Medication Nonadherence, "Professional Subjects," and Apparent Placebo Responders: Overlapping Challenges for Medications Development.

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 8 psychiatric trials conducted between 2001 and 2011. With nonadherence defined as greater than 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 2 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.

An Integrated Bioinformatics Approach for Identifying Genetic Markers that Predict Cerebrospinal Fluid Biomarker p-tau181/Aß1-42 Ratio in ApoE4-Negative Mild Cognitive Impairment Patients.

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/Aß1-42 and p-tau181/Aß1-42 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/Aß1-42 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/Aß1-42 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.

Effective visualization of integrated knowledge and data to enable informed decisions in drug development and translational medicine.

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.

Pretreatment data is highly predictive of liver chemistry signals in clinical trials.

PURPOSE: The 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 METHODS: Based 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. RESULTS: Predictive 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. CONCLUSION: It 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.

The fellowship of the RING: the RING-B-box linker region interacts with the RING in TRIM21/Ro52, contains a native autoantigenic epitope in Sjögren syndrome, and is an integral and conserved region in TRIM proteins.

Ro52 is a major autoantigen that is targeted in the autoimmune disease Sjögren syndrome and belongs to the tripartite motif (TRIM) protein family. Disease-related antigenic epitopes are mainly found in the coiled-coil domain of Ro52, but one such epitope is located in the Zn(2+)-binding region, which comprises an N-terminal RING followed by a B-box, separated by a approximately 40-residue linker peptide. In the present study, we extend the structural, biophysical, and immunological knowledge of this RING-B-box linker (RBL) by employing an array of methods. Our bioinformatic investigations show that the RBL sequence motif is unique to TRIM proteins and can be classified into three distinct subtypes. The RBL regions of all three subtypes are as conserved as their known flanking domains, and all are predicted to comprise an amphipathic helix. This helix formation is confirmed by circular dichroism spectroscopy and is dependent on the presence of the RING. Immunological studies show that the RBL is part of a conformation-dependent epitope, and its antigenicity is likewise dependent on a structured RING domain. Recombinant Ro52 RING-RBL exists as a monomer in vitro, and binding of two Zn(2+) increases its stability. Regions stabilized by Zn(2+) binding are identified by limited proteolysis and matrix-assisted laser desorption/ionization mass spectrometry. Furthermore, the residues of the RING and linker that interact with each other are identified by analysis of protection patterns, which, together with bioinformatic and biophysical data, enabled us to propose a structural model of the RING-RBL based on modeling and docking experiments. Sequence similarities and evolutionary sequence patterns suggest that the results obtained from Ro52 are extendable to the entire TRIM protein family.

Characterization of oligopeptide patterns in large protein sets.

BACKGROUND: Recent sequencing projects and the growth of sequence data banks enable oligopeptide patterns to be characterized on a genome or kingdom level. Several studies have focused on kingdom or habitat classifications based on the abundance of short peptide patterns. There have also been efforts at local structural prediction based on short sequence motifs. Oligopeptide patterns undoubtedly carry valuable information content. Therefore, it is important to characterize these informational peptide patterns to shed light on possible new applications and the pitfalls implicit in neglecting bias in peptide patterns. RESULTS: We have studied four classes of pentapeptide patterns (designated POP, NEP, ORP and URP) in the kingdoms archaea, bacteria and eukaryotes. POP are highly abundant patterns statistically not expected to exist; NEP are patterns that do not exist but are statistically expected to; ORP are patterns unique to a kingdom; and URP are patterns excluded from a kingdom. We used two data sources: the de facto standard of protein knowledge Swiss-Prot, and a set of 386 completely sequenced genomes. For each class of peptides we looked at the 100 most extreme and found both known and unknown sequence features. Most of the known sequence motifs can be explained on the basis of the protein families from which they originate. CONCLUSION: We find an inherent bias of certain oligopeptide patterns in naturally occurring proteins that cannot be explained solely on the basis of residue distribution in single proteins, kingdoms or databases. We see three predominant categories of patterns: (i) patterns widespread in a kingdom such as those originating from respiratory chain-associated proteins and translation machinery; (ii) proteins with structurally and/or functionally favored patterns, which have not yet been ascribed this role; (iii) multicopy species-specific retrotransposons, only found in the genome set. These categories will affect the accuracy of sequence pattern algorithms that rely mainly on amino acid residue usage. Methods presented in this paper may be used to discover targets for antibiotics, as we identify numerous examples of kingdom-specific antigens among our peptide classes. The methods may also be useful for detecting coding regions of genes.

Ontology annotation treebrowser : an interactive tool where the complementarity of medical subject headings and gene ontology improves the interpretation of gene lists.

