Predicting clinical progression trajectories of early Alzheimer's disease patients

BACKGROUND

Models for forecasting individual clinical progression trajectories in early Alzheimer's disease (AD) are needed for optimizing clinical studies and patient monitoring.

METHODS

Prediction models were constructed using a clinical trial training cohort (TC; n = 934) via a gradient boosting algorithm and then evaluated in two validation cohorts (VC 1, n = 235; VC 2, n = 421). Model inputs included baseline clinical features (cognitive function assessments, APOE ε4 status, and demographics) and brain magnetic resonance imaging (MRI) measures.

RESULTS

The model using clinical features achieved R2 of 0.21 and 0.31 for predicting 2-year cognitive decline in VC 1 and VC 2, respectively. Adding MRI features improved the R2 to 0.29 in VC 1, which employed the same preprocessing pipeline as the TC. Utilizing these model-based predictions for clinical trial enrichment reduced the required sample size by 20% to 49%.

DISCUSSION

Our validated prediction models enable baseline prediction of clinical progression trajectories in early AD, benefiting clinical trial enrichment and various applications.

Proceedings of the 14th European immunogenicity platform open symposium on immunogenicity of biopharmaceuticals

Biologics have revolutionized disease management in many therapeutic areas by addressing unmet medical needs and overcoming resistance to standard-of-care treatment in numerous patients. However, the development of unwanted immune responses directed against these drugs, humoral and/or cellular, can hinder their efficacy and have safety consequences with various degrees of severity. Health authorities ask that a thorough immunogenicity risk assessment be conducted during drug development to incorporate an appropriate monitoring and mitigation plan in clinical studies. With the rapid diversification and complexification of biologics, which today include modalities such as multi-domain antibodies, cell-based products, AAV delivery vectors, and nucleic acids, developers are faced with the challenge of establishing a risk assessment strategy sometimes in the absence of specific regulatory guidelines. The European Immunogenicity Platform (EIP) Open Symposium on Immunogenicity of Biopharmaceuticals and its one-day training course gives experts and newcomers across academia, industry, and regulatory agencies an opportunity to share experience and knowledge to overcome these challenges. Here, we report the discussions that took place at the EIP's 14th Symposium, held in April 2023. The topics covered included immunogenicity monitoring and clinical relevance, non-clinical immunogenicity risk assessment, regulatory aspects of immunogenicity assessment and reporting, and the challenges associated with new modalities, which were discussed in a dedicated session.

The age-specific comorbidity burden of mild cognitive impairment: a US claims database study

BACKGROUND

Identifying individuals with mild cognitive impairment (MCI) who are likely to progress to Alzheimer's disease and related dementia disorders (ADRD) would facilitate the development of individualized prevention plans. We investigated the association between MCI and comorbidities of ADRD. We examined the predictive potential of these comorbidities for MCI risk determination using a machine learning algorithm.

METHODS

Using a retrospective matched case-control design, 5185 MCI and 15,555 non-MCI individuals aged ≥50 years were identified from MarketScan databases. Predictive models included ADRD comorbidities, age, and sex.

RESULTS

Associations between 25 ADRD comorbidities and MCI were significant but weakened with increasing age groups. The odds ratios (MCI vs non-MCI) in 50-64, 65-79, and ≥ 80 years, respectively, for depression (4.4, 3.1, 2.9) and stroke/transient ischemic attack (6.4, 3.0, 2.1). The predictive potential decreased with older age groups, with ROC-AUCs 0.75, 0.70, and 0.66 respectively. Certain comorbidities were age-specific predictors.

CONCLUSIONS

The comorbidity burden of MCI relative to non-MCI is age-dependent. A model based on comorbidities alone predicted an MCI diagnosis with reasonable accuracy.

Improving the Robustness of Variable Selection and Predictive Performance of Regularized Generalized Linear Models and Cox Proportional Hazard Models

High-dimensional data applications often entail the use of various statistical and machine-learning algorithms to identify an optimal signature based on biomarkers and other patient characteristics that predicts the desired clinical outcome in biomedical research. Both the composition and predictive performance of such biomarker signatures are critical in various biomedical research applications. In the presence of a large number of features, however, a conventional regression analysis approach fails to yield a good prediction model. A widely used remedy is to introduce regularization in fitting the relevant regression model. In particular, a L 1 penalty on the regression coefficients is extremely useful, and very efficient numerical algorithms have been developed for fitting such models with different types of responses. This L 1 -based regularization tends to generate a parsimonious prediction model with promising prediction performance, i.e., feature selection is achieved along with construction of the prediction model. The variable selection, and hence the composition of the signature, as well as the prediction performance of the model depend on the choice of the penalty parameter used in the L 1 regularization. The penalty parameter is often chosen by K-fold cross-validation. However, such an algorithm tends to be unstable and may yield very different choices of the penalty parameter across multiple runs on the same dataset. In addition, the predictive performance estimates from the internal cross-validation procedure in this algorithm tend to be inflated. In this paper, we propose a Monte Carlo approach to improve the robustness of regularization parameter selection, along with an additional cross-validation wrapper for objectively evaluating the predictive performance of the final model. We demonstrate the improvements via simulations and illustrate the application via a real dataset.

