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Emerging Roles for Clinical Pharmacometrics in Cancer Precision Medicine

  • Precision Medicine and Pharmacogenomics (S Nair, Section Editor)
  • Published:
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Abstract

Purpose of Review

Although significant progress has been made in cancer research, there exist unmet needs in patient care as reflected by the “Cancer Moonshot” goals. This review appreciates the potential utility of quantitative pharmacology in cancer precision medicine.

Recent Findings

Precision oncology has received federal funding largely due to “The Precision Medicine Initiative.” Precision medicine takes into account the inter-individual variability and allows for tailoring the right medication or the right dose of drug to the best subpopulation of patients who will likely respond to the intervention, thus enhancing therapeutic success and reducing “financial toxicity” to patients, families, and caregivers. The National Cancer Institute (NCI) committed US$70 million from its fiscal year 2016 budget to advance precision oncology research. Through the “Critical Path Initiative,” pharmacometrics has gained an important role in drug development; however, it is yet to find widespread clinical applicability.

Summary

Stakeholders including clinicians and pharmacometricians need to work in concert to ensure that benefits of model-based approaches are harnessed to personalize cancer care to the individual needs of the patient via better dosing strategies, companion diagnostics, and predictive biomarkers. In medical oncology, where immediate patient care is the clinician’s primary concern, pharmacometric approaches can be tailored to build models that rely on patient data already digitally available in the electronic health record (EHR) to facilitate quick collaboration and avoid additional funding needs. Taken together, we offer a roadmap for the future of precision oncology which is fraught with both challenges and opportunities for pharmacometricians and clinicians alike.

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References

Papers of particular interest, published recently, have been highlighted as: • Of importance •• Of major importance

  1. US Food and Drug Administration. The Precision Medicine Initiative https://www.fda.gov/MedicalDevices/ProductsandMedicalProcedures/InVitroDiagnostics/PrecisionMedicine-MedicalDevices/default.htm

  2. Obama White House Archives. The Precision Medicine Initiative https://obamawhitehouse.archives.gov/node/333101

  3. Collins FS, Varmus H. A new initiative on precision medicine. N Engl J Med. 2015;372(9):793–5.

    Article  CAS  Google Scholar 

  4. National Cancer Institute. NCI and the Precision Medicine Initiative 2017; https://www.cancer.gov/research/areas/treatment/pmi-oncology

  5. Cook SF, Bies RR. Disease progression modeling: key concepts and recent developments. Curr Pharmacol Rep. 2016;2(5):221–30.

    Article  Google Scholar 

  6. National Cancer Institute. Cancer Moonshot https://www.cancer.gov/research/key-initiatives/moonshot-cancer-initiative

  7. US Food and Drug Administration. Innovation or stagnation: challenge and opportunity on the critical path to new medical products 2004; https://www.fda.gov/ScienceResearch/SpecialTopics/CriticalPathInitiative/default.htm

  8. Ribba B, Holford NH, Magni P, Troconiz I, Gueorguieva I, Girard P, et al. A review of mixed-effects models of tumor growth and effects of anticancer drug treatment used in population analysis. CPT Pharmacometrics Syst Pharmacol. 2014;3:e113.

    Article  CAS  Google Scholar 

  9. Kloft C. Pharmacometrics and systems biology in oncology: is there an intersection? Int J Clin Pharmacol Ther. 2013;51(1):89–90.

    Article  Google Scholar 

  10. Buil-Bruna N, Lopez-Picazo JM, Martin-Algarra S, Troconiz IF. Bringing model-based prediction to oncology clinical practice: a review of pharmacometrics principles and applications. Oncologist. 2016;21(2):220–32.

    Article  Google Scholar 

  11. Patel JN, O'Neil BH, Deal AM, Ibrahim JG, Sherrill GB, Olajide OA, et al. A community-based multicenter trial of pharmacokinetically guided 5-fluorouracil dosing for personalized colorectal cancer therapy. Oncologist. 2014;19(9):959–65.

    Article  CAS  Google Scholar 

  12. Kline CL, Schiccitano A, Zhu J, Beachler C, Sheikh H, Harvey HA, et al. Personalized dosing via pharmacokinetic monitoring of 5-fluorouracil might reduce toxicity in early- or late-stage colorectal cancer patients treated with infusional 5-fluorouracil-based chemotherapy regimens. Clin Colorectal Cancer. 2014;13(2):119–26.

    Article  Google Scholar 

  13. Pauley JL, Panetta JC, Crews KR, Pei D, Cheng C, McCormick J, et al. Between-course targeting of methotrexate exposure using pharmacokinetically guided dosage adjustments. Cancer Chemother Pharmacol. 2013;72(2):369–78.

    Article  CAS  Google Scholar 

  14. Takashina Y, Naito T, Mino Y, Yagi T, Ohnishi K, Kawakami J. Impact of CYP3A5 and ABCB1 gene polymorphisms on fentanyl pharmacokinetics and clinical responses in cancer patients undergoing conversion to a transdermal system. Drug Metab Pharmacokinet. 2012;27(4):414–21.

