Current Practices and Future Outlook on The Integration of Biomarkers in The Drug Development Process

References

• O’Connell D , RoblinD. Translational research in the pharmaceutical industry: from bench to bedside. Drug Discov. Today11 (17–18), 833–838 (2006).
   PubMed Web of Science ®Google Scholar
• Lesko LJ , AtkinsonAJJr. Use of biomarkers and surrogate endpoints in drug development and regulatory decision making: criteria, validation, strategies. Annu. Rev. Pharmacol. Toxicol. 41, 347–366 (2001).
   PubMed Web of Science ®Google Scholar
• Frank R , HargreavesR. Clinical biomarkers in drug discovery and development. Nat. Rev. Drug Discov. 2 (7), 566–580 (2003).
   PubMed Web of Science ®Google Scholar
• Horig H , PullmanW. From bench to clinic and back: perspective on the 1st IQPC Translational Research conference. J. Transl. Med. 2 (1), 44 (2004).
   PubMedGoogle Scholar
• Simon GM , NiphakisMJ, CravattBF. Determining target engagement in living systems. Nat. Chem. Biol. 9 (4), 200–205 (2013).
   PubMed Web of Science ®Google Scholar
• Grimwood S , HartigPR. Target site occupancy: emerging generalizations from clinical and preclinical studies. Pharmacol. Ther. 122 (3), 281–301 (2009).
   PubMed Web of Science ®Google Scholar
• Colburn WA . Biomarkers in drug discovery and development: from target identification through drug marketing. J. Clin. Pharmacol. 43 (4), 329–341 (2003).
   PubMed Web of Science ®Google Scholar
• Amadoz A , Sebastian-LeonP, VidalE, SalavertF, DopazoJ. Using activation status of signaling pathways as mechanism-based biomarkers to predict drug sensitivity. Sci. Rep. 5, 18494 (2015).
   PubMed Web of Science ®Google Scholar
• Danhof M , AlvanG, DahlSG, KuhlmannJ, PaintaudG. Mechanism-based pharmacokinetic-pharmacodynamic modeling-a new classification of biomarkers. Pharm. Res. 22 (9), 1432–1437 (2005).
   PubMed Web of Science ®Google Scholar
• Biomarkers Definitions Working Group . Biomarkers and surrogate endpoints: preferred definitions and conceptual framework. Clin. Pharmacol. Ther. 69 (3), 89–95 (2001).
   PubMed Web of Science ®Google Scholar
• Sistare FD , DegeorgeJJ. Preclinical predictors of clinical safety: opportunities for improvement. Clin. Pharmacol. Ther. 82 (2), 210–214 (2007).
   PubMed Web of Science ®Google Scholar
• Amur S , FruehFW, LeskoLJ, HuangSM. Integration and use of biomarkers in drug development, regulation and clinical practice: a US regulatory perspective. Biomark. Med. 2 (3), 305–311 (2008).
   PubMed Web of Science ®Google Scholar
• Wehling M . Translational medicine: can it really facilitate the transition of research “from bench to bedside”?Eur. J. Clin. Pharmacol. 62 (2), 91–95 (2006).
   PubMed Web of Science ®Google Scholar
• Mullane K , WilliamsM. Translational semantics and infrastructure: another search for the emperor’s new clothes?Drug Discov. Today17 (9–10), 459–468 (2012).
   PubMed Web of Science ®Google Scholar
• Olson H , BettonG, RobinsonDet al. Concordance of the toxicity of pharmaceuticals in humans and in animals. Regul. Toxicol. Pharmacol. 32 (1), 56–67 (2000).
   PubMed Web of Science ®Google Scholar
• Van Der Worp HB , HowellsDW, SenaESet al. Can animal models of disease reliably inform human studies? PLoS Med . 7 (3), e1000245 (2010).
   PubMed Web of Science ®Google Scholar
• McGonigle P , RuggeriB. Animal models of human disease: challenges in enabling translation. Biochem. Pharmacol. 87 (1), 162–171 (2014).