CSF peptides from VGF and other markers enhance prediction of MCI to AD progression using the ATN framework

The amyloid beta, tau, neurodegenerative markers framework has been proposed to serve as a system to classify and combine biomarkers for Alzheimer's Disease (AD). Although cerebrospinal (CSF) fluid AT (amyloid beta and tau)-based biomarkers have a well-established track record to distinguish AD from control subjects and to predict conversion from mild cognitive impairment (MCI) to AD, there is not an established non-tau based neurodegenerative ("N") marker from CSF. Here, we examine the ability of several candidate peptides in the CSF to serve as "N" markers to both classify disease state and predict MCI to AD conversion. We observed that although many putative N markers involved in synaptic processing and neuroinflammation were able to, when examined in isolation, distinguish MCI converters from non-converters, a derivative from VGF, when combined with AT markers, most strongly enhanced prediction of MCI to AD conversion. Low CSF VGF levels were also predictive of MCI to dementia conversion in the setting of normal AT markers, suggesting that it may serve as a very early predictor of dementia conversion. Other markers derived from neuronal pentraxin 2, GAP-43 and a 14-3-3 protein were also able to enhance MCI to AD prediction when used as a marker of neurodegeneration, but VGF had the highest predictive capacity. Thus, we propose that low levels of VGF in CSF may serve as "N" in the amyloid beta, tau, neurodegenerative markers framework to enhance the prediction of MCI to AD conversion.

Predictive signature development based on maximizing the area between receiver operating characteristic curves

Development of marker signatures to predict treatment benefits for a new therapeutic is an important scientific component in advancing the drug discovery and is an important first step toward the goal of precision medicine. In this article, we focus on developing an algorithm to search for optimal linear combination of markers that maximizes the area between two receiver operating characteristic curves of the new therapeutic and the control groups without assuming any parametric model. We further generalize the proposed algorithm for predictive signature development to maximize the difference of Harrel's C-index of the new therapeutic and the control groups when the outcome of interest is time-to-event. The performance of this proposed method is evaluated and compared to existing methods via simulations and real clinical trial data.

Comparison of Titer and Signal to Noise (S/N) for Determination of Anti-drug Antibody Magnitude Using Clinical Data from an Industry Consortium

During biotherapeutic drug development, immunogenicity is evaluated by measuring anti-drug antibodies (ADAs). The presence and magnitude of ADA responses is assessed using a multi-tier workflow where samples are screened, confirmed, and titered. Recent reports suggest that the assay signal to noise ratio (S/N) obtained during the screening tier correlates well with titer. To determine whether S/N could more broadly replace titer, anonymized ADA data from a consortium of sponsors was collected and analyzed. Datasets from clinical programs with therapeutics of varying immunogenicity risk levels (low to high), common ADA assay platforms (ELISA and MSD) and formats (bridging, direct, solid-phase extraction with acid dissociation), and titration approaches (endpoint and interpolated) were included in the analysis. A statistically significant correlation between S/N and titer was observed in all datasets, with a strong correlation (Spearman’s r > 0.8) in 11 out of 15 assays (73%). For assays with available data, conclusions regarding ADA impact on pharmacokinetics and pharmacodynamics were similar using S/N or titer. Subject ADA kinetic profiles were also comparable using the two measurements. Determination of antibody boosting in patients with pre-existing responses could be accomplished using similar approaches for titer and S/N. Investigation of factors that impacted the accuracy of ADA magnitude measurements revealed advantages and disadvantages to both approaches. In general, S/N had superior precision and ability to detect potentially low affinity/avidity responses compared to titer. This analysis indicates that S/N could serve as an equivalent and in some cases preferable alternative to titer for assessing ADA magnitude and evaluation of impact on clinical responses.

Graphical Abstract

Best practices for the development and fit-for-purpose validation of biomarker methods: a conference report

This conference report summarized a full-day workshop, “best practices for the development and fit-for-purpose validation of biomarker methods,” which was held prior to the American Association of Pharmaceutical Scientists (AAPS) PharmSci360 Congress, San Antonio, TX, November 2019. The purpose of the workshop was to bring together thought leaders in biomarker assay development in order to identify which assay parameters and key statistical measures need to be considered when developing a biomarker assay. A diverse group of more than 40 scientists participated in the workshop. The workshop and subsequent working dinner stimulated robust discussion. While a consensus on best practices was not achieved, some common themes and major points to consider for biomarker assay development have been identified and agreed on. The focus of this conference report is to summarize the presentations and discussions which occurred at the workshop. Biomarker assay validation is a complex and an evolving area with discussions ongoing.