    Article  CAS  Google Scholar 

  15. Cai X, Fang JM, Xue P, Song WF, Hu J, Gu HL, et al. The role of IVS14+1 G > A genotype detection in the dihydropyrimidine dehydrogenase gene and pharmacokinetic monitoring of 5-fluorouracil in the individualized adjustment of 5-fluorouracil for patients with local advanced and metastatic colorectal cancer: a preliminary report. Eur Rev Med Pharmacol Sci. 2014;18(8):1247–58.

    CAS  PubMed  Google Scholar 

  16. Pfreundschuh M, Murawski N, Zeynalova S, Ziepert M, Loeffler M, Hanel M, et al. Optimization of rituximab for the treatment of DLBCL: increasing the dose for elderly male patients. Br J Haematol. 2017;179(3):410–20.

    Article  CAS  Google Scholar 

  17. Zhao W, Zhang D, Fakhoury M, Fahd M, Duquesne F, Storme T, et al. Population pharmacokinetics and dosing optimization of vancomycin in children with malignant hematological disease. Antimicrob Agents Chemother. 2014;58(6):3191–9.

    Article  Google Scholar 

  18. Buil-Bruna N, Sahota T, Lopez-Picazo JM, Moreno-Jimenez M, Martin-Algarra S, Ribba B, et al. Early prediction of disease progression in small cell lung cancer: toward model-based personalized medicine in oncology. Cancer Res. 2015;75(12):2416–25.

    Article  CAS  Google Scholar 

  19. Wilbaux M, Tod M, De Bono J, Lorente D, Mateo J, Freyer G, et al. A joint model for the kinetics of CTC count and PSA concentration during treatment in metastatic castration-resistant prostate cancer. CPT Pharmacometrics Syst Pharmacol. 2015;4(5):277–85.

    Article  CAS  Google Scholar 

  20. Schindler E, Krishnan SM, Mathijssen R, Ruggiero A, Schiavon G, Friberg LE. Pharmacometric modeling of liver metastases’ diameter, volume, and density and their relation to clinical outcome in imatinib-treated patients with gastrointestinal stromal tumors. CPT Pharmacometrics Syst Pharmacol. 2017;6(7):449–57.

    Article  CAS  Google Scholar 

  21. Schindler E, Amantea MA, Karlsson MO, Friberg LE. PK-PD modeling of individual lesion FDG-PET response to predict overall survival in patients with sunitinib-treated gastrointestinal stromal tumor. CPT Pharmacometrics Syst Pharmacol. 2016;5(4):173–81.

    Article  CAS  Google Scholar 

  22. Kletting P, Meyer C, Reske SN, Glatting G. Potential of optimal preloading in anti-CD20 antibody radioimmunotherapy: an investigation based on pharmacokinetic modeling. Cancer Biother Radiopharm. 2010;25(3):279–87.

    Article  CAS  Google Scholar 

  23. Gallo JM, Birtwistle MR. Network pharmacodynamic models for customized cancer therapy. Wiley Interdiscip Rev Syst Biol Med. 2015;7(4):243–51.

    Article  Google Scholar 

  24. •• Majid O, Gupta A, Reyderman L, Olivo M, Hussein Z. Population pharmacometric analyses of eribulin in patients with locally advanced or metastatic breast cancer previously treated with anthracyclines and taxanes. J Clin Pharmacol. 2014;54(10):1134–43. This manuscript is a very good study combining systems pharmacology and pharmacometric analyses of eribulin.

    Article  CAS  Google Scholar 

  25. Xu C, Goggin TK, Su XY, Taverna P, Oganesian A, Lowder JN, et al. Simultaneous modeling of biomarker and toxicity response predicted optimal regimen of guadecitabine (SGI-110) in myeloid malignancies. CPT Pharmacometrics Syst Pharmacol. 2017;6:712–8.

    Article  CAS  Google Scholar 

  26. Benzekry S, Pasquier E, Barbolosi D, Lacarelle B, Barlesi F, Andre N, et al. Metronomic reloaded: theoretical models bringing chemotherapy into the era of precision medicine. Semin Cancer Biol. 2015;35(53–61.

    Article  Google Scholar 

  27. Hutchinson L. Metronomics—an alternative P4 medicine. Nat Rev Clin Oncol. 2016;13(8):461.

    Article  Google Scholar 

  28. Bocci G, Kerbel RS. Pharmacokinetics of metronomic chemotherapy: a neglected but crucial aspect. Nat Rev Clin Oncol. 2016;13(11):659–73.

    Article  CAS  Google Scholar 

  29. Ciccolini J, Barbolosi D, Meille C, Lombard A, Serdjebi C, Giacometti S, et al. Pharmacokinetics and pharmacodynamics-based mathematical modeling identifies an optimal protocol for metronomic chemotherapy. Cancer Res. 2017;77(17):4723–33.

    Article  CAS  Google Scholar 

  30. McCune JS, Bemer MJ, Barrett JS, Scott Baker K, Gamis AS, Holford NH. Busulfan in infant to adult hematopoietic cell transplant recipients: a population pharmacokinetic model for initial and Bayesian dose personalization. Clin Cancer Res. 2014;20(3):754–63.

