Protein Profiling in Alanine Aminotransferase Induced Patient cohort using Acetaminophen
Sensitive and predictive DILI (Drug Induced Liver
Injury) biomarkers are needed in drug R&D to improve early
detection of hepatotoxicity. The discovery of DILI biomarkers that
demonstrate the predictive power to identify individuals at risk to
DILI would represent a major advance in the development of
personalized healthcare approaches. In this healthy volunteer
acetaminophen study (4g/day for 7 days, with 3 monitored nontreatment
days before and 4 after), 450 serum samples from 32
subjects were analyzed using protein profiling by antibody
suspension bead arrays. Multiparallel protein profiles were generated
using a DILI target protein array with 300 antibodies, where the
antibodies were selected based on previous literature findings of
putative DILI biomarkers and a screening process using pre dose
samples from the same cohort. Of the 32 subjects, 16 were found to
develop an elevated ALT value (2Xbaseline, responders). Using the
plasma profiling approach together with multivariate statistical
analysis some novel findings linked to lipid metabolism were found
and more important, endogenous protein profiles in baseline samples
(prior to treatment) with predictive power for ALT elevations were
identified.
[1] Chalasani N, Fontana RJ, Bonkovsky HL, Watkins PB, Davern T,
Serrano J, Yang H, Rochon J; Drug Induced Liver Injury Network
(DILIN), "Causes, clinical features, and outcomes from prospective
study of drug-induced liver injury in the United States."
Gastroenterology, 2008, Dec;135(6):1924-34. Epub 2008 Sep 17.
[2] Toru Usui, Masashi Mise, Takanori Hashizume, Masashi Yabuki and
Setsumo Komuro;"Evaluation of the Potential for Drug-Induced Liver
Injury Based on in Vitro Covalent Binding to Human Liver Proteins"
Drug Metabolism and Disposition, The American Society for
Pharmacology and Experimental Therapeutics, DMD 37:2383-2392,
2009, Vol. 37, No. 12 28860/3529781.
[3] Russman,S, Kullak-Ublick G A and Grattagliano I;Current Concepts of
Mechanisms in Drug-Induced Hepatotoxicity. Curr Med Chem, 2009
August; 16(23):3041-3053.
[4] Rawlins & Thompson, Textbook of adverse drug reactions, Oxford
University Press 1991:18-45. Gruchalla, Lancet 2000,356:1505-11.
[5] Patel SJ, Milwid JM, King KR, Bohr S, Iracheta-Velle A, Li M, Vitalo
A, Parekkadan B, Jindal R and Yarmush ML. "Gap junction inhibition
prevents drug-induced liver toxicity and fulminant hepatic failure". Nat
Biotechnology 2012 Jan 15;30(2):179-83.doi: 10.1038/nbt.2089.
[6] O-Connel TM, Watkins PB. "The application of metabonomics to
predict drug-induced liver injury". Clin. Pharmacol. Ther. 2010
Sep;88(3):394-9. Epub 2010 Jul 28.
[7] Neiman M, Hedberg J, Dönnes P, Schuppe-Koistinen I, Hanschke S,
Schindler R, Uhlén M, Schwenk JM and Nilsson P. "Plasma profiling
reveals human fibulin-1 as candidate marker for renal impairment". J
Proteome Res. 2011 Nov 4;10(11):4925-34. Epub 2011 Sep 29.
[8] Dieterle F, Ross A, Schlotterbeck G and Senn H. "Probabilistic quotient
normalization as robust method to account for dilution of complex
biological mixtures. Application in 1H NMR metabonomics.". Anal.
Chem. 2006 Jul 1;78(13):4281-90.
[9] Yao F, Coquery J, Le Cao KA. "Independent Principal Component
Analysis for biologically meaningful dimension reduction of large
biological data sets". BMC Bioinformatics. 2012 Feb 3;13(1):24.
[10] Westerhuis JA, van Velzen EJ, Hoefsloot HC and Smilde
AK."Multivariate paired data analysis: multilevel PLSDA versus
OPLSDA". Metabolomics. 2010 Mar;6(1):119-128. Epub 2009 Oct 28.J.
[11] Golugula A, Lee G, Medabhushi A. "Evaluating feature selection
strategies for high dimensional, small sample size datasets". Conf Proc
IEEE Eng Med Biol Soc. 2011 Aug;2011:949-52. PMID:22254468.
[12] Kuhn M, Contributions from Wing J, Weston S, Williams A, Keefer C
and Engelhardt A. "Classification and regression training".
URL:http://caret-r.forge.r-project.org/. Published:2012-02-06.
[13] IPA. The data was analyzed using the Ingenuity Pathway Analysis
(www.ingenuity.com).
[1] Chalasani N, Fontana RJ, Bonkovsky HL, Watkins PB, Davern T,
Serrano J, Yang H, Rochon J; Drug Induced Liver Injury Network
(DILIN), "Causes, clinical features, and outcomes from prospective
study of drug-induced liver injury in the United States."
