Protocol of the Cognitive Health in Ageing Register: Investigational, Observational and Trial Studies in Dementia Research (CHARIOT): Prospective Readiness cOhort (PRO) SubStudy.

INTRODUCTION: The Cognitive Health in Ageing Register: Investigational, Observational and Trial Studies in Dementia Research (CHARIOT): Prospective Readiness cOhort (PRO) SubStudy (CPSS), sponsored by Janssen Pharmaceutical Research & Development LLC, is an Alzheimer's disease (AD) biomarker enriched observational study that began 3 July 2015 CPSS aims to identify and validate determinants of AD, alongside cognitive, functional and biological changes in older adults with or without detectable evidence of AD pathology at baseline. METHODS AND ANALYSIS: CPSS is a dual-site longitudinal cohort (3.5 years) assessed quarterly. Cognitively normal participants (60-85 years) were recruited across Greater London and Edinburgh. Participants are classified as high, medium (amnestic or non-amnestic) or low risk for developing mild cognitive impairment-Alzheimer's disease based on their Repeatable Battery for the Assessment of Neuropsychological Status performance at screening. Additional AD-related assessments include: a novel cognitive composite, the Global Preclinical Alzheimer's Cognitive Composite, brain MRI and positron emission tomography and cerebrospinal fluid analysis. Lifestyle, other cognitive and functional data, as well as biosamples (blood, urine, and saliva) are collected. Primarily, study analyses will evaluate longitudinal change in cognitive and functional outcomes. Annual interim analyses for descriptive data occur throughout the course of the study, although inferential statistics are conducted as required. ETHICS AND DISSEMINATION: CPSS received ethical approvals from the London-Central Research Ethics Committee (15/LO/0711) and the Administration of Radioactive Substances Advisory Committee (RPC 630/3764/33110) The study is at the forefront of global AD prevention efforts, with frequent and robust sampling of the well-characterised cohort, allowing for detection of incipient pathophysiological, cognitive and functional changes that could inform therapeutic strategies to prevent and/or delay cognitive impairment and dementia. Dissemination of results will target the scientific community, research participants, volunteer community, public, industry, regulatory authorities and policymakers. On study completion, and following a predetermined embargo period, CPSS data are planned to be made accessible for analysis to facilitate further research into the determinants of AD pathology, onset of symptomatology and progression. TRIAL REGISTRATION NUMBER: The CHARIOT:PRO SubStudy is registered with clinicaltrials.gov (NCT02114372). Notices of protocol modifications will be made available through this trial registry.

Longitudinal CSF proteomics identifies NPTX2 as a prognostic biomarker of Alzheimer's disease.

INTRODUCTION: Biomarkers that reflect pathologic processes affecting neuronal function during preclinical and early stages of Alzheimer's disease (AD) are needed to aid drug development. METHODS: A targeted, stable isotope, quantitative mass spectrometry-based investigation of longitudinal changes in concentrations of previously identified candidate biomarkers was performed in cerebrospinal fluid (CSF) of Alzheimer's Disease Neuroimaging Initiative participants who were classified as cognitively normal (CN; n = 76) or with mild cognitive impairment (MCI; n = 111) at baseline. RESULTS: Of the candidate biomarkers, the CSF concentration of neuronal pentraxin 2 (NPTX2), a protein involved in synaptic function, exhibited rates of change that were significantly different between three comparison groups (i.e., CN vs. MCI participants; AD pathology positive vs. negative defined by phosphorylated tau181/amyloid beta1-42 ratio; and clinical progressors vs. non-progressors). The rate of change of NPTX2 also significantly correlated with declining cognition. DISCUSSION: CSF NPTX2 concentration is a strong prognostic biomarker candidate of accelerated cognitive decline with potential use as a therapeutic target.

Findings of Efficacy, Safety, and Biomarker Outcomes of Atabecestat in Preclinical Alzheimer Disease: A Truncated Randomized Phase 2b/3 Clinical Trial.

