In vitro studies and in silico predictions of fluconazole and CYP2C9 genetic polymorphism impact on siponimod metabolism and pharmacokinetics

SNPs-Panel Polymorphism Variations in GHRL and GHSR Genes Are Not Associated with Prostate Cancer

Prostate cancer (PCa) is a major public health problem worldwide. Recent studies have suggested that ghrelin and its receptor could be involved in the susceptibility to several cancers such as PCa, leading to their use as an important predictive way for the clinical progression and prognosis of cancer. However, conflicting results of single nucleotide polymorphisms (SNPs) with ghrelin (GHRL) and its receptor (GHSR) genes were demonstrated in different studies. Thus, the present case-control study was undertaken to investigate the association of GHRL and GHSR polymorphisms with the susceptibility to sporadic PCa. A cohort of 120 PCa patients and 95 healthy subjects were enrolled in this study. Genotyping of six SNPs was performed: three tag SNPs in GHRL (rs696217, rs4684677, rs3491141) and three tag SNPs in the GHSR (rs2922126, rs572169, rs2948694) using TaqMan. The allele and genotype distribution, as well as haplotypes frequencies and linked disequilibrium (LD), were established. Multifactor dimensionality reduction (MDR) analysis was used to study gene-gene interactions between the six SNPs. Our results showed no significant association of the target polymorphisms with PCa (p > 0.05). Nevertheless, SNPs are often just markers that help identify or delimit specific genomic regions that may harbour functional variants rather than the variants causing the disease. Furthermore, we found that one GHSR rs2922126, namely the TT genotype, was significantly more frequent in PCa patients than in controls (p = 0.040). These data suggest that this genotype could be a PCa susceptibility genotype. MDR analyses revealed that the rs2922126 and rs572169 combination was the best model, with 81.08% accuracy (p = 0.0001) for predicting susceptibility to PCa. The results also showed a precision of 98.1% (p < 0.0001) and a PR-AUC of 1.00. Our findings provide new insights into the influence of GHRL and GHSR polymorphisms and significant evidence for gene-gene interactions in PCa susceptibility, and they may guide clinical decision-making to prevent overtreatment and enhance patients' quality of life.

Advancing pharmacogenetic testing in a tertiary hospital: a retrospective analysis after 10 years of activity

The field of pharmacogenetics (PGx) holds great promise in advancing personalized medicine by adapting treatments based on individual genetic profiles. Despite its benefits, there are still economic, ethical and institutional barriers that hinder its implementation in our healthcare environment. A retrospective analysis approach of anonymized data sourced from electronic health records was performed, encompassing a diverse patient population and evaluating key parameters such as prescribing patterns and test results, to assess the impact of pharmacogenetic testing. A head-to-head comparison with previously published activity results within the same pharmacogenetic laboratory was also conducted to contrast the progress made after 10 years. The analysis revealed significant utilization of pharmacogenetic testing in daily clinical practice, with 1,145 pharmacogenetic tests performed over a 1-year period and showing a 35% growth rate increase over time. Of the 17 different medical departments that sought PGx tests, the Oncology department accounted for the highest number, representing 58.47% of all genotyped patients. A total of 1,000 PGx tests were requested for individuals susceptible to receive a dose modification based on genotype, and 76 individuals received a genotype-guided dose adjustment. This study presents a comprehensive descriptive analysis of real-world data obtained from a public tertiary hospital laboratory specialized in pharmacogenetic testing, and presents data that strongly endorse the integration of pharmacogenetic testing into everyday clinical practice.

An update on the use of sphingosine 1-phosphate receptor modulators for the treatment of relapsing multiple sclerosis

INTRODUCTION

Multiple sclerosis (MS) is an immune-mediated disorder of the CNS manifested by recurrent attacks of neurological symptoms (related to focal inflammation) and gradual disability accrual (related to progressive neurodegeneration and neuroinflammation). Sphingosine-1-phosphate-receptor (S1PR) modulators are a class of oral disease-modifying therapies (DMTs) for relapsing MS. The first S1PR modulator developed and approved for MS was fingolimod, followed by siponimod, ozanimod, and ponesimod. All are S1P analogues with different S1PR-subtype selectivity. They restrain the S1P-dependent lymphocyte egress from lymph nodes by binding the lymphocytic S1P-subtype-1-receptor. Depending on their pharmacodynamics and pharmacokinetics, they can also interfere with other biological functions.

