Metrum Research Group

Tariffville, Connecticut, United States

Metrum Research Group

Tariffville, Connecticut, United States

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Robbie G.J.,MedImmune | Zhao L.,MedImmune | Zhao L.,U.S. Food and Drug Administration | Mondick J.,Metrum Research Group | And 2 more authors.
Antimicrobial Agents and Chemotherapy | Year: 2012

Although it has been on the market for over a decade, confusion remains regarding the pharmacokinetics (PK) and optimal dosing of palivizumab, a humanized IgG1κ monoclonal antibody indicated for the prevention of serious lower respiratory tract disease caused by respiratory syncytial virus (RSV) in pediatric patients at high risk of RSV disease. The objectives of this analysis were to characterize the population PK of palivizumab in adults and children using nonlinear mixed-effect modeling, quantify the effects of individual covariates on variability in palivizumab disposition, and compare palivizumab exposures for various dosing scenarios. Palivizumab PK data from 22 clinical studies were used for model development. The model was developed using a two-stage approach: (i) a 2-compartment model with first-order absorption after intramuscular administration was fitted to adult data, and (ii) the same structural model was fitted to the sparse pediatric data using the NONMEM $PRIOR subroutine, with informative priors obtained from the adult analysis. Body weight and an age descriptor that combines gestational age and postnatal age (PAGE) using an asymptotic-exponential model best described palivizumab clearance in pediatric patients. Palivizumab clearance increased slightly from 10.2 ml/day to 11.9 ml/day as a function of PAGE ranging from 7 to 18 months. Covariate analysis indicated a 20% higher clearance in children with chronic lung disease and in children with antidrug antibody titer values of ≥ 80. These covariates did not substantially explain interindividual variability. In the label-indicated pediatric population, body weight was the primary demographic factor affecting palivizumab PK. Body weight-based dosing of 15 mg/kg yields similar palivizumab concentrations in children of different gestational and postnatal ages. Simulations demonstrated that there was little difference in palivizumab PK between healthy term and premature infants. Simulations also demonstrated that the 5 monthly palivizumab doses of 15 mg/kg, consistent with the label and studied in two randomized, clinical trials, provided greater and more prolonged palivizumab exposure than did an abbreviated dosing regimen of 3 monthly doses. Copyright © 2012 American Society for Microbiology. All Rights Reserved.


Ahansson E.K.,Uppsala University | Ma G.,Uppsala University | Ma G.,Pfizer | Amantea M.A.,Pfizer | And 5 more authors.
CPT: Pharmacometrics and Systems Pharmacology | Year: 2013

A modeling framework relating exposure, biomarkers (vascular endothelial growth factor (VEGF), soluble vascular endothelial growth factor receptor (sVEGFR)-2,-3, soluble stem cell factor receptor (sKIT)), and tumor growth to overall survival (OS) was extended to include adverse effects (myelosuppression, hypertension, fatigue, and hand-foot syndrome (HFS)). Longitudinal pharmacokinetic-pharmacodynamic models of sunitinib were developed based on data from 303 patients with gastrointestinal stromal tumor. Myelosuppression was characterized by a semiphysiological model and hypertension with an indirect response model. Proportional odds models with a first-order Markov model described the incidence and severity of fatigue and HFS. Relative change in sVEGFR-3 was the most effective predictor of the occurrence and severity of myelosuppression, fatigue, and HFS. Hypertension was correlated best with sunitinib exposure. Baseline tumor size, time courses of neutropenia, and relative increase of diastolic blood pressure were identified as predictors of OS. The framework has potential to be used for early monitoring of adverse effects and clinical response, thereby facilitating dose individualization to maximize OS. © 2013 ASCPT.


