Pharmacodynamic Interaction Model Research Articles

An improved PKPD modeling approach to characterize the pharmacodynamic interaction over time between ceftazidime/avibactam and colistin from in vitro time-kill experiments against multidrug-resistant Klebsiella pneumoniae isolates.

In contrast to the checkerboard method, bactericidal experiments [time-kill curves (TKCs)] allow an assessment of pharmacodynamic (PD) interactions over time. However, TKCs in combination pose interpretation problems. The objective of this study was to characterize the PD interaction over time between ceftazidime/avibactam (CZA) and colistin (CST) using TKC against four multidrug-resistant Klebsiella pneumoniae susceptible to both antibiotics and expressing a widespread carbapenemase determinant KPC-3. In vitro TKCs were performed and analyzed using pharmacokinetic/pharmacodynamic (PKPD) modeling. The general pharmacodynamic interaction model was used to characterize PD interactions between drugs. The 95% confidence intervals (95%CIs) of the expected additivity and of the observed interaction were built using parametric bootstraps and compared to evaluate the in vitro PD interaction over time. Further simulations were conducted to investigate the effect of the combination at varying concentrations typically observed in patients. Regrowth was observed in TKCs at high concentrations of drugs alone [from 4 to 32× minimum inhibitory concentrations (MIC)], while the combination systematically prevented the regrowth at concentrations close to the MIC. Significant synergy or antagonism were observed under specific conditions but overall 95%CIs overlapped widely over time indicating an additive interaction between antibiotics. Moreover, simulations of typical PK profile at standard dosages indicated that the interaction should be additive in clinical conditions. The nature of the PD interaction varied with time and concentration in TKC. Against the four K. pneumoniae isolates, the bactericidal effect of CZA + CST combination was predicted to be additive and to prevent the emergence of resistance at clinical concentrations.

Pharmacodynamic modelling reveals synergistic interaction between docetaxel and SCO-101 in a docetaxel-resistant triple negative breast cancer cell line

One of the primary barriers in treating cancer patients is the development of resistance to the available treatments. This is the case for treatment of triple negative breast cancer (TNBC) with docetaxel, which is part of the neoadjuvant treatment for TNBC. The novel compound SCO-101 is under investigation for its potential treatment effect in several types of drug resistant cancer. The aim of this study was to establish a pharmacodynamic model that captures the effect of docetaxel, SCO-101, and the combination on cell survival in docetaxel resistant MDA-MB-231 TNBC cells. Several combination models were compared and a recently published combination model, the general pharmacodynamic interaction model (GPDI), provided the best fit. The model allowed for description and quantification of the interaction between docetaxel and SCO-101 with respects to both maximal effect and potency. Based on this model, SCO-101 has a synergistic effect with docetaxel. This synergy is not present in the maximal effect, but the combination of SCO-101 and docetaxel showed an approximately 60% increase in potency compared to docetaxel alone. Furthermore, the predicted model surface for the combination provided key information regarding promising dose ratios and dose levels for further studies of the combination. Lastly, the study presents a use case for the GPDI model, which provides a way to quantify and interpret drug-drug interactions.

Identification of the potential biological target of N-benzenesulfonyl-1,2,3,4-tetrahydroquinoline compounds active against gram-positive and gram-negative bacteria

The development of new antibiotics with activity towards a broad spectrum of bacteria, including multiresistant strains, is a very important topic for global public health. As part of previous works, N-benzenesulfonyl-1,2,3,4-tetrahydroquinoline (BSTHQ) derivatives were described as antimicrobial agents active against gram-positive and gram-negative pathogens. In this work, experimental and molecular modelling studies were performed in order to identify their potential biological target in the light of structure-based design efforts towards further BSTHQ derivatives. First, a carboxyfluorescein leakage assay was performed using liposomes to mimic bacterial membranes, which found no significative membrane disruption effects with respect to control samples. These results support a non-surfactant antimicrobial activity of the tested compounds. In a second stage, the inhibition of potential antimicrobial targets was screened using molecular modelling methods, taking into account previously reported druggable targets deposited in the ChEMBL database for Escherichia coli and Staphylococcus aureus. Two enzymes, namely D-glutamic acid-adding enzyme (MurD) and N-acetylglucosamine-1-phophate-uridyltransferase (GlmU), both involved in bacterial membrane synthesis, were identified as potential targets. Pharmacodynamic interaction models were developed using known MurD and GlmU inhibitors by applying state-of-the-art chemoinformatic methods (molecular docking, molecular dynamics and free energy of interaction analyses). These models were further extended to the analysis of the studied BSTHQ derivatives. Overall, our results demonstrated that the studied BSTHQ derivatives elicit their antibacterial activity by interacting with a specific molecular target, GlmU being the highly feasible one. Based on the presented results, further structure-aided design efforts towards the obtaining of novel BSTHQ derivatives are envisioned.Communicated by Ramaswamy H. Sarma

