A quantitative systems pharmacology model for simulating OFF-Time in augmentation trials for Parkinson's disease: application to preladenant.

The clinical impact of therapeutic interventions in Parkinson's disease is often measured as a reduction in OFF-time when the beneficial effects of the standard-of-care L-DOPA formulations wanes off. We investigated the pharmacodynamic interactions of augmentation therapy to standard-of-care using a quantitative systems pharmacology (QSP) model of the basal ganglia motor circuit, essentially a computer model of neuronal firing in the different subregions with anatomically informed connectivity, cell-specific expression of 17 different G-protein coupled receptors and corresponding coupling to voltage-gated ion channel effector proteins based on experimentally observed intracellular signaling. The calculated beta/gamma (b/g) power spectrum of the local field potentials in the subthalamic nucleus was previously calibrated on the clinically relevant Unified Parkinson's Disease Rating Scale (UPDRS). When combining this QSP model with PK modeling of different formulations of L-DOPA, we calculated the b/g fluctuations over a 16 h awake period and used a weighted distance from a specific threshold to determine the cumulative liability of OFF-Time. Prediction of OFF-time with clinical observations of different L-DOPA formulations showed a significant correlation. Simulations show that augmentation with the adenosine A2A antagonist preladenant reduces OFF-time with 6 min for carbidopa/levodopa 950 mg 5-times daily to 37 min for 100 mg L-DOPA - 3 or 5 times daily. Exploring delays between preladenant and L-DOPA intake did not improve the outcome. Hypothetical A2A antagonists with an ideal PK and pharmacology profile can achieve OFF-Time reductions ranging from 9.5 min with DuoDopa to 55 min with low dose L-DOPA formulations. Combination of the QSP model with PK modeling can predict the anticipated OFF-Time reduction of novel A2A antagonists with standard of care. With the large number of GPCR in the model, this combination can support both the design of clinical trials with new therapeutic agents and the optimization of combination therapy in clinical practice.

Effects of the herbicide Irgarol 1051 on the transcriptome of hermatypic coral Acropora tenuis and its symbiotic dinoflagellates.

Coral reefs face multiple threats, including climate change, agricultural runoff, shipping activities, coastal development, and chemical pollutants. Irgarol 1051, a PSII herbicide, has been used as an antifouling booster since the previously used antibiofouling agent tributyltin (TBT) was banned worldwide. Although the mechanisms through which elevated temperatures cause coral bleaching have been reported, it remains unclear how PSII herbicides cause bleaching. Thus, in this study, we investigated the transcriptomes of Acropora tenuis and its symbiotic dinoflagellates by RNA-sequencing (RNA-Seq) to elucidate the molecular mechanisms underlying Irgarol-induced bleaching. Coral exposure to 10 µg/L Irgarol for 7 d affected coral body colour, specifically by an increase in their red, green, and blue (RGB) values; however, no such effect was observed in corals exposed to 1 µg/L Irgarol. RNA-Seq revealed the differentially expressed genes (DEGs) in corals and symbiotic dinoflagellates following Irgarol exposure. Coral DEGs encoded green fluorescent protein, blue-light-sensing photoreceptor (cryptochrome), chromoprotein, caspase 8, and nuclear receptors; DEGs in symbiotic dinoflagellates encoded light-harvesting proteins, photosystem II proteins, and heat shock proteins (i.e. HSP70 and HSP90), and ubiquitin. Bioinformatic analyses revealed that both Irgarol treatments disrupted various gene ontology terms, pathways, and protein interaction networks; these are different in corals (e.g. oxidative phosphorylation, metabolic pathway, transforming growth factor-ß signalling pathway, adherens junction, and apoptosis) and symbiotic dinoflagellates (e.g. protein processing in endoplasmic reticulum, carbon fixation in photosynthetic organisms, metabolic pathway, and photosynthesis). Our data suggest that Irgarol disrupts the expression of various coral genes, thereby affecting various gene ontology terms, pathways, and protein interaction networks. Our study provides new insights into the potential molecular mechanisms underlying the bleaching effect of PSII herbicides, such as Irgarol, on corals and symbiotic dinoflagellates.

Succession of delayed fluorescence correlated with coral bleaching in the hermatypic coral Acropora tenuis.

We investigated coral bleaching by monitoring colour changes and measuring the delayed fluorescence (DF) of symbiotic dinoflagellates in the hermatypic coral Acropora tenuis, exposed to 1.0 µg/L Irgarol 1051 (photosystem II herbicide) for 14 d. The Irgarol concentration corresponded to those from international port regions of the world. The coral colour and DFs under the control treatment were stable throughout the experiment, whereas under the Irgarol treatment the corals showed gradual bleaching. The Irgarol treatment caused a rapid decrease in the slow decay DF component (10.1-60.0 s), while the fast decay DF component (0.1-10.0 s) decreased significantly after 6 d. The significant correlation between the latter values and the coral colour indicates that if the electron accumulation function of quinones QA and QB is compromised, corals will bleach. The present study will contribute to the understanding of the mechanism involved in bleaching of coral exposed to herbicides.

STEFTR: A Hybrid Versatile Method for State Estimation and Feature Extraction From the Trajectory of Animal Behavior.

Animal behavior is the final and integrated output of brain activity. Thus, recording and analyzing behavior is critical to understand the underlying brain function. While recording animal behavior has become easier than ever with the development of compact and inexpensive devices, detailed behavioral data analysis requires sufficient prior knowledge and/or high content data such as video images of animal postures, which makes it difficult for most of the animal behavioral data to be efficiently analyzed. Here, we report a versatile method using a hybrid supervised/unsupervised machine learning approach for behavioral state estimation and feature extraction (STEFTR) only from low-content animal trajectory data. To demonstrate the effectiveness of the proposed method, we analyzed trajectory data of worms, fruit flies, rats, and bats in the laboratories, and penguins and flying seabirds in the wild, which were recorded with various methods and span a wide range of spatiotemporal scales-from mm to 1,000 km in space and from sub-seconds to days in time. We successfully estimated several states during behavior and comprehensively extracted characteristic features from a behavioral state and/or a specific experimental condition. Physiological and genetic experiments in worms revealed that the extracted behavioral features reflected specific neural or gene activities. Thus, our method provides a versatile and unbiased way to extract behavioral features from simple trajectory data to understand brain function.