Model-Informed Dose Selection for Xentuzumab, a Dual Insulin-Like Growth Factor-I/II-Neutralizing Antibody.

Over the past decade, the insulin-like growth factor (IGF)-signaling pathway has gained substantial interest as potential therapeutic target in oncology. Xentuzumab, a humanized IgG1 monoclonal antibody, binds to IGF-I and IGF-II thereby inhibiting the downstream signaling essential for survival and tumor growth. This pathway is further regulated by circulating IGF binding proteins (IGFBPs). In this work, a mechanistic model characterizing the dynamics and interactions of IGFs, IGFBPs, and Xentuzumab has been developed to guide dose selection. Therefore, in vitro and in vivo literature information was combined with temporal IGF-I, IGF-II, and IGFBP-3 total plasma concentrations from two phase I studies. Based on the established quantitative framework, the time-course of free IGFs as ultimate drug targets not measured in clinics was predicted. Finally, a dose of 1000 mg/week-predicted to reduce free IGF-I and free IGF-II at steady-state by at least 90% and 64%, respectively-was suggested for phase II.

Optimal dynamic control approach in a multi-objective therapeutic scenario: Application to drug delivery in the treatment of prostate cancer.

Numerous problems encountered in computational biology can be formulated as optimization problems. In this context, optimization of drug release characteristics or dosing schedules for anticancer agents has become a prominent area not only for the development of new drugs, but also for established drugs. However, in complex systems, optimization of drug exposure is not a trivial task and cannot be efficiently addressed through trial-error simulation exercises. Finding a solution to those problems is a challenging task which requires more advanced strategies like optimal control theory. In this work, we perform an optimal control analysis on a previously developed computational model for the testosterone effects of triptorelin in prostate cancer patients with the goal of finding optimal drug-release characteristics. We demonstrate how numerical control optimization of non-linear models can be used to find better therapeutic approaches in order to improve the final outcome of the patients.

Pharmacological profiles of acute myeloid leukemia treatments in patient samples by automated flow cytometry: a bridge to individualized medicine.

BACKGROUND: We have evaluated the ex vivo pharmacology of single drugs and drug combinations in malignant cells of bone marrow samples from 125 patients with acute myeloid leukemia using a novel automated flow cytometry-based platform (ExviTech). We have improved previous ex vivo drug testing with 4 innovations: identifying individual leukemic cells, using intact whole blood during the incubation, using an automated platform that escalates reliably data, and performing analyses pharmacodynamic population models. PATIENTS AND METHODS: Samples were sent from 24 hospitals to a central laboratory and incubated for 48 hours in whole blood, after which drug activity was measured in terms of depletion of leukemic cells. RESULTS: The sensitivity of single drugs is assessed for standard efficacy (EMAX) and potency (EC50) variables, ranked as percentiles within the population. The sensitivity of drug-combination treatments is assessed for the synergism achieved in each patient sample. We found a large variability among patient samples in the dose-response curves to a single drug or combination treatment. CONCLUSION: We hypothesize that the use of the individual patient ex vivo pharmacological profiles may help to guide a personalized treatment selection.

Target-mediated disposition model describing the dynamics of IL12 and IFNγ after administration of a mifepristone-inducible adenoviral vector for IL-12 expression in mice.

Interleukin-12 (IL12) is a cytokine with potential applications in the treatment of cancer given the potent immune response that it triggers, in part due to its ability to stimulate expression of interferon-γ (IFNγ). To avoid the toxicity associated with systemic exposure to IL12, a high-capacity adenoviral vector carrying a liver-specific, mifepristone-inducible IL12 expression system (HC-Ad/RUmIL12) has been developed. However, the maintenance of IL12 expression at therapeutic levels is compromised by the inhibitory effect of IFNγ on inducible systems. The aim of this work is to develop a semi-mechanistic model to characterize the relationship between IL12 and IFNγ in wild-type and knock-out mice for the IFNγ receptor treated with HC-Ad/RUmIL12 under different dosing regimens in order to better understand the key mechanisms controlling the system. Rapid binding was considered to account for target-mediated disposition exhibited by both cytokines (equilibrium dissociation constant were 18 and 2.28 pM for IL12 and IFNγ, respectively). The final model included: (1) IFNγ receptor turnover, (2) irreversible free cytokine elimination from the serum compartment, (3) internalization of the IL12 receptor complex, (4) IL12 expression upregulated by the co-administration of the adenoviral vector and mifepristone and downregulated by the IFNγ receptor, and (5) synthesis of IFNγ controlled by the relative increments in the bound IL12. In conclusion, a model simultaneously describing the kinetics of IL12 and IFNγ in the context of gene therapy was developed and validated with additional data. The model was applied to design an experimental dosing protocol intended to maintain sustained therapeutic IL12 levels.