Effects of cold stress and ether stress on aminopyrine demethylation kinetics in vivo

The effect of cold stress and ether stress on mixed function oxidase activity in vivo has been studied using the aminopyrine-−14CO2 exhalation rate (CER) method. Cold stress following both acute and chronic exposure resulted in enhanced rate of demethylation of both aminopyrine (CER α half-life) and its monomethyl metabolite (CER β half-life). However the time course of the response to cold stress differs for the two demethylations. The characteristics of the cold-induced enhancement of aminopyrine-CER would appear to differ in several aspects from that reported for phenobarbitone pretreatment. In contrast, ether exposure did not produce any consistent effect on drug metabolizing status of the rat.

Assessment of algorithms for predicting drug-drug interactions via inhibition mechanisms: comparison of dynamic and static models

WHAT IS ALREADY KNOWN ABOUT THIS SUBJECT

The prediction of drug-drug interactions (DDIs) from in vitro data usually utilizes an average dosing interval estimate of inhibitor concentration in an equation-based static model. Simcyp®, a population-based ADME simulator, is becoming widely used for the prediction of DDIs and has the ability to incorporate the time-course of inhibitor concentration and hence generate a temporal profile of the inhibition process within a dynamic model.

WHAT THIS PAPER ADDS

Prediction of DDIs for 35 clinical studies incorporating a representative range of drug-drug interactions, with multiple studies across different inhibitors and victim drugs. Assessment of whether the inclusion of the time course of inhibition in the dynamic model improves prediction in comparison with the static model. Investigation of the impact of different inhibitor and victim drug parameters on DDI prediction accuracy including dosing time and the inclusion of active metabolites. Assessment of ability of the dynamic model to predict inter-individual variability in the DDI magnitude.

AIMS

Static and dynamic models (incorporating the time course of the inhibitor) were assessed for their ability to predict drug-drug interactions (DDIs) using a population-based ADME simulator (Simcyp®V8). The impact of active metabolites, dosing time and the ability to predict inter-individual variability in DDI magnitude were investigated using the dynamic model.

METHODS

Thirty-five in vivo DDIs involving azole inhibitors and benzodiazepines were predicted using the static and dynamic model; both models were employed within Simcyp for consistency in parameters. Simulations comprised of 10 trials with matching population demographics and dosage regimen to the in vivo studies. Predictive utility of the static and dynamic model was assessed relative to the inhibitor or victim drug investigated.

RESULTS

Use of the dynamic and static models resulted in comparable prediction success, with 71 and 77% of DDIs predicted within two-fold, respectively. Over 40% of strong DDIs (>five-fold AUC increase) were under-predicted by both models. Incorporation of the itraconazole metabolite into the dynamic model resulted in increased prediction accuracy of strong DDIs (80% within two-fold). Bias and imprecision in prediction of triazolam DDIs were higher in comparison with midazolam and alprazolam; >50% of triazolam DDIs were under-predicted regardless of the model used. Predicted inter-individual variability in the AUC ratio (coefficient of variation of 45%) was consistent with the observed variability (50%).

CONCLUSIONS

High prediction accuracy was observed using both the Simcyp dynamic and static models. The differences observed with the dose staggering and the incorporation of active metabolite highlight the importance of these variables in DDI prediction.

Critique of the two-fold measure of prediction success for ratios: application for the assessment of drug-drug interactions

Current assessment of drug-drug interaction (DDI) prediction success is based on whether predictions fall within a two-fold range of the observed data. This strategy results in a potential bias toward successful prediction at lower interaction levels [ratio of the area under the concentration-time profile (AUC) in the presence of inhibitor/inducer compared with control is <2]. This scenario can bias any assessment of different DDI prediction algorithms if databases contain large proportion of interactions in this lower range. Therefore, the current study proposes an alternative method to assess prediction success with a variable prediction margin dependent on the particular AUC ratio. The method is applicable for assessment of both induction and inhibition-related algorithms. The inclusion of variability into this predictive measure is also considered using midazolam as a case study. Comparison of the traditional two-fold and the new predictive method was performed on a subset of midazolam DDIs collated from previous databases; in each case, DDIs were predicted using the dynamic model in Simcyp simulator. A 21% reduction in prediction accuracy was evident using the new predictive measure, in particular at the level of no/weak interaction (AUC ratio <2). However, inclusion of variability increased the prediction success at these levels by two-fold. The trend of lower prediction accuracy at higher potency of DDIs reported in previous studies is no longer apparent when predictions are assessed via the new predictive measure. Thus, the study proposes a more logical method for the assessment of prediction success and its application for induction and inhibition DDIs.

Metabolite formation kinetics and intrinsic clearance of phenacetin, tolbutamide, alprazolam, and midazolam in adenoviral cytochrome P450-transfected HepG2 cells and comparison with hepatocytes and in vivo

Cryopreserved human hepatocytes and other in vitro systems often underpredict in vivo intrinsic clearance (CL(int)). The aim of this study was to explore the potential utility of HepG2 cells transduced with adenovirus vectors expressing a single cytochrome P450 enzyme (Ad-CYP1A2, Ad-CYP2C9, or Ad-CYP3A4) for metabolic clearance predictions. The kinetics of metabolite formation from phenacetin, tolbutamide, and alprazolam and midazolam, selected as substrates probes for CYP1A2, CYP2C9, and CYP3A4, respectively, were characterized in this in vitro system. The magnitude of the K(m) or S(50) values observed in Ad-P450 cells was similar to those found in the literature for other human liver-derived systems. For each substrate, CL(int) (or CL(max)), values from Ad-P450 systems were scaled to human hepatocytes in primary culture using the relative activity factor (RAF) approach. Scaled Ad-P450 CL(int) values were approximately 3- to 6-fold higher (for phenacetin O-deethylation, tolbutamide 4-hydroxylation, and alprazolam 4-hydroxyaltion) or lower (midazolam 1'-hydroxylation) than those reported for human cryopreserved hepatocytes in suspension. Comparison with the in vivo data reveals that Ad-P450 cells provide a favorable prediction of CL(int) for the substrates studied (in a range of 20-200% in vivo observed CL(int)). This is an improvement compared with the consistent underpredictions (<10-50% in in vivo observed CL(int)) found in cryopreserved hepatocyte studies with the same substrates. These results suggest that the Ad-P450 cell is a promising in vitro system for clearance predictions of P450-metabolized drugs.

Building National Capacity for Child and Family Disaster Mental Health Research

Disaster mental health is a burgeoning field with numerous opportunities for professional involvement in preparedness, response, and recovery efforts. Research is essential to advance professional understanding of risk and protective factors associated with disaster outcomes; to develop an evidence base for acute, intermediate, and long-term mental health approaches to address child, adult, family, and community disaster-related needs; and to inform policy and guide national and local disaster preparedness, response, and recovery programs. To address the continued need for research in this field, we created the Child & Family Disaster Research Training & Education (DRT) program, which is focused specifically on enhancing national capacity to conduct disaster mental health research related to children, a population particularly vulnerable to disaster trauma. This paper describes the structure and organization of the DRT program, reviews the training curriculum, discusses implementation and evaluation of the program, and reviews obstacles encountered in establishing the program. Finally, key lessons learned are reviewed for the purpose of guiding replication of the DRT model to address other areas of community mental health.