Reported Hearing Loss in Alzheimer's Disease Is Associated With Loss of Brainstem and Cerebellar Volume

Multiple epidemiological studies have revealed an association between presbycusis and Alzheimer’s Disease (AD). Unfortunately, the neurobiological underpinnings of this relationship are not clear. It is possible that the two disorders share a common, as yet unidentified, risk factor, or that hearing loss may independently accelerate AD pathology. Here, we examined the relationship between reported hearing loss and brain volumes in normal, mild cognitive impairment (MCI) and AD subjects using a publicly available database. We found that among subjects with AD, individuals that reported hearing loss had smaller brainstem and cerebellar volumes in both hemispheres than individuals without hearing loss. In addition, we found that these brain volumes diminish in size more rapidly among normal subjects with reported hearing loss and that there was a significant interaction between cognitive diagnosis and the relationship between reported hearing loss and these brain volumes. These data suggest that hearing loss is linked to brainstem and cerebellar pathology, but only in the context of the pathological state of AD. We hypothesize that the presence of AD-related pathology in both the brainstem and cerebellum creates vulnerabilities in these brain regions to auditory deafferentation-related atrophy. These data have implications for our understanding of the potential neural substrates for interactions between hearing loss and AD.

Serum Phosphatidylethanolamine and Lysophosphatidylethanolamine Levels Differentiate Alzheimer's Disease from Controls and Predict Progression from Mild Cognitive Impairment

BACKGROUND

There is intense interest in the development of blood-based biomarkers, not only that can differentiate Alzheimer's disease (AD) from controls, but that can also predict conversion from mild cognitive impairment (MCI) to AD. Serum biomarkers carry the potential advantage over imaging or spinal fluid markers both in terms of cost and invasiveness.

OBJECTIVE

Our objective was to measure the potential for serum lipid markers to differentiate AD from age-matched healthy controls as well as to predict conversion from MCI to AD.

METHODS

Using a publicly-available dataset, we examined the relationship between baseline serum levels of 349 known lipids from 16 classes of lipids to differentiate disease state as well as to predict the conversion from MCI to AD.

RESULTS

We observed that several classes of lipids (cholesteroyl ester, phosphatidylethanolamine, lysophosphatidylethanolamine, and acylcarnitine) differentiated AD from normal controls. Among these, only two classes, phosphatidylethanolamine (PE) and lysophosphatidylethanolamine (lyso-PE), predicted time to conversion from MCI to AD. Low levels of PE and high levels of lyso-PE result in two-fold faster median time to progression from MCI to AD, with hazard ratios 0.62 and 1.34, respectively.

CONCLUSION

These data suggest that serum PE and lyso-PE may be useful biomarkers for predicting MCI to AD conversion. In addition, since PE is converted to lyso-PE by phospholipase A2, an important inflammatory mediator that is dysregulated in AD, these data suggest that the disrupted serum lipid profile here may be related to an abnormal inflammatory response early in the AD pathologic cascade.

Hearing Loss in Alzheimer's Disease Is Associated with Altered Serum Lipidomic Biomarker Profiles

Recent data have found that aging-related hearing loss (ARHL) is associated with the development of Alzheimer’s Disease (AD). However, the nature of the relationship between these two disorders is not clear. There are multiple potential factors that link ARHL and AD, and previous investigators have speculated that shared metabolic dysregulation may underlie the propensity to develop both disorders. Here, we investigate the distribution of serum lipidomic biomarkers in AD subjects with or without hearing loss in a publicly available dataset. Serum levels of 349 known lipids from 16 lipid classes were measured in 185 AD patients. Using previously defined co-regulated sets of lipids, both age- and sex-adjusted, we found that lipid sets enriched in phosphatidylcholine and phosphatidylethanolamine showed a strong inverse association with hearing loss. Examination of biochemical classes confirmed these relationships and revealed that serum phosphatidylcholine levels were significantly lower in AD subjects with hearing loss. A similar relationship was not found in normal subjects. These data suggest that a synergistic relationship may exist between AD, hearing loss and metabolic biomarkers, such that in the context of a pathological state such as AD, alterations in serum metabolic profiles are associated with hearing loss. These data also point to a potential role for phosphatidylcholine, a molecule with antioxidant properties, in the underlying pathophysiology of ARHL in the context of AD, which has implications for our understanding and potential treatment of both disorders.

