PK/PD modeling analysis for dosing regimen selection of isatuximab as single agent and in combination therapy in patients with multiple myeloma.

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.

Drug-Disease Interaction and Time-Dependent Population Pharmacokinetics of Isatuximab in Relapsed/Refractory Multiple Myeloma Patients.

Isatuximab, a monoclonal antibody (mAb) of immunoglobulin G (IgG) isotype, specifically targets the cluster of differentiation 38 antigen overexpressed in malignant plasma cells. Isatuximab is used to treat multiple myeloma (MM), characterized by the excessive production of abnormal "myeloma proteins" (M-proteins) that may interact with therapeutic IgG mAb on the neonatal Fc receptor (FcRn)-mediated recycling pathway. The clinical pharmacology profile of isatuximab was investigated by population pharmacokinetics (PKs) modeling in 476 patients with MM who received 1-20 mg/kg isatuximab either as single agent or in combination with pomalidomide-dexamethasone in 4 clinical trials. Isatuximab PKs were characterized by a two-compartment model with parallel time-varying linear clearance (CL) and nonlinear elimination. Due to a mechanism-based drug-disease interaction, patients secreting IgG M-protein exhibited a twofold lower drug exposure compared with patients with non-IgG MM. No dose adjustment was required based on MM immunoglobulin type because efficacy and safety profiles were comparable between IgG and non-IgG MM subpopulations. ß2-microglobulin, body weight, sex, drug material, and race have a limited effect on drug exposure and do not require any dose adjustment. A typical 50% decrease in linear CL from initial treatment to steady-state was predicted, and this decrease correlated with the best overall response rate and was slower for patients with IgG MM. These findings suggest that the time-dependent effect of isatuximab is likely mediated by a combined factor of both disease state evolution and the perturbation of the FcRn-mediated recycling pathway.

Validation of Neutrophil Count as An Algorithm-Based Predictive Factor of Progression-Free Survival in Patients with Metastatic Soft Tissue Sarcomas Treated with Trabectedin.

Introduction: Based on a mathematical model of trabectedin-induced neutropenia, we assessed the predictive value of absolute neutrophil count (ANC) on progression-free survival (PFS) in an independent validation cohort of patients treated with trabectedin. Methods: We collected data from 87 patients in two expert centers who received at least two cycles of trabectedin for soft tissue sarcomas (STS) treatment. Correlations between ANC, patients' characteristics, and survival were assessed, and a multivariate model including tumor grade, performance status, ANC, and hemoglobin level was developed. Results: Therapeutic ANC ≥ 7.5 G/L level was associated with shorter PFS: 3.22 months (95% confidence interval (CI), 1.57⁻4.87) in patients with ANC ≥ 7.5 G/L vs. 5.78 months (95% CI, 3.95⁻7.61) in patients with ANC < 7.5 G/L (p = 0.009). Age, primary localization, lung metastases, dose reduction, hemoglobin, and albumin rates were also associated with PFS. In multivariate analysis, ANC ≥ 7.5 G/L was independently associated with poor PFS and overall survival. Conclusion: We validated increased pre-therapeutic ANC as a predictive factor of short PFS in patients starting trabectedin for STS. ANC appears to have an impact on survival rates and may be used as a decision-making tool for personalizing second-line strategies in patients with metastatic STS.

Revisiting Bevacizumab + Cytotoxics Scheduling Using Mathematical Modeling: Proof of Concept Study in Experimental Non-Small Cell Lung Carcinoma.

Concomitant administration of bevacizumab and pemetrexed-cisplatin is a common treatment for advanced nonsquamous non-small cell lung cancer (NSCLC). Vascular normalization following bevacizumab administration may transiently enhance drug delivery, suggesting improved efficacy with sequential administration. To investigate optimal scheduling, we conducted a study in NSCLC-bearing mice. First, experiments demonstrated improved efficacy when using sequential vs. concomitant scheduling of bevacizumab and chemotherapy. Combining this data with a mathematical model of tumor growth under therapy accounting for the normalization effect, we predicted an optimal delay of 2.8 days between bevacizumab and chemotherapy. This prediction was confirmed experimentally, with reduced tumor growth of 38% as compared to concomitant scheduling, and prolonged survival (74 vs. 70 days). Alternate sequencing of 8 days failed in achieving a similar increase in efficacy, thus emphasizing the utility of modeling support to identify optimal scheduling. The model could also be a useful tool in the clinic to personally tailor regimen sequences.

Meningioma growth dynamics assessed by radiocarbon retrospective birth dating.

