Introduction: The preclinical in vivo assay for QT prolongation is critical for predicting torsadogenic risk, but still difficult to extrapolate to humans. This study ran preclinical tests in cynomolgus monkeys on seven QT reference drugs containing the drugs used in the IQ-CSRC clinical trial and applied exposure-response (ER) analysis to the data to investigate the potential for translational information on the QT effect. Methods: In each of six participating facilities in the J-ICET project, telemetered monkeys were monitored for 24 h following administration of vehicle or 3 doses of test drugs, and pharmacokinetic profiles at the same doses were evaluated separately. An individual rate-corrected QT interval (QTca) was derived and the vehicle-adjusted change in QTca from baseline (Delta Delta QTca) was calculated. Then the relationship of concentration to QT effect was evaluated by ER analysis. Results: For QT-positive drugs in the IQ-CSRC study (dofetilide, dolasetron, moxifloxacin, ondansetron, and quinine) and levofloxacin, the slope of the total concentration-QTca effect was significantly positive, and the QT-prolonging effect, taken as the upper bound of the confidence interval for predicted Delta Delta QTca, was confirmed to exceed 10 ms. The ER slope of the negative drug levocetirizine was not significantly positive and the QTca effect was below 10 ms at observed peak exposure. Discussion: Preclinical QT assessment in cynomolgus monkeys combined with ER analysis could identify the small QT effect induced by several QT drugs consistently with the outcomes in humans. Thus, the ER method should be regarded as useful for translational prediction of QT effects in humans.

医薬品副作用データベース(英名: Japanese Adverse Drug Event Report database,略称; JADER)が,2012年4月に公開され,医薬品の適正使用情報としての活用が期待されている.本論文では,副作用発現時期の新たな評価方法として,副作用発現日を Weibull 分布にあてはめて推定した形状パラメータによる発現時期プロファイルの分析を取り上げ,自殺関連または糖尿病関連副作用のインターフェロン製剤間の違いを検討した.2013年8月の JADER から重複を除いた薬剤と副作用の組合せ件数 702,925 件のデータを用いた.自殺関連または糖尿病関連副作用は,PRR 等でシグナルと判断された.糖尿病関連副作用は,製剤間で副作用発現時期の分布が異なり,Weibull 分布の形状パラメータは,α 製剤では1.49(1.09-1.94)(点推定値および両側 95%信頼区間)と下側 95%信頼区間が有意に 1 を超え,摩耗故障型副作用時期プロファイルが示唆された.β 製剤では 0.84(0.66-1.05)と上側 95%信頼区間が 1 をわずかに上回るため初期故障型に近く,ペグ製剤 は,1.07(0.92-1.23)と点推定値はほぼ 1 であることから偶発故障型と考えられた.自殺関連副作用では,副作用発現時期の分布は製剤間で類似しており,形状パラメータはいずれの製剤も,点推定値は 0.89~1.01,95%信頼区間が 1 を含むことから副作用発現時期プロファイルは偶発故障型と判断された.この情報に,ヒストグラムや箱ひげ図などのグラフ表示による視覚的評価を併用することで,より具体的な安全性監視対策を検討することが可能となり,本評価方法は有用であると考えられた.

In clinical pharmacological studies, the test drug is considered to show linear pharmacokinetics when AUC and Cmax increase proportionally to dose level. In this paper, we reviewed three statistical analysis methods such as linear regression analysis, one-way analysis of variance, and a power model, which have been used to assess the dose proportionality. Power model is a simple regression analysis of log of the pharmacokinetic parameter and log of the dose. We also assessed the validity of these methods by means of computer simulation, and confirmed the usefulness of the power model. We therefore recommend the use of the power model, and reporting both the point estimate and its confidence interval of the slope.

Statistical analysis method on nonclinical pharmacokinetic data was studied. In this paper, the AUC estimation with its variance for sparse sampling data was considered. The methods to evaluate the variance of AUC based on the mean concentration and also to estimate the approximated confidence interval were presented.

When pharmaceutical scientists describe characteristics of a drug or when they decide whether it is appropriate to initiate clinical trials to determine the drug's effects in humans, their inferences are frequently grounded in information drawn from non-clinical studies. Therefore, certain and highly objective information is required. By introducing the concept of design of experiments to control some nuisance factors and performing confirmatory studies based on sample size estimation, trustworthy information can be efficiently obtained. This paper does not demand that researchers conduct an additional confirmatory study in a series of studies conducted so far. This is a reconsideration how a series of studies should be carried forward. Statistics ought to contribute much more not only to estimation or hypothetical tests after data are collected, but also to methodology of preliminary experiments and planning of studies. Cooperation with statisticians from an early stage of the studies is all the more helpful in non-clinical studies, in which, in a sense, "perfect" experiments can be conducted more than in clinical studies.

Statistical hypothesis tests are used as a flagging device to highlight differences worth further attention in the evaluation of repeated dose toxicity studies in the rat. Raw data of quantitative parameters of 19 regulatory toxicity studies were collected with their final interpretation of each study. An investigation was done on the consistency between flagging by statistical tests and biological significance by final interpretation. Williams’s test at 2.5% of the significance level showed as much accuracy (correct results compared with the sum of false negative and false positive results) as the rate of Dunnett’s test at the 5% significance level. Since a monotonic dose-response relationship is usually assumed in selection of dose levels, Williams’s test with ordered alternative hypotheses is recommended as a routine procedure instead of the currently used Dunnett’s test. A supplementary procedure, using Steel’s test, was shown to be effective for flagging unexpected ‘downturn’ dose response.