Identification of a Simple and Novel Cut-Point Based Cerebrospinal Fluid and MRI Signature for Predicting Alzheimer's Disease Progression that Reinforces the 2018 NIA-AA Research Framework

The 2018 NIA-AA research framework proposes a classification system with Amyloid-β deposition, pathologic Tau, and Neurodegeneration (ATN) for diagnosis and staging of Alzheimer's disease (AD). Data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database can be utilized to identify diagnostic signatures for predicting AD progression, and to determine the utility of this NIA-AA research framework. Profiles of 320 peptides from baseline cerebrospinal fluid (CSF) samples of 287 normal, mild cognitive impairment (MCI), and AD subjects followed over a 3-10-year period were measured via multiple reaction monitoring mass spectrometry. CSF Aβ42, total-Tau (tTau), phosphorylated-Tau (pTau-181), and hippocampal volume were also measured. From these candidate markers, optimal signatures with decision thresholds to separate AD and normal subjects were first identified via unbiased regression and tree-based algorithms. The best performing signature determined via cross-validation was then tested in an independent group of MCI subjects to predict future progression. This multivariate analysis yielded a simple diagnostic signature comprising CSF pTau-181 to Aβ42 ratio, MRI hippocampal volume, and low CSF levels of a novel PTPRN peptide, with a decision threshold on each marker. When applied to a separate MCI group at baseline, subjects meeting these signature criteria experience 4.3-fold faster progression to AD compared to a 2.2-fold faster progression using only conventional markers. This novel 4-marker signature represents an advance over the current diagnostics based on widely used markers, and is easier to use in practice than recently published complex signatures. This signature also reinforces the ATN construct from the 2018 NIA-AA research framework.

Recommendations for the Development and Validation of Immunogenicity Assays in Support of Biosimilar Programs

For biosimilar drug development programs, it is essential to demonstrate that there are no clinically significant differences between the proposed biosimilar therapeutic (biosimilar) and its reference product (originator). Based on a stepwise comprehensive comparability exercise, the biosimilar must demonstrate similarity to the originator in physicochemical characteristics, biological activity, pharmacokinetics, efficacy, and safety, including immunogenicity. The goal of the immunogenicity assessment is to evaluate potential differences between the proposed biosimilar product and the originator product in the incidence and severity of human immune responses. Establishing that there are no clinically meaningful differences in the immune response between the products is a key element in the demonstration of biosimilarity. An issue of practical, regulatory, and financial importance is to establish whether a two-assay (based on the biosimilar and originator respectively) or a one-assay approach (based on the biosimilar) is optimal for the comparative immunogenicity assessment. This paper recommends the use of a single, biosimilar-based assay for assessing immunogenic similarity in support of biosimilar drug development. The development and validation of an ADA assay used for a biosimilar program should include all the assessments recommended for an innovator program (10-16, 29). In addition, specific parameters also need to be evaluated, to gain confidence that the assay can detect antibodies against both the biosimilar and the originator. Specifically, the biosimilar and the originator should be compared in antigenic equivalence, to assess the ability of the biosimilar and the originator to bind in a similar manner to the positive control(s), as well as in the confirmatory assay and drug tolerance experiments. Practical guidance for the development and validation of anti-drug antibody (ADA) assays to assess immunogenicity of a biosimilar in comparison to the originator, using the one-assay approach, are described herein.

A multivariate predictive modeling approach reveals a novel CSF peptide signature for both Alzheimer's Disease state classification and for predicting future disease progression

To determine if a multi-analyte cerebrospinal fluid (CSF) peptide signature can be used to differentiate Alzheimer’s Disease (AD) and normal aged controls (NL), and to determine if this signature can also predict progression from mild cognitive impairment (MCI) to AD, analysis of CSF samples was done on the Alzheimer’s Disease Neuroimaging Initiative (ADNI) dataset. The profiles of 320 peptides from baseline CSF samples of 287 subjects over a 3–6 year period were analyzed. As expected, the peptide most able to differentiate between AD vs. NL was found to be Apolipoprotein E. Other peptides, some of which are not classically associated with AD, such as heart fatty acid binding protein, and the neuronal pentraxin receptor, also differentiated disease states. A sixteen-analyte signature was identified which differentiated AD vs. NL with an area under the receiver operating characteristic curve of 0.89, which was better than any combination of amyloid beta (1–42), tau, and phospho-181 tau. This same signature, when applied to a new and independent data set, also strongly predicted both probability and rate of future progression of MCI subjects to AD, better than traditional markers. These data suggest that multivariate peptide signatures from CSF predict MCI to AD progression, and point to potentially new roles for certain proteins not typically associated with AD.

