Predicting the clinical subcutaneous absorption rate constant of monoclonal antibodies using only the primary sequence: a machine learning approach

References

• Swartz M. The physiology of the lymphatic system. Adv Drug Deliv Rev. 2001;50(1–2):3–11. doi:10.1016/S0169-409X(01)00150-8.
   PubMed Web of Science ®Google Scholar
• Supersaxo A, Hein WR, Steffen H. Effect of molecular weight on the lymphatic absorption of water-soluble compounds following subcutaneous administration. Pharm Res. 1990;7(2):167–69. doi:10.1023/A:1015880819328.
   PubMed Web of Science ®Google Scholar
• Gabrielsson J, Weiner D, Weiner D. Pharmacokinetic & pharmacodynamic data analysis: concepts and applications. In: Gabrielsson J, editors. Expanded.; PK/PD data analysis: concepts and applications; Apotekarsocieteten, Swedish Acedemy of Pharmaceutical Sciences. Stockholm: Swedish Pharmaceutical Press; 2006. p. 43.
   Google Scholar
• Shah DK, Betts AM. Towards a platform PBPK model to characterize the plasma and tissue disposition of monoclonal antibodies in preclinical species and human. J Pharmacokinet Pharmacodyn. 2012;39(1):67–86. doi:10.1007/s10928-011-9232-2.
   PubMed Web of Science ®Google Scholar
• Cao Y, Jusko WJ. Applications of minimal physiologically-based pharmacokinetic models. J Pharmacokinet Pharmacodyn. 2012;39(6):711–23. doi:10.1007/s10928-012-9280-2.
   PubMed Web of Science ®Google Scholar
• Jones H, Rowland-Yeo K. Basic concepts in physiologically based pharmacokinetic modeling in drug discovery and development. CPT Pharmacomet Syst Pharmacol. 2013;2(8):63. doi:10.1038/psp.2013.41.
   PubMedGoogle Scholar
• Li Z, Yu X, Li Y, Verma A, Chang HP, Shah DK. A two-pore physiologically based pharmacokinetic model to predict subcutaneously administered different-size antibody/antibody fragments. Aaps J. 2021;23(3):62. doi:10.1208/s12248-021-00588-8.
   PubMed Web of Science ®Google Scholar
• Hu S, D’Argenio DZ. Predicting monoclonal antibody pharmacokinetics following subcutaneous administration via whole-body physiologically-based modeling. J Pharmacokinet Pharmacodyn. 2020;47(5):385–409. doi:10.1007/s10928-020-09691-3.
   PubMed Web of Science ®Google Scholar
• Gill KL, Gardner I, Li L, Jamei M. A bottom-up whole-body physiologically based pharmacokinetic model to mechanistically predict tissue distribution and the rate of subcutaneous absorption of therapeutic proteins. Aaps J. 2016;18(1):156–70. doi:10.1208/s12248-015-9819-4.
   PubMed Web of Science ®Google Scholar
• Chemical Computing Group ULC. Molecular operating environment (MOE); 1010 Sherbooke St. West, Suite #910 Montreal, QC, Canada, H3A 2R7; 2022.
   Google Scholar
• Datta-Mannan A, Estwick S, Zhou C, Choi H, Douglass NE, Witcher DR, Lu J, Beidler C, Millican R. Influence of physiochemical properties on the subcutaneous absorption and bioavailability of monoclonal antibodies. Mabs-austin. 2020;12(1):1770028. doi:10.1080/19420862.2020.1770028.
   PubMed Web of Science ®Google Scholar
• Reddy ST, Berk DA, Jain RK, Swartz MA. A sensitive in vivo model for quantifying interstitial convective transport of injected macromolecules and nanoparticles. J Appl Physiol. 2006;101(4):1162–69. doi:10.1152/japplphysiol.00389.2006.
   PubMed Web of Science ®Google Scholar
• Richter WF, Bhansali SG, Morris ME. Mechanistic determinants of biotherapeutics absorption following SC administration. Aaps J. 2012;14(3):559–70. doi:10.1208/s12248-012-9367-0.
   PubMed Web of Science ®Google Scholar
• Datta-Mannan A. Mechanisms influencing the pharmacokinetics and disposition of monoclonal antibodies and peptides. Drug Metab Dispos. 2019;47(10):1100–10. doi:10.1124/dmd.119.086488.
