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Closed-loop control of circulating drug levels in live animals

Nature Biomedical Engineering volume 1, Article number: 0070 (2017) Cite this article

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Abstract

Current methods of drug dosing rely on physical parameters (such as sex, age and weight) that do not account for genetic and physiological differences among individual patients. These differences can greatly affect how drugs are processed in the body and can result in ineffective underdosing or toxic overdosing. Here, we describe a generalizable closed-loop system consisting of a biosensor, controller and infusion pump, and a model of drug pharmacokinetics that continuously monitors and adjusts the concentration of a given drug in the body. As proof of concept, we demonstrate that the system can maintain the concentration of doxorubicin—a widely used chemotherapy drug—in live rabbits and rats at any desired set point and in real time, while automatically compensating for large pharmacokinetic differences among individual animals and stabilizing dramatic perturbations arising from acute drug–drug interactions. The feedback-loop system opens up the possibility of tightly controlled, patient-specific dosing of chemotherapeutics and other drugs within narrow therapeutic windows.

Current protocols for predicting the optimal drug dose for a patient generally rely on easily obtained physical parameters, such as sex, age, body weight or body surface area1–3. Unfortunately, these parameters do not account for individual differences in pharmacokinetics (PK; how the drug is processed by the body) and pharmacodynamics (how the body is affected by the drug), which can lead to ineffective underdosing or toxic overdosing4,5. This is an especially important problem for drugs that exhibit narrow therapeutic windows within which the concentration of drug in the bloodstream is both effective and safe, such as chemotherapeutics6,7, immunosuppressants8,9, anticoagulants10, insulin11 and anaesthetics12.

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Figure 1: Closed-loop control of in vivo drug levels with real-time biosensing.
Figure 2: Closed-loop feedback control of Dox in vivo.
Figure 3: Closed-loop control of plasma drug levels in animals with varying PK.
Figure 4: Closed-loop infusion during acute drug–drug interactions.
Figure 5: Closed-loop feedback control of Dox in live, anaesthetized Sprague–Dawley rats.

References

  1. Baker, S. D. et al. Role of body surface area in dosing of investigational anticancer agents in adults, 1991–2001. J. Natl Cancer I. 94, 1883–1888 (2002).

    Article  CAS  Google Scholar 

  2. Gurney, H. How to calculate the dose of chemotherapy. Brit. J. Cancer 86, 1297–1302 (2002).

    Article  CAS  Google Scholar 

  3. Khalil, F. & Läer, S. Physiologically based pharmacokinetic modeling: methodology, applications, and limitations with a focus on its role in pediatric drug development. J. Biomed. Biotechnol. 2011, 907461 (2011).

  4. Mathijssen, R. H. J. et al. Flat-fixed dosing versus body surface area based dosing of anticancer drugs in adults: does it make a difference? Oncologist 12, 913–923 (2007).

    Article  Google Scholar 

  5. Muller, P. Y. & Milton, M. N. The determination and interpretation of the therapeutic index in drug development. Nat. Rev. Drug Discov. 11, 751–761 (2012).

    Article  CAS  Google Scholar 

  6. Gao, B., Klumpen, H.-J. & Gurney, H. Dose calculation of anticancer drugs. Expert Opin. Drug Met. 4, 1307–1319 (2008).

    Article  CAS  Google Scholar 

  7. Undevia, S. D., Gomez-Abuin, G. & Ratain, M. J. Pharmacokinetic variability of anticancer agents. Nat. Rev. Cancer 5, 447–458 (2005).

    Article  CAS  Google Scholar 

  8. Johnston, A. & Holt, D. W. Therapeutic drug monitoring of immunosuppressant drugs. Brit. J. Clin. Pharmaco. 47, 339–350 (1999).

    Article  CAS  Google Scholar 

  9. van Gelder, T., van Schaik, R. H. & Hesselink, D. A. Pharmacogenetics and immunosuppressive drugs in solid organ transplantation. Nat. Rev. Nephrol. 10, 725–731 (2014).

