Use of biomarkers and surrogate endpoints in drug development and regulatory decision making: criteria, validation, strategies

Biomarkers and surrogate endpoints in glaucoma clinical trials

Surrogate endpoints are often used as replacements for true clinically relevant endpoints in several areas of medicine, as they enable faster and less expensive clinical trials. However, without proper validation, the use of surrogates may lead to incorrect conclusions about the efficacy and safety of treatments. This article reviews the general requirements for validating surrogate endpoints and provides a critical assessment of the use of intraocular pressure (IOP), visual fields, and structural measurements of the optic nerve as surrogate endpoints in glaucoma clinical trials. A valid surrogate endpoint must be able to predict the clinically relevant endpoint and fully capture the effect of an intervention on that endpoint. Despite its widespread use in clinical trials, no proper validation of IOP as a surrogate endpoint has ever been conducted for any class of IOP-lowering treatments. Evidence has accumulated with regard to the role of imaging measurements of optic nerve damage as surrogate endpoints in glaucoma. These measurements are predictive of functional losses in the disease and may explain, at least in part, treatment effects on clinically relevant endpoints. The use of composite endpoints in glaucoma trials may overcome weaknesses of the use of structural or functional endpoints in isolation. Unless research is dedicated to fully develop and validate suitable endpoints that can be used in glaucoma clinical trials, we run the risk of inappropriate judgments about the value of new therapies.

Biomarkers of HIV-associated Cancer

Cancer biomarkers have provided great opportunities for improving the management of cancer patients by enhancing the efficiency of early detection, diagnosis, and efficacy of treatment. Every cell type has a unique molecular signature, referred to as biomarkers, which are identifiable characteristics such as levels or activities of a myriad of genes, proteins, or other molecular features. Biomarkers can facilitate the molecular definition of cancer, provide information about the course of cancer, and predict response to chemotherapy. They offer the hope of early detection as well as tracking disease progression and recurrence. Current progress in the characterization of molecular genetics of HIV-associated cancers may form the basis for improved patient stratification and future targeted or individualized therapies. Biomarker use for cancer staging and personalization of therapy at the time of diagnosis could improve patient care. This review focuses on the relevance of biomarkers in the most common HIV-associated malignancies, namely, Kaposi sarcoma, non-Hodgkin’s lymphoma, and invasive cervical cancer.

Transition from cyclosporine-induced renal dysfunction to nephrotoxicity in an in vivo rat model

Cyclosporin A (CsA), a calcineurin inhibitor, remain the cornerstone of immunosuppressive regimens, regardless of nephrotoxicity, which depends on the duration of drug exposure. The mechanisms and biomarkers underlying the transition from CsA-induced renal dysfunction to nephrotoxicity deserve better elucidation, and would help clinical decisions. This study aimed to clarify these issues, using a rat model of short- and long-term CsA (5 mg/kg bw/day) treatments (3 and 9 weeks, respectively). Renal function was assessed on serum and urine; kidney tissue was used for histopathological characterization and gene and/or protein expression of markers of proliferation, fibrosis and inflammation. In the short-term, creatinine and blood urea nitrogen (BUN) levels increased and clearances decreased, accompanied by glomerular filtration rate (GFR) reduction, but without kidney lesions; at that stage, CsA exposure induced proliferating cell nuclear antigen (PCNA), transforming growth factor beta 1 (TGF-β1), factor nuclear kappa B (NF-κβ) and Tumor Protein P53 (TP53) kidney mRNA up-regulation. In the long-term treatment, renal dysfunction data was accompanied by glomerular and tubulointerstitial lesions, with remarkable kidney mRNA up-regulation of the mammalian target of rapamycin (mTOR) and the antigen identified by monoclonal antibody Ki-67 (Mki67), accompanied by mTOR protein overexpression. Transition from CsA-induced renal dysfunction to nephrotoxicity is accompanied by modification of molecular mechanisms and biomarkers, being mTOR one of the key players for kidney lesion evolution, thus suggesting, by mean of molecular evidences, that early CsA replacement by mTOR inhibitors is indeed the better therapeutic choice to prevent chronic allograft nephropathy.

Conversion to sirolimus ameliorates cyclosporine-induced nephropathy in the rat: focus on serum, urine, gene, and protein renal expression biomarkers

Protocols of conversion from cyclosporin A (CsA) to sirolimus (SRL) have been widely used in immunotherapy after transplantation to prevent CsA-induced nephropathy, but the molecular mechanisms underlying these protocols remain nuclear. This study aimed to identify the molecular pathways and putative biomarkers of CsA-to-SRL conversion in a rat model. Four animal groups (n = 6) were tested during 9 weeks: control, CsA, SRL, and conversion (CsA for 3 weeks followed by SRL for 6 weeks). Classical and emergent serum, urinary, and kidney tissue (gene and protein expression) markers were assessed. Renal lesions were analyzed in hematoxylin and eosin, periodic acid-Schiff, and Masson's trichrome stains. SRL-treated rats presented proteinuria and NGAL (serum and urinary) as the best markers of renal impairment. Short CsA treatment presented slight or even absent kidney lesions and TGF-β, NF-κβ, mTOR, PCNA, TP53, KIM-1, and CTGF as relevant gene and protein changes. Prolonged CsA exposure aggravated renal damage, without clear changes on the traditional markers, but with changes in serums TGF-β and IL-7, TBARs clearance, and kidney TGF-β and mTOR. Conversion to SRL prevented CsA-induced renal damage evolution (absent/mild grade lesions), while NGAL (serum versus urine) seems to be a feasible biomarker of CsA replacement to SRL.

Cytokine levels as biomarkers of radiation fibrosis in patients treated with breast radiotherapy

BACKGROUND

Radiation fibrosis is not easily measurable although clinical scores have been developed for this purpose. Biomarkers present an alternative more objective approach to quantification, and estimation in blood provides accessible samples. We investigated if blood cytokines could be used to measure established fibrosis in patients who have undergone radiotherapy for breast cancer.