   PubMed Web of Science ®Google Scholar
• Turner MR, Balu-Iyer SV. Challenges and opportunities for the subcutaneous delivery of therapeutic proteins. J Pharm Sci. 2018;107(5):1247–60. doi:10.1016/j.xphs.2018.01.007.
   PubMed Web of Science ®Google Scholar
• Zheng F, Hou P, Corpstein CD, Park K, Li T. Multiscale pharmacokinetic modeling of systemic exposure of subcutaneously injected biotherapeutics. J Controlled Release. 2021;337:407–16. doi:10.1016/j.jconrel.2021.07.043.
   PubMed Web of Science ®Google Scholar
• Wasserman RL. Overview of recombinant human hyaluronidase-facilitated subcutaneous infusion of IgG in primary immunodeficiencies. Immunotherapy. 2014;6(5):553–67. doi:10.2217/imt.14.34.
   PubMed Web of Science ®Google Scholar
• Yang L, Allan J. Pharmapendium. Amsterdam, Netherlands: Elsevier.
   Google Scholar
• Kinnunen HM, Mrsny RJ. Improving the outcomes of biopharmaceutical delivery via the subcutaneous route by understanding the chemical, physical and physiological properties of the subcutaneous injection site. J Controlled Release. 2014;182:22–32. doi:10.1016/j.jconrel.2014.03.011.
   PubMed Web of Science ®Google Scholar
• Velliangiri S, Alagumuthukrishnan S, Thankumar Joseph SI. A review of dimensionality reduction techniques for efficient computation. Procedia Comput Sci. 2019;165:104–11. doi:10.1016/j.procs.2020.01.079.
   Google Scholar
• Thorsteinson N, Gunn JR, Kelly K, Long W, Labute P. Structure-based charge calculations for predicting isoelectric point, viscosity, clearance, and profiling antibody therapeutics. Mabs-austin. 2021;13(1):1981805. doi:10.1080/19420862.2021.1981805.
   PubMed Web of Science ®Google Scholar
• Chen T, Guestrin C. Xgboost: a scalable tree boosting system. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining; 2016; p. 785–94. 10.1145/2939672.2939785.
   Google Scholar
• Liu Y, Just A. Package Version 0.1.0.; 2020. Shapforxgboost: SHAP plots for “XGBoost”. R.
   Google Scholar
• R Core Team. R: a language and environment for statistical computing. Vienna, Austria: R Foundation for Statistical Computing; 2021.
   Google Scholar
• Kuhn M. Building predictive models in R using the caret package. J Stat Softw. 2008;28:5. doi:10.18637/jss.v028.i05.
   Web of Science ®Google Scholar
• Wickham H. Ggplot2. Cham: Use R!; Springer International Publishing; 2016. doi:10.1007/978-3-319-24277-4.
   Google Scholar
• Vidarsson G, Dekkers G, Rispens T. IgG subclasses and Allotypes: from structure to effector functions. Front Immunol. 2014;5. doi:10.3389/fimmu.2014.00520.
   PubMed Web of Science ®Google Scholar
• Tang Y, Cain P, Anguiano V, Shih JJ, Chai Q, Feng Y. Impact of IgG subclass on molecular properties of monoclonal antibodies. Mabs-austin. 2021;13(1):1993768. doi:10.1080/19420862.2021.1993768.
   PubMed Web of Science ®Google Scholar
• Shwartz-Ziv R, Armon A. Tabular data: deep learning is not all you need. arXiv November 23, 2021. (accessed 2022 07 14) http://arxiv.org/abs/2106.03253.
   Google Scholar
• Bhattacharya S, S SRK, Maddikunta PKR, Kaluri R, Singh S, Gadekallu TR, Alazab M, Tariq U. A novel PCA-Firefly based XGBoost classification model for intrusion detection in networks using GPU. Electronics. 2020;9(2):219. doi:10.3390/electronics9020219.
   Web of Science ®Google Scholar
• Nobre J, Neves RF. Combining Principal component analysis, discrete wavelet transform and XGBoost to trade in the financial markets. Expert Syst Appl. 2019;125:181–94. doi:10.1016/j.eswa.2019.01.083.
   Web of Science ®Google Scholar
• Molnar C. Interpretable machine learning: a guide for making black box models explainable. 2nd. Munich, Germany: Christoph Molnar; 2022.
   Google Scholar
• Tibbitts J, Canter D, Graff R, Smith A, Khawli LA. Key factors influencing ADME properties of therapeutic proteins: a need for ADME characterization in drug discovery and development. mAbs-austin. 2016;8(2):229–45. doi:10.1080/19420862.2015.1115937.
