This book focuses on proteomics biomarker discovery and validation procedures from the clinical perspective. Topics covered include: sample selection; regulation of biomarker approval; sample storage and preparation; comprehensive LC-MS profiling and data preprocessing; statistical analysis, and biomarker validation. It discusses the current status of the science and technology involved and their limitations. Future developments needed to improve the success rate of translating biomarker discovery into useful clinical tests are also included. Common pitfalls and success stories are discussed and best practice guidelines are provided. Broad and interdisciplinary in approach, the book provides and excellent source of information for industrial and academic researchers, and those managing biobanks.
About the Author
Peter Horvatovich and Rainer Bischoff have worked at the University of Groningen Faculty of Mathematics and Natural Science for more than five years. Rainer Bischoff is professor in analytical biochemistry and has been studying protein analysis and proteomics for over 20 years. He obtained his PhD at the University of Göttingen before undertaking postdoctoral research at Purdue University in the USA. He also worked as a group and project leader at Transgene S. A. in Strasburg and a section manager at AstraZeneca R&D in Lund. Peter Horvatovich is an Assistant Professor. He has studied proteomics related bioinformatics for more than eight years and has an analytical chemistry background. Dr Horvatovich received his PhD at the University of Strasbourg for work related to the detection of irradiated food. He then worked at Sanofi-Synthelabo in Budapest and as a postdoctoral researcher at the Bundesinstitute für Risikobewertung in Berlin before moving to the University of Groningen.
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Comprehensive Biomarker Discovery and Validation for Clinical Application
By Péter Horvatovich, Rainer Bischoff
The Royal Society of ChemistryCopyright © 2013 The Royal Society of Chemistry
All rights reserved.
Introduction: Biomarkers in Translational and Personalized Medicine
CHANCHAL KUMAR AND ALAIN J. VAN GOOL
This chapter covers strategic and practical aspects related to optimal ways in which biomarkers for translational and personalized medicine can be applied to innovate pharmaceutical drug development, and contribute to improved health and disease management.
Biomarkers have been around since the beginning of medicine when the colour of skin, various characteristics of urine (exemplified by the diagnostic "urine wheel" published in 1506 by Ullrich Pinder, in his book Epiphanie Medicorum), and other qualitative assessments were interpreted as biological markers of a person's well-being. For a long time, phenotypic analyses combined with a patient's self-assessment were the only tools for diagnosis of disease and monitoring of treatment effects. Recent breakthroughs in molecular technologies to identify, understand and measure biomarkers have strongly increased the possibilities towards a person-specific assessment of disease. These include accurate prediction of a person's risk to develop a specific disease, early detection of a prevalent disease, prediction of disease progression, and prediction and monitoring of the effects of disease treatment, all in a personalized manner.
Biomarkers can be diverse. Ten years ago a useful definition of a biomarker was drafted, being "a characteristic that is objectively measured and evaluated as an indicator of normal biological processes, pathogenic processes, or pharmacologic responses to a therapeutic intervention". There are several important aspects in this definition, both in terms of what is described and what is missing. First, a biomarker can be an indicator of a normal process, of a derailed process related to disease or of the effects of a certain treatment thereon. Although this covers many of the applications, biomarker scientists argue it does not describe biomarkers that indicate disease risk, e.g. through genetic predisposition or brought about by a certain lifestyle. Secondly, the biomarker is a characteristic, meaning it can have multiple identities ranging from a single protein in serum to a complex three-dimensionally reconstructed image of the brain. This has caused several discussions in the field; indeed, would an established biochemical assay that has been operational long before the current biomarker hype, such as estadiol analysis, qualify as a biomarker? If so, how about a mechanical read-out such as a pressure meter in a pen, used by anxiety patients filling in a questionnaire? How about the questionnaire itself? Thirdly, the biomarker is to be objectively measured and evaluated, implying the biomarker assay read-out is trustable and actionable. This leaves room to decide exactly how objective a biomarker should be measured before enabling a clinical decision, fueling discussions on fit-for-purpose robustness of the assay. Despite these alternative views, the stated definition is still a useful one and used by many to focus their attentions to the output defined.
Because of their potential in clinical applications, biomarkers have received much interest in the biomedical field. Their main applications seem to reside in two areas. In translational medicine, knowledge from preclinical models is translated to clinical practice and back using biomarkers that can reflect various aspects of a biological system including molecular pathways, functional cell–cell interactions and tissue metabolism. Such studies are expected to greatly increase the molecular knowledge of the mechanisms of human disease and pathophysiology, leading to a better diagnosis and more effective clinical treatment. In personalized medicine, biomarkers are used to profile patients and to define which treatment should be given to which patient at what time and at what dose. Such stratification biomarkers are expected to strongly increase the chance of a successful clinical treatment by selecting patients that are most likely to respond to a drug and/or to deselect patients that are predicted to exhibit adverse effects.
