Abstract
With the rapid evolution of technologies capable of generating high-dimensional data sets such as those from the ‘omic’ platforms commonly encountered during pharmacogenetic/genomic clinical trials, there is need for computationally efficient methodologies capable of integrating that information into the drug development pipeline; however, the computational cost of identifying covariates and interactions through traditional parametric statistical approaches has impeded their utilization for these large data sets. Within the context of population pharmacokinetic/pharmacodynamic modeling, the potential for detecting interactions on such data sets is of great interest: Specifically, the applications of interactions in this context would be the creation of more comprehensive and biologically sound covariate models, leading to better prediction of individual values for PK/PD parameters of interest, and moving one step closer to the goal of personalized medicine. However, there are currently no commercially available software packages, or computational approaches, that can handle covariate interaction detection or model synthesis at a genome scale. Thus, the most immediate and tractable benefit from such interaction analyses at this scale would be the identification of the most informative subset of predictors that could be used for ‘formal’ covariate model synthesis. This chapter will provide a discussion on the following topics of interest in this area: A general discussion on covariates and interactions; specific challenges and opportunities that arise when large datasets are considered; search metrics that are applicable on high-dimensional data sets ; and a justification for the need to distinguish between covariate detection and formal covariate model synthesis in this context.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Bauer RJ, Guzy S, Ng C (2007) A survey of population analysis methods and software for complex pharmacokinetic and pharmacodynamic models with examples. AAPS J 9:E60–E83
Bellman R (1961) Adaptive control processes: a guided tour. Princeton University Press, Princeton
Breiman L (2001) Random forests. Mach Learn 45:5–32
Bush WS, Dudek SM, Ritchie MD (2006) Parallel multifactor dimensionality reduction: a tool for the large-scale analysis of gene-gene interactions. Bioinformatics 22:2173–2174
Chanda P, Zhang A, Brazeau D, Sucheston L, Freudenheim JL, Ambrosone C, Ramanathan M (2007) Information-theoretic metrics for visualizing gene-environment interactions. Am J Hum Genet 81:939–963
Chanda P, Sucheston L, Zhang A, Brazeau D, Freudenheim JL, Ambrosone C, Ramanathan M (2008) AMBIENCE: a novel approach and efficient algorithm for identifying informative genetic and environmental associations with complex phenotypes. Genetics 180:1191–1210
Chanda P, Sucheston L, Liu S, Zhang A, Ramanathan M (2009) Information-theoretic gene-gene and gene-environment interaction analysis of quantitative traits. BMC Genom 10:509
Chanda P, Zhang A, Ramanathan M (2011) Modeling of environmental and genetic interactions with AMBROSIA, an information-theoretic model synthesis method. Heredity 107:320–327
Cordell HJ (2002) Epistasis: what it means, what it doesn’t mean, and statistical methods to detect it in humans. Hum Mol Genet 11:2463–2468
Cordell HJ (2009) Detecting gene–gene interactions that underlie human diseases. Nat Rev Genet 10:392–404
Cosgun E, Limdi NA, Duarte CW (2011) High-dimensional pharmacogenetic prediction of a continuous trait using machine learning techniques with application to warfarin dose prediction in African Americans. Bioinformatics 27:1384–1389
Cover T, Thomas J (1991) Elements of information theory. Wiley, New York
Culverhouse R (2007) The use of the restricted partition method with case-control data. Hum Hered 63:93–100
Culverhouse RC (2012) A comparison of methods sensitive to interactions with small main effects. Genet Epidemiol 36:303–311
Culverhouse R, Klein T, Shannon W (2004) Detecting epistatic interactions contributing to quantitative traits. Genet Epidemiol 27:141–152
Fisher D, Shafer S (2007) Fisher/Shafer NONMEM workshop pharmacokinetic and pharmacodynamic analysis with NONMEM
Games PA, Howell JF (1976) Pairwise multiple comparison procedures with unequal n’s and/or variances: a Monte Carlo study. J Educ Behav Stat 1:113–125
Garcia-Magarinos M, Lopez-de-Ullibarri I, Cao R, Salas A (2009) Evaluating the ability of tree-based methods and logistic regression for the detection of SNP–SNP interaction. Ann Hum Genet 73:360–369
