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Clinical biomarkers in drug discovery and development

Abstract

Biomarkers enable the characterization of patient populations and quantitation of the extent to which new drugs reach intended targets, alter proposed pathophysiological mechanisms and achieve clinical outcomes. In genomics, the biomarker challenge is to identify unique molecular signatures in complex biological mixtures that can be unambiguously correlated to biological events in order to validate novel drug targets and predict drug response. Biomarkers can stratify patient populations or quantify drug benefit in primary prevention or disease-modification studies in poorly served areas such as neurodegeneration and cancer. Clinically useful biomarkers are required to inform regulatory and therapeutic decision making regarding candidate drugs and their indications in order to help bring new medicines to the right patients faster than they are today.

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Figure 1: Reasons for attrition.
Figure 2: Biomarker categories: target, mechanism and clinical.
Figure 3: Reasons surrogate endpoints 'fail'.
Figure 4: Correlation of brain iron staining and T2 mapping.
Figure 5: Computed tomography lung nodule.
Figure 6: Whole-body PET-CT.
Figure 7: NK1 receptor occupancy required for efficacy.
Figure 8: Positron emission tomography across species.
Figure 9: In vivo response depends on haplotype pair.
Figure 10: Metabonomics array.

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References

  1. Roberts, S. A. Drug metabolism and pharmacokinetics in drug discovery. Curr. Opin. Drug Discov. Devel. 6, 66–80 (2003).

    CAS  PubMed  Google Scholar 

  2. Laska, D. et al. Characterization and application of a vinblastine-selected Caco-2 cell line for evaluation of P-glycoprotein. In Vitro Cell. Devel. Biol. Anim. 38, 401–410 (2002).

    CAS  Google Scholar 

  3. Yuan, R. P. et al. In vitro metabolic interaction studies: Experience of the Food and Drug Administration. Clin. Pharmacol. Ther. 66, 9–15 (1999).

    CAS  PubMed  Google Scholar 

  4. Schadt, E. E., Monks, S. A. & Friend, S. H. A new paradigm for drug discovery: integrating clinical genetic, genomic and molecular phenotype data to identify drug targets. Biochem. Soc. Trans. 31, 437–443 (2003). Discussion of the potential value in a drug discovery setting of combining genetic analyses with gene expression studies to discover and validate therapeutic targets for complex phenotypic traits. This approach will also be applicable to the search for biomarkers.

    CAS  PubMed  Google Scholar 

  5. van de Vijver, M. et al. A gene expression signature as a predictor of survival in breast cancer. N. Engl. J. Med. 347, 1999–2009 (2002). Expression patterns of 70 genes revealed 2 signatures correlating clearly with good and poor prognosis independently of axillary lymph node involvement. This study has important lessons for the clinical and investigational use of microarray approaches in oncology today.

    CAS  PubMed  Google Scholar 

  6. Lesko, L. J. & Woodcock, J. Pharmacogenomic-guided drug development: Regulatory perspective. Pharmacogenomics J. 2, 20–24 (2002).

    CAS  PubMed  Google Scholar 

  7. US Department of Health and Human Services, Food and Drug Administration, Center for Drug Evaluation and Research (CDER) & (CBER). Guidance for Industry and Reviewers Estimating the Safe Starting Dose in Clinical Trials for Therapeutics in Adult Healthy Volunteers (2002).

  8. Rolan, P., Atkinson, A. & Lesko, L. J. Use of biomarkers from drug discovery through clinical practice: Report of the Ninth European Federation of Pharmaceutical Sciences Conference on optimizing drug development. Clin. Pharm. Ther. 73, 284–291 (2003).

    Google Scholar 

  9. De Gruttola, V. G. et al. Considerations in the evaluation of surrogate endpoints in clinical trials: Summary of a National Institutes of Health Workshop. Control. Clin. Trials 22, 485–502 (2001).

    CAS  PubMed  Google Scholar 

  10. De Meyer, G. & Shapiro, F. Biomarker development: The road to clinical utility. Curr. Drug Discov. 12, 23–27 (2003).

    Google Scholar 

  11. Atkinson, A. J., Jr. et al. Biomarkers and surrogate endpoints: Preferred definitions and conceptual framework. Clin. Pharmacol. Ther. 69, 89–95 (2001).

    Google Scholar 

  12. Prentice, R. L. Surrogate endpoints in clinical trials: definition and operational criteria. Stat. Med. 8, 431–440 (1989).

    CAS  PubMed  Google Scholar 

  13. Sormani, M. P., Bruzzi, P., Comi, G. & Filippi, M. MRI metrics as surrogate markers for clinical relapse rate in relapsing-remitting MS patients. Neurology 58, 417–421 (2002).

    PubMed  Google Scholar 

  14. Lin, D. Y., Fleming, T. R. & De Gruttola, V. Estimating the proportion of treatment effect explained by a surrogate marker. Stat. Med. 16, 1515–1527 (1997). The authors measure the extent to which a biological marker is a surrogate endpoint for a clinical event by the proportional reduction in the regression coefficient for the treatment indicator due to the inclusion of the marker in the Cox regression model, then estimate this proportion by applying the partial likelihood function to two Cox models postulated on the same failure time variable. One can construct confidence intervals for the proportion by using the direct normal approximation to the point estimator or by using Fieller's theorem. Extensive simulation studies demonstrate that the proposed methods are appropriate for practical use. The method is applied to HIV/AIDS clinical trials.

    CAS  PubMed  Google Scholar 

  15. Flandre, P. & Saidi, Y. Estimating the proportion of treatment effect explained by a surrogate marker. Stat. Med. 16, 1515–1527 (1997).

    Google Scholar 

  16. Cowles, M. K. Bayesian estimation of the proportion of treatment effect captured by a surrogate marker. Stat. Med. 21, 811–834 (2002). The 'proportion of treatment effect' (PTE) captured by a surrogate endpoint is a frequentist measure intended to address the question of whether trials based on a surrogate endpoint reach the same conclusions as would have been reached using the true endpoint. The authors developed a Markov chain Monte-Carlo-based method for estimating the Bayesian posterior distribution of PTE. Obtaining the full posterior distribution enables direct statements such as 'the posterior probability that PTE >0. 5 is 0.085'. Furthermore, credible sets do not depend on asymptotic approximations and can be computed using data sets for which the frequentist methods might be inaccurate or even impossible to apply.

