The prediction of response to galantamine treatment in patients with mild to moderate Alzheimer's disease.

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 ADAScog 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.

Predicting pharmacokinetic stability by multiple oral administration of atypical antipsychotics.

Lower fluctuation, i.e., lower peak-to-trough plasma-concentration variation at steady-state pharmacokinetics, has several advantages for the treatment of schizophrenia with antipsychotics. The reduction of peak concentration can decrease the risk of dose-dependent side effects, such as extrapyramidal symptom and somnolence, and by contrast the increase in trough concentration can decrease the incidence of lack of efficacy due to subtherapeutic drug concentration. Using a one-compartment simulation technique with pharmacokinetic parameters of each atypical antipsychotic collected from package inserts, the fluctuation index was calculated. Among the antipsychotics, the indices varied from 0.018 to 1.9, depending on dosing regimens, formulations and several pharmacokinetic properties. The order of simulated fluctuation index is active-moiety aripiprazole (b.i.d.)

The use of machine learning and nonlinear statistical tools for ADME prediction.

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.

Pathophysiological characteristics of dimethylnitrosamine-induced liver fibrosis in acute and chronic injury models: a possible contribution of KLF5 to fibrogenic responses.

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.

Predicting human liver microsomal stability with machine learning techniques.

To ensure a continuing pipeline in pharmaceutical research, lead candidates must possess appropriate metabolic stability in the drug discovery process. In vitro ADMET (absorption, distribution, metabolism, elimination, and toxicity) screening provides us with useful information regarding the metabolic stability of compounds. However, before the synthesis stage, an efficient process is required in order to deal with the vast quantity of data from large compound libraries and high-throughput screening. Here we have derived a relationship between the chemical structure and its metabolic stability for a data set of in-house compounds by means of various in silico machine learning such as random forest, support vector machine (SVM), logistic regression, and recursive partitioning. For model building, 1952 proprietary compounds comprising two classes (stable/unstable) were used with 193 descriptors calculated by Molecular Operating Environment. The results using test compounds have demonstrated that all classifiers yielded satisfactory results (accuracy > 0.8, sensitivity > 0.9, specificity > 0.6, and precision > 0.8). Above all, classification by random forest as well as SVM yielded kappa values of approximately 0.7 in an independent validation set, slightly higher than other classification tools. These results suggest that nonlinear/ensemble-based classification methods might prove useful in the area of in silico ADME modeling.

A novel automated method for measuring the effect of analgesics on formalin-evoked licking behavior in rats.

The behavioral assessment of pain is essential for the analysis of pain mechanisms and the evaluation of analgesic drugs. The formalin test is one of such methods widely used as a model of injury-induced pain in rodents. This test is manually demanding and the recording of results is left to the subjectivity of the experimenters. Thus we developed a novel automated method to estimate the pharmacological response in formalin-induced licking behavior in rats using a multicolor detection technique. Two color markers were preliminarily applied to rats-yellow dye on the mouth and fluorescent green tape on the right hind paw. Behaviors of the animals were recorded from both above and below the subject, by a dual-view digital video camera system. After injection with formalin into the hind paw, rats exhibited a biphasic display of licking behavior. Licking time was measured by the sum of frames where the distance between these markers was less than an appropriate threshold of distance (TD). The split-plot analysis of variance demonstrated that the sum of squares of differences in licking time between manual and automated measurement was minimized when TD = 20mm. In addition, frames in which moving velocity of these markers is less than 2.5mm/s was neglected for calculation in order to eliminate sedative effect on the recorded data. On these conditions, subcutaneous administration of morphine in rats dose-dependently decreased formalin-elicited nociceptive responses. These results suggest that under optimal conditions the automated technique when applied to pharmacological studies are more reliable and efficient than if they are manually recorded.

[Analysis of censored data in multi-factorial analgesic test].

Animal pain testing is essential for the development of new analgesic drugs, where appropriate data analyses as well as appropriate multi-factorial design of experiments are necessary to obtain meaningful results in an efficient fashion. The tail withdrawal experiment is one of the pain tests in which a rhesus monkey is restrained in a chair from which its tail hangs free by so it can be immersed in warm water. The monkeys consistently kept their tails in 38-40 degrees C water for an extended period of time, and thus, the data were censored at 120 sec. The effect of temperature on the tail withdrawal latency was evaluated using three monkeys with a randomized block design. The effect of morphine on the thermal sensitivity was also evaluated. A Friedman-type two-way analysis of variance (Mack-Skillings test) demonstrated that the effects of both temperature and the animals were significant. The effect of repeated measurement in one animal was not significant using the Friedman test, indicating that the significance of the effect of animals could be attributed to the difference in the intrinsic thermal sensitivity between animals. This method, together with a graphical approach, may prove to be valuable for assessing the sensitivity and reproducibility of an experimental condition, as well as the pharmacological effects of analgesic drugs.

Stimulation of adenosine A1 receptors decreases in vivo dopamine D1 receptor binding of [11C]SCH23390 in the cat striatum revealed by positron emission tomography.

