Abstract
In vitro systems that accurately model in vivo conditions in the gastrointestinal tract may aid the development of oral drugs with greater bioavailability. Here we show that the interaction profiles between drugs and intestinal drug transporters can be obtained by modulating transporter expression in intact porcine tissue explants via the ultrasound-mediated delivery of small interfering RNAs and that the interaction profiles can be classified via a random forest model trained on the drug–transporter relationships. For 24 drugs with well-characterized drug–transporter interactions, the model achieved 100% concordance. For 28 clinical drugs and 22 investigational drugs, the model identified 58 unknown drug–transporter interactions, 7 of which (out of 8 tested) corresponded to drug-pharmacokinetic measurements in mice. We also validated the model’s predictions for interactions between doxycycline and four drugs (warfarin, tacrolimus, digoxin and levetiracetam) through an ex vivo perfusion assay and the analysis of pharmacologic data from patients. Screening drugs for their interactions with the intestinal transportome via tissue explants and machine learning may help to expedite drug development and the evaluation of drug safety.
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Data availability
The raw and analysed datasets generated during the study are available for research purposes from the corresponding author on reasonable request.
Code availability
All training data and code used for machine learning and to make predictions are available on GitHub at https://github.com/RekerLab/Transportome.
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Acknowledgements
D.R. is a Swiss National Science Foundation Fellow (grant P2EZP3_168827 and P300P2_177833). This work was in part supported by NIH grant EB000244 (G.T., R.L.), the Karl van Tassel (1925) Career Development Professorship and Department of Mechanical Engineering MIT and Division of Gastroenterology, Brigham and Women’s Hospital (G.T.). The clinical work was in part supported by a Prostate Cancer Foundation Young Investigator Award (J.D.B.). Z.F. is supported by the Department of Defense through the National Defense Science and Engineering Graduate Fellowship Program. Z.Z. is supported by a Fellowship from the Department of Biomedical Engineering at Duke University. The authors acknowledge the use of resources of Microscopy Core Facilities at Swanson Biotechnology Center, David H. Koch Institute for Integrative Cancer Research at MIT, and G. Church for sharing the cell line PK15. The authors also acknowledge Partners RPDR team for their assistance with identifying patients and M. Jimenez for his substantial contributions to editing the manuscript.
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Y.S., D.R., J.D.B., A.R.K. and G.T. conceived the study, designed experiments and analysed data. K.H., Z.W., N.N., Z.F., Z.Z., A.L., V.S., J.W., T.v.E., C.C.Y. and L.M. performed experiments and analysed data. Y.S., D.R., J.D.B., A.R.K. and G.T. wrote the manuscript. R.L. and G.T. supervised the study. All authors discussed the results and assisted in the preparation of the manuscript.
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Y.S., D.R., V.S., R.L. and G.T. are co-inventors on a provisional patent application encompassing the work described. T.v.E., R.L. and G.T. have a financial interest in Vivtex, a biotechnology company applying ex vivo models for high-throughput drug formulation development. D.R. acts as a consultant to the pharmaceutical and biotechnology industry. Complete details of all relationships for profit and not for profit for G.T. and for R.L. can be found in the Supplementary Information.
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Extended data
Extended Data Fig. 1 Tissue viability assay and H&E histology staining to assess the safety and toxicity effect of ex vivo culture and low-frequency ultrasound on pig small intestine.
ATP and CellTiter-Glo assay were performed on ex vivo tissue cultured tissue with different time points to monitor the metabolic activity (a) and viability (b) of sliced tissue. c) ATP assay on ex vivo tissue at different ultrasound conditions to monitor damage caused by ultrasound delivery. d) TEER and FITC-labeled Dextran permeability assays to evaluate the possibility for changes in tissue permeability in response to low-frequency ultrasound treatment. In all experiments, three separate animal tissues were included and four repeats were performed for each tissue (n = 12). e) H&E histology staining using fresh tissue, tissue after a short ultrasound treatment, and tissue after a prolonged ultrasound treatment.
Extended Data Fig. 2 Validation of the ex vivo system for fold decrease of substrates/non-substrates of PEPT1 (A) and MCT1 (B) through the siRNA-mediated knock down of drug transporters of GI-TRIS.
Perfusion decreases for substrates because these enzymes are influx transporters.
Extended Data Fig. 3 Original data for substrate perfusion with siRNA treatment in the ex vivo system.
For each drug, the data is collected from 62–72 trials (n = 62–72) of tissue in four different pigs (m = 4). For siP-gp treated samples, Colchicine, Irinotecan, Loperamide, Nicardipine and Ranitidine are drug transporter specific substrates, while Carbamazepine, Chlorpheniramine, and Doxorubicin are non-specific substrates. For siBCRP treated samples, 4-methylumbelliferone sulfate, Daunorubicin, Mitoxantrone, Pitavastatin, and Rosuvastatin are BCRP specific substrates, while Doxorubicin, LysoTracker Green, and Rhodamine 123 are non-specific substrates. For siMRP2 treated samples, Etoposide, Irinotecan, Olmesartan, Para-aminohippurate, and Valsartan are MRP2 specific substrates, while Colchicine, Digoxin, and Nitrofurantoin are non-specific substrates.
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Shi, Y., Reker, D., Byrne, J.D. et al. Screening oral drugs for their interactions with the intestinal transportome via porcine tissue explants and machine learning. Nat. Biomed. Eng 8, 278–290 (2024). https://doi.org/10.1038/s41551-023-01128-9
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DOI: https://doi.org/10.1038/s41551-023-01128-9