title: A method for optimizing dosage regimens in oncology by visualizing the safety and efficacy response surface: analysis of inotuzumab ozogamicin.
abstract: OBJECTIVE
  The aim of this investigation was to develop a quantitative method to optimize inotuzumab ozogamicin (InO) dosage regimen in patients with indolent non-Hodgkin lymphoma (NHL) by simultaneously balancing safety and efficacy.
  
  
  METHODS
  Pharmacokinetics (PK), safety and efficacy data were obtained from a phase 2 trial of InO administered intravenously to patients (n = 81) with indolent NHL. The PK was described by a two-compartment model which was linked to: (1) an exponential tumor growth model to describe tumor size time course (efficacy determinant expressed as objective response rate) and (2) a precursor-dependent platelet inhibition model to describe platelet time course (safety determinant expressed as thrombocytopenia grade). The model was used to simulate virtual trials to construct safety and efficacy response surfaces. Using the simulated safety and efficacy contours, a clinical utility index (CUI) contour was then constructed, from which optimal InO regimens were then selected.
  
  
  RESULTS
  The model-simulated efficacy response surface indicated near-optimal efficacy of InO at the dosage regimen used in the trial (1.8 mg/m(2) every 4 weeks). The model-simulated safety response surface indicated that modifying the dosage regimen resulted in modest improvements in safety with little compromise in efficacy. The CUI contour identified 2 mg/m(2) every 10, 11, or 12 weeks as the "sweet spot" for optimal InO dosage regimen in patients with indolent NHL.
  
  
  CONCLUSIONS
  An approach to dosage regimen optimization was developed for simultaneously balancing safety and efficacy. This approach allows objective identification of optimal dosage regimens from early trial information and thus has broad utility across oncology trials.
authors.affiliation: Department of Clinical Pharmacology, Pfizer Oncology, La Jolla, CA, USA.|Ionis Pharmaceuticals, Inc., 2855 Gazelle Court, Carlsbad, CA, 92010, USA.
authors.forename: Kenneth T
authors.initials: KT
authors.lastname: Luu
authors.affiliation: Department of Clinical Pharmacology, Pfizer Oncology, 500 Arcola Rd, Collegeville, PA, 19426, USA. Joseph.boni@pfizer.com.
authors.forename: Joseph
authors.initials: J
authors.lastname: Boni
mesh_terms: D000465:Algorithms; D061067:Antibodies, Monoclonal, Humanized / Q000008:administration & dosage* / Q000009:adverse effects / Q000493:pharmacokinetics; D000970:Antineoplastic Agents / Q000008:administration & dosage* / Q000009:adverse effects / Q000493:pharmacokinetics; D003198:Computer Simulation; D018450:Disease Progression; D004334:Drug Administration Schedule; D054796:Drug Dosage Calculations; D006801:Humans; D000080045:Inotuzumab Ozogamicin; D008228:Lymphoma, Non-Hodgkin / Q000188:drug therapy; D015233:Models, Statistical; D009369:Neoplasms / Q000188:drug therapy*; D010976:Platelet Count; D013921:Thrombocytopenia / Q000139:chemically induced; D016896:Treatment Outcome
chemical_list: D061067:Antibodies, Monoclonal, Humanized; D000970:Antineoplastic Agents; D000080045:Inotuzumab Ozogamicin
keywords: Clinical utility index; Dose optimization; Inotuzumab ozogamicin; Non-Hodgkin lymphoma; Pharmacodynamics; Pharmacokinetics