Our results contribute to a better understanding of the molecular basis of ethnic differences in drug response, which may help to improve individualization of drug therapy and offer a preliminary basis for more rational use of drugs that are substrates for CYP2B6 and CYP3A5 in the Japanese population.

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To investigate the association between NAT2 genotypes and the incidence of isoniazid (INH)-induced adverse reactions, in the hope of identifying a pharmacogenetic approach that could be useful in the prediction and prevention of adverse reactions in Japanese patients, we retrospectively studied the genotypes of NAT2 in 102 Japanese patients treated with INH (without rifampicin co-administration). The subjects were classified into three groups according to their genotypes: rapid-type, intermediate-type, and slow-type. The clinical conditions of the patients were followed-up in order to evaluate the development of any adverse drug reactions (ADRs) and correlate them with patient genotypes. Six out of the 102 patients (5.9%) developed various ADRs following INH treatment. These reactions included nausea/vomiting, fever, visual impairment, and peripheral neuritis. We found a statistically significant difference between the incidence of ADRs and NAT2 genotype. The incidence of ADRs was significantly higher in the slow type than in the other two types, as 5 out of the 6 ADR patients were of the slow-type, and the other one was of the intermediate-type, while no patients of the rapid-type developed any ADRs. The results indicated that the genes coding for slow acetylation were associated with the incidence of serious ADRs following INH treatment. Our findings suggest that determination of NAT2 genotype might be clinically useful in the evaluation of patients at high risk of developing ADRs induced by INH.

Carvedilol ((Ϯ)-1-carbazol-4-yloxy)-3-[[2-(o-methoxyphenoxy)ethyl]-amino]-2-propanol) is an anti-hypertensive andanti-anginal drug that has b-adrenergic blocking and vasodilating activities. 1,2) This drug has also recently been used to treat chronic heart failure (CHF). However, for treatment of CHF, it is recommended that the dose of carvedilol be gradually and carefully increased because of its negative inotropic activity.2-5) Therefore, it is important to clarify factors that affect the pharmacokinetics of the drug.Carvedilol is known to be metabolized into various metabolites by both oxidation and conjugation pathways in the liver. It is thought that the main pathway is direct glucuronidation of carvedilol because the main metabolite in plasma and urine was found to be the glucuronide of unchanged carvedilol (22% and 32%, respectively).6,7) Three UDP-glucuronosyltransferase (UGT) isoforms have been reported to be capable of conjugating carvedilol into two forms of its glucuronide (G1 and G2). 8) UGT2B4 forms both glucuronides, whereas UGT1A1 (G2) and UGT2B7 (G1) forms either one. On the other hand, oxidation pathways are mainly catalyzed by CYP2D6. 9) CYP2D6 is responsible for the formation of 4Ј-hydroxy carvedilol and 5Ј-hydroxy carvedilol, and both metabolites are excreted into urine (6.4%).7) Therefore, it is thought that the pharmacokinetics of carvedilol is complicated and that there are many factors in interindividual and intraindividual variation of its disposition.We have reported that the frequencies of UGT1A1*6, UGT2B7*3 and CYP2D6*10 in patients who have a low level of ability of glucuronidation were significantly higher than those in patients with a high level of ability of glucuronidation. The same tendency was found in the frequency of CYP2D6*5, though there was no significant difference. 10) On the other hand, Honda et al. have reported that CYP2D6*10 affected oral clearance of carvedilol but that CYP2C9*3, CYP2C19*2, CYP2C19*3, CYP3A5*3, UGT2B7*2, and MDR1 C3435T did not significantly affect the pharmacokinetics of this drug in Japanese subjects. 11)The aim of this study was to determine the effects of polymorphisms in UGTs and CYP2D6 on pharmacokinetics of carvedilol using population pharmacokinetic analysis. MATERIALS AND METHODSThe study protocol was approved by the Ethics Committee of the Graduate School of Medicine, Hokkaido University. Written informed consent for participation in the study was obtained from all subjects.Patient Data The study population consisted of 41 patients (33 males and 8 females) with CHF or angina pectoris who were being treated with carvedilol. Data on plasma concentrations of carvedilol and its glucuronides, and that on their polymorphism for metabolic enzymes reported in our previous report with an additional subject were used.10) The patients with CHF were classified into New York Heart Association (NYHA) classes II-III. The daily doses of carvedilol administered to the patients ranged from 1.25 to 40 mg, and the drug was taken in one or two doses daily. Th...