Importance: Atabecestat, a nonselective oral ß-secretase inhibitor, was evaluated in the EARLY trial for slowing cognitive decline in participants with preclinical Alzheimer disease. Preliminary analyses suggested dose-related cognitive worsening and neuropsychiatric adverse events (AEs). Objective: To report efficacy, safety, and biomarker findings in the EARLY trial, both on and off atabecestat treatment, with focus on potential recovery of effects on cognition and behavior. Design, Setting, and Participants: Randomized, double-blind, placebo-controlled, phase 2b/3 study conducted from November 2015 to December 2018 after being stopped prematurely. The study was conducted at 143 centers across 14 countries. Participants were permitted to be followed off-treatment by the original protocol, collecting safety and efficacy data. From 4464 screened participants, 557 amyloid-positive, cognitively normal (Clinical Dementia Rating of 0; aged 60-85 years) participants (approximately 34% of originally planned 1650) were randomized before the trial sponsor stopped enrollment. Interventions: Participants were randomized (1:1:1) to atabecestat, 5 mg (n = 189), 25 mg (n = 183), or placebo (n = 185). Main Outcomes and Measures: Primary outcome: change from baseline in Preclinical Alzheimer Cognitive Composite score. Secondary outcomes: change from baseline in the Cognitive Function Index and the Repeatable Battery for the Assessment of Neuropsychological Status total scale score. Safety was monitored throughout the study. Results: Of 557 participants, 341 were women (61.2%); mean (SD) age was 70.4 (5.56) years. In May 2018, study medication was stopped early owing to hepatic-related AEs; participants were followed up off-treatment for 6 months. Atabecestat, 25 mg, showed significant cognitive worsening vs placebo for Preclinical Alzheimer Cognitive Composite at month 6 (least-square mean difference, -1.09; 95% CI, -1.66 to -0.53; P < .001) and month 12 (least-square mean, -1.62; 95% CI, -2.49 to -0.76; P < .001), and at month 3 for Repeatable Battery for the Assessment of Neuropsychological Status (least-square mean, -3.70; 95% CI, -5.76 to -1.63; P < .001). Cognitive Function Index participant report showed nonsignificant worsening at month 12. Systemic and neuropsychiatric-related treatment-emergent AEs were greater in atabecestat groups vs placebo. After stopping treatment, follow-up cognitive testing and AE assessment provided evidence of reversibility of drug-induced cognitive worsening and AEs in atabecestat groups. Conclusions and Relevance: Atabecestat treatment was associated with dose-related cognitive worsening as early as 3 months and presence of neuropsychiatric treatment-emergent AEs, with evidence of reversibility after 6 months off treatment. Trial Registration: ClinicalTrials.gov Identifier: NCT02569398.

ABC gene-ranking for prediction of drug-induced cholestasis in rats.

As legacy toxicogenomics databases have become available, improved data mining approaches are now key to extracting and visualizing subtle relationships between toxicants and gene expression. In the present study, a novel "aggregating bundles of clusters" (ABC) procedure was applied to separate cholestatic from non-cholestatic drugs and model toxicants in the Johnson & Johnson (Janssen) rat liver toxicogenomics database [3]. Drug-induced cholestasis is an important issue, particularly when a new compound enters the market with this liability, with standard preclinical models often mispredicting this toxicity. Three well-characterized cholestasis-responsive genes (Cyp7a1, Mrp3 and Bsep) were chosen from a previous in-house Janssen gene expression signature; these three genes show differing, non-redundant responses across the 90+ paradigm compounds in our database. Using the ABC procedure, extraneous contributions were minimized in comparisons of compound gene responses. All genes were assigned weights proportional to their correlations with Cyp7a1, Mrp3 and Bsep, and a resampling technique was used to derive a stable measure of compound similarity. The compounds that were known to be associated with rat cholestasis generally had small values of this measure relative to each other but also had large values of this measure relative to non-cholestatic compounds. Visualization of the data with the ABC-derived signature showed a very tight, essentially identically behaving cluster of robust human cholestatic drugs and experimental cholestatic toxicants (ethinyl estradiol, LPS, ANIT and methylene dianiline, disulfiram, naltrexone, methapyrilene, phenacetin, alpha-methyl dopa, flutamide, the NSAIDs--indomethacin, flurbiprofen, diclofenac, flufenamic acid, sulindac, and nimesulide, butylated hydroxytoluene, piperonyl butoxide, and bromobenzene), some slightly less active compounds (3'-acetamidofluorene, amsacrine, hydralazine, tannic acid), some drugs that behaved very differently, and were distinct from both non-cholestatic and cholestatic drugs (ketoconazole, dipyridamole, cyproheptadine and aniline), and many postulated human cholestatic drugs that in rat showed no evidence of cholestasis (chlorpromazine, erythromycin, niacin, captopril, dapsone, rifampicin, glibenclamide, simvastatin, furosemide, tamoxifen, and sulfamethoxazole). Most of these latter drugs were noted previously by other groups as showing cholestasis only in humans. The results of this work suggest that the ABC procedure and similar statistical approaches can be instrumental in combining data to compare toxicants across toxicogenomics databases, extract similarities among responses and reduce unexplained data varation.

