Singh Bhupinder, Kumar Rajiv, Ahuja Naveen
Pharmaceutics Division, University Institute of Pharmaceutical Sciences, Panjab University, Chandigarh, India.
Crit Rev Ther Drug Carrier Syst. 2005;22(1):27-105. doi: 10.1615/critrevtherdrugcarriersyst.v22.i1.20.
Design of an impeccable drug delivery product normally encompasses multiple objectives. For decades, this task has been attempted through trial and error, supplemented with the previous experience, knowledge, and wisdom of the formulator. Optimization of a pharmaceutical formulation or process using this traditional approach involves changing one variable at a time. Using this methodology, the solution of a specific problematic formulation characteristic can certainly be achieved, but attainment of the true optimal composition is never guaranteed. And for improvement in one characteristic, one has to trade off for degeneration in another. This customary approach of developing a drug product or process has been proved to be not only uneconomical in terms of time, money, and effort, but also unfavorable to fix errors, unpredictable, and at times even unsuccessful. On the other hand, the modern formulation optimization approaches, employing systematic Design of Experiments (DoE), are extensively practiced in the development of diverse kinds of drug delivery devices to improve such irregularities. Such systematic approaches are far more advantageous, because they require fewer experiments to achieve an optimum formulation, make problem tracing and rectification quite easier, reveal drug/polymer interactions, simulate the product performance, and comprehend the process to assist in better formulation development and subsequent scale-up. Optimization techniques using DoE represent effective and cost-effective analytical tools to yield the "best solution" to a particular "problem." Through quantification of drug delivery systems, these approaches provide a depth of understanding as well as an ability to explore and defend ranges for formulation factors, where experimentation is completed before optimization is attempted. The key elements of a DoE optimization methodology encompass planning the study objectives, screening of influential variables, experimental designs, postulation of mathematical models for various chosen response characteristics, fitting experimental data into these model(s), mapping and generating graphic outcomes, and design validation using model-based response surface methodology. The broad topic of DoE optimization methodology is covered in two parts. Part I of the review attempts to provide thought-through and thorough information on diverse DoE aspects organized in a seven-step sequence. Besides dealing with basic DoE terminology for the novice, the article covers the niceties of several important experimental designs, mathematical models, and optimum search techniques using numeric and graphical methods, with special emphasis on computer-based approaches, artificial neural networks, and judicious selection of designs and models.
设计一款完美的药物递送产品通常涉及多个目标。几十年来，人们一直通过反复试验来尝试这项任务，并辅以配方师以往的经验、知识和智慧。使用这种传统方法优化药物制剂或工艺时，每次只改变一个变量。使用这种方法，特定问题制剂特性的解决方案当然可以实现，但真正的最佳配方却无法保证。而且为了改善一个特性，必须在另一个特性上做出牺牲。这种开发药品或工艺的传统方法已被证明不仅在时间、金钱和精力方面不经济，而且不利于修复错误、不可预测，有时甚至不成功。另一方面，采用系统实验设计（DoE）的现代制剂优化方法在各种药物递送装置的开发中得到了广泛应用，以改善此类不规则性。这种系统方法具有更大的优势，因为它们只需较少的实验就能获得最佳配方，使问题追踪和纠正更加容易，揭示药物/聚合物相互作用，模拟产品性能，并理解该过程以协助更好地进行制剂开发和后续放大生产。使用DoE的优化技术是产生特定“问题”“最佳解决方案”的有效且具有成本效益的分析工具。通过对药物递送系统进行量化，这些方法提供了深入的理解以及探索和确定配方因素范围的能力，即在尝试优化之前先完成实验。DoE优化方法的关键要素包括规划研究目标、筛选有影响的变量、实验设计、为各种选定的响应特性建立数学模型、将实验数据拟合到这些模型中、绘制和生成图形结果，以及使用基于模型的响应面方法进行设计验证。DoE优化方法这个广泛的主题分为两部分进行阐述。本综述的第一部分试图按七个步骤的顺序，提供关于DoE各个方面经过深思熟虑且全面的信息。除了为新手讲解基本的DoE术语外，本文还涵盖了几种重要实验设计、数学模型以及使用数值和图形方法的最优搜索技术的细节，特别强调基于计算机的方法、人工神经网络以及设计和模型的明智选择。
