Solid dispersions in the development of a nimodipine floating tablet formulation and optimization by artificial neural networks and genetic programming

The present study investigates the use of nimodipine-polyethylene glycol solid dispersions for the development of effervescent controlled release floating tablet formulations. The physical state of the dispersed nimodipine in the polymer matrix was characterized by differential scanning calorimetry, powder X-ray diffraction, FT-IR spectroscopy and polarized light microscopy, and the mixture proportions of polyethylene glycol (PEG), polyvinyl-pyrrolidone (PVP), hydroxypropylmethylcellulose (HPMC), effervescent agents (EFF) and nimodipine were optimized in relation to drug release (% release at 60 min, and time at which the 90% of the drug was dissolved) and floating properties (tablet’s floating strength and duration), employing a 25-run D-optimal mixture design combined with artificial neural networks (ANNs) and genetic programming (GP). It was found that nimodipine exists as mod I microcrystals in the solid dispersions and is stable for at least a three-month period. The tablets showed good floating properties and controlled release profiles, with drug release proceeding via the concomitant operation of swelling and erosion of the polymer matrix. ANNs and GP both proved to be efficient tools in the optimization of the tablet formulation, and the global optimum formulation suggested by the GP equations consisted of PEG = 9%, PVP = 30%, HPMC = 36%, EFF = 11%, nimodipine = 14%.     

Development of PVP/PEG mixtures as appropriate carriers for the preparation of drug solid dispersions by melt mixing technique and optimization of dissolution using artificial neural networks

The effect of plasticizer’s (PEG) molecular weight (MW) on PVP based solid dispersions (SDs), prepared by melt mixing, was evaluated in the present study using Tibolone as a poorly water soluble model drug. PEGs with MW of 400, 600, and 2000 g/mol were tested, and the effect of drug content, time and temperature of melt mixing on the physical state of Tibolone, and the dissolution characteristics from SDs was investigated. PVP blends with PEG400 and PEG600 were completely miscible, while blends were heterogeneous. Furthermore, a single T_g recorded in all samples, indicating that Tibolone was dispersed in a molecular lever (or in the form of nanodispersions), varied with varying PEG’s molecular weight, melt mixing temperature, and drug content, while FTIR analysis indicated significant interactions between Tibolone and PVP/PEG matrices. All prepared solid dispersion showed long-term physical stability (18 months in room temperature). The extent of interaction between mixture components was verified using Fox and Gordon–Taylor equations. Artificial neural networks, used to correlate the studied factors with selected dissolution characteristics, showed good prediction ability.     

应用HSAB理论通过孤对电子性质对有机化合物亲核性/碱性的定量分析研究(英文)

在密度泛函理论的基础上,根据软、硬酸碱原理,通过孤对电子计算得到的各种化学性质,我们建立了一种定量研究有机化合物亲核性/碱性的方法。这些化学性质描述符包括全局柔性,Fukui方程,局部柔性,局部Mulliken电荷等,它们是通过PC Spartan Pro软件,在密度泛函理论SVWN/DN~*层面上计算得到的量。在本研究工作中,选择了28个化合物,基于计算得到的密度将它们按照亲核性/碱性进行了分类。神经网络中的BP算法被用来研究所选取描述符与亲核性/碱性之间的关系。通过交叉验证避免了在神经网络训练中可能会出现的过度拟合问题。结果显示基于所建立神经网络的预测结果与已知的实验结果符合的很好。局部柔性与Fukui方程与亲核性之间有着相当好的对应关系,而局部Mulliken电荷更好地反映了碱性。我们期望所建立和训练得到的BP网络模型可以被用来预测未知化合物和功能基团的亲核性/碱性。     

人工神经网络及Logistic回归模型对预测体外冲击波治疗上尿路结石的疗效分析

目的 探讨人工神经网络和Logistic回归模型对预测体外冲击波治疗上尿路结石的治疗效果预测.方法 从2010年1月至2015年1月,本院泌尿科共接受ESWL治疗的肾结石患者340例,将治疗前的病例资料10项(年龄大小、体重指数大小、病程时间长短、性别、尿路刺激症、血尿、肾绞痛、结石位置、患侧和大小)纳入预测参数,建立人工神经网络和Logistic回归模型,预测体外冲击波治疗上尿路结石的临床疗效.结果 人工神经网络得到预测参数重要性的前5位依次为结石大小、病程时间、血尿、结石位置、体重指数,进行显著性检验时,P＜ 0.05.Logistic回归模型中重要的参数分别为病程时间、血尿和结石位置,差异有统计学意义,P＜ 0.05.结论 人工神经网络和Logistic回归模型预测ESWL治疗上尿路结石成功率有较好的准确性,可以在临床上广泛推广.