Pharmacogenomics strategies to optimize treatments for multiple sclerosis: Insights from clinical research

Grossman, Iris; Knappertz, Volker; Laifenfeld, Daphna; Ross, Colin; Zeskind, Ben; Kolitz, Sarah; Ladkani, David; Hayardeny, Liat; Loupe, Pippa S.; Laufer, Ralph; Hayden, Michael R.

doi:10.1016/j.pneurobio.2016.02.001

Cited by 27 publications

(28 citation statements)

References 129 publications

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“…In addition, results from RCTs are based on group-level differences, while treatment decisions require individual-level information to enable optimal treatment allocation for an individual patient. This can cause a 'trial-and-error paradigm of treatment allocation' [1], in which the individual patient undergoes several therapy switches until a suitable treatment is found. Real-world data are gaining increasing importance to fill this gap between RCTs and utilization of treatment options in daily practice.…”

Section: Introductionmentioning

confidence: 99%

Framework for personalized prediction of treatment response in relapsing remitting multiple sclerosis

Stühler

Braune

Lionetto

et al. 2020

BMC Med Res Methodol

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Background: Personalized healthcare promises to successfully advance the treatment of heterogeneous neurological disorders such as relapsing remitting multiple sclerosis by addressing the caveats of traditional healthcare. This study presents a framework for personalized prediction of treatment response based on real-world data from the NeuroTransData network. Methods: A framework for personalized prediction of response to various treatments currently available for relapsing remitting multiple sclerosis patients was proposed. Two indicators of therapy effectiveness were used: number of relapses, and confirmed disability progression. The following steps were performed: (1) Data preprocessing and selection of predictors according to quality and inclusion criteria; (2) Implementation of hierarchical Bayesian generalized linear models for estimating treatment response; (3) Validation of the resulting predictive models based on several performance measures and routines, together with additional analyses that focus on evaluating the usability in clinical practice, such as comparing predicted treatment response with the empirically observed course of multiple sclerosis for different adherence profiles. Results: The results revealed that the predictive models provide robust and accurate predictions and generalize to new patients and clinical sites. Three different out-of-sample validation schemes (10-fold cross-validation, leave-one-site-out cross-validation, and excluding a test set) were employed to assess generalizability based on three different statistical performance measures (mean squared error, Harrell's concordance statistic, and negative log-likelihood). Sensitivity to different choices of the priors, to the characteristics of the underlying patient population, and to the sample size, was assessed. Finally, it was shown that model predictions are clinically meaningful. Conclusions: Applying personalized predictive models in relapsing remitting multiple sclerosis patients is still new territory that is rapidly evolving and has many challenges. The proposed framework addresses the following challenges: robustness and accuracy of the predictions, generalizability to new patients and clinical sites and comparability of the predicted effectiveness of different therapies. The methodological and clinical soundness of the results builds the basis for a future support of patients and doctors when the current treatment is not generating the desired effect and they are considering a therapy switch.

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Section: Introductionmentioning

confidence: 99%

Framework for personalized prediction of treatment response in relapsing remitting multiple sclerosis

Stühler

Braune

Lionetto

et al. 2020

BMC Med Res Methodol

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“…To date, GWA studies have identified different polymorphisms associated with response to IFN‐β in MS patients . Recently, genetic biomarkers for IFN‐β response in MS patients have been extensively reviewed by Coyle and other authors . More than a decade ago, Sriram et al evaluated the effects of 8 polymorphisms in the IFNAR1 and IFNAR2 in MS patients undergoing open‐label IFN‐β therapy.…”

Section: Current Knowledge About Pharmacogenetics Of Adsmentioning

confidence: 99%

“…[191][192][193] Recently, genetic biomarkers for IFN-β response in MS patients have been extensively reviewed by Coyle 196 and other authors. 197,198 More than a decade ago, Sriram et al 199 In another attempt at more efficient treatment, promoters of 100 interferon-inducible genes were sequenced. 200 Four genes (IFNAR, LMP7, CTSS and MxA) were recognized as being independently associated with treatment response to IFN-β therapy.…”