The availability of the human genome sequence in the late 1900s prompted many to believe that by 2010 personalized medicine would be fully implemented as each person would have his/her genome on a chip to enable a physician to determine the best personalized care. Former president of the USA Bill Clinton phrased it in his 1998 State of the Union Address as: "Gene chips will offer a road map for prevention of illness through a lifetime". There are many shining examples where hard work has indeed resulted in good clinical utility of biomarkers, but there still is a long way to go, as discussed in this chapter.
Biomarkers have become part of our daily lives as illustrated by advertisement of the positive effects of nutrition based on biomarkers (e.g. cholesterol lowering), by media-supported general education about the molecular processes in a human body and how biomarkers represent those processes, by availability and acceptance of biomarker-based "health checks" that can be performed through dedicated providers or even main-street pharmacies, by smartphone apps that provide a health check based on biomarker data, and so on. This all leads to more aware and vocal patients who debate with their physician about their best treatment, rather than "following doctor's orders".
Regarding industrial implementation, biomarkers in the pharmaceutical drug development field have been leading the way, as they matured from explorative pharmacological parameters to essential tools to characterize a patient in molecular detail and to monitor drug action after dosing. In development of neutriceuticals (functional ingredients of food) biomarkers can have a similar role and potentially similar biomarkers representing biological mechanisms or metabolic physiological states can be used. Also, cosmetics can be an interesting biomarker application area, whereby the biomarker read-outs can demonstrate absence of side-effects of the cosmetics. Interestingly, biomarkers are receiving increasing interest to quantify health. Health is described to be "not merely the absence of disease but the ability to adapt to one's environment", also called resilience. Health biomarkers thus indicate the risk of an individual to develop a disease and can be key drivers of prevention strategies, including timely correction by lifestyle change, neutriceuticals or pharmaceuticals.
Despite these positive developments, it was anticipated that progress in translational and personalized medicine would be more advanced than it is today. The discovery of a biomarker and its maturation to a clinically usable test has been shown to require a thorough and long-lasting research and development process.
In this chapter we will mainly focus on biomarkers in pharmaceutical drug development, as lessons learned there can be applied to biomarkers in other application areas. After outlining how biomarkers do play a role in decision making during development of drugs, and more specifically their role in translational and personalized medicine, we will review trends, challenges and opportunities related to biomarkers in biomedical science.
Before discussing the role of biomarkers in pharmaceutical drug development, translational medicine and personalized medicine, we would first like to list useful definitions of biomarkers and their utilities in this field that will guide the thought process.
I. Biomarker: A characteristic that is objectively measured and evaluated as an indicator of normal biological processes, pathogenic processes, or pharmacologic responses to a therapeutic intervention.
II. Disease biomarkers: Biomarkers that are correlated with the disease where correlation is established via rigorous biological and clinical validation. Disease biomarkers are not necessarily causally associated with the mechanism of the disease. Correlation to important phenotypes of the disease, relationships to its initiation, propagation, regression or relapses, however, must be established. Disease biomarkers can serve as diagnostic biomarkers (distinguishing patients from nonpatients), as prognostic biomarkers (identifying "rapid vs. slow progressing" patients) or as disease-classification biomarkers (elucidating molecular mechanisms of the observed pathophysiology). All three functions are crucial parameters to drive the selection of subjects in clinical studies.
III. Target Engagement Biomarker: Biomarkers that represent the direct interaction of the drug (small molecule or biological) with the molecular target. These are highly important to guide drug exposure as they reflect distribution of the drug to the specific location of target, the residency time of the drug on the target and the extent of the drug target modulation by the bound drug.
IV. Pharmacokinetic Biomarkers: Biomarkers that represent the level of the pharmaceutical drug in circulating body fluids and/or at the site of action, and that are important to calculate the dose needed to induce a certain pharmacological response.
V. Pharmacodynamic Biomarkers: Biomarkers that represent the functional outcome of the interaction of a drug with its target (also called pharmacological biomarkers). These biomarkers can have various identities, can be analyzed by a variety of methodologies (including enzymology, omics, imaging), but generally represent a read-out of complex biology. Pharmacodynamic biomarkers are specifically used to rationalize clinical therapeutic efficacy and adverse effects, typically measured as a multiparameter panel of biomarkers representing distinct functional events.
VI. Predictive Biomarker: Biomarkers that are used for the selection of patients for clinical studies. These biomarkers serve to predict which patients are likely to respond to a particular treatment or drug's specific mechanism of action, or potentially predict those patients who may experience adverse effects.
