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domingo, 2 de diciembre de 2012


The Pharmacogenomics Journal (2012) 12, 453–461; published online 9 October 2012
Pharmacogenomic update on multiple sclerosis: a focus on actual and new therapeutic strategies
V Foti Cuzzola1, E Palella1, D Celi1, M Barresi1, S Giacoppo1, P Bramanti1 and S Marino1
1Experimental Neurology Laboratory, IRCCS Centro Neurolesi ‘Bonino-Pulejo’, Messina, Italy
Correspondence: Dr V Foti Cuzzola, Experimental Neurology Laboratory, IRCCS Centro Neurolesi ‘Bonino Pulejo’, SS 113, Via Palermo, C.da Casazza, Messina, 98124, Italy. E-mail:
Multiple sclerosis (MS) is an inflammatory and demyelinating disease of central nervous system comprising several subtypes. Pharmacological treatment involves only few drugs. Among these, interferon beta (IFN-β) and glatiramer acetate were the most used. Although evidence supports the efficacy of these agents in treating MS symptoms, actual studies allowed to introduce new innovative drugs in clinical practice. Applying pharmacogenetic approach to MS, IFN-β and several other immune pathways were abundantly investigated. Numerous reports identified some promising therapy markers but only few markers have emerged as clinically useful. This may be partially due to differences in clinical and methodological criteria in the studies. Indeed, responder and non-responder definitions lack standardized clinical definition. The goal of this review is to treat advances in research on the pharmacogenetic markers of MS drugs and to highlight possible correlations between type of responses and genetic profile, with regard to clinical and methodological discrepancies in the studies.
Keywords: pharmacogenetics; multiple sclerosis; IFN-β; glatiramer acetate; MxA; IFNAR
Multiple sclerosis (MS) is a chronic inflammatory disease of the central nervous system affecting about 2.5 million people around the world. In Europe, the prevalence ranges between 10 and 187 per 100 000 with a higher rate in northern countries.1 Clinically, it is possible to distinguish several subtypes of the disease. Relapsing remitting MS (RRMS), the most frequent form, is characterized by acute exacerbations with complete remission or partial residues of symptoms. Inflammatory demyelination of this form evolves to a neurodegenerative condition, then distinguishing the secondary-progressive MS. Here, diffuse axonal injury clinically expresses as a persistent progression of disease with reduction of remission episodes. The primary-progressive MS form, in which progressive course of disease is present from the beginning, involves about 15% of MS patients. Finally, progressive relapsing MS describes, from onset, a steady neurologic decline with relapse episodes.2, 3 Clinical diagnosis of MS relies on criteria including clinical, laboratory and radiologic assessments. Currently, criteria of McDonald, recently revised by Polman et al.,4 are officially used in clinical practice.
In the last two decades, approved disease-modifying drugs—that is, interferon-beta (IFN-β), glatiramer acetate (GA) and mitoxantrone (MTX)—have shown to unequivocally ameliorate the natural history of MS.5 They are used for secondary-progressive MS and RRMS treatment, whereas there are no effective therapy to halt progression of disease and reduce disability in primary-progressive MS. Subsequently, two new innovative disease-modifying drugs, natalizumab and, more recently, fingolimod, have been added to the therapeutic arsenal. Many new agents in the pipeline, such as laquinimod, teriflunomide, dimethylfumarate (BG-12), daclizumab and alemtuzumab, potentially may bring to amplify the MS pharmacological armamentarium.6 Nevertheless, available clustered data on therapy efficacy and safety do not reflect the high variability of individual drug response, which is partly due to the genetic complex background behind MS.
The aim of identifying genetic markers able to predict treatment response and susceptibility to drug toxicity has led to the attempt of applying pharmacogenetics and pharmacogenomics approach to MS therapy. Pharmacogenetics is ‘the study of variations in DNA sequence in relation to drug response’ ( Screened genetic variants are single-nucleotide polymorphisms (SNPs), single-tandem repeats, gene deletions or amplifications, complex polymorphisms, haplotypes as well as, more recently, germline variants, RNA and epistatic regulations. The aim of this review is to address the most recent findings on pharmacogenetics/pharmacogenomics applied to MS, with regard to therapeutic response to approved drugs. Moreover, a comparison of various IFN-β studies on methods and clinical criteria used will be performed.
