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A Population Approach to Characterize Interferon Beta-1b Effect on Contrast Enhancing Lesions in Patients With Relapsing Remitting Multiple Sclerosis.


ABSTRACT: In patients with relapsing-remitting multiple sclerosis (RRMS), interferon beta-1b (IFNβ-1b) reduces the occurrence of contrast enhancing lesions (CELs) on magnetic resonance imaging (MRI). Questions remain on the stability of IFNβ-1b effect over time and its action beyond the reduction of CELs. In this study, we described the IFNβ-1b effect by a mixed effects model, quantifying the interpatient variability associated with its parameters. Using a negative binomial distribution model as a natural history model, the effect of IFNβ-1b was evaluated using different mathematical functions of time. IFNβ-1b produced a decrease in the expected CEL numbers, inhibiting the formation of new CELs but did not promote the resolution of the already-formed ones. Based on the final selected model, simulations were carried out to optimize the combined IFNβ-1b-corticosteroid therapy as a proof-of-concept. In summary, we provide evidence on the dynamics of CELs under IFNβ-1b treatment that can be used to monitor the effects of therapies in MS.

SUBMITTER: Gulati A 

PROVIDER: S-EPMC4452935 | biostudies-literature | 2015 May

REPOSITORIES: biostudies-literature

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A Population Approach to Characterize Interferon Beta-1b Effect on Contrast Enhancing Lesions in Patients With Relapsing Remitting Multiple Sclerosis.

Gulati A A   Bagnato F F   Villoslada P P   Velez de Mendizabal N N  

CPT: pharmacometrics & systems pharmacology 20150424 5


In patients with relapsing-remitting multiple sclerosis (RRMS), interferon beta-1b (IFNβ-1b) reduces the occurrence of contrast enhancing lesions (CELs) on magnetic resonance imaging (MRI). Questions remain on the stability of IFNβ-1b effect over time and its action beyond the reduction of CELs. In this study, we described the IFNβ-1b effect by a mixed effects model, quantifying the interpatient variability associated with its parameters. Using a negative binomial distribution model as a natural  ...[more]

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