Ueckert S.,Uppsala University | Plan E.L.,Uppsala University | Plan E.L.,Metrum Research Group | Ito K.,Pfizer | And 3 more authors.
Pharmaceutical Research | Year: 2014

Purpose: This work investigates improved utilization of ADAS-cog data (the primary outcome in Alzheimer's disease (AD) trials of mild and moderate AD) by combining pharmacometric modeling and item response theory (IRT). Methods: A baseline IRT model characterizing the ADAS-cog was built based on data from 2,744 individuals. Pharmacometric methods were used to extend the baseline IRT model to describe longitudinal ADAS-cog scores from an 18-month clinical study with 322 patients. Sensitivity of the ADAS-cog items in different patient populations as well as the power to detect a drug effect in relation to total score based methods were assessed with the IRT based model. Results: IRT analysis was able to describe both total and item level baseline ADAS-cog data. Longitudinal data were also well described. Differences in the information content of the item level components could be quantitatively characterized and ranked for mild cognitively impairment and mild AD populations. Based on clinical trial simulations with a theoretical drug effect, the IRT method demonstrated a significantly higher power to detect drug effect compared to the traditional method of analysis. Conclusion: A combined framework of IRT and pharmacometric modeling permits a more effective and precise analysis than total score based methods and therefore increases the value of ADAS-cog data. © 2014 The Author(s).


Peterson M.C.,Pfizer | Riggs M.M.,Metrum Research Group
CPT: Pharmacometrics and Systems Pharmacology | Year: 2015

In the evolving discipline of quantitative systems pharmacology (QSP), QSP model (QSPM) applications are expanding. Recently, a QSPM was used by US Food and Drug Administration (FDA) clinical pharmacologists to evaluate the appropriateness of a proposed dosing regimen for a new biologic. This application expands the use-horizon for QSPMs into the regulatory domain. Here we retrace the evolution of the model and suggest a question-based approach to directing model scope, identifying applications, and understanding overall QSPM value. © 2015 The Authors CPT: Pharmacometrics & Systems Pharmacology published by Wiley Periodicals, Inc. on behalf of American Society for Clinical Pharmacology and Therapeutics.


Hansson E.K.,Uppsala University | Amantea M.A.,Pfizer | Westwood P.,Uppsala University | Milligan P.A.,Pfizer | And 5 more authors.
CPT: Pharmacometrics and Systems Pharmacology | Year: 2013

The predictive value of longitudinal biomarker data (vascular endothelial growth factor (VEGF), soluble VEGF receptor (sVEGFR)-2, sVEGFR-3, and soluble stem cell factor receptor (sKIT)) for tumor response and survival was assessed based on data from 303 patients with imatinib-resistant gastrointestinal stromal tumors (GIST) receiving sunitinib and/or placebo treatment. The longitudinal tumor size data were well characterized by a tumor growth inhibition model, which included, as significant descriptors of tumor size change, the model-predicted relative changes from baseline over time for sKIT (most significant) and sVEGFR-3, in addition to sunitinib exposure. Survival time was best described by a parametric time-to-event model with baseline tumor size and relative change in sVEGFR-3 over time as predictive factors. Based on the proposed modeling framework to link longitudinal biomarker data with overall survival using pharmacokinetic-pharmacodynamic models, sVEGFR-3 demonstrated the greatest predictive potential for overall survival following sunitinib treatment in GIST. © 2013 ASCPT.


Strand V.,Biopharmaceutical Consultant | Ahadieh S.,Pfizer | French J.,Metrum Research Group | Geier J.,Pfizer | And 8 more authors.
Arthritis Research and Therapy | Year: 2015

