Publications by authors named "Steven Novick"

42 Publications

Mean comparisons and power calculations to ensure reproducibility in preclinical drug discovery.

Stat Med 2021 Mar 9;40(6):1414-1428. Epub 2020 Dec 9.

Data Sciences and Quantitative Biology, Discovery Sciences, Biopharmaceuticals R&D, AstraZeneca, Gaithersburg, Maryland, USA.

In the pharmaceutical industry, in vivo animal experiments are conducted to test the effects of novel preclinical drug compounds. Well-planned animal studies involve a sample size and statistical power analysis to provide a basis for the number of animals allocated into comparator arms of a future study. These calculations require approximate values for the parameters of a statistical model that will be applied to the future data and used to test for differences via statistical hypotheses. If the prestudy parameter estimates are nearly correct, the power analysis guarantees that a difference will be detected from the study data, up to a prespecified probability. Traditional power computations, however, are not calculated with reproducibility in mind. In this work, the issue of reproducibility in drug discovery is tackled from the point of view that study-to-study variability is not included in a typical sample size and power analysis. Three proposed methods that yield a reproducible mean-comparison analysis are derived and compared.
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http://dx.doi.org/10.1002/sim.8848DOI Listing
March 2021

The current state of Bayesian methods in nonclinical pharmaceutical statistics: Survey results and recommendations from the DIA/ASA-BIOP Nonclinical Bayesian Working Group.

Pharm Stat 2021 Mar 6;20(2):245-255. Epub 2020 Oct 6.

PharmaLex Statistical Solutions, Mont-Saint-Guibert, Belgium.

The use of Bayesian methods to support pharmaceutical product development has grown in recent years. In clinical statistics, the drive to provide faster access for patients to medical treatments has led to a heightened focus by industry and regulatory authorities on innovative clinical trial designs, including those that apply Bayesian methods. In nonclinical statistics, Bayesian applications have also made advances. However, they have been embraced far more slowly in the nonclinical area than in the clinical counterpart. In this article, we explore some of the reasons for this slower rate of adoption. We also present the results of a survey conducted for the purpose of understanding the current state of Bayesian application in nonclinical areas and for identifying areas of priority for the DIA/ASA-BIOP Nonclinical Bayesian Working Group. The survey explored current usage, hurdles, perceptions, and training needs for Bayesian methods among nonclinical statisticians. Based on the survey results, a set of recommendations is provided to help guide the future advancement of Bayesian applications in nonclinical pharmaceutical statistics.
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http://dx.doi.org/10.1002/pst.2072DOI Listing
March 2021

A Bayesian Statistical Approach to Continuous Qualification of a Bioassay.

PDA J Pharm Sci Technol 2021 Jan-Feb;75(1):8-23. Epub 2020 Aug 14.

Department of Bioassay Impurity and Quality, Biopharmaceutical Development, BioPharmaceuticals R&D, AstraZeneca, Gaithersburg, MD.

A validated bioassay is used to measure the potency of commercial lots, and as such, must be accurate, precise, and fit for its intended purpose. Regulatory expectations for a bioassay include a characterization of features, such as accuracy, precision, linearity, and range. The journey of a bioassay typically starts in a development lab, where it is initially qualified and used to support the release and stability testing of clinical lots. As a program moves through the different clinical phases, it may be optimized further, used to support process development, or transferred to new laboratories, with each activity generating additional bioassay data. Finally, the bioassay is fully validated as part of the transfer to the commercial quality control testing laboratories. In this work, rather than capturing the data from each study as a separate, independent report, it is proposed that, beginning with the qualification study, the accuracy and precision of the bioassay be continuously characterized, with each subsequent study result building upon the preceding ones. We call this approach Such a proposition is naturally carried out using Bayesian statistical methods in which the historical study data is used to construct prior knowledge that is blended with the current study data. By doing so, the bioassay accuracy and precision may be assessed with high confidence well ahead of commercial manufacturing. Further, by following the total-variance approach, the method also allows for a robust construction of system suitability and control limits for potency.
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http://dx.doi.org/10.5731/pdajpst.2019.011221DOI Listing
August 2020

Preclinical Characterization of an Antibody-Drug Conjugate Targeting CS-1 and the Identification of Uncharacterized Populations of CS-1-Positive Cells.

Mol Cancer Ther 2020 08 13;19(8):1649-1659. Epub 2020 May 13.

Research and Development, AstraZeneca, Gaithersburg, Maryland.

Multiple myeloma is a hematologic cancer that disrupts normal bone marrow function and has multiple lines of therapeutic options, but is incurable as patients ultimately relapse. We developed a novel antibody-drug conjugate (ADC) targeting CS-1, a protein that is highly expressed on multiple myeloma tumor cells. The anti-CS-1 mAb specifically bound to cells expressing CS-1 and, when conjugated to a cytotoxic pyrrolobenzodiazepine payload, reduced the viability of multiple myeloma cell lines In mouse models of multiple myeloma, a single administration of the CS-1 ADC caused durable regressions in disseminated models and complete regression in a subcutaneous model. In an exploratory study in cynomolgus monkeys, the CS-1 ADC demonstrated a half-life of 3 to 6 days; however, no highest nonseverely toxic dose was achieved, as bone marrow toxicity was dose limiting. Bone marrow from dosed monkeys showed reductions in progenitor cells as compared with normal marrow. cell killing assays demonstrated that the CS-1 ADC substantially reduced the number of progenitor cells in healthy bone marrow, leading us to identify previously unreported CS-1 expression on a small population of progenitor cells in the myeloid-erythroid lineage. This finding suggests that bone marrow toxicity is the result of both on-target and off-target killing by the ADC.
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http://dx.doi.org/10.1158/1535-7163.MCT-19-0482DOI Listing
August 2020

Comparisons of outlier tests for potency bioassays.

