Targeted Biomarker Quantitation by LC-MS

Naidong Weng, Wenying Jian, Naidong Weng, Wenying Jian

• Publisher: Wiley
• Year: 2017
• Print ISBN: 9781119103066
• eBook ISBN: 9781119413059
• Edition: 1
• Topic: Medicine
• Subtopic: Pharmacology

The first book to offer a blueprint for overcoming the challenges to successfully quantifying biomarkers in living organisms

The demand among scientists and clinicians for targeted quantitation experiments has experienced explosive growth in recent years. While there are a few books dedicated to bioanalysis and biomarkers in general, until now there were none devoted exclusively to addressing critical issues surrounding this area of intense research. Target Biomarker Quantitation by LC-MS provides a detailed blueprint for quantifying biomarkers in biological systems. It uses numerous real-world cases to exemplify key concepts, all of which were carefully selected and presented so as to allow the concepts they embody to be easily expanded to future applications, including new biomarker development.

Target Biomarker Quantitation by LC-MS primarily focuses on the assay establishment for biomarker quantitation—a critical issue rarely treated in depth. It offers comprehensive coverage of three core areas of biomarker assay establishment: the relationship between the measured biomarkers and their intended usage; contemporary regulatory requirements for biomarker assays (a thorough understanding of which is essential to producing a successful and defendable submission); and the technical challenges of analyzing biomarkers produced inside a living organism or cell.

• Covers the theory of and applications for state-of-the-art mass spectrometry and chromatography and their applications in biomarker analysis
• Features real-life examples illustrating the challenges involved in target biomarker quantitation and the innovative approaches which have been used to overcome those challenges
• Addresses potential obstacles to obtain effective biomarker level and data interpretation, such as specificity establishment and sample collection
• Outlines a tiered approach and fit-for-purpose assay protocol for target biomarker quantitation
• Highlights the current state of the biomarker regulatory environment and protocol standards

Target Biomarker Quantitation by LC-MS is a valuable resource for bioanalytical scientists, drug metabolism and pharmacokinetics scientists, clinical scientists, analytical chemists, and others for whom biomarker quantitation is an important tool of the trade. It also functions as an excellent text for graduate courses in pharmaceutical, biochemistry and chemistry.

Part I
Overview

1
Overview of Targeted Quantitation of Biomarkers and Its Applications

Naidong Weng

Bioanalytical & Pharmacokinetics, Janssen Research & Development, LLC, Spring House, PA, USA

1.1 Introduction

In the last two decades, utilization of biomarkers in drug discovery and development has seen rapid growth as a result of the advancement of laboratory techniques and bioanalytical assays including ligand‐binding assays (LBA) such as enzyme‐linked immunosorbent assay (ELISA), quantitative polymerase chain reaction (qPCR), and mass spectrometry (MS)‐based technologies, and so on (Anderson and Kodukula, 2014). Currently, pharmaceutical companies and regulatory authorities are actively engaged in developing robust efficacy and safety biomarkers that can be used in a translational manner to assist drug development by making the right choice for “go” and “no‐go” decisions at the earliest possible stage. In order to most efficiently utilize the resources and maximize the benefits of biomarker research, most of the drug companies have established internal biomarker research centers and are also pursuing extensive collaborations with academia, hospitals, and research institutes. A biomarker can assist target and candidate selection in drug discovery, toxicity assessment, dose selection, and pharmacokinetics (PK)/pharmacodynamics (PD) modeling in drug development. In clinical Phase I–IV, a biomarker can help in patient stratification, drug–drug interaction (DDI) evaluation, efficacy assessment, safety monitoring, and companion diagnosis as well as postapproval surveillance. Biomarkers measured in patients before treatment can also be used to select patients for inclusion in a clinical trial. Changes in biomarkers following treatment may predict or identify safety problems related to a candidate drug or reveal a pharmacological activity that is expected to predict an eventual benefit from treatment. Biomarkers can also be used as diagnostic tools for the identification of population with an underlying disease and its progressive stage.

In fact, most of the drug programs in development stages have requirements of biomarkers to be incorporated in the preclinical and clinical development strategy as they can help ensure safety and efficacy of the drug candidates. Indeed, it has been reported that the ability of biomarkers to improve treatment and reduce healthcare costs is potentially greater than in any other area of current medical research (Drucker and Krapfenbauer, 2013). A search of one major clinical trial registry on December 5, 2015 (https://ClinicalTrials.gov), using the search term “biomarker,” generated 17,366 results, almost twofold increases, compared to what had been previously reported 5 years ago (Boulton and Dally, 2010). Less than a year late (November 8, 2016), this number is 19,611.