    Article  CAS  Google Scholar 

  10. Burns, M. Management of narrow therapeutic index drugs. J. Thromb. Thrombolys. 7, 137–143 (1999).

    Article  CAS  Google Scholar 

  11. Cohen, M. R. Pharmacists’ role in ensuring safe and effective hospital use of insulin. Am. J. Health-Syst. Ph. 67, S17–S21 (2010).

    Article  Google Scholar 

  12. Kanto, J. & Gepts, E. Pharmacokinetic implications for the clinical use of propofol. Clin. Pharmacokinet. 17, 308–326 (1989).

    Article  CAS  Google Scholar 

  13. Relling, M. V. & Evans, W. E. Pharmacogenomics in the clinic. Nature 526, 343–350 (2015).

    Article  CAS  Google Scholar 

  14. Momper, J. D. & Wagner, J. A. Therapeutic drug monitoring as a component of personalized medicine: applications in pediatric drug development. Clin. Pharmacol. Ther. 95, 138–140 (2014).

    Article  CAS  Google Scholar 

  15. Zanger, U. M. Pharmacogenetics—challenges and opportunities ahead. Front. Pharmacol. 1, 112 (2010).

    PubMed  PubMed Central  Google Scholar 

  16. Wang, B., Canestaro, W. J. & Choudhry, N. K. Clinical evidence supporting pharmacogenomic biomarker testing provided in US Food and Drug Administration drug labels. JAMA Intern. Med. 174, 1938–1944 (2014).

    Article  Google Scholar 

  17. Touw, D. J., Neef, C., Thomson, A. H. & Vinks, A. A. Cost-effectiveness of therapeutic drug monitoring: a systematic review. Ther. Drug Monit. 27, 10–17 (2005).

    Article  CAS  Google Scholar 

  18. Swen, J. J. et al. Translating pharmacogenomics: challenges on the road to the clinic. PLoS Med. 4, 1317–1324 (2007).

    Article  Google Scholar 

  19. DiFrancesco, R. et al. Quality assessment for therapeutic drug monitoring in AIDS Clinical Trials Group (ACTG 5146): a multicenter clinical trial. Ther. Drug Monit. 32, 458–466 (2010).

    Article  Google Scholar 

  20. Klonoff, D. C. Smart sensors for maintaining physiologic homeostasis. J. Diabetes Sci. Technol. 5, 470–475 (2011).

    Article  Google Scholar 

  21. Uemura, K. & Sugimachi, M. Automated cardiovascular drug infusion system to control hemodynamics. Adv. Biomed. Eng. 2, 32–37 (2013).

    Article  Google Scholar 

  22. Struys, M. M. et al. Comparison of closed-loop controlled administration of propofol using Bispectral Index as the controlled variable versus ‘standard practice’ controlled administration. Anesthesiology 95, 6–17 (2001).

    Article  CAS  Google Scholar 

  23. Doyle, F. J., Huyett, L. M., Lee, J. B., Zisser, H. C. & Dassau, E. Closed-loop artificial pancreas systems: engineering the algorithms. Diabetes Care 37, 1191–1197 (2014).

    Article  Google Scholar 

  24. Ferguson, B. S. et al. Real-time, aptamer-based tracking of circulating therapeutic agents in living animals. Sci. Transl. Med. 5, 213ra165 (2013).

    Article  Google Scholar 

  25. Chatelut, E. et al. Dose banding as an alternative to body surface area-based dosing of chemotherapeutic agents. Brit. J. Cancer 107, 1100–1106 (2012).

    Article  CAS  Google Scholar 

  26. Eksborg, S., Strandler, H.-S., Edsmyr, F., Näslund, I. & Tahvanainen, P. Pharmacokinetic study of IV infusions of adriamycin. Eur. J. Clin. Pharmacol. 28, 205–212 (1985).