Pharm Stat 2020 05 24;19(3):230-242. Epub 2019 Nov 24.

Statistical Science, AstraZeneca, Gaithersburg, Maryland.

Potency bioassays are used to measure biological activity. Consequently, potency is considered a critical quality attribute in manufacturing. Relative potency is measured by comparing the concentration-response curves of a manufactured test batch with that of a reference standard. If the curve shapes are deemed similar, the test batch is said to exhibit constant relative potency with the reference standard, a critical requirement for calibrating the potency of the final drug product. Outliers in bioassay potency data may result in the false acceptance/rejection of a bad/good sample and, if accepted, may yield a biased relative potency estimate. To avoid these issues, the USP<1032> recommends the screening of bioassay data for outliers prior to performing a relative potency analysis. In a recently published work, the effects of one or more outliers, outlier size, and outlier type on similarity testing and estimation of relative potency were thoroughly examined, confirming the USP<1032> outlier guidance. As a follow-up, several outlier detection methods, including those proposed by the USP<1010>, are evaluated and compared in this work through computer simulation. Two novel outlier detection methods are also proposed. The effects of outlier removal on similarity testing and estimation of relative potency were evaluated, resulting in recommendations for best practice.
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http://dx.doi.org/10.1002/pst.1984DOI Listing
May 2020

Population Pharmacokinetics of an Anti-PD-L1 Antibody, Durvalumab in Patients with Hematologic Malignancies.

Clin Pharmacokinet 2020 02;59(2):217-227

Celgene Corporation, Summit, NJ, USA.

Background And Objectives: Durvalumab, a human monoclonal antibody targeting programmed cell death ligand 1, has been approved for urothelial carcinoma and stage III non-small cell lung cancer by the US Food and Drug Administration and is being evaluated in various malignancies. The objective of this study was to develop a population-pharmacokinetic model of durvalumab in patients with various hematologic malignancies and to investigate the effects of demographic and disease factors on the pharmacokinetics in this population.

Methods: A total of 1812 concentrations from 267 patients with myelodysplastic syndromes, acute myeloid leukemia, multiple myeloma, non-Hodgkin lymphoma, or Hodgkin lymphoma were included in the analysis.

Results: The pharmacokinetics of durvalumab was adequately described by a two-compartment model with first-order elimination. A decrease in durvalumab clearance over time was mainly explained by incorporation of time-dependent changes in albumin (in all patients) and immunoglobulin G (in patients with multiple myeloma) into the model. For multiple myeloma, patients with immunoglobulin G ≥ 20 g/L showed a 30% lower area under the concentration-time curve at cycle 1 compared with patients with immunoglobulin G < 20 g/L. The impact of any baseline covariates on durvalumab pharmacokinetics did not appear to be clinically relevant. The pharmacokinetics of durvalumab in hematologic malignancies was generally consistent with previously reported pharmacokinetics in solid tumors.

Conclusions: These results support the same dosing regimen (1500 mg every 4 weeks) for both solid tumors and hematologic malignancies from the perspective of adequate exposure. Additionally, total immunoglobulin G level could be a critical covariate for the pharmacokinetics of monoclonal antibodies in patients with multiple myeloma.
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http://dx.doi.org/10.1007/s40262-019-00804-xDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7007418PMC
February 2020

Detecting bliss synergy in in vivo combination studies with a tumor kinetic model.

Pharm Stat 2019 11 29;18(6):688-699. Epub 2019 May 29.

Statistical Sciences, AstraZeneca PLC, Cambridge, UK.

Linear models are generally reliable methods for analyzing tumor growth in vivo, with drug effectiveness being represented by the steepness of the regression slope. With immunotherapy, however, not all tumor growth follows a linear pattern, even after log transformation. Tumor kinetics models are mechanistic models that describe tumor proliferation and tumor killing macroscopically, through a set of differential equations. In drug combination studies, although an additional drug-drug interaction term can be added to such models, however, the drug interactions suggested by tumor kinetics models cannot be translated directly into synergistic effects. We have developed a novel statistical approach that simultaneously models tumor growth in control, monotherapy, and combination therapy groups. This approach makes it possible to test for synergistic effects directly and to compare such effects among different studies.
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http://dx.doi.org/10.1002/pst.1952DOI Listing
November 2019

A fast and reliable test for parallelism in bioassay.

J Biopharm Stat 2019 4;29(6):1011-1023. Epub 2019 Feb 4.

Department of Statistical Sciences, MedImmune LLC, Gaithersburg, Maryland, USA.

Parallelism in bioassay is a synonym of similarity between two concentration-response curves. Before the determination of relative potency in bioassays, it is necessary to test for and claim parallelism between the pair of concentration-response curves of reference standard and test sample. Methods for parallelism testing include -value-based significance tests and interval-based equivalence tests. Most of the latter approaches make statistical inference about the equivalence of parameters of the concentration-response curve models. An apparent drawback of such methods is that equivalence in model parameters does not guarantee similarity between the reference and test sample. In contrast, a Bayesian method was recently proposed that directly tests the parallelism hypothesis that the concentration-response curve of the test sample is a horizontal shift of that of the reference. In other words, the testing sample is a dilution or concentration of the reference standard. The Bayesian approach is shown to protect against type I error and provides sufficient statistical power for parallelism testing. In practice, however, it is challenging to implement the method as it requires both specialized Bayesian software and a relatively long run time. In this paper, we propose a frequentist version of the test with split-second run time. The empirical properties of the frequentist parallelism test method are evaluated and compared with the original Bayesian method. It is demonstrated that the frequentist method is both fast and reliable for parallelism testing for a variety of concentration-response models.
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http://dx.doi.org/10.1080/10543406.2019.1572615DOI Listing
October 2020

Effect of a statistical outlier in potency bioassays.

Pharm Stat 2018 11 15;17(6):701-709. Epub 2018 Aug 15.