METHODS

We studied two cohorts treated by breast-conserving surgery and radiotherapy in the UK START Trial A, one with breast fibrosis (cases) and one with no or minimal fibrosis (controls). Two candidate cytokines, plasma connective tissue growth factor (CTGF) and serum interleukin-6 (IL6) were estimated by ELISA. Comparisons between cases and controls used the t-test or Mann-Whitney test and associations between blood concentration and clinical factors were assessed using the Spearman rank correlation coefficient.

RESULTS

Seventy patients were included (26 cases, 44 controls). Mean time since radiotherapy was 9.9 years (range 8.3-12.0). No statistically significant differences between cases and controls in serum IL6 (median (IQR) 0.84 pg/ml (0.57-1.14), 0.75 pg/ml (0.41-1.43) respectively) or plasma CTGF (331.4 pg/ml (234.8-602.9), 334.5 pg/ml (270.0-452.8) were identified. There were no significant associations between blood cytokine concentration and age, fibrosis severity, breast size or time since radiotherapy.

CONCLUSIONS

No significant difference in IL6 or CTGF concentrations was detected between patients with breast fibrosis and controls with minimal or no fibrosis.

A population pharmacodynamic model for lactate dehydrogenase and neuron specific enolase to predict tumor progression in small cell lung cancer patients

The development of individualized therapies poses a major challenge in oncology. Significant hurdles to overcome include better disease monitoring and early prediction of clinical outcome. Current clinical practice consists of using Response Evaluation Criteria in Solid Tumors (RECIST) to categorize response to treatment. However, the utility of RECIST is restricted due to limitations on the frequency of measurement and its categorical rather than continuous nature. We propose a population modeling framework that relates circulating biomarkers in plasma, easily obtained from patients, to tumor progression levels assessed by imaging scans (i.e., RECIST categories). We successfully applied this framework to data regarding lactate dehydrogenase (LDH) and neuron specific enolase (NSE) concentrations in patients diagnosed with small cell lung cancer (SCLC). LDH and NSE have been proposed as independent prognostic factors for SCLC. However, their prognostic and predictive value has not been demonstrated in the context of standard clinical practice. Our model incorporates an underlying latent variable ("disease level") representing (unobserved) tumor size dynamics, which is assumed to drive biomarker production and to be influenced by exposure to treatment; these assumptions are in agreement with the known physiology of SCLC and these biomarkers. Our model predictions of unobserved disease level are strongly correlated with disease progression measured by RECIST criteria. In conclusion, the proposed framework enables prediction of treatment outcome based on circulating biomarkers and therefore can be a powerful tool to help clinicians monitor disease in SCLC.

The future of osteoarthritis therapeutics: targeted pharmacological therapy

Osteoarthritis (OA) is one of the most common forms of degenerative joint disease and a major cause of pain and disability affecting the aging population. It is estimated that more than 20 million Americans and 35 to 40 million Europeans suffer from OA. Analgesics and non-steroidal anti-inflammatory drugs (NSAIDs) are the only therapeutic treatment options for OA. Effective pharmacotherapy for OA, capable of restoring the original structure and function of damaged cartilage and other synovial tissue, is urgently needed, and research into such disease-modifying osteoarthritis drugs (DMOADs) is in progress. This is the first of three reviews focusing on OA therapeutics. This paper provides an overview of current research into potential structure-modifying drugs and more appropriately targeted pharmacological therapy. The challenges and opportunities in this area of research and development are reviewed, covering the most up-to-date initiatives, trends, and topics.

Biomarkers for osteoarthritis: current position and steps towards further validation

Historically disease knowledge development and treatment innovation in osteoarthritis (OA) has been considered to be slow. One of the many reasons purported as responsible for this slow pace has been the alleged lack of valid and responsive biomarkers to ascertain efficacy, which itself has been dependent upon the slow evolution of the understanding of the complex nature of joint tissue biology. This narrative review outlines the rationale for why we need OA biomarkers with regard to biomarker validation and qualification. The main biomarkers in current development for OA are biochemical and imaging markers. We describe an approach to biomarker validation and qualification for OA clinical trials that has recently commenced with the Foundation of NIH OA Biomarkers Consortium study cosponsored by the Osteoarthritis Research Society International (OARSI). With this approach we endeavor to identify, develop, and qualify biological markers (biomarkers) to support new drug development, preventive medicine, and medical diagnostics for osteoarthritis.

The role of molecular imaging in modern drug development

Drug development represents a highly complex, inefficient and costly process. Over the past decade, the widespread use of nuclear imaging, owing to its functional and molecular nature, has proven to be a determinant in improving the efficiency in selecting the candidate drugs that should either be abandoned or moved forward into clinical trials. This helps not only with the development of safer and effective drugs but also with the shortening of time-to-market. The modern concept and future trends concerning molecular imaging will assumedly be hybrid or multimodality imaging, including combinations between high sensitivity and functional (molecular) modalities with high spatial resolution and morphological techniques.

Peptides in body fluids and tissues as markers of disease

The general awareness of the importance of peptides in physiology and pathophysiology has increased strongly over the last few years. With worldwide progress in the analysis of whole genomes, the knowledge base in gene sequence and expression data useful for protein and peptide analysis has drastically increased. The medical need for relevant biomarkers is enormous. This is particularly true for the many types of cancer, but other diseases such as Type 2 diabetes also lack useful and adequate diagnostic markers with high specificity and sensitivity. Despite advances in imaging technologies for early detection of diseases, proteomic and peptidomic multiplex techniques have evolved in recent years. This review focuses on the application of peptidomics technologies to peptides in health and disease. Peptidomics technologies provide new opportunities for the detection of low-molecular-weight proteome biomarkers (peptides) by mass spectrometry. Improvements in peptidomics research are based on separation of peptides and/or proteins by their physicochemical properties in combination with mass spectrometric detection, identification and sophisticated bioinformatics tools for data analysis. Therefore, peptidomics technologies offer an opportunity to discover novel biomarkers for diagnosis and management of disease (e.g., prognosis, treatment decision and monitoring response to therapy).