Identifying amyloid pathology-related cerebrospinal fluid biomarkers for Alzheimer's disease in a multicohort study.

INTRODUCTION: The dynamic range of cerebrospinal fluid (CSF) amyloid ß (Aß1-42) measurement does not parallel to cognitive changes in Alzheimer's disease (AD) and cognitively normal (CN) subjects across different studies. Therefore, identifying novel proteins to characterize symptomatic AD samples is important. METHODS: Proteins were profiled using a multianalyte platform by Rules Based Medicine (MAP-RBM). Due to underlying heterogeneity and unbalanced sample size, we combined subjects (344 AD and 325 CN) from three cohorts: Alzheimer's Disease Neuroimaging Initiative, Penn Center for Neurodegenerative Disease Research of the University of Pennsylvania, and Knight Alzheimer's Disease Research Center at Washington University in St. Louis. We focused on samples whose cognitive and amyloid status was consistent. We performed linear regression (accounted for age, gender, number of APOE e4 alleles, and cohort variable) to identify amyloid-related proteins for symptomatic AD subjects in this largest ever CSF-based MAP-RBM study. ANOVA and Tukey's test were used to evaluate if these proteins were related to cognitive impairment changes as measured by mini-mental state examination (MMSE). RESULTS: Seven proteins were significantly associated with Aß1-42 levels in the combined cohort (false discovery rate adjusted P < .05), of which lipoprotein a (Lp(a)), prolactin (PRL), resistin, and vascular endothelial growth factor (VEGF) have consistent direction of associations across every individual cohort. VEGF was strongly associated with MMSE scores, followed by pancreatic polypeptide and immunoglobulin A (IgA), suggesting they may be related to staging of AD. DISCUSSION: Lp(a), PRL, IgA, and tissue factor/thromboplastin have never been reported for AD diagnosis in previous individual CSF-based MAP-RBM studies. Although some of our reported analytes are related to AD pathophysiology, others' roles in symptomatic AD samples worth further explorations.

Novel Statistically-Derived Composite Measures for Assessing the Efficacy of Disease-Modifying Therapies in Prodromal Alzheimer's Disease Trials: An AIBL Study.

BACKGROUND: There is a growing consensus that disease-modifying therapies must be given at the prodromal or preclinical stages of Alzheimer's disease (AD) to be effective. A major unmet need is to develop and validate sensitive measures to track disease progression in these populations. OBJECTIVE: To generate novel statistically-derived composites from standard scores, which have increased sensitivity in the assessment of change from baseline in prodromal AD. METHODS: An empirically based method was employed to generate domain specific, global, and cognitive-functional novel composites. The novel composites were compared and contrasted with each other, as well as standard scores for their ability to track change from baseline. The longitudinal characteristics and power to detect decline of the measures were evaluated. Data from participants in the Australian Imaging, Biomarkers and Lifestyle (AIBL) Study characterized as mild cognitively impaired with high neocortical amyloid-ß burden were utilized for the study. RESULTS: The best performing standard scores were CDR Sum-of-Boxes and MMSE. The statistically-derived novel composites performed better than the standard scores from which they were derived. The domain-specific composites generally did not perform as well as the global composites or the cognitive-functional composites. CONCLUSION: A systematic method was employed to generate novel statistically-derived composite measures from standard scores. Composites comprised of measures including function and multiple cognitive domains appeared to best capture change from baseline. These composites may be useful to assess progression or lack thereof in prodromal AD. However, the results should be replicated and validated using an independent clinical sample before implementation in a clinical trial.

Optimal composite scores for longitudinal clinical trials under the linear mixed effects model.

