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| 2. |
- Brekkan, Ari, et al.
(författare)
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Characterization of anti-drug antibody dynamics using a bivariate mixed hidden-markov model by nonlinear-mixed effects approach
- 2024
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Ingår i: Journal of Pharmacokinetics and Pharmacodynamics. - : Springer. - 1567-567X .- 1573-8744. ; 51:1, s. 65-75
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Tidskriftsartikel (refereegranskat)abstract
- Biological therapies may act as immunogenic triggers leading to the formation of anti-drug antibodies (ADAs). Population pharmacokinetic (PK) models can be used to characterize the relationship between ADA and drug disposition but often rely on the ADA bioassay results, which may not be sufficiently sensitive to inform on this characterization.In this work, a methodology that could help to further elucidate the underlying ADA production and impact on the drug disposition was explored. A mixed hidden-Markov model (MHMM) was developed to characterize the underlying (hidden) formation of ADA against the biologic, using certolizumab pegol (CZP), as a test drug. CZP is a PEGylated Fc free TNF-inhibitor used in the treatment of rheumatoid arthritis and other chronic inflammatory diseases.The bivariate MHMM used information from plasma drug concentrations and ADA measurements, from six clinical studies (n = 845), that were correlated through a bivariate Gaussian function to infer about two hidden states; production and no-production of ADA influencing PK. Estimation of inter-individual variability was not supported in this case. Parameters associated with the observed part of the model were reasonably well estimated while parameters associated with the hidden part were less precise. Individual state sequences obtained using a Viterbi algorithm suggested that the model was able to determine the start of ADA production for each individual, being a more assay-independent methodology than traditional population PK. The model serves as a basis for identification of covariates influencing the ADA formation, and thus has the potential to identify aspects that minimize its impact on PK and/or efficacy.
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| 3. |
- Kroeze, Leonie I., et al.
(författare)
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Evaluation of a Hybrid Capture-Based Pan-Cancer Panel for Analysis of Treatment Stratifying Oncogenic Aberrations and Processes
- 2020
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Ingår i: Journal of Molecular Diagnostics. - : Elsevier. - 1525-1578 .- 1943-7811. ; 22:6, s. 757-769
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Tidskriftsartikel (refereegranskat)abstract
- Stratification of patients for targeted and immune-based therapies requires extensive genomic profiling that enables sensitive detection of clinically relevant variants and interrogation of biomarkers, such as tumor mutational burden (TMB) and microsatellite instability (MSI). Detection of single and multiple nucleotide variants, copy number variants, MSI, and TMB was evaluated using a commercially available next-generation sequencing panel containing 523 cancer-related genes (1.94 megabases). Analysis of formalin-fixed, paraffin-embedded tissue sections and cytologic material from 45 tumor samples showed that all previously known MSI-positive samples (n = 7), amplifications (n = 9), and pathogenic variants (n = 59) could be detected. TMB and MSI scores showed high intralaboratory and interlaboratory reproducibility (eight samples tested in 11 laboratories). For reliable TMB analysis, 20 ng DNA was shown to be sufficient, even for relatively poor-quality samples. A minimum of 20% neoplastic cells was required to minimize variations in TMB values induced by chromosomal instability or tumor heterogeneity. Subsequent analysis of 58 consecutive lung cancer samples in a diagnostic setting was successful and revealed sufficient somatic mutations to generate mutational signatures in 14 cases. In conclusion, the 523-gene assay can be applied for evaluation of multiple DNA-based biomarkers relevant for treatment selection.
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| 4. |
- Lacroix, Brigitte, 1978-, et al.
(författare)
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A Pharmacodynamic Markov Mixed-Effects Model for Determining the Effect of Exposure to Certolizumab Pegol on the ACR20 Score in Patients With Rheumatoid Arthritis
- 2009
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Ingår i: Clinical Pharmacology and Therapeutics. - : Nature Publishing Group. - 0009-9236 .- 1532-6535. ; 86:4, s. 387-395
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Tidskriftsartikel (refereegranskat)abstract
- The American College of Rheumatology 20% preliminary definition of improvement of rheumatoid arthritis (ACR20) is widely used in clinical trials to assess response to treatment. The objective of this analysis was to develop an exposure-response model of ACR20 in subjects treated with certolizumab pegol, and to predict clinical outcome following various treatment schedules. At each visit, subjects were classified as being ACR20 responders, ACR20 non-responders, or having dropped out. A Markov mixed-effect model was developed to investigate the drug effect on the transitions between the 3 defined states. Increasing certolizumab pegol exposure predicted an increasing probability of becoming a responder and remaining a responder, as well as a reduced probability of dropping out of treatment. Simulations of the ACR20 response rate support dosing regimens of 400 mg at weeks 0, 2 and 4 followed by 200 mg every 2 weeks, or alternative maintenance regimen of 400 mg every 4 weeks.
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| 5. |
- Lacroix, Brigitte D., et al.