Rapid translation of circulating tumor cell biomarkers into clinical practice: technology development, clinical needs and regulatory requirements

The great hope in circulating tumor cell (CTC) research lies in the use of these rare cells as an accessible "fluid biopsy" that would permit frequent, minimally invasive sampling of tumor cells for similar molecular assays that are performed on traditional biopsies. Given the rarity of CTCs in peripheral circulation, microscale methods show great promise and superiority to capture and analyze these cells from patients with solid tumors. Novel technologies that produce validated CTC biomarkers may finally provide medical oncologists the tools needed to provide precise, personalized medical care for patients with advanced cancer. However, few CTC technologies demonstrate both experimental and clinical evidence of an accurate, reliable and reproducible assay that also meets the regulatory requirements to enter routine clinical practice. Many opportunities exist to incorporate clinical needs and regulatory benchmarks into technology development to more quickly garner FDA approval to direct decisions on patient care. This review will address: 1) device development tailored to address predictive, prognostic and/or therapeutic needs across the multitude of malignancies and disease stages; 2) validation benchmarks for clinical assay development; 3) early establishment of standard operating procedures for sample acquisition and analysis; 4) demonstration of clinical utility; 5) clinical qualification of a novel biomarker; and 6) integration of a newly validated and qualified technology into routine clinical practice. Early understanding and incorporation of these regulatory requirements into assay development can simplify and speed the integration of these novel technologies into patient care. Meeting these benchmarks will lead to the true personalization of cancer therapies, directing initial and subsequent treatments for each individual based on initial tumor characteristics while monitoring for emerging mechanisms of resistance in these continually evolving tumors.

Surrogate outcomes: experiences at the Common Drug Review

BACKGROUND

Surrogate outcomes are a significant challenge in drug evaluation for health technology assessment (HTA) agencies. The research objectives were to: identify factors associated with surrogate use and acceptability in Canada's Common Drug Review (CDR) recommendations, and compare the CDR with other HTA or regulatory agencies regarding surrogate concerns.

METHODS

Final recommendations were identified from CDR inception (September 2003) to December 31, 2010. Recommendations were classified by type of outcome (surrogate, final, other) and acceptability of surrogates (determined by the presence/absence of statements of concern regarding surrogates). Descriptive and statistical analyses examined factors related to surrogate use and acceptability. For thirteen surrogate-based submissions, recommendations from international HTA and regulatory agencies were reviewed for statements about surrogate acceptability.

RESULTS

Of 156 final recommendations, 68 (44%) involved surrogates. The overall 'do not list' (DNL) rate was 48%; the DNL rate for surrogates was 41% (p = 0.175). The DNL rate was 64% for non-accepted surrogates (n = 28) versus 25% for accepted surrogates (odds ratio 5.4, p = 0.002). Clinical uncertainty, use of economic evidence over price alone, and a premium price were significantly associated with non-accepted surrogates. Surrogates were used most commonly for HIV, diabetes, rare diseases, cardiovascular disease and cancer. For the subset of drugs studied, other HTA agencies did not express concerns for most recommendations, while regulatory agencies frequently stated surrogate acceptance.

CONCLUSIONS

The majority of surrogates were accepted at the CDR. Non-accepted surrogates were significantly associated with clinical uncertainty and a DNL recommendation. There was inconsistency of surrogate acceptability across several international agencies. Stakeholders should consider collaboratively establishing guidelines on the use, validation, and acceptability of surrogates.

Biomarkers in prostate cancer surveillance and screening: past, present, and future

The use of biomarkers for prostate cancer (PCa) screening, detection, and prognostication have revolutionized the diagnosis and management of the disease. Current clinical practice has been driven largely by the utilization of prostate-specific antigen (PSA). The lack of specificity of PSA for PCa has led to both unnecessary biopsies and overdiagnosis of indolent cancers. The recent controversial recommendation by the United States Preventive Services Task Force against PCa screening has highlighted the need for novel clinically useful biomarkers. We review the literature on PCa biomarkers in serum, urine, and tissue. While these markers show promise, none seems poised to replace PSA, but rather may augment it. Further validation and consideration of how these novel markers improve clinical outcome is necessary. The discovery of new genetic markers shows promise in stratifying men with aggressive PCa.

Molecular Targeted Drugs and Biomarkers in NSCLC, the Evolving Role of Individualized Therapy

Lung cancer first line treatment has been directed from the non-specific cytotoxic doublet chemotherapy to the molecular targeted. The major limitation of the targeted therapies still remains the small number of patients positive to gene mutations. Furthermore, the differentiation between second line and maintenance therapy has not been fully clarified and differs in the clinical practice between cancer centers. The authors present a segregation between maintenance treatment and second line and present a possible definition for the term “maintenance” treatment. In addition, cancer cell evolution induces mutations and therefore either targeted therapies or non-specific chemotherapy drugs in many patients become ineffective. In the present work pathways such as epidermal growth factor, anaplastic lymphoma kinase, met proto-oncogene and PI3K are extensively presented and correlated with current chemotherapy treatment. Future, perspectives for targeted treatment are presented based on the current publications and ongoing clinical trials.

Role of biomarkers in sepsis care

Sepsis is one of the leading causes of mortality and morbidity, even with the current availability of extended-spectrum antibiotics and advanced medical care. Biomarkers offer a tool in facilitating early diagnosis, in identifying patient populations at high risk of complications, and in monitoring progression of the disease, which are critical assessments for appropriate therapy and improvement in patient outcomes. Several biomarkers are already available for clinical use in sepsis; however, their effectiveness in many instances is limited by the lack of specificity and sensitivity to characterize the presence of an infection and the complexity of the inflammatory and immune processes and to stratify patients into homogenous groups for specific treatments. Current advances in molecular techniques have provided new tools facilitating the discovery of novel biomarkers, which can vary from metabolites and chemical products present in body fluids to genes and proteins in circulating blood cells. The purpose of this review was to examine the current status of sepsis biomarkers, with special emphasis on emerging markers, which are undergoing validation and may transition into clinical practice for their informative value in diagnosis, prognosis, or response to therapy. We will also discuss the new concept of combination biomarkers and biomarker risk models, their existing challenges, and their potential use in the daily management of patients with sepsis.