Current therapeutic options in MS
Drug treatment for MS is based on the use of several agents that have been licensed for their property to modify the disease course. They are classified as immunomodulatory or immunosuppressive agents, according to the targeted mechanism in the pathogenesis of MS.7 IFN-β with its formulations (IFNb-1a Avonex (Biogen-Idec), IFNb-1a Rebif (Merck-Serono), IFNb-1b Betaseron (Betaseron/Betaferon, Bayer-Schering)), GA (Copaxone, Teva), natalizumab (Tysabri, Biogen-Idec/Elan) and the recently licensed fingolimod (FTY720, Novartis) are immunomodulatory drugs, whereas MTX (Novantrone, Wyeth/Serono) is an immunosuppressive agent. Current approved therapies deal with the inflammatory phase of the disease, classically consisting in the activation of peripheral immune system with a self-tolerance breakdown in genetically susceptible individuals. Subsequently, activated T and B cells cross the blood–brain barrier and attack myelinated neurons.8 Neurodegenerative phase of the disease, starting with and following the inflammatory component, is characterized by axonal transection and neuronal loss.9 At that time, no approved or promising drug compounds are effective against neurodegeneration. Available drugs target different phases of the inflammatory process. In 1993, IFN-β was the first approved therapy by Food and Drug Administration (FDA) for MS, and it has been considered the mainstay of RRMS treatment. Instead, no drugs have been licensed for the treatment of primary-progressive MS.10, 11, 12 Adverse effects of IFN-β, flu-like syndrome, fatigue and depression, is known to interfere with compliance and long-term therapy continuation.13 Although GA as IFN-β, is able to reduce the relapse rate, disability progression and the formation of active and new lesion at magnetic resonance imaging (MRI), MTX is recommended for both active RRMS and secondary-progressive MS.14, 15 All three agents may block autoreactive T-cell reactivation within the central nervous system, thus reducing microglia stimulation and cytotoxic action on myelinated neurons.16
Natalizumab is the first monoclonal antibody approved for the treatment of RRMS.17 It is extremely efficient in inhibiting T-cell migration across the blood–brain barrier, mechanism that involves the interaction between leukocyte adhesion molecules and their complementary ligands on vascular endothelial cells.8
Although these drugs are able to reduce the frequency of relapses and partially delay disease progression, they are still far from providing the desired risk–benefit profile. In line with these considerations, the use of natalizumab and MTX, more efficacious than IFN-β and GA, is limited by unfavorable adverse effect profiles, that is, progressive multifocal leucoencephalopathy from JC virus infection for natalizumab and leukemia and cardiotoxicity for MTX therapy.18 Fingolimod, approved by FDA in October 2010, has unique immunomodulatory properties. It modulates sphyngosine-1-phosphate receptor activity by inducing a continued signaling via internalization of the receptor on T lymphocytes and preventing their egress to inflammatory tissue.19 As a consequence, CD4 and T helper 17 (Th17) cells are mainly retained within lymph nodes. Fingolimod has shown in clinical trials an outstanding efficacy in reducing the number of relapses (54%), disease progression and the number of active lesions at MRI, significantly higher than weekly IFN-β 1a.20 Adverse events occurred during fingolimod trials were generalized herpetic infections (three cases, two deaths) and transient bradycardia. However, to assess the real incidence of adverse events during fingolimod therapy, long-term post-marketing observational studies are needed.
Pharmacogenetics in MS
The most pharmacogenetic studies published until now on MS drugs have been related with IFN-β. Consequently, this review will focus on IFN-β with respect to GA, MTX, natalizumab and fingolimod.
