MPE Values Research Articles

Examination of the antiallodynic effect of rosmarinic acid in neuropathic pain and possible mechanisms of action

This study aimed to explore the potential antiallodynic effects of rosmarinic acid, a natural antioxidant with a demonstrated safety profile across a broad dose range. Using a chronic constriction injury-induced neuropathic pain model, the impact of rosmarinic acid on allodynia was investigated. Furthermore, the involvement of adrenergic and opioidergic mechanisms in its activity was assessed. To evaluate rosmarinic acid’s efficacy, doses of 10, 20, and 40 mg/kg were administered and the electronic von Frey test was utilized along with an activity cage apparatus. % MPE values were calculated to gauge the extent of pain relief. Mechanistic insights were obtained by pretreating animals with the β-adrenergic receptor antagonist propranolol, the α1-adrenergic receptor antagonist prazosin, α2-adrenergic receptor antagonist yohimbine, and the opioid receptor antagonist naloxone. Rosmarinic acid demonstrated a statistically significant antiallodynic effect that was independent of locomotor activity. This effect was noteworthy as it resembled both the level and duration of relief provided by pregabalin. Additionally, the %MPE value of the group treated with 40 mg/kg rosmarinic acid showed a significant difference compared to the value of the pregabalin-treated group (P<0.001). Pre-administration of the antagonists revealed that the antiallodynic activity was shown to be mediated by the stimulation of opioid and adrenergic receptors, with a primary contribution from α2-adrenergic receptor stimulation. Our findings suggest that rosmarinic acid may hold promise as a potential therapeutic agent for neuropathic pain. By elucidating the involvement of adrenergic and opioidergic mechanisms, we have provided valuable preclinical data that could inform novel treatment approaches.

Performance Evaluation of a Simple Strategy to Optimize Formula Constants for Zero Mean or Minimal Standard Deviation or Root-Mean-Squared Prediction Error in Intraocular Lens Power Calculation

PurposeTo investigate the performance of a simple prediction scheme for the formula constants optimized for a mean (MPE), standard deviation (SDPE) or root-mean-squared refractive prediction error (RMSPE). DesignRetrospective cross-sectional study. MethodsUsing IOLMaster 700 biometric data from 888 eyes treated with the Hoya Vivinex lens and 821 eyes treated with the Alcon SA60AT lens, plus the power of the implanted lens and postoperative spherical equivalent refraction, optimized constants for SRKT, Hoffer Q, Holladay 1, Haigis, and K6 formulae were calculated using an iterative nonlinear optimization for zero MPE and minimal SDPE and RMSPE. Start values were detuned by ±1.5 from the MPE optimized constants and formula constants generated using the simple prediction scheme were compared to the corresponding directly optimized constants. ResultsFor all 5 formulae under test and with both datasets, constants optimized using the simple scheme showed excellent agreement with those from the iterative method with either MPE or RMSPE used as the optimization metric, and good agreement with SDPE as the metric. Constants optimized for zero MPE or minimal RMSPE agreed within 0.05, whereas constants for minimal SDPE could be systematically off by up to 0.6 from the MPE values, making SDPE unsuitable as an optimization metric. ConclusionsThis simple formula constant optimization scheme performs excellently for 4 disclosed formulae and one nondisclosed formula in our 2 monocentric datasets with zero MPE or minimal RMSPE as metrics. Multicentric studies with other study populations and biometers are required to further investigate the clinical applicability.

Forecasting of Monthly Crude Oil Prices in Kenya Using Comparative Time Series Models