TLR 2 and 4 responsiveness from isolated peripheral blood mononuclear cells from rats and humans as potential chronic pain biomarkers

Background

Chronic pain patients have increased peripheral blood mononuclear cell Interkeukin-1β production following TLR2 and TLR4 simulation. Here we have used a human-to-rat and rat-to-human approach to further investigate whether peripheral blood immune responses to TLR agonists might be suitable for development as possible systems biomarkers of chronic pain in humans.

Methods and Results

Study 1: using a graded model of chronic constriction injury in rats, behavioral allodynia was assessed followed by in vitro quantification of TLR2 and TLR4 agonist-induced stimulation of IL-1β release by PBMCs and spinal cord tissues (n = 42; 6 rats per group). Statistical models were subsequently developed using the IL-1β responses, which distinguished the pain/no pain states and predicted the degree of allodynia. Study 2: the rat-derived statistical models were tested to assess their predictive utility in determining the pain status of a published human cohort that consists of a heterogeneous clinical pain population (n = 19) and a pain-free population (n = 11). The predictive ability of one of the rat models was able to distinguish pain patients from controls with a ROC AUC of 0.94. The rat model was used to predict the presence of pain in a new chronic pain cohort and was able to accurately predict the presence of pain in 28 out of the 34 chronic pain participants.

Conclusions

These clinical findings confirm our previous discoveries of the involvement of the peripheral immune system in chronic pain. Given that these findings are reflected in the prospective graded rat data, it suggests that the TLR response from peripheral blood and spinal cord were related to pain and these clinical findings do indeed act as system biomarkers for the chronic pain state. Hence, they provide additional impetus to the neuroimmune interaction to be a drug target for chronic pain.

Safety biomarkers in preclinical development: translational potential

The identification, application, and qualification of safety biomarkers are becoming increasingly critical to successful drug discovery and development as companies are striving to develop drugs for difficult targets and for novel disease indications in a risk-adverse environment. Translational safety biomarkers that are minimally invasive and monitor drug-induced toxicity during human clinical trials are urgently needed to assess whether toxicities observed in preclinical toxicology studies are relevant to humans at therapeutic doses. The interpretation of data during the biomarker qualification phase should include careful consideration of the analytic method used, the biology, pharmacokinetic and pharmacodynamic properties of the biomarker, and the pathophysiology of the process studied. The purpose of this review is to summarize commonly employed technologies in the development of fluid- and tissue-based safety biomarkers in drug discovery and development and to highlight areas of ongoing novel assay development.

A conceptual review on systems biology in health and diseases: from biological networks to modern therapeutics

Human physiology is an ensemble of various biological processes spanning from intracellular molecular interactions to the whole body phenotypic response. Systems biology endures to decipher these multi-scale biological networks and bridge the link between genotype to phenotype. The structure and dynamic properties of these networks are responsible for controlling and deciding the phenotypic state of a cell. Several cells and various tissues coordinate together to generate an organ level response which further regulates the ultimate physiological state. The overall network embeds a hierarchical regulatory structure, which when unusually perturbed can lead to undesirable physiological state termed as disease. Here, we treat a disease diagnosis problem analogous to a fault diagnosis problem in engineering systems. Accordingly we review the application of engineering methodologies to address human diseases from systems biological perspective. The review highlights potential networks and modeling approaches used for analyzing human diseases. The application of such analysis is illustrated in the case of cancer and diabetes. We put forth a concept of cell-to-human framework comprising of five modules (data mining, networking, modeling, experimental and validation) for addressing human physiology and diseases based on a paradigm of system level analysis. The review overtly emphasizes on the importance of multi-scale biological networks and subsequent modeling and analysis for drug target identification and designing efficient therapies.

Markers for nutrition studies: review of criteria for the evaluation of markers

INTRODUCTION

Markers are important tools to assess the nutrition status and effects of nutrition interventions. There is currently insufficient consensus in nutrition sciences on how to evaluate markers, despite the need for properly evaluating them.

OBJECTIVES

To identify the criteria for the evaluation of markers related to nutrition, health and disease and to propose generic criteria for evaluation.

METHOD

The report on "Evaluation of Biomarker and Surrogate Endpoints in Chronic Disease" from the Institute of Medicine was the starting point for the literature search. Additionally, specific search strategies were developed for Pubmed.

RESULTS

In nutrition, no set of criteria or systematic approach to evaluate markers is currently available. There is a reliance on the medical area where statistical methods have been developed to quantify the evaluation of markers. Even here, a systematic approach is lacking-markers are still evaluated on a case-by-case basis. The review of publications from the literature search resulted in a database with definitions, criteria for validity and the rationale behind the criteria. It was recognized that, in nutrition, a number of methodological aspects differ from medical research.

CONCLUSIONS

The following criteria were identified as essential elements in the evaluation of markers: (1) the marker has a causal biological link with the endpoint, (2) there is a significant association between marker and endpoint in the target population, (3) marker changes consistently with the endpoint, e.g., in response to an intervention, and (4) change in the marker explains a substantial proportion of the change in the endpoint in response to the intervention.

Optimal thresholds by maximizing or minimizing various metrics via ROC-type analysis

RATIONALE AND OBJECTIVES

Based on imaging features, the optimal thresholds are typically determined as cutoff points to dichotomize the corresponding measurement scales.

MATERIALS AND METHODS

Five metrics (ie, the Youden index, Euclidian distance, percent of correct diagnosis, kappa statistic, and mutual information) are individually maximized or minimized to derive the corresponding optimal threshold. These optimal thresholds are estimated under the parametric binormal assumption. Monte Carlo simulation studies are conducted to compare the performances of these different methods. A published radiological example on the choice of treatment outcomes following ureteral stones is used to illustrate and compare the estimated thresholds both empirically and parametrically.

