Robotics and Laboratory Automation in Pharmaceuticals Analysis
Authors: North Nigel 
Published: September 2006

Laboratory robotics is being increasingly applied in pharmaceutical development to help meet the needs of increasing productivity, decreasing drug development time and reducing costs. A robot can be defined as a device that can perform a variable but programmed series of physical manipulations including moving objects, sample identification, weighing, extraction, filtration and dilution. Robots can be classified according to the type of movement the robotic arm can perform. Three of the most common geometries for laboratory robots are: Cartesian (three mutually perpendicular axes); cylindrical (parallel action arm pivoted about a central point); and anthropomorphic (multijointed, human‐like configuration). Each has been used in the pharmaceutical industry with varying degrees of success. Cylindrical and anthropomorphic robots generally provide more flexible “human‐like” automation that includes transfer, weighing, extraction and filtration of samples.

In a typical analysis, four major steps are performed: sampling; sample preparation; sample measurement; and data collection/analysis/reduction to provide a report for the customer. Most laboratory robotic systems are employed to automate the sample preparation stage which is normally the most labor‐intensive step of the analysis process. Automation has been applied in pharmaceutical analysis in a number of areas: raw materials release; in‐process testing; release of finished products; and stability testing of the dosage form. In pharmaceutical analysis the assay, content uniformity and dissolution tests are the most common methods that use automation. Developing an automated assay or content uniformity method for a dosage form requires several aspects to be investigated before validation of the procedure can proceed, including: extraction; filter; carry‐over studies; and stability of solution.

Validation can be defined as establishing documented evidence which provides a high degree of assurance that a specific process will generate a product, in our case an automated method, that will meet predetermined specifications and quality attributes. Qualification is part of validation and has been defined for installation, operation and running of the system under workload for a specific application. In the pharmaceutical industry the “4Qs” model is generally used to qualify the unit. Validation of manual analytical methods is well understood but documented in a number of guidelines in slightly different ways. It has been only recently that single documents (International Conference on Harmonisation) have emerged that describe the terminology and methodology for method validation. For robotic methods, equivalency between the manual and automated procedure is an important extra validation test that is often performed since it is unusual not to have a manual already in use. This test may not be relevant if accuracy has been also demonstrated for the automated method. For successful transfer of automated methods, the robotic systems used in research and development (R&D) should mirror those used at the commercial production site. The automated equipment must be rugged and easy to use for the production quality control (QC) laboratory to invest in the technology. Complex robotic systems that need specialist, dedicated staff, have not been generally successful in the QC laboratory.

Robotic systems are a high capital cost and this is often justified by increasing throughput since automatic systems can operate 24 h a day and over weekends unattended, thereby also reducing the cost per analysis. During the 1990s, drug development times have significantly reduced, which has required more responsive “just‐in‐time analysis” and this has been an integral part of cost reduction for the release of raw materials and commercial products. Automated systems also enable scientists to use their time more productively by spending time on evaluating data and on more innovative tasks rather than performing routine repetitive operations.

Although the application of laboratory robotics is increasing, limitations including high capital cost, relative complexity of operation and poor connectivity between the robot and laboratory information management systems, are hindering its implementation.