Crude oil is one of the most vital products that ever existed and variation in prices affects all sectors of the economy and variation in its prices is very crucial. Therefore, without an accurate and appropriate predictive model for crude oil prices, it has proven difficult to predict future oil prices. Therefore, appropriate modeling is crucial for the oil companies to adjust strategies used in the production and supply as well as structural optimization. This study sought to fit several time series forecasting models namely; ARIMA, Naïve, Seasonal Naïve, Time Series Linear Model (TSLM), and Exponential Smoothing (ETS) to model and forecast crude oil prices. The study used Kenya’s monthly crude oil prices from Jan 2003 to Dec 2023, giving a sample of 252 observations. The selection of the best model was based on the minimization of the information criteria, where ARIMA, ETS, and Seasonal Naïve attained an AIC of 1294.193, 1846.780, and 1403.821, respectively. Similarly, the models attained the BIC of 1304.991, 1863.071, and 1450.489, respectively. Since the Naïve and TSLM could not provide the AIC and BIC, model selection was entirely based on the forecast accuracy measures. From the results, the Naive model reported the lowest RMSE (19.6329), indicating that it has the smallest average squared error, with the lowest MAE (15.5212), suggesting it has the smallest average absolute error. Besides, the Naive model reported an ME (1.9819) which is relatively low but not the lowest. The automatic ETS model has a slightly lower ME (3.478211). The naive model reported lower MPE and MAPE values (-3.9525 and 26.8022, respectively) compared to most models, indicating less percentage error. Similarly, a lower MASE and RMSSE were reported by the naïve model, (0.7343624) and RMSSE (0.7228792), respectively, indicating that the model performs well relative to forecasting crude oil prices in Kenya. The naïve model demonstrated a higher consistency and reliability in forecasting crude oil prices in Kenya.

Biomass equations and annual growth of various Eucalyptus clones in commercial plantations across Thailand

Plantation forestry is pivotal in meeting global timber and fiber demands while promoting sustainable practices. Eucalyptus plantations, renowned for their rapid growth and adaptability, have significantly expanded in tropical regions such as Thailand. Improved Eucalyptus clones have enhanced productivity, emphasizing the role of genetic improvement programs. Accurate biomass estimation in these plantations is crucial for sustainable management and bioenergy production. This study employs a nonlinear mixed model approach to evaluate tree variables combined with rotation, clones, and region on aboveground biomass (AGB) estimation. The results showed that the "rotation, clones, region" model emerged as the most precise, achieving the highest R² and the lowest SEE, ASE, and MPSE values. However, over-parameterization is a concern. The more straightforward "clone" model performed well, achieving a high R² and relatively low prediction error, with no systematic bias and comparable ASE, MPE, and MPSE values, making it a strong choice when fewer predictor variables are preferred. Our results revealed that clones H4 and K7 for the northeastern region and K58 for the eastern region show the highest annual productivity, with growth rates up to 20 t ha−1 year−1. The rapid AGB increment in clones K58, K62, and K7 during the first rotation suggests improved performance in subsequent rotations. As in eastern Thailand, selecting less arid sites can enhance these clones' AGB productivity. Additionally, intensive silvicultural practices could further boost their productive efficiency.

Construction of Compatible Volume Model for Populus in Beijing, China

The accurate assessment of tree volume is crucial for developing forest management plans, and this can be achieved using tree volume models. In this study, data on individual trees were collected and calculated, including the diameter at breast height (D), ground diameter (DG), tree height (H), and tree volume (V). A total of 400 Populus × tomentosa Carrière, 400 Populus tomentosa Carr, and 400 Populus × canadensis Moench trees were sampled. Two compatible volume model systems were established using corresponding methods. The models consisted of the following five types: V-DH, V-D, V-DG, H-D, and DG-D. In our calculations, before the horizontal line was the dependent variable, and behind the horizontal line was the independent variable. Variations in preferences for the H-D models were observed among the tree species, with the logistic function proving the most suitable for Populus × tomentosa Carrière, Chapman–Richard function for Populus tomentosa Carr, and power function for Populus × canadensis Moench. Among the three volume models, the V-DH model exhibited a superior performance, with its R2 values ranging from 0.965 to 0.984 and mean estimated error (MPE) values ranging from 1.26% to 1.78%. Following this was the V-D model, with R2 values between 0.9359 and 0.9704 and MPE values between 1.71% and 2.46%. The V-DG model ranked third, with R2 values ranging from 0.8746 to 0.9501 and MPE values ranging from 2.33% to 3.16%. In the H-D model, the R2 and MPE values ranged from 0.4998 to 0.7851 and from 1.31% to 1.45%, respectively. For the DG-D model, the R2 values ranged from 0.9563 to 0.9868 and the MPE values ranged from 0.47% to 0.68%. Comparing both compatible methods, the nonlinear seemingly uncorrelated regression (NSUR) was more effective. The three volume models demonstrated high levels of accuracy and compatibility, providing a reliable scientific foundation for forest management and the formulation of harvesting plans in Beijing, with significant practical implications.