(författare)
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Evaluation of IPPSE, an alternative method for sequential population PKPD analysis
- 2012
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Ingår i: Journal of Pharmacokinetics and Pharmacodynamics. - : Springer Science and Business Media LLC. - 1567-567X .- 1573-8744. ; 39:2, s. 177-193
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Tidskriftsartikel (refereegranskat)abstract
- The aim of this study is to present and evaluate an alternative sequential method to perform population pharmacokinetic-pharmacodynamic (PKPD) analysis. Simultaneous PKPD analysis (SIM) is generally considered the reference method but may be computationally burdensome and time consuming. Evaluation of alternative approaches aims at speeding up the computation time and stabilizing the estimation of the models, while estimating the model parameters with good enough precision. The IPPSE method presented here uses the individual PK parameter estimates and their uncertainty (SE) to propagate the PK information to the PD estimation step, while the IPP method uses the individual PK parameters only and the PPP&D method utilizes the PK data. Data sets (n = 200) with various study designs were simulated according to a one-compartment PK model and a direct Emax PD model. The study design of each dataset was randomly selected. The same PK and PD models were fitted to the simulated observations using the SIM, IPP, PPP&D and IPPSE methods. The performances of the methods were compared with respect to estimation precision and bias, and computation time. Estimated precision and bias for the IPPSE method were similar to that of SIM and PPP&D, while IPP had higher bias and imprecision. Compared with the SIM method, IPPSE saved more computation time (61%) than PPP&D (39%), while IPP remained the fastest method (86% run time saved). The IPPSE method is a promising alternative for PKPD analysis, combining the advantages of the SIM (higher precision and lower bias of parameter estimates) and the IPP (shorter run time) methods.
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| 6. |
- Lacroix, Brigitte
(författare)
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Pharmacometric Modeling in Rheumatoid Arthritis
- 2015
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Biologic therapies have revolutionized the treatment of rheumatoid arthritis, a common chronic inflammatory disease, mainly characterized by the chronic inflammation of the joints. The activity and progression of the disease are highly variable, both between subjects and between the successive assessments for the same subject. Standardized assessments of clinical variables have been developed to reflect the disease activity and evaluate new therapies. Pharmacokinetics-pharmacodynamic (PKPD) models and methods for analyzing the generated time-course data are needed to improve the interpretation of the clinical trials’ outcomes, and to describe the variability between subjects, including patients characteristics, disease factors and the use of concomitant treatments that may affect the response to treatment. In addition, good simulation properties are also desirable for predicting clinical responses for various populations or for different dosing schedules. The aim of this thesis was to develop methods and models for analyzing pharmacokinetic and pharmacokinetic-pharmacodynamic (PKPD) data from rheumatoid arthritis patients, illustrated by treatment with a new anti-TNFα biologic drug under clinical development, certolizumab pegol.Two models were developed that characterized the relationship between the exposure to the drug and the efficacy ACR variables that represent improvement of the disease; a logistic-type Markov model for 20% improvement (ACR20) and a continuous-type Markov model for simultaneous analysis of 20% (ACR20), 50% (ACR50) and 70% (ACR70) improvement. Both models accounted for the within-subjects correlation in the successive clinical assessments and were able to capture the observed ACR responses over time. Simulations from these models of the ACR20 response rate supported dosing regimens of 400 mg at weeks 0, 2 and 4 to achieve a rapid onset of response to the treatment, followed by 200 mg every 2 weeks, or alternative maintenance regimen of 400 mg every 4 weeks.The immunogenicity induced by the biologic drug was characterized by a time to event model describing the time to appearance of antibodies directed against the drug. The immunogenicity was predicted to appear mainly during the first 3 months following the start of the treatment and to be reduced at higher trough concentrations of CZP, as well as with concomitant administration of MTX.The full time-course of sequential events, such as dose-exposure-efficacy relations, is most accurately described by a simultaneous analysis of all data. However, due to the complexity and runtime limitations of such an analysis, alternatives are often used. In this thesis, a method, IPPSE, was developed and compared to the reference simultaneous method and to existing alternative methods. The IPPSE method was shown to provide accuracy and precision of estimates similar to the simultaneous method, but with easier implementation and shorter run times.In conclusion, two PKPD models and one immunogenicity model were developed for evaluation of the response of a biologic drug against rheumatoid arthritis that allowed accurate analysis and simulation of clinical trial data, as well as serving as examples for how a model-informed basis for decisions about biological drugs can be created.
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| 7. |
- Lacroix, Brigitte, et al.
(författare)
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Simultaneous Exposure-Response Modeling of ACR20, ACR50, and ACR70 Improvement Scores in Rheumatoid Arthritis Patients With Certolizumab Pegol
- 2014
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Ingår i: CPT. - Nature Publishing Group : Wiley. - 2163-8306. ; 3:10, s. 1-11
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Tidskriftsartikel (refereegranskat)abstract
- The Markovian approach has been proposed to model ACR response (ACR20, ACR50 or ACR70) reported in rheumatoid arthritis clinical trials to account for the dependency of the scores over time. However, dichotomizing the composite ACR assessment discards much information. Here we propose a new approach for modeling together the 3 thresholds: a continuous-time Markov exposure-response model was developed, based on data from 5 placebo-controlled certolizumab pegol clinical trials. This approach allows adequate prediction of individual ACR20/50/70 time-response, even for non-periodic observations. An exposure-response was established over a large range of licensed and unlicensed doses including phase II dose-ranging data. Simulations from the model (50 to 400 mg every other week) illustrated the range and sustainability of response (ACR20: 56 to 68%, ACR50: 27 to 42%, ACR70: 11 to 22% at week 24) with maximum clinical effect achieved at the recommended maintenance dose of 200 mg every other week.
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