Application of multiobjective optimization in the deisgn of simulated moving bed systems for chiral drug separation

手性药物的销售接近全球所有药物销售的三分之一。手性药物的普通化学生产方法产生含有化学成分相同但空间结构取向不同的对映体的外消旋混合物。手性药物的一种同分异构体形式对人体有治疗作用，而其对映体是有害的，这是很常见的。当一种异构体为“好”而另一种为“坏”时，分离两种对映体可以明显提高其安全性和耐受性。模拟移动床（SMB）系统是获得纯单一对映体的可行技术。最近，由于手性固定相和非线性色谱理论的最新发展，以及严格的药物管理政策，制药行业对使用SMB技术进行对映体分离的兴趣越来越大。如果对SMB进行优化设计，则可以提高生产率，满足严格的产品质量，并显著节省成本。在这个项目中，我们建议详细研究手性药物的分离和纯化，特别是含有两个以上对映体的外消旋体混合物。我们的重点是在SMB系统的新设计和开发，以及多目标优化和先进的实时控制的应用。详细的模拟模型将被开发出来，随后将被实验验证。然后，使用多个目标和约束条件计算最优运行条件。将采用最先进的优化技术——遗传算法。最后，通过实验验证了该系统的最佳运行性能。这些研究将有助于优化几个目标，同时满足工业中遇到的许多现实限制。

The sale of chiral drugs is close to one third of all drug sales worldwide. Ordinary chemical production methods for chiral drugs produce a racemic mixture containing enantiomers having identical chemical compositions but different structrtal orientation in space. It is quite common to find that one isomeric form of a chiral drug has a therapeutic effect on the human body while its enantiomer is harmful. When one isomer is 'good' and the other 'bad', there is obvious benefit from separating the two enantiomers to enhance its safety and tolerability. The Simulated Moving Bed (SMB) system is a viable technology for obtaining pure single enantiomers. Lately, there has been increased interest in the pharmaceutical industry for using SMB technology for enantio-separations due to recent developments in chiral stationary phases and nonlinear chromatographic theory, as well as stringent drug administration policy. Enhanced productivity satisfying stringent product qualities and significant cost savings are possible if optimal design of the SMB is made. In this project, we propose to study in detail the separation and purification of chiral drugs, particularly, for racemate mixtures containing more than two enantiomers. Our main emphasis is in new design and development, and application of multi-objective optimization and advanced real-time control to SMB systems. Detailed simulation models would be developed, which will subsequently be verified experimentally. Thereafter, optimal operating conditions will be computed using multiple objectives and constraints. An adaptation of the state-of-the-art optimization technique, genetic algorithm, will be used. Finally, optimal operating performance would be validated experimentally. These studies would help optimize several objectives while simultaneously satisfying numerous real-life constraints encountered in industry.