Impact of gut permeability on estimation of oral bioavailability for chemicals in commerce and the environment
Main Article Content
Abstract
Performance of pharmacokinetic models developed using in vitro-to-in vivo extrapolation (IVIVE) methods may be improved by refining assumptions regarding fraction absorbed (Fabs) through the intestine, a component of oral bioavailability (Fbio). Although in vivo measures of Fabs are often unavailable for non-pharmaceuticals, in vitro measures of apparent permeability (Papp) using the Caco-2 cell line have been highly correlated with Fabs. We measured bidirectional Papp for over 400 non-pharmaceutical chemicals using the Caco-2 assay. A random forest quantitative structure-property relationship (QSPR) model was developed using these and peer-reviewed pharmaceutical data. Both Caco-2 data (R2=0.37) and the QSPR model (R2=0.29) were better at predicting human bioavailability compared to in vivo rat data (R2=0.23). After incorporation into a high throughput toxicokinetics (HTTK) framework for IVIVE, the Caco-2 data were used to estimate in vivo administered equivalent dose (AED) for bioactivity assessed in vitro, The HTTK-predicted plasma steady state concentrations (Css) for IVIVE were revised, with modest changes predicted for poorly absorbed chemicals. Experimental data were evaluated for sources of measurement uncertainty, which were then accounted for using the Monte Carlo method. Revised AEDs were subsequently compared with exposure estimates to evaluate effects on bioactivity:exposure ratios, a surrogate for risk. Only minor changes in the margin between chemical exposure and predicted bioactive doses were observed due to the preponderance of highly absorbed chemicals.
Plain language summary
When assessing any chemical risk posed to the public health, there is a crucial difference between a dose ingested orally and the amount that enters the bloodstream (the rest of the chemical is eliminated from the body). The in vitro Caco-2 permeability assay is used by the pharmaceutical industry to identify chemical compounds that will be well absorbed. Here we have used that same Caco-2 assay to screen more than 400 chemicals occuring in commerce and the environment to see if they are well-absorbed. We have further developed a machine learning model to predict this property for other chemicals without in vitro data. Due to interspecies differences, in vitro and machine learning approaches both appear to work better than animal studies for predicting human bioavailability. We predict that many, but not all, non-pharmaceuticals are well absorbed. We show how these new data refine health risk-based chemical priotizations.
Article Details
This work is licensed under a Creative Commons Attribution 4.0 International License.
Articles are distributed under the terms of the Creative Commons Attribution 4.0 International license (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium, provided the original work is appropriately cited (CC-BY). Copyright on any article in ALTEX is retained by the author(s).
Archer, K. J. and Kimes, R. V. (2008). Empirical characterization of random forest variable importance measures. Computational Statistics & Data Analysis 52, 2249-2260. doi:10.1016/j.csda.2007.08.015
Artursson, P. and Karlsson, J. (1991). Correlation between oral drug absorption in humans and apparent drug permeability coefficients in human intestinal epithelial (caco-2) cells. Biochemical and biophysical Research communications 175, 880-885. doi:10.1016/0006-291X(91)91647-U
Artursson, P., Palm, K. and Luthman, K. (2012). Caco-2 monolayers in experimental and theoretical predictions of drug transport. Advanced Drug Delivery Reviews 64, 280-289. doi:10.1016/j.addr.2012.09.005
