Automated multiple development optimization

Lodi, G. Betti, A. Menziani, E. Brandohni, V. Tosi, B. Some aspects and examples of automated multiple development (AMD) gradient optimization. J. Planar Chromatogr. 1991, 4 (3/4), 106-110. 

All approaches to method optimization based on multiple experiments have the requirement that all components be detected and that they be tracked between runs. For complex samples, this is typically the most labor-intensive aspect of method development. For unattended method development, the instrument is required to monitor the change in retention of each component automatically. The historical limitations to this technology have been a key stumbling block in the widespread adoption of automated method development. 

Method development remains the most challenging aspect of chiral chromatographic analysis, and the need for rapid method development is particularly acute in the pharmaceutical industry. To complicate matters, even structurally similar compounds may not be resolved under the same chromatographic conditions, or even on the same CSP. Rapid column equilibration in SFC speeds the column screening process, and automated systems accommodating multiple CSPs and modifiers now permit unattended method optimization in SFC [36]. Because more compounds are likely to be resolved with a single set of parameters in SFC than in LC, the analyst stands a greater chance of success on the first try in SFC [37]. The increased resolution obtained in SFC may also reduce the number of columns that must be evaluated to achieve the desired separation. 

As noted in the introduction, a major aim of the current research is the development of "black-box" automated reactors that can produce particles with desired physicochemical properties on demand and without any user intervention. In operation, an ideal reactor would behave in the manner of Figure 12. The user would first specify the required particle properties. The reactor would then evaluate multiple reaction conditions until it eventually identified an appropriate set of reaction conditions that yield particles with the specified properties, and it would then continue to produce particles with exactly these properties until instructed to stop. There are three essential parts to any automated system—(1) physical machinery to perform the process at hand, (2) online detectors for monitoring the output of the process, and (3) decision-making software that repeatedly updates the process parameters until a product with the desired properties is obtained. The effectiveness of the automation procedure is critically dependent on the performance of these three subsystems, each of which must satisfy a number of key criteria the machinery should provide precise reproducible control of the physical process and should carry out the individual process steps as rapidly as possible to enable fast screening the online detectors should provide real-time low-noise information about the end product and the decision-making software should search for the optimal conditions in a way that is both parsimonious in terms of experimental measurements (in order to ensure a fast time-to-solution) and tolerant of noise in the experimental system. 

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