   PubMed Web of Science ®Google Scholar
• Biomarker letter of support, rodent skeletal and exploratory clinical . www.fda.gov/downloads/Drugs/DevelopmentApprovalProcess/DrugDevelopmentToolsQualificationProgram/UCM432653.pdf
   Google Scholar
• Letter of Support for skeletal muscle injury biomarkers. www.ema.europa.eu/docs/en_GB/document_library/Other/2015/03/WC500184458.pdf
   Google Scholar
• Drucker E , KrapfenbauerK. Pitfalls and limitations in translation from biomarker discovery to clinical utility in predictive and personalised medicine. EPMA J. 4 (1), 7 (2013).
   PubMedGoogle Scholar
• Matheis K , LaurieD, AndriamandrosoCet al. A generic operational strategy to qualify translational safety biomarkers. Drug Discov. Today16 (13–14), 600–608 (2011).
   PubMed Web of Science ®Google Scholar
• Wehling M . Assessing the translatability of drug projects: what needs to be scored to predict success?Nat. Rev. Drug Discov. 8 (7), 541–546 (2009).
   PubMed Web of Science ®Google Scholar
• Greco I , DayN, Riddoch-ContrerasJet al. Alzheimer’s disease biomarker discovery using in silico literature mining and clinical validation. J. Transl. Med. 10, 217 (2012).
   PubMed Web of Science ®Google Scholar
• Garner JP . The significance of meaning: why do over 90% of behavioral neuroscience results fail to translate to humans, and what can we do to fix it?ILAR J. 55 (3), 438–456 (2014).
   PubMed Web of Science ®Google Scholar
• Maurer TS , GhoshA, Haddish-BerhaneNet al. Pharmacodynamic model of sodium-glucose transporter 2 (SGLT2) inhibition: implications for quantitative translational pharmacology. AAPS J. 13 (4), 576–584 (2011).
   PubMed Web of Science ®Google Scholar
• Visser SA , AurellM, JonesRDet al. Model-based drug discovery: implementation and impact. Drug Discov. Today18 (15–16), 764–775 (2013).
   PubMed Web of Science ®Google Scholar
• Sistare FD , DieterleF, TrothSet al. Towards consensus practices to qualify safety biomarkers for use in early drug development. Nat. Biotechnol. 28 (5), 446–454 (2010).
   PubMed Web of Science ®Google Scholar
• Hanley JA , McneilBJ. The meaning and use of the area under a receiver operating characteristic (ROC) curve. Radiology143 (1), 29–36 (1982).
   PubMed Web of Science ®Google Scholar
• Kofoed K , AndersenO, KronborgGet al. Use of plasma C-reactive protein, procalcitonin, neutrophils, macrophage migration inhibitory factor, soluble urokinase-type plasminogen activator receptor, and soluble triggering receptor expressed on myeloid cells-1 in combination to diagnose infections: a prospective study. Crit. Care11 (2), R38 (2007).
   PubMed Web of Science ®Google Scholar
• Wall RJ , ShaniM. Are animal models as good as we think?Theriogenology69 (1), 2–9 (2008).
   PubMed Web of Science ®Google Scholar
• Finkelman FD , HoganSP, HersheyGK, RothenbergME, Wills-KarpM. Importance of cytokines in murine allergic airway disease and human asthma. J. Immunol. 184 (4), 1663–1674 (2010).
   PubMed Web of Science ®Google Scholar
• Mestas J , HughesCC. Of mice and not men: differences between mouse and human immunology. J. Immunol. 172 (5), 2731–2738 (2004).
   PubMed Web of Science ®Google Scholar
• Roep BO . Are insights gained from NOD mice sufficient to guide clinical translation? Another inconvenient truth. Ann. NY Acad. Sci. 1103, 1–10 (2007).
   PubMed Web of Science ®Google Scholar
• ICH guideline M3(R2) on non-clinical safety studies for the conduct of human clinical trials and marketing authorisation for pharmaceuticals. www.ema.europa.eu/docs/en_GB/document_library/Scientific_guideline/2009/09/WC500002720.pdf
   Google Scholar
• Campion S , AubrechtJ, BoekelheideKet al. The current status of biomarkers for predicting toxicity. Expert Opin. Drug Metab. Toxicol. 9 (11), 1391–1408 (2013).