    PubMed  Google Scholar 

  17. Buyse, M. & Molenberghs, G. Criteria for the validation of surrogate endpoints in randomized experiments. Biometrics 54, 1014–1029 (1998).

    CAS  PubMed  Google Scholar 

  18. Congress, O. h. f. Senate Bill 830 Food and Drug Administration Modernization Act of 1997. 10 (1997).

  19. Temple, R. Are surrogate markers adequate to assess cardiovascular disease drugs? JAMA. 282, 790–795 (1999).

    CAS  PubMed  Google Scholar 

  20. Alonso, A., Geys, H., Molenberghs, G. & Vangeneugden, T. Investigating the criterion validity of psychiatric symptom scales using surrogate marker validation methodology. J. Biopharm. Stat. 12, 161–178 (2002).

    PubMed  Google Scholar 

  21. Streiner, D. & Norman, G. Health Measurement Scales: A Practical Guide to Their Development and Use (Oxford University Press, Oxford, 1995).

    Google Scholar 

  22. Swanson, B. N. Delivery of high-quality biomarker assays. Dis. Markers 18, 47–56 (2002).

    CAS  PubMed  PubMed Central  Google Scholar 

  23. Bossuyt, P. & Reitsma, J. B. The STARD Initiative. Lancet 361, 71 (2003). A STARD steering committee did an extensive literature search and extracted a list of 75 potential items which were consolidated to form a 25-item checklist and led to the development of a flow diagram for diagnostic studies to communicate vital information about the design of a study — including method recruitment and the order of test execution — and the flow of participants in a transparent manner. The STARD group assembled the final, single-page checklist that is published online.

    PubMed  Google Scholar 

  24. Crum, W. R., Scahill, R. I. & Fox, N. C. Automated hippocampal segmentation by regional fluid registration of serial MRI: Validation and application in Alzheimer's disease. Neuroimage 13, 847–855 (2001).

    CAS  PubMed  Google Scholar 

  25. Rolan, P. The contribution of clinical pharmacology surrogates and models to drug development — a critical appraisal. Br. J. Clin. Pharmacol. 44, 219–225 (1997).

    CAS  PubMed  PubMed Central  Google Scholar 

  26. Wesnes, K., Simpson, P. & Kidd, A. An investigation of the range of cognitive impairments induced by scopolamine 0. 6 mg s. c. Hum. Psychopharmacol. 3, 27–41 (1988).

    Google Scholar 

  27. Kitagawa, H. et al. Safety, pharmacokinetics, and effects on cognitive function of multiple doses of GTS-21 in healthy, male volunteers. Neuropsychopharmacology 28, 542–551 ((2003)).

    CAS  PubMed  Google Scholar 

  28. Huestis Marilyn, A. et al. Blockade of effects of smoked marijuana by the CB1-selective cannabinoid receptor antagonist SR141716. Arch. Gen. Psychiatry 58, 322–328 (2001).

    Google Scholar 

  29. Lonn, E. The use of surrogate endpoints in clinical trials: Focus on clinical trials in cardiovascular diseases. Pharmacoepidemiol. Drug Saf. 10, 497–508 (2001). This review provides a definition of surrogate endpoints, proposes practical criteria for establishing their validity, outlines some of the advantages, disadvantages and specific statistical considerations associated with their use in clinical trials and attempts to also highlight drug approval issues associated with the use of these endpoints. A number of examples are also provided related to the use of surrogate endpoints in clinical trials with special emphasis on their use in cardiovascular medicine.

    CAS  PubMed  Google Scholar 

  30. Daniels, M. J. & Hughes, M. D. Meta-analysis for the evaluation of potential surrogate markers. Stat. Med. 16, 1965–1982 (1997).

    CAS  PubMed  Google Scholar 

  31. Gail, M. H., Pfeiffer, R., van Houwelingen, H. C. & Carroll, R. J. On meta-analytic assessment of surrogate outcomes. Biostat. 1, 231–246 (2000).

    CAS  Google Scholar 

  32. Becker, N. G. & Marschner, I. C. Advances in medical statistics arising from the AIDS epidemic. Stat. Methods Med. Res. 10, 117–140 (2001).

    CAS  PubMed  Google Scholar 

  33. Lesko, L. J. & Atkinson, A. J. Use of biomarkers and surrogate endpoints in drug development and regulatory decision making: Criteria, validation, strategies. Ann. Rev. Pharmacol. Toxicol. 41, 347–366 (2001). Biomarkers will play an increasingly important role in all phases of drug development including regulatory review although only a few of these biomarkers will become established well enough to serve in regulatory decision making as surrogate endpoints. Even generally accepted surrogate endpoints are unlikely to capture all the therapeutic benefits and potential adverse effects a drug will have in a diverse patient population. Accordingly, combinations of biomarkers probably will be needed to provide a more complete characterization of the spectrum of pharmacological response. In the future, pharmacogenomic approaches, including those based on differential expression of gene arrays, will provide panels of relevant biomarkers that can be expected to transform the drug development process.

    CAS  Google Scholar 

  34. Pearson, T. A. et al. Markers of inflammation and cardiovascular disease application to clinical and public health practice — A statement for healthcare professionals from the centers for disease control and prevention and the American Heart Association. Circulation 107, 499–511 (2003).

    PubMed  Google Scholar 

  35. Fleming, T. R. & DeMets, D. L. Surrogate end points in clinical trials: are we being misled?[comment]. Ann. Intern. Med. 125, 605–613 (1996).

    CAS  PubMed  Google Scholar 

  36. Colburn, W. A. Optimizing the use of biomarkers, surrogate endpoints, and clinical endpoints for more efficient drug development. J. Clin. Pharmacol. 40, 1419–1427 (2000).