Statistical Science, MedImmune LLC, Gaithersburg, MD, USA.

The USP<1032> guidelines recommend the screening of bioassay data for outliers prior to performing a relative potency (RP) analysis. The guidelines, however, do not offer advice on the size or type of outlier that should be removed prior to model fitting and calculation of RP. Computer simulation was used to investigate the consequences of ignoring the USP<1032> guidance to remove outliers. For biotherapeutics and vaccines, outliers in potency data may result in the false acceptance/rejection of a bad/good lot of drug product. Biological activity, measured through a potency bioassay, is considered a critical quality attribute in manufacturing. If the concentration-response potency curve of a test sample is deemed to be similar in shape to that of the reference standard, the curves are said to exhibit constant RP, an essential criterion for the interpretation of a RP. One or more outliers in the concentration-response data, however, may result in a failure to declare similarity or may yield a biased RP estimate. Concentration-response curves for test and reference were computer generated with constant RP from four-parameter logistic curves. Single outlier, multiple outlier, and whole-curve outlier scenarios were explored for their effects on the similarity testing and on the RP estimation. Though the simulations point to situations for which outlier removal is unnecessary, the results generally support the USP<1032> recommendation and illustrate the impact on the RP calculation when application of outlier removal procedures are discounted.
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http://dx.doi.org/10.1002/pst.1893DOI Listing
November 2018

A Novel Method for Qualification of a Potency Assay through Partial Computer Simulation.

PDA J Pharm Sci Technol 2018 May-Jun;72(3):249-263. Epub 2018 Feb 14.

MedImmune LLC, Gaithersburg, MD.

For biotherapeutics and vaccines, potency is measured in a bioassay that compares the concentration-response curves of a new batch to that of a reference standard. Acceptable accuracy and precision of potency measurement is critical to the manufacturing of these products. These characteristics of a bioassay are typically assessed in a procedure that is carried out with samples spanning the acceptable range for the product. During early development, however, a full validation study such as that which is carried out in late development can be costly as it relates to the likelihood of eventual program success. For these reasons, the laboratory may look for alternative ways to ensure the validity of the bioassay across a range that will support product development. One such alternative combines information from a reduced procedure using only reference standard and 100% relative potency concentration-response data sets, together with computer simulation, to estimate missing relative potency values across the desired range. Fits to the reduced dataset provide estimates of bioassay model parameters such as those for an S-shaped potency assay that follows a four-parameter logistic relationship, along with estimates of their variance-covariance structure and independent experimental unit (e.g., well-to-well or animal-to-animal) errors. Using Bayesian Markov Chain Monte Carlo modeling, the predictive distribution of the concentration-response data for the desired levels of relative potency is generated. Results from use of the reduced procedure are compared to results calculated from a full dataset in Monte Carlo simulation and in a motivating example. For biotherapeutics and vaccines, potency is measured in a bioassay that compares the concentration-response curves of a new batch to that of a reference standard. Acceptable accuracy and precision of potency measurement is critical to the manufacturing of these products. These characteristics of a bioassay are typically assessed in a procedure that is carried out with samples spanning the acceptable range for the product. During early development, however, a full validation study such as that which is carried out in late development can be costly as it relates to the likelihood of eventual program success. For these reasons, the laboratory may look for alternative ways to ensure the validity of the bioassay across a range that will support product development. One such alternative combines information from a reduced procedure using only reference standard and 100% relative potency concentration-response data sets, together with computer simulation, to estimate missing relative potency values across the desired range. Bayesian Markov Chain Monte Carlo modeling is used to generate the distributions of the missing potency levels. Results from use of the reduced procedure are compared to results calculated from a full dataset in Monte Carlo simulation and in a motivating example.
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http://dx.doi.org/10.5731/pdajpst.2017.008094DOI Listing
March 2019

Content uniformity testing for stratified samples via parametric tolerance interval testing.

J Biopharm Stat 2018 8;28(3):463-474. Epub 2017 May 8.

b Manager of Statistics, GMS Technical, GlaxoSmithKline , Zebulon , NC , USA.

Historically in the biopharmaceutical setting, USP<905> has been used to establish that a batch of drug product has acceptable content uniformity. More recently, alternative approaches such as the two one-sided parametric tolerance interval test (PTI-TOST) have been proposed to establish content uniformity. Traditionally, the PTI-TOST is implemented as a sequential, two-tiered test, under the generally accepted assumption that the data are independently and identically distributed. Since the material is sequenced through the manufacturing process over a period of time, there are conceptually arguable locations within each batch, for instance: beginning, middle, and end. In such a situation, a practitioner may wish to evaluate potential effects of these batch locations, for example, during process validation. If location (stratified) differences exist within the batch and if multiple samples are taken from each location, significant within-location correlations may be induced in the data. In such a case, the traditional PTI-TOST underestimates the total variability, thereby improperly boosting the power of the test method. When there is reason to believe that location variances exist, the batch may be evaluated using stratified sampling, and the location effect may be modeled. In this paper, a two-tiered PTI-TOST that accounts for both between-location and within-location variance components is introduced. Operating characteristic curves and practical advice are given to aid the practitioner's uptake of the proposed method.
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http://dx.doi.org/10.1080/10543406.2017.1321005DOI Listing
July 2019

Setting Alert and Action Limits in the Presence of Significant Amount of Censoring in Data.

PDA J Pharm Sci Technol 2017 1/2;71(1):20-32. Epub 2016 Aug 11.

MedImmune LLC, Gaithersburg, MD.