Background: Tofacitinib is an oral Janus kinase inhibitor for the treatment of rheumatoid arthritis (RA). Tofacitinib modulates the signaling of cytokines that are integral to lymphocyte activation, proliferation, and function. Thus, tofacitinib therapy may result in suppression of multiple elements of the immune response. Serious infections have been reported in tofacitinib RA trials. However, limited head-to-head comparator data were available within the tofacitinib RA development program to directly compare rates of serious infections with tofacitinib relative to biologic agents, and specifically adalimumab (employed as an active control agent in two randomized controlled trials of tofacitinib). Methods: A systematic literature search of data from interventional randomized controlled trials and long-term extension studies with biologics in RA was carried out. Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) consensus was followed for reporting results of the review and meta-analysis. Incidence rates (unique patients with events/100 patient-years) for each therapy were estimated based on data from randomized controlled trials and long-term extension studies using a random-effects model. Relative and absolute risk comparisons versus placebo used Mantel-Haenszel methods. Results: The search produced 657 hits. In total, 66 randomized controlled trials and 22 long-term extension studies met the selection criteria. Estimated incidence rates (95% confidence intervals [CIs]) for abatacept, rituximab, tocilizumab, and tumor necrosis factor inhibitors were 3.04 (2.49, 3.72), 3.72 (2.99, 4.62), 5.45 (4.26, 6.96), and 4.90 (4.41, 5.44), respectively. Incidence rates (95% CIs) for tofacitinib 5 and 10mg twice daily (BID) in phase 3 trials were 3.02 (2.25, 4.05) and 3.00 (2.24, 4.02), respectively. Corresponding incidence rates in long-term extension studies were 2.50 (2.05, 3.04) and 3.19 (2.74, 3.72). The risk ratios (95% CIs) versus placebo for tofacitinib 5 and 10mg BID were 2.21 (0.60, 8.14) and 2.02 (0.56, 7.28), respectively. Risk differences (95% CIs) versus placebo for tofacitinib 5 and 10mg BID were 0.38% (-0.24%, 0.99%) and 0.40% (-0.22%, 1.02%), respectively. Conclusions: In interventional studies, the risk of serious infections with tofacitinib is comparable to published rates for biologic disease-modifying antirheumatic drugs in patients with moderate to severely active RA. © 2015 Strand et al.


Ravva P.,Pfizer | Karlsson M.O.,Uppsala University | French J.L.,Metrum Research Group
Statistics in Medicine | Year: 2014

The application of model-based meta-analysis in drug development has gained prominence recently, particularly for characterizing dose-response relationships and quantifying treatment effect sizes of competitor drugs. The models are typically nonlinear in nature and involve covariates to explain the heterogeneity in summary-level literature (or aggregate data (AD)). Inferring individual patient-level relationships from these nonlinear meta-analysis models leads to aggregation bias. Individual patient-level data (IPD) are indeed required to characterize patient-level relationships but too often this information is limited. Since combined analyses of AD and IPD allow advantage of the information they share to be taken, the models developed for AD must be derived from IPD models; in the case of linear models, the solution is a closed form, while for nonlinear models, closed form solutions do not exist.Here, we propose a linearization method based on a second order Taylor series approximation for fitting models to AD alone or combined AD and IPD. The application of this method is illustrated by an analysis of a continuous landmark endpoint, i.e., change from baseline in HbA1c at week 12, from 18 clinical trials evaluating the effects of DPP-4 inhibitors on hyperglycemia in diabetic patients. The performance of this method is demonstrated by a simulation study where the effects of varying the degree of nonlinearity and of heterogeneity in covariates (as assessed by the ratio of between-trial to within-trial variability) were studied. A dose-response relationship using an Emax model with linear and nonlinear effects of covariates on the emax parameter was used to simulate data. The simulation results showed that when an IPD model is simply used for modeling AD, the bias in the emax parameter estimate increased noticeably with an increasing degree of nonlinearity in the model, with respect to covariates. When using an appropriately derived AD model, the linearization method adequately corrected for bias. It was also noted that the bias in the model parameter estimates decreased as the ratio of between-trial to within-trial variability in covariate distribution increased. Taken together, the proposed linearization approach allows addressing the issue of aggregation bias in the particular case of nonlinear models of aggregate data. © 2014 John Wiley & Sons, Ltd.


Grant
Agency: Department of Defense | Branch: Navy | Program: STTR | Phase: Phase I | Award Amount: 79.78K | Year: 2016

Differential equations are widely used to analyze and simulate the dynamics of complex systems in the physical, biological and social sciences. Inferences with such models are challenging due to both statistical and computational complexity. Stan is a widely used, open-source, probabilistic programming language and Bayesian inference engine. We propose to extend Stan by incorporating solvers for ordinary differential equations and differential algebraic equations. We expect to achieve a substantial speedup over the existing state-of-the-art, due to Stans automatic differentiation library and efficient estimation algorithms. We will also extend Stan to deal with events arising from external inputs such as multiple dosing in pharmacology. We will evaluate the tools produced using pharmacometric data with a range of sophisticated statistical and mathematical models in common use. The result will be an even more flexible Bayesian statistics platform that supports analysis of heterogeneous collections of data conditioned on models of great stochastic and deterministic complexity and quantitative prior knowledge. This work will be commercialized by incorporation of the enhanced Stan platform within Metrums Metworx cloud computing platform. The result will be a more efficient and flexible computational environment for data analysis and simulation relevant to a range of scientific and engineering applications.