AREAS COVERED

Our narrative review covers the PubMed English literature on S1PR modulators in MS until August 2022. We discuss their pharmacology, efficacy, safety profile, and risk management recommendations based on the results of phase II and III clinical trials. We briefly address their impact on the risk of infections and vaccines efficacy.

EXPERT OPINION

S1PR modulators decrease relapse rate and may modestly delay disease progression in people with relapsing MS. Aside their established benefit, their place and timing within the long-term DMT strategy in MS, as well as their immunological effects in the new and evolving context of the post-COVID-19 pandemic and vaccination campaigns warrant further study.

Can Mechanistic Static Models for Drug-Drug Interactions Support Regulatory Filing for Study Waivers and Label Recommendations?

BACKGROUND AND OBJECTIVE

Mechanistic static and dynamic physiologically based pharmacokinetic models are used in clinical drug development to assess the risk of drug-drug interactions (DDIs). Currently, the use of mechanistic static models is restricted to screening DDI risk for an investigational drug, while dynamic physiologically based pharmacokinetic models are used for quantitative predictions of DDIs to support regulatory filing. As physiologically based pharmacokinetic model development by sponsors as well as a review of models by regulators require considerable resources, we explored the possibility of using mechanistic static models to support regulatory filing, using representative cases of successful physiologically based pharmacokinetic submissions to the US Food and Drug Administration under different classes of applications.

METHODS

Drug-drug interaction predictions with mechanistic static models were done for representative cases in the different classes of applications using the same data and modelling workflow as described in the Food and Drug Administration clinical pharmacology reviews. We investigated the hypothesis that the use of unbound average steady-state concentrations of modulators as driver concentrations in the mechanistic static models should lead to the same conclusions as those from physiologically based pharmacokinetic modelling for non-dynamic measures of DDI risk assessment such as the area under the plasma concentration-time curve ratio, provided the same input data are employed for the interacting drugs.

RESULTS

Drug-drug interaction predictions of area under the plasma concentration-time curve ratios using mechanistic static models were mostly comparable to those reported in the Food and Drug Administration reviews using physiologically based pharmacokinetic models for all representative cases in the different classes of applications.

CONCLUSIONS

The results reported in this study should encourage the use of models that best fit an intended purpose, limiting the use of physiologically based pharmacokinetic models to those applications that leverage its unique strengths, such as what-if scenario testing to understand the effect of dose staggering, evaluating the role of uptake and efflux transporters, extrapolating DDI effects from studied to unstudied populations, or assessing the impact of DDIs on the exposure of a victim drug with concurrent mechanisms. With this first step, we hope to trigger a scientific discussion on the value of a routine comparison of the two methods for regulatory submissions to potentially create a best practice that could help identify examples where the use of dynamic changes in modulator concentrations could make a difference to DDI risk assessment.

Pharmacokinetic Characteristics of Siponimod in Healthy Volunteers and Patients With Multiple Sclerosis: Analyses of Published Clinical Trials

Objectives: This study aimed to investigate the pharmacokinetic characteristics of siponimod in healthy volunteers and patients with MS based on aggregated data from published clinical trials, and to explore the factors influencing siponimod exposure.

Methods: A total of 476 siponimod plasma concentrations aggregated from 28 dosage groups (corresponding to 294 healthy volunteers and 207 patients with MS) were collected from published clinical trials. Population pharmacokinetic (PPK) analysis was performed using a nonlinear, mixed-effect modeling approach. The pharmacokinetic properties of siponimod in healthy volunteers and patients with MS were compared, and the influence of covariates on siponimod exposure was evaluated using both PPK analysis and noncompartmental analysis (NCA).