In manufacturing settings, control limits are often set using a three-sigma rule (i.e., three estimated standard deviations above and below the estimated mean). More sophisticated statistical methods might include the use of confidence, prediction, or tolerance intervals. However, in environmental monitoring of microbial excursions in aseptic manufacturing operations, most of the assayed measurements fall below the limit of quantitation. In such circumstances, it is inappropriate to directly calculate control limits with a mean plus two or three standard deviations to represent the center and spread of the data. The system under consideration assumes that microbial assayed values stem from a log-normal distribution with two sources of variability to account for testing occasions and measurements made within a testing occasion. Bayesian statistical methods and a Tobit likelihood are used to model the observed and left-censored data in order to predict the distribution of new data. Control limits are generated from quantiles of the posterior predictive distribution.

Lay Abstract: In manufacturing settings, control limits are used to ensure either the manufacturing process or environment is in a state of control. These limits can be set using a three-sigma rule (i.e., three estimated standard deviations above and below the estimated mean) or via more sophisticated statistical methods. In this paper, we consider setting the control limits for a clean room setting in which microbial excursions are rare. Under such circumstance, the measurements of microbial counts are often below the limit of quantification. We develop methods based on Bayesian analysis and a Tobit regression to more accurately estimate control limits.
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http://dx.doi.org/10.5731/pdajpst.2016.006684DOI Listing
June 2018

On Statistical Approaches for Demonstrating Analytical Similarity in the Presence of Correlation.

PDA J Pharm Sci Technol 2016 11/12;70(6):547-559. Epub 2016 Jun 20.

Elion Labs.

Analytical similarity is the foundation for demonstration of biosimilarity between a proposed product and a reference product. For this assessment, currently the U.S. Food and Drug Administration (FDA) recommends a tiered system in which quality attributes are categorized into three tiers commensurate with their risk and approaches of varying statistical rigor are subsequently used for the three-tier quality attributes. Key to the analyses of Tiers 1 and 2 quality attributes is the establishment of equivalence acceptance criterion and quality range. For particular licensure applications, the FDA has provided advice on statistical methods for demonstration of analytical similarity. For example, for Tier 1 assessment, an equivalence test can be used based on an equivalence margin of 1.5 σ, where σ is the reference product variability estimated by the sample standard deviation S from a sample of reference lots. The quality range for demonstrating Tier 2 analytical similarity is of the form X̄ ± K × σ where the constant K is appropriately justified. To demonstrate Tier 2 analytical similarity, a large percentage (e.g., 90%) of test product must fall in the quality range. In this paper, through both theoretical derivations and simulations, we show that when the reference drug product lots are correlated, the sample standard deviation S underestimates the true reference product variability σ As a result, substituting S for σ in the Tier 1 equivalence acceptance criterion and the Tier 2 quality range inappropriately reduces the statistical power and the ability to declare analytical similarity. Also explored is the impact of correlation among drug product lots on Type I error rate and power. Three methods based on generalized pivotal quantities are introduced, and their performance is compared against a two-one-sided tests (TOST) approach. Finally, strategies to mitigate risk of correlation among the reference products lots are discussed.

Lay Abstract: A biosimilar is a generic version of the original biological drug product. A key component of a biosimilar development is the demonstration of analytical similarity between the biosimilar and the reference product. Such demonstration relies on application of statistical methods to establish a similarity margin and appropriate test for equivalence between the two products. This paper discusses statistical issues with demonstration of analytical similarity and provides alternate approaches to potentially mitigate these problems.
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http://dx.doi.org/10.5731/pdajpst.2016.006551DOI Listing
May 2018

A new PK equivalence test for a bridging study.

J Biopharm Stat 2016 16;26(5):992-1002. Epub 2016 Feb 16.

a Statistical Sciences, MedImmune LLC , Gaithersburg , Maryland , USA.

In a bridging study, the plasma drug concentration-time curve is generally used to assess bioequivalence between the two formulations. Selected pharmacokinetic (PK) parameters including the area under the concentration-time curve, the maximum plasma concentration or peak exposure (Cmax), and drug half-life (T1/2) are compared to ensure comparable bioavailability of the two formulations. Comparability in these PK parameters, however, does not necessarily imply equivalence of the entire concentration-time profile. In this article, we propose an alternative metric of equivalence based on the maximum difference between PK profiles of the two formulations. A test procedure based on Bayesian analysis and accounting for uncertainties in model parameters is developed. Through both theoretical derivation and empirical simulation, it is shown that the new method provides better control over consumer's risk.
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http://dx.doi.org/10.1080/10543406.2016.1148712DOI Listing
November 2017

Pharmacokinetics/Pharmacodynamics of Peptide Deformylase Inhibitor GSK1322322 against Streptococcus pneumoniae, Haemophilus influenzae, and Staphylococcus aureus in Rodent Models of Infection.

Antimicrob Agents Chemother 2016 01 19;60(1):180-9. Epub 2015 Oct 19.

Antibacterial Discovery Performance Unit, Infectious Disease Therapeutic Area, GlaxoSmithKline, Collegeville, Pennsylvania, USA.

GSK1322322 is a novel inhibitor of peptide deformylase (PDF) with good in vitro activity against bacteria associated with community-acquired pneumonia and skin infections. We have characterized the in vivo pharmacodynamics (PD) of GSK1322322 in immunocompetent animal models of infection with Streptococcus pneumoniae and Haemophilus influenzae (mouse lung model) and with Staphylococcus aureus (rat abscess model) and determined the pharmacokinetic (PK)/PD index that best correlates with efficacy and its magnitude. Oral PK studies with both models showed slightly higher-than-dose-proportional exposure, with 3-fold increases in area under the concentration-time curve (AUC) with doubling doses. GSK1322322 exhibited dose-dependent in vivo efficacy against multiple isolates of S. pneumoniae, H. influenzae, and S. aureus. Dose fractionation studies with two S. pneumoniae and S. aureus isolates showed that therapeutic outcome correlated best with the free AUC/MIC (fAUC/MIC) index in S. pneumoniae (R(2), 0.83), whereas fAUC/MIC and free maximum drug concentration (fCmax)/MIC were the best efficacy predictors for S. aureus (R(2), 0.9 and 0.91, respectively). Median daily fAUC/MIC values required for stasis and for a 1-log10 reduction in bacterial burden were 8.1 and 14.4 for 11 S. pneumoniae isolates (R(2), 0.62) and 7.2 and 13.0 for five H. influenzae isolates (R(2), 0.93). The data showed that for eight S. aureus isolates, fAUC correlated better with efficacy than fAUC/MIC (R(2), 0.91 and 0.76, respectively), as efficacious AUCs were similar for all isolates, independent of their GSK1322322 MIC (range, 0.5 to 4 μg/ml). Median fAUCs of 2.1 and 6.3 μg · h/ml were associated with stasis and 1-log10 reductions, respectively, for S. aureus.
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http://dx.doi.org/10.1128/AAC.01842-15DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4704172PMC
January 2016