PubMed | Metrum Research Group and Boehringer Ingelheim
Type: Journal Article | Journal: Diabetes, obesity & metabolism | Year: 2016

To quantify the effect of the sodium-glucose co-transporter 2 inhibitor, empagliflozin, on renal glucose reabsorption in patients with type 2 diabetes, and to evaluate covariate effects, using a mechanistic population pharmacokinetic-pharmacodynamic (PK-PD) model.Four phase I/II trials were used for model development. Empagliflozins PK characteristics were characterized by a two-compartmental model with sequential zero- and first-order absorption. Urinary glucose excretion (UGE) was described as dependent on renal glucose filtration and reabsorption; splay of the glucose reabsorption/excretion curves was considered. The modelling assumed that empagliflozin lowers the maximum renal glucose reabsorption capacity and, thereby, the renal threshold for glucose (RTg). Covariate effects were investigated using a full covariate modelling approach, emphasizing parameter estimation.The PK-PD model provided a reasonable description of the PK characteristics of empagliflozin and its effects on UGE across a range of renal function levels. Its parameters are consistent with reported values for renal physiology. Using this model, the effect of empagliflozin on renal glucose reabsorption was quantified. Steady-state empagliflozin doses (1, 5, 10 and 25mg) reduced RTg from 12.5mmol/L [95% confidence interval (CI) 12.0, 13.1] to 5.66 (95% CI 4.62, 6.72), 3.01 (95% CI 2.33, 3.69), 2.53 (95% CI 1.83, 3.14) and 2.21 (95% CI 1.47, 2.84)mg/dl, respectively. Covariate analysis showed the effect of empagliflozin on UGE was not influenced, to a clinically relevant extent, by sex, age or race.A method for characterizing renal glucose reabsorption was developed that does not require complex glucose clamp experiments. These analyses indicate that empagliflozin provided concentration-dependent RTg reductions, with 10 and 25mg providing near-maximum RTg-lowering.


PubMed | Metrum Research Group, Boehringer Ingelheim and Boehringer Ingelheim Pharmaceuticals
Type: Journal Article | Journal: Diabetes therapy : research, treatment and education of diabetes and related disorders | Year: 2016

The aim of the analysis was to characterize the population pharmacokinetics (PKs) and exposure-response (E-R) for efficacy (fasting plasma glucose, glycated hemoglobin) and safety/tolerability [hypoglycemia, genital infections, urinary tract infection (UTI), and volume depletion] of the sodium glucose cotransporter 2 inhibitor, empagliflozin, in patients with type 2 diabetes mellitus. This study extends the findings of previous analyses which described the PK and pharmacodynamics (PD) using early clinical studies of up to 12weeks in duration.Population pharmacokinetic and E-R models were developed based on two Phase I, four Phase II, and four Phase III studies.Variability in empagliflozin exposure was primarily affected by estimated glomerular filtration rate (eGFR) (less than twofold increase in exposure in patients with severe renal impairment). Consistent with its mode of action, the efficacy of empagliflozin was increased with elevated baseline plasma glucose levels and attenuated with decreasing renal function, but was still maintained to nearly half the maximal effect with eGFR as low as 30mL/min/1.73m(2). All other investigated covariates, including sex, body mass index, race, and age did not alter the PK or efficacy of empagliflozin to a clinically relevant extent. Compared with placebo, empagliflozin administration was associated with an exposure-independent increase in the incidence of genital infections and no significant change in the risk of UTI, hypoglycemia, or volume depletion.Based on the results from the PK and E-R analysis, no dose adjustment is required for empagliflozin in the patient population for which the drug is approved.Boehringer Ingelheim.

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