Effect of polymorphisms in drug metabolism and transportation on plasma concentration of atorvastatin and its metabolites in patients with chronic kidney disease

Quantification of urinary albumin in clinical samples using smartphone enabled LFA reader incorporating automated segmentation

Smartphone-assisted urine analyzers estimate the urinary albumin by quantifying color changes at sensor pad of test strips. These strips yield color variations due to the total protein present in the sample, making it difficult to relate to color changes due to specific analyte. We have addressed it using a Lateral Flow Assay (LFA) device for automatic detection and quantification of urinary albumin. LFAs are specific to individual analytes, allowing color changes to be linked to the specific analyte, minimizing the interference. The proposed reader performs automatic segmentation of the region of interest (ROI) using YOLOv5, a deep learning-based model. Concentrations of urinary albumin in clinical samples were classified using customized machine learning algorithms. An accuracy of 96% was achieved on the test data using the k-Nearest Neighbour (k-NN) algorithm. Performance of the model was also evaluated under different illumination conditions and with different smartphone cameras, and validated using standard nephelometer.

Genotype-Guided Model for Prediction of Tacrolimus Initial Dosing After Lung Transplantation

The determination of the appropriate initial dose for tacrolimus is crucial in achieving the target concentration promptly and avoiding adverse effects and poor prognosis. However, the trial-and-error approach is still common practice. This study aimed to establish a prediction model for an initial dosing algorithm of tacrolimus in patients receiving a lung transplant. A total of 210 lung transplant recipients were enrolled, and 26 single nucleotide polymorphisms (SNP) from 18 genes that could potentially affect tacrolimus pharmacokinetics were genotyped. Associations between SNPs and tacrolimus concentration/dose ratio were analyzed. SNPs that remained significant in pharmacogenomic analysis were further combined with clinical factors to construct a prediction model for tacrolimus initial dose. The dose needed to reach steady state tacrolimus concentrations and achieve the target range was used to validate model prediction efficiency. Our final model consisted of 7 predictors-CYP3A5 rs776746, SLCO1B3 rs4149117, SLC2A2 rs1499821, NFATc4 rs1955915, alanine aminotransferase, direct bilirubin, and hematocrit-and explained 41.4% variance in the tacrolimus concentration/dose ratio. It achieved an area under the receiver operating characteristic curve of 0.804 (95% confidence interval, 0.746-0.861). The Hosmer-Lemeshow test yielded a nonsignificant P value of .790, suggesting good fit of the model. The predicted dose exhibited good correlation with the observed dose in the early postoperative period (r = 0.748, P less than .001). Our study provided a genotype-guided prediction model for tacrolimus initial dose, which may help to guide individualized dosing of tacrolimus in the lung transplant population in clinical practice.

Atorvastatin-associated myotoxicity: A toxicokinetic review of pharmacogenetic associations to evaluate the feasibility of precision pharmacotherapy

Atorvastatin (ATV) and other statins are highly effective in reducing cholesterol levels. However, in some patients, the development of drug-associated muscle side effects remains an issue as it compromises the adherence to treatment. Since the toxicity is dose-dependent, exploring factors modulating pharmacokinetics (PK) appears fundamental. The purpose of this review aims at reporting the current state of knowledge about the singular genetic susceptibilities influencing the risk of developing ATV muscle adverse events through PK modulations. Multiple single nucleotide polymorphisms (SNP) in efflux (ABCB1, ABCC1, ABCC2, ABCC4 and ABCG2) and influx (SLCO1B1, SLCO1B3 and SLCO2B1) transporters have been explored for their association with ATV PK modulation or with statin-related myotoxicities (SRM) development. The most convincing pharmacogenetic association with ATV remains the influence of the rs4149056 (c.521 T > C) in SLCO1B1 on ATV PK and pharmacodynamics. This SNP has been robustly associated with increased ATV systemic exposure and consequently, an increased risk of SRM. Additionally, the SNP rs2231142 (c.421C > A) in ABCG2 has also been associated with increased drug exposure and higher risk of SRM occurrence. SLCO1B1 and ABCG2 pharmacogenetic associations highlight that modulation of ATV systemic exposure is important to explain the risk of developing SRM. However, some novel observations credit the hypothesis that additional genes (e.g. SLCO2B1 or ABCC1) might be important for explaining local PK modulations within the muscle tissue, indicating that studying the local PK directly at the skeletal muscle level might pave the way for additional understanding.

When the same treatment has different response: The role of pharmacogenomics in statin therapy

Statins, also known as HMG-CoA reductase inhibitors, are one of the most potently prescribed and thoroughly researched medications, predominantly utilized for managing cardiovascular diseases by modulating serum cholesterol levels. Despite the well-documented efficacy of statins in reducing overall mortality via attenuating the risk of cardiovascular diseases, notable interindividual variability in therapeutic responses persists as such variability could compromise the lipid-lowering efficacy of the drug, potentially increasing susceptibility to adverse effects or attenuating therapeutic outcomes.This phenomenon has catalysed a growing interest in the scientific community to explore common genetic polymorphisms within genes that encode for pivotal enzymes within the pharmacokinetic pathways of statins. In our review, we focus to provide insight into potentially clinically relevant polymorphisms associated with statins' pharmacokinetic participants and assess their consequent implications on modulating the therapeutic outcomes of statins among distinct genetic carrier.