Results: A one-compartment model with first-order absorption and elimination adequately described siponimod pharmacokinetics. The typical population parameter estimates of clearance (CL/F), apparent volume of distribution (V/F), and absorption rate constant (ka) were 3.17 L/h, 112.70 L, and 0.38 h⁻¹, respectively. An 11.85% lower siponimod clearance was estimated for patients with MS relative to healthy volunteers. Subgroup analyses using NCA assessments revealed that siponimod presented an accumulation index of approximately 2 after multiple administration. Compared with nonobese participants, obese participants had a relatively lower dose-corrected area under the concentration-time curve (AUC_0-∞/D) (0.31 vs. 0.42 h/L) and V/F (120.95 vs. 133.75 L), and a relatively higher CL/F (3.25 vs. 3.21 L/h). Participants with CYP2C9*2/*3, *1/*3, and *3/*3 genotypes experienced an increased (1.3- and 3.4-fold, respectively) AUC_0-∞/D and a decreased (0.7- and 0.3-fold, respectively) CL/F compared with those in participants with the CYP2C9*1/*1, *1*2, and *2*2 genotypes. Fluconazole combination led to a decrease in CL/F (approximately 0.5 times) and an increase in AUC_0-∞/D (approximately 1.3 times).

Conclusion: Siponimod pharmacokinetic properties in healthy volunteers and patients with MS were explored using complementary model-based meta-analysis (MBMA) and NCA approaches. A slightly lower siponimod clearance was observed in patients with MS than in healthy volunteers. The dosage regimen, body mass index, CYP2C9 genetic polymorphism and fluconazole combination may had influences on siponimod pharmacokinetics. Such model paves the road to more population-based analyses in different patient populations with MS to quantify the effect of any influencing factors on siponimod pharmacokinetics.

Application of PBPK Modeling and Simulation for Regulatory Decision Making and Its Impact on US Prescribing Information: An Update on the 2018-2019 Submissions to the US FDA's Office of Clinical Pharmacology

Since 2016, results from physiologically based pharmacokinetic (PBPK) analyses have been routinely found in the clinical pharmacology section of regulatory applications submitted to the US Food and Drug Administration (FDA). In 2018, the Food and Drug Administration's Office of Clinical Pharmacology published a commentary summarizing the application of PBPK modeling in the submissions it received between 2008 and 2017 and its impact on prescribing information. In this commentary, we provide an update on the application of PBPK modeling in submissions received between 2018 and 2019 and highlight a few notable examples.

Therapeutic Value of Single Nucleotide Polymorphisms on the Efficacy of New Therapies in Patients with Multiple Sclerosis

The introduction of new therapies for the treatment of multiple sclerosis (MS) is a very recent phenomenon and little is known of their mechanism of action. Moreover, the response is subject to interindividual variability and may be affected by genetic factors, such as polymorphisms in the genes implicated in the pathologic environment, pharmacodynamics, and metabolism of the disease or in the mechanism of action of the medications, influencing the effectiveness of these therapies. This review evaluates the impact of pharmacogenetics on the response to treatment with new therapies in patients diagnosed with MS. The results suggest that polymorphisms detected in the GSTP1, ITGA4, NQO1, AKT1, and GP6 genes, for treatment with natalizumab, ZMIZ1, for fingolimod and dimethyl fumarate, ADA, for cladribine, and NOX3, for dimethyl fumarate, may be used in the future as predictive markers of treatment response to new therapies in MS patients. However, there are few existing studies and their samples are small, making it difficult to generalize the role of these genes in treatment with new therapies. Studies with larger sample sizes and longer follow-up are therefore needed to confirm the results of these studies.