IFN-β is a pleiotropic cytokine secreted by nucleated cells binding to a heterodimeric receptor (IFNAR1/IFNAR2). Numerous biological effects of IFN-β were identified even if its action mechanism is not completely clarified. The activation of Janus kinase (JAK)–signal transducers and activators of transcription (STAT) signaling pathway, following interaction between IFN-β and IFNAR1 and IFNAR2, is crucial in the regulation of immune system functions, leading to the expression of genes that influence cell proliferation, apoptosis and responses to viruses (Figure 1).21, 22 More than 100 genes are involved in IFN-β pathway and all may be considered attractive candidates for pharmacogenetic responsive markers. Among these, it was observed that interleukin-8 (IL-8) is a single informative gene downregulated in responders both ex vivo and in vitro.23, 24 This finding was also strengthen by the observation that IL-8 receptor is downregulated during MS treatment with natalizumab.25
Figure 1.
Action mechanism of IFN-α/β. (a) The interaction between IFNAR receptor, composed of two subunits IFNAR1 and IFNAR2, and IFN-α/β induces an immediate transcriptional response through JAK–STAT signal transduction pathway. (b) The JAK–STAT system involves a cascade of molecular effectors that includes the membrane receptor, the cytoplasmic JAK and the transcription factors STATs. After signal, JAK autophosporylates itself, then activating STAT proteins. (c) STATs pass from the cytoplasm to the nucleus, promoting transcription of genes responsive to STAT. AAF, IFN-α-activated factor; IFN, interferon; ISRE, IFN-stimulated response elements; JAK, Janus kinase; GAS, IFN-γ-activated sequence; ISGF3, interferon-stimulated gene factor 3; IRF9, interferon regulatory factor 9; STAT, signal transducers and activators of transcription; TYK2, tyrosine kinase 2.
Several other genes are differentially expressed in partial, good and no responders. In a complementary DNA array study, a good response was associated with a lower expression of IFNAR1, IL-8 and CASP10 genes and a higher expression of MxA, STAT1, IFNAR2, IRF1, B2M, IFITM1, IL-6 and TGFB2 genes.23 Chronic administration of IFN-β attenuated differences of expression among these genes, mainly by reducing the expression of genes involved in the type I IFN pathway in long-term non-responder patients. The role of type I IFN signaling pathway genes in determining the clinical response to IFN-β has been confirmed in the other studies. Comabella et al.26 found that eight of these genes, in particular five IFN-inducible genes, IFIT3, IFIT1, OASL, IFI44 and IFIT2, were able to predict IFN-β response.
MxA gene Myxovirus-influenza-resistance A (MxA) protein, involved in JAK–STAT and immune pathways, is the most widely investigated. It belongs to dynamin superfamily of high molecular mass GTPases found in yeast, plant and animal cells.27 These GTPases have a key role in fundamental cellular processes and they are particularly involved in resistance to virus infection.28 A study conducted by Lampe et al.29 on human tissue from autopsy or biopsy of different brain pathologies showed that MxA is not expressed in MS samples. In contrast, a recent study of Al-Masri et al.30 on autoptic MS brain lesions revealed a positive staining for MxA protein in active MS, associated to early myelin degradation products and/or perivascular inflammation. In any case, the presence of different functional IFN-stimulated response elements in the promoter of MxA gene led to investigate them as a possible quantitative biomarker of the effect of IFN-β treatment in MS.31
By enzyme-linked immunosorbent assay, Kracke et al.32 observed an increase in MxA protein in peripheral blood mononuclear cells from patients with RRMS after IFN-β treatment. In an open-label pharmacodynamic study of Weinstock-Guttman et al.,23 time-dependent mRNA expression changes were evaluated. Several IFN-inducible genes underlying antiviral responses, JAK-–STAT and immune pathways, including MxA, seemed to be prominently induced after IFN-β treatment.21 A following study, in which 22 patients were involved, the effect of the first IFN-β dose to that of chronic weekly administration (6 and 12 months) was compared focusing on individual genetic pattern and therapeutic efficacy. Authors concluded that although MxA protein is not an ideal marker to distinguish good from partial IFN-β responders, it may be considered a good marker of IFN activity.23 This consideration was already confirmed by Serrano-Fernandez et al.33 through microarray assay on MS patients during one year of follow-up.