Solar Radiation Prediction in Adrar, Algeria: A Case Study of Hybrid Extreme Machine-Based Techniques

This study delves into the application of hybrid extreme machine-based techniques for solar radiation prediction in Adrar, Algeria. The models under evaluation include the Extreme Learning Machine (ELM), Weighted Extreme Learning Machine (WELM), and Self-Adaptive Extreme Learning Machine (SA-ELM), with a comparative analysis based on various performance metrics. The results show that SA-ELM achieves the highest accuracy with an R2 of 0.97, outperforming ELM and WELM by 4.6% and 15.4% respectively in terms of R2. SA-ELM also has the lowest MPE, RMSE and RRMSE values, indicating a higher accuracy in predicting global radiation. Furthermore, comparison with previously employed prediction techniques solidifies SA-ELM’s superiority, evident in its 0.275 RMSE.The study explores different input combinations for predicting global radiation in the study region, concluding that incorporating all relevant inputs yields optimal performance, although reduced input scenarios can still provide practical accuracy when data availability is limited. These results highlight the effectiveness of the SA-ELM model in accurately predicting global radiation, which is expected to have significant implications for renewable energy applications in the region. However, further testing and evaluation of the models in different regions and under different weather conditions is recommended to improve the generalizability and robustness of the results.

Evaluation of L-ascorbic acid as a green low dosage hydrate inhibitor in water-based drilling fluid for the drilling of gas hydrate reservoirs

Hydrate plug formation in the drilling fluid flow line is a significant issue in the oil and gas industry. Green hydrate inhibitors have recently gained much interest in flow assurance problems as they are being used as alternatives for existing hydrate inhibitors. The present study described that L-ascorbic acid (LA), a natural organic compound, has been identified as a Low Dosage Hydrate Inhibitor and has exhibited better results than Polyvinylcaprolactum (PVCap) and Polyvinylpyrrolidone (PVP). The temperature-augmented visual method and a self-fabricated set-up have been used to determine the first hydrate crystal formation in the tetrahydrofuran (THF)-water hydrate system. LA has shown a better inhibition effect in terms of induction time (i.e., >1440 min) than PVCap (119.67–180.67 min) and PVP (85.33–240.67 min). Carboxymethyl cellulose (CMC), polyanionic cellulose (PAC), xanthan gum (XG), and potassium chloride (KCl) are mixed with water to make the water-based drilling fluids used in this study. The R2 values showed a good agreement with the Herschel-Bulkley Model (R2 ranges from 0.993 to 0.999 for 0.5 w/v% PVCap and 0.1 w/v% PVP-containing fluids) than Bingham Plastic Model (R2 ranges from 0.836 to 0.952 for 0.1 w/v% PVP and base fluids). The MPE values are less for Herschel-Bulkley Model (From 1.418 to 6.015 for 0.5 w/v% PVCap and 0.1 w/v% PVP) than for Bingham Plastic (From 9.985 to 29.718 for 1.0 w/v% PVP and 0.1 w/v% PVP). Cross Model is also used to determine the zero and infinite shear viscosities for the formulated fluid system, which showed the viscosities are in the permissible range. These observations suggest that L-ascorbic acid (LA) may be an effective hydrate inhibitor in drilling fluids.

Comparative Valuation of Global Solar Radiation on Horizontal and Tilt Surfaces in Owerri, Nigeria

Comparative valuation of global solar radiation on horizontal and tilt surfaces with the use of Angstrom-Page and four isotropic models for Owerri has been carried out in this paper. Angstrom linear regression model is developed from thirty years (1992-2022) of sunshine hour and average monthly global solar radiation sourced from NIMET and NASA, respectively. The Angstrom regression coefficients 𝑎 and 𝑏determined for Owerri is 0.210 and 0.696, respectively. The developed model is used to estimate average monthly global solar radiation on horizontal surface and consequently, the amount of incident solar radiation received on tilt surface by the isotropic models. The MBE, RMSE and MPE values are estimated to be 0.0083, 0.0029 and -0.0041%, respectively, for the developed Angstrom-Page model. A strong coefficient of determination of 76.6% was obtained and Analysis of Variance (ANOVA) significant of 100% was observed, which shows the goodness of fitness of the model. Among the isentropic models studied, one of the models poorly estimated solar radiation on horizontal and tilt surfaces for the study location

Additive biomass model for Heritiera fomes (Buch.-Hum.) in the Sundarbans Reserved Forest, Bangladesh

