Pharmaceutical automation– expensive lego?

Citation

Piggin, R. (2002), "Pharmaceutical automation– expensive lego?", Industrial Robot, Vol. 29 No. 1. https://doi.org/10.1108/ir.2002.04929aaa.002

Pharmaceutical automation– expensive lego?

Keywords: Pharmaceuticals industry, Robots, Automation

The pharmaceutical industry has been transformed through mergers, acquisitions, increasing health care costs, government intervention and increasing consumer sophistication. Half of the top 25 pharmaceutical companies have been publicly involved in mergers and acquisitions in the last 2 years. This major consolidation and restructuring has increased the pressures on the pharmaceutical sector to reduce costs, increase productivity and improve asset utilisation.

One of the principal challenges to the sector lies in the high costs and low discovery rate of new compound entities (NCEs). To take a drug from “bench to bottle” can cost between $400m-$900m (and rising) and takes between 10-12 years, with 75 per cent of the cost attributable to drug failures. Less than 5 per cent of the candidates identified in drug discovery enter pre-clinical trials and less than 2 per cent of these become medicines. It is widley held within the industry that pharmaceutical companies need to increase average productivity from 0.45 of a new chemical entity per year, to about 2.2 NCEs per year. This is just to maintain their league ranking and sustain anticipated annual earnings growth, that has been double digit but, the increasingly hostile market environment will probably stretch this to close to triple the productivity. This equates to the need for companies to launch 3 new $500m “blockbuster” drugs each year, however, only 4 per cent of drugs currently generate sales of this magnitude. In response, pharmaceutical companies are optimising their operations, increasing manufacturing efficiency and have invested significantly in R&D and automation.

Automation of the drug discovery process using High Throughput Screening (HTS) has attracted significant attention and high levels of investment, since it is arguably the only discovery path that can offer the promise of a five-fold NCE increase. This, along with combinatorial chemistry will create pressures further down the development pipeline, with more compounds making both development and manufacturing increasingly demanding. However, the technology needs to be made to deliver the results. Other factors that drive the use of robotics in life sciences have played a part, these include the “Lego robotics” enthusiast, attracted by the fun element and the multi-disciplinary approach. Early ventures involved laboratory automation, with relatively low investment and without the robustness of the industrial automation used in manufacturing. As the market pressures have grown and technology requirements have developed and matured, the same people often continue to influence the aspirations of pharmaceutical companies, which in turn affects the level of performance realised. Companies like to be seen to pursue automation and robotics solutions by financial analysts, keen to draw attention to expected improvements and financial returns. The analyst community has also been accused of viability claims (more so recently) that have led to a fashion element in technology selection, with subsequent poor results discrediting technology for non-technical reasons. More recently pharmaceutical companies have been looking to utilize a more “industrial” approach to automation in drug discovery and analytics, to increase process speed, capability and capacity.

Key drivers for pharmaceutical companies are the rising cost of new product to market, particularly in pre-launch activities, management of the risk and economics of regulatory compliance (again an increasing cost). Automation can offer solutions to both challenges. Pharmaceutical processes have distinct parallels with manufacturing, with various industries optimising overall system performance, as opposed to individual elements. Importantly, it is recognised that the nominal peak speed of an automated machine or process can be irrelevant to the total system throughput. Automation may be purchased on the basis of peak output, rather than the net realisable. The difference between these being infrastructure constraints, where a lower nominal speed machine used more efficiently could increase total system performance.

Capability in the automation context relates to what a machine can actually achieve in a particular situation, given process repeatability, tolerances, reliability, peak and net throughput and includes allowances for set-up and maintenance operations. System capacity can then be estimated from the capability for a given configuration of a number of machines or processes, giving the best estimate under realistic conditions. This may bear little resemblance to individual process performance.

A critical factor observed in the successful implementation of automation in pharmaceutical R&D has been measured by the availability of professional engineers to support users. Unfortunately, the nature of the life science industry has meant that the engineering animal is not yet fully understood. This needs to change, since the engineering requirements of the complex automation systems differ from the skills required by the users of the technology.

Richard PigginPilz Automation Technology, Medlicott Close, Oakley Hay Business Park, Corby, Northants. NN18 9NF, United Kingdom