It is sometimes assumed that government regulations governing the use of animal models in drug development hamper good science, either by accident or design. But reality is rather different: focus on the 3Rs of replacement, reduction and refinement can lead to more reliable results, quicker, at lower cost and with improved animal welfare and reduced animal use as well.

There are a number of strategies that can reduce the number of animals used during the development of a new drug. The most obvious is to combine several types of study, investigating efficacy, safety and drug disposition simultaneously. As well as reducing the number of animals required, it has scientific benefits too: instead of relying on measuring drug levels to assess exposure, you can observe the safety of the drug in exactly the same animals where efficacy is investigated. For drugs with simple distribution characteristics, measuring exposure in the blood is useful for comparing different studies, but as soon as the distribution becomes complex (for example, with drugs that accumulate in some tissues, or are excluded from others) comparing different end-points in different studies becomes challenging and fraught with risk of misinterpretation.

Quite simply, then, its simply better to look at safety and efficacy in the same animals in the same study. The results are easier to interpret, particularly early in drug development when knowledge of distribution characteristics may be imperfect. Not only is it scientifically better, but it reduces the use of animals, and it reduces the overall cost of obtaining the data. A combination study may be as much as 30% cheaper than running two separate studies.

For these reasons, Total Scientific plan to launch in 2012 a comprehensive range of combination study packages, combining our industry-standard models of chronic inflammatory diseases with conventional assessment of toxicity, including clinical chemistry, haematology, urinalysis, organ weights and histopathology. For anyone involved in early stage drug development in immunology and inflammation, these study designs will offer more reliable de-risking of an early stage programme at a lower cost than conventional development routes.

If the data is better and the costs are lower, why haven’t such combination designs become the norm before now? Perhaps its because of a misunderstanding of what kind of safety information is needed during the early stages of developing a first-in-class compound. Conventional toxicology (such as that required for regulatory filings) requires driving dosing levels very high to ensure that adverse effects are identified. Clearly, for a drug to be successful, the adverse events must be occurring at much higher doses than the beneficial effects – which is at odds with a combination study design.

That’s fine once you have selected your clinical candidate (and conventional toxicology studies of this kind will still be needed prior to regulatory submission even if you ran a combination study). But for earlier stage development, the combination design makes perfect sense: before you ask how big the therapeutic index might be, first you simply want to know whether it is safe at the doses required for efficacy.

A previous blog by DrugBaron has already commented on the over-focus on efficacy in early drug development as a contributor to costly attrition later in the pipeline. Why would you be interested in a compound that offered benefit but only at doses that cause unacceptable side-effects (whether mechanism-related or molecule-specific it matters not)? Continuing to invest either time or money in such a compound ignorant of the safety issues until later down the path is a recipe for failure.

Looking at early stage opportunities being touted for venture capital investment paints a similar picture: almost all have, as their centerpiece, a compelling package of efficacy data in one (or often several) animal models. Far fewer have any assessment of safety beyond the obvious (that the animals in the efficacy studies survived the treatment period). Since almost any first-in-class compound, by definition hitting a target unvalidated in the clinic, is associated with “expected” side-effects, this lack of any information to mitigate that risk is the most common reason for failing to attract commercial backing for those early stage projects. Total Scientific’s combination study designs rectify these defects, reducing risk earlier, and at lower cost.

Why stop there? Relatively simple changes to the study design also allow investigation of pharmacokinetics, metabolism and distribution – all in the same animals where efficacy and safety are already being investigated. Such “super-studies” that try and address simultaneously many different aspects of the drug development cascade may be unusual, and may not provide definitive (that is “regulator-friendly”) results for any of the individual study objectives. However, in early stage preclinical development they will provide an extremely cost-effective method of identifying potential problems early, while reducing use of animals still further.

Combining different objectives into one study is only one way Total Scientific refines animal model designs in order to reduce animal requirements. Being biomarker specialists, we can improve the phenotyping of our animal models in several different ways. Firstly, by using multiple end-points (and an appropriate multi-objective statistical framework) we can detect efficacy with fewer animals per group than when relying on a single primary end-point. There can be no doubt that a single primary end-point design, used for regulatory clinical studies for example, is the gold-standard – and is entirely appropriate for deciding whether to approve a drug. But once again its not the most appropriate design for early preclinical investigations. It’s much better to trade a degree of certainty for the extra information that comes from multiple end-points. In any case, the consistency of the whole dataset provides that certainty in a different way.

Learning how a new compound affects multiple pathways that compose the disease phenotype provides a lot of additional value. In respiratory disease, for example, understanding whether the effect is similar on neutrophils and eosinophils, or heavily biased towards one or the other provides an early indication as to whether the compound may be more effective in allergic asthma or in severe steroid-resistant asthma. Compounds that hit multiple end-points in an animal model are much more likely to translate to efficacy in the clinic.

Equally importantly, we focus on end-points that have lower inter-animal variability – and hence greater statistical power. There is a tendency for end-points to become established in the literature simply on the basis of being used in the first studies to be published. Through an understandable desire to compare new studies with those that have been published, those initial choices of end-points tend to become locked in and used almost without thinking. But often there are better choices, with related measures providing similar information, but with markedly better statistical power. This is particularly true of semi-quantative scoring systems that have evolved to combine several measures into one number. Frequently, most of the relevant information is in one component of the composite variable, while others contribute most of the noise – destroying statistical power and requiring larger studies.

What all these refinements have in common is that they improve the quality of the data (driving better decisions), while reducing the number of animals required on the other (with ethical and cost benefits). Its not often you get a win:win situation like this – better decisions typically cost more rather than less. But the forthcoming introduction of Total Scientific’s new range of preclinical model study designs promises benefits all round.