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Genetic polymorphisms and adverse effects that affect the natalizumab clinical response: a review
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The clinical response to natalizumab in patients with multiple sclerosis (MS) may be significantly influenced by genetic variation. Mutations in genes related to the drug’s mechanism of action or the pathological milieu of MS can contribute substantially to interindividual differences in treatment outcomes. This review aims to provide an overview of previous studies that have examined genetic polymorphisms associated with the clinical efficacy of natalizumab. A systematic literature search was conducted across the PubMed, Google Scholar, and ResearchGate databases using targeted keywords relevant to the subject matter. Several genetic loci were found to be linked to natalizumab responsiveness, including the integrin subunit alpha 4 (ITGA4), the nicotinamide adenine dinucleotide phosphate (NADPH) quinone oxidoreductase 1 (NQO1), the glutathione S-transferase pi 1 (GSTP1), the glycoprotein VI platelet (GP6), and the alpha serine/threonine-protein kinase (AKT1) genes. Further research is warranted in order to explore the influence of genetic factors on treatment response across diverse populations. By synthesizing existing evidence, this review underscores the role of pharmacogenomics in optimizing the use of natalizumab and highlights its efficacy and safety in improving clinical outcomes.

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Publication Date
Mon Mar 31 2025
Journal Name
Iraqi Statisticians Journal
Hypothesis Testing for Non-Normal Multiple Compact Regression Model
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Generalized multivariate transmuted Bessel distribution belongs to the family of probability distributions with a symmetric heavy tail. It is considered a mixed continuous probability distribution. It is the result of mixing the multivariate Gaussian mixture distribution with the generalized inverse normal distribution. On this basis, the paper will study a multiple compact regression model when the random error follows a generalized multivariate transmuted Bessel distribution. Assuming that the shape parameters are known, the parameters of the multiple compact regression model will be estimated using the maximum likelihood method and Bayesian approach depending on non-informative prior information. In addition, the Bayes factor was used

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