Feasibility of Exposure-Response Analyses for Clinical Dose-Ranging Studies of Drug Combinations.

The exposure-response relationship of combinatory drug effects can be quantitatively described using pharmacodynamic interaction models, which can be used for the selection of optimal dose combinations. The aim of this simulation study was to evaluate the reliability of parameter estimates and the probability for accurate dose identification for various underlying exposure-response profiles, under a number of different phase II designs. An efficacy variable driven by the combined exposure of two theoretical compounds was simulated and model parameters were estimated using two different models, one estimating all parameters and one assuming that adequate previous knowledge for one drug is readily available. Estimation of all pharmacodynamic parameters under a realistic, in terms of sample size and study design, phase II trial, proved to be challenging. Inaccurate estimates were found in all exposure-response scenarios, except for situations where no pharmacodynamic interaction was present, with the drug potency and interaction parameters being the hardest to estimate. When previous knowledge of the exposure-response relationship of one of the monocomponents is available, such information should be utilized, as it enabled relevant improvements in parameter estimation and in correct dose identification. No general trends for classification of the performance of the tested study designs across different scenarios could be identified. This study shows that pharmacodynamic interactions models can be used for the exposure-response analysis of clinical endpoints especially when accompanied by appropriate dose selection in regard to the expected drug potencies and appropriate trial size and if information regarding the exposure-response profile of one monocomponent is available.

A general pharmacodynamic interaction model identifies perpetrators and victims in drug interactions

Assessment of pharmacodynamic (PD) drug interactions is a cornerstone of the development of combination drug therapies. To guide this venture, we derive a general pharmacodynamic interaction (GPDI) model for ≥2 interacting drugs that is compatible with common additivity criteria. We propose a PD interaction to be quantifiable as multidirectional shifts in drug efficacy or potency and explicate the drugs’ role as victim, perpetrator or even both at the same time. We evaluate the GPDI model against conventional approaches in a data set of 200 combination experiments in Saccharomyces cerevisiae: 22% interact additively, a minority of the interactions (11%) are bidirectional antagonistic or synergistic, whereas the majority (67%) are monodirectional, i.e., asymmetric with distinct perpetrators and victims, which is not classifiable by conventional methods. The GPDI model excellently reflects the observed interaction data, and hence represents an attractive approach for quantitative assessment of novel combination therapies along the drug development process.

Assessing Pharmacodynamic Interactions in Mice Using the Multistate Tuberculosis Pharmacometric and General Pharmacodynamic Interaction Models.

The aim of this study was to investigate pharmacodynamic (PD) interactions in mice infected with Mycobacterium tuberculosis using population pharmacokinetics (PKs), the Multistate Tuberculosis Pharmacometric (MTP) model, and the General Pharmacodynamic Interaction (GPDI) model. Rifampicin, isoniazid, ethambutol, or pyrazinamide were administered in monotherapy for 4 weeks. Rifampicin and isoniazid showed effects in monotherapy, whereas the animals became moribund after 7 days with ethambutol or pyrazinamide alone. No PD interactions were observed against fast‐multiplying bacteria. Interactions between rifampicin and isoniazid on killing slow and non‐multiplying bacteria were identified, which led to an increase of 0.86 log10 colony‐forming unit (CFU)/lungs at 28 days after treatment compared to expected additivity (i.e., antagonism). An interaction between rifampicin and ethambutol on killing non‐multiplying bacteria was quantified, which led to a decrease of 2.84 log10 CFU/lungs at 28 days after treatment (i.e., synergism). These results show the value of pharmacometrics to quantitatively assess PD interactions in preclinical tuberculosis drug development.

Pharmacodynamic interaction models in pediatric anesthesia.

