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

手性药物的销售额接近全球药品销售额的三分之一。手性药物的常规化学生产方法产生一种外消旋混合物，其中含有化学成分相同但在空间上结构取向不同的对映体。手性药物的一种异构体对人体有治疗作用，而其对映体是有害的，这是很常见的。当一种异构体“好”，另一种“坏”时，分离两种对映体有明显的好处，以提高其安全性和耐受性。模拟移动床(SMB)系统是一种获得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给药政策，SMB技术在医药行业的应用引起了越来越大的兴趣。如果对SMB进行优化设计，就有可能提高生产率，满足严格的产品质量，并显著节省成本。在这个项目中，我们打算详细研究手性药物的分离和纯化，特别是对于含有两个以上对映体的外消旋混合物。我们的重点是新的设计和开发，以及多目标优化和先进的实时控制在SMB系统中的应用。将开发详细的模拟模型，随后将进行实验验证。此后，将使用多个目标和约束来计算最佳运行条件。将使用最先进的优化技术--遗传算法的改编。最后，通过实验验证了最优运行性能。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生活中的限制。

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.