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A hybrid intelligent system for formulation of BCS Class II drugs in hard gelatin capsules

机译:一种混合智能制度,用于在硬明胶胶囊中制定BCS II类药物

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In this paper, we describe a hybrid intelligent system for formulation of BCS Class II drugs in hard gelatin capsules. Several significant challenges are involved in drug-formulation: the active ingredients and the fillers in the capsule must be chemically compatible according to bio-pharmaceutical principles; the formulation must be manufacturable; and it must meet the prescribed drug release requirement. Traditional trial and error approach to drug-formulation design is too costly and time consuming to meet the increasing demand for new drugs. To answer these challenges, we have developed a prototype hybrid intelligent system for automatic drug formulation. This system consists of a rule-based Expert System (ES) that conducts formulation design according to Biopharmaceutical Classification of drugs and a neural network (NN) that predicts the quality of the formulation recommended by ES. Through interaction between the two modules, the hybrid system forms a (re) formulation-prediction cycle, and the quality of the formulation is improved with each iteration. The hybrid system is tested with sample drugs and is shown to be able to produce formulations with desirable performance measures.
机译:在本文中,我们描述了一种用于在硬明胶胶囊中配制BCS II类药物的混合智能系统。药物制剂的几种重大挑战:胶囊中的活性成分和胶囊中的填料必须根据生物药物原则进行化学相容;制剂必须是制造的;它必须符合规定的药物释放要求。药物配方设计的传统试验和误差方法过于昂贵且耗时,以满足对新药的日益增长的需求。为了回答这些挑战,我们开发了一种用于自动药物配方的原型混合智能系统。该系统由基于规则的专家系统组成,根据药物的生物制药分类和神经网络(NN)进行制定设计,其预测ES推荐的制剂的质量。通过两个模块之间的相互作用,混合系统形成(RE)制剂预测循环,并且每次迭代改善配方的质量。杂交系统用样品药物测试,并显示能够产生具有所需性能措施的制剂。

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