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Pharmaceutical Manufacturing and Packing Sourcer

Creative Targets

David Harris and Ross Jones at Sagentia look at innovations in drug delivery, and ask whether the drive for quality is stifling creativity

A drive for uniformity – from drug formulation to packaging and dispatching – has always characterised the pharmaceutical industry, leading to the exceptional quality required by regulators today and expected by practitioners and patients. In order to ensure this uniformity throughout the drug development process, the use of formal quality, testing and process control methodologies (such as Six Sigma and QFD) has become more widespread. Although such methodologies can significantly improve quality and efficiency within the process environment, their use is now extending into areas such as innovation. In this context, there is a significant risk that the underlying philosophy of control that process methodologies promote can contradict the free exploration of ideas necessary for true breakthroughs in product development. Is quality therefore stifling innovation? If so, how can a workable compromise be achieved?


A number of process management methodologies (including Six Sigma, which is perhaps one of the best known) are currently employed across the pharmaceutical industry, and the drivers for their adoption are well documented. Pharmaceutical companies have to balance the delivery of consistent quality – essential for regulatory compliance – against the constant reduction of operational costs (primarily achieved through increased efficiency) essential in a highly competitive market environment. But with many key product lines facing patent expiry, innovation is also vital if the product pipeline is to stay healthy. The recent abrupt economic downturn has further underlined the importance of innovation in ensuring market survival. Companies able to deliver a real, step-change product or technology can recapture market share, and in addition, even reshape markets entirely by changing buying or usage patterns. The experience of one particular drug delivery sector – inhaler technology – serves as a good example of how transformative step-change innovation can be, and how hard it is to maintain the balance between operational efficiency and innovation.

When the pressurised metered dose inhaler (pMDI) was launched, it revolutionised both asthmatic patients’ lives and market opportunities. At the time of its development in the mid-1950s, asthma patients commonly gained therapeutic relief through the use of a squeeze bulb nebuliser, which was unreliable and cumbersome to refill. The inspiration for the pMDI came from the daughter of a president at Riker Laboratories, who asked “Why can’t they put my asthma medicine in a spray can, like they do hair spray?” This question prompted the inventor, Charles Thiel, to develop the technology that laid the foundation for the field of respiratory drug delivery and, more importantly, is now used to treat over 70 million asthmatic patients worldwide.

Such step-change innovation is the Holy Grail of pharmaceutical research, but despite its obvious value, replicating innovation at this level – or even on a more modest scale – seems harder than ever for many pharmaceutical companies. Incremental change remains the predominant market entry route; Thiel’s original inhaler, for example, required a specific user technique that was not always easy to master. As a result, a breath-actuated alternative was developed, but this technology, which is still in use, is now over 20 years old. Improvements are possible, but would result in a more complex device, that is more expensive and less efficient to manufacture; therefore running counter to process methodology. As a result, the ‘good-enough’ inhaler continues to dominate the market, even though much better products could be produced.


This is not to say that incremental change is not important, as there will always be sound business reasons for improving those established and much loved products with which customers have developed long-term relationships. But with new products being an essential part of the mix, it could now be argued that the drive for process control has put a brake upon innovation, a situation further exacerbated by some absent specialisms that could assist in many pharmaceutical organisations. A deep knowledge of complex physics, for example, could be put to great effect in the development of inhalers, but is rarely found in a pharmaceutical R&D context.

At its most basic, a process methodology demands that defects are reduced to an acceptable minimum; to achieve this each process must be so well documented or so easy to implement that almost anyone could manage it if required. Although ensuring quality, this also results in highly structured ‘black box’ processes which few people, at any point in the production process, truly understand. Individuals are therefore actively dissuaded from trying to amend, or improve, a process which, in turn, deskills the workforce and creates an environment focused almost entirely upon replication.

This deskilling is not restricted to the production line. The infrastructure required to support quality processes (the greater use of quality consultants, for example, and the development of personnel hierarchies such as ‘master champions’ and ‘black belts’) encourages a dependency upon individuals with highly specific skill sets, who know only about the products currently being manufactured. Extending this approach into R&D can shackle an innovation team both creatively – as there is no driver to think beyond what they already know – and in terms of innovation assessment. As a result, many organisations consider themselves highly innovative, but in practice they are not, for a number of reasons.

Firstly, when used in R&D, process management methodologies emphasise quality far too early in the innovation process, with new ideas tested in the same way as finished products. When a concept fails, it is then rejected with little thought as to why it failed or whether the product concept could be improved and refined. This is particularly evident when companies test large numbers of candidate concepts, opting to use an established ‘pass or fail’ method as an apparently straightforward way of evaluating all options equally. This approach rejects the fact that, in truly effective innovation, most new ideas are not immediately successful. For example, inhalers are routinely tested for their fine particle fraction, but consider an innovative dry powder inhaler concept (and one already in development) that deagglomerates the drug from the carrier particles by entraining the agglomerates in an airstream which then smashes them against a ‘wall’ within the device. To fully realise the potential of such a device, initial tests would have to focus upon the physical dynamics of the impact event rather than the standard test for fine particle fraction. The question being answered here is not ‘Does it work (in the manner required of a finished product)?’ but ‘Does it work the way we intended it to and if not, how can it be improved?’ A technical understanding of the product, rather than quality per se, should therefore be the focus of initial development.