Gassó P, Mas S, Álvarez S, Trias G, Bioque M, Oliveira C, Bernardo M, Lafuente A (2010) Xenobiotic metabolizing and transporter genes: gene–gene interactions in schizophrenia and related disorders. Pharmacogenomics 11:1725–1731
Gastonguay MR (2004) A full model estimation approach for covariate effects: inference based on clinical importance and estimation precision. AAPS J 6:W4354
Goldstein B, Hubbard A, Cutler A, Barcellos L (2010) An application of random forests to a genome-wide association dataset: methodological considerations & new findings. BMC Genet 11:49
Grady BJ, Torstenson E, Dudek SM, Giles J, Sexton D, Ritchie MD (2010) Finding unique filter sets in PLATO: a precursor to efficient interaction analysis in GWAS data. In: Pacific Symposium on Biocomputing. World Scientific, pp 315–326
Hahn LW, Moore JH (2004) Ideal discrimination of discrete clinical endpoints using multilocus genotypes. In Silico Biol 4:183–194
Hahn LW, Ritchie MD, Moore JH (2003) Multifactor dimensionality reduction software for detecting gene-gene and gene-environment interactions. Bioinformatics 19:376–382
Hair JF (1995) Multivariate data analysis with readings. Prentice Hall, Englewood Cliffs
Han TS (1980) Multiple mutual informations and multiple interactions in frequency data. Inf Control 46:26–45
Jakulin A (2005) Machine learning based on attribute interactions. Univerza v Ljubljani
Joerger M (2012) Covariate pharmacokinetic model building in oncology and its potential clinical relevance. AAPS J 14:119–132
Jonsson EN, Karlsson MO (1998) Automated covariate model building within NONMEM. Pharm Res 15:1463–1468
Khandelwal A, Harling K, Jonsson EN, Hooker AC, Karlsson MO (2011) A fast method for testing covariates in population PK/PD models. AAPS J 13:464–472
Kim Y, Wojciechowski R, Sung H, Mathias RA, Wang L, Klein AP, Lenroot RK, Malley J, Bailey-Wilson JE (2009) Evaluation of random forests performance for genome-wide association studies in the presence of interaction effects. BMC Proc 3(Suppl 7):S64
Knights J, Ramanathan M (2012) An information theory analysis of gene-environmental interactions in count/rate data. Hum Hered 73:123–138
Knights J, Ramanathan M (2013) Genetic factors associated with gemcitabine pharmacokinetics, disposition, and toxicity. Submitted: Pharmacogenomics
Knights J, Chanda P, Sato Y, Kaniwa N, Saito Y, Ueno H, Zhang A, Ramanathan M (2013a) Vertical integration of pharmacogenetics in population PK/PD modeling: a novel information theoretic method. CPT Pharmacomet Syst Pharmacol 2:e25
Knights J, Yang J, Chanda P, Zhang A, Ramanathan M (2013b) SYMPHONY, an information-theoretic method for gene–gene and gene–environment interaction analysis of disease syndromes. Heredity
Leary B, Dunlavey M, Chittenden J, Matzuka B, Guzy S. QRPEM—a new standard of accuracy, precision, and efficiency in NLME population PK/PD methods
Li MD, Lou X-Y, Chen G, Ma JZ, Elston RC (2008) Gene–gene interactions among CHRNA4, CHRNB2, BDNF, and NTRK2 in nicotine dependence. Biol Psychiatry 64:951–957
Lombardo F, Obach RS, DiCapua FM, Bakken GA, Lu J, Potter DM, Gao F, Miller MD, Zhang Y (2006) A hybrid mixture discriminant analysis-random forest computational model for the prediction of volume of distribution of drugs in human. J Med Chem 49:2262–2267
Lou XY, Chen GB, Yan L, Ma JZ, Zhu J, Elston RC, Li MD (2007) A generalized combinatorial approach for detecting gene-by-gene and gene-by-environment interactions with application to nicotine dependence. Am J Hum Genet 80:1125–1137
Ludden TM, Beal SL, Sheiner LB (1994) Comparison of the akaike information criterion, the Schwarz criterion and the F test as guides to model selection. J Pharmacokinet Biopharm 22:431–445
Mandema JW, Verotta D, Sheiner LB (1992) Building population pharmacokineticpharmacodynamic models. I. Models for covariate effects. J Pharmacokinet Biopharm 20:511–528
Moore JH, Williams SM (2005) Traversing the conceptual divide between biological and statistical epistasis: systems biology and a more modern synthesis. BioEssays 27:637–646
Moore JH, Gilbert JC, Tsai CT, Chiang FT, Holden T, Barney N, White BC (2006) A flexible computational framework for detecting, characterizing, and interpreting statistical patterns of epistasis in genetic studies of human disease susceptibility. J Theor Biol 241:252–261
Motsinger‐Reif AA (2012) Developments in analyses in pharmacogenetic datasets. Pharmacogenet Individ Ther, 415–435
Nelson M, Kardia S, Ferrell R, Sing C (2001) A combinatorial partitioning method to identify multilocus genotypic partitions that predict quantitative trait variation. Genome Res 11:458–470