Pharmacokinetic (PK) and pharmacodynamic (PD) models are important tools for summarizing drug dose, concentration, and effect relationships. Co-administration of drugs may alter PK and PD relationships. Traditional methods of evaluating PD interactions include using isoboles, shifts in dose-response curves, or interaction indices based on parameters of potency derived from separate monotherapy and combination therapy analyses. These methods provide an estimation of the magnitude of effect for dose or concentration combinations, but they do not inform us on the time course of that effect, or its associated variability. A better way to investigate PD interactions is to use modeling, and to take advantage of the benefits of population analyses. A population analysis is a statistical method in which a model describing the typical (or population) response, and the variability between individuals within that population, is developed. Models for monotherapy, derived using a population approach, can be combined and extended to incorporate PD interactions between two or more drugs. The purpose of this article was to provide a general road map for understanding and interpreting PD interaction models, including the 'response surface' models. Several types of response surface models exist, and here we review these with examples taken from the literature. We also consider current and future applications for this type of analysis for clinical anesthesia and pediatrics.

Quantitative evaluation of the combination between cytotoxic drug and efflux transporter inhibitors based on a tumour growth inhibition model

ATP-Binding Cassette transporters such as ABCG2 confer resistance to various anticancer drugs including irinotecan and its active metabolite, SN38. Early quantitative evaluation of efflux transporter inhibitors-cytotoxic combination requires quantitative drug-disease models. A proof-of-concept study has been carried out for studying the effect of a new ABCG2 transporter inhibitor, MBLI87 combined to irinotecan in mice xenografted with cells overexpressing ABCG2. Mice were treated with irinotecan alone or combined to MBLI87, and tumour size was periodically measured. To model those data, a tumour growth inhibition model was developed. Unperturbed tumour growth was modelled using Simeoni's model. Drug effect kinetics was accounted for by a Kinetic-Pharmacodynamic approach. Effect of inhibitor was described with a pharmacodynamic interaction model where inhibitor enhances activity of cytotoxic. This model correctly predicted tumour growth dynamics from our study. MBLI87 increased irinotecan potency by 20% per μmol of MBLI87. This model retains enough complexity to simultaneously describe tumour growth and effect of this type of drug combination. It can thus be used as a template to early evaluate efflux transporter inhibitors in-vivo.

Interaction of Fentanyl and Buprenorphine in an Experimental Model of Pain and Central Sensitization in Human Volunteers

: There is controversy about combining opioids with different receptor affinities. We assessed the analgesic and antihyperalgesic effects of the μ-agonist fentanyl and the partial μ-agonist/κ-antagonist buprenorphine in a human pain model, when given alone or in combination. : Fifteen healthy male volunteers (22 to 35 y) were included in this randomized, double-blind, placebo-controlled, cross-over study. Transcutaneous electrical stimulation induced spontaneous acute pain and stable areas of secondary hyperalgesia. Pain intensities, measured on a numeric rating scale from 0 to 10, and the size of the hyperalgesic areas were assessed before, during, and after an intravenous infusion of 1.5 µg/kg fentanyl, 1.5 µg/kg buprenorphine, a combination of 0.75 µg/kg fentanyl and buprenorphine each, or saline 0.9%. Maximum effects of the treatments were compared by repeated measurement analysis of variance, and pharmacodynamic interaction models were fitted to the data. : Starting from a baseline value of numeric rating scale=6, the maximum reduction of pain intensity after correction for placebo effects was 43.9 ± 22.2% after fentanyl, 35.0 ± 23.0% after buprenorphine, and 39.4 ± 20.8% after the combination (mean ± SD, P=0.24). The maximum reduction of the hyperalgesic area was 38.3 ± 39.0% for fentanyl, 34.4 ± 32.7% for buprenorphine, and 30.0 ± 53.8% for the combination (mean ± SD, P=0.82). The time courses were best described by pharmacodynamic models assuming an additive interaction. : For the doses administered in this study, buprenorphine and fentanyl showed an additive interaction.

