The role of a statistician in drug development: Pre-Clinical Drug Development
Before a novel compound is evaluated in human trials, preliminary characterization of the toxicology profile as well as the efficacy and drug metabolism should be assessed in pre-clinical studies. This can be performed using basic techniques or more advanced strategies such as pharmacokinetic-pharmacodynamic (PK-PD) modelling. The findings may be used to predict how humans would react to the drug, as the disease progression and the PK-PD relationship in humans could be consistent, to some extent, with animal study results. Furthermore, data accumulated from animal experiments that have been well designed, conducted and analysed will contribute to a more successful translation into humans. Innovative study designs and sophisticated statistical methods can therefore potentially improve the efficiency and accelerate the development of pharmaceuticals. However, recent reports (Roberts et al., 2002; Hackam and Redelmeier, 2006; American Council for Science and Health, 2006) show that the quality of animal experiments in general is dishearteningly unsatisfactory. Criticism also involves the deficiency of applying statistical approaches adequately and properly in pre-clinical research. These issues arise fundamentally due to the scant awareness that statisticians can play a significant role in this stage of early drug development.
What are the responsibilities of a pre-clinical statistician in this phase?
Pre-clinical statisticians give statistical support to in-vitro and in-vivo studies (i.e. studies performed with cells and animals respectively). The responsibilities of a statistician in this phase are varied: they can involve data analysis of toxicological studies, assay validation, synergy calculation, biomarker exploration, planning and analysis of dose finding studies and involvement in pharmacogenomics, pharmacokinetic and pharmacodynamic modelling. As in other phases, the pre-clinical statistician is involved in the design, power calculation, data management and analysis of studies, communication of results and report writing. Nevertheless, the role of pre-clinical statisticians is more “pioneering” (Lendrem, 2002), as the pre-clinical phase of drug development is characterized by poor knowledge of a compound’s mechanism of action and of new scientific technologies. Therefore, the inherent innovative nature of this phase also requires the development of new statistical methods, which is another important role of the pre-clinical statistician.
With whom do pre-clinical statisticians collaborate?
Pre-clinical statisticians mostly collaborate with pre-clinical scientists (project managers, laboratory heads, laboratory assistants, toxicologists, computational biologists etc.) in order to address statistical questions and communicate results, and with IT specialists in activities such as data generation and storage. Statisticians involved in the pre-clinical phase also work with later stage scientists and statisticians to assess translatability of the results.
What are the major challenges emerging from these collaborations?
Pre-clinical statisticians sometimes struggle to demonstrate the importance and value of statistics to pre-clinical scientists. It is often difficult to work in an interdisciplinary working environment and to reach a common goal starting from different scientific backgrounds. Some pre-clinical scientists have limited statistical knowledge and conduct trials without adequately taking statistics into consideration. Therefore, statisticians are sometimes not involved, or they are not involved early enough to prevent statistical errors. Even in situations where pre-clinical statisticians are involved, pre-clinical scientists often require analyses which are too much focused-on p-values and testing (Wasserstein and Lazar, 2016), whereas the true scientific question is about estimation.
What are the major statistical challenges in this phase?
Unlike late phase studies, pre-clinical studies are characterized by small sample sizes. The importance of pre-clinical statisticians in this phase lies in drawing the big picture to answer a complex scientific question from small experiments. The difficulties are the distributional assumptions, many confounding variables and unknown sources of variability (these are unverifiable in small sample sizes). In such context, all additional sources of information (e.g. historical controls, additional/external databases) need to be combined, cleaned and corrected in order to support and assess the current experiment. Moreover, statistical models should be able to have also a biological (pharmacological), not only a statistical, meaningfulness. Strategies such as complex Bayesian (hierarchical) models and equivalence methods have great potential in this phase. Robust statistical methods (e.g. for microbiome data analysis), mixed effects models for excess zero/sparse data (data with a majority of cells with zero counts) and process validation are important as well. Often, a specific statistical approach has to be developed in a short period of time and improper post-hoc- and subgroup analyses have to be avoided.
What is the connection between pre-clinical and Phase I studies?
Statisticians working in the preclinical phase provide critical input into quantification of risk when moving from animal to human studies. Pre-clinical results evaluation leads to go/no go decisions about first-in-human studies.
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Journal of Developing Drugs