   PubMed Web of Science ®Google Scholar
• Cook N , JodrellDI, TuvesonDA. Predictive in vivo animal models and translation to clinical trials. Drug Discov. Today17 (5), 253–260 (2012).
   PubMed Web of Science ®Google Scholar
• Pandolfi P . APL as a paradigm in biomedical research: a journey toward the cure. In:Acute Promyelocytic Leukemia. Springer - Verlag Berlin Heidelberg, 1–2 (2007).
   Google Scholar
• Lallemand-Breitenbach V , GuilleminM-C, JaninAet al. Retinoic acid and arsenic synergize to eradicate leukemic cells in a mouse model of acute promyelocytic leukemia. J. Exp. Med. 189 (7), 1043–1052 (1999).
   PubMed Web of Science ®Google Scholar
• Brown D , KoganS, LagasseEet al. A PMLRARalpha transgene initiates murine acute promyelocytic leukemia. Proc. Natl Acad. Sci. USA94 (6), 2551–2556 (1997).
   PubMed Web of Science ®Google Scholar
• Mullane K , WilliamsM. Alzheimer’s therapeutics: continued clinical failures question the validity of the amyloid hypothesis-but what lies beyond?Biochem. Pharmacol. 85 (3), 289–305 (2013).
   PubMed Web of Science ®Google Scholar
• Pound P , EbrahimS, SandercockP, BrackenMB, RobertsI, Reviewing Animal Trials Systematically Group. Where is the evidence that animal research benefits humans?BMJ328 (7438), 514–517 (2004).
   PubMed Web of Science ®Google Scholar
• Webb DR . Animal models of human disease: inflammation. Biochem. Pharmacol. 87 (1), 121–130 (2014).
   PubMed Web of Science ®Google Scholar
• Mullane K , WilliamsM. Animal models of asthma: reprise or reboot?Biochem. Pharmacol. 87 (1), 131–139 (2014).
   PubMed Web of Science ®Google Scholar
• Anderson MS , BluestoneJA. The NOD mouse: a model of immune dysregulation. Annu. Rev. Immunol. 23, 447–485 (2005).
   PubMed Web of Science ®Google Scholar
• Giarratana N , PennaG, AdoriniL. Animal models of spontaneous autoimmune disease: Type 1 diabetes in the nonobese diabetic mouse. Methods Mol. Biol. 380, 285–311 (2007).
   PubMedGoogle Scholar
• Roep BO . The role of T cells in the pathogenesis of Type 1 diabetes: from cause to cure. Diabetologia46 (3), 305–321 (2003).
   PubMed Web of Science ®Google Scholar
• Driver JP , SerrezeDV, ChenYG. Mouse models for the study of autoimmune Type 1 diabetes: a NOD to similarities and differences to human disease. Semin. Immunopathol. 33 (1), 67–87 (2011).
   PubMed Web of Science ®Google Scholar
• Roep BO , AtkinsonM. Animal models have little to teach us about Type 1 diabetes: 1. in support of this proposal. Diabetologia47 (10), 1650–1656 (2004).
   PubMed Web of Science ®Google Scholar
• Atkinson MA , LeiterEH. The NOD mouse model of Type 1 diabetes: as good as it gets?Nat. Med. 5 (6), 601–604 (1999).
   PubMed Web of Science ®Google Scholar
• Bach JF . The effect of infections on susceptibility to autoimmune and allergic diseases. N. Engl. J. Med. 347 (12), 911–920 (2002).
   PubMed Web of Science ®Google Scholar
• Bresson D , Von HerrathM. Immunotherapy for the prevention and treatment of Type 1 diabetes: optimizing the path from bench to bedside. Diabetes Care32 (10), 1753–1768 (2009).
   PubMed Web of Science ®Google Scholar
• Roep BO , AtkinsonM, Von HerrathM. Satisfaction (not) guaranteed: re-evaluating the use of animal models of Type 1 diabetes. Nat. Rev. Immunol. 4 (12), 989–997 (2004).