Mangroves are recognised as an important carbon sequester and therefore demand accurate biomass and carbon stock estimations. This study aimed to develop additive biomass models for Heritiera fomes, the most dominant tree species of the Sundarbans Reserved Forest in Bangladesh. Using a non-destructive method, 219 small branches (diameter < 7 cm) were harvested from 97 individual trees to develop biomass models for leaves and smaller branches. The biomass of bigger branches (diameter > 7 cm) and stem was calculated from the volume and mean wood density value after debarking while the biomass of all other components was derived from the determined fresh to oven dry weight conversion ratio. Finally, the biomass of one individual tree was calculated by adding the biomass of trimmed and untrimmed leaves, small and large branches, foliage and stem. An independent data set was used to validate the best-fit model. A component-wise (leaves, branches, bark and stem) biomass model was developed by recovering subsequent cross-component correlations which were then aggregated using the weighted Gaussian maximum likelihood estimation method. Among the components model, D (diameter at breast height) alone performed best for leaves and branches while the product of D and H (total tree height) proved the better results for stem and bark. Our best-fit model (Biomass = 0.0389D 2.3773 H 0.4178 + 0.0492D 2.3027 + 0.0112D 1.1144 H 1.4572 + 0.0306D 1.8507) showed the highest model efficiency with the lowest AIC, RMSE%, MAE, and MPE values. The efficiency of our non-destructive model has shown that it is as effective as other widely used pan-tropical models. Our built models can therefore be used for accurate estimation of biomass and carbon stock in H. fomes of the Sundarbans Reserved Forest, Bangladesh.

Forecasting the price of crude oil

Crude oil is the mixture of petroleum liquids and gases that is extracted from the ground by oil wells. It is an important source of fuel and is used in the production of several products. Given the important role price of the crude oil plays, it becomes extremely important for managers to predict future oil price while making operational decisions such as: when to purchase material, how much to produce and what modes of transportation to use. The goal of this paper is to develop a forecasting model to predict the oil prices that aid management to reduce operational costs, increase profit and enhance competitive advantage. We first analyze the primary theories related to the forecast of oil price followed by the reviews of two main streams of forecast theory, which are Target Capacity Utilization Rule (TCU) and Exhaustible Resources Theory. We implement a Target Capacity Utilization Rule recursive simulation model and test it on the historical data from 1987 through 2017 to predict crude oil prices for 1991 through 2017. We tried several variations of the base model and the best method produced MAD, MSE, MAPE and MPE of 12.676, 280.92, 0.2597, 0.028, respectively. We further estimated the forecasts of the oil prices at a monthly level based on our yearly forecast of oil prices from our best method. The calculated MAD, MSE, MAPE and MPE values are 5.66, 82.1163, 0.1246 and 0.038, respectively, which shows our model is promising again at a monthly level.

Comparative Stability of Two Anti-hyperpigmentation Agents: Kojic Acid as a Natural Metabolite and Its Di-Palmitate Ester, Under Oxidative Stress; Application to Pharmaceutical Formulation Design.

Purpose: Kojic acid (KA) a natural metabolite and its dipalmitate ester, kojic acid dipalmitate(Kadp) are both prescribed to treat skin hyperpigmentation. Stress test reveals the intrinsicstability of active ingredients and leads to selection of the suitable formulations. This researchevaluates the comparative stability of KA and its di-palmitate ester under liquid oxidative stress. Methods: The HPLC-UV/PDA method with a C18 column was utilized. Liquid oxidative stresswas induced using hydrogen peroxide (H2O2). Degradation was separately induced for eachdrug, and they were compared to each other. Results: Kadp degraded more rapidly in similar liquid oxidative stress conditions than KA did.The superior degradation model was the first order for both drugs based on the mean percentage error (MPE) values, indicating the dependency of the reaction rate on the initial concentrationof the reactive substance. Ring opening was proposed as the most possible theory for KA andKadp oxidative degradation. Conclusion: It is suggested to use KA instead of Kadp in less stable formulations, such asextemporaneous preparations. The incorporation of antioxidant excipients in Kadp formulationsis recommended for yielding better stability results. Formulating Kadp in the internal phase ofo/w emulsion formulations may protect this susceptive molecule from oxidative degradationduring the shelf life of the pharmaceutical preparation. Further studies are required to study theexact mechanism of the degradation process.