Moreover, 227 genes differentially regulated were individuated by an IFN-β trial with healthy volunteers. Among these, MxA gene displayed dynamic expression kinetics with low inter-individual variability.34
IFNAR1 and IFNAR2 genes A pharmacogenetic study evidenced an association between rs1012334 polymorphism of IFNRA1 gene and the type of response to IFN-β.35 Nevertheless, other studies failed to demonstrate that IFNRA1 gene polymorphisms, included rs1012334, can distinguish responders from non-responders.36 Also Leyva et al.37 confirmed that genomic variations in IFNAR1 and IFNAR2 genes are not relevant to IFN-β therapy response while, for the first time, it reported an association between allelic variations, such as IFNAR1 18417 and IFNAR2 11876, with MS. Specifically, IFNAR118417 C/C and IFNAR2 11876 G/G genotypes were associated with a fivefold and twofold higher risk to develop MS, respectively. This study lacks of a link between individuated SNPs and data expression.
Expression results of a recent work revealed that a good clinical response had a significant decrease in IFNAR1 and IFNAR2 expression.38 These data suggest that the modulation in IFNAR subunits expression may be one of the main mechanisms regulating cell responsiveness to type I IFN. Nevertheless, the study did not find any correlation between the expression of IFNAR1 and IFNAR2 genes and the IFN-activity MxA marker. These results contrast with other studies. Dividing IFN-treated patients in MxA-induced and -non-induced subgroups, Serana et al.39 demonstrated that IFNAR1 expression level was similar to controls in MxA-induced subgroup, remaining instead significantly lower in the other subgroup. Therefore, it seems that IFNAR1 expression may be influenced by IFN biological activity, then revealing a possible association between MxA and IFNAR1 proteins.
Finally, a genome-wide association study (GWAS) showed that IFNAR1 and IFNAR2 SNPs are not associated with IFN-β response.40
Other genes as possible markers Several experimental approaches were utilized to individuate other possible markers (Table 1). As the only two performed GWASs reported, several novel SNPs able to significantly distinguish responders from non-responders to IFN-β were individuated.26, 40 It was speculated that glypican, encoded by GPC5 gene, might participate in the IFN-β/IFNAR1/2 interaction, increasing or decreasing the binding affinity.41 Glypicans are heparan sulfate proteoglycans that are anchored to the outer surface of the plasma membrane by a glycosyl-phosphatidylinositol residue. They are implicated in synapse formation, axon regeneration and they are found in dense networks in active MS plaques, where they may be involved in sequestering pro-inflammatory chemokines.42 According to Byun et al.,40 IFN-β may affect the expression of glypicans, then influencing neuronal growth and repair. The association between GPC5 gene and the response to IFN-β therapy was observed by an independent case–control study.43

Polymorphisms in extracellular matrix proteins, such as hyaluronan proteoglycans and collagen, might also alter the efficacy of IFN-β therapy.40 Nevertheless, the involvement of hyaluronan proteoglycan link protein was not confirmed by Cenit et al.43 According to Comabella et al.,26 the strongest association with the clinical response to IFN-β was found for ZFAT, ZFHX4, STARD13 and GRIA3 genes. ZFAT and ZFHX4 genes encode zinc-finger proteins involved in immune-regulation, apoptosis and neural differentiation.44, 45 STARD13 protein belongs to Rho family guanosine triphosphatase-activating protein, whereas the GRIA3 protein is an AMPA-type glutamate receptor.26 In contrast, some genetic variants of IL-10, such as rs1800872 and rs1800896, showed no association with the response to type I IFN therapy in MS,46 whereas in hepatitis C they were correlated to IFN-β response.26, 40, 47
Finally, a recent performed microarray analysis confirmed not only previously reported associations but it firstly individuated two upregulated genes, suppressor of cytokine signaling-1 (SOCS1—alias CISH) and insulin-like growth factor-binding protein 7 (IGFBP7).48 SOCS1 is involved in the negative regulation of cytokine signaling by the inhibiting STAT proteins and CD40. Aberrant expression of this protein is implicated in human diseases, such as MS and rheumatoid arthritis.49 Instead, no association data are available on IGFBP7 gene and diseases. Finally, in two more recent works APOE epsilon (ε) genotypes were investigated. In the first studies, the APOE ε2 or ε4 alleles showed not significantly association with clinical response to IFN-β. Moreover, also the second study failed to find relevant associations, although a positive relation between the allele ε2 and a moderate disability was reported.50, 51