   PubMed Web of Science ®Google Scholar
• Eastwood D , FindlayL, PooleSet al. Monoclonal antibody TGN1412 trial failure explained by species differences in CD28 expression on CD4+ effector memory T cells. Br. J. Pharmacol. 161 (3), 512–526 (2010).
   PubMed Web of Science ®Google Scholar
• Luhder F , HuangY, DennehyKMet al. Topological requirements and signaling properties of T cell-activating, anti-CD28 antibody superagonists. J. Exp. Med. 197 (8), 955–966 (2003).
   PubMed Web of Science ®Google Scholar
• Beyersdorf N , HankeT, KerkauT, HunigT. CD28 superagonists put a break on autoimmunity by preferentially activating CD4+ CD25+ regulatory T cells. Autoimmun. Rev. 5 (1), 40–45 (2006).
   PubMed Web of Science ®Google Scholar
• Attarwala H . TGN1412: from discovery to disaster. J. Young Pharm. 2 (3), 332–336 (2010).
   PubMedGoogle Scholar
• Hunig T . The storm has cleared: lessons from the CD28 superagonist TGN1412 trial. Nat. Rev. Immunol. 12 (5), 317–318 (2012).
   PubMed Web of Science ®Google Scholar
• Stebbings R , FindlayL, EdwardsCet al. “Cytokine storm” in the phase I trial of monoclonal antibody TGN1412: better understanding the causes to improve preclinical testing of immunotherapeutics. J. Immunol. 179 (5), 3325–3331 (2007).
   PubMed Web of Science ®Google Scholar
• Sistare FD , DegeorgeJJ. Promise of new translational safety biomarkers in drug development and challenges to regulatory qualification. Biomark. Med. 5 (4), 497–514 (2011).
   PubMed Web of Science ®Google Scholar
• Ledwith BJ , DegeorgeJJ. Changes to ICH guideline M3: new and revised guidance on nonclinical safety studies to support human clinical trials and marketing authorization. Clin. Pharmacol. Ther. 89 (2), 295–299 (2011).
   PubMed Web of Science ®Google Scholar
• Ferri N , SieglP, CorsiniA, HerrmannJ, LermanA, BenghoziR. Drug attrition during pre-clinical and clinical development: understanding and managing drug-induced cardiotoxicity. Pharmacol. Ther. 138 (3), 470–484 (2013).
   PubMed Web of Science ®Google Scholar
• Kola I , LandisJ. Can the pharmaceutical industry reduce attrition rates?Nat. Rev. Drug Discov. 3 (8), 711–715 (2004).
   PubMed Web of Science ®Google Scholar
• Food and Drug Administration . Biomarker qualification program. FDA (2013). www.fda.gov/drugs/developmentapprovalprocess/drugdevelopmenttoolsqualificationprogram/ucm284076.htm
   Google Scholar
• BEST (Biomarkers, EndpointS, and other Tools). www.ncbi.nlm.nih.gov/books/NBK338448/
   Google Scholar
• Center for Drug Evaluation and Research . Guidance for Industry and FDA Staff Qualification Process for Drug Development Tools. www.fda.gov/downloads/drugs/guidancecomplianceregulatoryinformation/guidances/ucm230597.pdf
   Google Scholar
• Mattes WB , WalkerEG, AbadieEet al. Research at the interface of industry, academia and regulatory science. Nat. Biotechnol. 28 (5), 432–433 (2010).
   PubMed Web of Science ®Google Scholar
• Sauer J-M , WalkerEG, PorterAC. The Predictive Safety Testing Consortium: safety bio-markers, collaboration, and qualification. J. Med. Dev. Sci. 1 (1), 34–45 (2016).
   Google Scholar
• Warnock DG , PeckCC. A roadmap for biomarker qualification. Nat. Biotechnol. 28 (5), 444–445 (2010).
   PubMed Web of Science ®Google Scholar
• Vaidya VS , OzerJS, DieterleFet al. Kidney injury molecule-1 outperforms traditional biomarkers of kidney injury in preclinical biomarker qualification studies. Nat. Biotechnol. 28 (5), 478–485 (2010).
   PubMed Web of Science ®Google Scholar
• Dieterle F , SistareF, GoodsaidFet al. Renal biomarker qualification submission: a dialog between the FDA-EMEA and Predictive Safety Testing Consortium. Nat. Biotechnol. 28 (5), 455–462 (2010).