Abstract 3709: Synergistic effects of obatoclax (GX15-070) and bortezomib (BTZ) in non-Hodgkin's lymphoma

Abstract Non-Hodgkins Lymphoma (NHL) is the second most rapidly increasing cause of cancer-related death in the US, making selection of appropriate combinations against B-cell lymphoma increasingly important. Our aim was to develop a pharmacodynamic (PD) model to characterize the interaction of obatoclax and bortezomib (BTZ), which target pathways associated with acquired resistance to therapies of rituximab and chemotherapy in NHL. Primary tumor cells from patients (n=45) with previously untreated relapsed/refractory (rel/ref) B-cell lymphoma were utilized. Sample subtypes included: activated B-cell (ABC), diffuse large B-cell lymphoma (DLBCL), germinal center B-cell (GCB), Transformed DLBCL, Follicular lymphoma (FL), Hodgkin's lymphoma (HL), mantle cell lymphoma (MCL), marginal zone lymphoma (MZL) and small lymphocytic lymphoma (SLL). Primary tumor cells were exposed to obatoclax and BTZ for 48-hours, and cell viability was determined by luminescent cell viability assays. Single agent IC50 values for obatoclax (0.0003-6.3nM) and BTZ (0.0005-0.01nM) were determined across all cell lines. Tumors were exposed to combinations of obatoclax and BTZ and a PD interaction model was developed to assess synergy; (α=1, additive; Φ<1, synergy; ≤ >1, antagonism). Patient demographics, BCL-2 status, Ki67%, CD20 status, lifetime rituximab dose and response to therapy were described for all patients. A total of 14 male (M) and 9 female (F) tumor cells were evaluable for synergy. Values of ≤ were: 0.93-1.0 (DLBCL-ABC-Denovo (D)); 0.64-1.6 (DLBCL-ABC Refractory (R)); 0.64-0.71 (DLBCL/GCB-D); 0.48-1.1 (FL-D); 0.97-2.5 (FL-R); 0.58-1.2 (HL-D); and (1.0-1.2) SLL-D. Synergy was observed in both MCL-R (α = 0.55) and MZL-D (α = 0.56). Patients with progressive disease (n=3) after therapy all had Ψ>1. Tumor cells from patients who had a complete (n=6) or partial response (n=8) post-biopsy had a synergistic or additive effect to obatoclax and BTZ. A non-significant difference in ≤ was observed between M and F (p=0.5510), D and R cell types (p=0.31), and CD20 expression (p=0.678) by a two tailed Mann-Whitney. The ≤ for the interaction between obatoclax and BTZ was correlated with lifetime rituximab dose (r =0.61, t-score = 2.4, p<0.05), %Ki67 (r =0.50, t-score = 2.04, p>0.05) and age (r =−0.21, t-score = 0.085, p>0.05). A one-way ANOVA indicates a significant difference observed in ≤ with patients showing complete or partial response compared with progressive disease (p<0.05). These results indicate combination of obatoclax and BTZ have a synergistic effect in GCB-DLBCL-D, MCL-R and MZL-D cell types. A partial synergy or additive effect was observed in FL-D, HL-D, SLL-D and ABC-DLBCL-D. This approach provides a modeling strategy that may serve as a framework for bridging in vitro and ex vivo work to provide clinical benefit to patients, and supports the use of obatoclax in combination with BTZ in the future. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 103rd Annual Meeting of the American Association for Cancer Research; 2012 Mar 31-Apr 4; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2012;72(8 Suppl):Abstract nr 3709. doi:1538-7445.AM2012-3709