It is not known how long it takes from the initial neoplastic transformation of a cell to the detection of a tumor, which would be valuable for understanding tumor growth dynamics. Meningiomas show a broad histological, genetic and clinical spectrum, are usually benign and considered slowly growing. There is an intense debate regarding their age and growth pattern and when meningiomas should be resected. We have assessed the age and growth dynamics of 14 patients with meningiomas (WHO grade I: n=6 with meningothelial and n=6 with fibrous subtype, as well as n=2 atypical WHO grade II meningiomas) by combining retrospective birth-dating of cells by analyzing incorporation of nuclear-bomb-test-derived 14C, analysis of cell proliferation, cell density, MRI imaging and mathematical modeling. We provide an integrated model of the growth dynamics of benign meningiomas. The mean age of WHO grade I meningiomas was 22.1±6.5years, whereas atypical WHO grade II meningiomas originated 1.5±0.1years prior to surgery (p<0.01). We conclude that WHO grade I meningiomas are very slowly growing brain tumors, which are resected in average two decades after time of origination.

A multiscale modelling approach for the regulation of the cell cycle by the circadian clock.

We present a multiscale mathematical model for the regulation of the cell cycle by the circadian clock. Biologically, the model describes the proliferation of a population of heterogeneous cells connected to each other. The model consists of a high dimensional transport equation structured by molecular contents of the cell cycle-circadian clock coupled oscillator. We propose a computational method for resolution adapted from the concept of particle methods. We study the impact of molecular dynamics on cell proliferation and show an example where discordance of division rhythms between population and single cell levels is observed. This highlights the importance of multiscale modeling where such results cannot be inferred from considering solely one biological level.

Model driven optimization of antiangiogenics + cytotoxics combination: application to breast cancer mice treated with bevacizumab + paclitaxel doublet leads to reduced tumor growth and fewer metastasis.

Bevacizumab is the first-in-class antiangiogenic drug and is almost always administrated in combination with cytotoxics. Reports have shown that bevacizumab could induce a transient phase of vascular normalization, thus ensuring a better drug delivery when cytotoxics administration is adjuvant. However, determining the best sequence remains challenging. We have developed a mathematical model describing the impact of antiangiogenics on tumor vasculature. A 3.4 days gap between bevacizumab and paclitaxel was first proposed by our model. To test its relevance, 84 mice were orthotopically xenografted with human MDA-231Luc+ refractory breast cancer cells. Two sets of experiments were performed, based upon different bevacizumab dosing (10 or 20 mg/kg) and inter-cycle intervals (7 or 10 days), comprising several combinations with paclitaxel. Results showed that scheduling bevacizumab 3 days before paclitaxel improved antitumor efficacy (48% reduction in tumor size compared with concomitant dosing, p < 0.05) and reduced metastatic spreading. Additionally, bevacizumab alone could lead to more aggressive metastatic disease with shorter survival in animals. Our model was able to fit the experimental data and provided insights on the underlying dynamics of the vasculature's ability to deliver the cytotoxic agent. Final simulations suggested a new, data-informed optimal gap of 2.2 days. Our experimental data suggest that current concomitant dosing between bevacizumab and paclitaxel could be a sub-optimal strategy at bedside. In addition, this proof of concept study suggests that mathematical modelling could help to identify the optimal interval among a variety of possible alternate treatment modalities, thus refining the way experimental or clinical studies are conducted.

Implication of the Autologous Immune System in BCR-ABL Transcript Variations in Chronic Myelogenous Leukemia Patients Treated with Imatinib.

Imatinib and other tyrosine kinase inhibitors (TKI) have improved treatment of chronic myelogenous leukemia (CML); however, most patients are not cured. Deeper mechanistic understanding may improve TKI combination therapies to better control the residual leukemic cell population. In analyzing our patients' data, we found that many patients who otherwise responded well to imatinib therapy still showed variations in their BCR-ABL transcripts. To investigate this phenomenon, we applied a mathematical model that integrates CML and an autologous immune response to the patients' data. We define an immune window or a range of leukemic loads for which the autologous immune system induces an improved response. Our modeling results suggest that, at diagnosis, a patient's leukemic load is able to partially or fully suppress the autologous immune response developed in a majority of patients, toward the CML clone(s). Imatinib therapy drives the leukemic population into the "immune window," allowing the patient's autologous immune cells to expand and eventually mount an efficient recognition of the residual leukemic burden. This response drives the leukemic load below this immune window, allowing the leukemic population to partially recover until another weaker immune response is initiated. Thus, the autologous immune response may explain the oscillations in BCR-ABL transcripts regularly observed in patients on imatinib.