    CAS  PubMed  Google Scholar 

  37. Morris, J. C. et al. Mild cognitive impairment represents early-stage Alzheimer disease. Arch. Neurol. 58, 397–405 (2001).

    CAS  PubMed  Google Scholar 

  38. de Leon, M. J. et al. Prediction of cognitive decline in normal elderly subjects with 2-(18F)fluoro-2-deoxy-D-glucose/ positron-emission tomography (FDG/PET). Proc. Natl Acad. Sci. USA 98, 10966–10971 (2001).

    CAS  PubMed  Google Scholar 

  39. Jack, C. R. et al. Rates of hippocampal atrophy correlate with change in clinical status in aging and AD. Neurology 55, 484–489 (2000).

    PubMed  PubMed Central  Google Scholar 

  40. Hodis, H. N. et al. Estrogen in the prevention of atherosclerosis — a randomized, double-blind, placebo-controlled trial. Ann. Intern. Med. 135, 939–953 (2001).

    CAS  PubMed  Google Scholar 

  41. Choudhury, R. P., Fuster, V., Badimon, J. J., Fisher, E. A. & Fayad, Z. A. MRI and characterization of atherosclerotic plaque: Emerging applications and molecular imaging. Arterioscler. Thromb. Vasc. Biol. 22, 1065–1074 (2002). Comprehensive review of MRI characterization of composition and microanatomy of plaque. The use of MRI in both human disease and experimental models in animals is discussed

    CAS  PubMed  Google Scholar 

  42. Yuan, C. P. et al. Identification of fibrous cap rupture with magnetic resonance imaging is highly associated with recent transient ischemic attack or stroke. Circulation 105, 181–185 (2002).

    PubMed  Google Scholar 

  43. Corti, R. et al. Lipid lowering by simvastatin induces regression of human atherosclerotic lesions: two years' follow up by high resolution noninvasive magnetic resonance imaging. Circulation 106, 2884–2887 (2002). Groundbreaking study using non-invasive methodology to show vascular remodelling and treatment of vessel wall disease. The technique is of importance to assess new therpaeutic approaches that treat vessel wall lesions without altering biomarkers such as cholesterol.

    CAS  PubMed  Google Scholar 

  44. Stefanadis, C. M. D. et al. Thermal heterogeneity within human atherosclerotic coronary arteries detected in vivo: a new method of detection by application of a special thermography catheter. Circulation 99, 1965–1971 (1999).

    CAS  PubMed  Google Scholar 

  45. Rudd, J. H. F. et al. Imaging atherosclerotic plaque inflammation with [18f]-fluorodeoxyglucose positron emission tomography. Circulation 105, 2708–2711 (2002).

    CAS  PubMed  Google Scholar 

  46. De Franco, A. C. & Nissen, S. E. Coronary intravascular ultrasound: implications for understanding the development and potential regression of atherosclerosis. Am. J. Cardiol. 88, 7M–20M (2001).

    CAS  PubMed  Google Scholar 

  47. Nissen, S. Application of intravascular ultrasound to characterize coronary artery disease and assess the progression or regression of atherosclerosis. Am. J. Cardiol. 89, 24B–31B (2002). Description of the evolution, and advantages, of the most advanced imaging technique in use today in clinical trials for the study of atherosclerosis progression and regression.

    PubMed  Google Scholar 

  48. Nieman, K. et al. Reliable noninvasive coronary angiography with fast submillimeter multislice spiral computed tomography. Circulation 106, 2051–2054 (2002).

    PubMed  Google Scholar 

  49. Randomized trial of cholesterol lowering in 4444 patients with coronary heart disease: the Scandinavian Simvastatin Survival Study (4S). Lancet 344, 1383–1389 (1994). Landmark study linking cholesterol lowering to coronary mortality and morbidity endpoints.

  50. Heart Protection study collaborative group (2002) MRC/BHF Heart Protection study of cholesterol lowering with simvastatin in 20,536 high risk individuals: a randomized placebo controlled trial. Lancet 360, 7–22 (2002). One of the largest ever trials of the long-term effects of sustained lowering of low-density lipoprotein cholesterol on vascular and non-vascular mortality and major morbidities. Findings showed expected beneficial effects on heart disease and, surprisingly, that simvastatin reduced the incidence of ischaemic stroke, as this event is not well correlated with high cholesterol levels.

  51. Taubes, G. Cardiovascular disease: Does inflammation cut to the heart of the matter? Science 296, 242–245 (2002).

    CAS  PubMed  Google Scholar 

  52. Ridker, P. M., Hennekens, C. H., Buring, J. E. & Rifai, N. C-reactive protein and other markers of inflammation in the prediction of cardiovascular disease in women. N. Engl. J. Med. 342, 836–843 (2000).

    CAS  PubMed  Google Scholar 

  53. van der Wal, A. C. et al. Recent activation of the plaque immune response in coronary lesions underlying acute coronary syndromes. Heart 80, 14–18 (1998).

    CAS  PubMed  PubMed Central  Google Scholar 

  54. Albert, M. A., Danielson, E., Rifai, N. & Ridker, P. M. Effect of statin therapy on C-reactive protein levels: The pravastatin inflammation/CRP evaluation (PRINCE): A randomized trial and cohort study. JAMA 286, 64–70 (2001).

    CAS  PubMed  Google Scholar 

  55. Szalai, A. J., Cooper, G. S., McCrory, M. A. & Kimberly, R. P. Association between a GT-repeat polymorphism in the intron of the C-reactive protein (CRP) gene and plasma CRP. FASEB J. 15, A685 (2001).

    Google Scholar 

  56. Sides, G. D. QT interval prolongation as a biomarker for Torsades de Pointes and sudden death in drug development. Dis. Markers 18, 57–62 (2002).

    CAS  PubMed  PubMed Central  Google Scholar 

  57. Herman, E. H. et al. The use of serum levels of cardiac troponin T to compare the protective activity of dexrazoxane against doxorubicin- and mitoxantrone-induced cardiotoxicity. Cancer Chemother. Pharmacol. 48, 297–304 (2001).