Sedative and hypnotic interactions between sevoflurane and etomidate

Objective To evaluate sedative and hypnotic interactions between sevoflurane and etomidate. Methods Twenty-four ASA Ⅰ or Ⅱ patients aged 18-59 yr with body mass index 17-27 kg/m2 undergoing elective surgery under general anesthesia were included in this study. Part Ⅰ Twelve patients received etemidate by TCI at target effect-site concentrations (Ce) of 0.05, 0.1, 0.2, 0.3, 0.4 and 0.5 μg/ml in turn. When the preset concentrations were reached for 2 min, the spectral entropy index (response entropy, RE; state entropy, SE) and Observer's Assessment of Alertuess/Sedation (OAA/S) score were recorded and TCI of etomidate was stopped. The patients were randomly divided into 3 groups ( n = 4 each), inhaling sevoflurane with end-tidal concentrations of 0.5% (group A1 ), 1% (group B1 ) and 2% (group C1 ). When the sevoflurane concentration reached 95% predetermined end-tidal concentration or more, etomidate was administered via TCI at Ce of 0.05, 0.1, 0.2, 0.3, 0.4 and 0.5 μg/ml in turn. When the preset concentrations were reached for 2 min, RE, SE and OAA/S score were recorded. Part Ⅱ Twelve patients inhaled sevoflurane with end-tidal concentrations of 0.5%, 1%, 2%, 3%, 4% and 5% in turn. When the predetermined end-tidal concentrations were reached, RE, SE and OAA/S score were recorded and inhalation of sevoflurane was stopped. Sevoflurane was washed out. Then the patients were randomly divided into 3 groups ( n = 4 each), receiving etomidate by TCI at Ce of 0.05 μg/ml (group A2), 0.1 μg/ml ( group B2) and 0.2 μg/ml ( group C2) respectively. The patients inhaled sevoflurane with end-tidal concentrations of 0.5%, 1%, 2%, 3%, 4% and 5% in turn 2 min after the preset concentrations were reached. When the predetermined end-tidal concentrations were reached, RE, SE and OAA/S score were recorded. Response surface pharmacodynamic interaction models were constructed to evaluate RE, SE and OAA/S score and determine sedative and hypnotic interactions between sevoflurane and etomidate. Results The fitted values of the interaction index a of RE and SE (95% confidence intervals, 95% CI) were 0.32 ( - 0.07-0.71 ) and 0.25 (-0.12-0.63) respectively (P > 0.05). The fitted value of the interaction index a of OAA/S score (95% CI) was 2.25 (0.58-3.93) (P <0.05).Conclusion The interaction between etomidate and sevoflurane is additive and synergistic for sedation and hypnosis assessed by spectral entropy index and by OAA/S score respectively. Key words: Anesthetics, inhalation; Etomidate; Drug interactions; Conscious sedation; Hypnosis, anesthetic

Mechanism-Based Pharmacodynamic Modeling ofS(–)-Atenolol: Estimation of in Vivo Affinity for the β1-Adrenoceptor with an Agonist-Antagonist Interaction Model

The aim of this study was the development of an agonist-antagonist interaction model to estimate the in vivo affinity of S(-)-atenolol for the beta(1)-adrenoreceptor. Male Wistar-Kyoto (WKY) rats were used to characterize the interaction between the model drugs isoprenaline (to induce tachycardia) and S(-)-atenolol. Blood samples were taken to determine plasma pharmacokinetics. Reduction of isoprenaline-induced tachycardia was used as a pharmacodynamic endpoint. The pharmacokinetic-pharmacodynamic relationship of isoprenaline was first characterized with the operational model of agonism using the literature value for the affinity (K(A)) of isoprenaline (3.2 x 10(-8) M; left atria WKY rats). Resulting estimates for baseline (E(0)), maximal effect (E(max)), and efficacy (tau) were 374 (1.9%), 130 (5.9%), and 247 (33%) beats per minute, respectively. In addition, the interaction between isoprenaline and S(-)-atenolol was characterized using a pharmacodynamic interaction model based on the operational model of agonism that describes the heart rate response based on the affinity of the agonist (K(A)), the affinity of the antagonist (K(B)), the efficacy (tau), the maximal effect (E(max)), the Hill coefficient (n(H)), the concentrations of isoprenaline and atenolol, and the displacement of the endogenous agonist adrenaline. The estimated in vivo affinity (K(B)) of S(-)-atenolol for the beta(1) -receptor was 4.6 x 10(-8) M. The obtained estimate for in vivo affinity of S(-)-atenolol (4.6 x 10(-8) M) is comparable to literature values for the in vitro affinity in functional assays. In conclusion, a meaningful estimate of in vivo affinity for S(-)-atenolol could be obtained using a mechanism-based pharmacodynamic modeling approach.

When Is a Bispectral Index of 60 Too Low?

