitus (T2DM) indicating that there could be shared underlying pathophysiological mechanisms.

Objective:

This study aims to systematically model relevant knowledge at the molecular level to find a mechanistic rationale explaining the existing comorbid association between AD and T2DM.

Method:

We have used a knowledge-based modeling approach to build two network models for AD and T2DM using Biological Expression Language (BEL), which is capable of capturing and representing causal and correlative relationships at both molecular and clinical levels from various knowledge resources.

Results:

Using comparative analysis, we have identified several putative “shared pathways”. We demonstrate, at a mechanistic level, how the insulin signaling pathway is related to other significant AD pathways such as the neurotrophin signaling pathway, PI3K/AKT signaling, MTOR signaling, and MAPK signaling and how these pathways do cross-talk with each other both in AD and T2DM. In addition, we present a mechanistic hypothesis that explains both favorable and adverse effects of the anti-diabetic drug metformin in AD.

Conclusion:

The two computable models introduced here provide a powerful framework to identify plausible mechanistic links shared between AD and T2DM and thereby identify targeted pathways for new therapeutics. Our approach can also be used to provide mechanistic answers to the question of why some T2DM treatments seem to increase the risk of AD.