Multi-stage artificial neural network structure-based optimization of geothermal energy powered Kalina cycle

In this study, the geothermal energy powered Kalina cycle (GEP-KC) was optimized by using a multi-stage artificial neural network (ANN) analysis. The ANN model was basically composed of two stages. The first stage has one network, and the second stage consists of three networks in this ANN model. The 365 GEP-KCs were designed for four variable parameters. These designs were analytically analyzed by means of thermodynamic and economic analysis. The obtained data were used for modeling of multi-stage ANN structure. This multi-stage ANN model was designed with the aim of maximizing net present value (NPV). Turbine inlet pressure, geothermal water outlet temperature at evaporator, condenser pressure and ammonia mass fraction were input parameters of the multi-stage ANN model. Energy efficiency and exergy efficiency of the GEP-KC were outputs of the first stage, and the NPV of the GEP-KC was the output of the third network of the second stage. The most suitable network structure for the optimization of GEP-KC was performed by using the Levenberg–Marquardt variant of back-propagation learning algorithm for multi-stage ANN. The cov, MPE, RMSE and R2 values of multi-stage ANN were calculated as 2.558308, 1.077997189, 1.777658128 and 0.994693, respectively, for NPV. The calculated masses and biases of this structure were used to determine the optimum operating parameters of GEP-KC. The analytical findings of the NPV, energy and exergy efficiencies of the optimum GEP-KC model were, respectively, determined as 113.0732 M$, 6.7285% and 46.8701% in a high accuracy with the ANN results.

Effects of 5‐HT1 and 5‐HT2 Receptor Agonists on Electromagnetic Field‐Induced Analgesia in Rats

Much evidence demonstrates the antinociceptive effect of magnetic fields (MFs). However, the analgesic action mechanism of the electromagnetic field (EMF) is not exactly understood. The aim of the present study was to investigate the effects of 5-HT1 and 5-HT2 receptor agonists (serotonin HCl and 2,5-dimethoxy-4-iodoamphetamine [DOI] hydrochloride) on EMF-induced analgesia. In total, 66 adult male Wistar albino rats with an average body mass of 225 ± 13 g were used in this study. The animals were subjected to repeated exposures of alternating 50 Hz and 5 mT EMF for 2 h a day for 15 days. Prior to analgesia tests, serotonin HCl (5-HT1 agonist) 4 mg/kg, WAY 100635 (5-HT1 antagonist) 0.04 mg/kg, DOI hydrochloride (5-HT2 receptor agonist) 4 mg/kg, and SB 204741 (5-HT2 antagonist) 0.5 mg/kg doses were injected into rats. For statistical analysis of the data, analysis of variance was used and multiple comparisons were determined by Tukey's test. Administration of serotonin HCl MF (5 mT)-exposed rats produced a significant increase in percent maximal possible effect (% MPE) as compared with EMF group (P < 0.05). On the contrary, injection of WAY 100635 to MF-exposed rats produced a significant decrease in analgesic activity (P < 0.05). Similarly, the administration of DOI hydrochloride significantly increased % MPE values as compared with the EMF group while SB 204741 reduced it (P < 0.05). In conclusion, our results suggested that serotonin 5-HT1 and 5-HT2 receptors play an important role in EMF-induced analgesia; however, further research studies are necessary to understand the mechanism. Bioelectromagnetics. 2019;40:319-330. © 2019 Bioelectromagnetics Society.

Activation of GPR40 produces mechanical antiallodynia via the spinal glial interleukin-10/\u03b2-endorphin pathway