Breen, M., Ring, C. L., Kreutz, A. et al. (2021). High-throughput pbtk models for in vitro to in vivo extrapolation. Expert Opinion on Drug Metabolism & Toxicology 17, 903-921. doi:10.1080/17425255.2021.1935867
Breen, M., Wambaugh, J. F., Bernstein, A. et al. (2022). Simulating toxicokinetic variability to identify susceptible and highly exposed populations. Journal of Exposure Science & Environmental Epidemiology
Breiman, L. (2001). Random forests. Machine Learning 45, 5-32. doi:10.1023/A:1010933404324
Cabrera-Pérez, M. Á. and Pham-The, H. (2018). Computational modeling of human oral bioavailability: What will be next? Expert opinion on drug discovery 13, 509-521. doi:10.1080/17460441.2018.1463988
Caldwell, J. C., Evans, M. V. and Krishnan, K. (2012). Cutting edge pbpk models and analyses: Providing the basis for future modeling efforts and bridges to emerging toxicology paradigms. Journal of Toxicology 2012, doi:10.1155/2012/852384
Coecke, S., Pelkonen, O., Leite, S. B. et al. (2013). Toxicokinetics as a key to the integrated toxicity risk assessment based primarily on non-animal approaches. Toxicology in Vitro 27, 1570-1577. doi:10.1016/j.tiv.2012.06.012
Dahan, A., Miller, J. M. and Amidon, G. L. (2009). Prediction of solubility and permeability class membership: Provisional bcs classification of the world’s top oral drugs. The AAPS Journal 11, 740-746. doi:10.1208/s12248-009-9144-x
Dahlgren, D., Roos, C., Sjögren, E. et al. (2015). Direct in vivo human intestinal permeability (peff) determined with different clinical perfusion and intubation methods. Journal of Pharmaceutical Sciences 104, 2702-2726. doi:10.1002/jps.24258
Davies, B. and Morris, T. (1993). Physiological parameters in laboratory animals and humans. Pharmaceutical Research 10, 1093-1095. doi:10.1023/A:1018943613122
De Angelis, I. and Turco, L. (2011). Caco-2 cells as a model for intestinal absorption. Current Protocols in Toxicology 47, 20.26.21-20.26.15. doi:10.1002/0471140856.tx2006s47
Dougherty, J., Kohavi, R. and Sahami, M. (1995). Supervised and unsupervised discretization of continuous features. In (eds.), Machine learning proceedings 1995. Elsevier. doi:10.1016/B978-1-55860-377-6.50032-3
Drozdzik, M., Busch, D., Lapczuk, J. et al. (2018). Protein abundance of clinically relevant drug‐metabolizing enzymes in the human liver and intestine: A comparative analysis in paired tissue specimens. Clinical pharmacology & therapeutics 104, 515-524. doi:10.1002/cpt.967
Fagerholm, U., Johansson, M. and Lennernäs, H. (1996). Comparison between permeability coefficients in rat and human jejunum. Pharmaceutical Research 13, 1336-1342. doi:10.1023/A:1016065715308
Fagerholm, U. (2007). Prediction of human pharmacokinetics—gut‐wall metabolism. Journal of Pharmacy and Pharmacology 59, 1335-1343. doi:10.1211/jpp.59.10.0002
Fedi, A., Vitale, C., Ponschin, G. et al. (2021). In vitro models replicating the human intestinal epithelium for absorption and metabolism studies: A systematic review. Journal of Controlled Release 335, 247-268. doi:10.1016/j.jconrel.2021.05.028
Filer, D. L., Kothiya, P., Setzer, R. W. et al. (2017). Tcpl: The toxcast pipeline for high-throughput screening data. Bioinformatics 33, 618-620. doi:10.1093/bioinformatics/btw680
Gaulton, A., Bellis, L. J., Bento, A. P. et al. (2012). Chembl: A large-scale bioactivity database for drug discovery. Nucleic acids research 40, D1100-D1107. doi:10.1093/nar/gkr777
Gehlke, C. E. and Biehl, K. (1934). Certain effects of grouping upon the size of the correlation coefficient in census tract material. Journal of the American Statistical Association 29, 169-170. doi:10.1080/01621459.1934.10506247
Grandoni, S., Cesari, N., Brogin, G. et al. (2019). Building in-house pbpk modelling tools for oral drug administration from literature information. ADMET and DMPK 7, 4-21. doi:10.5599/admet.638
