A mechanistic assessment of the nature of pharmacodynamic drug-drug interaction in vivo and in vitro.

Combination pharmacotherapy is becoming increasingly necessary because most diseases are pathophysiologically controlled at the subcellular level by target proteins in a combinatorial manner. We demonstrate the application of the stimulus-response mechanistic model in characterising the drug and physiological properties of pharmacodynamic drug-drug interactions (PDDI) using previously published in vitro and in vivo drug combination experiments. The in vitro experiment tested the effect of a combination of SCH66336 and 4-HPR on the survival of in squamous cell carcinoma cell lines, while the in vivo experiment tested the effect of a combination of cetuximab and cisplatin on tumour growth inhibition in female xenograft mice. The model adequately described both experiments, quantified both system and drug properties and predicted the nature of the PDDI mechanism. Strong baseline signals of 7.35 and 610 units existed in the in vitro and in vivo experiments respectively. An overall synergistic relationship (interaction index = 1.03E-8) was detected in the in vitro experiment. In the in vivo model, the overall interaction index was 70,139.45 implying an antagonistic interaction between the cisplatin and the cetuximab signals.

The new big is small: Leveraging knowledge from small trials for rare disease drug development: Blarcamesine for Rett syndrome.

Big data in drug development may not satisfactorily address the demands of precision medicine in a rare disease population, making the use of smaller clinical trials necessary. Consequently, the use of innovative design and analysis of these clinical trials using model-informed approaches have become indispensable. This requires informative exposure-outcome analysis, together with formal statistical analysis, which should include the strength of evidence for a study outcome. We demonstrate how knowledge can be gained, with supporting strength of evidence, from a small (data) clinical trial with a low dose of blarcamesine in the treatment of Rett syndrome. Based on a small data paradigm, pharmacometrics item response theory modelling and Bayes factor analysis were used to demonstrate the efficacy of blarcamesine in Rett syndrome.

Plasma concentration and eGFR in preterm and term neonates receiving gentamicin or successive amikacin therapy.

BACKGROUND: Gentamicin and amikacin are aminoglycoside antibiotics which are renally excreted and known to be nephrotoxic. Estimate of glomerular filtration rate (eGFR) per body surface area is lower in neonates than in adults and exposure to these drugs could lead to more suppression in kidney function. The aim of this study was to determine maximum and minimum plasma concentrations (Cmax and Cmin), time to reach Cmin levels of gentamicin and amikacin, and to assess eGFR in preterm and term neonates. METHODS: Two groups of patients were recruited, 44 neonates receiving gentamicin (5 mg/kg/24 h) and 35 neonates receiving amikacin (15 mg/kg/24 h) by slow intravenous injection. Patients on amikacin had been on gentamicin before being switched to amikacin. Two blood samples were drawn for the determination of the maximum and minimum plasma concentration. Primary outcomes were determination of Cmax, Cmin, and the time it took to clear the aminoglycoside to a plasma concentration below the toxicity threshold (gentamicin: < 1 mcg/mL; amikacin: < 5 mcg/mL. RESULTS: Therapeutic range for Cmax of gentamicin (15-25 mcg/mL) or amikacin (30-40 mcg/mL) was achieved in only 27.3 and 2.9% of neonates, respectively. Percentage of neonates reaching plasma concentrations below the toxicity threshold within the 24-hour dosing interval was 72.7% for gentamicin and 97.1% for amikacin. Positive correlation between gentamicin clearance and postnatal age borderline statistical significance (p = 0.007), while the correlation between amikacin clearance and postnatal age was poor and not statistically significant (r2 = - 0.30, p = 0.971). CONCLUSION: Although eGFR decreased significantly as a function of postnatal age in neonates receiving amikacin, the majority (91.4%) of these neonates were able to clear the drug to < 5 mcg/mL within a 24-hour dosing interval.

Maximizing Knowledge Extraction From Patient-Reported Outcome Data Using Exposure-Outcome Item Response Modeling Approach: Understanding Efavirenz-Induced Central Nervous System Toxicity.