Nilotinib versus imatinib for GIST - Authors' reply.

Lancet Oncol 2015 Jul;16(7):e311-2

Novartis Pharmaceuticals Corporation, East Hanover, NJ 07936, USA.

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http://dx.doi.org/10.1016/S1470-2045(15)00016-9DOI Listing
July 2015

Nilotinib in patients with systemic mastocytosis: analysis of the phase 2, open-label, single-arm nilotinib registration study.

J Cancer Res Clin Oncol 2015 Nov 23;141(11):2047-60. Epub 2015 May 23.

The University of Texas, MD Anderson Cancer Center, Houston, TX, USA.

Purpose: Activating KIT mutations are part of the pathogenesis of systemic mastocytosis (SM). Nilotinib is a tyrosine kinase inhibitor that potently inhibits activated forms of KIT. This phase 2, open-label, single-arm study (CAMN107A2101; www.clinicaltrials.gov NCT00109707) evaluated nilotinib in patients with SM.

Methods: Patients with SM [aggressive SM (ASM), indolent SM, or other] received nilotinib 400 mg twice daily. C-findings were collected retrospectively to assess response using criteria proposed after trial initiation. Response was evaluated using improvements in laboratory findings (for all patients) and ASM response criteria (for the ASM subgroup).

Results: In 61 patients enrolled, the median nilotinib exposure was 232 days (range 3-1274 days) with a median follow-up of 34.7 months. In patients with ASM (n = 37), the overall response rate was 21.6 %. In the eight responders, all of whom had a KIT D816V mutation at any time, mast cell infiltration and tryptase level decreased by 70 % and 29.8 %, respectively; absolute neutrophil count increased by 94.7 %. Laboratory parameters also improved in the non-ASM subgroups. Overall survival at 24 months was 81.2 % (95 % CI 70.6-91.8 %) with median survival not yet reached. New or worsening grade 3/4 hematologic adverse events (AEs) included thrombocytopenia (10.3 %), anemia (10.0 %), and neutropenia (6.9 %). The most common grade 3/4 nonhematologic drug-related AEs were diarrhea (6.6 %) and headache (4.9 %). Eleven patients (9 with ASM, 2 with MCL) died, 10 due to progressive disease; 7 deaths occurred ≥28 days after treatment discontinuation.

Conclusions: Nilotinib 400 mg twice daily was effective in some patients with SM, including patients with mutated KIT D816V.
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http://dx.doi.org/10.1007/s00432-015-1988-0DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4768228PMC
November 2015

Testing drug additivity based on monotherapies.

Pharm Stat 2015 Jul-Aug;14(4):332-40. Epub 2015 May 12.

MedImmune LLC, One MedImmune Way, Gaithersburg, MD, USA.

Under the Loewe additivity, constant relative potency between two drugs is a sufficient condition for the two drugs to be additive. Implicit in this condition is that one drug acts like a dilution of the other. Geometrically, it means that the dose-response curve of one drug is a copy of another that is shifted horizontally by a constant over the log-dose axis. Such phenomenon is often referred to as parallelism. Thus, testing drug additivity is equivalent to the demonstration of parallelism between two dose-response curves. Current methods used for testing parallelism are usually based on significance tests for differences between parameters in the dose-response curves of the monotherapies. A p-value of less than 0.05 is indicative of non-parallelism. The p-value-based methods, however, may be fundamentally flawed because an increase in either sample size or precision of the assay used to measure drug effect may result in more frequent rejection of parallel lines for a trivial difference. Moreover, similarity (difference) between model parameters does not necessarily translate into the similarity (difference) between the two response curves. As a result, a test may conclude that the model parameters are similar (different), yet there is little assurance on the similarity between the two dose-response curves. In this paper, we introduce a Bayesian approach to directly test the hypothesis that the two drugs have a constant relative potency. An important utility of our proposed method is in aiding go/no-go decisions concerning two drug combination studies. It is illustrated with both a simulated example and a real-life example.
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http://dx.doi.org/10.1002/pst.1689DOI Listing
May 2016

Nilotinib versus imatinib as first-line therapy for patients with unresectable or metastatic gastrointestinal stromal tumours (ENESTg1): a randomised phase 3 trial.

Lancet Oncol 2015 May 14;16(5):550-60. Epub 2015 Apr 14.

Center for Sarcoma and Bone Oncology, Dana-Farber Cancer Institute, and Ludwig Center at Harvard, Harvard Medical School, Boston, MA, USA.

Background: Nilotinib inhibits the tyrosine kinase activity of ABL1/BCR-ABL1 and KIT, platelet-derived growth factor receptors (PDGFRs), and the discoidin domain receptor. Gain-of-function mutations in KIT or PDGFRα are key drivers in most gastrointestinal stromal tumours (GISTs). This trial was designed to test the efficacy and safety of nilotinib versus imatinib as first-line therapy for patients with advanced GISTs.