Section: 165-167mentioning

confidence: 99%

Pharmacogenetics: A strategy for personalized medicine for autoimmune diseases

2018

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For many years, a considerable number of patients with autoimmune diseases (ADs) have suffered from a lack of drug response and drug-related toxicity. Despite the emergence of new therapeutic options such as biological agents, patients continue to struggle with these problems. Unfortunately, new challenges, including the paradoxical effects of biological drugs, have complicated the situation. In recent decades, efforts have been made to predict drug response as well as drug-related side effects. Thanks to the many advances in genetics, evaluation of markers to predict drug response/toxicity before the initiation of treatment may be an avenue toward personalizing treatments. Implementing pharmacogenetics and pharmacogenomics in the clinic could improve clinical care; however, obstacles remain to effective personalized medicine for ADs. The present study attempted to clarify the concept of pharmacogenetics/pharmacogenomics for ADs. After an overview on the pathogenesis of the most common types of treatments, this paper focuses on pharmacogenetic studies related to the selected ADs. Bridging the gap between pharmacogenetics and personalized medicine is also discussed. Moreover, the advantages, disadvantages and recommendations related to making personalized medicine practical for ADs have been addressed.

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“…Genetic factors whilst very few are known are thought to contribute to MS co-morbidities, drug detoxification, adverse effects, and overall drug response. The disparity in patient response to MS disease-modifying drugs have gravitated MS research towards discovery of novel treatments and advocacy for personalized MS treatment research [4,5]. In recent years, second-line treatments have been developed to target MS patients that do not respond to first-line treatments such as beta-interferon derivatives and glatiramer acetate [6].…”

Section: Introductionmentioning

confidence: 99%

GP6 rs2304166 polymorphism is associated with response to natalizumab in multiple sclerosis patients

AlMojel

Alroughani

Kannankeril

et al. 2019

Mult Scler Demyelinating Disord

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Background: Currently, there are few established pharmacogenetic predictors of response to treatment in multiple sclerosis (MS) patients. Commonly used second line treatments include natalizumab that has proven to be successful in slowing MS progression in a subset of patients. Our aim was to identify pharmacogenetic factor(s) associated with MS patients' response to natalizumab.Methods: Ten MS patients not responding to natalizumab and 23 MS patients responsive to natalizumab were selected for exome sequencing. Exome sequences were analyzed and resultant variant was assessed in an additional cohort of 86 MS patients.Results: Exome analysis revealed a missense polymorphism (rs2304166) in glycoprotein VI (GP6) gene (19q13.42, c.940C > G, p.P314A) associated in homozygosity with MS poor response to natalizumab (p = 0.0012). An additional cohort of 86 MS patients of which 19 were natalizumab unresponsive and 67 responsive confirmed rs2304166 CC significant association with poor response to natalizumab (p = 0.0000027). In addition, rs2304166 CC MS patients had increased MS disability despite taking natalizumab (Odds ratio 22.18, 95% CI: 5.758-95.88, p = 0.00000001).Conclusion: Natalizumab is a commonly used second line treatment for MS, and poor response to natalizumab appears to be associated with rs2304166 CC genotype in MS patients. Our findings suggest the need to investigate GP6's rs2304166 role as a potential pharmacogenetic predictor for MS patients' response to natalizumab.

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Pharmacogenomics strategies to optimize treatments for multiple sclerosis: Insights from clinical research

Cited by 27 publications

References 129 publications

Framework for personalized prediction of treatment response in relapsing remitting multiple sclerosis

Framework for personalized prediction of treatment response in relapsing remitting multiple sclerosis

Pharmacogenetics: A strategy for personalized medicine for autoimmune diseases

GP6 rs2304166 polymorphism is associated with response to natalizumab in multiple sclerosis patients

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