BackgroundThe G protein-coupled receptor 40 (GPR40), broadly expressed in various tissues such as the spinal cord, exerts multiple physiological functions including pain regulation. This study aimed to elucidate the mechanisms underlying GPR40 activation-induced antinociception in neuropathic pain, particularly related to the spinal glial expression of IL-10 and subsequent β-endorphin.MethodsSpinal nerve ligation-induced neuropathic pain model was used in this study. β-Endorphin and IL-10 levels were measured in the spinal cord and cultured primary microglia, astrocytes, and neurons. Double immunofluorescence staining of β-endorphin with glial and neuronal cellular biomarkers was also detected in the spinal cord and cultured primary microglia, astrocytes, and neurons.ResultsGPR40 was expressed on microglia, astrocytes, and neurons in the spinal cords and upregulated by spinal nerve ligation. Intrathecal injection of the GPR40 agonist GW9508 dose-dependently attenuated mechanical allodynia and thermal hyperalgesia in neuropathic rats, with Emax values of 80% and 100% MPE and ED50 values of 6.7 and 5.4 μg, respectively. Its mechanical antiallodynia was blocked by the selective GPR40 antagonist GW1100 but not GPR120 antagonist AH7614. Intrathecal GW9508 significantly enhanced IL-10 and β-endorphin immunostaining in spinal microglia and astrocytes but not in neurons. GW9508 also markedly stimulated gene and protein expression of IL-10 and β-endorphin in cultured primary spinal microglia and astrocytes but not in neurons, originated from 1-day-old neonatal rats. The IL-10 antibody inhibited GW9508-stimulated gene expression of the β-endorphin precursor proopiomelanocortin (POMC) but not IL-10, whereas the β-endorphin antibody did not affect GW9508-stimulated IL-10 or POMC gene expression. GW9508 increased phosphorylation of mitogen-activated protein kinases (MAPKs) including p38, extracellular signal-regulated kinase (ERK), and c-Jun N-terminal kinase (JNK), and its stimulatory effects on IL-10 and POMC expression were blocked by each MAPK isoform inhibitor. Spinal GW9508-induced mechanical antiallodynia was completely blocked by intrathecal minocycline, IL-10 neutralizing antibody, β-endorphin antiserum, and μ-opioid receptor-preferred antagonist naloxone.ConclusionsOur results illustrate that GPR40 activation produces antinociception via the spinal glial IL-10/β-endorphin antinociceptive pathway.

Solvent co-deposition during oxygen reduction on Au in DMSO LiPF6

Rotating ring disk electrode (RRDE) and electrochemical quartz crystal microbalance (EQCM) have been employed for chronoamperometry of the oxygen reduction reaction (ORR) on gold electrodes in O2 saturated LiPF6/DMSO electrolyte. The Au ring electrode (ER=3.0V) detects a small fraction of soluble superoxide generated at the disk while EQCM detects the mass of ORR insoluble products. By integration of the ORR current transient the mass to charge plots exhibit mass per electron (mpe) values which largely exceed those expected for simple O2 to O2Li or Li2O2 reactions. Therefore the co-deposition of solvent and/or side reactions such as electrolyte degradation should be taken into consideration to explain the experimental evidence.

Climate-based empirical model for PV module temperature estimation in tropical environment

The paper proposes new mathematical models to estimating PV module temperature for poly and mono crystalline technologies in tropical climate such as in Malaysia. The developed models are based on measured hourly global solar radiation, ambient temperature, relative humidity, wind speed and module temperature. All data were collected over the year 2009 at GreenTech 92 kWp installed PV system in Selangor, Malaysia. The models were compared using r, MBE, RMSE, and MPE. The results showed that the proposed models give the highest value of correlation coefficient r, and good result when considering statistical indicators i.e. low RMSE, low MBE, and low MPE values. The results show that the proposed regression models have advantages over the conventional approaches for calculating the hourly and day-average PV module temperature, and give the closest results comparing to the actual measurements. The proposed approaches can be used as effective tools for predicting the PV module temperature, whether a simple PV module, open rack system, BIPV installations, or even PV/Thermal collector, in remote and rural locations with no direct measurement equipment. The proposed models can be very useful in studying PV system performance and estimating its energy output.

Modelling swine supply response using a structural time series approach

Swine supply response was analyzed using a structural time series model (STSM) with both seasonal and yearly effects introduced stochastically. Swine production is divided into four production phases. Parameters of each phase were then estimated using a structural time series model. Out-of-sample forecasting robustness of the STSM was compared against a commonly used deterministic trend and deterministic seasonally (DTDS) using root mean square error and mean absolute percentage error criteria. The STSM model produced lower RMSE and MPE values than those obtained using a DTDS modeling approach.

By-Products in Earth Construction: Environmental Assessments

In Finland, some industrial residuals (by-products) have been used in earth construction since the 1970s. The lack of a general code of practice on environmental assessments of by-product utilization has led to nonuniform approval practices. In most countries, environmental standards are mainly based on health risks related to contaminant leachability. Leaching tests and solubility standards vary. Standards on waste composition and mix proportions have also been adopted in many countries. Based on a survey of practices in various countries, a hierarchical procedure is presented for the environmental assessments relevant to the utilization of by-products. In this model, the verification of environmental suitability can be based on a composition analysis, studies on leaching of contaminants, or a risk assessment procedure. In the next stage, composition and solubility standards (MPE values) need to be issued. Despite some identified drawbacks and data gaps, the Dutch methodology proved to be the most appropriate with respect to the derivation of substance-specific MPEs.