RESULTS

The optimal threshold can be a "moving target" because it would depend on modeling assumptions, metrics, and variability in the data. Even with large samples, disease prevalence has an impact on the robustness of the metrics.

CONCLUSIONS

It is recommended that researchers compare different optimal cutoff points using several metrics and select one that is most clinically relevant. The ultimate goal is to maximize diagnostic performances that are clinically meaningful to achieve improved global health.

Use of functional imaging across clinical phases in CNS drug development

The use of novel brain biomarkers using nuclear magnetic resonance imaging holds potential of making central nervous system (CNS) drug development more efficient. By evaluating changes in brain function in the disease state or drug effects on brain function, the technology opens up the possibility of obtaining objective data on drug effects in the living awake brain. By providing objective data, imaging may improve the probability of success of identifying useful drugs to treat CNS diseases across all clinical phases (I-IV) of drug development. The evolution of functional imaging and the promise it holds to contribute to drug development will require the development of standards (including good imaging practice), but, if well integrated into drug development, functional imaging can define markers of CNS penetration, drug dosing and target engagement (even for drugs that are not amenable to positron emission tomography imaging) in phase I; differentiate objective measures of efficacy and side effects and responders vs non-responders in phase II, evaluate differences between placebo and drug in phase III trials and provide insights into disease modification in phase IV trials.

Integration of proteomics, bioinformatics, and systems biology in traumatic brain injury biomarker discovery

Traumatic brain injury (TBI) is a major medical crisis without any FDA-approved pharmacological therapies that have been demonstrated to improve functional outcomes. It has been argued that discovery of disease-relevant biomarkers might help to guide successful clinical trials for TBI. Major advances in mass spectrometry (MS) have revolutionized the field of proteomic biomarker discovery and facilitated the identification of several candidate markers that are being further evaluated for their efficacy as TBI biomarkers. However, several hurdles have to be overcome even during the discovery phase which is only the first step in the long process of biomarker development. The high-throughput nature of MS-based proteomic experiments generates a massive amount of mass spectral data presenting great challenges in downstream interpretation. Currently, different bioinformatics platforms are available for functional analysis and data mining of MS-generated proteomic data. These tools provide a way to convert data sets to biologically interpretable results and functional outcomes. A strategy that has promise in advancing biomarker development involves the triad of proteomics, bioinformatics, and systems biology. In this review, a brief overview of how bioinformatics and systems biology tools analyze, transform, and interpret complex MS datasets into biologically relevant results is discussed. In addition, challenges and limitations of proteomics, bioinformatics, and systems biology in TBI biomarker discovery are presented. A brief survey of researches that utilized these three overlapping disciplines in TBI biomarker discovery is also presented. Finally, examples of TBI biomarkers and their applications are discussed.

Optimizing Dose Selection with Modeling and Simulation: Application to the Vasopeptidase Inhibitor M100240

Dual inhibition of neutral endopeptidase 24.11 (NEP) and angiotensin-converting enzyme (ACE) has gained increasing interest in the treatment of hypertension, heart failure, and renoprotection. Specifically, M100240, the thioester of the dual ACE/NEP inhibitor MDL100,173, has been evaluated in the management of hypertension. A model-based analysis, including simulations, was employed to characterize the relationship between individual M100240 drug exposure and neurohormonal response and to optimize the dose selection for future clinical studies. Sixty-two healthy subjects and 189 hypertensive patients were studied after oral once-daily administration of 2.5, 5, 10, 25, or 50 mg M100240. Pharmacokinetic-biomarker and blood pressure response models were fitted to the data with the computer program NONMEM. A direct inhibitory E(max) model adequately described the relationship between MDL100,173 concentration and ACE activity. No clear concentration or dose-dependent NEP or blood pressure responses were evident. Given a target 90% ACE inhibition, simulation reveals that (1). 50 mg M100240 once daily produces adequate ACE inhibition 24 hours postdose in only 20% of subjects, and (2). higher and/or more frequent doses on the order of 25 mg three times daily or 50 mg twice daily are required to achieve the target ACE inhibition in at least 50% of patients over 24 hours. Insufficient blood pressure-lowering effects were observed in healthy subjects and hypertensive patients due to inadequate ACE and NEP inhibition with once-daily oral doses of up to 50 mg of M100240. Divided doses might provide target ACE inhibition in more patients.

Proteomics and metabolomics in renal transplantation-quo vadis?

The improvement of long-term transplant organ and patient survival remains a critical challenge following kidney transplantation. Proteomics and biochemical profiling (metabolomics) may allow for the detection of early changes in cell signal transduction regulation and biochemistry with high sensitivity and specificity. Hence, these analytical strategies hold the promise to detect and monitor disease processes and drug effects before histopathological and pathophysiological changes occur. In addition, they will identify enriched populations and enable individualized drug therapy. However, proteomics and metabolomics have not yet lived up to such high expectations. Renal transplant patients are highly complex, making it difficult to establish cause-effect relationships between surrogate markers and disease processes. Appropriate study design, adequate sample handling, storage and processing, quality and reproducibility of bioanalytical multi-analyte assays, data analysis and interpretation, mechanistic verification, and clinical qualification (=establishment of sensitivity and specificity in adequately powered prospective clinical trials) are important factors for the success of molecular marker discovery and development in renal transplantation. However, a newly developed and appropriately qualified molecular marker can only be successful if it is realistic that it can be implemented in a clinical setting. The development of combinatorial markers with supporting software tools is an attractive goal.