Gastroenterology, 2008, Dec;135(6):1924-34. Epub 2008 Sep 17.
[2] Toru Usui, Masashi Mise, Takanori Hashizume, Masashi Yabuki and
Setsumo Komuro;"Evaluation of the Potential for Drug-Induced Liver
Injury Based on in Vitro Covalent Binding to Human Liver Proteins"
Drug Metabolism and Disposition, The American Society for
Pharmacology and Experimental Therapeutics, DMD 37:2383-2392,
2009, Vol. 37, No. 12 28860/3529781.
[3] Russman,S, Kullak-Ublick G A and Grattagliano I;Current Concepts of
Mechanisms in Drug-Induced Hepatotoxicity. Curr Med Chem, 2009
August; 16(23):3041-3053.
[4] Rawlins & Thompson, Textbook of adverse drug reactions, Oxford
University Press 1991:18-45. Gruchalla, Lancet 2000,356:1505-11.
[5] Patel SJ, Milwid JM, King KR, Bohr S, Iracheta-Velle A, Li M, Vitalo
A, Parekkadan B, Jindal R and Yarmush ML. "Gap junction inhibition
prevents drug-induced liver toxicity and fulminant hepatic failure". Nat
Biotechnology 2012 Jan 15;30(2):179-83.doi: 10.1038/nbt.2089.
[6] O-Connel TM, Watkins PB. "The application of metabonomics to
predict drug-induced liver injury". Clin. Pharmacol. Ther. 2010
Sep;88(3):394-9. Epub 2010 Jul 28.
[7] Neiman M, Hedberg J, Dönnes P, Schuppe-Koistinen I, Hanschke S,
Schindler R, Uhlén M, Schwenk JM and Nilsson P. "Plasma profiling
reveals human fibulin-1 as candidate marker for renal impairment". J
Proteome Res. 2011 Nov 4;10(11):4925-34. Epub 2011 Sep 29.
[8] Dieterle F, Ross A, Schlotterbeck G and Senn H. "Probabilistic quotient
normalization as robust method to account for dilution of complex
biological mixtures. Application in 1H NMR metabonomics.". Anal.
Chem. 2006 Jul 1;78(13):4281-90.
[9] Yao F, Coquery J, Le Cao KA. "Independent Principal Component
Analysis for biologically meaningful dimension reduction of large
biological data sets". BMC Bioinformatics. 2012 Feb 3;13(1):24.
[10] Westerhuis JA, van Velzen EJ, Hoefsloot HC and Smilde
AK."Multivariate paired data analysis: multilevel PLSDA versus
OPLSDA". Metabolomics. 2010 Mar;6(1):119-128. Epub 2009 Oct 28.J.
[11] Golugula A, Lee G, Medabhushi A. "Evaluating feature selection
strategies for high dimensional, small sample size datasets". Conf Proc
IEEE Eng Med Biol Soc. 2011 Aug;2011:949-52. PMID:22254468.
[12] Kuhn M, Contributions from Wing J, Weston S, Williams A, Keefer C
and Engelhardt A. "Classification and regression training".
URL:http://caret-r.forge.r-project.org/. Published:2012-02-06.
[13] IPA. The data was analyzed using the Ingenuity Pathway Analysis
(www.ingenuity.com).
@article{"International Journal of Medical, Medicine and Health Sciences:58549", author = "Gry M and Bergström J and Lengquist J and Lindberg J and Drobin K and Schwenk J and Nilsson P and Schuppe-Koistinen I.", title = "Protein Profiling in Alanine Aminotransferase Induced Patient cohort using Acetaminophen", abstract = "Sensitive and predictive DILI (Drug Induced Liver
Injury) biomarkers are needed in drug R&D to improve early
detection of hepatotoxicity. The discovery of DILI biomarkers that
demonstrate the predictive power to identify individuals at risk to
DILI would represent a major advance in the development of
personalized healthcare approaches. In this healthy volunteer
acetaminophen study (4g/day for 7 days, with 3 monitored nontreatment
days before and 4 after), 450 serum samples from 32
subjects were analyzed using protein profiling by antibody
suspension bead arrays. Multiparallel protein profiles were generated
using a DILI target protein array with 300 antibodies, where the
antibodies were selected based on previous literature findings of
putative DILI biomarkers and a screening process using pre dose
samples from the same cohort. Of the 32 subjects, 16 were found to
develop an elevated ALT value (2Xbaseline, responders). Using the
plasma profiling approach together with multivariate statistical
analysis some novel findings linked to lipid metabolism were found
and more important, endogenous protein profiles in baseline samples
(prior to treatment) with predictive power for ALT elevations were
identified.", keywords = "DILI, Plasma profiling, PLSDA, Randomforest.", volume = "6", number = "4", pages = "91-3", }