This investigation was undertaken to maximally extract hidden knowledge from an efavirenz-based trial data set using an item response theory-based approach to exposure-outcome analysis. The aim was to understand the influence of efavirenz exposure on the underlying neuropsychiatric impairment in HIV/AIDS patients. Data from 196 individuals with 4136 neuropsychiatric impairment symptom observations at baseline and 2 and 12 weeks of 600-mg efavirenz-based therapy was analyzed. The 7 symptoms were categorized as sleep disorders (3), hallucinations (3), and cognitive impairment (1). A longitudinal item response theory model incorporating 3 latent variables based on the symptom categories and a linear disease progression model with a symptomatic drug effect was developed in NONMEM 7.4.1. The model adequately characterized the observed symptoms and revealed the hidden knowledge on the informativeness of symptoms in characterizing the underlying neuropsychiatric impairment. Informativeness, which was affected by underlying impairment severity and efavirenz therapy duration, varied among symptoms. Sleep disorders were the most efavirenz-sensitive symptom category. Vivid dreams and auditory hallucinations were most informative in their respective symptom categories. Mini-Mental State Examination score cutoff levels used to classify the severity of cognitive impairment did not distinctively correspond with neuropsychiatric impairment severity. Efavirenz treatment effect on the severity of neuropsychiatric impairment was not more than 15%. Simulation of individual symptoms at the 400-mg dose only reduced the prevalence of sleep disorder symptoms. Use of the exposure-item response theory modeling approach maximally extracted hidden knowledge about efavirenz-induced neuropsychiatric impairment and appropriately characterized the impact of dose reduction on specific neuropsychiatric impairment symptoms.

Pharmacometrics of clobazam in pediatrics: Prediction of effective clobazam doses for Dravet syndrome.

OBJECTIVE: To describe the use of a population pharmacokinetic (PopPK) model incorporating weight and ontogeny to identify effective clobazam (CLB) dosing for use in a clinical trial in pediatric patients with Dravet syndrome. METHODS: Pharmacokinetic data were combined from 3 CLB trials (OV-1012, OV-1017, and study 301) and a simulated study (study 401) for a total of 1306 CLB and 1305 N-desmethyl clobazam (N-CLB) samples from 193 Lennox-Gastaut syndrome patients and healthy subjects aged 6 months to 45 years. A structured approach based on US Food and Drug Administration guidance and pharmacometric knowledge discovery was developed using a nonlinear mixed-effects approach. Graphing and fitting using logistical weight regression were used to identify covariates for inclusion in the final model, which was evaluated using goodness-of-fit criteria and validated using prediction-corrected visual predictive check (pcVPC). Using the final PopPK model, a simulation study determined CLB and N-CLB distributions after 4 weeks of 1.5 and 2.0 mg/kg CLB. RESULTS: The parameters of the final PopPK model were similar to previous reports. Fixed-effect parameters were precisely estimated, with no significant increase in NONMEM objective function value. Intersubject variability estimates were similar to previous reports, with <35% shrinkage associated with parameter variability, except for intercompartmental clearance and apparent volumes of distribution of peripheral compartments. Goodness-of-fit plots and pcVPC show that the model adequately described CLB and N-CLB data. The CLB/N-CLB ratio in virtual study subjects aged <3 years was 0.23 for 1.5 and 2.0 mg/kg and was 0.14 for subjects aged ≥3 years, which is 2 to 3 times those reported in a previous stiripentol/CLB/valproate study in which seizure improvement was reported. SIGNIFICANCE: The PopPK model dosing parameters of 1.5 and 2.0 mg/kg are likely to result in efficacious concentrations of CLB and N-CLB in pediatric patients as young as 16 months. Dosages exceeding 1.5 mg/kg should be monitored for tolerability, particularly in patients aged <2 years, as there may be a higher incidence of sedation.

Transition modeling of neuropsychiatric impairment in HIV.

Few studies have reported analyses of neuropsychiatric impairment (NPI) data from HIV patients, in a real world clinical setting with the aim of establishing association between anti-retroviral drug concentrations and NPI development and resolution. No study has modeled the effect of efavirenz exposure beyond the pre-steady state period on the frequency and duration of NPI. The data used consists of 196 HIV patients whose efavirenz pharmacokinetic parameters were previously determined. Neuropsychiatric evaluation was done at baseline, week 2 and week 12. Patients were classified into NORMAL and NPI states. The duration of NPI was further classified as transient (NPI at week 2 but not at week 12), persistent (NPI at week 2 and 12) and delayed (NPI at week 12 but not at week 2). The proportion of patients in each duration category out of the total NPI patients was calculated. A continuous time Markov model was developed in NONMEM 7.3 and used to describe the relationship between efavirenz exposure and the duration of NPI. Monte Carlo simulations with the model were used to describe the effect of efavirenz dose reduction from 600mg to 400mg on the duration of NPI. The model adequately described the data. The influence of efavirenz exposure on the rate of development of NPI decayed with a half-life of 8.4 days. Efavirenz dose reduction to 400mg significantly reduces the duration of NPI, but has no impact on delayed NPI symptoms or efficacy.