   PubMed Web of Science ®Google Scholar
• Kramer AB , Van TimmerenMM, SchuursTAet al. Reduction of proteinuria in adriamycin-induced nephropathy is associated with reduction of renal kidney injury molecule (Kim-1) over time. Am. J. Physiol. Renal Physiol. 296 (5), F1136–1145 (2009).
   PubMed Web of Science ®Google Scholar
• Prozialeck WC , VaidyaVS, LiuJet al. Kidney injury molecule-1 is an early biomarker of cadmium nephrotoxicity. Kidney Int. 72 (8), 985–993 (2007).
   PubMed Web of Science ®Google Scholar
• Zhou Y , VaidyaVS, BrownRPet al. Comparison of kidney injury molecule-1 and other nephrotoxicity biomarkers in urine and kidney following acute exposure to gentamicin, mercury, and chromium. Toxicol. Sci. 101 (1), 159–170 (2008).
   PubMed Web of Science ®Google Scholar
• Liangos O , PerianayagamMC, VaidyaVSet al. Urinary N-acetyl-beta-(D)-glucosaminidase activity and kidney injury molecule-1 level are associated with adverse outcomes in acute renal failure. J. Am. Soc. Nephrol. 18 (3), 904–912 (2007).
   PubMed Web of Science ®Google Scholar
• Vaidya VS , WaikarSS, FergusonMAet al. Urinary biomarkers for sensitive and specific detection of acute kidney injury in humans. Clin. Transl. Sci. 1 (3), 200–208 (2008).
   PubMed Web of Science ®Google Scholar
• Han WK , BaillyV, AbichandaniR, ThadhaniR, BonventreJV. Kidney Injury Molecule-1 (KIM-1): a novel biomarker for human renal proximal tubule injury. Kidney Int. 62 (1), 237–244 (2002).
   PubMed Web of Science ®Google Scholar
• Lavezzari G , WomackAW. Industry perspectives on biomarker qualification. Clin. Pharmacol. Ther. 99 (2), 208–213 (2016).
   PubMed Web of Science ®Google Scholar
• Harpur E , WalkerEG. Qualification of safety biomarkers for use in drug development: what has been achieved and what is the path forward?Curr. Opin. Toxicol. 4, 66–73 (2017).
   Google Scholar
• Lee JW , HallM. Method validation of protein biomarkers in support of drug development or clinical diagnosis/prognosis. J. Chromatogr. B Analyt. Technol. Biomed. Life Sci. 877 (13), 1259–1271 (2009).
   PubMed Web of Science ®Google Scholar
• Lee JW , DevanarayanV, BarrettYCet al. Fit-for-purpose method development and validation for successful biomarker measurement. Pharm. Res. 23 (2), 312–328 (2006).
   PubMed Web of Science ®Google Scholar
• Guidance for Industry Bioanalytical Method Validation. www.fda.gov/downloads/Drugs/…/ucm070107.pdf
   Google Scholar
• Draft Guidance for Industry Bioanalytical Method Validation. www.fda.gov/downloads/drugs/guidancecomplianceregulatoryinformation/guidances/ucm368107.pdf
   Google Scholar
• Lowes S , AckermannBL. AAPS and US FDA Crystal City VI workshop on bioanalytical method validation for biomarkers. Bioanalysis8 (3), 163–167 (2016).
   PubMed Web of Science ®Google Scholar
• Arnold ME , BoothB, KingL, RayC. Workshop report: crystal city VI-bioanalytical method validation for biomarkers. AAPS J. 18 (6), 1366–1372 (2016).
   PubMed Web of Science ®Google Scholar
• Lee JW , FigeysD, VasilescuJ. Biomarker assay translation from discovery to clinical studies in cancer drug development: quantification of emerging protein biomarkers. Adv. Cancer Res. 96, 269–298 (2007).
   PubMed Web of Science ®Google Scholar
• Valentin MA , MaS, ZhaoA, LegayF, AvrameasA. Validation of immunoassay for protein biomarkers: bioanalytical study plan implementation to support pre-clinical and clinical studies. J. Pharm. Biomed. Anal. 55 (5), 869–877 (2011).
