PK/PD modeling analysis for dosing regimen selection of isatuximab as single agent and in combination therapy in patients with multiple myeloma.
Kimiko KoiwaiRaouf El-CheikhHoai-Thu ThaiClaire BrillacJean-Baptiste FauChristine Veyrat-FolletMarie-Laure RisseHelgi van de VeldeDorothée SemiondLaurent NguyenPublished in: CPT: pharmacometrics & systems pharmacology (2021)
This analysis describes the pharmacokinetic/pharmacodynamic (PK/PD) modeling framework that supported selection of the isatuximab (anti-CD38 monoclonal antibody) dosing regimen alongside its early clinical development in patients with relapsed/refractory multiple myeloma (RRMM). The PK/PD mathematical model characterized the variations of patient serum M-protein concentrations, the primary marker of tumor burden in multiple myeloma (MM). Three separate PK/PD models were built sequentially as data became available from phase I clinical trials. The primary PK/PD analysis was initiated using monotherapy phase I study data (n = 122), followed by analysis of data collected from phase Ib combination studies with lenalidomide and dexamethasone (Rd, n = 40) and then with pomalidomide and dexamethasone (Pd, n = 31). Using the PK/PD model, abnormal "myeloma" protein (M-protein) profiles under different isatuximab dosing regimens were simulated. Overall, simulations revealed that regimens which included a loading period of four weekly administrations followed by administration every 2 weeks thereafter (QW4-Q2W), reduced M-protein levels more than a Q2W regimen without a loading period. For isatuximab monotherapy, a 20 mg/kg dose induced greater reduction in serum M-protein levels compared with doses equal or lower than 10 mg/kg. For isatuximab in combination with either Rd or Pd, simulations yielded no substantial benefit in terms of M-protein reduction between isatuximab 10 mg/kg and 20 mg/kg. These PK/PD analyses supported the use of isatuximab 10 mg/kg QW4-Q2W in combination with Pd in the phase III trial.
Keyphrases
- multiple myeloma
- clinical trial
- combination therapy
- phase iii
- protein protein
- open label
- binding protein
- monoclonal antibody
- low dose
- electronic health record
- oxidative stress
- machine learning
- molecular dynamics
- artificial intelligence
- diffuse large b cell lymphoma
- acute myeloid leukemia
- high dose
- data analysis
- endothelial cells
- single cell
- deep learning
- high glucose