No abstract

To clarify age-related changes in dopamine D1-like and D2-like receptor binding in the striatum, positron emission tomography (PET) and in vitro receptor autoradiography (in vitro ARG) were performed using F344/N rats of various ages (6, 12, 18, and 24 months). In the PET study, [11C]SCH23390 and [11C]raclopride were used to image dopamine D1-like receptors and dopamine D2-like receptors, respectively, while [3H]SCH23390 and [3H]raclopride were used for the in vitro ARG study. With PET, we calculated the binding potential (= k3/k4, Bmax/Kd) of [11C]SCH23390 and [11C]raclopride in the striatum according to the curve fitting (CF) and the Logan plot (LP) methods. The binding potential of [11C]SCH23390 in the striatum demonstrated significant decrease as a function of age (max. decrease -26%) by the LP method, while this was not observed in the data analyzed by the CF method. In contrast, the binding potential of [11C]raclopride in the striatum decreased significantly with age by both the CF (max. decrease -28%) and the LP (max. decrease -36%) methods. However, no significant difference by either method was observed in rats between 6 and 12 months old using either ligand. In the in vitro ARG study, the specific binding (fmol/mg tissue) of [3H]SCH23390 and [3H]raclopride in the striatum were determined. Both [3H]SCH23390 and [3H]raclopride binding declined considerably with age as noted by comparing 12, 18, and 24-month-old rats against those 6 months old (max. decrease -29% and -31%, respectively). The substantial difference in binding shown in 12-month-olds in comparison with 6-month-olds using either ligand with in vitro ARG was in contrast with the PET results. These distinctions between the PET and the in vitro ARG studies may be attributed to the receptor microenvironment created under these experimental conditions. The results indicate that PET with LP analysis is useful in obtaining age-related changes of D1-like and D2-like receptor binding in the striatum of living rats.

Absorption, distribution, metabolism and excretion (ADME)-related failure of drug candidates is a major issue for the pharmaceutical industry today. Prediction of ADME by in silico tools has now become an inevitable paradigm to reduce cost and enhance efficiency in pharmaceutical research. Recently, machine learning as well as nonlinear statistical tools has been widely applied to predict routine ADME end points. To achieve accurate and reliable predictions, it would be a prerequisite to understand the concepts, mechanisms and limitations of these tools. Here, we have devised a small synthetic nonlinear data set to help understand the mechanism of machine learning by 2D-visualisation. We applied six new machine learning methods to four different data sets. The methods include Naive Bayes classifier, classification and regression tree, random forest, Gaussian process, support vector machine and k nearest neighbour. The results demonstrated that ensemble learning and kernel machine displayed greater accuracy of prediction than classical methods irrespective of the data set size. The importance of interaction with the engineering field is also addressed. The results described here provide insights into the mechanism of machine learning, which will enable appropriate usage in the future.

Dimethylnitrosamine administration induces a rapid increase in collagen deposition with concomitant proliferation of hepatic stellate cells in rats. Here, we investigated the pathophysiological profiles of acute and chronic hepatic fibrosis states and attempted to determine the possible role of Kruppel-like factor-5 (KLF5) in this model. In acute study using a single drug injection, we observed a rapid transient increase of ALT and mRNA levels of KLF5 followed by increases in fibrosis-related genes. Repeated administration of dimethylnitrosamine once a week caused early damage with severe fibrosis and sustained hepatocyte injury, while intermittent injections at 2-week intervals induced only modest fibrosis from 3 weeks. Weekly administration also induced profound upregulation of collagen I, alpha-smooth muscle actin, and KLF5 mRNA. In contrast, such continued augmentation was not observed after intermittent injections; KLF5 increased only after 3 weeks. These results suggested that dimethylnitrosamine induced a rapid hepatic fibrogenic response with a possible participation of KLF5.

The prediction of efficacy in long-term treatment of acetylcholinesterase inhibitors (AChEIs) is a major clinical issue, although no consistently strong predictive factors have emerged thus far. The present analyses aimed to identify factors for predicting long-term outcome of galantamine treatment. Analyses were conducted with data from a 24 weeks randomized, double-blind, placebo controlled trial to evaluate the efficacy and the safety of galantamine in the treatment of 303 patients with mild to moderate AD. Patients were divided into responders (4 or more point improvement of ADAS-cog scores at 24 weeks of treatment) and non-responders. We explored whether patients’ background (e.g. sex, age, and duration of disease) and scores of cognitive scales at early stage, are relevant to the long-term response to AChEIs. Predictive values were estimated by the logistic regression model. The responder rate was 31.7 %. We found that changes in scores of ADAS-J cog subscales between week 4 and baseline, especially word recognition, can be a good variable to predict subsequent response to galantamine, with approximately 75% of predictive performance. Characteristics of patients, including demographic characteristics, severity of disease and neuropsychological features before treatment were poorly predictive. The present study indicate that initial response to galantamine administration in patients with mild to moderate AD seems to be a reliable predictor of response of consequent galantamine treatment. Patients who show improvement of episodic memory function during the first 4 weeks of galantamine administration may be likely to particularly benefit from galantamine treatment.