An integrative population pharmacokinetics approach to the characterization of the effect of hepatic impairment on clobazam pharmacokinetics.

An integrative population pharmacokinetics (PPK)-based approach was used to characterize the effect of hepatic impairment on clobazam PK and its major metabolite in systemic circulation, N-desmethylclobazam (N-CLB). At therapeutic clobazam dosages, N-CLB plasma concentrations are 3-5 times greater than the parent compound. PK data from clinical trials in patients with Lennox-Gastaut syndrome (LGS; OV-1002 and OV-1012), healthy participants (OV-1016), and participants with and without renal impairment (OV-1032), as well as those from a publication describing the effects of hepatic impairment on clobazam PK, were merged to create the PPK model. Individual patient clobazam PK parameters from the publication were used to generate patient plasma-concentration data. Clobazam PK was linear and the formation of N-CLB was elimination-rate limited. Hepatic impairment did not affect the total apparent clearance of clobazam but may affect the PK of N-CLB. Because the formation of N-CLB is elimination-rate limited and the total apparent clearance of clobazam is unaffected by hepatic impairment, the PPK model suggests that patients with LGS and hepatic impairment may not require clobazam dosage modification.

Drug-metabolism mechanism: Knowledge-based population pharmacokinetic approach for characterizing clobazam drug-drug interactions.

A metabolic mechanism-based characterization of antiepileptic drug-drug interactions (DDIs) with clobazam in patients with Lennox-Gastaut syndrome (LGS) was performed using a population pharmacokinetic (PPK) approach. To characterize potential DDIs with clobazam, pharmacokinetic (PK) data from 153 patients with LGS in study OV-1012 (NCT00518713) and 18 healthy participants in bioavailability study OV-1017 were pooled. Antiepileptic drugs (AEDs) were grouped based on their effects on the cytochrome P450 (CYP) isozymes responsible for the metabolism of clobazam and its metabolite, N-desmethylclobazam (N-CLB): CYP3A inducers (phenobarbital, phenytoin, and carbamazepine), CYP2C19 inducers (valproic acid, phenobarbital, phenytoin, and carbamazepine), or CYP2C19 inhibitors (felbamate, oxcarbazepine). CYP3A4 inducers-which did not affect the oral clearance of clobazam-significantly increased the formation of N-CLB by 9.4%, while CYP2C19 inducers significantly increased the apparent elimination rate of N-CLB by 10.5%, resulting in a negligible net change in the PK of the active metabolite. CYP2C19 inhibitors did not affect N-CLB elimination. Because concomitant use of AEDs that are either CYP450 inhibitors or inducers with clobazam in the treatment of LGS patients had negligible to no effect on clobazam PK in this study, dosage adjustments may not be required for clobazam in the presence of the AEDs investigated here.

Markov model for characterizing neuropsychologic impairment and Monte Carlo simulation for optimizing efavirenz therapy.

The study was undertaken to develop a pharmacokinetic-pharmacodynamic model to characterize efavirenz-induced neuropsychologic impairment, given preexistent impairment, which can be used for the optimization of efavirenz therapy via Monte Carlo simulations. The modeling was performed with NONMEM 7.2. A 1-compartment pharmacokinetic model was fitted to efavirenz concentration data from 196 Ugandan patients treated with a 600-mg daily efavirenz dose. Pharmacokinetic parameters and area under the curve (AUC) were derived. Neuropsychologic evaluation of the patients was done at baseline and in week 2 of antiretroviral therapy. A discrete-time 2-state first-order Markov model was developed to describe neuropsychologic impairment. Efavirenz AUC, day 3 efavirenz trough concentration, and female sex increased the probability (P01) of neuropsychologic impairment. Efavirenz oral clearance (CL/F) increased the probability (P10) of resolution of preexistent neuropsychologic impairment. The predictive performance of the reduced (final) model, given the data, incorporating AUC on P01and CL /F on P10, showed that the model adequately characterized the neuropsychologic impairment observed with efavirenz therapy. Simulations with the developed model predicted a 7% overall reduction in neuropsychologic impairment probability at 450 mg of efavirenz. We recommend a reduction in efavirenz dose from 600 to 450 mg, because the 450-mg dose has been shown to produce sustained antiretroviral efficacy.

