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Clin Pharmacol Ther. 2021 Jan;109(1):47-50. doi: 10.1002/cpt.2064. Epub 2020 Oct 26.

Precision Dosing: An Industry Perspective.

Clinical pharmacology and therapeutics

Richard W Peck

Affiliations

  1. Clinical Pharmacology, Pharma Research & Early Development, Roche Innovation Center, Basel, Switzerland.

PMID: 33107023 PMCID: PMC7820949 DOI: 10.1002/cpt.2064

[No abstract available.]

References

  1. Vinks, A.A., Peck, R.W., Neely, M. & Mould, D.R. Development and implementation of electronic health record-integrated model-informed clinical decision support tools for the precision dosing of drugs. Clin. Pharmacol. Ther. 107, 129-135 (2020). - PubMed
  2. Maloney, A. A new paradigm. “Learn-learn more”; Dose-exposure-response at the center of drug development and regulatory approval. Clin. Pharmacol. Ther. 102, 942-950 (2017). - PubMed
  3. Schuck, R.N., Pacanowski, M., Kim, S., Madabushi, R. & Zineh, I. Use of titration as a therapeutic individualization strategy: an analysis of Food and Drug Administration-approved drugs. Clin. Transl. Sci. 12, 236-239 (2019). - PubMed
  4. Xolair [prescribing information]. (Novartis, East Hanover, NJ, 07936-1080) Accessed July 8, 2020. - PubMed
  5. Gammagard liquid [prescribing information]. (Baxter Healthcare Corporation, Westlake Village, CA, 2012) Accessed July 8, 2020. - PubMed
  6. Wilate [prescribing information]. (Octapharma, Vienna, Austria, 2019) Accessed July 8, 2020. - PubMed
  7. US Food and Drug Administration.Guidance for industry and FDA staff: software as a medical device (SAMD): Clinical evaluation (2017) Accessed July 8, 2020. - PubMed
  8. Peck, R.W. Precision medicine is not just genomics: the right dose for every patient. Annu. Rev. Pharmacol. Toxicol. 58, 105-122 (2018). - PubMed
  9. Ribba, B., Dudal, S., Lavé, T. & Peck, R.W. Model-informed artificial intelligence: reinforcement learning for precision dosing. Clin. Pharmacol. Ther. 107, 853-857 (2020). - PubMed
  10. Bica, I., Alaa, A.M., Lambert, C. & van der Schaar, M. From real-world patient data to individualized treatment effects using machine learning: current and future methods to address underlying treatment challenges. Clin. Pharmacol. Ther. 109, 87-100 (2021). - PubMed

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