Gene expression and proteomics analysis allow the investigation of thousands of biomolecules in parallel. This results in a long list of interesting genes or proteins and a list of annotation terms in the order of thousands. It is not a trivial task to understand such a gene list and it would require extensive efforts to bring together the overwhelming amounts of associated information from the literature and databases. Thus, it is evident that we need ways of condensing and filtering this information. An excellent way to represent knowledge is to use ontologies, where it is possible to group genes or terms with overlapping context, rather than studying one-dimensional lists of keywords. Therefore, we have built the ontology annotation treebrowser (OAT) to represent, condense, filter and summarise the knowledge associated with a list of genes or proteins. The OAT system consists of two disjointed parts; a MySQL database named OATdb, and a treebrowser engine that is implemented as a web interface. The OAT system is implemented using Perl scripts on an Apache web server and the gene, ontology and annotation data is stored in a relational MySQL database. In OAT, we have harmonized the two ontologies of medical subject headings (MeSH) and gene ontology (GO), to enable us to use knowledge both from the literature and the annotation projects in the same tool. OAT includes multiple gene identifier sets, which are merged internally in the OAT database. We have also generated novel MeSH annotations by mapping accession numbers to MEDLINE entries. The ontology browser OAT was created to facilitate the analysis of gene lists. It can be browsed dynamically, so that a scientist can interact with the data and govern the outcome. Test statistics show which branches are enriched. We also show that the two ontologies complement each other, with surprisingly low overlap, by mapping annotations to the Unified Medical Language System. We have developed a novel interactive annotation browser that is the first to incorporate both MeSH and GO for improved interpretation of gene lists. With OAT, we illustrate the benefits of combining MeSH and GO for understanding gene lists. OAT is available as a public web service at: http://www.ifm.liu.se/bioinfo/oat.

Bioinformatic and enzymatic characterization of the MAPEG superfamily.

The membrane associated proteins in eicosanoid and glutathione metabolism (MAPEG) superfamily includes structurally related membrane proteins with diverse functions of widespread origin. A total of 136 proteins belonging to the MAPEG superfamily were found in database and genome screenings. The members were found in prokaryotes and eukaryotes, but not in any archaeal organism. Multiple sequence alignments and calculations of evolutionary trees revealed a clear subdivision of the eukaryotic MAPEG members, corresponding to the six families of microsomal glutathione transferases (MGST) 1, 2 and 3, leukotriene C4 synthase (LTC4), 5-lipoxygenase activating protein (FLAP), and prostaglandin E synthase. Prokaryotes contain at least two distinct potential ancestral subfamilies, of which one is unique, whereas the other most closely resembles enzymes that belong to the MGST2/FLAP/LTC4 synthase families. The insect members are most similar to MGST1/prostaglandin E synthase. With the new data available, we observe that fish enzymes are present in all six families, showing an early origin for MAPEG family differentiation. Thus, the evolutionary origins and relationships of the MAPEG superfamily can be defined, including distinct sequence patterns characteristic for each of the subfamilies. We have further investigated and functionally characterized representative gene products from Escherichia coli, Synechocystis sp., Arabidopsis thaliana and Drosophila melanogaster, and the fish liver enzyme, purified from pike (Esox lucius). Protein overexpression and enzyme activity analysis demonstrated that all proteins catalyzed the conjugation of 1-chloro-2,4-dinitrobenzene with reduced glutathione. The E. coli protein displayed glutathione transferase activity of 0.11 micromol.min(-1).mg(-1) in the membrane fraction from bacteria overexpressing the protein. Partial purification of the Synechocystis sp. protein yielded an enzyme of the expected molecular mass and an N-terminal amino acid sequence that was at least 50% pure, with a specific activity towards 1-chloro-2,4-dinitrobenzene of 11 micromol.min(-1).mg(-1). Yeast microsomes expressing the Arabidopsis enzyme showed an activity of 0.02 micromol.min(-1).mg(-1), whereas the Drosophila enzyme expressed in E. coli was highly active at 3.6 micromol.min(-1).mg(-1). The purified pike enzyme is the most active MGST described so far with a specific activity of 285 micromol.min(-1).mg(-1). Drosophila and pike enzymes also displayed glutathione peroxidase activity towards cumene hydroperoxide (0.4 and 2.2 micromol.min(-1).mg(-1), respectively). Glutathione transferase activity can thus be regarded as a common denominator for a majority of MAPEG members throughout the kingdoms of life whereas glutathione peroxidase activity occurs in representatives from the MGST1, 2 and 3 and PGES subfamilies.