VII. Validated Biomarkers: Biomarkers that are measured in an analytical test system with well-established performance characteristics, and with established scientific framework or body of evidence that elucidates the physiologic, pharmacologic, toxicologic, or clinical significance of the test results.
VIII. Surrogate Endpoint: A biomarker that is intended to substitute for a clinical endpoint. A surrogate endpoint is expected to predict clinical benefit (or harm or lack of benefit or harm) based on epidemiologic, therapeutic, pathophysiologic, or other scientific evidence.
IX. Therapeutic Biomarker: A biomarker that indicates the effect of a therapeutic intervention and can be used to assess its efficacy and/or safety liability.
1.3 Biomarkers in Pharmaceutical Drug Development
1.3.1 The Pharmaceutical Research and Development Process
The pharmaceutical drug-development process (Figure 1.1) is a multistep process that on average takes 14 years from initiation of research to marketing of the new medicine.
It starts with the discovery of a drug target, the molecular target that the drug will act upon. In this target discovery phase researchers investigate how a particular disease is caused and what factors play a key role. The inhibition or stimulation of those key factors will be the basis for the new pharmaceutical drug used to treat the selected disease. For example, postmenopausal complaints are caused by a decrease in endogenously produced estrogens and the objective is to find estrogen-like compounds that can supplement the natural pool of estrogens. The drug target in this case is the estrogen receptor, which is a nuclear protein present in cells of specific tissues and acts as an estrogen-activated transcription factor with specific effects on each specific tissue. Indeed, the activated estrogen receptor in osteoblasts mediates the synthesis of new bone, whereas the estrogen receptor in breast epithelial cells is a key player in cell growth.
The next step is the identification of compounds that have the desired effect on the drug target, for example by inhibiting or stimulating its activity. This discovery is done in the lead discovery phase, during which large numbers of chemical or biological compounds are tested for the desired effects in biochemical and cellular assays. Often mechanistic biomarkers or derivatives thereof are used as read-outs of the screening assays. In the estrogen receptor example, a screening assay to identify compounds with agonistic or antagonistic estrogenic activity may comprise of a cell line expressing the receptor and containing an estrogen-receptor sensitive luciferase reporter module.
Following selection of the most promising hits and limited optimization, the lead optimization phase starts where systematically up to a thousand variants of the original positive substances are synthesized and tested in various tests. A stringent selection process aims to select those compounds that display improved efficacy, specificity, safety, bioavailability and/or production efficiency (depending on the objectives of the project). Assays used during lead optimization include biochemical and cellular test systems, followed by in vivo assays to assess bioavailability, pharmacological and toxicological effects of the drug. Typically one or two of the best compounds are nominated to progress from research to development.
In preclinical development the substance is first investigated in animal models to test whether it is sufficiently bioavailable and safe. After successfully passing this phase, similar studies are performed in the phase 1 clinical trials, during which under strictly controlled conditions the compound is tested in human subjects. Typically, healthy human subjects participate in such trials; the exception being oncology trials whereby drugs are often tested directly in small numbers of patients. Subsequently, the compound is tested in patients, which is the first time the drug developer will determine whether the originally chosen approach of affecting the drug target has a positive effect on treatment of the disease. This occurs in a phase 2 clinical trial in which a relatively small group of patients are tested. A positive outcome of this trial, with an acceptable level of side effects, is very important as it is then proved that the approach chosen to treat the disease works, also known as the clinical proof of concept. After this milestone, the clinical phase 3 starts in which the effect of the substance is tested on large numbers of patients. Such a study can be very substantial. For instance, a phase 3 trial of testing estrogenic compounds in osteoporosis involves administration of the candidate drug in thousands of postmenopausal women per dose group for three years while recording how often a participant breaks a hip, a reduction of which is the currently accepted clinical endpoint of efficacy.
Excerpted from Comprehensive Biomarker Discovery and Validation for Clinical Application by Péter Horvatovich, Rainer Bischoff. Copyright © 2013 The Royal Society of Chemistry. Excerpted by permission of The Royal Society of Chemistry.
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Table of Contents
Introduction to biomarker discovery and validation; Clinical context of proteomics biomarker discovery and validation, authority regulations; Biomarker discovery: Patient selection; Biomarker discovery: Experimental design and biobanking; Biomarker discovery: body fluids; Biomarker discovery: tissues; Biomarker discovery: mass spectrometry based profiling platforms; Biomarker discovery: array based profiling platforms; Biomarker discovery: data preprocessing for LC-MS data; Biomarker discovery: data preprocessing for Maldi imaging, and 2D electrophoresis and protein arrays data; Biomarker discovery: statistical analysis and validation; Biomarker validation: biomarker validation methods; Functional biomarker validation; Clinical application: case studies Clinical application: summary and future technology development trends