Methodological considerations on pharmacogenetic IFN-β studies
The use of innovative strategies of study and integrated efforts of more actual disciplines, for example, molecular biology, neurology and bioinformatics, led to the individuation of several potential IFN-β response markers. Nevertheless, not all studies showed the same results. The major discrepancies between pharmacogenetic studies may depend on differential strategies of studies or molecular screening methods. In order to test a pharmacogenetic question, two most common strategies were designed: the candidate-gene and GWAS. Actually, GWAS is the more applied in pharmacogenetics. Large sets of SNPs across the genome are examined conducting the analysis as a case–control, cohort, or family study. The requirement for a large clinical sample size and the high cost of whole-genome SNP panels for GWASs compared with the candidate-gene approach are the limiting factors to using this method, although they hold great potential for contributing to the understanding of complex disease development. In MS, genome-wide pharmacogenomic analysis are very recent. The first study conducted by Byun et al.40 consisted in a multianalytical approach by using judicious implementation of DNA pooling on SNP microarrays (Affymetrix 100K, Santa Clara, CA, USA). Although genetic associations previously reported were not confirmed, the study identified candidate genes and under-scored the genetic heterogeneity underlying response to IFN-β therapy in MS. The following GW Scan of Comabella et al.26 was performed using a higher-density SNP array (Affymetrix 500K) and a more stringent clinical criteria to identify treatment failure. In this manner, the study confirmed the relationship between neuronal excitation and IFN-β treatment, as suggested by Byun et al.,40 but did not replicate associations with the same genes but only with rs4131514 ADAR mutation. The lack of replicated results may be due to different power of study. In particular, several association studies show inappropriate sample size, then revealing unrepeatable results when the case group is enlarged.
Although in the future higher-density SNP screens and further replication studies are needing, these studies are instrumental in bringing the concept of personalized medicine a step closer to the MS patient. In addition, they have generated a flurry of novel information of likely importance in furthering our understanding of type I IFN biology.
Also the contribution of expression studies was fundamental in revealing candidate markers of IFN-β activity. Most of these were performed by different in vitro cell types, such as human fibrosarcoma cell line and human umbilical vein endothelial cells and by microarray techniques.52, 53 In order to overcome limitations imposed by in vitro model, Sturzebecher et al.24 realized an innovative complementary DNA array approach. It compared in vitro pre-treatment gene expression profile, obtained by Mini-Lymphochip cDNA microarrays, with in vivo biological response and ex vivo experiments. Moreover, the transition to in vivo IFN-β exposure analysis was necessary. In 2003, Weinstock-Guttman et al.21 submitted RNA (isolated by monocytes depleted from the peripheral blood mononuclear cell) of MS patients to GeneFilters GF211 DNA arrays containing >4000 known genes. In this manner, it was able to demonstrate that in vivo IFN-β treatment induces specific and time-dependent mRNA changes in lymphocytes of MS patients. This provided a framework for rapid monitoring of the response to therapy. However, the use of spotted DNA arrays on nylon filters involved a significantly lower sensibility than slide-based microarrays. Then, in 2008 the same author replicated the array analysis using a computational approach to compare pharmacogenomic effects of the first IFN-β dose with chronic administration (6 and 12 months of dosing) for the once-weekly dose. By this prospective approach, he found a relationships between pharmacogenomic factors and therapeutic efficacy, evidencing that gene expression may be a valuable tool for understanding the molecular mechanisms of IFN-β action.23
In order to achieve this object, it might be useful to correlate genotype versus gene expression changes. So far, reported studies lack of this aspect. Several other points may be discussed.