ポリエチレングリコールを担体とするモルヒネの放出制御

An implantable drug delivery system consisting of morphine and polyethylene glycol (PEG) was studied for the purpose of prolonged duration of analgesia effect by the controlled slow release of morphine. Because, a long continuous administration of analgesic through catheter and the supply equipment has given great inconveniences and uncomfortableness for the patients after operations and the patients having chronic pains and cancer pains. PEG of two different molecular weights (1000 and 1500) was mixed with morphine (2.5 mg, 5.0 mg and 7.5 mg respectively in one sample) so as to have a melting point of 40°C. The mixture was molten and injected into epidural of rats. The mixture was solidified in the epidural and gradually released morphine for a certain period. The duration period of analgesia effect was tested and evaluated by tail-flick test method and the MPE values. The results showed that PEG-morphine delivery system containing morphine more than 5.0 mg had a remarkable prolongation effect for analgesia function. The system containing 5.0 mg morphine kept 6 days duration of efficacy and the system containing 7.5 mg morphine showed 13 days duration of analgesia effect. An injection of control system containing no morphine (PEG only) showed no effect. Conclusively, it was proved that a prolonged duration of analgesia effect over several days∼2 weeks was achieved with a new administration technique of PEG-morphine (more than 5.0 mg) delivery system into epidural by the liquid injection and the followed solidification in vivo.

Morphine-3-glucuronide: evidence to support its putative role in the development of tolerance to the antinociceptive effects of morphine in the rat

Antinociceptive tolerance to morphine (MOR) was induced in groups of Sprague-Dawley rats receiving continuous intravenous infusions of morphine sulphate administered by 3 different MOR dosing regimes. At appropriate intervals throughout each infusion period, antinociceptive testing was performed using the tail-flick latency test and blood samples were collected. Groups of saline (SAL)-infused control rats also underwent antinociceptive testing and blood sample collection. Complete antinociceptive tolerance developed during each MOR infusion period and was characterized by a marked decline in the degree of antinociception from values greater than 90% of the maximum possible effect (%MPE) to pre-dosing baseline values. By contrast, %MPE values in SAL-infused control animals and in sham-operated rats were not significantly different from pre-dosing values throughout the infusion period, indicating that the experimental procedures themselves did not contribute to the development of antinociceptive tolerance to MOR. In addition, the rate of MOR tolerance development was inversely proportional to the MOR infusion rate. A very significant inverse relationship was observed between the mean degree of antinociception (%MPE) and the mean plasma molar concentration ratio, [morphine-3-glucuronide]/[MOR], for each of the 3 MOR dosing regimes and for the cumulated data. This relationship showed that near-maximum antinociception was attainable at ratio values less than approximately 0.50, whilst at ratio values above approximately 1.5, little or no antinociception was observed. Although %MPE was highly inversely correlated with the mean plasma morphine-3-glucuronide (M3G) concentrations for rats receiving regimes A and B, this was not the case for rats receiving regime C where antinociceptive tolerance was partially reversed by an increase in the morphine infusion rate part-way through the infusion period. In addition, a poor relationship was observed between %MPE and the mean plasma MOR concentration, possibly due to the confounding presence of M3G in all samples. Thus, we may conclude from this study in Sprague-Dawley rats that irrespective of the rate of antinociceptive tolerance development, the level of antinociception achievable appears to be highly inversely correlated with the mean [M3G]/[MOR] plasma molar concentration ratio and poorly correlated with the plasma MOR concentration, consistent with the notion that it is perhaps the balance between the excitatory effects of M3G and the inhibitory effects of MOR at the functional level which is the important determinant. Further research is required in carefully conducted studies in cancer patients to evaluate the possible contribution of the MOR metabolites. M3G and morphine-6-glucuronide (MbG), to increasing dosing requirements of MOR. Other factors such as disease progression and increasing psychological distress must also be borne in mind, as these will have a major influence on increasing dosing requirements of MOR in cancer patients (Portenoy 1994).