Clinical trials of chronic, progressive conditions use rate of change on continuous measures as the primary outcome measure, with slowing of progression on the measure as evidence of clinical efficacy. For clinical trials with a single prespecified primary endpoint, it is important to choose an endpoint with the best signal-to-noise properties to optimize statistical power to detect a treatment effect. Composite endpoints composed of a linear weighted average of candidate outcome measures have also been proposed. Composites constructed as simple sums or averages of component tests, as well as composites constructed using weights derived from more sophisticated approaches, can be suboptimal, in some cases performing worse than individual outcome measures. We extend recent research on the construction of efficient linearly weighted composites by establishing the often overlooked connection between trial design and composite performance under linear mixed effects model assumptions and derive a formula for calculating composites that are optimal for longitudinal clinical trials of known, arbitrary design. Using data from a completed trial, we provide example calculations showing that the optimally weighted linear combination of scales can improve the efficiency of trials by almost 20% compared with the most efficient of the individual component scales. Additional simulations and analytical results demonstrate the potential losses in efficiency that can result from alternative published approaches to composite construction and explore the impact of weight estimation on composite performance.

Development and evaluation of a multiplexed mass spectrometry based assay for measuring candidate peptide biomarkers in Alzheimer's Disease Neuroimaging Initiative (ADNI) CSF.

PURPOSE: We describe the outcome of the Biomarkers Consortium CSF Proteomics Project (where CSF is cerebral spinal fluid), a public-private partnership of government, academia, nonprofit, and industry. The goal of this study was to evaluate a multiplexed MS-based approach for the qualification of candidate Alzheimer's disease (AD) biomarkers using CSF samples from the AD Neuroimaging Initiative. EXPERIMENTAL DESIGN: Reproducibility of sample processing, analytic variability, and ability to detect a variety of analytes of interest were thoroughly investigated. Multiple approaches to statistical analyses assessed whether panel analytes were associated with baseline pathology (mild cognitive impairment (MCI), AD) versus healthy controls or associated with progression for MCI patients, and included (i) univariate association analyses, (ii) univariate prediction models, (iii) exploratory multivariate analyses, and (iv) supervised multivariate analysis. RESULTS: A robust targeted MS-based approach for the qualification of candidate AD biomarkers was developed. The results identified several peptides with potential diagnostic or predictive utility, with the most significant differences observed for the following peptides for differentiating (including peptides from hemoglobin A, hemoglobin B, and superoxide dismutase) or predicting (including peptides from neuronal pentraxin-2, neurosecretory protein VGF (VGF), and secretogranin-2) progression versus nonprogression from MCI to AD. CONCLUSIONS AND CLINICAL RELEVANCE: These data provide potential insights into the biology of CSF in AD and MCI progression and provide a novel tool for AD researchers and clinicians working to improve diagnostic accuracy, evaluation of treatment efficacy, and early diagnosis.

Optimizing regions-of-interest composites for capturing treatment effects on brain amyloid in clinical trials.

BACKGROUND: Pittsburgh Compound B (PiB) positron emission tomography (PET) neuroimaging is a powerful research tool to characterize amyloid evolution in the brain. Quantification of amyloid load critically depends on (i) the choice of a reference region (RR) and (ii) on the selection of regions of interest (ROIs) to derive the standard uptake value ratios (SUVRs). OBJECTIVE: To evaluate the stability, i.e., negligible amyloid accumulation over time, of different RRs, and the performance of different PiB summary measures defined by selected ROIs and RRs for their sensitivity to detecting longitudinal change in amyloid burden. METHODS: To evaluate RRs, cross-sectional and longitudinal analyses of focal regional and composite measures of amyloid accumulation were carried out on the standardized PiB-PET regional data for cerebellar grey matter (CER), subcortical white matter (SWM), and pons (PON). RRs and candidate composite SUVR measures were further evaluated to select regions and develop novel composites, using standardized 2-year change from baseline. RESULTS: Longitudinal trajectories of PiB4-average of anterior cingulate (ACG), frontal cortex (FRC), parietal cortex, and precuneus-demonstrated marked variability and small change from baseline when normalized to CER, larger changes and less variability when normalized to SWM, which was further enhanced for the composite in PON-normalized settings. Novel composite PiB3, comprised of the average SUVRs of lateral temporal cortex, ACG, and FRC was created. CONCLUSION: PON and SWM appeared to be more stable RRs than the CER. PiB3 showed compelling sample size reduction and gains in power calculations for clinical trials over conventional PiB4 composite.

Disease progression model for Clinical Dementia Rating-Sum of Boxes in mild cognitive impairment and Alzheimer's subjects from the Alzheimer's Disease Neuroimaging Initiative.

