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.