A pragmatic approach to the design of population pharmacokinetic studies.

The publication of a seminal article on nonlinear mixed-effect modeling led to a revolution in pharmacokinetics (PKs) with the introduction of the population approach. Since then, interest in obtaining accurate and precise estimates of population PK parameters has led to work on population PK study design that extended previous work on optimal sampling designs for individual PK parameter estimation. The issues and developments in the design of population PK studies are reviewed as a prelude to investigating, via simulation, the performance of 2 approaches (population Fisher information matrix D-optimal design and informative block [profile] randomized [IBR] design) for designing population PK studies. The results of our simulation study indicate that the designs based on the 2 approaches yielded efficient parameter estimates. The designs based on the 2 approaches performed similarly, and in some cases designs based on the IBR approach were slightly better. The ease with which the IBR designs can be generated makes them preferable in drug development, where pragmatism and time are of great consideration. We, therefore, refer to the IBR designs as pragmatic designs. Pragmatic designs that achieve high efficiency in the estimation parameters should be used in the design of population PK studies, and simulation should be used to determine the efficiency of the designs.

A random sampling approach for robust estimation of tissue-to-plasma ratio from extremely sparse data.

his study was performed to develop a new nonparametric approach for the estimation of robust tissue-to-plasma ratio from extremely sparsely sampled paired data (ie, one sample each from plasma and tissue per subject). Tissue-to-plasma ratio was estimated from paired/unpaired experimental data using independent time points approach, area under the curve (AUC) values calculated with the naïve data averaging approach, and AUC values calculated using sampling based approaches (eg, the pseudoprofile-based bootstrap [PpbB] approach and the random sampling approach [our proposed approach]). The random sampling approach involves the use of a 2-phase algorithm. The convergence of the sampling/resampling approaches was investigated, as well as the robustness of the estimates produced by different approaches. To evaluate the latter, new data sets were generated by introducing outlier(s) into the real data set. One to 2 concentration values were inflated by 10% to 40% from their original values to produce the outliers. Tissue-to-plasma ratios computed using the independent time points approach varied between 0 and 50 across time points. The ratio obtained from AUC values acquired using the naive data averaging approach was not associated with any measure of uncertainty or variability. Calculating the ratio without regard to pairing yielded poorer estimates. The random sampling and pseudoprofile-based bootstrap approaches yielded tissue-to-plasma ratios with uncertainty and variability. However, the random sampling approach, because of the 2-phase nature of its algorithm, yielded more robust estimates and required fewer replications. Therefore, a 2-phase random sampling approach is proposed for the robust estimation of tissue-to-plasma ratio from extremely sparsely sampled data.

Data supplementation: a pharmacokinetic/pharmacodynamic knowledge creation approach for characterizing an unexplored region of the response surface.

PURPOSE: To develop a data supplementation [i.e., a pharmacokinetic/pharmacodynamics (PK/PD) knowledge creation] approach for generating supplemental data to be used in characterizing a targeted unexplored segment of the response surface. METHODS: The procedure for data supplementation can be summarized as follows: 1) statement of the objective of data supplementation for PK/PD knowledge creation, 2) performance of PK knowledge discovery, 3) PK data synthesis for target dose group(s), 4) covariate data synthesis for virtual subjects in the target dose group(s), 5) discovery of hidden knowledge from real data set to which supplemental data will be added, 6) implementation of a data supplementation methodology, and 7) discovery and communication of the created knowledge. A nonparametric approximate Bayesian multiple supplementation and its modification, structure-based multiple supplementation, which is an adaptation of the approximate Bayesian bootstrap, is proposed as a method of data supplementation for PK/PD knowledge creation. The structured-based multiple supplementation methodology was applied to characterize the effect of a target dose of 100 mg that was unexplored in a previously concluded study that investigated the effect of 200- and 600-mg doses on biomarker response. RESULTS: The target dose of 100 mg was found to produce a response comparable with that of the 200 mg and better than that obtained with the 600 mg. CONCLUSIONS: Implementation of the PK/PD knowledge creation process through data supplementation resulted in gaining knowledge about a targeted region of a response surface (i.e., the effect of a target dose) that was not previously studied in a completed study without expending resources in conducting a new study.