Griffin, B. T. and O’Driscoll, C. M. (2008). An examination of the effect of intestinal first pass extraction on intestinal lymphatic transport of saquinavir in the rat. Pharmaceutical research 25, 1125-1133. doi:10.1007/s11095-007-9473-3
Hishamuddin, M. N. F., Hassan, M. F. and Mokhtar, A. A. (2020). Improving classification accuracy of random forest algorithm using unsupervised discretization with fuzzy partition and fuzzy set intervals. Proceedings of the 2020 9th International Conference on Software and Computer Applications, 99-104. doi:10.1145/3384544.3384590
Hu, M., Ling, J., Lin, H. et al. (2004). Use of caco-2 cell monolayers to study drug absorption and metabolism. Optimization in drug discovery: in vitro methods 19-35. doi:10.1385/1-59259-800-5:019
Ichikawa, M., Akamine, H., Murata, M. et al. (2021). Generation of tetracycline-controllable cyp3a4-expressing caco-2 cells by the piggybac transposon system. Scientific Reports 11, 11670. doi:10.1038/s41598-021-91160-z
Kilford, P. J., Gertz, M., Houston, J. B. et al. (2008). Hepatocellular binding of drugs: Correction for unbound fraction in hepatocyte incubations using microsomal binding or drug lipophilicity data. Drug Metabolism and Disposition 36, 1194-1197. doi:10.1124/dmd.108.020834
Kim, M. T., Sedykh, A., Chakravarti, S. K. et al. (2014). Critical evaluation of human oral bioavailability for pharmaceutical drugs by using various cheminformatics approaches. Pharmaceutical research 31, 1002-1014. doi:10.1007/s11095-013-1222-1
Kroes, R., Kleiner, J. and Renwick, A. (2005). The threshold of toxicological concern concept in risk assessment. Toxicological sciences 86, 226-230. doi:10.1093/toxsci/kfi169
Kuhn, M. (2008). Building predictive models in r using the caret package. Journal of statistical software 28, 1-26. doi:10.18637/jss.v028.i05
Lanevskij, K. and Didziapetris, R. (2019). Physicochemical qsar analysis of passive permeability across caco-2 monolayers. Journal of Pharmaceutical Sciences 108, 78-86. doi:10.1016/j.xphs.2018.10.006
Larregieu, C. A. and Benet, L. Z. (2013). Drug discovery and regulatory considerations for improving in silico and in vitro predictions that use caco-2 as a surrogate for human intestinal permeability measurements. The AAPS Journal 15, 483-497. doi:10.1208/s12248-013-9456-8
Lennernäs, H. (1997). Human jejunal effective permeability and its correlation with preclinical drug absorption models. Journal of Pharmacy and Pharmacology 49, 627-638. doi:10.1111/j.2042-7158.1997.tb06084.x
Lennernäs, H. (2007). Animal data: The contributions of the ussing chamber and perfusion systems to predicting human oral drug delivery in vivo. Advanced drug delivery reviews 59, 1103-1120. doi:10.1016/j.addr.2007.06.016
Liaw, A. and Wiener, M. (2002). Classification and regression by randomforest. R news 2, 18-22.
Mansouri, K., Grulke, C. M., Judson, R. S. et al. (2018). Opera models for predicting physicochemical properties and environmental fate endpoints. Journal of Cheminformatics 10, 10. doi:10.1186/s13321-018-0263-1
Mansouri, K., Cariello, N. F., Korotcov, A. et al. (2019). Open-source qsar models for pka prediction using multiple machine learning approaches. Journal of cheminformatics 11, 1-20. doi:10.1186/s13321-019-0384-1
Musther, H., Olivares-Morales, A., Hatley, O. J. et al. (2014). Animal versus human oral drug bioavailability: Do they correlate? European Journal of Pharmaceutical Sciences 57, 280-291. doi:10.1016/j.ejps.2013.08.018
Obringer, C., Manwaring, J., Goebel, C. et al. (2016). Suitability of the in vitro caco-2 assay to predict the oral absorption of aromatic amine hair dyes. Toxicology in Vitro 32, 1-7. doi:10.1016/j.tiv.2015.11.007
Paine, M. F., Khalighi, M., Fisher, J. M. et al. (1997). Characterization of interintestinal and intraintestinal variations in human cyp3a-dependent metabolism. Journal of Pharmacology and Experimental Therapeutics 283, 1552-1562.