    CAS  PubMed  Google Scholar 

  58. Schaefer, P. W. et al. Predicting cerebral ischemic infarct volume with diffusion and perfusion MR imaging. Am. J. Neuroradiol. 23, 1785–1794 (2002).

    PubMed  Google Scholar 

  59. Wintermark, M. Prognostic accuracy of cerebral blood flow measurement by perfusion computed tomography, at the time of emergency room admission, in acute stroke patients. Ann. Neurol. 51, 417–432 (2002). Studies show that perfusion CT is an accurate predictor of final infarct size allowing early decisions on management of acute stroke patints and providing a potentuial endpoint for novel neuroprotective agents.

    PubMed  Google Scholar 

  60. Morrish, P., Bailey, D., Sawle, G. & Brooks, D. Measuring the rate of progression and estimating the preclinical period of Parkinson's disease with [18F]dopa PET. J. Neurol. Neurosurg. Psychiatry 64, 314–319 (1998).

    CAS  PubMed  PubMed Central  Google Scholar 

  61. Du, A. T. et al. Atrophy rates of entorhinal cortex in AD and normal aging. Neurology 60, 481–486 (2003).

    CAS  PubMed  PubMed Central  Google Scholar 

  62. Grundman, M. et al. Brain MRI hippocampal volume and prediction of clinical status in a mild cognitive impairment trial. J. Mol. Neurosci. 19, 23–27 (2002).

    CAS  PubMed  Google Scholar 

  63. Jacobs, L. D. et al. Intramuscular interferon-β-1a therapy initiated during a first demyelinating event in multiple sclerosis. N. Engl. J. Med. 343, 898–904 (2000). A recent example of the use of MR in multiple sclerosis diagnosis and treatment. The use of MR in drug evaluation and labelling can be seen by reading the PDR for interferon-β1a.

    CAS  PubMed  Google Scholar 

  64. Jack, C. R. et al. MRI as a biomarker of disease progression in a therapeutic trial of milameline for AD. Neurology 60, 253–260 (2003). Demonstrates the consistency of MRI measurements of hippocampus across multiple sites giving confidence that multicenter studies to monitor Alzheimer's disease progression and drug treatment effects using MR as biomarker are technically feasible.

    PubMed  PubMed Central  Google Scholar 

  65. Fox, N. C., Cousens, S., Scahill, R., Harvey, R. J. & Rossor, M. N. Using serial registered brain magnetic resonance imaging to measure disease progression in Alzheimer disease — power calculations and estimates of sample size to detect treatment effects. Arch. Neurol. 57, 339–344 (2000).

    CAS  PubMed  Google Scholar 

  66. Fox, N. C. et al. Presymptomatic hippocampal atrophy in Alzheimers disease — a longitudinal MRI study. Brain 119, 2001–2007 (1996).

    PubMed  Google Scholar 

  67. Thompson, P. M. et al. Dynamics of gray matter loss in Alzheimer's disease. J. Neurosci. 23, 994–1005 (2003).

    CAS  PubMed  Google Scholar 

  68. Silverman, D. H. S. et al. Positron emission tomography in evaluation of dementia: regional brain metabolism and long-term outcome. JAMA 286, 2120–2127 (2001).

    CAS  PubMed  Google Scholar 

  69. Zhuang, Z. P. et al. Structure–activity relationship of imidazo. J. Med. Chem. 46, 237–243 (2003).

    CAS  PubMed  Google Scholar 

  70. Cagnin, A., Gerhard, A. & Banati, R. B. In vivo imaging of neuroinflammation. Eur. Neuropsychopharmacol. 12, 581–586 (2002). Outline of possible PET studies using [11C] PK11195 for the identification of activated microglia as biomarker for active CNS disease.

    CAS  PubMed  Google Scholar 

  71. Frank, R. et al. Biological markers for therapeutic trials in Alzheimer's disease. Proceedings of the Biological Markers Working Group; NIA Initiative on Neuroimaging in Alzheimers Disease. Neurobiol. Aging 24 (in the press).

  72. Sunderland, T. et al. Decreased β-amyloid 1-42 and increased Tau levels in cerebrospinal fluid of patients with Alzheimer's disease. JAMA 289, 2094–2103 (2003). Preliminary studies and meta-analysis supporting the view that decreases in cerebrospinal fluid Aβ1–42 and increased cerebrospinal fluid Tau could be biomarkers for predictive diagnostic or treatment evaluation of disease modifying therapies in Alzheimer's disease.

    PubMed  Google Scholar 

  73. Gurwitz, D. Targeting Alzheimer's disease: Is there a light at the end of the tunnel? Drug Dev. Res. 56, 45–48 (2002).

    CAS  Google Scholar 

  74. Selkoe, D. J. Alzheimer's disease: Genes, proteins, and therapy. Physiol. Rev. 81, 741–766 (2001).

    CAS  PubMed  Google Scholar 

  75. Morris, J. C. & Price, J. L. Pathologic correlates of nondemented aging, mild cognitive impairment, and early-stage Alzheimer's disease. J. Mol. Neurosci. 17, 101–118 (2001).

    CAS  PubMed  Google Scholar 

  76. Petersen, R. C. et al. Current concepts in mild cognitive impairment. Arch. Neurol. 58, 1985–1992 (2001).

    CAS  PubMed  Google Scholar 

  77. Trojanowski, J. Q. Alzheimer's disease centers and the dementias of aging program of the national institute on aging: A brief overview. J. Alzheimer's Dis. 3, 249–251 (2001).

    Google Scholar 

  78. Duffy, M. J. Carcinoembryonic antigen as a marker for colorectal cancer: is it clinically useful? Clin. Chem. 47, 624–630 (2001).

    CAS  PubMed  Google Scholar 

  79. Grizzle, W. E., Manne, U., Jhala, N. C. & Weiss, H. L. Molecular characterization of colorectal neoplasia in translational research. Arch. Pathol. Lab. Med. 125, 91–98 (2001).

    CAS  PubMed  Google Scholar 

  80. Therasse, P. et al. New guidelines to evaluate the response to treatment in solid tumors. J. Natl Cancer Inst. 92, 205–216 (2000).