Population pharmacokinetics III: design, analysis, and application of population pharmacokinetic Studies.

OBJECTIVE: To present a framework within which population pharmacokinetic (PPK) studies should be designed and analyzed and discuss the application of developed PPK models. METHODS: Information on PPK was retrieved from a MEDLINE search (1979-December 2003) of the literature and a bibliographic evaluation of review articles and books. This information is used in conjunction with experience to explain the design and analysis of PPK studies. Also, examples are included to demonstrate the usefulness of PPK. SYNTHESIS: A great deal of thought must be given to the design and analysis of PPK studies (ie, development of PPK models). Models are of 2 primary types--descriptive and predictive--and the process applied to these models is necessarily different. An approach that ensures model applicability is presented. CONCLUSIONS: PPK models have great utility, and the applications are many. They are very different from single-subject pharmacokinetic models and therefore require different approaches to model estimation.

Population pharmacokinetics II: estimation methods.

OBJECTIVE: To present, compare, and contrast the various approaches to estimating population pharmacokinetic (PPK) models with respect to the mathematical foundation, statistical aspects, software programs for implementation, and underlying assumptions. DATA SOURCES: Information on PPK was retrieved from a MEDLINE search (1977-August 2004) of literature and a bibliographic review of review articles and books. This information is used in conjunction with experience to explain the various methodologic approaches to PPK. STUDY SELECTION AND DATA EXTRACTION: All articles indentified from data sources were evaluated and relevant information was included in this review. DATA SYNTHESIS: Over 80 articles dealing with PPK estimation methods and/or their implementation were identified and reviewed. Sixty-four of these were chosen for their direct relevance to the subject of this article. Different estimation methods ranging from the naive averaging and naive pooled approaches through the standard two-stage approach to the nonlinear mixed-effects modeling approaches for estimating PPK are reviewed with their advantages and limitations. CONCLUSIONS: PPK estimation methods that rely on the characterizing of mixed (fixed and random) effects are known to produce PPK parameter estimates that are less biased than those obtained using the naive and standard two-stage approaches. The NONMEM software is the most widely used software for the characterization of PPK.

Population pharmacokinetics I: background, concepts, and models.

OBJECTIVE: To present and emphasize the background, foundations, utility, and conceptual underlying theory of the population pharmacokinetic (PPK) approach with an examination of the advantages when compared with other approaches of pharmacokinetic modeling. DATA SOURCES: Information on PPK was retrieved from a MEDLINE search (1979-June 2002) of literature and a bibliographic review of review articles and books. STUDY SELECTION AND DATA EXTRACTION: All articles identified from data sources were evaluated and relevant information was included in this review. DATA SYNTHESIS: PPK plays a pivotal role in developing dosing strategies for direct patient care and in drug development. PPK is valuable because it targets the patient group that will eventually receive the drug of interest, quantitates pharmacokinetic variability at several levels, and seeks to explain those sources of variability. CONCLUSIONS: PPK models have great utility and the applications are many. They are very different from single-subject pharmacokinetic models and therefore require different approaches to model development.

Model appropriateness and population pharmacokinetic modeling.

The purpose of this study was to define model appropriateness, identifying the individual elements thereof, and to set out a framework within which model appropriateness could be determined for population pharmacokinetic (PPK) models. Model appropriateness was defined by stating the problem to be solved, with the intended use of the model being the pivotal event. The elements of model appropriateness were identified with the type of model (descriptive vs. predictive) determining which elements of model appropriateness need to be executed. An example is presented to show how model appropriateness is determined for the optimal application of PPK models. It was determined that PPK models are developed to solve problems. Model appropriateness depends on identifying the problem, as well as stating the intended use of the model, and requires evaluation of the model for goodness of fit, reliability, and stability if intended for descriptive purposes; for predictive models, validation would be an additional requirement. Descriptive models are used to explain variability in the pharmacokinetics (PK) of a drug, while predictive models are developed to extrapolate beyond the immediate study population. For those models used for predictive purposes, strong assumptions are made about the relationship to the underlying population from which the data were collected. As an example of determining model appropriateness, a PPK model for 5-fluorocytosine was developed, using NONMEM, version IV. The model was evaluated and validated by the process of percentile bootstrapping. From the PPK model, the range of expected serum concentrations based on two widely used dosing methods (Sanford and the University of California at San Diego [UCSD]) was simulated (Pharsight Trial Designer software). These results indicated that the UCSD method performed well and has the advantage of recommending convenient dosing intervals. In conclusion, considering and applying the principles of model appropriateness to PPK models will result in models that can be applied for their intended use with confidence. Model appropriateness was efficiently established and determined to address the problem of comparing competing dosing strategies.