Effect of CYP2C9 genetic polymorphism and breviscapine on losartan pharmacokinetics in healthy subjects

1. Breviscapine was an active ingredient of flavonoid glycosides. Our present study was conducted to evaluate the impact of breviscapine on the pharmacokinetics of losartan and its active metabolite E-3174, and that relationship with the gene polymorphism of CYP2C9 in healthy Chinese volunteers, to provide a basis for clinical rational drug use.2. The genotypes of 217 healthy Chinese subjects were determined using PCR-RFLP. Twelve healthy subjects were selected and were known CYP2C9 genotypes (six CYP2C9*1/*3 and six CYP2C9*1/*1) in a two-phase randomised crossover design study. These subjects were given daily doses of 120 mg (40 mg, three times a day) of breviscapine or a placebo for 14 days, followed by 50 mg losartan on day 15.3. Compared with individuals carrying the CYP2C9*1/*1 genotype, the CYP2C9*1/*3 genotype showed an increase in the AUC_(0-36) (833.6 ± 379.8 ng h ml^-1 vs. 526.1 ± 140.1 ng h ml^-1, p < 0.05) and a decrease in the MR (the metabolic ratio of losartan, AUC_E-3174/AUC_losartan) (2.67 ± 1.40 vs. 4.56 ± 0.83, p < 0.05) of losartan during the placebo treatment phase. Individuals with genotype CYP2C9*1/*3 showed a significant increase in AUC_(0-36) (2335 ± 851.8 ng h ml^-1 vs. 1927 ± 949.5 ng h ml^-1, p < 0.05) and AUC_(0-∞) (2363 ± 875.6 ng h ml^-1 vs. 1966 ± 966.1 ng h ml^-1, p < 0.05) of E-3174 after breviscapine treatment compared to the placebo group.4. In healthy subjects, breviscapine had no significant effect on the pharmacokinetics of losartan. The activity of CYP2C9 enzyme to losartan metabolism was more significant in subjects with CYP2C9*1/*3 than those with CYP2C9*1/*1 genotype.

Physiologically Based Pharmacokinetic/Pharmacodynamic Modeling to Predict the Impact of CYP2C9 Genetic Polymorphisms, Co-Medication and Formulation on the Pharmacokinetics and Pharmacodynamics of Flurbiprofen

Physiologically based pharmacokinetic/pharmacodynamic (PBPK/PD) models can serve as a powerful framework for predicting the influence as well as the interaction of formulation, genetic polymorphism and co-medication on the pharmacokinetics and pharmacodynamics of drug substances. In this study, flurbiprofen, a potent non-steroid anti-inflammatory drug, was chosen as a model drug. Flurbiprofen has absolute bioavailability of ~95% and linear pharmacokinetics in the dose range of 50–300 mg. Its absorption is considered variable and complex, often associated with double peak phenomena, and its pharmacokinetics are characterized by high inter-subject variability, mainly due to its metabolism by the polymorphic CYP2C9 (fmCYP2C9 ≥ 0.71). In this study, by leveraging in vitro, in silico and in vivo data, an integrated PBPK/PD model with mechanistic absorption was developed and evaluated against clinical data from PK, PD, drug-drug and gene-drug interaction studies. The PBPK model successfully predicted (within 2-fold) 36 out of 38 observed concentration-time profiles of flurbiprofen as well as the CYP2C9 genetic effects after administration of different intravenous and oral dosage forms over a dose range of 40–300 mg in both Caucasian and Chinese healthy volunteers. All model predictions for C_max, AUC_inf and CL/F were within two-fold of their respective mean or geometric mean values, while 90% of the predictions of C_max, 81% of the predictions of AUC_inf and 74% of the predictions of Cl/F were within 1.25 fold. In addition, the drug-drug and drug-gene interactions were predicted within 1.5-fold of the observed interaction ratios (AUC, C_max ratios). The validated PBPK model was further expanded by linking it to an inhibitory E_max model describing the analgesic efficacy of flurbiprofen and applying it to explore the effect of formulation and genetic polymorphisms on the onset and duration of pain relief. This comprehensive PBPK/PD analysis, along with a detailed translational biopharmaceutic framework including appropriately designed biorelevant in vitro experiments and in vitro-in vivo extrapolation, provided mechanistic insight on the impact of formulation and genetic variations, two major determinants of the population variability, on the PK/PD of flurbiprofen. Clinically relevant specifications and potential dose adjustments were also proposed. Overall, the present work highlights the value of a translational PBPK/PD approach, tailored to target populations and genotypes, as an approach towards achieving personalized medicine.