Methods: In this randomised, open-label, multicentre, phase 3 trial (ENESTg1), participants from academic centres were aged 18 years or older and had previously untreated, histologically confirmed, metastatic or unresectable GISTs. Patients were stratified by previous adjuvant therapy and randomly assigned (1:1) via a randomisation list to receive oral imatinib 400 mg once daily or oral nilotinib 400 mg twice daily. The primary endpoint was centrally reviewed progression-free survival. Efficacy endpoints were assessed by intention-to-treat. This trial is registered with ClinicalTrials.gov, number NCT00785785.

Findings: Because the futility boundary was crossed at a preplanned interim analysis, trial accrual terminated in April, 2011. Between March 16, 2009, and April 21, 2011, 647 patients were enrolled; of whom 324 were allocated nilotinib and 320 were allocated imatinib. At final analysis of the core study (data cutoff, October, 2012), 2-year progression-free survival was higher in the imatinib group (59·2% [95% CI 50·9-66·5]) than in the nilotinib group (51·6% [43·0-59·5]; hazard ratio 1·47 [95% CI 1·10-1·95]). In the imatinib group, the most common grade 3-4 adverse events were hypophosphataemia (19 [6%]), anaemia (17 [5%]), abdominal pain (13; 4%), and elevated lipase level (15; 5%), and in the nilotinib group were anaemia (18; 6%), elevated lipase level (15; 5%), elevated alanine aminotransferase concentration (12; 4%), and abdominal pain (11; 3%). The most common serious adverse event in both groups was abdominal pain (11 [4%] in the imatinib group, 14 [4%] in the nilotinib group).

Interpretation: Nilotinib cannot be recommended for broad use to treat first-line GIST. However, future studies might identify patient subsets for whom first-line nilotinib could be of clinical benefit.

Funding: Novartis Pharmaceuticals.
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http://dx.doi.org/10.1016/S1470-2045(15)70105-1DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4521211PMC
May 2015

Inhibitory effect of single and repeated doses of nilotinib on the pharmacokinetics of CYP3A substrate midazolam.

J Clin Pharmacol 2015 Apr 27;55(4):401-8. Epub 2015 Feb 27.

Oncology Global Development, Novartis Pharmaceuticals Corporation, One Health Plaza, East Hanover, NJ, USA.

Effects of single and repeated doses of nilotinib on the pharmacokinetics of midazolam, a cytochrome P450 3A (CYP3A) substrate, were assessed in 2 separate studies. In the single-dose nilotinib study, 18 healthy subjects were randomized to 6 treatment sequences to receive single dose of nilotinib 600 mg, midazolam 4 mg, and coadministration of both in a crossover manner. In the repeated-dose nilotinib study, 19 chronic myeloid leukemia patients took a single dose of midazolam 2 mg on days 1 and 13, and nilotinib 400 mg twice daily from days 2-13. In the single-dose study, the geometric mean ratio of the area under the plasma concentration time curve extrapolated to infinity (AUC(inf)) of midazolam plus nilotinib vs. midazolam was 1.3 (90%CI, 1.2-1.5) and the maximum observed serum concentration (C(max)) was 1.2 (90%CI, 1.0-1.4). In the repeated-dose study, the values for AUC(inf) and C(max) were 2.6 (90%CI, 2.1-3.3) and 2.0 (90%CI, 1.7-2.4), respectively. These results indicate that single-dose and repeated-dose administration of nilotinib results in weak and moderate inhibition of CYP3A, respectively. Therefore, appropriate monitoring and dose adjustment may be needed for drugs that are mainly metabolized by CYP3A, and have narrow therapeutic index, when coadministered with nilotinib.
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http://dx.doi.org/10.1002/jcph.434DOI Listing
April 2015

The acute extracellular flux (XF) assay to assess compound effects on mitochondrial function.

J Biomol Screen 2015 Mar 7;20(3):422-9. Epub 2014 Nov 7.

GlaxoSmithKline, Research Triangle Park, NC, USA.

Numerous investigations have linked mitochondrial dysfunction to adverse health outcomes and drug-induced toxicity. The pharmaceutical industry is challenged with identifying mitochondrial liabilities earlier in drug development and thereby reducing late-stage attrition. Consequently, there is a demand for reliable, higher-throughput screening methods for assessing the impact of drug candidates on mitochondrial function. The extracellular flux (XF) assay described here is a plate-based method in which galactose-conditioned HepG2 cells were acutely exposed to test compounds, then real-time changes in the oxygen consumption rate and extracellular acidification rate were simultaneously measured using a Seahorse Bioscience XF-96 analyzer. The acute XF assay was validated using marketed drugs known to modulate mitochondrial function, and data analysis was automated using a spline curve fitting model developed at GlaxoSmithKline. We demonstrate that the acute XF assay is a robust, sensitive screening platform for evaluating drug-induced effects on mitochondrial activity in whole cells.
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http://dx.doi.org/10.1177/1087057114557621DOI Listing
March 2015

Dissolution curve comparisons through the F(2) parameter, a Bayesian extension of the f(2) statistic.

J Biopharm Stat 2015 ;25(2):351-71

a GlaxoSmithKline Pharmaceuticals , Research Triangle Park , North Carolina , USA.

Dissolution (or in vitro release) studies constitute an important aspect of pharmaceutical drug development. One important use of such studies is for justifying a biowaiver for post-approval changes which requires establishing equivalence between the new and old product. We propose a statistically rigorous modeling approach for this purpose based on the estimation of what we refer to as the F2 parameter, an extension of the commonly used f2 statistic. A Bayesian test procedure is proposed in relation to a set of composite hypotheses that capture the similarity requirement on the absolute mean differences between test and reference dissolution profiles. Several examples are provided to illustrate the application. Results of our simulation study comparing the performance of f2 and the proposed method show that our Bayesian approach is comparable to or in many cases superior to the f2 statistic as a decision rule. Further useful extensions of the method, such as the use of continuous-time dissolution modeling, are considered.
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http://dx.doi.org/10.1080/10543406.2014.971175DOI Listing
July 2015

Testing assay linearity over a pre-specified range.