    CAS  PubMed  Google Scholar 

  81. Hopper, K. D., Singapuri, K. & Finkel, A. Body CT and oncologic imaging. Radiology 215, 27–40 (2000).

    CAS  PubMed  Google Scholar 

  82. Yankelevitz, D. F., Reeves, A. P., Kostis, W. J., Zhao, B. & Henscke, C. I. Small pulmonary nodules: volumetrically determined growth rates based on CT evaluation. Radiology 217, 251–256 (2000).

    CAS  Google Scholar 

  83. Henschke C. I. et al. Early Lung Cancer Action Project: overall design and findings from baseline screening. Lancet 354, 99–105 (1999).

    CAS  PubMed  Google Scholar 

  84. Gupta, N. C., Graeber, G. M., Rogers, J. S. & Bishop, H. A. Comparative efficacy of positron emission tomography with FDG and computed tomographic scanning in preoperative staging of non-small cell lung cancer. Ann. Surgery. 229, 286–291 (1999).

    CAS  Google Scholar 

  85. Van den Abbeele, A. D. & Badawi, R. D. Use of positron emission tomography in oncology and its potential role to assess response to imatinib mesylate therapy in gastrointestinal stromal tumors (GISTs). Eur. J. Cancer 38, S60–65 (2002).

    PubMed  Google Scholar 

  86. Eubank, W. B. et al. Detection of locoregional and distant recurrences in breast cancer patients by using FDG PET. Radiographics 22, 5–17 (2002).

    PubMed  Google Scholar 

  87. Mortimer, J. E. et al. Metabolic flare: Indicator of hormone responsiveness in advanced breast cancer. J. Clin. Oncol. 19, 2797–2803 (2001).

    CAS  PubMed  Google Scholar 

  88. Shields, A. et al. Imaging proliferation in vivo with [F-18] and positron emission tomography. Nature Med. 4, 1334–1336 (1998).

    CAS  PubMed  Google Scholar 

  89. Eriksson, B. et al. The role of PET in localization of neuroendocrine and adrenocortical tumors. Ann. NY Acad. Sci. 970 159–169 (2002).

    CAS  PubMed  Google Scholar 

  90. Mankoff, D. A., Dehdashti, F. & Shields, A. F. Characterizing tumors using metabolic imaging: PET imaging of cellular proliferation and steroid receptors. Neoplasia 2, 71–88 (2000).

    CAS  PubMed  PubMed Central  Google Scholar 

  91. Chin, B. B. et al. Quantitative evaluation of 2-deoxy-2-[18F] fluoro-D-glucose uptake in hepatic metastases with combined PET-CT: Iterative reconstruction with CT attenuation correction versus filtered back projection with 68Germanium attenuation correction. Mol. Imag. Biol. 4, 399–409 (2002).

    Google Scholar 

  92. Beyer, T., Townsend, D. W. & Blodgett, T. M. Dual-modality PET/CT tomography for clinical oncology. Q. J. Nucl. Med. 46, 24–34 (2002).

    CAS  PubMed  Google Scholar 

  93. Antoch, G. et al. Whole-body positron emission tomography-CT: Optimized CT using oral and IV contrast materials. Am. J. Roentgenol. 179, 1555–1560 (2002).

    Google Scholar 

  94. Masood, S. Standardization of immunobioassays as surrogate endpoints. J. Cell. Biochem. 19, S28–S35 (1994).

    Google Scholar 

  95. Grouse, L. H., Munson, P. J. & Nelson, P. S. Sequence databases and microarrays as tools for identifying prostate cancer biomarkers. Urology 57, 154–159 (2001).

    CAS  PubMed  Google Scholar 

  96. Scherf, U. et al. A gene expression database for the molecular pharmacology of cancer. Nature Genet. 24, 236–244 (2000).

    CAS  PubMed  Google Scholar 

  97. Shipp, M. A. et al. Diffuse large B-cell lymphoma outcome prediction by gene-expression profiling and supervised machine learning. Nature Med. 8, 68–74 (2002).

    CAS  PubMed  Google Scholar 

  98. Sorlie, T. et al. Gene expression patterns of breast carcinomas distinguish tumor subclasses with clinical implications. Proc. Natl Acad. Sci. USA 98, 10869–10874 (2001).

    CAS  PubMed  Google Scholar 

  99. Bittner, M. et al. Molecular classification of cutaneous malignant melanoma by gene expression profiling. Nature 406, 536–540 (2000).

    CAS  PubMed  Google Scholar 

  100. Van't Veer, L. J. & De Jong, D. The microarray way to tailored cancer treatment. Nature Med. 8, 13–14 (2002).

    CAS  Google Scholar 

  101. Di Leo, A. et al. Predictive molecular markers in the adjuvant therapy of breast cancer: state of the art in the year 2002. Int. J. Clin. Oncol. 7, 245–53 (2002).

    CAS  PubMed  Google Scholar 

  102. Rosenwald, A. et al. The use of molecular profiling to predict survival after chemotherapy for diffuse large-B-cell lymphoma. N. Engl. J. Med. 346, 1937–1947 (2002).

    PubMed  Google Scholar 

  103. Simon, R., Radmacher, M. D., Dobbin, K. & McShane, L. M. Pitfalls in the use of DNA microarray data for diagnostic and prognostic classification. J. Natl Cancer Inst. Cancer Spectrum 95, 14–18 (2003).

    CAS  PubMed  Google Scholar 

  104. Quackenbush, J. Computational analysis of microarray data. Nature Rev. Genet. 2, 418–427 (2001).

    CAS  PubMed  Google Scholar 

  105. Raap, D. a. V. d. K., LD. Mini-review: selective serotonin reuptake inhibitors and neuroendocrine function. Life Sci. 65 (1999).

  106. Grillon, C., Codova, J., Levine, L. R. & Morgan, C. A. Anxiolytic effects of a novel group II metabotropic glutamate receptor agonist (LY 354740) in the fear potentiated startle paradigm in humans. Psychopharmacology 2003 April 23 (DOI: 10.1007/s00213-003-1444-8).