Siponimod: A Review in Secondary Progressive Multiple Sclerosis

Oral siponimod (Mayzent®), a next-generation, selective sphingosine 1-phosphate receptor (S1PR) 1 and 5 modulator, is approved in several countries for the treatment of secondary progressive multiple sclerosis (SPMS), with specific indications varying between individual countries. In the pivotal EXPAND trial (median duration double-blind treatment 18 months) in a broad spectrum of patients with SPMS, once-daily oral siponimod 2 mg (initial dose titration over 6 days) was significantly more effective than placebo in reducing clinical and MRI-defined outcomes of disease activity and disability progression, including 3-month confirmed disability progression on the Expanded Disability Status Scale (EDSS), and was generally well tolerated in the core phase of the study. These beneficial effects of siponimod appeared to be sustained during up to 5 years of treatment in the ongoing open-label extension phase of EXPAND. The safety profile of siponimod is similar to that of other agents in its class, including adverse events of special interest (i.e. those known to be associated with S1PR modulators). No new safety signals were identified during up to 5 years' treatment in the open-label extension phase. Albeit further long-term efficacy and safety data from the real-world setting are required to fully define its role, given the paucity of current treatment options and its convenient dosage regimen, siponimod represents an important emerging option for the treatment of adult patients with SPMS with active disease evidenced by relapses or imaging-features of inflammatory activity.

Computational Approaches in Preclinical Studies on Drug Discovery and Development

Because undesirable pharmacokinetics and toxicity are significant reasons for the failure of drug development in the costly late stage, it has been widely recognized that drug ADMET properties should be considered as early as possible to reduce failure rates in the clinical phase of drug discovery. Concurrently, drug recalls have become increasingly common in recent years, prompting pharmaceutical companies to increase attention toward the safety evaluation of preclinical drugs. In vitro and in vivo drug evaluation techniques are currently more mature in preclinical applications, but these technologies are costly. In recent years, with the rapid development of computer science, in silico technology has been widely used to evaluate the relevant properties of drugs in the preclinical stage and has produced many software programs and in silico models, further promoting the study of ADMET in vitro. In this review, we first introduce the two ADMET prediction categories (molecular modeling and data modeling). Then, we perform a systematic classification and description of the databases and software commonly used for ADMET prediction. We focus on some widely studied ADMT properties as well as PBPK simulation, and we list some applications that are related to the prediction categories and web tools. Finally, we discuss challenges and limitations in the preclinical area and propose some suggestions and prospects for the future.

Association Between Ghrelin Gene Polymorphism and Cerebral Infarction

Background

The aim of this study was to explore the associations of ghrelin gene polymorphisms at rs26312, rs26802 and rs27647 with cerebral infarction.

Material/Methods

A total of 200 cerebral infarction patients in our hospital were enrolled as the disease group, while 200 healthy people were enrolled as the control group. Peripheral venous blood was collected from both groups, and the ghrelin gene polymorphisms at rs26312, rs26802, and rs27647 in nucleated cells were detected through sequencing.

Results

The genotype distribution at ghrelin gene loci rs26802 and rs27647 in the disease group was significantly different from that in the control group. The distribution of recessive model at ghrelin gene locus rs26802 in the disease group was different from that in the control group, in which the TG+GG frequency was evidently higher in the disease group. The AA genotype at ghrelin gene locus rs26312 was remarkably associated with the ghrelin gene expression level, and the expression level of ghrelin gene in the disease group was remarkably lower than that in the control group. The genotype at ghrelin gene locus rs26312 was associated with activated partial thromboplastin time (APTT), and APTT was significantly shorter in patients with GG genotype. The genotype at ghrelin gene locus rs26802 was associated with D-dimer, and the D-dimer level was significantly lower in patients with TG genotype. The genotype at ghrelin gene locus rs27647 was associated with prothrombin time (PT), and PT was obviously shorter in patients with TT genotype.

Conclusions

The ghrelin gene polymorphisms are remarkably associated with the occurrence of cerebral infarction.