J Biopharm Stat 2015 ;25(2):339-50

a MedImmune, LLC , Gaithersburg , Maryland , USA.

Validation of linearity is a regulatory requirement. Although many methods are proposed, they suffer from several deficiencies including difficulties of setting fit-for-purpose acceptable limits, dependency on concentration levels used in linearity experiment, and challenges in implementation for statistically lay users. In this article, a statistical procedure for testing linearity is proposed. The method uses a two one-sided test (TOST) of equivalence to evaluate the bias that can result from approximating a higher-order polynomial response with a linear function. By using orthogonal polynomials and generalized pivotal quantity analysis, the method provides a closed-form solution, thus making linearity testing easy to implement.
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http://dx.doi.org/10.1080/10543406.2014.972513DOI Listing
July 2015

Application of physiologically based absorption modeling to formulation development of a low solubility, low permeability weak base: mechanistic investigation of food effect.

AAPS PharmSciTech 2014 Apr 17;15(2):400-6. Epub 2014 Jan 17.

Novartis Pharmaceuticals Corporation, Florham Park, New Jersey, USA,

Physiologically based pharmacokinetic (PBPK) modeling has been broadly used to facilitate drug development, hereby we developed a PBPK model to systematically investigate the underlying mechanisms of the observed positive food effect of compound X (cpd X) and to strategically explore the feasible approaches to mitigate the food effect. Cpd X is a weak base with pH-dependent solubility; the compound displays significant and dose-dependent food effect in humans, leading to a nonadherence of drug administration. A GastroPlus Opt logD Model was selected for pharmacokinetic simulation under both fasted and fed conditions, where the biopharmaceutic parameters (e.g., solubility and permeability) for cpd X were determined in vitro, and human pharmacokinetic disposition properties were predicted from preclinical data and then optimized with clinical pharmacokinetic data. A parameter sensitivity analysis was performed to evaluate the effect of particle size on the cpd X absorption. A PBPK model was successfully developed for cpd X; its pharmacokinetic parameters (e.g., C max, AUCinf, and t max) predicted at different oral doses were within ±25% of the observed mean values. The in vivo solubility (in duodenum) and mean precipitation time under fed conditions were estimated to be 7.4- and 3.4-fold higher than those under fasted conditions, respectively. The PBPK modeling analysis provided a reasonable explanation for the underlying mechanism for the observed positive food effect of the cpd X in humans. Oral absorption of the cpd X can be increased by reducing the particle size (<100 nm) of an active pharmaceutical ingredient under fasted conditions and therefore, reduce the cpd X food effect correspondingly.
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http://dx.doi.org/10.1208/s12249-014-0075-1DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3969476PMC
April 2014

Effect of the tyrosine kinase inhibitor nilotinib in patients with hypereosinophilic syndrome/chronic eosinophilic leukemia: analysis of the phase 2, open-label, single-arm A2101 study.

J Cancer Res Clin Oncol 2013 Dec 22;139(12):1985-93. Epub 2013 Sep 22.

Abteilung Hämatologie/Onkologie, Universitätsklinikum Jena, Jena, Germany,

Purpose: Hypereosinophilic syndrome (HES) and chronic eosinophilic leukemia (CEL) are characterized by sustained overproduction of eosinophils and organ dysfunction. CEL involves the presence of clonal genetic markers, such as a fusion of FIP1-like 1 protein and platelet-derived growth factor receptor α (FIP1L1-PDGFRα, or F/P) or PDGFRα-activating mutations.

Methods: Sixteen patients with HES/CEL were enrolled in the phase 2 nilotinib registration trial (NCT00109707) and treated with nilotinib 400 mg twice daily. The median duration of treatment was 95 days (range 3-1,079).

Results: Twelve patients had HES: 1 achieved a complete hematologic response (CHR), 3 achieved stable disease, 3 had progressive disease, and 5 were not evaluable for response. Four patients had CEL: 2 with the F/P fusion and 2 with PDGFRα-activating mutations. Both patients with an F/P fusion achieved a CHR; 1 also achieved a complete molecular response (CMR). Of the 2 patients with PDGFRα-activating mutations, 1 had stable disease and the other achieved CMR. At 24 months, overall survival in the HES group was 75.0 % (95 % CI 50.5-100.0) and no patients in the CEL group died. Median survival was not yet reached after a median follow-up of 32 months. The most common grade 3/4 hematologic laboratory abnormalities were lymphocytopenia (31.3 %) and neutropenia (25.0 %). The most common drug-related nonhematologic grade 3/4 adverse event was pruritus, which occurred in 2 patients (12.5 %).

Conclusions: Nilotinib 400 mg twice daily was effective in some patients with HES/CEL regardless of F/P mutation status, and the safety profile was consistent with other nilotinib studies.
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http://dx.doi.org/10.1007/s00432-013-1529-7DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5556980PMC
December 2013

A simple test for synergy for a small number of combinations.

Authors:
Steven J Novick

Stat Med 2013 Dec 1;32(29):5145-55. Epub 2013 Aug 1.

Department of Statistics, GlaxoSmithKline, NC, U.S.A.

A method for detecting deviations from the Loewe additive drug combination reference model for in vitro drug combination experimentation is described. It is often difficult to fit a response surface model to drug combination data, especially in situations where the experimental design contains a sparse set of combinations. The literature does contain good response surface modeling approaches, but they tend to be complex and can be difficult to execute. It is especially difficult to check model quality when fitting to more than two combined agents. A simple method based on sound statistical principles is proposed that examines the mean response deviation of each combination from the predicted response under Loewe additivity. The method can readily handle any number of combined agents, does not require sophisticated modeling, and can even be programmed into Microsoft Excel without the use of macros. Several potential extensions to the method are discussed in detail. Computer-generated simulations demonstrate the statistical capabilities of the approach, and a real-data example is given to illustrate the method.
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http://dx.doi.org/10.1002/sim.5905DOI Listing
December 2013

The effect of initial purity on the stability of solutions in storage.