  107. Riba, J. et al. Differential effects of alprazolam on the baseline and fear-potentiated startle reflex in humans: A dose response study. Psychopharmacology 157, 358–367 (2001).

    CAS  PubMed  Google Scholar 

  108. Guimares, F. S. et al. A simple simulated public speaking test for evaluating anxiolytic drugs. Braz. J. Med. Biol. Res. 22, 1083–1089 (1989).

    Google Scholar 

  109. Abelson, J. L., Weg, J. G., Nesse, R. M. & Curtis, G. C. Neuroendocrine responses to laboratory panic: cognitive intervention in the doxapram model. Psychoneuroendocrinology 21, 375–390 (1996).

    CAS  PubMed  Google Scholar 

  110. Liebowitz, M. R. et al. Effects of intravenous diazepam pretreatment on lactate-induced panic. Psychiatry Res. 58, 127–138 (1995).

    CAS  PubMed  Google Scholar 

  111. Valenca, A. M. N. A., Nascimento, I., Zin, W. A. & Versiani, M. Carbon dioxide test as an additional clinical measure of treatment response in panic disorder. Arq. Neuropsiquiatr. 60, 358–361 (2002).

    PubMed  Google Scholar 

  112. de Visser, S. J. et al. Biomarkers for the effects of benzodiazepines in healthy volunteers. Br. J. Clin. Pharmacol. 55, 39–50 (2003).

    CAS  PubMed  PubMed Central  Google Scholar 

  113. Lesko, L. J., Rowland, M., Peck, C. C. & Blaschke, T. F. Optimizing the science of drug development: opportunities for better candidate selection and accelerated evaluation in humans. Eur. J. Pharm. Sci. 10, iv–xiv (2000).

    CAS  PubMed  Google Scholar 

  114. Cherry, S. R. & Gambhir, S. S. Use of positron emission tomography in animal research. ILAR J. 42, 219–232 (2001).

    CAS  PubMed  Google Scholar 

  115. Hargreaves, R. Imaging substance P NK1 Receptors in the living human brain using positron emission tomography. J. Clin. Psychiatry 63, 18–24 (2003).

    Google Scholar 

  116. Chawla, S. P. et al. Establishing the dose of the oral NK-1 antagonist MK-869 for chemotherapy-induced nausea and vomiting. Am. Soc. Clin. Oncol. A1527 (2001).

  117. Bradley, M. M., Cuthbert, B. N. & Lang, P. J. Picture media and emotion — effects of a sustained affective context. Psychophysiology 33, 662–670 (1996).

    CAS  PubMed  Google Scholar 

  118. Cuthbert, B. N., Bradley, M. M. & Lang, P. J. Probing picture perception — activation and emotion. Psychophysiology 33, 103–111 (1996).

    CAS  PubMed  Google Scholar 

  119. Sheline, Y. I. et al. Increased amygdala response to masked emotional faces in depressed subjects resolves with antidepressant treatment: An fMRI study. Biol. Psychiatry 50, 651–658 (2001).

    CAS  PubMed  Google Scholar 

  120. Gross, J. J. & Levenson, R. W. Emotion elicitation using films. Cognition Emotion 9, 87–108 (1995). Hollywood knows that films are better than still photographs for eliciting emotion and so their use in neuropsychiatry research will no doubt increase.

    Google Scholar 

  121. Rottenberg, J., Gross, J. J., Wilhelm, F. H. & Gotlib, I. H. Crying threshold and intensity in major depressive disorder. J. Abnorm. Psychol. 111, 302–312 (2002).

    PubMed  Google Scholar 

  122. Yamasaki, H., LaBar, K. S. & McCarthy, G. Dissociable prefrontal brain systems for attention and emotion. Proc. Natl Acad. Sci. USA 99, 11447–11451 (2002).

    CAS  PubMed  Google Scholar 

  123. Ploghaus, A. et al. Exacerbation of pain by anxiety is associated with activity in a hippocampal network. J. Neurosci. 21, 9896–9903 (2001).

    CAS  PubMed  Google Scholar 

  124. Wise, R. G. et al. Combining fMRI with a pharmacokinetic model to determine which brain areas activated by painful stimulation are specifically modulated by remifentanil. Neuroimage 16, 999–1014 (2002).

    PubMed  Google Scholar 

  125. Borsook, D. & Bercerra, L. Utilizing brain imaging for analgesic drug development. Curr. Opin. Investig. Drugs 3, 1342–1347 (2002). Use of MRI as a means to dissociate the sensory, affective, and cognitive components of pain, providing a potential paradigm to screen for CNS activity of novel drugs.

    CAS  PubMed  Google Scholar 

  126. Peterfy, C. G. Imaging of the disease process. Curr. Opin. Rheumatol. 14, 590–596 (2002). Comprehensive overview of magnetic resonance imaging approaches to osteoarthritis.

    PubMed  Google Scholar 

  127. Rudin, M. & Weissleder, R. Molecular imaging in drug discovery and development. Nature Rev. Drug Discov. 2, 123–131 (2003). A thorough review of emerging imaging technology and its application to clinical studies of new drugs.

    CAS  Google Scholar 

  128. Santos-Rosa, H. et al. Active genes are tri-methylated at K4 of histone H3. Nature 419, 407–411 (2002).

    CAS  PubMed  Google Scholar 

  129. Weiss, S. T., Silverman, E. K. & Palmer, L. J. Case-control association studies in pharmacogenetics. Pharmacogenomics J. 1, 157–8 (2001).

    CAS  PubMed  Google Scholar 

  130. Kiberstis, P. & Roberts, L. It's not just the genes. Science 296, 685 (2002).

    CAS  Google Scholar 

  131. US Department of Health and Human Services, Food and Drug Administration, Center for Drug Evaluation and Research (CDER) & (CBER). Guidance for Industry In Vivo Drug Metabolism/Drug Interaction Studies — Study Design, Data Analysis, and Recommendations for Dosing and Labeling (1999).