   PubMed Web of Science ®Google Scholar
• Stevenson LF , PurushothamaS. Parallelism: considerations for the development, validation and implementation of PK and biomarker ligand-binding assays. Bioanalysis6 (2), 185–198 (2014).
   PubMed Web of Science ®Google Scholar
• Fraser S , FleenerC, OgborneK, SoderstromC. When close is not close enough: a comparison of endogenous and recombinant biomarker stability samples. Bioanalysis7 (11), 1355–1360 (2015).
   PubMed Web of Science ®Google Scholar
• Sloan JH , AckermannBL, CarpenterJWet al. A novel, high-sensitivity and drug-tolerant sandwich immunoassay for the quantitative measurement of circulating proteins. Bioanalysis4 (3), 241–248 (2012).
   PubMed Web of Science ®Google Scholar
• Stevenson L , KelleyM, GorovitsBet al. Large molecule specific assay operation: recommendation for best practices and harmonization from the global bioanalysis consortium harmonization team. AAPS J. 16 (1), 83–88 (2014).
   PubMed Web of Science ®Google Scholar
• Komoroski B , VachharajaniN, FengY, LiL, KornhauserD, PfisterM. Dapagliflozin, a novel, selective SGLT2 inhibitor, improved glycemic control over 2 weeks in patients with Type 2 diabetes mellitus. Clin. Pharmacol. Ther. 85 (5), 513–519 (2009).
   PubMed Web of Science ®Google Scholar
• Whaley JM , TirmensteinM, ReillyTPet al. Targeting the kidney and glucose excretion with dapagliflozin: preclinical and clinical evidence for SGLT2 inhibition as a new option for treatment of Type 2 diabetes mellitus. Diabetes Metab. Syndr. Obes. 5, 135–148 (2012).
   PubMedGoogle Scholar
• List JF , WooV, MoralesE, TangW, FiedorekFT. Sodium-glucose cotransport inhibition with dapagliflozin in Type 2 diabetes. Diabetes Care32 (4), 650–657 (2009).
   PubMed Web of Science ®Google Scholar
• Sha S , DevineniD, GhoshAet al. Canagliflozin, a novel inhibitor of sodium glucose co-transporter 2, dose dependently reduces calculated renal threshold for glucose excretion and increases urinary glucose excretion in healthy subjects. Diabetes Obes. Metab. 13 (7), 669–672 (2011).
   PubMed Web of Science ®Google Scholar
• Rieg T , MasudaT, GerasimovaMet al. Increase in SGLT1-mediated transport explains renal glucose reabsorption during genetic and pharmacological SGLT2 inhibition in euglycemia. Am. J. Physiol. Renal Physiol. 306 (2), F188–F193 (2014).
   PubMed Web of Science ®Google Scholar
• Hasan FM , AlsahliM, GerichJE. SGLT2 inhibitors in the treatment of Type 2 diabetes. Diabetes Res. Clin. Pract. 104 (3), 297–322 (2014).
   PubMed Web of Science ®Google Scholar
• Van’t Veer A , CarlezonWAJr. Role of kappa-opioid receptors in stress and anxiety-related behavior. Psychopharmacology (Berl.)229 (3), 435–452 (2013).
   PubMed Web of Science ®Google Scholar
• Peuskens J , PaniL, DetrauxJ, De HertM. The effects of novel and newly approved antipsychotics on serum prolactin levels: a comprehensive review. CNS Drugs28 (5), 421–453 (2014).
   PubMed Web of Science ®Google Scholar
• Nielsen DA , HamonSC, KostenTR. The kappa-opioid receptor gene as a predictor of response in a cocaine vaccine clinical trial. Psychiatr. Genet. 23 (6), 225–232 (2013).
   PubMed Web of Science ®Google Scholar
• Chang C , ByonW, LuYet al. Quantitative PK-PD model-based translational pharmacology of a novel kappa opioid receptor antagonist between rats and humans. AAPS J. 13 (4), 565–575 (2011).
   PubMedGoogle Scholar