J Biomol Screen 2014 Feb 19;19(2):308-16. Epub 2013 Jun 19.

1GlaxoSmithKline, Research Triangle Park, NC, USA.

Many modern compound-screening technologies are highly miniaturized, resulting in longer-lasting solution stocks in compound management laboratories. As the ages of some stocks stretch into years, it becomes increasingly important to ensure that the DMSO solutions remain of high quality. It can be a burden to check the quality of a large library of compound solutions continuously, and so a study was devised to link the effects of initial compound purity and physicochemical properties of the compounds with the current purity of DMSO solutions. Approximately 5000 compounds with initial purity of at least 80% were examined. Storage conditions were held or observed to be relatively constant and so were eliminated as potential predictors. This allowed the evaluation of the effects of other factors on the stability of solutions, such as initial purity, number of freeze-thaw cycles, age of the solution, and multiple calculated physicochemical parameters. Of all the factors investigated, initial purity was the only one that had a clear effect on stability. None of the other parameters investigated (physicochemical properties, number of freeze-thaw cycles, age of solutions) had a statistically significant effect on stability.
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http://dx.doi.org/10.1177/1087057113492201DOI Listing
February 2014

Development of a high-throughput electrophysiological assay for the human ether-à-go-go related potassium channel hERG.

J Pharmacol Toxicol Methods 2013 Jan-Feb;67(1):33-44. Epub 2012 Oct 26.

Biological Reagents and Assay Development, GlaxoSmithKline, 5 Moore Drive, Research Triangle Park, NC 27709, USA.

Introduction: Drug-induced prolongation of the QT interval via block of the hERG potassium channel is a major cause of attrition in drug development. The advent of automated electrophysiology systems has enabled the detection of hERG block earlier in drug discovery. In this study, we have evaluated the suitability of a second generation automated patch clamp instrument, the IonWorks Barracuda, for the characterization of hERG biophysics and pharmacology.

Methods: All experiments were conducted with cells stably expressing hERG. Recordings were made in perforated patch mode either on a conventional patch clamp setup or on the IonWorks Barracuda. On the latter, all recordings were population recordings in 384-well patch plates.

Results: HERG channels activated with a V(1/2)=-3.2±1.6mV (n=178) on the IonWorks Barracuda versus -11.2±6.1mV (n=9) by manual patch clamp. On the IonWorks Barracuda, seal resistances and currents were stable (<30% change) with up to six cumulative drug additions and 1-min incubations per addition. Over 27 experiments, an average of 338 concentration-response curves were obtained per experiment (96% of the 352 test wells on each plate). HERG pharmacology was examined with a set of 353 compounds that included well-characterized hERG blockers. Astemizole, terfenadine and quinidine inhibited hERG currents with IC(50) values of 159nM, 224nM and 2μM, respectively (n=51, 10 and 18). This set of compounds was also tested on the PatchXpress automated electrophysiology system. We determined through statistical methods that the two automated systems provided equivalent results.

Discussion: Evaluating drug effects on hERG channels is best performed by electrophysiological methods. HERG activation and pharmacology on the IonWorks Barracuda automated electrophysiology platform were in good agreement with published electrophysiology results. Therefore, the IonWorks Barracuda provides an efficient way to study hERG biophysics and pharmacology.
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http://dx.doi.org/10.1016/j.vascn.2012.10.002DOI Listing
October 2013

Effects of famotidine or an antacid preparation on the pharmacokinetics of nilotinib in healthy volunteers.

Cancer Chemother Pharmacol 2013 Jan 16;71(1):219-26. Epub 2012 Oct 16.

Oncology Clinical Pharmacology, Novartis Pharmaceuticals Corporation, Florham Park, NJ 07932, USA.

Purpose: This study evaluated the effects of either famotidine or antacid on the pharmacokinetics of nilotinib in healthy subjects, with the specific focus to explore different dosing separation schemes leading to a minimized drug-drug interaction.

Methods: Fifty-two subjects were randomized to receive the following treatments in a crossover manner: (A) single oral nilotinib 400 mg alone; (B) famotidine 20 mg twice a day for 3 days, followed by a single administration of nilotinib 400 mg and famotidine 20 mg on Day 4, where famotidine was given 2 h after nilotinib; (C) single oral nilotinib 400 mg and antacid suspension 20 mL, where antacid was given 2 h before nilotinib; (D) single oral nilotinib 400 mg and antacid suspension 20 mL, where antacid was given 2 h after nilotinib.

Results: Comparing Treatment B to Treatment A, the geometric mean ratios of nilotinib C(max), AUC(0-tlast), and AUC(0-inf) were 0.966, 0.984, and 0.911, respectively (90% confidence intervals (CIs), 0.875-1.066, 0.905-1.069, and 0.798-1.039, respectively). Nilotinib pharmacokinetic parameters following Treatment C or Treatment D were similar to those after Treatment A; the corresponding 90% CIs of the geometric mean ratios of C(max), AUC(0-tlast), and AUC(0-inf) all fell within the bioequivalence range of 0.8-1.25.

Conclusions: Neither famotidine nor antacid significantly affected nilotinib pharmacokinetics. When concurrent use of an H2 blocker or an antacid is necessary, the H2 blocker may be administered 10 h before and 2 h after nilotinib dose, or the antacid may be administered 2 h before or 2 h after nilotinib dose.
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http://dx.doi.org/10.1007/s00280-012-1999-3DOI Listing
January 2013