There is a clear case for drug treatments to be selected according to the characteristics of an individual patient, in order to improve efficacy and reduce the number and severity of adverse drug reactions. However, such personalization of drug treatments requires the ability to predict how different individuals will respond to a particular drug/dose combination. After initial optimism, there is increasing recognition of the limitations of the pharmacogenomic approach, which does not take account of important environmental influences on drug absorption, distribution, metabolism and excretion. For instance, a major factor underlying inter-individual variation in drug effects is variation in metabolic phenotype, which is influenced not only by genotype but also by environmental factors such as nutritional status, the gut microbiota, age, disease and the co- or pre-administration of other drugs. Thus, although genetic variation is clearly important, it seems unlikely that personalized drug therapy will be enabled for a wide range of major diseases using genomic knowledge alone. Here we describe an alternative and conceptually new 'pharmaco-metabonomic' approach to personalizing drug treatment, which uses a combination of pre-dose metabolite profiling and chemometrics to model and predict the responses of individual subjects. We provide proof-of-principle for this new approach, which is sensitive to both genetic and environmental influences, with a study of paracetamol (acetaminophen) administered to rats. We show pre-dose prediction of an aspect of the urinary drug metabolite profile and an association between pre-dose urinary composition and the extent of liver damage sustained after paracetamol administration.

A valid surrogate endpoint allows correct inference to be drawn regarding the effect of an intervention on the unobserved true clinical endpoint of interest. The perceived practical and ethical advantages of substituting a surrogate endpoint for a clinical endpoint have led to a considerable number of statistical methods being proposed for the evaluation of a biomarker as a surrogate endpoint. We review the main statistical schools of thought which have developed and consider how the validation process might be arranged within the regulatory and practical constraints of the drug development process. We conclude by assessing which of the candidate statistical methods offer the best approach for surrogate endpoint evaluation.

The goal of clinical trial research is to deliver safe and efficacious new treatments to patients in need in a timely and cost-effective manner. There is precedent in using historical control data to reduce the number of concurrent control subjects required in developing medicines for rare diseases and other areas of unmet need. The purpose of this paper is to provide a review for a regulatory and industry audience of the current state of relevant statistical methods, and of the uptake of these approaches and the opportunities for broader use of historical data in confirmatory clinical trials. General principles to consider when incorporating historical control data in a new trial are presented. Bayesian and frequentist approaches are outlined including how the operating characteristics for such a trial can be obtained. Finally, examples of approved new treatments that incorporated historical controls in their confirmatory trials are presented.

This paper illustrates how the design and statistical analysis of the primary endpoint of a proof-of-concept study can be formulated within a Bayesian framework and is motivated by and illustrated with a Pfizer case study in chronic kidney disease. It is shown how decision criteria for success can be formulated, and how the study design can be assessed in relation to these, both using the traditional approach of probability of success conditional on the true treatment difference and also using Bayesian assurance and pre-posterior probabilities. The case study illustrates how an informative prior on placebo response can have a dramatic effect in reducing sample size, saving time and resource, and we argue that in some cases, it can be considered unethical not to include relevant literature data in this way.

Metabonomics is a relatively new field of research in which the total pool of metabolites in body fluids or tissues from different patient groups is subjected to comparative analysis. Nuclear magnetic resonance (NMR) spectroscopy is the technology that is currently most widely used for the analysis of these highly complex metabolite mixtures, and hundreds of metabolites can be detected without any upfront separation. We have investigated in this study whether gas chromatography (GC) separation in combination with flame ionisation detection (FID) and mass spectrometry (MS) detection can be used for metabolite profiling from urine. We show that although GC sample preparation is much more involved than for NMR, hundreds of metabolites can reproducibly be detected and analysed by GC. We show that the data quality is sufficiently high -particularly if appropriate baseline correction and time-warping methods are applied -to allow for data comparison by chemometrics methods. A sample set of urines from eleven healthy human volunteers was analysed independently by GC and NMR, and subsequent chemometrics analysis of the two datasets showed some similar features. As judged by NIST database searches of the GC/MS data some of the major metabolites that are detected by NMR are also visible by GC/MS. Since in contrast to NMR every peak in GC corresponds to a single metabolite, the electron ionisation spectra can be used to quickly identify metabolites of interest if their reference spectra are present in a searchable database. In summary, we show that GC is a method that can be used as a complementary tool to NMR for metabolite profiling of urine samples.