Patient subgroup identification for clinical drug development

Causal mechanism of relationship between the clinical outcome (efficacy or safety endpoints) and putative biomarkers, clinical baseline, and related predictors is usually unknown and must be deduced empirically from experimental data. Such relationships enable the development of tailored therapeutics and implementation of a precision medicine strategy in clinical trials to help stratify patients in terms of disease progression, clinical response, treatment differentiation, and so on. These relationships often require complex modeling to develop the prognostic and predictive signatures. For the purpose of easier interpretation and implementation in clinical practice, defining a multivariate biomarker signature in terms of thresholds (cutoffs/cut points) on individual biomarkers is preferable. In this paper, we propose some methods for developing such signatures in the context of continuous, binary and time-to-event endpoints. Results from simulations and case study illustration are also provided. Copyright © 2017 John Wiley & Sons, Ltd.

Big data to smart data in Alzheimer's disease: The brain health modeling initiative to foster actionable knowledge

Massive investment and technological advances in the collection of extensive and longitudinal information on thousands of Alzheimer patients results in large amounts of data. These "big-data" databases can potentially advance CNS research and drug development. However, although necessary, they are not sufficient, and we posit that they must be matched with analytical methods that go beyond retrospective data-driven associations with various clinical phenotypes. Although these empirically derived associations can generate novel and useful hypotheses, they need to be organically integrated in a quantitative understanding of the pathology that can be actionable for drug discovery and development. We argue that mechanism-based modeling and simulation approaches, where existing domain knowledge is formally integrated using complexity science and quantitative systems pharmacology can be combined with data-driven analytics to generate predictive actionable knowledge for drug discovery programs, target validation, and optimization of clinical development.

Recommendations for Use and Fit-for-Purpose Validation of Biomarker Multiplex Ligand Binding Assays in Drug Development

Multiplex ligand binding assays (LBAs) are increasingly being used to support many stages of drug development. The complexity of multiplex assays creates many unique challenges in comparison to single-plexed assays leading to various adjustments for validation and potentially during sample analysis to accommodate all of the analytes being measured. This often requires a compromise in decision making with respect to choosing final assay conditions and acceptance criteria of some key assay parameters, depending on the intended use of the assay. The critical parameters that are impacted due to the added challenges associated with multiplexing include the minimum required dilution (MRD), quality control samples that span the range of all analytes being measured, quantitative ranges which can be compromised for certain targets, achieving parallelism for all analytes of interest, cross-talk across assays, freeze-thaw stability across analytes, among many others. Thus, these challenges also increase the complexity of validating the performance of the assay for its intended use. This paper describes the challenges encountered with multiplex LBAs, discusses the underlying causes, and provides solutions to help overcome these challenges. Finally, we provide recommendations on how to perform a fit-for-purpose-based validation, emphasizing issues that are unique to multiplex kit assays.

Evaluation of miR-122 as a Serum Biomarker for Hepatotoxicity in Investigative Rat Toxicology Studies

MicroRNAs are short noncoding RNAs involved in regulation of gene expression. Certain microRNAs, including miR-122, seem to have ideal properties as biomarkers due to good stability, high tissue specificity, and ease of detection across multiple species. Recent reports have indicated that miR-122 is a highly liver-specific marker detectable in serum after liver injury. The purpose of the current study was to assess the performance of miR-122 as a serum biomarker for hepatotoxicity in short-term (5–28 days) repeat-dose rat toxicology studies when benchmarked against routine clinical chemistry and histopathology. A total of 23 studies with multiple dose levels of experimental compounds were examined, and they included animals with or without liver injury and with various hepatic histopathologic changes. Serum miR-122 levels were quantified by reverse transcription quantitative polymerase chain reaction. Increases in circulating miR-122 levels highly correlated with serum elevations of liver enzymes, such as alanine aminotransferase (ALT), aspartate aminotransferase (AST) and glutamate dehydrogenase (GLDH). Statistical analysis showed that miR-122 outperformed ALT as a biomarker for histopathologically confirmed liver toxicity and was equivalent in performance to AST and GLDH. Additionally, an increase of 4% in predictive accuracy was obtained using a multiparameter approach incorporating miR-122 with ALT, AST, and GLDH. In conclusion, serum miR-122 levels can be utilized as a biomarker of hepatotoxicity in acute and subacute rat toxicology studies, and its performance can rival or exceed those of standard enzyme biomarkers such as the liver transaminases.

Comparison of RNA-seq and microarray-based models for clinical endpoint prediction

Background

Gene expression profiling is being widely applied in cancer research to identify biomarkers for clinical endpoint prediction. Since RNA-seq provides a powerful tool for transcriptome-based applications beyond the limitations of microarrays, we sought to systematically evaluate the performance of RNA-seq-based and microarray-based classifiers in this MAQC-III/SEQC study for clinical endpoint prediction using neuroblastoma as a model.