    Article  CAS  Google Scholar 

  27. Dobbs, N. A. et al. Gender affects doxorubicin pharmacokinetics in patients with normal liver biochemistry. Cancer Chemoth. Pharm. 36, 473–476 (1995).

    Article  CAS  Google Scholar 

  28. Elis, A. et al. Doxorubicin in lymphoma: association between pharmacokinetic variability and clinical response. Ther. Drug Monit. 32, 50–52 (2010).

    Article  CAS  Google Scholar 

  29. Barpe, D. R., Rosa, D. D. & Froehlich, P. E. Pharmacokinetic evaluation of doxorubicin plasma levels in normal and overweight patients with breast cancer and simulation of dose adjustment by different indexes of body mass. Eur. J. Pharm. Sci. 41, 458–463 (2010).

    Article  CAS  Google Scholar 

  30. Ang, K. H., Chong, G. & Li, Y. PID control system analysis, design, and technology. IEEE T. Contr. Syst. T. 13, 559–576 (2005).

    Article  Google Scholar 

  31. Greene, R. F., Collins, J. M., Jenkins, J. F., Speyer, J. L. & Myers, C. E. Plasma pharmacokinetics of adriamycin and adriamycinol: implications for the design of in vitro experiments and treatment protocols. Cancer Res. 43, 3417–3421 (1983).

    CAS  PubMed  Google Scholar 

  32. Camaggi, C. M. et al. Epirubicin and doxorubicin comparative metabolism and pharmacokinetics. A cross-over study. Cancer Chemoth. Pharm. 21, 221–228 (1988).

    CAS  Google Scholar 

  33. Mross, K. et al. Pharmacokinetics and metabolism of epidoxorubicin and doxorubicin in humans. J. Clin. Oncol. 6, 517–526 (1988).

    Article  CAS  Google Scholar 

  34. Mross, K., Mayer, U., Hamm, K., Burk, K. & Hossfeld, D. K. Pharmacokinetics and metabolism of iodo-doxorubicin and doxorubicin in humans. Eur. J. Clin. Pharmacol. 39, 507–513 (1990).

    Article  CAS  Google Scholar 

  35. Schoukroun-Barnes, L. R. et al. Reagentless, structure-switching, electrochemical aptamer-based sensors. Annu. Rev. Anal. Chem. 9, 163–181 (2016).

    Article  CAS  Google Scholar 

  36. Thorslund, S., Sanchez, J., Larsson, R., Nikolajeff, F. & Bergquist, J. Functionality and stability of heparin immobilized onto poly(dimethylsiloxane). Colloid. Surface B 45, 76–81 (2005).

    Article  CAS  Google Scholar 

  37. Maniez-Devos, D. M., Baurain, R., Trouet, A. & Lesne, M. Doxorubicin pharmacokinetics in the rabbit. J. Pharmacol. 16, 159–169 (1985).

    CAS  PubMed  Google Scholar 

  38. Scripture, C. D. & Figg, W. D. Drug interactions in cancer therapy. Nat. Rev. Cancer 6, 546–558 (2006).

    Article  CAS  Google Scholar 

  39. Watanabe, K. Current chemotherapeutic approaches for hepatoblastoma. Int. J. Clin. Oncol. 18, 955–961 (2013).

    Article  CAS  Google Scholar 

  40. Najjar, T. A. O. & Saad, S. Y. Effect of treatment schedule on the toxicity and pharmacokinetics of cisplatin-doxorubicin combination in rabbits. J. Egypt. Nat. Cancer I. 12, 259–265 (2000).

    Google Scholar 

  41. Rahman, A., Carmichael, D., Harris, M. & Roh, J. K. Comparative pharmacokinetics of free doxorubicin and doxorubicin entrapped in cardiolipin liposomes. Cancer Res. 46, 2295–2299 (1986).

    CAS  PubMed  Google Scholar 

  42. Ueda, Y. et al. Comparison of efficacy, toxicity and pharmacokinetics of free adriamycin and adriamycin linked to oxidized dextran in rats. Chem. Pharm. Bull. 37, 1639–1641 (1989).