Effect of Fluconazole Coadministration and CYP2C9 Genetic Polymorphism on Siponimod Pharmacokinetics in Healthy Subjects

Objectives

The aim of this study was to assess the pharmacokinetics (PK) and safety/tolerability of siponimod in healthy subjects when coadministered with (1) the moderate cytochrome P450 (CYP) 2C9 and CYP3A inhibitor fluconazole (Study A), and (2) with three different CYP2C9 genotype variants (Study B).

Methods

Study A was an open-label, single-dose study comprising periods 1 (14 days; day 1: siponimod 4 mg) and 2 (20 days; day 1: fluconazole 200 mg twice daily; days 2–19: fluconazole 200 mg once daily; day 3: siponimod 4 mg) in healthy subjects (n = 14) with the wild-type CYP2C9 genotype (CYP2C9*1/*1). Study B was a multicentre, open-label study comprising parts 1 (day 1: siponimod 0.25 mg once daily in the CYP2C9*1/*1, CYP2C9*2/*3 and CYP2C9*3/*3 genotypes) and 2 (days 1–2: 0.25 mg once daily; day 3: 0.5 mg once daily in the CYP2C9*2/*3 and CYP2C9*3/*3 genotypes only) in healthy subjects with polymorphic variants of CYP2C9 (n = 24). Pharmacokinetic parameters were calculated using noncompartmental methods.

Results

In Study A, coadministration with fluconazole produced an approximately twofold increase in mean area under the curve (AUC) versus siponimod alone (from 1110 to 2160 h*ng/mL), and an increase in maximum plasma concentration (C_max; from 31.2 to 34.0 ng/mL) and elimination half-life (T_½; from 40.6 to 61.6 h). In Study B, the AUCs of siponimod were approximately two to fourfold greater in subjects with the CYP2C9*2/*3 and CYP2C9*3/*3 genotypes, with a minor increase in C_max versus the CYP2C9*1/*1 genotype. The mean T_½ was prolonged in the CYP2C9*2/*3 (51 h) and CYP2C9*3/*3 (126 h) genotypes versus the CYP2C9*1/*1 (28 h) genotype. Siponimod did not result in increased adverse events in healthy subjects in both studies.

Conclusions

Changes in siponimod PK, when coadministered with fluconazole at steady-state and in subjects with different CYP2C9 genotypes, indicate that the reduced CYP2C9 enzymatic activity does not affect the absorption phase of siponimod but prolongs the elimination phase. These results confirm the relevance of CYP2C9 activity on siponimod metabolism in humans.

Electronic supplementary material

The online version of this article (10.1007/s40262-018-0700-3) contains supplementary material, which is available to authorized users.

Siponimod in the treatment of multiple sclerosis

Introduction: Multiple sclerosis (MS) causes focal lesions of immune-mediated demyelinating events followed by slow progressive accumulation of disability. Over the past 2 decades, multiple medications have been studied and approved for use in MS. Most of these agents work by modulating or suppressing the peripheral immune system. Siponimod is a newer-generation sphingosine 1 phosphate (S1P) receptor modulator that internalizes S1P1 receptors, thereby inhibiting efflux of lymphocytes from lymph nodes and thymus. There are promising data suggesting that it may also have a direct neuroprotective property independent of peripheral lymphocytopenia.Areas covered: We reviewed the pharmacology and the clinical and radiological effects of siponimod.Expert opinion: The selective effect of siponimod on the S1P1 and S1P5 receptors offers a favorable side-effect profile and transient bradycardia can be avoided by dose titration. A phase-II study showed that siponomod has dose-dependent beneficial effects in patients with relapsing remitting disease. The results of a phase-III study suggest that siponimod may be beneficial in secondary progressive MS, at least in patients with disease activity.

