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Toward pharmacogenetic SLCO1B1-guided dosing of methotrexate in arthritis using a murine Slco1b2 knockout model.

ABSTRACT: Low-dose methotrexate (MTX) is a first-line therapy for the treatment of arthritis. However, there is considerable interindividual variability in MTX exposure following standard dosing. Polymorphisms in SLCO1B1 significantly effect MTX clearance, altering therapeutic response. One decreased function variant, rs4149056 (c.521T>C, Val174Ala), slows MTX clearance and in vitro uptake of MTX. This phenotype was recapitulated in a mouse model using a knockout (KO) of the murine orthologue, Slco1b2. Our objective was to investigate the impact of this phenotype on the pharmacokinetics and therapeutic outcomes of low-dose MTX in a murine model of collagen-induced arthritis (CIA). We evaluated response to MTX in mice with CIA using wildtype (WT), heterozygous, and KO Slco1b2 mice on a DBA1/J background. Arthritis was macroscopically evaluated daily to quantify disease progression. Mice received 2 mg/kg or a pharmacogenetically guided MTX dose subcutaneously 3 times a week for 2 weeks. MTX concentrations were collected at the end of the study and exposure (day*µM) was estimated using a two-compartment model. Mice displayed a seven-fold range in MTX exposure and revealed a significant exposure-response relationship (p = 0.0027). KO mice receiving the 2 mg/kg dosing regimen had 2.3-fold greater exposure to MTX (p < 0.0001) and a 66% reduction in overall disease progression (p = 0.011) compared to WT mice. However, exposure and response were equivalent when pharmacogenetically guided dosing was used. These studies demonstrate that an exposure-response relationship exists for MTX and that Slco1b2 genotype affects MTX exposure and therapeutic response. Such evidence supports the use of SLCO1B1-pharmacogenetic dosing of low-dose MTX for patients with arthritis.

SUBMITTER: Taylor ZL

PROVIDER: S-EPMC8604247 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:Warfarin is the most common oral anticoagulant. Because of a narrow therapeutic range, interindividual differences in drug responses, and the risk of bleeding, there are many challenges in using warfarin. We need to predict the warfarin maintenance dose. However, ethnic-specific algorithms may be required, and some Chinese algorithms do not perform adequately. Therefore, we aimed to establish a Han Chinese appropriate algorithm.We recruited a study group consisting of 361 Han Chinese patients receiving warfarin treatment who had heart valve replacements. Genotyping of 38 single nucleotide polymorphisms (SNPs) in 13 candidate genes was carried out using the MassARRAY. In the derivation cohort, a multiple linear regression model was constructed to predict the warfarin dosage. We evaluated the accuracy of our algorithm in the validation cohort and compared it with the other 5 algorithms based on Han Chinese and other races.We established a Han Chinese-specific pharmacogenetic-guided warfarin dosing algorithm. Warfarin maintenance dosage (mg/day) = 1.787 - 0.023 × (Age) + 1.151 × (BSA [m]) + 0.917 × (VKORC1 AG) + 4.619 × (VKORC1 GG) + 0.595 × (CYP4F2 TT) + 0.707 × (CYP2C19 CC). It explained 58.3% of the variance in warfarin doses in Han Chinese patients and was superior to the other 5 algorithms. The ability of the 6 algorithms which estimate the required dose correctly was tested. Our model had a mean absolute error of 0.74 mg/day, the other 5 models have mean absolute error of 0.81 mg/day,1.05 mg/day, 1.24 mg/day, 1.18 mg/day, and 0.85 mg/day, respectively. Our model had a mean percentage error of 26.9%, the other 5 models have the mean percentage error of 27.7%, 27.2%, 52.3%, 45.7%, and 29.3%, respectively.Physicians can not adopt algorithm from other race directly to predict warfarin dose in patients with heart valve replacements, they should establish a new algorithm or adjust another algorithm to fit their patients. The algorithm established in this study has the potential to assist physicians in determining warfarin doses that are close to the appropriate doses.

Project description:ObjectiveMultiple linear regression (MLR) and machine learning techniques in pharmacogenetic algorithm-based warfarin dosing have been reported. However, performances of these algorithms in racially diverse group have never been objectively evaluated and compared. In this literature-based study, we compared the performances of eight machine learning techniques with those of MLR in a large, racially-diverse cohort.MethodsMLR, artificial neural network (ANN), regression tree (RT), multivariate adaptive regression splines (MARS), boosted regression tree (BRT), support vector regression (SVR), random forest regression (RFR), lasso regression (LAR) and Bayesian additive regression trees (BART) were applied in warfarin dose algorithms in a cohort from the International Warfarin Pharmacogenetics Consortium database. Covariates obtained by stepwise regression from 80% of randomly selected patients were used to develop algorithms. To compare the performances of these algorithms, the mean percentage of patients whose predicted dose fell within 20% of the actual dose (mean percentage within 20%) and the mean absolute error (MAE) were calculated in the remaining 20% of patients. The performances of these techniques in different races, as well as the dose ranges of therapeutic warfarin were compared. Robust results were obtained after 100 rounds of resampling.ResultsBART, MARS and SVR were statistically indistinguishable and significantly out performed all the other approaches in the whole cohort (MAE: 8.84-8.96 mg/week, mean percentage within 20%: 45.88%-46.35%). In the White population, MARS and BART showed higher mean percentage within 20% and lower mean MAE than those of MLR (all p values < 0.05). In the Asian population, SVR, BART, MARS and LAR performed the same as MLR. MLR and LAR optimally performed among the Black population. When patients were grouped in terms of warfarin dose range, all machine learning techniques except ANN and LAR showed significantly higher mean percentage within 20%, and lower MAE (all p values < 0.05) than MLR in the low- and high- dose ranges.ConclusionOverall, machine learning-based techniques, BART, MARS and SVR performed superior than MLR in warfarin pharmacogenetic dosing. Differences of algorithms' performances exist among the races. Moreover, machine learning-based algorithms tended to perform better in the low- and high- dose ranges than MLR.

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Toward pharmacogenetic SLCO1B1-guided dosing of methotrexate in arthritis using a murine Slco1b2 knockout model.

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