Tomás Sou (1), Sebastien Lorenzo (1), Romain Sechaud (1), Hans-Jochen Weber (1)
(1) Novartis, Basel, Switzerland
Introduction:
Overexpression of murine double minute 2 (MDM2), a key negative regulator of the tumour suppressor protein p53, has been reported in a variety of cancers [1]. Siremadlin, a MDM2 inhibitor, is being investigated as a new treatment for acute myeloid leukaemia (AML). The effect of siremadlin on delayed thrombocytopenia in patients with solid tumours and haematological malignancies have been previously reported [2,3]. However, the impact of disease on drug action as well as the interwinding effect of drug and disease on thrombocytopenia have not been fully evaluated. Specifically, the progression of the underlying condition may impact on the clinical manifestation of thrombocytopenia. A population PK/PD model characterising the relationship between plasma pharmacokinetics (PK) of siremadlin and platelet levels considering the impact of haematological conditions and disease progression can therefore be invaluable to support dose selection.
Objectives:
This work aims to develop a population PK/PD model characterising the relationship between siremadlin plasma PK and platelet levels in patients with haematological malignancies to support dose selection.
Methods:
Plasma drug concentrations and platelet data were obtained from a phase I study on patients with p53 wild-type solid tumours and haematological malignancies following different dosing regimens. The data were analysed using the Monolix Suite 2023R1. Using a sequential approach, a population PK (PopPK) model was first developed using the available PK data and the individual PK predictions from the PopPK model were used to drive drug effect on platelets. The platelet model was a cell maturation model adapted from Friberg et al (2002) [4] and different drug effect functions and disease mechanisms were evaluated. The selected model was built into a Shiny application [5,6] to allow interactive exploration of different dosing scenarios. Dosing regimens from 10 to 40 mg QD for 5 days with different periods of drug holiday and number of cycles were evaluated. In the population simulations, platelet profiles from 1000 virtual subjects with solid tumour and haematological malignancies were generated to assess the risk of thrombocytopenia resulting from the different dosing regimens.
Results:
The plasma PK profiles of siremadlin were well-described by a one-compartment disposition model with linear clearance (CL/F) and delayed absorption using a transit compartment model. Body weight was included as a covariate on volume of distribution (V/F) and the correlation between CL/F and V/F was considered. The population estimates (RSE%) of the PopPK model were: ktr = 6.69 h-1 (8.31%); MTT = 0.793 h (4.27%); ka = 3.69 h-1 (19.5%); V/F = 115 L (2.58%); CL/F = 5.90 L/h (4.22%); beta_V_tBWKG = 0.883 (9.36%); corr_V_Cl = 0.631 (9.09%). In the current analysis, drug effect on platelets, driven by drug concentrations in the central compartment, was described by a drug effect function potentiating cell apoptosis in the proliferating precursor compartment representing bone marrow cells. The latest model incorporated two subpopulations of precursor cells with different susceptibility to the drug. The model described the data and was able to simulate the delayed thrombocytopenia resulting from different dosing regimens. Compared to solid tumour patients, baseline platelet count (PLTZ) in haematological malignancies was noticeably lower (PLTZ: 30.8 vs 229 G/L). The population estimates (RSE%) of the platelet model in haematological malignancies were: PLTZ = 30.8 (10.5%); MMT = 142 (14.8%); gamma = 0.113 (13.8%), SLP = 0.00184 (11.2%) and kSR = 0.0000131 (74.8%). The simulations showed that following a single cycle of treatment in haematological malignancies, platelet count decreased to the lowest level after approximately 15 days for a typical subject before a gradual recovery. In addition, compared to solid tumour patients, platelet levels were more responsive to drug effect in haematological malignancies as shown by a more prominent initial decline upon treatment and a less delayed recovery time.
Conclusion:
This work shows the invaluable role population PK/PD modelling can play in dose selection. In particular, the semi-mechanistic model helped investigate the interwinding effect of drug and disease on platelet dynamics to support dosing decision. Model development to improve the understanding of drug action and disease effect in haematological malignancies is ongoing.
References:
[1] Guerreiro N et al. 2021. “Translational modeling of anticancer efficacy to predict clinical outcomes in a first-in-human phase 1 study of MDM2 inhibitor HDM201.” AAPS J. 23(2):28
[2] Meille C et al. PAGE 27 (2018) Abstr 8612 [www.page-meeting.org/?abstract=8612]
[3] Sou T et al. PAGE 31 (2023) Abstr 10337 [www.page-meeting.org/?abstract=10337]
[4] Friberg L et al. 2002. “Model of chemotherapy-induced myelosuppression with parameter consistency across drugs.” J. Clin. Oncol. 20:4713–4721.
[5] https://CRAN.R-project.org/package=shiny
[6] https://CRAN.R-project.org/package=mrgsolve
Reference: PAGE 32 (2024) Abstr 10920 [www.page-meeting.org/?abstract=10920]
Poster: Drug/Disease Modelling - Oncology