Prediction of the Impact of Cytochrome P450 2C9 Genotypes on the Drug-Drug Interaction Potential of Siponimod With Physiologically-Based Pharmacokinetic Modeling: A Comprehensive Approach for Drug Label Recommendations

We predicted the drug–drug interaction (DDI) potential of siponimod in presence of cytochrome P450 (CYP)2C9/CYP3A4 inhibitors/inducers in subjects with different CYP2C9 genotypes by physiologically‐based pharmacokinetic (PK) modeling. The model was established using in vitro and clinical PK data and verified by adequately predicting siponimod PK when coadministered with rifampin. With strong and moderate CYP3A4 inhibitors, an increased DDI risk for siponimod was predicted for CYP2C9*3/*3 genotype vs. other genotypes area under the curve ratio (AUCR): 3.03–4.20 vs. ≤ 1.49 for strong; 2.42 vs. 1.14–1.30 for moderate. AUCRs increased with moderate (2.13–2.49) and weak (1.12–1.42) CYP3A4/CYP2C9 inhibitors to the same extent for all genotypes. With strong CYP3A4/moderate CYP2C9 inducers and moderate CYP3A4 inducers, predicted AUCRs were 0.21–0.32 and 0.35–0.71, respectively. This complementary analysis to the clinical PK‐DDI studies confirmed the relevant influence of CYP2C9 polymorphism on the DDI behavior of siponimod and represented the basis for the DDI labeling recommendations.

Siponimod pharmacokinetics, safety, and tolerability in combination with the potent CYP3A4 inhibitor itraconazole in healthy subjects with different CYP2C9 genotypes

PURPOSE

To evaluate the PK and safety of siponimod, a substrate of CYP2C9/3A4, in the presence or absence of a CYP3A4 inhibitor, itraconazole.

METHODS

This was an open-label study in healthy subjects (aged 18-50 years; genotype: CYP2C9 *1*2 [cohort 1; n = 17] or *1*3 [cohort 2; n = 13]). Subjects received siponimod 0.25-mg single dose in treatment period 1 (days 1-14), itraconazole 100 mg twice daily in treatment period 2 (days 15-18), and siponimod 0.25-mg single dose (day 19) with itraconazole until day 31 (cohort 1) or day 35 (cohort 2) in treatment period 3. PK of siponimod alone and with itraconazole and safety were assessed.

RESULTS

Overall, 29/30 subjects completed the study. In treatment period 1, geometric mean AUC_inf, T_1/2, and median T_max were higher while systemic clearance was lower in cohort 2 than cohort 1. In treatment period 3, siponimod AUC decreased by 10% (geo-mean ratio [90% confidence intervals]: 0.90 [0.84; 0.96]) and 24% (0.76 [0.69; 0.82]) in cohorts 1 and 2, respectively. Siponimod C_max was similar between treatment periods 1 and 3. In both cohorts, the C_max and AUC of the metabolites (M17, M3, and M5) decreased in the presence of itraconazole. All adverse events were mild.

CONCLUSIONS

The minor albeit significant reduction in plasma exposure of siponimod and its metabolites by itraconazole was unexpected. While the reason is unclear, the results suggest that coadministration of the two drugs would not cause a considerable increase of siponimod exposure independent of CYP2C9 genotype.

Siponimod for the treatment of secondary progressive multiple sclerosis

INTRODUCTION

Multiple sclerosis (MS) is a chronic central nervous system immune-mediated disease with an important inflammatory component associated with focal demyelination and widespread neurodegeneration. In most cases, the clinical presentation is relapsing-remitting, followed by a secondary progressive phase, characterized by disability accrual unrelated to relapses. In a minority, the phenotype is progressive from the beginning. Major therapeutic achievements have been made concerning the relapsing phase but modifying the evolution of progressive MS remains an unmet need. Areas covered: This review covers siponimod (BAF312), a new sphingosine 1-phosphate receptor modulator, and its role in the treatment of secondary progressive MS. The authors reviewed PubMed English literature using the keywords 'siponimod' or 'BAF312' and 'multiple sclerosis.' They also present the pharmacological profile of siponimod, as well as clinical efficacy and safety, with emphasis on the recently published results of a Phase III trial. Phase II data in relapsing MS are also summarized. Expert opinion: Siponimod may reduce the activity of the disease and has a modest effect on the gradual disability accrual. If approved, it may become one of the few available therapy options for secondary progressive MS.

Siponimod pharmacokinetics, safety, and tolerability in combination with rifampin, a CYP2C9/3A4 inducer, in healthy subjects

PURPOSE

To assess the potential pharmacokinetic (PK) interactions between siponimod and rifampin, a strong CYP3A4/moderate CYP2C9 inducer, in healthy subjects.