Artificial neural networks are the main tools for data mining and were inspired by the human brain and nervous system. Studies have demonstrated their usefulness in medicine. However, no studies have used artificial neural networks for the prediction of adverse drug reactions. We aimed to validate the usefulness of artificial neural networks for the prediction of adverse drug reactions and focused on vancomycin -induced nephrotoxicity. For constructing an artificial neural network, a multilayer perceptron algorithm was employed. A 10-fold cross validation method was adopted for evaluating the resultant artificial neural network. In total, 1141 patients who received vancomycin at Hokkaido University Hospital from November 2011 to February 2019 were enrolled. Among these patients, 179 (15.7%) developed vancomycin -induced nephrotoxicity. The top three risk factors of vancomycin -induced nephrotoxicity which are relatively important in the artificial neural networks were average vancomycin trough concentration ≥ 13.0 mg/L and concomitant use of piperacillin–tazobactam and vasopressor drugs. The predictive accuracy of the artificial neural network was 86.3% and that of the multiple logistic regression model (conventional statistical method) was 85.1%. Moreover, area under the receiver operating characteristic curve (AUROC) of the artificial neural network was 0.83. In the 10-fold cross-validation, the accuracy obtained was 86.0% and AUROC was 0.82. The artificial neural network model predicting the vancomycin -induced nephrotoxicity showed good predictive performance. This appears to be the first report of the usefulness of artificial neural networks for an adverse drug reactions risk prediction model.

No abstract

This study aimed to construct an optimal algorithm for initial dose settings of vancomycin (VCM) using machine learning (ML) with decision tree (DT) analysis. Patients who were administered intravenous VCM and underwent therapeutic drug monitoring (TDM) at the Hokkaido University Hospital were enrolled. The study period was November 2011 to March 2019. In total, 654 patients were included in the study. Patients were divided into two groups, training (patients who received VCM from November 2011 to December 2017; n 496) and testing (patients who received VCM from January 2018 to March 2019; n 158) groups. For the training group, DT analysis of the classification and regression tree algorithm was performed to construct an algorithm (called DT algorithm) for the initial dose settings of VCM. For the testing group, the rates of attaining the VCM therapeutic range (trough value 10-15 and 10-20 mg/L) with the DT algorithm and three conventional dose-setting methods were compared for model evaluation. The DT algorithm was constructed to be used for patients with estimated glomerular filtration rate ≥50 mL/min and body weight ≥40 kg. As a result, the recommended daily doses ranged from 20.0 to 58.1 mg/kg. In model evaluation, the DT algorithm obtained the highest rates of attaining the VCM therapeutic range compared to conventional dose-setting methods. Therefore, our DT algorithm can be applied to clinical practice. In addition, ML is useful for setting drug doses.

A general method for predicting the intestinal absorption of a wide range of drugs using multiple regression analysis of their physicochemical properties and the drug-membrane electrostatic interaction was developed. The absorption rates of tested drugs from rat jejunum were measured by the in situ single-pass perfusion technique. The drugs used in this study were divided into three groups for regression analysis, and a smaller "test" set of compounds was used to assess the predictive capacity of the regression equation. When the analysis was applied to each respective group of drugs (i.e., anionic, cationic, and nonionized compounds), obtained regression coefficients were 0.569, 0.821, 0.728 by using the organic solvent (n-octanol)/buffer partition coefficient, 0.730, 0.734, 0.914 using the permeation rate across a silicon membrane, and 0.790, 0.915, 0.941 using an EVA membrane, respectively. However, smaller regression coefficients of 0.377, 0. 468, and 0.718 were obtained when these three groups of drugs were put together for prediction. Meanwhile, correlation was improved remarkably when drug-membrane electrostatic interactions, namely, hydrogen-bonding donor (Halpha) and acceptor (Hbeta) activity or index of electricity (Ec), were added to the other parameters of lipophilicity and permeation rate across the EVA membrane (r = 0.880 and 0.883, respectively). Moreover, the equation obtained from these regression analyses was applicable even to the prediction of the absorption of the zwitterionic drugs. These results suggest that including the electrostatic interaction parameters in addition to lipophilicity and permeability across artificial membranes would afford a better prediction for the intestinal absorption of the vast majority of drugs.