Use of a biomarker in exposure-response analysis to support dose selection for fingolimod.

Fingolimod is the first orally administered drug which has been approved for the treatment of patients with relapsing and remitting forms of multiple sclerosis (RRMS).1,2 Fingolimod is the first sphingosine-1-phosphate (S1P) receptor modulator. Fingolimod needs to be phosphorylated to the active moiety, S-enantiomer fingolimod-P.3 Fingolimod-P is a functional antagonist of the S1P receptor, which reduces the peripheral lymphocyte count by down-modulation of the S1P1 receptor on lymphocytes.4 This results in a reduced egress of lymphocytes from the lymph nodes; in particular, autoaggressive T-cells that perform a central role in the MS inflammatory disease process are prevented from recirculating to the central nervous system. Fingolimod-P is reversibly dephosphorylated back to the inactive form, fingolimod. At steady state (SS), fingolimod and fingolimod-P are in a dynamic equilibrium. Due to the presence of S1P receptors in multiple tissues, fingolimod manifests a number of other biological effects in addition to the reduction in circulating lymphocytes. These include a transient reduction in heart rate and atrioventricular conduction on treatment initiation, a dose-dependent mild increase in airway resistance, macular edema, a mild increase in blood pressure, and asymptomatic elevation in serum levels of hepatic transaminases.5,6,7 Fingolimod at both dose groups (0.5 and 1.25 mg q.d.) showed superior efficacy in reducing the frequency of relapse relative to placebo in a 2-year study in patients with RRMS, and also demonstrated superior efficacy over a current standard of care, interferon β-1a (Avonex) in a 1-year study with little difference between the two dose groups.1 The low dose (0.5 mg q.d.) was proposed and approved for the treatment of RRMS.1 Although fingolimod has demonstrated superior efficacy over both placebo and an active control, there was a dose-dependent increase in several adverse events such as increase in alanine transaminase, macular edema, and bradycardia (Figure 1). As a result, it was reasonable to raise the question whether doses even lower than 0.5 mg would provide sufficient efficacy with less safety concerns. Therefore, our analysis focused on estimating the efficacy at doses lower than 0.5 mg based on analysis of the exposure (fingolimod concentration)–response (relapse rate reduction) relationship. Figure 1 The benefit and risk profile of fingolimod. The left figure presents the dose-primary endpoint (ARR) relationship. The right graph shows the percentage of patients having adverse events during clinical studies for the selected safety endpoints which showed ... A typical efficacy analysis focuses on group comparison using a statistical method such as analysis of variance or analysis of covariance, and a decision is made based on the P value. In the exposure–response analysis, we use individual concentration levels in the blood instead of the assigned dose because patients at the same dose could have different concentration levels. The concentration level is related to the response based on certain assumptions on the structure of the relationship. A maximum effect (Emax) model is a commonly used model for pharmacodynamic or clinical endpoints, and the hill coefficient parameter (h) governs the shape of the relationship. Figure 2 illustrates the role of the hill coefficient parameter in the shape of the exposure–response relationship for an Emax model. Figure 2 Illustration of role of hill coefficient parameter in Emax model. ARR, annualized relapse rate; Emax, maximum effect. Under certain circumstances, a biomarker can provide valuable information. For example, the key pharmacodynamic effect of fingolimod is a dose-dependent reduction of the peripheral lymphocyte count mediated by down-modulation of the S1P1 receptor on lymphocyte counts, which is believed to be the mechanism to produce clinical benefit in terms of reduction in relapse rate. This served as the major rationale for the sequential linkage among exposure, biomarker, and the clinical endpoint. As the ultimate goal is to predict the clinical endpoint (annualized relapse rate (ARR) which was computed as the total number of confirmed relapses for all the patients divided by the total number of days on the study for all the patients, multiplied by 365.25) at exposure levels of unstudied doses, confidence in the model structure is crucial. The main challenge in analyzing the relationship between the fingolimod exposure and the clinical endpoint was that the hill coefficient in the model could not be estimated successfully due to the lack of data at lower exposure levels (declining part of the relationship in Figure 2). As a result, it was not possible to predict ARR precisely at doses lower than 0.5 mg. In order to bridge this gap, pharmacodynamic biomarker information was used to link ARR and drug exposure. Specifically, the average lymphocyte count at the SS was used to link drug exposure to ARR rather than directly modeling the relationship between drug exposure and ARR. By doing this it was expected that the fingolimod exposure–ARR relationship would be quantified more precisely given the availability of lymphocyte count data and the presumed relationship between lymphocyte counts and ARR. In this article, we aim to show how biomarker data can be used as a bridge in exposure–response analysis to estimate clinical endpoint response at certain dose levels when the direct relationship between exposure and the clinical endpoint can not be quantified reliably.