    Article  CAS  Google Scholar 

  43. Bendetowicz, A. V, Béguin, S., Caplain, H. & Hemker, H. C. Pharmacokinetics and pharmacodynamics of a low molecular weight heparin (enoxaparin) after subcutaneous injection, comparison with unfractionated heparin—a three way cross over study in human volunteers. Thromb. Haemostasis 71, 305–313 (1994).

    Article  CAS  Google Scholar 

  44. Barbour, A., Scaglione, F. & Derendorf, H. Class-dependent relevance of tissue distribution in the interpretation of anti-infective pharmacokinetic/pharmacodynamic indices. Int. J. Antimicrob. Ag. 35, 431–438 (2010).

    Article  CAS  Google Scholar 

  45. Absalom, A. R., Mani, V., De Smet, T. & Struys, M. M. R. F. Pharmacokinetic models for propofol—defining and illuminating the devil in the detail. Brit. J. Anaesth. 103, 26–37 (2009).

    Article  CAS  Google Scholar 

  46. Williams, C. K. & Maddox, R. R. Implementation of an i.v. medication safety system. Am. J. Health-Syst. Ph. 62, 530–536 (2005).

    Google Scholar 

  47. Hortobágyi, G. N. Anthracyclines in the treatment of cancer. Drugs 54, (Suppl. 4) 1–7 (1997).

    Article  Google Scholar 

  48. Singal, P. K. & Iliskovic, N. Doxorubicin-induced cardiomyopathy. N. Engl. J. Med. 339, 900–905 (1998).

    Article  CAS  Google Scholar 

  49. Minotti, G., Menna, P., Salvatorelli, E., Cairo, G. & Gianni, L. Anthracyclines: molecular advances and pharmacologic developments in antitumor activity and cardiotoxicity. Pharmacol. Rev. 56, 185–229 (2004).

    Article  CAS  Google Scholar 

  50. Fulbright, J. M., Huh, W., Anderson, P. & Chandra, J. Can anthracycline therapy for pediatric malignancies be less cardiotoxic? Curr. Oncol. Rep. 12, 411–419 (2010).

    Article  Google Scholar 

  51. Hutson, J. R. et al. Pharmacokinetic and pharmacogenetic determinants and considerations in chemotherapy selection and dosing in infants. Expert Opin. Drug Met. 8, 709–722 (2012).

    Article  CAS  Google Scholar 

  52. Grosman, B., Dassau, E., Zisser, H. C., Jovanovic, L. & Doyle, F. J. Zone model predictive control: a strategy to minimize hyper- and hypoglycemic events. J. Diabetes Sci. Technol. 4, 961–975 (2010).

    Article  Google Scholar 

  53. Greenblatt, D. J., Sellers, E. M. & Koch-Weser, J. Importance of protein binding for the interpretation of serum or plasma drug concentrations. J. Clin. Pharmacol. 22, 259–263 (1982).

    Article  CAS  Google Scholar 

  54. Pavlovic, E. et al. Microfluidic device architecture for electrochemical patterning and detection of multiple DNA sequences. Langmuir 24, 1102–1107 (2008).

    Article  CAS  Google Scholar 

  55. Turksoy, K., Bayrak, E. S., Quinn, L., Littlejohn, E. & Cinar, A. Multivariable adaptive closed-loop control of an artificial pancreas without meal and activity announcement. Diabetes Technol. The. 15, 386–400 (2013).

    Article  CAS  Google Scholar 

  56. Magni, L. et al. Run-to-run tuning of model predictive control for type 1 diabetes subjects: in silico trial. J. Diabetes Sci. Technol. 3, 1091–1098 (2009).

    Article  Google Scholar 

  57. Ho, J. S. et al. Wireless power transfer to deep-tissue microimplants. Proc. Natl Acad. Sci. USA 111, 7974–7979 (2014).