Certainly, innovation requires a process of refinement in order to sharpen development focus upon those products with the potential to deliver. But this sharpening should encompass both an understanding of failure and a better idea of the customer analysis – a more fundamental unpicking of customer needs, that will identify opportunities for new product development. The aim should be to deliver products which hit a target that sits in the middle of customer needs and quality drivers. When the sharpening process is driven by quality testing then the final result is not necessarily on target – the end result will be another ‘good enough’ product, which delivers excellent replicability, but never sets the market on fire.


Most process engineering systems rely upon statistical methods to determine quality, and these methods are, in turn, based upon fairly simple mathematical assumptions such as the normal distribution of variables. It is rare that engineers test whether these assumptions apply in every given situation, and even rarer for them to respond in an appropriate manner if these assumptions are found to fail. In fact, a general criticism of the Six Sigma style approach is that it is based on what could be considered an arbitrary measure of quality – a mechanical approach to defect management that does not reflect the different needs of every situation to which it is applied. This lack of questioning is evidence, perhaps, that many R&D teams lack the mathematical knowledge required to understand and question process methodologies, and also that the skill sets employed rarely focus on areas of science and technology beyond those required by the existing product range.

True innovation depends upon the pooling of expertise from outside core skill areas, as determining the worth of a new idea requires real insight – such as engineering, fluid dynamics, sensor technology or even signal processing. For example, a recent inhaler innovation incorporates an inexpensive condenser microphone. This is used to enable an acoustic analysis of key performance characteristics, such as flow rate and inhaled volume, together with confirmation of events such as the firing of the breath actuation mechanism. Evidence shows that inhaler users often fail to use their devices properly, inadvertently exacerbating the severity of their condition and therefore requiring increased levels of treatment, for a longer period of time. Acoustic measurements can provide valuable user feedback, enabling better drug delivery, and also provide evidence of compliance that allows the physician to monitor the patient more effectively. They also show the value of employing a wider skill set – bringing signal processing skills into inhaler development, just as Thiel explored aerosol technologies used in the cosmetic industry in his drive to find an alternative to the nebuliser.


Process methodologies still play an essential role in many aspects of pharmaceutical production, but as they are not designed to encourage an understanding of process, they can easily stifle the inventiveness required to generate new and innovative products. Creativity is a core ingredient of innovation, but it is not a skill easily managed within a Six Sigma style environment.

The best solution may therefore be to cherry pick elements of process theory and apply these to limited areas within the production chain (thereby achieving quality and regulatory targets), or to tailor the statistical method behind the selected methodology to suit the application (but this requires both mathematical skill and physical insight – skills which are rarely found together in practising engineers). A more pragmatic approach also avoids an overemphasis on cost (cost equalling efficiency) – a parameter which can all too easily be used to reject new lines of discovery. Inhaler technology, for example, is an ideal means of delivering pain-killing drugs and is already used for acute conditions such as cancer, but it could also provide an alternative to the paracetamol tablets that so many customers routinely swallow, delivering pain relief in seconds rather than minutes. Paracetamol in tablet form is very cheap to produce however, and offers attractive profit margins even for the cheapest versions. There is no driver, in terms of cost, to offer consumers a much more efficient method of pain relief, which also means that consumers are deprived from even the option of accessing a more effective alternative.

A better understanding of the statistics behind process methodologies is therefore essential. When a generic statistical method is used inappropriately, it lacks power and this usually translates into additional cost and effort during the testing phase. The most powerful statistical methods – those which provide the best inference with the least data – are based upon mathematical models of how a system behaves. Methods such as Bayesian statistics can also generate evolving statistical knowledge of device behaviour which can even incorporate the level of intuitive understanding that an engineer may have, but which cannot be expressed in simple mathematical terms. Such a tailored statistical approach could then give all those involved in production the opportunity to explore new ideas – even where considered somewhat unrealistic – in order to generate new product development, and in turn, encourage the more insightful analysis that is required for establishing the true potential of a new product.


Successful innovation has always relied upon a balance of market pull and technology push, and there are many high profile failures which clearly demonstrate what can happen when this balance is ignored. Inhaled insulin is a prime example. In the drive to produce an inhaler device which could replace the insulin injection, an innovation team (supported by huge investment) did not adequately consider the user and the process, and produced a large, cumbersome, noisy device that was over-engineered and expensive to produce; something prospective users rejected. Perhaps it could be argued that the application of process engineering earlier on in the innovation process would have saved a lot of time and money – but perhaps the use of better innovation strategies would have been even more efficient.

Innovation experts also accept that their target is always hazy and that a scattergun effect is required at the outset, which will require a constant process of refinement. The innovation problem is not entirely without structure – frameworks can and should be used to establish the parameters for exploration and to help determine success. But a qualitybased approach, which attempts to systemise innovation in line with later production processes, is almost certainly not a route to breakthrough products.

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David Harris joined Sagentia’s Drug Delivery Group as a Principal Consultant in 2009. Prior to this he was the Chief Technologist of Cambridge Consultant’s MedTech division, where he invented the Conix inhaler technology. He is an expert in aerosol science, fluid dynamics, cyclones and inhaler design with more than 15 years experience. David has over 30 patents in the field of inhalers, cyclone systems and medical products, and is widely published, predominantly in the area of pulmonary drug delivery.

Ross Jones joined Sagentia in 1995, where he specialises in mathematical modelling, data analysis and algorithm development. Much of his work is focused on new product development, where he uses mathematical methods to identify design rules and determine how to test and optimise new devices. He received a PhD from the University of Cambridge in dynamics systems after graduating from the University of Tasmania.

David Harris
Ross Jones
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