BACKGROUND: The objective of this analysis was to develop a nonlinear disease progression model, using an expanded set of covariates that captures the longitudinal Clinical Dementia Rating Scale-Sum of Boxes (CDR-SB) scores. These were derived from the Alzheimer's Disease Neuroimaging Initiative ADNI-1 study, of 301 Alzheimer's disease and mild cognitive impairment patients who were followed for 2-3 years. METHODS: The model describes progression rate and baseline disease score as a function of covariates. The covariates that were tested fell into five groups: a) hippocampal volume; b) serum and cerebrospinal fluid (CSF) biomarkers; c) demographics and apolipoprotein Epsilon 4 (ApoE4) allele status; d) baseline cognitive tests; and e) disease state and comedications. RESULTS: Covariates associated with baseline disease severity were disease state, hippocampal volume, and comedication use. Disease progression rate was influenced by baseline CSF biomarkers, Trail-Making Test part A score, delayed logical memory test score, and current level of impairment as measured by CDR-SB. The rate of disease progression was dependent on disease severity, with intermediate scores around the inflection point score of 10 exhibiting high disease progression rate. The CDR-SB disease progression rate in a typical patient, with late mild cognitive impairment and mild Alzheimer's disease, was estimated to be approximately 0.5 and 1.4 points/year, respectively. CONCLUSIONS: In conclusion, this model describes disease progression in terms of CDR-SB changes in patients and its dependency on novel covariates. The CSF biomarkers included in the model discriminate mild cognitive impairment subjects as progressors and nonprogressors. Therefore, the model may be utilized for optimizing study designs, through patient population enrichment and clinical trial simulations.

Disease progression model in subjects with mild cognitive impairment from the Alzheimer's disease neuroimaging initiative: CSF biomarkers predict population subtypes.

AIM: The objective is to develop a semi-mechanistic disease progression model for mild cognitive impairment (MCI) subjects. The model aims to describe the longitudinal progression of ADAS-cog scores from the Alzheimer's disease neuroimaging initiative trial that had data from 198 MCI subjects with cerebrospinal fluid (CSF) information who were followed for 3 years. METHOD: Various covariates were tested on disease progression parameters and these variables fell into six categories: imaging volumetrics, biochemical, genetic, demographic, cognitive tests and CSF biomarkers. RESULTS: CSF biomarkers were associated with both baseline disease score and disease progression rate in subjects with MCI. Baseline disease score was also correlated with atrophy measured using hippocampal volume. Progression rate was also predicted by executive functioning as measured by the Trail B-test. CONCLUSION: CSF biomarkers have the ability to discriminate MCI subjects into sub-populations that exhibit markedly different rates of disease progression on the ADAS-cog scale. These biomarkers can therefore be utilized for designing clinical trials enriched with subjects that carry the underlying disease pathology.

Coalition Against Major Diseases: Precompetitive Collaborations and Regulatory Paths to Accelerating Drug Development for Neurodegenerative Diseases.

Precompetitive collaborations have been successful in several disease areas and industries. Such collaborations are critical to address the gaps and challenges in therapeutic development for chronic neurodegenerative diseases. On November 5, 2012, members of the scientific community, advocates, regulators, industry, and government officials met at the US Food and Drug Administration to develop tools to expedite drug development and maximize the potential for success in future drug trials for Alzheimer disease and Parkinson disease. The meeting established that multiple collaborative approaches are essential for accelerating drug development. Such approaches include precompetitive data sharing, regulatory qualification of biomarkers and clinical outcome assessments, implementation of data standards, and development of quantitative drug disease trial models. While challenges to collaboration among industry partners are formidable, they are not insurmountable. The Coalition Against Major Diseases (CAMD) has several positive examples to highlight. This review represents proceedings from CAMD's annual conference and discusses the key themes that are being advanced by the Critical Path Institute.

The ADAS-Cog revisited: novel composite scales based on ADAS-Cog to improve efficiency in MCI and early AD trials.