Biomarkers in Multiple Sclerosis: An Up-to-Date Overview

During the last decades, the effort of establishing satisfactory biomarkers for multiple sclerosis has been proven to be very difficult, due to the clinical and pathophysiological complexities of the disease. Recent knowledge acquired in the domains of genomics-immunogenetics and neuroimmunology, as well as the evolution in neuroimaging, has provided a whole new list of biomarkers. This variety, though, leads inevitably to confusion in the effort of decision making concerning strategic and individualized therapeutics. In this paper, our primary goal is to provide the reader with a list of the most important characteristics that a biomarker must possess in order to be considered as reliable. Additionally, up-to-date biomarkers are further divided into three subgroups, genetic-immunogenetic, laboratorial, and imaging. The most important representatives of each category are presented in the text and for the first time in a summarizing workable table, in a critical way, estimating their diagnostic potential and their efficacy to correlate with phenotypical expression, neuroinflammation, neurodegeneration, disability, and therapeutical response. Special attention is given to the "gold standards" of each category, like HLA-DRB1∗ polymorphisms, oligoclonal bands, vitamin D, and conventional and nonconventional imaging techniques. Moreover, not adequately established but quite promising, recently characterized biomarkers, like TOB-1 polymorphisms, are further discussed.

European Bioanalysis Forum recommendation on method establishment and bioanalysis of biomarkers in support of drug development

Biomarkers have become increasingly important in drug development and many bioanalysts are getting involved. Consequently, different views on how to approach the bioanalysis of biomarkers have been published or are being developed. The European Bioanalysis Forum has intensively discussed this topic since 2010 and is ready with their recommendation on method establishment and bioanalysis of biomarkers. Acknowledging that the challenges step outside the bioanalytical laboratory is a cornerstone of our recommendation. The importance of integrating all scientific aspects, from purely analytical aspects, all the way to understanding the biology and effects of the biomarker, prior to embarking on method establishment or sample analysis, cannot be underestimated. Close and iterative interactions with the teams requesting the data is imperative to develop a bioanalytical strategy that combines science, analytical performance and regulations. The European Bioanalysis Forum developed a straightforward decision tree to help the scientific community in developing a bioanalytical strategy for any biomarker in drug development.

Evaluation of novel renal biomarkers with a cisplatin model of kidney injury: gender and dosage differences

A number of novel urinary biomarkers have been identified and partially qualified for use as markers for renal injury in rats. We use two novel multiplex assays to quantify biomarker concentration in multiple urine collections made prior to and following administration of cisplatin, a common nephrotoxicant, to rats. We investigate the correlation of the magnitude of biomarker changes with the severity of histopathological observations and explore the relationship of these to both dose and sex. The novel biomarkers evaluated are urinary albumin, alpha glutathione s-transferase (α-GST), glutathione S-transferase-yb1 (GSTYb1), lipocalin-2, kidney injury molecule-1 (KIM-1), osteopontin, and renal papillary antigen 1 (RPA-1) and plasma cystatin C, alongside the traditional biomarkers of plasma urea, creatinine, and urinary n-acetyl-beta-d-glucosaminidase (NAG), total protein, and glucose. We show for all time points, and for almost all doses, that male rats consistently had either more severely graded or a higher incidence of histologically observed lesions than females; that changes in urinary glucose, total urinary protein, NAG, and the novel urinary biomarkers albumin, osteopontin, and KIM-1 are clearly temporally associated; and that changes are related to the severity of injury. We also found that receiver operating characteristic curve analysis and area under the curve are significantly higher than urea or creatinine for all new biomarkers except aGST, GSTYb1, cystatin c, and total protein in both sexes.

Microbiota-targeted therapies: an ecological perspective

The connection between disease and the disruption of homeostatic interactions between the host and its microbiota is now well established. Drug developers and clinicians are starting to rely more heavily on therapies that directly target the microbiota and on the ecology of the microbiota to understand the outcomes of these treatments. The effects of those microbiota-targeted therapies that alter community composition range in scale from eliminating individual strains of a single species (for example, with antibacterial conjugate vaccines) to replacing the entire community with a new intact microbiota (for example, by fecal transplantation). Secondary infections linked to antibiotic use provide a cautionary tale of the unintended consequences of perturbing a microbial species network and highlight the need for new narrow-spectrum antibiotics with rapid companion diagnostics. Insights into microbial ecology will also benefit the development of probiotics, whose therapeutic prospects will depend on rigorous clinical testing. Future probiotics may take the form of a consortium of long-term community residents: "a fecal transplant in a capsule." The efficacy of microbiota-targeted therapies will need to be assessed using new diagnostic tools that measure community function rather than composition, including the temporal response of a microbial community to a defined perturbation such as an antibiotic or probiotic.

ErbB3 mRNA leukocyte levels as a biomarker for major depressive disorder

BACKGROUND

In recent years, the identification of peripheral biomarkers that are associated with psychiatric diseases, such as Major Depressive Disorder (MDD), has become relevant because these biomarkers may improve the efficiency of the differential diagnosis process and indicate targets for new antidepressant drugs. Two recent candidate genes, ErbB3 and Fgfr1, are growth factors whose mRNA levels have been found to be altered in the leukocytes of patients that are affected by bipolar disorder in a depressive state. On this basis, the aim of the study was to determine if ErbB3 and Fgfr1 mRNA levels could be a biomarkers of MDD.

METHODS

We measured by Real Time PCR ErbB3 and Fgfr1 mRNA expression levels in leukocytes of MDD patients compared with controls. Successively, to assess whether ErbB3 mRNA levels were influenced by previous antidepressant treatment we stratified our patients sample in two cohorts, comparing drug-naive versus drug-free patients. Moreover, we evaluated the levels of the transcript in MDD patients after 12 weeks of antidepressant treatment, and in prefrontal cortex of rats stressed and treated with an antidepressant drug of the same class.

RESULTS

These results showed that ErbB3 but not Fgfr1 mRNA levels were reduced in leukocytes of MDD patients compared to healthy subjects. Furthermore, ErbB3 levels were not affected by antidepressant treatment in either human or animal models

CONCLUSIONS

Our data suggest that ErbB3 might be considered as a biomarker for MDD and that its deficit may underlie the pathophysiology of the disease and is not a consequence of treatment. Moreover the study supports the usefulness of leukocytes as a peripheral system for identifying biomarkers in psychiatric diseases.

