The consumer health market has had a bumper decade, combining elements of over-the-counter (OTC) medicine and a range of consumer products, while racing to become an industry with annual revenues of more than $200 billion globally. The sector is growing in breadth as well as volume as health & wellness continues to trend, and new entrants seek to capture a share of this market. A sector formerly composed largely of vitamins and aspirin is increasingly expanding into products in the personal care, cosmetics, and nutrition space – attaching science-backed claims of efficacy. The rapid growth of this market has increased the pace of product launches, and put increasing pressure on companies to find new benefit claims and ingredients to excite consumers. This increasing demand has begun to outstrip the traditional pipeline of evidence on which such claims are based.
At the core of the consumer health business are products with clear, relevant benefit claims. Making such claims requires regulatory approval based on clinical trials demonstrating efficacy. These trials can be extremely expensive to carry out, and traditionally the industry has tried to limit the risks involved in developing the evidence base for clinical claims by following the trail revealed by preclinical research. These exploratory studies on new ingredients effectively split the cost of development between academia and private companies. Preclinical evidence often takes the form of small initial trials in humans or validated animal models, without the full rigour or statistical power to generate approved claims, but with just enough to indicate the potential for effect and reduce the financial risk attached to funding a full clinical study.
Traditionally, the bulk of preclinical evidence was generated by academic institutions. However, a perception that these trials tended to take a scattergun ‘trial and error’ approach rather than being led by rigorous methodology has stuck, as has a reputation for poor reproducibility. This has led to a reduction in grant funding, with leading institutions now preferring to prioritize research in areas of more fundamental science. This has meant that, although academic preclinical research continues to be produced, it has failed to keep pace with the demands of industry. Additionally, the majority of current academic output in the field focuses on relatively well-known ingredients and cannot respond rapidly enough to changing consumer trends.
This is creating a noticeable bottleneck in preclinical research, which in turn is beginning to impact how consumer health companies plan, develop, and launch products. Companies are increasingly exploring other ways to reduce research costs and to develop product claims. Two of the most common new approaches are ‘synthetic’ (often known as ‘third degree’ claims) and mechanistic investigation.
The synthetic approach leverages the vast amounts of information that have been built up over the past few decades on known active molecules contained in well-characterised ingredients. Whole classes of common molecules (polyphenols, vitamins, minerals, fatty acids, etc.) are now associated with marketable claims, and the presence of one or more of these in a new ingredient or product formulation can be used as the basis of benefit claims for a new product. However, the language used for claims made under this approach is bound by a particular semantic structure: ‘contains ingredient X, rich in molecule Y, which has been shown to achieve effect Z’. While the precise wording can be massaged, the basic form has to be maintained. This creates a disconnect that is seen as undesirable; consumers are increasingly sensitive to the perceived risk of ‘false’ claims; and this synthetic approach lacks the authority of direct claims. In the case of novel active ingredients this approach may also completely miss new potential benefit claims as it can only refer back to pre-existing evidence and effects. However, when used appropriately this technique is flexible, quick to implement, and cost-effective as it removes the need for new clinical trials entirely.
Mechanistic investigation is not a way of circumventing clinical trials completely, but aims to reduce the uncertainty around an ingredient to the point where a company feels confident enough to invest in early investigative trials. Like synthetic claims, mechanistic investigations build on the wealth of granular understanding the scientific community has developed. By identifying the known active molecules in a new ingredient, or identifying molecules that appear to be similar in some way to known active molecules, it is possible to anticipate the likely effects of a new ingredient. This is advantageous compared to a straightforward synthetic claim, because it leaves room to recognise the potential for benefits achieved through the combination of active molecules within an ingredient, or the potential for benefits in areas that have not previously been identified. While this is an alternative to reliance on traditional preclinical data, the risk reduction that mechanistic investigation offers is still significantly lower. In reality, mechanistic investigation is used as a method for narrowing down candidates for investigation; an acceptance by companies that they will need to perform early preclinical validation themselves while attempting to limit the cost.
Greater integration of mechanistic investigation of candidate ingredients and leveraging of existing claims will be a key part of a more efficient claims pipeline. Both of these approaches are helping to ease dependence on traditional preclinical evidence, but neither is a complete replacement. In the longer term, new ways must be found to raise the output of preclinical research pipelines. One solution might be for companies to finance more of this research themselves. Indeed, corporate research funding has been increasing significantly in recent years – driven by a recognition that if industry requires a certain academic output, they must bear a greater share of the burden of financing it.
However, there is a potential disruptor to this traditional approach in the emergence of ‘big data’ as a research tool. Consumers produce huge amounts of data through their use of technology describing all aspects of their lives: their habits, their likes and dislikes, and, crucially, aspects of their health and wellbeing. The ability to gather and process vast quantities of user information is likely to open up new ways to gather preclinical indications of ingredients’ efficacy in products that consumers already use. At the present time it must be admitted that this exists more as a dream scenario in the minds of proponents than it does in any practical sense. While implementing big data has already had a significant impact on the costs of clinical trials themselves, applying these techniques to the preclinical phase will bring unique challenges around managing liability, safety, and the validity of results . Despite these short term difficulties however, the collection and analysis of big data is likely to fundamentally alter the landscape of consumer health claims development; offering a solution to a bottleneck that is becoming too cumbersome to ignore.
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