For example, most of pharmacogenetic investigations are designed as retrospective case–control rather than prospective follow-up studies. This last study might allow us to obtain a better profiling of sensitivity, absolute risks, predictive values and also a better evaluation of environmental influence. Moreover, works in which potential pharmacogenetic markers were individuated, showed as limit their low allelic frequency, then making difficult their validation and clinical implementation. SNP screenings more complete, involving the copy number variations, could give more information to easily identify potential risk factors in IFN-β response.
Clinical considerations on pharmacogenetic IFN-β studies
The meaning of ‘responder’ is highly variable in literature. The lack of consensus on response criteria causes discrepancies that may affect study results. Treatment failure may be defined according to the less rigorous criteria requiring one or more relapses or constant progression of 0.5 or more points on the Expanded Disability Status Scale (EDSS) after 1 year of treatment or more stringent criteria involving the presence of relapses and sustained progression of 1 or more points on EDSS after 2 years of treatment.54 According to Rio et al.,55 response criteria based on disability progression were more clinically relevant than those based only in relapse rate in RRMS, but a greater accuracy was obtained when clinical criteria were supplemented with radiological parameters. Indeed, MRI criteria are more sensitive than clinical criteria, but the low concordance between MRI and clinical data in relation to the progression of disability leads to the so-called clinical–MRI paradox. Until now, its relevance in the assessment of individual treatment response in MS patients in the daily clinical practice has not been demonstrated. Several MS pharmacogenetic/pharmacogenomic studies are based on clinical criteria;26, 37, 56, 57, 58 instead other authors used clinical and MRI criteria.24, 59, 60, 61 Waubant et al. distinguished responder and non-responder patients simply on the base of the reduction of annualized relapse rate. Non-responder criteria used by Leyva et al. were an increment of one or more relapses with respect to the previous year or an increase in EDSS score of 0.5 points or more after the first year of treatment. In contrast, Enevold et al. applied more stringent criteria to distinguish responder from non-responders patients. Disease progression was defined as a sustained increase in EDSS score for at least 6 months of 1 point, 1.5 points if the baseline EDSS score was 0, and 0.5 points if the baseline EDSS was 6.0 or above. Non-responders were defined as patients having either disease progression after 2 years of treatment, or a Sturzebecher's definition of drug-responder is based on a combination of clinical and MRI criteria, as previously defined by Stone et al.62 (60% reduction of total enhancing lesion). In a randomized placebo-controlled IFN-β study, Rudick et al.60 classified patients as responders or non-responders by the number of relapses during the 2-year trial, the number of new T2 lesions after 2 years and the number of gadolinium-enhancing lesions at year 1 and year 2 on study. MRI response was defined as
Clinical and MRI outcomes, relapse rate and number of enhancing lesions reflect the inflammatory component of MS, whereas the disability progression, as assessed by the EDSS and the number of new T2 lesions, partially reflects neurodegenerative component of MS. In attempt to individuate a consensus definition of ‘responder’, it seems more appropriate to favor neurodegenerative aspect with respect to inflammatory component of MS, because neurodegenerative component is considered the primary determinant of the neurological deficits in MS patients. For this reason, Macciardi et al.63 affirmed that a consensus definition may be that a sustained and confirmed worsening is equal to at least 1.0 point EDSS score if the baseline EDSS
All these reported data revealed that there are a considerable variability in defining who is a ‘responder’ patient. Moreover, the lack of rigorous and consistent clinical–MRI criteria introduces a high degree of inconsistency across the various studies making impossible any direct comparison. Consequently, the importance and utility of surrogate markers able to measure therapeutic response is increasing.
