Biochemical Pharmacology 87 (2014) 172–188 Contents lists available at ScienceDirect Biochemical Pharmacology jo urnal homepage: www.elsevier.com/locate/biochempharm Review – Part of the Special Issue – Pharmacology in 21st Century Biomedical Research Biomarkers in pharmacology and drug discovery a, a,b D.C. Anderson *, Krishna Kodukula a Center for Advanced Drug Research, SRI International, Harrisonburg, VA 22802, USA b Departments of Biology, and Chemistry and Biochemistry, James Madison University, Harrisonburg, VA 22802, USA A R T I C L E I N F O A B S T R A C T Article history: Biomarkers, quantitatively measurable indicators of biological or pathogenic processes, once validated Received 16 August 2013 play a critical role in disease diagnostics, the prediction of disease progression, and/or monitoring of the Accepted 19 August 2013 response to treatment. They may also represent drug targets. A number of different methods can be used Available online 31 August 2013 for biomarker discovery and validation, including proteomics methods, metabolomics, imaging, and genome wide association studies (GWASs) and can be analysed using receiver operating characteristic Keywords: (ROC) plots. The relative utility of single biomarkers compared to biomarker panels is discussed, along Biomarkers with paradigms for biomarker development, the latter in the context of three large-scale biomarker Disease diagnosis biomarker discovery consortia, the Critical Path Predictive Safety Testing Consortium (PSTC), the NCI Early Detection Research Polypharmacy Network (EDRN) and the Alzheimer’s Disease Neuroimaging Initiative (ADNI). The importance of Protein biomarkers systematic optimization of many parameters in biomarker analysis, including validation, reproducibili- ty, study design, statistical analysis and avoidance of bias are critical features used by these consortia. Problems including introduction of bias into study designs, data reporting or data analysis are also reviewed. ß 2013 Elsevier Inc. All rights reserved. Contents 1. Introduction . 173 2. Defining biomarkers . 173 2.1. Drug targets and biomarkers . 174 2.2. Polypharmacy . 174 3. Biomarker discovery . 175 3.1. Targeted methods . 175 3.2. Non-targeted methods . 176 3.2.1. Proteomics: 2D LC/MS/MS. 176 3.2.2. Proteomics: SELDI-TOF, MALDI protein profiling . 176 3.2.3. Metabolomics . 176 3.2.4. Imaging . 177 3.2.5. Mass spectrometry imaging . 177 3.2.6. Genome wide association studies (GWASs) . 178 4. Approaches to analysis . 178 4.1. A single biomarker versus a biomarker panel . 179 5. Biomarker qualification and subsequent development . 180 5.1. Predictive Safety Testing Consortium (PSTC). 180 5.2. Early Detection Research Network (EDRN) . 180 5.3. Alzheimer’s Disease Neuroimaging Initiative (ADNI) . 181 6. Biomarker validation . 181 7. Challenges . 182 7.1. Most published biomarker candidates do not enter clinical practice. 182 7.2. Problems with selection of preclinical samples. 182 7.3. Sample storage and handling . 182 * Corresponding author. E-mail address: [email protected] (D.C. Anderson). 0006-2952/$ – see front matter ß 2013 Elsevier Inc. All rights reserved. http://dx.doi.org/10.1016/j.bcp.2013.08.026 D.C. Anderson, K. Kodukula / Biochemical Pharmacology 87 (2014) 172–188 173 7.4. Pooling samples . 182 7.5. Biomarker dynamic range . 182 7.6. Data analysis and bias . 183 7.6.1. Batch effects. 183 7.6.2. Selective data reporting, incomplete validation and forensic bioinformatics . 183 7.7. Study design . 183 8. Conclusions . 183 Acknowledgements . 183 References . 183 1. Introduction of the drug discovery process; characterization of animal disease models; use in clinical trials to stratify patients; use as diagnostics Biomarkers, where these exist and have been appropriately or companion diagnostics; or as indicators of therapeutic validated [1–7] can play a critical role in biomedical research, drug response. discovery and development, including: Biomarkers, broadly defined, can be a variety of (ideally) quantitatively measured indicators of biological or pathphysiolo- Their use as diagnostics to establish the presence of a disease. gical processes, or the response to therapeutic intervention, predicting disease progression and the stratification of disease including molecular entities, images or other measured activities severity. or properties, or their combination as a biomarker panel. They assessing and predicting the clinical benefit(s) or toxicity include proteins, protein modifications, or activities, e.g., enzymes resulting from a therapeutic intervention. [12]. DNA-based biomarkers include DNA (e.g., circulating DNA can monitoring treatment response, including target engagement be used to diagnose genetic diseases such as Down’s Syndrome in where appropriate. unborn children [13]), single nucleotide polymorphisms (SNPs), gene copy number variations (CNVs; DNA insertions, deletions, Biomarkers can be used as part of a personalized medicine rearrangements such as inversions and translocations larger than paradigm to customize treatment to the specific disease char- 1 kb, and insertions/deletions less than 1 kb [14]), mRNA or long acteristics of an individual patient. They can also be used to better non-coding RNA [15]. Epigenetic biomarkers include methylated understand disease mechanisms and to identify novel disease DNA (e.g., fecal DNA tests for detection of colorectal cancer [16]), targets. microRNA (e.g., a microRNA panel for diagnosis of stage II-IV colon Issues that are addressed in this review include: (i) cancer [17]), or modified histones. Other more traditional biomarker discovery, development and analysis that requires biomarkers include gross phenotypes such as blood pressure, a combination of careful study design to avoid distorting or lung volume, blood sugar and urine volume, cellular metabolites or negating results due to bias; (ii) systematic development of lipids and other physical measurements including structural (e.g., assays; (iii) larger scale collaborations involving multiple computed tomography or magnetic resonance) or functional (e.g., patient populations to develop and refine robust and reproduc- positron emission tomography) images as used in neuroimaging ible assays and; (iv) for clinical validation [1–7], transparent [18], or electroencephalograms [19]. Combinations of different data analysis (particularly when large datasets are involved), biomarker types (e.g., fluid biomarkers, tissue images, allele with statistical analyses.