Estimating inestimable standard errors in population pharmacokinetic studies: the bootstrap with Winsorization.

A simulation study was performed to determine how inestimable standard errors could be obtained when population pharmacokinetic analysis is performed with the NONMEM software on data from small sample size phase I studies. Plausible sets of concentration-time data for nineteen subjects were simulated using an incomplete longitudinal population pharmacokinetic study design, and parameters of a drug in development that exhibits two compartment linear pharmacokinetics with single dose first order input. They were analyzed with the NONMEM program. Standard errors for model parameters were computed from the simulated parameter values to serve as true standard errors of estimates. The nonparametric bootstrap approach was used to generate replicate data sets from the simulated data and analyzed with NONMEM. Because of the sensitivity of the bootstrap to extreme values, winsorization was applied to parameter estimates. Winsorized mean parameters and their standard errors were computed and compared with their true values as well as the non-winsorized estimates. Percent bias was used to judge the performance of the bootstrap approach (with or without winsorization) in estimating inestimable standard errors of population pharmacokinetic parameters. Winsorized standard error estimates were generally more accurate than non-winsorized estimates because the distribution of most parameter estimates were skewed, sometimes with heavy tails. Using the bootstrap approach combined with winsorization, inestimable robust standard errors can be obtained for NONMEM estimated population pharmacokinetic parameters with > or = 150 bootstrap replicates. This approach was also applied to a real data set and a similar outcome was obtained. This investigation provides a structural framework for estimating inestimable standard errors when NONMEM is used for population pharmacokinetic modeling involving small sample sizes.

Safety and efficacy of the multidrug-resistance inhibitor biricodar (VX-710) with concurrent doxorubicin in patients with anthracycline-resistant advanced soft tissue sarcoma.

PURPOSE: Incel (biricodar, VX-710) restores drug sensitivity to P-glycoprotein and multidrug resistance-associated protein-1-expressing cells. This Phase I/II study evaluated the safety/tolerability, pharmacokinetics, and efficacy of VX-710 plus doxorubicin in patients with inoperable, locally advanced or metastatic, anthracycline-resistant/refractory, soft tissue sarcoma. EXPERIMENTAL DESIGN: In Phase I, i.v. bolus doxorubicin at 60, 75, or 67.5 mg/m(2) was administered 8 h after initiation of a 72-h continuous i.v. (CIV) infusion of VX-710 (120 mg/m(2)/h) to cohorts of patients to establish a maximum tolerated dose. For efficacy evaluations in Phase II, eligible patients had inoperable, locally advanced or metastatic, anthracycline-resistant/refractory soft tissue sarcoma; < or =225 mg/m(2) cumulative prior doxorubicin; and adequate hematological, liver, and kidney function. Cycles were repeated every 3 weeks. RESULTS: Fourteen patients were enrolled in Phase I. Myelosuppression was the dose-limiting toxicity with 75 and then 67.5 mg/m(2) doxorubicin, and the maximum tolerated dose was established at 60 mg/m(2) with VX-710, 120 mg/m(2)/h, 72-h CIV. VX-710 had no apparent effect on doxorubicin pharmacokinetics. Twenty-nine patients enrolled in Phase II were treated with VX-710, 120 mg/m(2)/h 72-h CIV, and 60 mg/m(2) doxorubicin. Among 26 evaluable patients, minimal activity was noted among 11 patients with gastrointestinal stromal tumors (GISTs); however, in 15 patients with anthracycline-resistant sarcomas of other histologies, 2 achieved partial responses and 7 patients had disease stabilization with an overall median progression-free interval of 3.4 months. CONCLUSION: Anthracycline resistance in GISTs appears to be independent of P-glycoprotein or multidrug resistance-associated protein-1 resistance mechanisms. However, the combination of VX-710 and doxorubicin resulted in objective responses or disease stabilization in patients with strictly defined anthracycline-refractory non-GIST sarcomas, which warrants further evaluation.