Glatiramer acetate
GA is a pool of synthetic peptides with an average length of 40 to 100 residues. It competes for ligand presentation and promotes the shift of the Th cells to the anti-inflammatory Th2 phenotype (Figure 2).64, 65 GA seems also to induce neurotrophic factor secretion by immune cells.66
Figure 2.
Action mechanism of GA. GA acts on ‘signal one’ of T-cell activation by binding to MHC class II molecules irrespective of haplotype, and possibly cross-reacts with several CNS antigens. It promotes the shift of the TH cells to the anti-inflammatory TH2 phenotype which, once reached the CNS, induce the production of anti-inflammatory factors. APC, antigen-presenting cell; BBB, blood–brain barrier; CNS, central nervous system; GA, glatiramer acetate; IL-4, interleukin-4; IL-10, interleukin-10; MHC, major histocompatibility complex; TGF-beta: transforming growth factor beta; TH2, T helper cells.
However, a significant proportion of MS patient appears to experience modest benefit from GA treatment. Genetic variants affecting the clinical response to GA may be believed to be relevant as biomarkers of GA treatment efficiency.
Fusco et al.67 investigated the role of 27 candidate genes in relation to GA response. Although this study was conducted in a small patient cohort, a correlation between HLA-DRB1*1501 and the response to GA was demonstrated. Cathepsin S (CTSS) gene was considerably investigated in relation to both IFN-β and GA treatments. This gene encodes CTSS, a lysosomal enzyme that has a significant role in the activation of certain immune responses. As such, its role in the degradation of antigens for presentation to major histocompatibility complex class II molecules is crucial.68 Moreover, in vitro CTSS protein was reported to be functional in the proteolytic processing of human myelin basic protein.69 An association between CTSS gene with both IFN-β treatment response and disease susceptibility was found.36 Grossman et al.70 demonstrated that also an association between GA response and CTSS gene was significant, although this result was not repeated in more recent studies. However, the study of Cunningham et al.36 revealed that rs3754212 of CTSS gene was significant associated to MS susceptibility, whereas another SNP, rs1136774, showed association with recombinant IFN-β response. The presence of association with both drugs suggests that CTSS gene may have a critical role in self-antigen generation in MS.71
Moreover, a higher expression level of IL-4/IFN-γ in ex vivo-stimulated peripheral blood mononuclear cells was significantly correlated with response to GA, leading to a reduction of relapse rates.72 Certainly, progresses in the pharmacogenetic field tend to advance. A recent work analyzed nine polymorphisms in candidate genes as possible determinants of GA response in 285 Russian MS patients. The combination of several alleles DRB1*15+TGFB1*T+CCR5*d+IFNAR1*G and DRB1*15+TGFB1*T+CCR5*d had a 14 to 15 times increased risk of ineffective response to GA therapy with respect to individual SNPs.73
MTX, natalizumab and fingolimod
MTX, a topoisomerase II inhibitor, prevents the successful unwinding of DNA and counteracts immune cell proliferation and migration, then promoting apoptosis (Figure 3).16
Pharmacogenomic studies have focused the attention on the correlation between MTX response and ATP-binding cassette (ABC) transporters, ABCB1 and ABCG2, in both cancer and MS. ABC transporters are multidomain integral membrane proteins that translocate solutes across cellular membranes in all mammalian species.74 They are central to many important biomedical phenomena including drug absorption, distribution and excretion.75 In the central nervous system, they influence the accumulation of different relevant pharmacological substances, for example, in brain cancer, infections, epilepsy and psychiatric diseases. The investigation of ABC SNP role in clinical MTX response was corroborated by in vitro and in vivo experimental data. As a results, ABCB1 2677 G>T, 3435C>T, ABCG2 V12M and Q141K were retrospectively correlated to clinical MTX response, whereas ABCB1/ABCG2-H had lowest responder rate (62.5%) than ABCB1/ABCG2-L (84.8%). It seems that biological effects of genetic patterns involved substrate efflux leading to enhance of cell sensitivity to MTX.76 In conclusion, these SNPs could be considered as pharmacogenetic markers of MTX therapy, although they were not replicated so far.
