Mechanisms involved in the antinociception induced by spinal administration of inosine or guanine in mice

It is well known that adenine-based purines exert multiple effects on pain transmission. Recently, we have demonstrated that guanine-based purines may produce some antinociceptive effects against chemical and thermal pain in mice. The present study was designed to investigate the antinociceptive effects of intrathecal (i.t.) administration of inosine or guanine in mice. Additionally, investigation into the mechanisms of action of these purines, their general toxicity and measurements of CSF purine levels were performed. Animals received an i.t. injection of vehicle (30mN NaOH), inosine or guanine (up to 600nmol) and submitted to several pain models and behavioural paradigms. Guanine and inosine produced dose-dependent antinociceptive effects in the tail-flick, hot-plate, intraplantar (i.pl.) glutamate, i.pl. capsaicin and acetic acid pain models. Additionally, i.t. inosine inhibited the biting behaviour induced by spinal injection of capsaicin and i.t. guanine reduced the biting behaviour induced by spinal injection of glutamate or AMPA. Intrathecal administration of inosine (200nmol) induced an approximately 115-fold increase on CSF inosine levels. This study provides new evidence on the mechanism of action of extracellular guanine and inosine presenting antinociceptive effects following spinal administration. These effects seem to be related, at least partially, to the modulation of A1 adenosine receptors.

Catalytic wet air oxidation of aniline with nanocasted Mn-Ce-oxide catalyst

The catalytic wet air oxidation of aqueous solution containing 1000 ppm aniline was conducted in a trickle-bed reactor packed with a novel nanocasted Mn-Ce-oxide catalyst (surface area of 300 m2/g) prepared using SBA-15 silica as a hard template. A range of liquid hourly space velocities (5-20 h(-1)) and temperatures (110-140 degrees C) at 10 bar of oxygen were tested. The experiments were conducted to provide the intrinsic performance of the catalysts. Complete aniline conversion, 90% TOC conversion, and 80% nitrogen mineralization were achieved at 140 degrees C and 5 h(-1). Blank experiments yielded relatively low homogeneous aniline (<35%) and negligible TOC conversions. Fast deactivation of the catalysts was experienced due to leaching caused by complexation with aniline. Acidification of the solution with HCI (molar HCI to aniline ratio of 1.2) was necessary to avoid colloidization and leaching of the nanoparticulate catalyst components. The catalyst displayed stable performance for over 200 h on stream.

Creating discrete joint densities from continuous ones: the moment matching-maximum entropy approach

An approach for converting continuous densities into discrete ones with a pre-assigned set of support points is developed. The algorithm performs this conversion in a most skeptical, least-informative way, by finding the discrete distribution with the maximum entropy that satisfies a set of moment constraints derived from the stated continuous distribution, such as means and variances. Individualized drug therapies based on multiple model control rely on the availability of discrete distributions to generate the underlying model set. The methods developed herein are especially compatible with individualized drug therapies based on multiple model control that rely on the availability of discrete densities to generate the model set.

Achieving target goals most precisely using nonparametric compartmental models and "multiple model" design of dosage regimens

Multiple model (MM) design and stochastic control of dosage regimens permit essentially full use of all the information contained in either a Bayesian prior nonparametric EM (NPEM) population pharmacokinetic model or in an MM Bayesian posterior updated parameter set, to achieve and maintain selected therapeutic goals with optimal precision (least predicted weighted squared error). The regimens are visibly more precise in the achievement of desired target goals than are current methods using mean or median population parameter values. Bayesian feedback has now also been incorporated into the MM software. An evaluation of MM dosage design using an NPEM population model versus dosage design based on conventional mean population parameter values is presented, using a population model of vancomycin. Further feedback control was also evaluated, incorporating realistic simulated uncertainties in the clinical environment such as those in the preparation and administration of doses.

Model-based, goal-oriented, individualised drug therapy. Linkage of population modelling, new 'multiple model' dosage design, bayesian feedback and individualised target goals

This article examines the use of population pharmacokinetic models to store experiences about drugs in patients and to apply that experience to the care of new patients. Population models are the Bayesian prior. For truly individualised therapy, it is necessary first to select a specific target goal, such as a desired serum or peripheral compartment concentration, and then to develop the dosage regimen individualised to best hit that target in that patient. One must monitor the behaviour of the drug by measuring serum concentrations or other responses, hopefully obtained at optimally chosen times, not only to see the raw results, but to also make an individualised (Bayesian posterior) model of how the drug is behaving in that patient. Only then can one see the relationship between the dose and the absorption, distribution, effect and elimination of the drug, and the patient's clinical sensitivity to it; one must always look at the patient. Only by looking at both the patient and the model can it be judged whether the target goal was correct or needs to be changed. The adjusted dosage regimen is again developed to hit that target most precisely starting with the very next dose, not just for some future steady state. Nonparametric population models have discrete, not continuous, parameter distributions. These lead naturally into the multiple model method of dosage design, specifically to hit a desired target with the greatest possible precision for whatever past experience and present data are available on that drug--a new feature for this goal-oriented, model-based, individualised drug therapy. As clinical versions of this new approach become available from several centers, it should lead to further improvements in patient care, especially for bacterial and viral infections, cardiovascular therapy, and cancer and transplant situations.

Pharmaco-informatics: more precise drug therapy from "multiple model" (MM) stochastic adaptive control regimens: evaluation with simulated vancomycin therapy

MM stochastic control of dosage regimens permits essentially full use of information, either in a population pharmacokinetic model or a Bayesian updated MM parameter set, to achieve and maintain selected therapeutic goals with optimal precision. The regimens are visibly more precise than those developed using mean parameter values. Bayesian MM feedback has now also been implemented.