Laboratory automation

Automation within the pharmaceutical industry is a complex process, made even more challenging because the terminology used is inconsistent. Pharmaceutical testing services provider SOTAX explores examples of and considerations for automated method development.

Laboratory automation spans pharmaceutical and nutraceutical discovery, development, manufacturing and quality testing. To the uninitiated, a logical starting point for understanding automation is to compare an automated process to an existing manual technique; however, before a comparison can begin, the scope that the automation encompasses must be defined.

Semi and fully automated systems

The definitions of the terms ‘semi’ and ‘fully automated’ are subjective and require knowledge of the unit of the work being automated. In drug dissolution testing, the unit of work was designed around a six-sample model and the type of apparatus used is well-defined and regulated. In this context, a semiautomated system offers complete walk-away from a single run of six samples; however, if more than a single set needs to be run, the user must intervene since typical semi-automated systems do not provide media preparation, nor do they automatically clean, prepare and initiate subsequent runs.

Fully automated dissolution systems extend the work envelope of what is automated to encompass the preparation and cleaning of the vessel, allowing the system to cycle through multiple individual runs without user intervention.


Throughput is a well-accepted term, referring to the number of samples, runs or other units of work to be accomplished. A common misconception is that fully automated processes bring greater return on investment (ROI) to an organisation due to higher throughput. While this is sometimes true, if the unit of work to be accomplished contains a time-dependent step such as a long high-performance liquid chromatography (HPLC) run time or a dissolution time, then the bottleneck in the automation of the process may be the method itself rather than the instrument.

A good example of this is the automation of controlled release dosage form testing. These assays often cannot be accelerated beyond certain limits without compromising the assay quality. For the same capital investment, multiple semi-automated systems operating in parallel can achieve a higher throughput than a fully automated system that sequentially processes batches of six samples. There will be more user intervention required, so managers need to weigh throughput against walk-away time before deciding on the best ROI for their laboratories.

Sequential, parallel and scheduled processing

Sequential processing refers to a system that handles one sample at a time through a multistep process to completion. Parallel processing is a ‘brute force’ automation approach where copies of the same automated system are used to run multiple assays simultaneously. These systems may be linked to share information and sort data; for example, if the bottleneck in a process is the speed of the HPLC run, multiple LC systems would be connected to the same automated system in order to accomplish greater throughput, but this is not common due to the higher capital investment required.

Scheduled automation takes advantage of waiting times to complete other tasks; for example, in a fully automated dissolution system, media is prepared for a subsequent run while the current run is underway to reduce the cycle time between runs.

Method development considerations

When validating or transferring existing methods into automated methods, remember that you are not confined to the same limits as a manual procedure; for example, in automating laboratory assays, you are no longer limited to the available volumetric glassware sizes and dilutions are not limited to typically available manual pipetting ranges.

Filtration, which may not have been possible due to the forces required or ergonomic health concerns, may take the place of centrifugation. Consider validation of the automated system holistically, taking into account the reaction space and the sampling system. Also consider the advantages of designing method parameters as variables, thereby opening up the assay design space to the application of design of experiments packages to achieve more robust assays.