Results

We generate gene expression profiles from 498 primary neuroblastomas using both RNA-seq and 44 k microarrays. Characterization of the neuroblastoma transcriptome by RNA-seq reveals that more than 48,000 genes and 200,000 transcripts are being expressed in this malignancy. We also find that RNA-seq provides much more detailed information on specific transcript expression patterns in clinico-genetic neuroblastoma subgroups than microarrays. To systematically compare the power of RNA-seq and microarray-based models in predicting clinical endpoints, we divide the cohort randomly into training and validation sets and develop 360 predictive models on six clinical endpoints of varying predictability. Evaluation of factors potentially affecting model performances reveals that prediction accuracies are most strongly influenced by the nature of the clinical endpoint, whereas technological platforms (RNA-seq vs. microarrays), RNA-seq data analysis pipelines, and feature levels (gene vs. transcript vs. exon-junction level) do not significantly affect performances of the models.

Conclusions

We demonstrate that RNA-seq outperforms microarrays in determining the transcriptomic characteristics of cancer, while RNA-seq and microarray-based models perform similarly in clinical endpoint prediction. Our findings may be valuable to guide future studies on the development of gene expression-based predictive models and their implementation in clinical practice.

Electronic supplementary material

The online version of this article (doi:10.1186/s13059-015-0694-1) contains supplementary material, which is available to authorized users.

Workshop Report: AAPS Workshop on Method Development, Validation, and Troubleshooting of Ligand-Binding Assays in the Regulated Environment

A novel format was introduced at the recent AAPS NBC Workshop on Method Development, Validation and Troubleshooting in San Diego on 18th May 2014. The workshop format was initiated by Binodh De Silva; Marie Rock and Sherri Dudal joined the initiative to develop and chair the workshop. Questions were solicited by a variety of avenues, including a Linked-In Discussion Group. Once collated and clarified, the topics covered assay development, validation, and analysis of PK, Immunogenicity, and Biomarkers with an additional topic on alternative bioanalytical technologies. A panel of experts (workshop report co-authors) was assigned to each topic to bring forward thought-provoking aspects of each topic. The format of the workshop was developed to target the needs of bioanalytical scientists with intermediate to advanced experience in the field ranging to enable robust discussion and to delve deeper into the current bioanalytical hot topics. While the new format allowed for an interactive session with the topical discussion driven by the audience members, it did not foster equal discussion time for all of the proposed topics, especially Biomarkers and alternative LBA technologies.

Randomized phase II study of carboplatin and paclitaxel with either linifanib or placebo for advanced nonsquamous non-small-cell lung cancer

PURPOSE

Linifanib, a potent, selective inhibitor of vascular endothelial growth factor (VEGF) and platelet-derived growth factor (PDGF) receptors, has single-agent activity in non-small-cell lung cancer (NSCLC). We evaluated linifanib with carboplatin and paclitaxel as first-line therapy of advanced nonsquamous NSCLC.

PATIENTS AND METHODS

Patients with stage IIIB/IV nonsquamous NSCLC were randomly assigned to 3-week cycles of carboplatin (area under the curve 6) and paclitaxel (200 mg/m(2)) with daily placebo (arm A), linifanib 7.5 mg (arm B), or linifanib 12.5 mg (arm C). The primary end point was progression-free survival (PFS); secondary efficacy end points included overall survival (OS) and objective response rate.

RESULTS

One hundred thirty-eight patients were randomly assigned (median age, 61 years; 57% men; 84% smokers). Median PFS times were 5.4 months (95% CI, 4.2 to 5.7 months) in arm A (n = 47), 8.3 months (95% CI, 4.2 to 10.8 months) in arm B (n = 44), and 7.3 months (95% CI, 4.6 to 10.8 months) in arm C (n = 47). Hazard ratios (HRs) for PFS were 0.51 for arm B versus A (P = .022) and 0.64 for arm C versus A (P = .118). Median OS times were 11.3, 11.4, and 13.0 months in arms A, B, and C, respectively. HRs for OS were 1.08 for arm B versus A (P = .779) and 0.88 for arm C versus A (P = .650). Both linifanib doses were associated with increased toxicity, including a higher incidence of adverse events known to be associated with VEGF/PDGF inhibition. Baseline plasma carcinoembryonic antigen/cytokeratin 19 fragments biomarker signature was associated with PFS improvement and a trend toward OS improvement with linifanib 12.5 mg.

CONCLUSION

Addition of linifanib to chemotherapy significantly improved PFS (arm B), with a modest trend for survival benefit (arm C) and increased toxicity reflective of known VEGF/PDGF inhibitory effects.