BACKGROUND: The Alzheimer's Disease Assessment Scale-Cognitive (ADAS-Cog) has been used widely as a cognitive end point in Alzheimer's Disease (AD) clinical trials. Efforts to treat AD pathology at earlier stages have also used ADAS-Cog, but failure in these trials can be difficult to interpret because the scale has well-known ceiling effects that limit its use in mild cognitive impairment (MCI) and early AD. A wealth of data exists in ADAS-Cog from both historical trials and contemporary longitudinal natural history studies that can provide insights about parts of the scale that may be better suited for MCI and early AD trials. METHODS: Using Alzheimer's Disease Neuroimaging Initiative study data, we identified the most informative cognitive measures from the ADAS-Cog and other available scales. We used cross-sectional analyses to characterize trajectories of ADAS-Cog and its individual subscales, as well as other cognitive, functional, or global measures across disease stages. Informative measures were identified based on standardized mean of 2-year change from baseline and were combined into novel composite endpoints. We assessed performance of the novel endpoints based on sample size requirements for a 2-year clinical trial. A bootstrap validation procedure was also undertaken to assess the reproducibility of the standardized mean changes of the selected measures and the corresponding composites. RESULTS: All proposed novel endpoints have improved standardized mean changes and thus improved statistical power compared with the ADAS-Cog 11. Further improvements were achieved by using cognitive-functional composites. Combining the novel composites with an enrichment strategy based on cerebral spinal fluid beta-amyloid (Aß(1-42)) in a 2-year trial yielded gains in power of 20% to 40% over ADAS-Cog 11, regardless of the novel measure considered. CONCLUSION: An empirical, data-driven approach with existing instruments was used to derive novel composite scales based on ADAS-Cog 11 with improved performance characteristics for MCI and early AD clinical trials. Together with patient enrichment based on Aß(1-42) pathology, these modified endpoints may allow more efficient clinical trials in these populations and can be assessed without modifying current test administration procedures in ongoing trials.

Sparse learning and stability selection for predicting MCI to AD conversion using baseline ADNI data.

BACKGROUND: Patients with Mild Cognitive Impairment (MCI) are at high risk of progression to Alzheimer's dementia. Identifying MCI individuals with high likelihood of conversion to dementia and the associated biosignatures has recently received increasing attention in AD research. Different biosignatures for AD (neuroimaging, demographic, genetic and cognitive measures) may contain complementary information for diagnosis and prognosis of AD. METHODS: We have conducted a comprehensive study using a large number of samples from the Alzheimer's Disease Neuroimaging Initiative (ADNI) to test the power of integrating various baseline data for predicting the conversion from MCI to probable AD and identifying a small subset of biosignatures for the prediction and assess the relative importance of different modalities in predicting MCI to AD conversion. We have employed sparse logistic regression with stability selection for the integration and selection of potential predictors. Our study differs from many of the other ones in three important respects: (1) we use a large cohort of MCI samples that are unbiased with respect to age or education status between case and controls (2) we integrate and test various types of baseline data available in ADNI including MRI, demographic, genetic and cognitive measures and (3) we apply sparse logistic regression with stability selection to ADNI data for robust feature selection. RESULTS: We have used 319 MCI subjects from ADNI that had MRI measurements at the baseline and passed quality control, including 177 MCI Non-converters and 142 MCI Converters. Conversion was considered over the course of a 4-year follow-up period. A combination of 15 features (predictors) including those from MRI scans, APOE genotyping, and cognitive measures achieves the best prediction with an AUC score of 0.8587. CONCLUSIONS: Our results demonstrate the power of integrating various baseline data for prediction of the conversion from MCI to probable AD. Our results also demonstrate the effectiveness of stability selection for feature selection in the context of sparse logistic regression.

A novel subject synchronization clinical trial design for Alzheimer's disease.

One of the challenges in developing a viable therapy for Alzheimer's disease has been demonstrating efficacy within a clinical trial. Using this as motivation, we sought to re-examine conventional clinical trial practices in order to determine whether efficacy can be better shown through alternative trial designs and novel analysis methods. In this work, we hypothesize that the confounding factors which hamper the ability to discern a treatment signal are the variability in observations as well as the insidious nature of the disease. We demonstrate that a two-phase trial design in which drug dosing is administered after a certain level of disease severity has been reached, coupled with a method to account more accurately for the progression of the disease, may allow us to compensate for these factors, and thus enable us to make treatment effects more apparent. Utilizing data from two previously failed trials which involved the evaluation of galantamine for indication in mild cognitive impairment, we were able to demonstrate that a clear treatment effect can be realized through both visual and statistical means, and propose that future trials may be more likely to show success if similar methods are utilized.

