Abuzarur-Aloul R, Gjellan K, Sjölund M, Graffner C
Pharmaceutical R&D, Astra Läkemedel AB, Södertälje, Sweden.
Drug Dev Ind Pharm. 1998 Apr;24(4):371-83. doi: 10.3109/03639049809085633.
The main aims of the present study were to establish an in vitro/in vivo correlation for multiple-unit capsules of paracetamol by means of statistical prediction models and to investigate the effect of a number of in vitro variables on the discussion rate of paracetamol from the formulation. A fractional factorial screening design was used to investigate the effects of the variables agitation, pH, osmolality, viscosity, and the presence of bile salt on the dissolution rate of paracetamol. The effects were evaluated in two separate partial least-squares models, in which the responses were expressed as the cumulative percentage of paracetamol dissolved at specified time-points (model I) and as the shape (beta) and scale (eta) parameters according to the Weibull function (model II). It was concluded that agitation and viscosity had significant effects on the dissolution rate of paracetamol. Statistical models based on the responses from models I and II were then used to predict the in vitro conditions most closely correlated with the in vitro dissolution of paracetamol after administration of the formulation to 10 healthy volunteers. The predicted optimal in vitro conditions were similar for the two models and not too far from what is expected from the gastrointestinal tract. The experimental verification of the in vitro conditions showed that both models were equally good, and contributed to high degrees of correlation with the in vivo dissolution behavior of the formulation during 9 hr. The relationships obtained when plotting the percentage dissolved in vitro versus in vivo were y = 1.1x (r2 = 0.98) and y = 1.1x (r2 = 0.94) for models I and II, respectively. Based on these results, it is difficult to state a preference for one of the models. Finally, the use of statistical prediction models to develop critical in vitro tests is a successful approach in the establishment of associations between dissolution behavior in vitro and in vivo for oral extended-release systems.
本研究的主要目的是通过统计预测模型建立对乙酰氨基酚多单元胶囊的体外/体内相关性,并研究一些体外变量对乙酰氨基酚从制剂中溶出速率的影响。采用部分因子筛选设计来研究搅拌、pH值、渗透压、粘度以及胆盐的存在对乙酰氨基酚溶出速率的影响。在两个单独的偏最小二乘模型中评估这些影响,其中响应分别表示为在指定时间点溶解的乙酰氨基酚的累积百分比(模型I)以及根据威布尔函数表示的形状(β)和尺度(η)参数(模型II)。得出的结论是,搅拌和粘度对乙酰氨基酚的溶出速率有显著影响。然后,基于模型I和II的响应建立的统计模型被用于预测在给10名健康志愿者服用该制剂后与乙酰氨基酚体外溶出最密切相关的体外条件。两个模型预测的最佳体外条件相似,且与胃肠道预期情况相差不远。体外条件的实验验证表明,两个模型同样出色,并且在9小时内与制剂的体内溶出行为具有高度相关性。对于模型I和II,绘制体外溶解百分比与体内溶解百分比的关系分别为y = 1.1x(r2 = 0.98)和y = 1.1x(r2 = 0.94)。基于这些结果,很难表明对其中一个模型有偏好。最后,使用统计预测模型来开发关键的体外试验是在建立口服缓释系统体外和体内溶出行为之间关联方面的一种成功方法。