Instead, few pharmacogenetic data are available in literature on natalizumab and fingolimod. Immune markers were investigated in natalizumab treatment response. Millonig et al.77 reported an association between natalizumab treatment and the reduction in the levels of the soluble vascular cell adhesion molecule-1 and expression of CD49d compared with untreated and IFN-β-treated MS patients and healthy controls. Moreover, a reduction of proinflammatory cytokine and chemokine levels in cerebrospinal fluid after natalizumab treatment (maintained for 1 year) was reported by Mellergard et al.78 In particular, IL-1β, IL-6, IL-8, CXCL9, CXCL10 and CCL2 were the most susceptible to treatment. However, these results are in disagreement with a previous study showing increased mRNA expression levels of proinflammatory cytokines such as IFN-γ and tumor necrosis factor-α in peripheral blood cells.79
It was demonstrated that fingolimod is able to inhibit lymphocyte egress from secondary lymphoid organs into the peripheral circulation, thereby reducing the number of circulating naive and central memory T cells, but not effector memory T cells in blood. A study conducted by Mehling et al.80 reported that in peripheral blood Th17 levels decreased after treatment with fingolimod. According to the authors, this result perfectly matches with the action mechanism of fingolimod, as previously described.
Certainly, new knowledge in clinical response to these drugs will allow the individuation of the causes at the base of individual response.
Conclusions and future perspectives
MS management suffered by a few modifications regarding strategy to using approved drugs. This scenario might ameliorate having biomarkers of response to therapy able to ensure good clinical response, patient compliance and protection against the more severe side effects. Recently, the armamentarium for the treatment of RRMS is rapidly increasing. Fingolimod was recently approved for RRMS by the FDA and by the Committee for Medicinal Products for Human Use (CHMP) of the European Medicines Agency. Other agents are testing and results of placebo-controlled phase 3 trials of oral MS treatment are encouraging ( Certainly, solid knowledge in pharmacogenetic/pharmacogenomic MS field may be useful also to identify drug targets and to introduce new therapeutic agents in clinical practice.
Unfortunately, efforts from current research on personalized medicine in MS were not sufficient for different reasons. Current design of MS studies often lacks a correct sample size and, as mentioned above, a consensus definition of ‘responder’. This is an absolute need to avoid discordant results from studies and to have, instead, reliable results. Furthermore, the achievement of response criteria more extreme may lead to a more decisive classification of intermediate responders.
Although the complex nature of the disease negatively influences genetic screens, it still can be done. For example, the role of gene-dose effects could be investigated than evaluating the copy number variation or by developing a more sophisticated computational analysis for genetic interaction networks.
Actually, high expectations are directed on GWA scans, which permit to carry out large studies. Then, although the two existent MS GW pharmacogenomic studies on IFN-β response are small and short of genome-wide significance, they allowed to individuate numerous associated markers with drug response. The following step, such as the validation of selected markers, implies the development of reproducible standardized analysis methods in controlled clinical settings.
However, the impact of pharmacogenetic testing on health economics might be enormous, by reducing unnecessary treatment and minimizing cost incurred during management of treatment-related toxicities and hospitalizations. In particular, natalizumab and fingolimod therapies are more expensive. Nevertheless, several important side effects are known, they are used in clinical practice because they are more efficacious. In this condition, a pharmacogenetic support for clinicians may be important to have a therapeutic approach more rigorous and accurate.
Considering the dropping cost of genotyping, the incorporation of genomic scans in patient evaluation becomes a dynamic and ongoing process that should be constantly submitted to check from authorities, in order to allow for more accessibility for genotyping and its benefits as more evidence becomes available.
Indeed, the provided guidelines for genomic data management, pharmacogenetic testing and designing of adaptive clinical trials have been implemented to support genomic and personalized pharmacological treatment.
Although the full application of pharmacogenetics into clinical practice will require dramatic changes in regulations and legislative protection, benefits will be notable.

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