DATA ANALYSIS IN PHARMACOLOGY


delivery and rapid breakdown. But these ratios vary from individual to individual and only a statistical estimate can be applied to populations. Furthermore, substances do not operate in isolation; they, and their pharmacologies, interact with each other and with their environment. Ten there are supplementary substances which may be used in an attempt to increase the ration by enhancing therapeutic and buffering or reducing toxic effects, each with their own pharmacologies which add further to the complexity. Well-defined models, and soundly-based data analytic tests against which these descriptors can be tested, are therefore vital in extracting meaningful, useful and reliable findings. A major focus of


pharmacological investigation is drug discovery for medical intervention, and here another imperative behind careful statistical treatment of pharmacological data arises from their high ethical and economic costs. Whatever one’s attitude to animal and human tests, there is (the occasional monomaniac tyranny aside) broad social consensus that their numbers should not exceed absolute necessity, especially given that every human trial potentially puts the welfare of a volunteer at risk. Such work is also expensive in financial terms and both costs are multiplied several thousand fold by the low proportion of programmes which eventually lead to a usable outcome. It therefore becomes a moral and fiscal priority to extract the maximum knowledge from the smallest


OXYGENE


Therapeutic interventions are what usually spring to mind when the word pharmacology is mentioned, but they cannot be meaningfully analysed without reference to their environment. As a fundamental component of animal metabolism, ever present in every context, oxygen is also a primary object of pharmacological attention – especially in its most reactive forms which play many crucial roles, particularly in association with kinases and in relation to genetic expression or deletion effects.


Comparative statistical analysis


of results from experiments investigating cancer preventative effects of gugulipid (a natural extract from a plant used in traditional Indian medicine) on prostate cells showed[4]


that


beneficial proapoptotic and angiogenesis suppression effects were dependent upon levels of c-Jun N-terminal kinase. Initiation of this regulatory effect, however, was in turn dependent upon available levels of reactive oxygen species (ROS) – a connection hypothesised from analyses of previous


experimental data on apoptotic kinases and built into the study as a result. This is one of many studies pointing to potential mechanisms for the suppression of cancers.


In plants, designed experiments linked to well- structured data analysis show that ROS suppression by negative feedback loops affects lignin production for repair processes[5]


are implicated with another kinase in heart failure. Analysis of recent data[7]


has shown, for


example, a ROS link to sodium and calcium overload in the muscle cells. Adopting a three-way data analytic approach to high levels of intravenously administered ascorbate (for example, experiments[8]


by and ROS-


related responses to oral insect secretions[6]


during predation. ROS have many damaging effects, however, and in particular


Levne and others at NIH Bethesda) has opened up new pharmacodynamic knowledge of hydroxide decomposition mechanisms and, again, impact on cancerous cells.


possible number of experiments by efficiently designed data generation and utilisation strategies. Computerised soſtware for


pharmacological work comes in the same sort of variety as in other fields. Generic products can, of course, be used – and oſten are, though an increasing number of them are supplying purpose designed functions. Others are designed to be life sciences oriented and there is the same mix of commercial, free and open-source solutions now familiar from every area of scientific computing. Unlike some market segments (office


“Computerised software for pharmacological work comes in the same sort of variety as in other fields”


suites, for example, and LIMS which I’ll mention later) data analytic soſtware within an organisation is oſten not monolithic throughout. Visiting and talking to people in preparation of this article I’ve seen many examples of one package being used at management level, others at laboratory level and several more on individual desktops. In other cases, the variation is between one site and another.


Te crucial issue is whether data and analytic results can travel transparently back and forth across soſtware and organisational boundaries. Given the wide range of import and export file format options available in most statistics products these days, it’s usually possible to find a lingua franca which allows everyone their favourite tools within an overall system. An example of the free-to-use, dedicated


sector is InVivoStat, the name of which gives clear notice of its purpose and focus:


WHAT’S A MOTHER TO DO?


Female birds have a surprising degree of control over the sex ratio of their offspring but we have imperfect understanding of how they do it. A GenStat designed and analysed study by Sarah Pryke and others sought to investigate, through a pharmacological approach, the hypothesis that variation in female hormones provide the mechanism. A number of possible influences might influence hormone balance, but ‘partner quality was found to directly affect female hormonal status and subsequent fitness. When constrained to breeding with low-quality males, females had highly elevated stress responses (corticosterone levels) and produced adaptive male-biased sex ratios, whereas when they bred with high-quality males, females had low corticosterone levels and produced an equal offspring sex ratio. There was no effect of other maternal hormones (e.g., testosterone) or body condition on offspring sex ratios.’[10]


‘designed specifically for researchers... where exploiting the experimental design is crucial for reliable statistical analyses’, to quote a comparative review[2]


by five such


researchers at the end of 2011. Examining the package shows the provided tools to be a subset chosen from those available in generic packages, with selection and presentation being the key to specificity. General statistics packages increasingly


include macros or wizards to cater for particular expectations of field-specific users. Tese routines serve as personalised guides through the maze of available methods to the features with which a researcher (in this case a pharmacologist) is familiar, then present a particular interface for control of those features, without altering or obscuring the generality of the product itself. Other


SCIENTIFIC COMPUTING WORLD


BEYOND THE NUMBERS A STATISTICS SPECIAL 19


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