Pant, A., Maiti, T. K., Mahajan, D. et al. (2023). Human gut microbiota and drug metabolism. Microbial Ecology 86, 97-111. doi:10.1007/s00248-022-02081-x
Pearce, R. G., Setzer, R. W., Davis, J. L. et al. (2017a). Evaluation and calibration of high-throughput predictions of chemical distribution to tissues. Journal of Pharmacokinetics and Pharmacodynamics 44, 549-565. doi:10.1007/s10928-017-9548-7
Pearce, R. G., Setzer, R. W., Strope, C. L. et al. (2017b). Httk: R package for high-throughput toxicokinetics. Journal of Statistical Software 79, 1-26. doi:10.18637/jss.v079.i04
Pham-The, H., Cabrera-Pérez, M. Á., Nam, N.-H. et al. (2018). In silico assessment of adme properties: Advances in caco-2 cell monolayer permeability modeling. Current Topics in Medicinal Chemistry 18, 2209-2229. doi:10.2174/1568026619666181130140350
Pham, L. L., Watford, S. M., Pradeep, P. et al. (2020). Variability in in vivo studies: Defining the upper limit of performance for predictions of systemic effect levels. Computational Toxicology 15, 100126. doi:10.1016/j.comtox.2020.100126
Punt, A., Louisse, J., Pinckaers, N. et al. (2021). Predictive performance of next generation physiologically based kinetic (pbk) model predictions in rats based on in vitro and in silico input data. Toxicological Sciences 186, 18-28. doi:10.1093/toxsci/kfab150
Richard, A. M., Judson, R. S., Houck, K. A. et al. (2016). Toxcast chemical landscape: Paving the road to 21st century toxicology. Chemical Research in Toxicology 29, 1225-1251. doi:10.1021/acs.chemrestox.6b00135
Ring, C. L., Pearce, R. G., Setzer, R. W. et al. (2017). Identifying populations sensitive to environmental chemicals by simulating toxicokinetic variability. Environment International 106, 105-118. doi:10.1016/j.envint.2017.06.004
Ring, C. L., Arnot, J. A., Bennett, D. H. et al. (2018). Consensus modeling of median chemical intake for the us population based on predictions of exposure pathways. Environmental Science & Technology 53, 719-732. doi:10.1021/acs.est.8b04056
Rostami‐Hodjegan, A. (2012). Physiologically based pharmacokinetics joined with in vitro–in vivo extrapolation of adme: A marriage under the arch of systems pharmacology. Clinical Pharmacology & Therapeutics 92, 50-61. doi:10.1038/clpt.2012.65
Rotroff, D. M., Wetmore, B. A., Dix, D. J. et al. (2010). Incorporating human dosimetry and exposure into high-throughput in vitro toxicity screening. Toxicological Sciences 117, 348-358. doi:10.1093/toxsci/kfq220
Roy, K., Kar, S. and Ambure, P. (2015). On a simple approach for determining applicability domain of qsar models. Chemometrics and Intelligent Laboratory Systems 145, 22-29. doi:10.1016/j.chemolab.2015.04.013
Ryu, S., Tess, D., Chang, G. et al. (2020). Evaluation of fraction unbound across 7 tissues of 5 species. Journal of Pharmaceutical Sciences 109, 1178-1190. doi:10.1016/j.xphs.2019.10.060
Sambuy, Y., De Angelis, I., Ranaldi, G. et al. (2005). The caco-2 cell line as a model of the intestinal barrier: Influence of cell and culture-related factors on caco-2 cell functional characteristics. Cell biology and toxicology 21, 1-26. doi:10.1007/s10565-005-0085-6
Sun, D., Lennernas, H., Welage, L. S. et al. (2002). Comparison of human duodenum and caco-2 gene expression profiles for 12,000 gene sequences tags and correlation with permeability of 26 drugs. Pharmaceutical Research 19, 1400-1416. doi:10.1023/A:1020483911355
Tateishi, S., Arima, S. and Futami, K. (1997). Assessment of blood flow in the small intestine by laser doppler flowmetry: Comparison of healthy small intestine and small intestine in crohn's disease. Journal of gastroenterology 32, 457-463. doi:10.1007/BF02934083
Varma, M. V., Obach, R. S., Rotter, C. et al. (2010). Physicochemical space for optimum oral bioavailability: Contribution of human intestinal absorption and first-pass elimination. Journal of medicinal chemistry 53, 1098-1108. doi:10.1021/jm901371v
von Jouanne-Diedrich, H. (2017). Oner: One rule machine learning classification algorithm with enhancements. R package version 2, 2.