METHODS

This was a confirmatory, open-label, multiple-dose two-period study in healthy subjects (aged 18-45 years). In Period 1 (Days 1-12), siponimod was up-titrated from 0.25 to 2 mg over 5 days (Days 1-6) followed by 2 mg once daily on days 7-12. In Period 2, siponimod 2 mg qd was co-administered with rifampin 600 mg qd (Days 13-24). Primary assessments included PK of siponimod (Days 12 and 24; maximum steady-state plasma concentration [C_max,ss], median time to achieve C_max,ss [T_{max, ss}], and area under the curve at steady state [AUC_tau,ss]). Key secondary assessments were PK of M3 and M5 metabolites, and safety/tolerability including absolute lymphocyte count (ALC).

RESULTS

Of the 16 subjects enrolled (age, mean ± standard deviation [SD] 31 ± 8.3 years; men, n = 15), 15 completed the study. In Period 1, siponimod geometric mean C_max,ss (28.6 ng/mL) was achieved in 4 h (median T_max,ss; range, 1.58-8.00) and the geometric mean AUC_tau,ss was 546 h × ng/mL. In Period 2, the siponimod geometric mean C_max,ss and AUC_tau,ss decreased to 15.7 ng/mL and 235 h × ng/mL, respectively; median T_max remained unchanged (4 h). Rifampin co-administration increased M3 C_max,ss by 53% while M5 C_max,ss remained unchanged. The AUC_tau,ss of M3 and M5 decreased by 10% and 37%, respectively. The majority of adverse events reported were mild, with a higher frequency during Period 2 (86.7%) versus Period 1 (50%). The mean ALC increased slightly under rifampin co-administration but remained below 1.0 × 10⁹/L.

CONCLUSIONS

The study findings suggest that in the presence of rifampin, a strong CYP3A4/moderate CYP2C9 inducer, siponimod showed significant decrease in C_max,ss (45%) and AUC_tau,ss (57%) in healthy subjects.

Metabolism and Disposition of Siponimod, a Novel Selective S1P1/S1P5 Agonist, in Healthy Volunteers and In Vitro Identification of Human Cytochrome P450 Enzymes Involved in Its Oxidative Metabolism

Siponimod, a next-generation selective sphingosine-1-phosphate receptor modulator, is currently being investigated for the treatment of secondary progressive multiple sclerosis. We investigated the absorption, distribution, metabolism, and excretion (ADME) of a single 10-mg oral dose of [¹⁴C]siponimod in four healthy men. Mass balance, blood and plasma radioactivity, and plasma siponimod concentrations were measured. Metabolite profiles were determined in plasma, urine, and feces. Metabolite structures were elucidated using mass spectrometry and comparison with reference compounds. Unchanged siponimod accounted for 57% of the total plasma radioactivity (area under the concentration-time curve), indicating substantial exposure to metabolites. Siponimod showed medium to slow absorption (median T_max: 4 hours) and moderate distribution (Vz/F: 291 l). Siponimod was mainly cleared through biotransformation, predominantly by oxidative metabolism. The mean apparent elimination half-life of siponimod in plasma was 56.6 hours. Siponimod was excreted mostly in feces in the form of oxidative metabolites. The excretion of radioactivity was close to complete after 13 days. Based on the metabolite patterns, a phase II metabolite (M3) formed by glucuronidation of hydroxylated siponimod was the main circulating metabolite in plasma. However, in subsequent mouse ADME and clinical pharmacokinetic studies, a long-lived nonpolar metabolite (M17, cholesterol ester of siponimod) was identified as the most prominent systemic metabolite. We further conducted in vitro experiments to investigate the enzymes responsible for the oxidative metabolism of siponimod. The selective inhibitor and recombinant enzyme results identified cytochrome P450 2C9 (CYP2C9) as the predominant contributor to the human liver microsomal biotransformation of siponimod, with minor contributions from CYP3A4 and other cytochrome P450 enzymes.