A PRIM approach to predictive-signature development for patient stratification

Patients often respond differently to a treatment because of individual heterogeneity. Failures of clinical trials can be substantially reduced if, prior to an investigational treatment, patients are stratified into responders and nonresponders based on biological or demographic characteristics. These characteristics are captured by a predictive signature. In this paper, we propose a procedure to search for predictive signatures based on the approach of patient rule induction method. Specifically, we discuss selection of a proper objective function for the search, present its algorithm, and describe a resampling scheme that can enhance search performance. Through simulations, we characterize conditions under which the procedure works well. To demonstrate practical uses of the procedure, we apply it to two real-world data sets. We also compare the results with those obtained from a recent regression-based approach, Adaptive Index Models, and discuss their respective advantages. In this study, we focus on oncology applications with survival responses.

Assessment and reporting of the clinical immunogenicity of therapeutic proteins and peptides-harmonized terminology and tactical recommendations

Immunogenicity is a significant concern for biologic drugs as it can affect both safety and efficacy. To date, the descriptions of product immunogenicity have varied not only due to different degrees of understanding of product immunogenicity at the time of licensing but also due to an evolving lexicon that has generated some confusion in the field. In recent years, there has been growing consensus regarding the data needed to assess product immunogenicity. Harmonization of the strategy for the elucidation of product immunogenicity by drug developers, as well as the use of defined common terminology, can benefit medical practitioners, health regulatory agencies, and ultimately the patients. Clearly, understanding the incidence, kinetics and magnitude of anti-drug antibody (ADA), its neutralizing ability, cross-reactivity with endogenous molecules or other marketed biologic drugs, and related clinical impact may enhance clinical management of patients treated with biologic drugs. To that end, the authors present terms and definitions for describing and analyzing clinical immunogenicity data and suggest approaches to data presentation, emphasizing associations of ADA development with pharmacokinetics, efficacy, and safety that are necessary to assess the clinical relevance of immunogenicity.

Association of baseline plasma biomarker signature with survival in advanced NSCLC patients on linifanib

e13583

Background: Linifanib is a potent and selective VEGF and PDGF receptor inhibitor that has activity in unselected, advanced NSCLC patients (pts) both as monotherapy in the relapsed setting and with carboplatin (C) and paclitaxel (P) in the first-line setting. A baseline plasma biomarker signature identifying NSCLC pts most sensitive to linifanib is needed. Methods: An exploratory retrospective analysis of four randomized clinical trials including linifanib or other treatments in relapsed NSCLC was conducted. Evaluable baseline plasma samples were obtained from 116 pts who received linifanib and 71 pts on other treatments. A signature combining established tumor markers (carcinoembryonic antigen [CEA] and fragments of cytokeratin 19 [CYFRA 21-1]) was derived using a sequential BATTing approach. The signature was then tested across a randomized trial of CP + placebo, linifanib 7.5 mg, or linifanib 12.5 mg in first-line advanced, non-squamous NSCLC. Results: In 2/3L NSCLC, the signature was associated with improvement in survival on linifanib monotherapy (HR=0.51 vs. signature negative; P=0.0017), but no improvement in survival on other treatments (P=0.87). In the first-line setting with CP, the signature was associated with significant PFS improvement with linifanib and a trend towards significant overall survival improvement at high dose (Table). Conclusions: A baseline plasma biomarker signature is associated with improved survival in advanced NSCLC patients on linifanib. Incorporation of this signature should be considered in any further investigation of linifanib in NSCLC. [Table: see text]

Minimum significant ratio of selectivity ratios (MSRSR) and confidence in ratio of selectivity ratios (CRSR): quantitative measures for selectivity ratios obtained by screening assays

Development of inhibitor compounds selective against undesirable targets is critical in drug discovery. Selectivity ratios for candidate compounds are evaluated by dividing potencies from two assays assessing the off-target and target. Because all potency measurements have underlying uncertainty, understanding error propagation is essential to interpreting selectivity data. Assay noise introduces ambiguity in the statistical significance of selectivity ratios, particularly at low replicate numbers when compounds are often prioritized for subsequent testing. The ability to differentiate potency results for any pair of compounds in one assay is evaluated using a metric called minimum significant ratio (MSR). Potency results of one compound tested in a pair of assays can be differentiated by the minimum significant selectivity ratio (MSSR). To differentiate selectivity ratios for any pair of compounds, we extend this concept by proposing two new parameters called the minimum significant ratio of selectivity ratios (MSRSR) and confidence in ratio of selectivity ratios (CRSR). Importantly, these tools can be used after a single selectivity measurement. We describe these methods and illustrate their usefulness using structure-activity relationship data from a Janus kinase inhibitor project, in which these tools informed a cogent retesting strategy and enabled rapid and objective decision making.

A hidden Markov model-based algorithm for identifying tumour subtype using array CGH data

Background

The recent advancement in array CGH (aCGH) research has significantly improved tumor identification using DNA copy number data. A number of unsupervised learning methods have been proposed for clustering aCGH samples. Two of the major challenges for developing aCGH sample clustering are the high spatial correlation between aCGH markers and the low computing efficiency. A mixture hidden Markov model based algorithm was developed to address these two challenges.