  132. Venkatakrishnan, K., Von Moltke, L. L. & Greenblatt, D. J. Application of the relative activity factor approach in scaling from heterologously expressed cytochromes P450 to human liver microsomes: Studies on amitriptyline as a model substrate. J. Pharmacol. Exp. Ther. 297, 326–337 (2001).

    CAS  PubMed  Google Scholar 

  133. Koukouritaki, S. B., Simpson, P., Yeung, C. K., Rettie, A. E. & Hines, R. N. Human hepatic flavin-containing monooxygenases 1 (FMO1) and 3 (FMO3) developmental expression. Pediatr. Res. 51, 236–243 (2002).

    CAS  PubMed  Google Scholar 

  134. Sonnier, M. & Cresteil, T. Delayed ontogenesis of cyp1a2 in the human liver. Eur. J. Biochem. 251, 893–898 (1998).

    CAS  PubMed  Google Scholar 

  135. Leeder, J. S. Pharmacogenetics and pharmacogenomics. Pediatr. Clin. North Am. 48, 765–781 (2001).

    CAS  PubMed  Google Scholar 

  136. Hoggard, P. G. & Back, D. J. Intracellular pharmacology of nucleoside analogues and protease inhibitors: Role of transporter molecules. Curr. Opin. Infect. Dis. 15, 3–8 (2002).

    CAS  PubMed  Google Scholar 

  137. SenGupta Dhruba, J. et al. A single glycine mutation in the equilibrative nucleoside transporter gene, hENT1, alters nucleoside transport activity and sensitivity to nitrobenzylthioinosine. Biochemistry 41, 1512–1519 (2002).

    CAS  PubMed  Google Scholar 

  138. Tanaka, C., Kawai, R. & Rowland, M. Physiologically based pharmacokinetics of cyclosporine a: reevaluation of dose-nonlinear kinetics in rats. J. Pharmacokinet. Biopharm. 27, 597–623 (1999).

    CAS  PubMed  Google Scholar 

  139. Slapak, C. A., Dahlheimer, J. & Piwnica-Worms, D. Reversal of multidrug resistance with LY335979: Functional analysis of P-glycoprotein-mediated transport activity and its modulation in vivo. J. Clin. Pharmacol. 41, 29S–38S (2001).

    CAS  PubMed  Google Scholar 

  140. Conrad, S. et al. A naturally occurring mutation in MRP1 results in a selective decrease in organic anion transport and in increased doxorubicin resistance. Pharmacogenetics 12, 321–330 (2002).

    CAS  PubMed  Google Scholar 

  141. Dresser, G. K., Schwarz, U. I., Wilkinson, G. R. & Kim, R. B. Coordinate induction of both cytochrome P4503A and MDR1 by St John's wort in healthy subjects. Clin. Pharmacol. Ther. 73, 41–50 (2003).

    CAS  PubMed  Google Scholar 

  142. Unadkat, J. D., Link, J., Muzi, M., Dupuis, A. & Mankoff, D. In vivo P-glycoprotein (P-gp) transport activity in the pregnant M. nemestrina as measured by biodistribution of (11C)–verapamil and positron emission tomography (PET). J. Med. Primatol. 31, 308 (2002).

    Google Scholar 

  143. Ballinger, J. R. Tc-99m-tetrofosmin for functional imaging of P-glycoprotein modulation in vivo. J. Clin. Pharmacol. (2001).

  144. Wandel, C., Kim, R., Wood, M. & Wood, A. Interaction of morphine, fentanyl, sufentanil, alfentanil, and loperamide with the efflux drug transporter P-glycoprotein. Anesthesiology 96, 913–920 (2002).

    CAS  PubMed  Google Scholar 

  145. Drysdale, C. et al. Complex promoter and coding region β2-adrenergic receptor haplotypes alter receptor expression and predict in vivo responsiveness. Proc. Natl Acad. Sci. 97, 10483–10488 (2000).

    CAS  PubMed  Google Scholar 

  146. Petricoin, E. F., Zoon, K. C., Kohn, E. C., Barrett, J. C. & Liotta, L. A. Clinical proteomics: translating benchside promise into bedside reality. Nature Rev. Drug Discov. 1, 683–695 (2002). The authors describe proteomic technologies that are being developed to detect cancer earlier, to discover the next generation of targets and imaging biomarkers, and finally to tailor the therapy to the patient.

    CAS  Google Scholar 

  147. Kantor, A. B. Comprehensive phenotyping and biological marker discovery. Dis. Markers 18, 91–97 (2002).

    CAS  PubMed  PubMed Central  Google Scholar 

  148. Walton, I. D. et al. A microvolume laser scanning cytometry platform for biological marker discovery. Proc. SPIE 3926, 192–201 (2000).

    CAS  Google Scholar 

  149. Wang, W. et al. Quantification of proteins and metabolites by mass spectrometry without isotopic labeling or spiked standards. Submitted for publication (2003).

  150. Crabb, J. W. et al. Drusen proteome analysis: An approach to the etiology of age-related macular degeneration. Proc. Natl Acad. Sci. USA 99, 14682–14687 (2002). This paper gives new insights into the composition of Drusen, a putative biomarker of age-related macular degeneration, that could reveal more on the disease etiology and suggest new therapeutic targets.

    CAS  PubMed  Google Scholar 

  151. Brindle, J. T. et al. Rapid and noninvasive diagnosis of the presence and severity of coronary heart disease using H-1-NMR-based metabonomics. Nature Med. 8, 1439–1444 (2002).

    CAS  PubMed  Google Scholar 

  152. Petricoin, E. F. et al. Use of proteomic patterns in serum to identify ovarian cancer. Lancet 359, 572–577 (2002).

    CAS  PubMed  Google Scholar 

  153. McDonald, W. H. & Yates, J. R. Shotgun proteomics and biomarker discovery. Dis. Markers 18, 99–105 (2002).

    CAS  PubMed  PubMed Central  Google Scholar 

  154. Renwick, A. G. & Walton, K. The use of surrogate endpoints to assess potential toxicity in humans. Toxicol. Lett. 120, 97–110 (2001).