Multi-biomarker pattern for tumor identification and prognosis

In last decades, the basic, clinical, and translational research efforts have been directed to the identification of standard biomarkers associated with the degree of malignancy. There is an increasingly public health concern for earlier detection of cancer development at stages in which successful treatments can be achieved. To meet this urgent clinical demand, early stage cancer biomarkers supported by reliable and robust experimental data that can be readily applicable in the clinical practice, are required. In the current standard protocols, when one or two of the canonical proliferating index biomarkers are analyzed, contradictory results are frequently reached leading to incorrect cancer diagnostic and unsuccessful therapies. Therefore, the identification of other cellular characteristics or signatures present in the tumor cells either alone or in combination with the well-established proliferation markers emerge as an alternative strategy in the improvement of cancer diagnosis and treatment. Because it is well known that several pathways and processes are altered in tumor cells, the concept of "single marker" in cancer results incorrect. Therefore, this review aims to analyze and discuss the proposal that the molecular profile of different genes or proteins in different altered tumor pathways must be established to provide a better global clinical pattern for cancer detection and prognosis.

Is blood pressure reduction a valid surrogate endpoint for stroke prevention? An analysis incorporating a systematic review of randomised controlled trials, a by-trial weighted errors-in-variables regression, the surrogate threshold effect (STE) and the Biomarker-Surrogacy (BioSurrogate) Evaluation Schema (BSES)

Background

Blood pressure is considered to be a leading example of a valid surrogate endpoint. The aims of this study were to (i) formally evaluate systolic and diastolic blood pressure reduction as a surrogate endpoint for stroke prevention and (ii) determine what blood pressure reduction would predict a stroke benefit.

Methods

We identified randomised trials of at least six months duration comparing any pharmacologic anti-hypertensive treatment to placebo or no treatment, and reporting baseline blood pressure, on-trial blood pressure, and fatal and non-fatal stroke. Trials with fewer than five strokes in at least one arm were excluded. Errors-in-variables weighted least squares regression modelled the reduction in stroke as a function of systolic blood pressure reduction and diastolic blood pressure reduction respectively. The lower 95% prediction band was used to determine the minimum systolic blood pressure and diastolic blood pressure difference, the surrogate threshold effect (STE), below which there would be no predicted stroke benefit. The STE was used to generate the surrogate threshold effect proportion (STEP), a surrogacy metric, which with the R-squared trial-level association was used to evaluate blood pressure as a surrogate endpoint for stroke using the Biomarker-Surrogacy Evaluation Schema (BSES3).

Results

In 18 qualifying trials representing all pharmacologic drug classes of antihypertensives, assuming a reliability coefficient of 0.9, the surrogate threshold effect for a stroke benefit was 7.1 mmHg for systolic blood pressure and 2.4 mmHg for diastolic blood pressure. The trial-level association was 0.41 and 0.64 and the STEP was 66% and 78% for systolic and diastolic blood pressure respectively. The STE and STEP were more robust to measurement error in the independent variable than R-squared trial-level associations. Using the BSES3, assuming a reliability coefficient of 0.9, systolic blood pressure was a B + grade and diastolic blood pressure was an A grade surrogate endpoint for stroke prevention. In comparison, using the same stroke data sets, no STEs could be estimated for cardiovascular (CV) mortality or all-cause mortality reduction, although the STE for CV mortality approached 25 mmHg for systolic blood pressure.

Conclusions

In this report we provide the first surrogate threshold effect (STE) values for systolic and diastolic blood pressure. We suggest the STEs have face and content validity, evidenced by the inclusivity of trial populations, subject populations and pharmacologic intervention populations in their calculation. We propose that the STE and STEP metrics offer another method of evaluating the evidence supporting surrogate endpoints. We demonstrate how surrogacy evaluations are strengthened if formally evaluated within specific-context evaluation frameworks using the Biomarker- Surrogate Evaluation Schema (BSES3), and we discuss the implications of our evaluation of blood pressure on other biomarkers and patient-reported instruments in relation to surrogacy metrics and trial design.

Imaging surrogate end points in heart failure trials

Despite the epidemiologic importance, large investments, and careful design, recent results of heart failure (HF) trials have been unable to demonstrate significant treatment improvements. This shortcoming has led to a reassessment of research methodology, particularly related to sample size and costs, for which end-point selection is a main issue. In comparing interventions in clinical trials, surrogate end points may be used to reduce the costs. To this end, ongoing research into the roles of imaging biomarkers as reliable surrogate end points may lead to better clinical trial design and more efficient development of new therapies for HF.

Evaluation of novel urinary renal biomarkers with a cisplatin model of kidney injury: effects of collection period

A number of novel urinary biomarkers have been identified and partially qualified for use as markers for renal injury in rats. To date, all evaluation studies have been made using 18 to 24 hour collection periods. However, shorter, more welfare friendly, urine collection periods are also used in industry. In this article, we quantify urinary biomarker concentration in serial paired sequential short and long urine collections from male rats administered varying concentrations of cisplatin. We calculate the rate of biomarker excretion in normal animals for both collection periods and the bias and correlation in urinary biomarker concentration between collection periods in dosed and control animals, and we estimate the level of agreement in biomarker concentration between both collection periods. We conclude that although there are minor differences in the concentration of some urinary biomarkers that are dependent upon the time and duration of collection, shorter collection protocols do not influence subsequent interpretation of normalized urinary biomarker data for most biomarkers.

A robust tool for discriminative analysis and feature selection in paired samples impacts the identification of the genes essential for reprogramming lung tissue to adenocarcinoma

BACKGROUND

Lung cancer is the leading cause of cancer deaths in the world. The most common type of lung cancer is lung adenocarcinoma (AC). The genetic mechanisms of the early stages and lung AC progression steps are poorly understood. There is currently no clinically applicable gene test for the early diagnosis and AC aggressiveness. Among the major reasons for the lack of reliable diagnostic biomarkers are the extraordinary heterogeneity of the cancer cells, complex and poorly understudied interactions of the AC cells with adjacent tissue and immune system, gene variation across patient cohorts, measurement variability, small sample sizes and sub-optimal analytical methods. We suggest that gene expression profiling of the primary tumours and adjacent tissues (PT-AT) handled with a rational statistical and bioinformatics strategy of biomarker prediction and validation could provide significant progress in the identification of clinical biomarkers of AC. To minimise sample-to-sample variability, repeated multivariate measurements in the same object (organ or tissue, e.g. PT-AT in lung) across patients should be designed, but prediction and validation on the genome scale with small sample size is a great methodical challenge.