Wahajuddin, M., Singh, S. P., Patel, K. et al. (2011). Prediction of human absorption of a trioxane antimalarial drug (cdri 99/411) using an in-house validated in situ single-pass intestinal perfusion model. Arzneimittelforschung 61, 532-537. doi:10.1055/s-0031-129624040
Wambaugh, J. F., Hughes, M. F., Ring, C. L. et al. (2018). Evaluating in vitro-in vivo extrapolation of toxicokinetics. Toxicological Sciences 163, 152-169. doi:10.1093/toxsci/kfy020
Wambaugh, J. F., Wetmore, B. A., Ring, C. L. et al. (2019). Assessing toxicokinetic uncertainty and variability in risk prioritization. Toxicological Sciences 172, 235-251. doi:10.1093/toxsci/kfz205
Wetmore, B. A., Wambaugh, J. F., Ferguson, S. S. et al. (2012). Integration of dosimetry, exposure, and high-throughput screening data in chemical toxicity assessment. Toxicological Sciences 125, 157-174. doi:10.1093/toxsci/kfr254
Wetmore, B. A., Wambaugh, J. F., Allen, B. et al. (2015). Incorporating high-throughput exposure predictions with dosimetry-adjusted in vitro bioactivity to inform chemical toxicity testing. Toxicological Sciences 148, 121-136. doi:10.1093/toxsci/kfv1711
Williams, A. J., Grulke, C. M., Edwards, J. et al. (2017). The comptox chemistry dashboard: A community data resource for environmental chemistry. Journal of Cheminformatics 9, 61. doi:10.1186/s13321-017-0247-6
Yamada, N., Negoro, R., Watanabe, K. et al. (2023). Generation of caco-2 cells with predictable metabolism by cyp3a4, ugt1a1 and ces using the pitch system. Drug Metabolism and Pharmacokinetics 50, 100497. doi:10.1016/j.dmpk.2023.100497
Yang, J., Jamei, M., Yeo, K. R. et al. (2007). Prediction of intestinal first-pass drug metabolism. Current Drug Metabolism 8, 676-684. doi:10.2174/138920007782109733
Yap, C. W. (2011). Padel‐descriptor: An open source software to calculate molecular descriptors and fingerprints. Journal of Computational Chemistry 32, 1466-1474. doi:10.1002/jcc.21707
Yim, D.-S., Choi, S. and Bae, S. H. (2020). Predicting human pharmacokinetics from preclinical data: Absorption. Translational and Clinical Pharmacology 28, 126. doi:10.12793/tcp.2020.28.e14
Yoon, M., Campbell, J. L., Andersen, M. E. et al. (2012). Quantitative in vitro to in vivo extrapolation of cell-based toxicity assay results. Critical Reviews in Toxicology 42, 633-652. doi:10.3109/10408444.2012.692115
Yu, L. X. and Amidon, G. L. (1999). A compartmental absorption and transit model for estimating oral drug absorption. International Journal of Pharmaceutics 186, 119-125. doi:10.1016/S0378-5173(99)00147-7