    CAS  PubMed  Google Scholar 

  155. US Department of Health and Human Services, Food and Drug Administration, Center for Drug Evaluation and Research (CDER) & Center for Biologics Evaluation and Research (CBER). Guidance for Industry and Reviewers Estimating the Safe Starting Dose in Clinical Trials for Therapeutics in Adult Healthy Volunteers (2002).

  156. Kantor, A. B. et al. Biomarker discovery by comprehensive phenotyping for autoimmune diseases. Clin. Immunol. (in the press).

  157. den Broeder, A. A. et al. Long term anti-tumour necrosis factor alpha monotherapy in rheumatoid arthritis: Effect on radiological course and prognostic value of markers of cartilage turnover and endothelial activation. Ann. Rheum. Dis. 61, 311–318 (2002).

    CAS  PubMed  PubMed Central  Google Scholar 

  158. Neuhold, L. A. et al. Postnatal expression in hyaline cartilage of constitutively active human collagenase-3 (MMP-13) induces osteoarthritis in mice. J. Clin. Invest. 107, 35–44 (2001).

    CAS  PubMed  PubMed Central  Google Scholar 

  159. Cunnane, G., FitzGerald, O., Beeton, C., Cawston, T. E. & Bresnihan, B. Early joint erosions and serum levels of matrix metalloproteinase 1, matrix metalloproteinase 3, and tissue inhibitor of metalloproteinases 1 in rheumatoid arthritis. Arthritis Rheum. 44, 2263–2274 (2001).

    CAS  PubMed  Google Scholar 

  160. Bayliss, M. T., Hutton, S., Hayward, J. & Maciewicz, R. A. Distribution of aggrecanase (ADAMts 4/5) cleavage products in normal and osteoarthritic human articular cartilage: The influence of age, topography and zone of tissue. Osteoarth. Cartil. 9, 553–560 (2001).

    CAS  Google Scholar 

  161. Armstrong, B. & Doll, R. Environmental factors and cancer incidence and mortality in different countries, with special reference to dietary practices. Int. J. Cancer 15, 617–631 (1975).

    CAS  PubMed  Google Scholar 

  162. Wakeland, E. K., Liu, K., Graham, R. R. & Behrens, T. W. Delineating the genetic basis of systemic lupus erythematosus. Immunity 15, 397–408 (2001).

    CAS  PubMed  Google Scholar 

  163. Cummings, D. E. & Schwartz, M. W. Genetics and pathophysiology of human obesity. Annu. Rev. Med. 54, 453–457 (2003).

    CAS  PubMed  Google Scholar 

  164. Townsend, D. W. & Beyer, T. A combined PET/CT scanner: the path to true image fusion. Br. J. Radiol. 75, S24–S30 (2002).

    PubMed  Google Scholar 

  165. Katz, R., Wagner, H. N., Fauntleroy, M., Kuwert, T. & Frank, R. The use of imaging as biomarkers in drug development: Regulatory issues worldwide. J. Clin. Pharmacol. 41, 118S–126S (2001).

    CAS  PubMed  Google Scholar 

  166. Schwaiger, M. Functional imaging for assessment of therapy. Br. J. Radiol. 75, S67–S73 (2002).

    PubMed  Google Scholar 

  167. Ferguson, S. M. Licensing and distribution of research tools: National institutes of health perspective. J. Clin. Pharmacol. 110S–112S (2001).

  168. Esmond, R. W. The patenting of tools for drug discovery and development. J. Clin. Pharmacol. 112S–115S (2001).

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DATABASES

LocusLink

α7 nicotinic acetylecholine receptor

CYP1A2

CYP2D6

ERBB2

FMO3

NK

Cancer.gov

Breast cancer

melanoma

Online Mendelain Inheritance in Man

Alzheimer's disease

multiple sclerosis

osteoarthritis

Parkinson's disease

Tangier disease

FURTHER INFORMATION

Center for Drug Evaluation and Research: Guidance for Screening INDs

National Institute of Mental Health Proposal for Treatment Units for Research on Neurocognition and Schizophrenia

American Association of Clinical Endocrinologists

Application of Genomics to Mechanism-based Risk Assessment

Biomakers and Surrogate Endpoints Conference

Genomics glossary

Gastrointestinal Drugs Advisory Committee

Guidance for Industry

Microsdosing Guidance

National Institute on Aging Alzheimer's disease neuroimaging initiative

National Institute of Mental Health Treatment Development Initiative

Osteoarthritis biomarker initiative

Osteoarthritis imaging initiative

Pharmacogenomics

Principles of software validation

Technical Committee of Application of Genomics to Mechanism-based Risk Assessment

Society for Non-Invasive Imaging in Drug Development

Standards for Reporting of Diagnostic Accuracy

Glossary

BIOLOGICAL MARKER (BIOMARKER)

A characteristic that is objectively measured and evaluated as an indicator of normal biologic processes, pathogenic processes, or pharmacologic responses to a therapeutic intervention.

SURROGATE ENDPOINT

A biomarker intended to substitute for a clinical endpoint. A clinical investigator uses epidemiologic, therapeutic, pathophysiologic, or other scientific evidence to select a surrogate endpoint that is expected to predict clinical benefit, harm, or lack of benefit or harm.

CLINICAL ENDPOINT

A characteristic or variable that reflects how a patient feels or functions, or how long a patient survives.

SINGLE-NUCLEOTIDE POLYMORPHISM

(SNP). A variant in a single base pair which can occur in any region of the gene including promoters, introns, exons, splice junctions or even untranslated regions.

MULTI-DRUG RESISTANCE

(MDR). A set of proteins which oppose tumour uptake of chemotherapeutic drugs by transporting them back into the bloodstream.

DRUSEN

Small yellowish protein–lipid deposits in the retina that develop in the early stages of dry (atrophic) macular degeneration.

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Frank, R., Hargreaves, R. Clinical biomarkers in drug discovery and development. Nat Rev Drug Discov 2, 566–580 (2003). https://doi.org/10.1038/nrd1130

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