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.
| Identifer | oai:union.ndltd.org:UPSALLA1/oai:DiVA.org:uu-247917 |
| Date | January 2015 |
| Creators | Lacroix, Brigitte |
| Publisher | Uppsala universitet, Institutionen för farmaceutisk biovetenskap, Uppsala |
| Source Sets | DiVA Archive at Upsalla University |
| Language | English |
| Detected Language | English |
| Type | Doctoral thesis, comprehensive summary, info:eu-repo/semantics/doctoralThesis, text |
| Format | application/pdf |
| Rights | info:eu-repo/semantics/openAccess |
| Relation | Digital Comprehensive Summaries of Uppsala Dissertations from the Faculty of Pharmacy, 1651-6192 ; 199 |
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