Nonyane BA, Foulkes AS (2008) Application of two machine learning algorithms to genetic association studies in the presence of covariates. BMC Genet 9:71
Olshen LBJFR, Stone CJ (1984) Classification and regression trees. Wadsworth International Group, Belmont
Paine SW, Barton P, Bird J, Denton R, Menochet K, Smith A, Tomkinson NP, Chohan KK (2010) A rapid computational filter for predicting the rate of human renal clearance. J Mol Graph Model 29:529–537
Patefield WM (1981) Algorithm AS 159: an efficient method of generating random R x C tables with given row and column totals. J R Stat Soc Ser C (Appl Stat) 30:91–97
Peters BJ, Rodin AS, De Boer A, Maitland-van der Zee AH (2010) Methodological and statistical issues in pharmacogenomics. J Pharm Pharmacol 62:161–166
Ribbing J (2007) Covariate model building in nonlinear mixed effects models. Uppsala University
Ribbing J, Nyberg J, Caster O, Jonsson EN (2007) The lasso—a novel method for predictive covariate model building in nonlinear mixed effects models. J Pharmacokinet Pharmacodyn 34:485–517
Ritchie MD, Hahn LW, Roodi N, Bailey LR, Dupont WD, Parl FF, Moore JH (2001a) Multifactor-dimensionality reduction reveals high-order interactions among estrogen-metabolism genes in sporadic breast cancer. Am J Hum Genet 69:138
Ritchie MD, Hahn LW, Roodi N, Bailey LR, Dupont WD, Parl FF, Moore JH (2001b) Multifactor-dimensionality reduction reveals high-order interactions among estrogen-metabolism genes in sporadic breast cancer. Am J Hum Genet 69:138–147
Ritchie MD, Hahn LW, Moore JH (2003) Power of multifactor dimensionality reduction for detecting gene-gene interactions in the presence of genotyping error, missing data, phenocopy, and genetic heterogeneity. Genet Epidemiol 24:150–157
Sabbagh A, Darlu P (2006) Data-mining methods as useful tools for predicting individual drug response: application to CYP2D6 data. Hum Hered 62:119–134
Sabbagh A, Darlu P (2009) Data mining methods as tools for predicting individual drug response. Pharm Data Min Approaches Appl Drug Discov 6:379
Shannon CE, Weaver W (1948) A mathematical theory of communication: American Telephone and Telegraph Company
Siemiatycki J, Thomas DC (1981) Biological models and statistical interactions: an example from multistage carcinogenesis. Int J Epidemiol 10:383–387
Sucheston L, Chanda P, Zhang A, Tritchler D, Ramanathan M (2010) Comparison of information-theoretic to statistical methods for gene-gene interactions in the presence of genetic heterogeneity. BMC Genom 11:487
Sun YV, Cai Z, Desai K, Lawrance R, Leff R, Jawaid A, Kardia SL, Yang H (2007) Classification of rheumatoid arthritis status with candidate gene and genome-wide single-nucleotide polymorphisms using random forests. BMC Proc 1(Suppl 1):S62
Tabachnick BG, Fidell LS (2001) Using multivariate statistics. Allyn and Bacon, Boston
Tritchler DL, Sucheston L, Chanda P, Ramanathan M (2011) Information metrics in genetic epidemiology. Stat Appl Genet Mol Biol 10:Article 12
Velez DR, White BC, Motsinger AA, Bush WS, Ritchie MD, Williams SM, Moore JH (2007) A balanced accuracy function for epistasis modeling in imbalanced datasets using multifactor dimensionality reduction. Genet Epidemiol 31:306–315
Wählby U (2002) Methodological studies on covariate model building in population pharmacokinetic–pharmacodynamic analysis. Uppsala University
Wahlby U, Jonsson EN, Karlsson MO (2002) Comparison of stepwise covariate model building strategies in population pharmacokinetic-pharmacodynamic analysis. AAPS PharmSci 4:E27
Wählby U, Jonsson EN, Karlsson MO (2001) Assessment of actual significance levels for covariate effects in NONMEM. J Pharmacokinet Pharmacodyn 28:231–252
Wan X, Yang C, Yang Q, Xue H, Fan X, Tang NL, Yu W (2010) BOOST: a fast approach to detecting gene-gene interactions in genome-wide case-control studies. Am J Hum Genet 87:325
Watanabe S (1960) Information theoretical analysis of multivariate correlation. IBM J Res Dev 4:66–82
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 American Association of Pharmaceutical Scientists
About this chapter
Cite this chapter
Knights, J., Ramanathan, M. (2016). Detecting Pharmacokinetic and Pharmacodynamic Covariates from High-Dimensional Data. In: Mager, D., Kimko, H. (eds) Systems Pharmacology and Pharmacodynamics. AAPS Advances in the Pharmaceutical Sciences Series, vol 23. Springer, Cham. https://doi.org/10.1007/978-3-319-44534-2_13
Download citation
DOI: https://doi.org/10.1007/978-3-319-44534-2_13
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-44532-8
Online ISBN: 978-3-319-44534-2
eBook Packages: Biomedical and Life SciencesBiomedical and Life Sciences (R0)