Koordination der Produktionsmenge mit dynamischen Transferpreisen

Amela-Adriana SadeanKlagenfurt, Alpen-Adria-Univ., Master-Arb., 2015(VLID)241243

Comparison of three a-priori models in the prediction of serum lithium concentration

Context: Mathematical models are valuable for optimizing drug dose and dosing regimens. Aims: To compare the precision and bias of three a-priori methods in the prediction of serum level of lithium in patients with bipolar disorder, and to determine their sensitivity and specificity in detecting serum lithium levels outside the therapeutic range. Settings and Design: Hospital-based, retrospective study. Materials and Methods: In a retrospective study of 31 in-patients, the serum level of lithium was calculated using three different a-priori methods. Mean Prediction Error was used as a measure of bias while Mean Absolute Error and Root Mean Squared Error were used as a measure of precision. The sensitivity and specificity of the methods was calculated. Results: All three models underestimated serum lithium level. Precision was best with the model described by Pepin et al., while bias of prediction was the least with the method of Abou Auda et al. The formula by Pepin et al. was able to predict serum lithium level with a mean error of 36.57%. The sensitivity and specificity of the models in identifying serum lithium levels outside the therapeutic range was 80% and 76.19% for Pepin et al., 90% and 74.19% for Zetin et al., and 90% and 66.67% for Abou-Auda et al., respectively. Conclusion: The study demonstrates the difference in precision and bias of three a-priori methods, with no one method being superior to the other in the prediction of serum concentration

Pharmacokinetic-pharmacodynamic modelling of opioids in healthy human volunteers. A minireview

Pain is characterized by its multi-dimensional nature, explaining in part why the pharmacokinetic/pharmacodynamic (PK/PD) relationships are not straightforward for analgesics. The first part of this MiniReview gives an overview of PK, PD and PK/PD models, as well as of population approach used in analgesic studies. The second part updates the state-of-the-art in the PK/PD relationship of opioids, focusing on data obtained on experimental human pain models, a useful tool to characterize the PD of analgesics. For the so-called weak opioids such as codeine, experimental human studies showed that analgesia relies mainly upon biotransformation into morphine. However, the time-course of plasma concentrations of morphine did not always reflect the time-course of effects, the major site of action being the central nervous system. For tramadol, a correlation has been observed between the analgesic response and the PK of the (+)R-O-demethyl-tramadol metabolite. For 'stronger' opioids such as oxycodone, studies assessing the PK/PD of oxycodone suggested that active metabolite oxymorphone also strongly contributes to the analgesia and that analgesia may also be partially related through an action to peripherally located κ-opioid receptors. Different models have been proposed to describe the time-course of buprenorphine. An effect-compartment model was adopted to describe the PK/PD of morphine and its active metabolite, morphine-6-glucuronide (M6G). A longer blood-effect site equilibration half-life t(1/2) k(e0) was observed for M6G, suggesting a longer onset of action. The studies assessing the PK/PD of fentanyl and its derivatives showed a short t(1/2) k(e0) for analgesia, between 0.2 and 9 min., reflecting a short onset of effect. In conclusion, depending on the speed of transfer between the plasma and the effect site as well as the participation of active metabolites, the time-course of the analgesic effects can be close to the plasma concentrations (alfentanil and derivates) or observed with a prolonged delay (codeine, buprenorphine, morphine). These PK/PD data can be used to better characterize the differences between opioids, and partly explain the important observed variability among opioids in experimental conditions and should be systematically evaluated during drug development to better predict their selection in specific clinical conditions