Association of SLCO1B1 and ABCB1 Genetic Variants with Atorvastatin-induced Myopathy in Patients with Acute Ischemic Stroke

Zhang, Limin; Lv, Hong; Zhang, Qian; Wang, Dongzhi; Kang, Xiaojian; Zhang, Guojun; Li, Xingang

doi:10.2174/1381612825666190705204614

Cited by 14 publications

(9 citation statements)

References 24 publications

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“…ABCB11, a primary transporter of bile salts [76], was found to be involved in the biliary excretion of statins [77]. SLCO1B1 and SLCO1B3 are responsible for the uptake of atorvastatin into hepatocytes [78, 79]. Thus, the removal of these three transporters from atorvastatin’s TET profile may increase its plasma concentration, increasing risks of ILD [78, 79].…”

Section: Resultsmentioning

confidence: 99%

Prediction of adverse drug reactions associated with drug-drug interactions using hierarchical classification

Kim¹,

Tatonetti

2021

Preprint

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Adverse drug reactions (ADRs) associated with drug-drug interactions (DDIs) represent a significant threat to public health. Unfortunately, most conventional methods for prediction of DDI-associated ADRs suffer from limited applicability and/or provide no mechanistic insight into DDIs. In this study, a hierarchical machine learning model was created to predict DDI-associated ADRs and pharmacological insight thereof for any drug pair. Briefly, the model takes drugs' chemical structures as inputs to predict their target, enzyme, and transporter (TET) profiles, which are subsequently utilized to assess occurrences of ADRs, with an overall accuracy of ~91%. The robustness of the model for ADR classification was validated with DDIs involving three widely prescribed drugs. The model was then applied for interstitial lung disease (ILD) associated with DDIs involving atorvastatin, identifying the involvement of multiple targets, enzymes, and transporters in ILD. The model presented here is anticipated to serve as a versatile tool for enhancing drug safety.

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Section: Resultsmentioning

confidence: 99%

Prediction of adverse drug reactions associated with drug-drug interactions using hierarchical classification

Kim¹,

Tatonetti

2021

Preprint

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“…SLCO1B1 has an important influence on the absorption, distribution and elimination of endogenous and exogenous substances, especially drugs ( Zhang et al, 2019 ). For anti-TB drugs, in addition to being closely related to drug metabolism enzymes as mentioned above, the mutation of SLCO1B1*15 may also increase the susceptibility to liver injury induced by rifampicin ( Li et al, 2012 ).…”

Section: Dili and Gene Polymorphismmentioning

confidence: 99%

Research Progress of Pharmacogenomics in Drug-Induced Liver Injury

et al. 2021

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Background: Drug-induced liver injury (DILI) is a common and serious adverse drug reaction with insufficient clinical diagnostic strategies and treatment methods. The only clinically well-received method is the Roussel UCLAF Causality Assessment Method scale, which can be applied to both individuals and prospective or retrospective studies. However, in severe cases, patients with DILI still would develop acute liver failure or even death. Pharmacogenomics, a powerful tool to achieve precision medicine, has been used to study the polymorphism of DILI related genes.Summary: We summarized the pathogenesis of DILI and findings on associated genes and variations with DILI, including but not limited to HLA genes, drug metabolizing enzymes, and transporters genes, and pointed out further fields for DILI related pharmacogenomics study to provide references for DILI clinical diagnosis and treatment.Key Messages: At present, most of the studies are mainly limited to CGS and GWAS, and there is still a long way to achieve clinical transformation. DNA methylation could be a new consideration, and ethnic differences and special populations also deserve attention.

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“…In tPA-naı ¨ve patients with high risk of bleeding, Aspirin is administered as a part of anti-coagulative therapy. Also, high-intensity statins like Atorvastatin can be administered to stabilize the patient (Tuttolomondo et al 2016;Zhang et al 2019;Amarenco et al 2019). Finally, if clinically appropriate, treatment of deep vein thrombosis can be considered (Ha et al 2020).…”

Section: Cerebrovascular Disease and Strokementioning

confidence: 99%

Central nervous system as a target of novel coronavirus infections: Potential routes of entry and pathogenic mechanisms

et al. 2021

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Since the COVID-19 pandemic started in December 2019, there have been several reports of patients succumbing to neurological complications. Early reports were suggestive of a possibility, while by early 2020 it was clearly evident that although SARS-CoV-2 primarily attacks the respiratory system, the brain is one of the most affected organs post-recovery. Although it may be premature to comment on the long-term effects of COVID-19 in brain, some reliable predictions can be made based on the data currently available. Further, exploring the CNS connections of SARS-CoV-2 is of keen interest for neuroscience researchers. As soon as the virus enters the nasal region, it is exposed to the olfactory nervous system which is interlinked with the visual system, and hence we explore the mechanism of entry of this virus into CNS, including brain, olfactory and retinal nervous systems. In this review, we have thoroughly reviewed reports about both SARS-CoV-1 and SARS-CoV-2 with respect to their ability to breach the blood-brain and blood-retinal barriers. We have compiled different neurological conditions resulting from COVID-19 and looked into viral infections related to COVID-19 to understand how the virus may gain control of the olfactory and visual systems. Once the dust settles on the pandemic, it would be interesting to explore the extent of viral infection in the CNS. The longterm effects of this virus in the CNS are not yet known, and several scientific research papers evolving in this field will throw light on the same.

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Association of SLCO1B1 and ABCB1 Genetic Variants with Atorvastatin-induced Myopathy in Patients with Acute Ischemic Stroke

Cited by 14 publications

References 24 publications

Prediction of adverse drug reactions associated with drug-drug interactions using hierarchical classification

Prediction of adverse drug reactions associated with drug-drug interactions using hierarchical classification

Research Progress of Pharmacogenomics in Drug-Induced Liver Injury

Central nervous system as a target of novel coronavirus infections: Potential routes of entry and pathogenic mechanisms

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