Nonadherence is a major problem in clinical trials of new medications. To evaluate the extent of nonadherence, this study evaluated pharmacokinetic sampling from 1765 subjects receiving active therapy across eight psychiatric trials conducted between 2001 and 2011. With nonadherence defined as > 50% of plasma samples below the limit of quantification for study drug, the percentage of nonadherent subjects ranged from 12.8% to 39.2%. There was a trend toward increased nonadherence in studies with greater numbers of subjects but an association with nonadherence was not apparent for other study design parameters or subject characteristics. For two trials with multiple recruitment sites in geographical proximity, several subjects attempted to simultaneously enroll at separate site locations. The construct of “professional subjects,” those who enroll in trials only for financial gain, is gaining attention, and we therefore modeled the impact of professional subjects on medication efficacy trials. The results indicate that enrollment of professional subjects who are destined to succeed (those who will appear to achieve treatment success regardless of study drug assignment) can substantially increase both the apparent placebo response rate and the sample size requirement for statistical power, while decreasing the observed effect size. The overlapping nature of nonadherence, professional subjects, and placebo response suggests that these issues should be considered and addressed together. Following this approach, we describe a novel clinical trial design to minimize the adverse effects of professional subjects on trial outcomes, and discuss methods to monitor adherence.

Integrative understanding of preclinical and clinical data is imperative to enable informed decisions and reduce the attrition rate during drug development. The volume and variety of data generated during drug development have increased tremendously. A new information model and visualization tool was developed to effectively utilize all available data and current knowledge. The Knowledge Plot integrates preclinical, clinical, efficacy and safety data by adding two concepts: knowledge from the different disciplines and protein binding.Internal and public available data were gathered and processed to allow flexible and interactive visualizations. The exposure was expressed as the unbound concentration of the compound and the treatment effect was normalized and scaled by including expert opinion on what a biologically meaningful treatment effect would be.The Knowledge Plot has been applied both retrospectively and prospectively in project teams in a number of different therapeutic areas, resulting in closer collaboration between multiple disciplines discussing both preclinical and clinical data. The Plot allows head to head comparisons of compounds and was used to support Candidate Drug selections and differentiation from comparators and competitors, back translation of clinical data, understanding the predictability of preclinical models and assays, reviewing drift in primary endpoints over the years, and evaluate or benchmark compounds in due diligence comparing multiple attributes.The Knowledge Plot concept allows flexible integration and visualization of relevant data for interpretation in order to enable scientific and informed decision-making in various stages of drug development. The concept can be used for communication, decision-making, knowledge management, and as a forward and back translational tool, that will result in an improved understanding of the competitive edge for a particular project or disease area portfolio. In addition, it also builds up a knowledge and translational continuum, which in turn will reduce the attrition rate and costs of clinical development by identifying poor candidates early.

Alzheimer's disease (AD) is the most common form of dementia, with no disease-modifying treatment yet available. Early detection of patients at risk of developing AD is of central importance. Blood-based genetic signatures can serve as early detection and as population-based screening tools. In this study, we aimed to identify genetic markers and gene signatures associated with cerebrospinal fluid (CSF) biomarkers levels of t-tau, p-tau181, and with the two ratios t-tau/Aβ1-42 and p-tau181/Aβ1-42 in the context of progression from mild cognitive impairment (MCI) to AD, and to identify a panel of genetic markers that can predict CSF biomarker p-tau181/Aβ1-42 ratio with consideration of APOE ε4 stratification. We analyzed genome-wide the Alzheimer's Disease Neuroimaging Initiative dataset with up to 48 months follow-up. In the first part of the analysis, the main effect of single nucleotide polymorphisms (SNPs) under an additive genetic model was assessed for each of the four CSF biomarkers. In the second part of the analysis, we performed an integrated analysis of genome-wide association study results with pathway enrichment analysis, predictive modeling and network analysis in the subgroup of ApoE4-negative subjects. We identified a panel of five SNPs, rs6766238, rs1143960, rs1249963, rs11975968, and rs4836493, that are predictive for p-tau181/Aβ1-42 ratio (high/low) with a sensitivity of 66% and a specificity of 70% (AUC 0.74). These results suggest that a panel of SNPs is a potential prognostic biomarker in ApoE4-negative MCI patients.

PurposeThe goal of this retrospective analysis was to assess how well predictive models could determine which patients would develop liver chemistry signals during clinical trials based on their pretreatment (baseline) information.Patients and methodsBased on data from 24 late-stage clinical trials, classification models were developed to predict liver chemistry outcomes using baseline information, which included demographics, medical history, concomitant medications, and baseline laboratory results.ResultsPredictive models using baseline data predicted which patients would develop liver signals during the trials with average validation accuracy around 80%. Baseline levels of individual liver chemistry tests were most important for predicting their own elevations during the trials. High bilirubin levels at baseline were not uncommon and were associated with a high risk of developing biochemical Hy’s law cases. Baseline γ-glutamyltransferase (GGT) level appeared to have some predictive value, but did not increase predictability beyond using established liver chemistry tests.ConclusionIt is possible to predict which patients are at a higher risk of developing liver chemistry signals using pretreatment (baseline) data. Derived knowledge from such predictions may allow proactive and targeted risk management, and the type of analysis described here could help determine whether new biomarkers offer improved performance over established ones.

No abstract