An improved model for disease progression in patients from the Alzheimer's disease neuroimaging initiative.

The objective of this analysis was to develop a semi-mechanistic nonlinear disease progression model using an expanded set of covariates that captures the longitudinal change of Alzheimer's Disease Assessment Scale (ADAS-cog) scores from the Alzheimer's Disease Neuroimaging Initiative study that consisted of 191 Alzheimer disease patients who were followed for 2 years. The model describes the rate of progression and baseline disease severity as a function of influential covariates. The covariates that were tested fell into 4 categories: (1) imaging volumetric measures, (2) serum biomarkers, (3) demographic and genetic factors, and (4) baseline cognitive tests. Covariates found to affect baseline disease status were years since disease onset, hippocampal volume, and ventricular volume. Disease progression rate in the model was influenced by age, total cholesterol, APOE ε4 genotype, Trail Making Test (part B) score, and current levels of impairment as measured by ADAS-cog. Rate of progression was slower for mild and severe Alzheimer patients compared with moderate Alzheimer patients who exhibited faster rates of deterioration. In conclusion, this model describes disease progression in Alzheimer patients using novel covariates that are important for understanding the worsening of ADAS-cog scores over time and may be useful in the future for optimizing study designs through clinical trial simulations.

Development and evaluation of a genomic signature for the prediction and mechanistic assessment of nongenotoxic hepatocarcinogens in the rat.

Evaluating the risk of chemical carcinogenesis has long been a challenge owing to the protracted nature of the pathology and the limited translatability of animal models. Although numerous short-term in vitro and in vivo assays have been developed, they have failed to reliably predict the carcinogenicity of nongenotoxic compounds. Extending upon previous microarray work (Fielden, M. R., Nie, A., McMillian, M., Elangbam, C. S., Trela, B. A., Yang, Y., Dunn, R. T., II, Dragan, Y., Fransson-Stehen, R., Bogdanffy, M., et al. (2008). Interlaboratory evaluation of genomic signatures for predicting carcinogenicity in the rat. Toxicol. Sci. 103, 28-34), we have developed and extensively evaluated a quantitative PCR-based signature to predict the potential for nongenotoxic compounds to induce liver tumors in the rat as a first step in the safety assessment of potential nongenotoxic carcinogens. The training set was derived from liver RNA from rats treated with 72 compounds and used to develop a 22-gene signature on the TaqMan array platform, providing an economical and standardized assay protocol. Independent testing on over 900 diverse samples (66 compounds) confirmed the interlaboratory precision of the assay and its ability to predict known nongenotoxic hepatocarcinogens (NGHCs). When tested under different experimental designs, strains, time points, dose setting criteria, and other preanalytical processes, the signature sensitivity and specificity was estimated to be 67% (95% confidence interval [CI] = 38-88%) and 59% (95% CI = 44-72%), respectively, with an area under the receiver operating characteristic curve of 0.65 (95% CI = 0.46-0.83%). Compounds were best classified using expression data from short-term repeat dose studies; however, the prognostic expression changes appeared to be preserved after longer term treatment. Exploratory evaluations also revealed that different modes of action for nongenotoxic and genotoxic compounds can be discriminated based on the expression of specific genes. These results support a potential early preclinical testing paradigm to catalyze broader understanding of putative NGHCs.

Quantifying the pathophysiological timeline of Alzheimer's disease.

Hypothetical models of AD progression typically relate clinical stages of AD to sequential changes in CSF biomarkers, imaging, and cognition. However, quantifying the continuous trajectories proposed by these models over time is difficult because of the difficulty in relating the dynamics of different biomarkers during a clinical trial that is significantly shorter than the duration of the disease. We seek to show that through proper synchronization, it is possible to de-convolve these trends and quantify the periods of time associated with different pathophysiological changes associated with Alzheimer's disease (AD). We developed a model that replicated the observed progression of ADAS-Cog 13 scores and used this as a more precise estimate of disease-duration and thus pathologic stage. We then synchronized cerebrospinal fluid (CSF) and imaging biomarkers according to our new disease timeline. By de-convolving disease progression via ADAS-Cog 13, we were able to confirm the predictions of previous hypothetical models of disease progression as well as establish concrete timelines for different pathobiological events. Specifically, our work supports a sequential pattern of biomarker changes in AD in which reduction in CSF Aß(42) and brain atrophy precede the increases in CSF tau and phospho-tau.