Strategies for the Selection of the Equipment

The ultimate goal would be to have each laboratory equipped for parallel working at all screening stages. Therefore, a complete ''cascade'' of screening apparatus (for process screening, process optimization, process validation) would have to be purchased or developed, which would require significant investment. Hence, some moderating decisions have to be made and preliminary questions have to be answered:

1 Automation versus manual work?

2 What is the right distribution and number of pieces of apparatus for different stages?

3 Buying commercially available systems or performing in-house development? Automation versus Manual Work?

Discussing adequate equipment for parallelization during different phases of process development raises the question of''What is the right degree of automation?'' Criteria that play a role in answering this question are wage costs versus investment costs, at what phase the parallel set-up should be run, and how flexible in terms of chemistry and time the set-up should be.

Our experience is that, on a screening scale of 5-100 mL, a technician can handle around 12 experiments in parallel per day with rather cheap, nonautomated, multiple reaction block equipment. These racks (for commercial examples, see Table 30.1) are easy to install, and they can be used for most organic chemistry to cover the developmental stages of process screening and, to a lesser extent, either process screening at an earlier phase or process optimization.

We have introduced these racks into our process development laboratories. In fine chemical synthesis, our statistical evaluation has revealed that the overall efficiency of the laboratories, given by the number of experiments performed per day, has approximately doubled using this equipment.

Expenses Development Time

2 4 8 16 32 64 128 256 512 1024

Fig. 30.9. Expenses versus simultaneous reactions.

If the parallelization is expanded to a degree much higher than tenfold, then a large increase in effort and expenditure results. This concept is illustrated in Fig. 30.9.

The sigmoid curve shows the increase from manual work to full automation. Instead of the hardware, the control software and the data-handling system become the cost-determining features. Moreover, software installation and adaptation, hardware integration via numerous interfaces, and training of the operating staff have to be taken into account.

Software plays an increasingly important role in higher degrees of automation. In our experience, a parallelization significantly greater than 101 can no longer be handled with standard programs and, especially if larger amounts of raw data have to be processed, stored and analyzed, special software and databases have to be used. The same is true for the control system that steers the components, pumps, mass flow controllers, pressure controllers, temperature monitors, chromatographs, etc. The more components from different suppliers with different protocols and interfaces that have to be integrated, the more complex the system becomes, and, a priori, the lower is its reliability. A fully integrated system is useless if it cannot run reliably on a day-to-day basis.

Costs and complexity (e.g. software, logistics) suggest that a dedicated laboratory with a staff who are experts in automation should operate this equipment. These activities can eventually be outsourced to central research units or to small research service companies.

Owing to the limited chemical flexibility of most workstations, the ''jump'' to automation is particularly rewarding for long-term projects with a specific chemistry-or technology-based screening. For example, when we test our enantiomerically pure ligand database in asymmetric hydrogenations, we test up to 96 combinations per run to see which catalyst system reveals the best enantiomeric excess for a given substrate. Where trial and error promises success, a large number of experi

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