Physiologically based pharmacokinetic modeling and simulation to predict drug-drug interactions of ivosidenib with CYP3A perpetrators in patients with acute myeloid leukemia

Cancer Chemother Pharmacol. 2020 Nov;86(5):619-632. doi: 10.1007/s00280-020-04148-3. Epub 2020 Sep 25.

Abstract

Purpose: Develop a physiologically based pharmacokinetic (PBPK) model of ivosidenib using in vitro and clinical PK data from healthy participants (HPs), refine it with clinical data on ivosidenib co-administered with itraconazole, and develop a model for patients with acute myeloid leukemia (AML) and apply it to predict ivosidenib drug-drug interactions (DDI).

Methods: An HP PBPK model was developed in Simcyp Population-Based Simulator (version 15.1), with the CYP3A4 component refined based on a clinical DDI study. A separate model accounting for the reduced apparent oral clearance in patients with AML was used to assess the DDI potential of ivosidenib as the victim of CYP3A perpetrators.

Results: For a single 250 mg ivosidenib dose, the HP model predicted geometric mean ratios of 2.14 (plasma area under concentration-time curve, to infinity [AUC0-∞]) and 1.04 (maximum plasma concentration [Cmax]) with the strong CYP3A4 inhibitor, itraconazole, within 1.26-fold of the observed values (2.69 and 1.0, respectively). The AML model reasonably predicted the observed ivosidenib concentration-time profiles across all dose levels in patients. Predicted ivosidenib geometric mean steady-state AUC0-∞ and Cmax ratios were 3.23 and 2.26 with ketoconazole, and 1.90 and 1.52 with fluconazole, respectively. Co-administration of the strong CYP3A4 inducer, rifampin, predicted a greater DDI effect on a single dose of ivosidenib than on multiple doses (AUC ratios 0.35 and 0.67, Cmax ratios 0.91 and 0.81, respectively).

Conclusion: Potentially clinically relevant DDI effects with CYP3A4 inducers and moderate and strong inhibitors co-administered with ivosidenib were predicted. Considering the challenges of conducting clinical DDI studies in patients, this PBPK approach is valuable in ivosidenib DDI risk assessment and management.

Keywords: Acute myeloid leukemia; CYP3A perpetrators; Drug–drug interactions; Ivosidenib; Physiologically based pharmacokinetic model.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Administration, Oral
  • Antineoplastic Agents / administration & dosage
  • Antineoplastic Agents / pharmacokinetics*
  • Area Under Curve
  • Computer Simulation
  • Cytochrome P-450 CYP3A / metabolism
  • Cytochrome P-450 CYP3A Inducers / administration & dosage
  • Cytochrome P-450 CYP3A Inducers / pharmacokinetics*
  • Cytochrome P-450 CYP3A Inhibitors / administration & dosage
  • Cytochrome P-450 CYP3A Inhibitors / pharmacokinetics*
  • Drug Interactions
  • Female
  • Fluconazole / administration & dosage
  • Fluconazole / pharmacokinetics
  • Glycine / administration & dosage
  • Glycine / analogs & derivatives
  • Glycine / pharmacokinetics
  • Healthy Volunteers
  • Humans
  • Itraconazole / administration & dosage
  • Itraconazole / pharmacokinetics*
  • Ketoconazole / administration & dosage
  • Ketoconazole / pharmacokinetics
  • Leukemia, Myeloid, Acute / drug therapy*
  • Male
  • Microsomes, Liver
  • Models, Biological
  • Pyridines / administration & dosage
  • Pyridines / pharmacokinetics
  • Rifampin / administration & dosage
  • Rifampin / pharmacokinetics

Substances

  • Antineoplastic Agents
  • Cytochrome P-450 CYP3A Inducers
  • Cytochrome P-450 CYP3A Inhibitors
  • Pyridines
  • Itraconazole
  • Fluconazole
  • Cytochrome P-450 CYP3A
  • CYP3A4 protein, human
  • ivosidenib
  • Ketoconazole
  • Glycine
  • Rifampin