RESULTS

To analyse PT-AT relationships efficiently in the statistical modelling, we propose an Extreme Class Discrimination (ECD) feature selection method that identifies a sub-set of the most discriminative variables (e.g. expressed genes). Our method consists of a paired Cross-normalization (CN) step followed by a modified sign Wilcoxon test with multivariate adjustment carried out for each variable. Using an Affymetrix U133A microarray paired dataset of 27 AC patients, we reviewed the global reprogramming of the transcriptome in human lung AC tissue versus normal lung tissue, which is associated with about 2,300 genes discriminating the tissues with 100% accuracy. Cluster analysis applied to these genes resulted in four distinct gene groups which we classified as associated with (i) up-regulated genes in the mitotic cell cycle lung AC, (ii) silenced/suppressed gene specific for normal lung tissue, (iii) cell communication and cell motility and (iv) the immune system features. The genes related to mutagenesis, specific lung cancers, early stage of AC development, tumour aggressiveness and metabolic pathway alterations and adaptations of cancer cells are strongly enriched in the AC PT-AT discriminative gene set. Two AC diagnostic biomarkers SPP1 and CENPA were successfully validated on RT-RCR tissue array. ECD method was systematically compared to several alternative methods and proved to be of better performance and as well as it was validated by comparison of the predicted gene set with literature meta-signature.

CONCLUSIONS

We developed a method that identifies and selects highly discriminative variables from high dimensional data spaces of potential biomarkers based on a statistical analysis of paired samples when the number of samples is small. This method provides superior selection in comparison to conventional methods and can be widely used in different applications. Our method revealed at least 23 hundreds patho-biologically essential genes associated with the global transcriptional reprogramming of human lung epithelium cells and lung AC aggressiveness. This gene set includes many previously published AC biomarkers reflecting inherent disease complexity and specifies the mechanisms of carcinogenesis in the lung AC. SPP1, CENPA and many other PT-AT discriminative genes could be considered as the prospective diagnostic and prognostic biomarkers of lung AC.

Variance decomposition of protein profiles from antibody arrays using a longitudinal twin model

BACKGROUND

The advent of affinity-based proteomics technologies for global protein profiling provides the prospect of finding new molecular biomarkers for common, multifactorial disorders. The molecular phenotypes obtained from studies on such platforms are driven by multiple sources, including genetic, environmental, and experimental components. In characterizing the contribution of different sources of variation to the measured phenotypes, the aim is to facilitate the design and interpretation of future biomedical studies employing exploratory and multiplexed technologies. Thus, biometrical genetic modelling of twin or other family data can be used to decompose the variation underlying a phenotype into biological and experimental components.

RESULTS

Using antibody suspension bead arrays and antibodies from the Human Protein Atlas, we study unfractionated serum from a longitudinal study on 154 twins. In this study, we provide a detailed description of how the variation in a molecular phenotype in terms of protein profile can be decomposed into familial i.e. genetic and common environmental; individual environmental, short-term biological and experimental components. The results show that across 69 antibodies analyzed in the study, the median proportion of the total variation explained by familial sources is 12% (IQR 1-22%), and the median proportion of the total variation attributable to experimental sources is 63% (IQR 53-72%).

CONCLUSION

The variability analysis of antibody arrays highlights the importance to consider variability components and their relative contributions when designing and evaluating studies for biomarker discoveries with exploratory, high-throughput and multiplexed methods.

Administrative database research has unique characteristics that can risk biased results

OBJECTIVE

The provision of health care frequently creates digitized data--such as physician service claims, medication prescription records, and hospitalization abstracts--that can be used to conduct studies termed "administrative database research." While most guidelines for assessing the validity of observational studies apply to administrative database research, the unique data source and analytical opportunities for these studies create risks that can make them uninterpretable or bias their results.

STUDY DESIGN

Nonsystematic review.

RESULTS

The risks of uninterpretable or biased results can be minimized by; providing a robust description of the data tables used, focusing on both why and how they were created; measuring and reporting the accuracy of diagnostic and procedural codes used; distinguishing between clinical significance and statistical significance; properly accounting for any time-dependent nature of variables; and analyzing clustered data properly to explore its influence on study outcomes.

CONCLUSION

This article reviewed these five issues as they pertain to administrative database research to help maximize the utility of these studies for both readers and writers.

Biomarkers in development of psychotropic drugs

Biomarkers have been receiving increasing attention, especially in the field of psychiatry. In contrast to the availability of potent therapeutic tools including pharmacotherapy, psychotherapy, and biological therapies, unmet needs remain in terms of onset of action, stability of response, and further improvement of the clinical course. Biomarkers are objectively measured characteristics which serve as indicators of the causes of illnesses, their clinical course, and modification by treatment. There exist a variety of markers: laboratory markers which comprise the determination of genetic and epigenetic markers, neurotransmitters, hormones, cytokines, neuropeptides, enzymes, and others as single measures; electrophysiological markers which usually comprise electroencephalography (EEG) measures, and in particular sleep EEG and evoked potentials, magnetic encephalography, electrocardiogram, facial electromyography, skin conductance, and others; brain imaging techniques such as cranial computed tomography, magnetic resonance imaging, functional MRl, magnetic resonance spectroscopy, positron emission tomography, and single photon emission computed tomography; and behavioral approaches such as cue exposure and challenge tests which can be used to induce especially emotional processes in anxiety and depression. Examples for each of these domains are provided in this review. With a view to developing more individually tailored therapeutic strategies, the characterization of patients and the courses of different types of treatment will become even more important in the future.