Opioids are commonly used in conjunction with sedative drugs to provide anesthesia. Previous studies have shown that opioids reduce the clinical requirements of sedatives needed to provide adequate anesthesia. Processed electroencephalographic parameters, such as the Bispectral Index (BIS; Aspect Medical Systems, Newton, MA) and Auditory Evoked Potential Index (AAI; Alaris Medical Systems, San Diego, CA), can be used intraoperatively to assess the depth of sedation. The aim of this study was to characterize how the addition of opioids sufficient to change the clinical level of sedation influenced the BIS and AAI. Twenty-four adult volunteers received a target-controlled infusion of remifentanil (0-15 ng/ml) and inhaled sevoflurane (0-6 vol%) at various target concentration pairs. After reaching pseudo-steady state drug levels, the modified Observer's Assessment of Alertness/Sedation score, BIS, and AAI were measured at each target concentration pair. Response surface pharmacodynamic interaction models were built using the pooled data for each pharmacodynamic endpoint. Response surface models adequately characterized all pharmacodynamic endpoints. Despite the fact that sevoflurane-remifentanil interactions were strongly synergistic for clinical sedation, BIS and AAI were minimally affected by the addition of remifentanil to sevoflurane anesthetics. Although clinical sedation increases significantly even with the addition of a small to moderate dose of remifentanil to a sevoflurane anesthetic, the BIS and AAI are insensitive to this change in clinical state. Therefore, during "opioid-heavy" sevoflurane-remifentanil anesthetics, targeting a BIS less than 60 or an AAI less than 30 may result in an unnecessarily deep anesthetic state.

Mechanism-Based Pharmacokinetic-Pharmacodynamic Modelling of the Reversal of Buprenorphine-Induced Respiratory Depression by Naloxone

Respiratory depression is a potentially life-threatening adverse effect of opioid therapy. It has been postulated that the difficulty of reversing buprenorphine-induced respiratory depression is caused by slow receptor association-dissociation kinetics at the opioid mu receptor. The aim of this study was to characterise the pharmacodynamic interaction between buprenorphine and naloxone in healthy volunteers. A competitive pharmacodynamic interaction model was proposed to describe and predict the time course of naloxone-induced reversal of respiratory depression. The model was identified using data from an adaptive naloxone dose-selection trial following intravenous administration of buprenorphine 0.2mg/70kg or 0.4mg/70kg. The pharmacokinetics of naloxone and buprenorphine were best described by a two-compartment model and a three-compartment model, respectively. A combined biophase equilibration-receptor association-dissociation pharmacodynamic model described the competitive interaction between buprenorphine and naloxone at the opioid mu receptor. For buprenorphine, the values of the rate constants of receptor association (k(on)) and dissociation (k(off)) were 0.203 mL/ng/min and 0.0172 min(-)(1), respectively. The value of the equilibrium dissociation constant (K(D)) was 0.18 nmol/L. The half-life (t((1/2))) of biophase equilibration was 173 minutes. These estimates of the pharmacodynamic parameters are similar to values obtained in the absence of naloxone co-administration. For naloxone, the half-life of biophase distribution was 6.5 minutes. Because of the slow receptor association-dissociation kinetics of buprenorphine in combination with the fast elimination kinetics of naloxone, naloxone is best administered as a continuous infusion for reversal of buprenorphine-induced respiratory depression.

This Month in ANESTHESIOLOGY

This Month in ANESTHESIOLOGY

Opioid–Volatile Anesthetic Synergy

Combining a hypnotic and an analgesic to produce sedation, analgesia, and surgical immobility required for clinical anesthesia is more common than administration of a volatile anesthetic alone. The aim of this study was to apply response surface methods to characterize the interactions between remifentanil and sevoflurane. Sixteen adult volunteers received a target-controlled infusion of remifentanil (0-15 ng/ml) and inhaled sevoflurane (0-6 vol%) at various target concentration pairs. After reaching pseudo-steady state drug levels, the Observer's Assessment of Alertness/Sedation score and response to a series of randomly applied experimental pain stimuli (pressure algometry, electrical tetany, and thermal stimulation) were observed for each target concentration pair. Response surface pharmacodynamic interaction models were built using the pooled data for sedation and analgesic endpoints. Using computer simulation, the pharmacodynamic interaction models were combined with previously reported pharmacokinetic models to identify the combination of remifentanil and sevoflurane that yielded the fastest recovery (Observer's Assessment of Alertness/Sedation score > or = 4) for anesthetics lasting 30-900 min. Remifentanil synergistically decreased the amount of sevoflurane necessary to produce sedation and analgesia. Simulations revealed that as the duration of the procedure increased, faster recovery was produced by concentration target pairs containing higher amounts of remifentanil. This trend plateaued at a combination of 0.75 vol% sevoflurane and 6.2 ng/ml remifentanil. Response surface analyses demonstrate a synergistic interaction between remifentanil and sevoflurane for sedation and all analgesic endpoints.