   PubMed Web of Science ®Google Scholar
• Khawli LA, Goswami S, Hutchinson R, Kwong ZW, Yang J, Wang X, Yao Z, Sreedhara A, Cano T, Tesar DB. et al. Charge variants in IgG1: isolation, characterization, in vitro binding properties and pharmacokinetics in rats. mAbs-austin. 2010;2(6):613–24. doi:10.4161/mabs.2.6.13333.
   PubMed Web of Science ®Google Scholar
• Boswell CA, Tesar DB, Mukhyala K, Theil F-P, Fielder PJ, Khawli LA. Effects of charge on antibody tissue distribution and pharmacokinetics. Bioconjug Chem. 2010;21(12):2153–63. doi:10.1021/bc100261d.
   PubMed Web of Science ®Google Scholar
• Kyte J, Doolittle RF. A simple method for displaying the hydropathic character of a protein. J Mol Biol. 1982;157(1):105–32. doi:10.1016/0022-2836(82)90515-0.
   PubMed Web of Science ®Google Scholar
• Koulakis JP, Rouch J, Huynh N, Wu HH, Dunn JCY, Putterman S. Tumescent injections in subcutaneous pig tissue disperse fluids volumetrically and maintain elevated local concentrations of additives for several hours, suggesting a treatment for drug resistant wounds. Pharm Res. 2020;37(3):51. doi:10.1007/s11095-020-2769-2.
   PubMed Web of Science ®Google Scholar
• Pace CN, Vajdos F, Fee L, Grimsley G, Gray T. How to measure and predict the molar absorption coefficient of a protein. Protein Sci. 1995;4(11):2411–23. doi:10.1002/pro.5560041120.
   PubMed Web of Science ®Google Scholar
• Ying X. An overview of overfitting and its solutions. J Phys Conf Ser. 2019;1168:022022. doi:10.1088/1742-6596/1168/2/022022.
   Google Scholar
• Kagan L. Pharmacokinetic modeling of the subcutaneous absorption of therapeutic proteins. Drug Metab Dispos. 2014;42(11):1890–905. doi:10.1124/dmd.114.059121.
   PubMedGoogle Scholar
• Lou H, Hageman MJ. Machine learning attempts for predicting human subcutaneous bioavailability of monoclonal antibodies. Pharm Res. 2021;38(3):451–60. doi:10.1007/s11095-021-03022-y.
   PubMed Web of Science ®Google Scholar
• Martin KP, Grimaldi C, Grempler R, Hansel S, Kumar S. Trends in industrialization of biotherapeutics: a survey of product characteristics of 89 antibody-based biotherapeutics. Mabs-austin. 2023;15(1):2191301. doi:10.1080/19420862.2023.2191301.
   PubMed Web of Science ®Google Scholar
• Porter C. Lymphatic transport of proteins after s.C. Injection: implications of animal model selection. Adv Drug Deliv Rev. 2001;50(1–2):157–71. doi:10.1016/S0169-409X(01)00153-3.
   PubMed Web of Science ®Google Scholar
• Thomas VA, Balthasar JP. Understanding inter-individual variability in monoclonal antibody disposition. Antibodies. 2019;8(4):56. doi:10.3390/antib8040056.
   Web of Science ®Google Scholar
• Gill KL, Machavaram KK, Rose RH, Chetty M. Potential sources of inter-subject variability in monoclonal antibody pharmacokinetics. Clin Pharmacokinet. 2016;55(7):789–805. doi:10.1007/s40262-015-0361-4.
   PubMed Web of Science ®Google Scholar
• Dirks NL, Meibohm B. Population pharmacokinetics of therapeutic monoclonal antibodies: Clin. Pharmacokinet. 2010;49(10):633–59. doi:10.2165/11535960-000000000-00000.
   Google Scholar
• Zou P, Wang F, Wang J, Lu Y, Tran D, Seo SK. Impact of injection sites on clinical pharmacokinetics of subcutaneously administered peptides and proteins. J Controlled Release. 2021;336:310–21. doi:10.1016/j.jconrel.2021.06.038.
   PubMed Web of Science ®Google Scholar
• Bown HK, Bonn C, Yohe S, Yadav DB, Patapoff TW, Daugherty A, Mrsny RJ. In vitro model for predicting bioavailability of subcutaneously injected monoclonal antibodies. J Controlled Release. 2018;273:13–20. doi:10.1016/j.jconrel.2018.01.015.
   PubMed Web of Science ®Google Scholar