More specifically, biomarkers have demonstrated the added values to every major disease area. For example, in oncology, with the growth in numbers of targeted therapies for oncology clinical testing, biomarkers are often used to select patient population (Arteaga, 2003). Biomarkers can also allow investigators to stratify patients for prospective or retrospective evaluation of different clinical responses and for identification of specific responder sub‐population (Mendelsohn and Baselga, 2003). A previous publication also proposed optimizing oncology drug development by using a tiered set of clinical biomarkers that predict compound efficacy with increasing confidences as well as increasing rigor of validation at each of the three levels (Floyd and McShane, 2004). Level‐1 biomarkers confirm biochemical or pharmacological mechanism of action by showing that the drug is modulating its target and provides correlation of PD and PK, which is the exposure of the drug and its active metabolites. Level‐2 biomarkers confirm that the drug is producing a desired PD effect directly related to its potential for efficacy such as altered downstream cell signaling in pathways related to target, decreased metabolic activity, or changes in tumor vascular perfusion. Level‐3 biomarkers have predictive power for a desired outcome and may be surrogate end points for in vivo symptoms, such as tumor size. It should be noted that even with the extensive research by many scientists over the last decades, very few biomarkers, that can be measured in the laboratory, qualify for Level‐3 biomarkers. Of course, this type of categorization of biomarkers can also be applied to other disease areas. Almost all of the biomarkers discussed in this book belong to the first two levels.

For Type 2 diabetes (T2DM), it was estimated that, in 2010, 285 million people had been diagnosed with diabetes mellitus worldwide, a prevalence of 6.4% of the total population. This is predicted to increase to 439 million (7.7% of total population), and by 2030, T2DM will account for about 90% of diabetic patients worldwide (Shaw et al., 2010). Biomarker search has lead to several promising biomarkers such as Chitinase‐3‐like protein 1 (CHI3L1) also known as YKL‐40, soluble CD36 (cluster of differentiation 36), leptin, resistin, interleukin 18 (IL‐18), retinol‐binding protein 4 (RBP4), and chemerin that could be indicative for the pathogenesis of insulin resistance and endothelial dysfunction in T2DM patients (Qhadijah et al., 2013). In another paper (Lyons and Basu, 2012), it was postulated that in blood, hemoglobin A1c (HbA1c) may be considered as a biomarker for the presence and severity of hyperglycemia, implying diabetes or prediabetes.

Alzheimer’s disease (AD) is an irreversible, progressive brain disorder that slowly destroys memory and thinking skills, and eventually the ability to carry out the simplest tasks. In most people with AD, symptoms first appear in their mid‐60s. Estimates vary, but experts suggest that more than five million Americans may have AD (https://www.nia.nih.gov/alzheimers/publication/alzheimers‐disease‐fact‐sheet). There is significant interest in the development of methods to validate novel biomarkers for diagnosis of AD. Cerebrospinal fluid (CSF) levels of β‐amyloid Aβ1–40 and Aβ1–42 peptides, total Tau protein, and phosphorylated Tau protein have diagnostic values in AD (Chintamaneni and Bhaskar, 2012). Tau protein is a highly soluble microtubule‐associated protein (MAP). In humans, these proteins are found mostly in neurons compared to non‐neuronal cells. Tau protein and phosphorylated Tau protein are measured by using ELISA (Herrmann et al., 1999). Liquid chromatography in conjunction with mass spectrometric detection (LC–MS)‐based assays have also been published for measuring β‐amyloid Aβ1–40 and Aβ1–42 peptides in CSF (Choi et al., 2013). A systematic review and meta‐analysis of the literature on whether or not CSF total tau, phosphorylated tau, and β‐amyloid Aβ1–42 peptide help predict progression of mild cognitive impairment to AD was conducted (Diniz et al., 2008).

1.2 Biomarker Definition

It is generally accepted in the pharmaceutical industry that a biological marker or a biomarker is a characteristic that is objectively measured and evaluated as an indicator of normal biologic processes, pathologic processes, or biological responses to a therapeutic intervention (Biomarkers Definitions Working Group, 2001). Biomarkers are typically classified into diagnostic, prognostic, and predictive biomarkers. Biomarker definition and usage are summarized in Appendix II of the Guidance for Industry and FDA Staff (Qualification Process Working Group, 2014).

A diagnostic biomarker is a disease characteristic that categorizes a person by the presence or absence of a specific physiological or pathophysiological state or disease.

A prognostic biomarker is a baseline characteristic that categorizes patients by degree of risk for disease occurrence or progression of a specific aspect of a disease.

A predictive biomarker is a baseline characteristic that categorizes patients by their likelihood of response to a particular treatment relative to no treatment.

In pharmaceutical industry research and development (R&D), biomarkers can also be described as efficacy or safety biomarkers. Division of common biomarkers into these two categories is probably better linked with the drug discovery and development process as deficiency in safety or efficacy is the major reason for termination of drug candidates. Efficacy biomarkers emphasize on mode of action and can be used to build early confidence in drug mechanism and can potentially substitute for clinical symptoms as a measurement of efficacy. Safety biomarkers are early markers of reversible or irreversible drug‐induced adverse events and can be used to understand the mechanism of drug‐induced toxicity.

An emerging area of extensive research is t...

Table of contents

• Cover
• Title Page
• Table of Contents
• List of Contributors
• Preface
• Abbreviations
• Part I: Overview
• Part II: Challenges and Approaches
• Part III: Applications
• Index
• End User License Agreement