Results

The hidden Markov model (HMM) was used to model the spatial correlation between aCGH markers. A fast clustering algorithm was implemented and real data analysis on glioma aCGH data has shown that it converges to the optimal cluster rapidly and the computation time is proportional to the sample size. Simulation results showed that this HMM based clustering (HMMC) method has a substantially lower error rate than NMF clustering. The HMMC results for glioma data were significantly associated with clinical outcomes.

Conclusions

We have developed a fast clustering algorithm to identify tumor subtypes based on DNA copy number aberrations. The performance of the proposed HMMC method has been evaluated using both simulated and real aCGH data. The software for HMMC in both R and C++ is available in ND INBRE website http://ndinbre.org/programs/bioinformatics.php.

Screening for new biomarkers for subcortical vascular dementia and Alzheimer's disease

Background

Novel biomarkers are important for identifying as well as differentiating subcortical vascular dementia (SVD) and Alzheimer's disease (AD) at an early stage in the disease process.

Methods

In two independent cohorts, a multiplex immunoassay was utilized to analyze 90 proteins in cerebrospinal fluid (CSF) samples from dementia patients and patients at risk of developing dementia (mild cognitive impairment).

Results

The levels of several CSF proteins were increased in SVD and its incipient state, and in moderate-to-severe AD compared with the control group. In contrast, some CSF proteins were altered in AD, but not in SVD. The levels of heart-type fatty acid binding protein (H-FABP) were consistently increased in all groups with dementia but only in some of their incipient states.

Conclusions

In summary, these results support the notion that SVD and AD are driven by different pathophysiological mechanisms reflected in the CSF protein profile and that H-FABP in CSF is a general marker of neurodegeneration.

Identification of distinct plasma biomarker signatures in patients with rapid and slow declining forms of COPD

Chronic obstructive pulmonary disease (COPD) is a prevalent pulmonary disease characterized by a progressive decline in lung function. The identification of biomarkers capable of predicting the rate of lung function decline or capable of giving an early read on drug efficacy in clinical trials would be very useful. The aim of this study was to identify plasma biomarkers capable of accurately distinguishing patients with COPD from healthy controls. Eighty-nine plasma markers in 40 COPD patients and 20 healthy smoker controls were analyzed. The COPD patients were divided into two subgroups, rapid and slow decliners based on their rate of lung function decline measured over 15 years. Univariate analysis revealed that 25 plasma markers were statistically different between rapid decliners and controls, 4 markers were different between slow decliners and controls, and 10 markers were different between rapid and slow decliners (p < 0.05). Multivariate analysis led to the identification of groups of plasma markers capable of distinguishing rapid decliners from controls (signature 1), slow decliners from controls (signature 2) and rapid from slow decliners (signature 3) with over 90% classification accuracy. Importantly, signature 1 was shown to be longitudinally stable using plasma samples taken a year later from a subset of patients. This study describes a novel set of plasma markers differentiating slow from rapid decline of lung function in COPD. If validated in distinct and larger cohorts, the signatures identified will have important implications in both disease diagnosis, as well as the clinical evaluation of new therapies.

Identification of expression signatures predictive of sensitivity to the Bcl-2 family member inhibitor ABT-263 in small cell lung carcinoma and leukemia/lymphoma cell lines

ABT-263 inhibits the antiapoptotic proteins Bcl-2, Bcl-x(L), and Bcl-w and has single-agent efficacy in numerous small cell lung carcinoma (SCLC) and leukemia/lymphoma cell lines in vitro and in vivo. It is currently in clinical trials for treating patients with SCLC and various leukemia/lymphomas. Identification of predictive markers for response will benefit the clinical development of ABT-263. We identified the expression of Bcl-2 family genes that correlated best with sensitivity to ABT-263 in a panel of 36 SCLC and 31 leukemia/lymphoma cell lines. In cells sensitive to ABT-263, expression of Bcl-2 and Noxa is elevated, whereas expression of Mcl-1 is higher in resistant cells. We also examined global expression differences to identify gene signature sets that correlated with sensitivity to ABT-263 to generate optimal signature sets predictive of sensitivity to ABT-263. Independent cell lines were used to verify the predictive power of the gene sets and to refine the optimal gene signatures. When comparing normal lung tissue and SCLC primary tumors, the expression pattern of these genes in the tumor tissue is most similar to sensitive SCLC lines, whereas normal tissue is most similar to resistant SCLC lines. Most of the genes identified using global expression patterns are related to the apoptotic pathway; however, all but Bcl-rambo are distinct from the Bcl-2 family. This study leverages global expression data to identify key gene expression patterns for sensitivity to ABT-263 in SCLC and leukemia/lymphoma and may provide guidance in the selection of patients in future clinical trials.