    Article  CAS  Google Scholar 

  58. Farra, R. et al. First-in-human testing of a wirelessly controlled drug delivery microchip. Sci. Transl. Med. 4, 122ra21 (2012).

    Article  Google Scholar 

  59. Phares, N., White, R. J. & Plaxco, K. W. Improving the stability and sensing of electrochemical biosensors by employing trithiol-anchoring groups in a six-carbon self-assembled monolayer. Anal. Chem. 81, 1095–1100 (2009).

    Article  CAS  Google Scholar 

  60. Herbert, J. M. et al. The antiaggregating and antithrombotic activity of clopidogrel is potentiated by aspirin in several experimental models in the rabbit. Thromb. Haemostasis 80, 512–518 (1998).

    Article  CAS  Google Scholar 

  61. Zehnder, A. M., Hawkins, M. G., Trestrail, E. A., Holt, R. W. & Kent, M. S. Calculation of body surface area via computed tomography-guided modeling in domestic rabbits (Oryctolagus cuniculus). Am. J. Vet. Res. 73, 1859–1863 (2012).

    Article  Google Scholar 

  62. Gilpin, D. A. Calculation of a new Meeh constant and experimental determination of burn size. Burns 22, 607–611 (1996).

    Article  CAS  Google Scholar 

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Acknowledgements

We are grateful for the financial support of the Garland Initiative, Army Research Office (W911NF-10-2-0114, W911QY-15-C-0026) and the W. M. Keck Foundation Medical Research Program. We thank B. Eisenhower, D. Hoggarth, J. Somerson, D. Mamerow, A. Pressman, G. Marcus, S. Hall, M. Eisenstein, M. Nakamoto, K. Plaxco and F. Doyle for helpful discussions. We also thank A. Griffin, R. Wynn, and M. Garcia at the UCSB Animal Resource Center for their technical expertise and assistance with the animal studies.

Author information

Author notes

  1. P. L. Mage

    Present address: Department of Electrical Engineering, Stanford University, Stanford, California, 94305, USA

  2. B. S. Ferguson

    Present address: Aptitude Medical Systems, Inc., Santa Barbara, California, 93105, USA

Authors and Affiliations

  1. Department of Materials, University of California, Santa Barbara, 93106, California, USA

    P. L. Mage & H. T. Soh

  2. Institute for Collaborative Biotechnologies, University of California, Santa Barbara, 93106, California, USA

    B. S. Ferguson, D. Maliniak, K. L Ploense, T. E. Kippin & H. T. Soh

  3. Department of Psychological and Brain Sciences, University of California, Santa Barbara, 93106, California, USA

    K. L Ploense & T. E. Kippin

  4. Neuroscience Research Institute, University of California, Santa Barbara, 93106, California, USA

    T. E. Kippin

  5. Department of Molecular, Cellular and Developmental Biology, University of California, Santa Barbara, 93106, California, USA

    T. E. Kippin

  6. Department of Electrical Engineering, Stanford University, Stanford, 94305, California, USA

    H. T. Soh

  7. Department of Radiology, Stanford University, Stanford, 94305, California, USA

    H. T. Soh

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Contributions

B.S.F. and H.T.S. conceived of the project. P.L.M. and B.S.F. designed the experiments, fabricated the sensors and created the control and simulation software. P.L.M. directed the animal studies, performed the controller simulations and analysed the data. D.M. and K.L.P. performed all animal procedures in the studies. T.E.K. assisted in design of, provided supervision for, and wrote the protocols for the animal studies. P.L.M. and H.T.S wrote and edited the manuscript. All authors discussed the results and commented on the manuscript.

Corresponding author

Correspondence to H. T. Soh.

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Mage, P., Ferguson, B., Maliniak, D. et al. Closed-loop control of circulating drug levels in live animals. Nat Biomed Eng 1, 0070 (2017). https://doi.org/10.1038/s41551-017-0070

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