It has been previously suggested that activation of adenosine A1 receptor modulates dopamine D1 receptor binding in vitro, although the direct mechanism of this interaction in vivo has not yet been demonstrated. Here, we conducted a positron emission tomography (PET) study to demonstrate in vivo the interaction between these receptors. The specific adenosine A1 receptor agonist N6-cyclopentyladenosine (CPA) was acutely administered to cats under anesthetized condition. Cats underwent repeated measurement of striatal and cerebellar radioactivity following intravenous injection of dopamine D1 receptor-specific [11C]SCH23390. The pretreatment with CPA decreased the striatum/cerebellum ratio of the uptake of [11C]SCH23390. Using the cerebellar radioactivity as an input function, kinetic analysis was performed and demonstrated that CPA caused about 40% decrease in the association rate constant. These results suggest that stimulation of adenosine A1 receptors modulates dopamine D1 receptor binding in vivo.

An atlas-based image registration method for dopamine receptor imaging with PET in rats.

For analysis of in vivo dopamine receptor binding in the rat brain by positron emission tomography (PET), a convenient method to obtain precise anatomical registration for striatum and cerebellum on the PET image was developed. On the PET measurements, a control, an anesthetized rat was positioned in a stereotaxic holder so that the horizontal plane of the PET image would be parallel to the horizontal plane of the brain atlas. After the positioning, [11C]raclopride was intravenously injected into the rats and scanned to obtain PET images of dopamine D2 receptor in the brain. The striatum was bilaterally identified in the obtained PET image. The atlas-based regions of interest (ROIs) of the whole brain were preliminarily created according to the atlas, and were superimposed on an early phase PET image. The early phase PET image was compatible to the whole brain ROI in the atlas, which enabled determination of striatal and cerebellar ROI difficult to determine by the PET image alone. Using the cerebellar radioactivity as a reference input function, rate constants between the free/nonspecific compartment and the receptor bound compartment (k3 and k4) were calculated by a two-parameter compartment model, and the binding potential (k3/k4) was estimated. The binding potential and its coefficients of variation were 1.56+/-0.30, 19.3% in Wistar rats, 1.05+/-0.14, 13.4% in Sprague-Dawley (SD) rats, and 1.29+/-0.07, 5.2% in Fischer F344 rats, in which binding potential in Wistar rats was significantly higher than that in SD rats. This method is objective and convenient in routine use for PET studies in rats, regardless of differences in the rat strains.

Increased binding potential of [(11)C]raclopride during unilateral continuous microinjection of nicotine in rat striatum observed by positron emission tomography.

Nicotine injections and nicotine skin patches significantly improve attention, memory, and learning in Alzheimer's disease. In animal studies, nicotine improves the performance of various memory-related tasks, an effect that is thought to be mediated by the neuronal dopaminergic system as systemic administration of nicotine decreased [(11)C]raclopride binding in the anesthetized state. Since high doses of systemically administered nicotine are harmful, we administrated it directly into the rat striatum via microdialysis. We then examined the acute effects of continuous central administration of high doses of nicotine on striatal dopamine concentrations by measuring [(11)C]raclopride binding by positron emission tomography. The concentration of dopamine in the dialysates was significantly increased from basal levels when microdialysis with 100 mM nicotine was initiated. However, contrary to expectations, the binding potential (BP) of [(11)C]raclopride in the nicotine-perfused striatum was significantly higher than that in control striatum. Preinjection of mecamylamine (3 mg/kg), a nicotinic antagonist, had no effect on either extracellular dopamine levels or on the BP of [(11)C]raclopride. These findings suggest that the high dose of local nicotine administration induced mecamylamine-insensitive local increases in extracellular dopamine, but might have decreased the total amount of extracellular dopamine in the striatum.

Acute treatment with pentobarbital alters the kinetics of in vivo receptor binding in the mouse brain.

The effect of pentobarbital, a sedative-hypnotic barbiturate, on the in vivo binding of benzodiazepine receptors in the mouse brain was investigated. Dose-related changes in the apparent binding of [3H]Ro15-1788 ([3H]flumazenil) in the cerebral cortex, cerebellum and pons-medulla were observed by pretreatment with pentobarbital. For quantification of the kinetic properties of the in vivo binding of [3H]Ro15-1788, time courses of radioactivity following its injection were examined, and kinetic analysis was performed using the compartment model. The time courses of radioactivity following injection of [3H]Ro15-1788 with 3 mg/kg Ro15-1788 were used as input function. In all regions studied, rate constants between input compartment and specific binding compartment were significantly decreased by pentobarbital. However, no significant alterations in the binding potential (BP=K3/K4) of benzodiazepine receptors by pentobarbital were observed in any of the regions. A saturation experiment indicated that the decrease in the input rate constant (K3), which includes both the association rate constant (k(on)) and the number of binding sites available (B(max)), was mainly due to decrease in k(on). These results suggest that apparent increases in binding at 20 min after tracer injection were due to the decrease in the association and dissociation rates of binding in vivo.