    Article  CAS  Google Scholar 

  31. Mizuno K, Dong M, Fukuda T, Chandra S, Mehta PA, McConnell S, et al. Population pharmacokinetics and optimal sampling strategy for model-based precision dosing of melphalan in patients undergoing hematopoietic stem cell transplantation. Clin Pharmacokinet. 2017;

  32. Sanghavi K, Wiseman A, Kirstein MN, Cao Q, Brundage R, Jensen K, et al. Personalized fludarabine dosing to reduce nonrelapse mortality in hematopoietic stem-cell transplant recipients receiving reduced intensity conditioning. Transl Res. 2016;175:103–115 e104.

    Article  CAS  Google Scholar 

  33. •• Nair S. Pharmacometrics and systems pharmacology of immune checkpoint inhibitor nivolumab in cancer translational medicine. Adv Modern Oncol Res. 2016;2(1):18–31. The manuscript gives a detailed account of pharmacometric and systems pharmacology variables in nivolumab immunotherapy.

    Article  CAS  Google Scholar 

  34. Nair S, Iyer A, Vijay V, Bandlamudi S, Llerena A. Pharmacokinetics and systems pharmacology of monoclonal antibody olaratumab for inoperable soft tissue sarcoma. Adv Modern Oncol Res. 2017;3(3):114–25.

    Article  Google Scholar 

  35. Khosravan R, Toh M, Garrett M, La Fargue J, Ni G, Marbury TC, et al. Pharmacokinetics and safety of sunitinib malate in subjects with impaired renal function. J Clin Pharmacol. 2010;50(4):472–81.

    Article  CAS  Google Scholar 

  36. Janus N, Launay-Vacher V. Pharmacokinetic/pharmacodynamic considerations for cancer patients undergoing hemodialysis. Expert Opin Drug Metab Toxicol. 2017;13(6):617–23.

    Article  CAS  Google Scholar 

  37. Llerena A. Population pharmacogenetics and global health. Drug Metab Pers Ther. 2015;30(2):73–4.

    PubMed  Google Scholar 

  38. •• Nair S, Llerena A. Editorial: new vistas in personalized medicine for ethnicity in cancer: population pharmacogenomics and pharmacometrics. Drug Metab Pers Ther 2018; 33(2):In Press June issue. The editorial provides perspectives on inter-individual variability and model-based approaches to cancer precision medicine.

  39. Myszka A, Nguyen-Dumont T, Karpinski P, Sasiadek MM, Akopyan H, Hammet F, et al. Targeted massively parallel sequencing characterises the mutation spectrum of PALB2 in breast and ovarian cancer cases from Poland and Ukraine. Familial Cancer. 2017;

  40. Nair S. Current insights into the molecular systems pharmacology of lncRNA-miRNA regulatory interactions and implications in cancer translational medicine. AIMS Mol Sci. 2016;3(2):104–24.

    Article  CAS  Google Scholar 

  41. Nair S, Kong A. Architecture of signature miRNA regulatory networks in cancer chemoprevention. Curr Pharmacol Rep. 2015;1(2):89–101.

    Article  CAS  Google Scholar 

  42. • Nair S, Kong A. Pharmacometrics of nutraceutical sulforaphane and its implications in prostate cancer prevention. J Chin Pharm Sci. 2016;25(1):12–22. The manuscript describes the pharmacokinetics guiding the anticancer activity of nutraceutical sulforaphane from broccoli and other cruciferous vegetables.

    Google Scholar 

  43. Yao Z, Hoffman EP, Ghimbovschi S, DuBois DC, Almon RR, Jusko WJ Pharmacodynamic/pharmacogenomic modeling of insulin resistance genes in rat muscle after methylprednisolone treatment: exploring regulatory signaling cascades. Gene Regul Syst Bio 2008; 2(141–161).

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Funding

This work is supported in part by NIH R01 CA200129 to Prof. Ah-Ng Tony Kong from the National Cancer Institute (NCI) and National Institutes of Health (NIH) and in part by institutional funds from Rutgers, The State University of New Jersey, USA.

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Authors and Affiliations

  1. Amrita Cancer Discovery Biology Laboratory, Amrita Vishwa Vidyapeetham University, Amritapuri, Clappana P.O., Kollam, Kerala, 690525, India

    Sujit Nair

  2. Center for Cancer Chemoprevention Research and Department of Pharmaceutics, Ernest Mario School of Pharmacy, Rutgers, The State University of New Jersey, 160 Frelinghuysen Road, Piscataway, NJ, 08854, USA

    Ah-Ng Tony Kong

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Correspondence to Sujit Nair or Ah-Ng Tony Kong.

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On behalf of all authors, the corresponding author states that there is no conflict of interest.

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This article does not contain any studies with human or animal subjects performed by any of the authors.

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This article is part of the Topical Collection on Precision Medicine and Pharmacogenomics

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Nair, S., Kong, AN.T. Emerging Roles for Clinical Pharmacometrics in Cancer Precision Medicine. Curr Pharmacol Rep 4, 276–283 (2018). https://doi.org/10.1007/s40495-018-0139-0

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