Mechanistic modelling of immuno-modulators in oncology drug development

-

Beyond Deterministic Models in Drug Discovery and Development

The model-informed drug discovery and development paradigm is now well established among the pharmaceutical industry and regulatory agencies. This success has been mainly due to the ability of pharmacometrics to bring together different modeling strategies, such as population pharmacokinetics/pharmacodynamics (PK/PD) and systems biology/pharmacology. However, there are promising quantitative approaches that are still seldom used by pharmacometricians and that deserve consideration. One such case is the stochastic modeling approach, which can be important when modeling small populations because random events can have a huge impact on these systems. In this review, we aim to raise awareness of stochastic models and how to combine them with existing modeling techniques, with the ultimate goal of making future drug–disease models more versatile and realistic

Advanced Boolean modeling of biological networks applied to systems pharmacology

Motivation Literature on complex diseases is abundant but not always quantitative. Many molecular pathways are qualitatively well described but this information cannot be used in traditional quantitative mathematical models employed in drug development. Tools for analysis of discrete networks are useful to capture the available information in the literature but have not been efficiently integrated by the pharmaceutical industry. We propose an expansion of the usual analysis of discrete networks that facilitates the identification/validation of therapeutic targets. Results In this article, we propose a methodology to perform Boolean modeling of Systems Biology/Pharmacology networks by using SPIDDOR (Systems Pharmacology for effIcient Drug Development On R) R package. The resulting models can be used to analyze the dynamics of signaling networks associated to diseases to predict the pathogenesis mechanisms and identify potential therapeutic targets

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

A quantitative systems pharmacology model for acute viral hepatitis B

Hepatitis B liver infection is caused by hepatitis B virus (HBV) and represents a major global disease problem when it becomes chronic, as is the case for 80–90% of vertical or early life infections. However, in the vast majority (>95%) of adult exposures, the infected individuals are capable of mounting an effective immune response leading to infection resolution. A good understanding of HBV dynamics and the interaction between the virus and immune system during acute infection represents an essential step to characterize and understand the key biological processes involved in disease resolution, which may help to identify potential interventions to prevent chronic hepatitis B. In this work, a quantitative systems pharmacology model for acute hepatitis B characterizing viral dynamics and the main components of the innate, adaptive, and tolerant immune response has been successfully developed. To do so, information from multiple sources and across different organization levels has been integrated in a common mechanistic framework. The final model adequately describes the chronology and plausibility of an HBV-triggered immune response, as well as clinical data from acute patients reported in the literature. Given the holistic nature of the framework, the model can be used to illustrate the relevance of the different immune pathways and biological processes to ultimate response, observing the negligible contribution of the innate response and the key contribution of the cellular response on viral clearance. More specifically, moderate reductions of the proliferation of activated cytotoxic CD8+ lymphocytes or increased immunoregulatory effects can drive the system towards chronicity

Population Pharmacokinetic Analysis of Lanreotide Autogel®/Depot in the Treatment of Neuroendocrine Tumors: Pooled Analysis of Four Clinical Trials

Background and Objectives Lanreotide Autogel® (lanreotide Depot in the USA) has demonstrated anti-tumor activity and control of the symptoms associated with hormone hypersecretion in patients with neuroendocrine tumors. The objectives of this study were to describe the pharmacokinetics of lanreotide Autogel® administered 4-weekly by deep subcutaneous injections of 60, 90, or 120 mg in patients with gastroenteropancreatic neuroendocrine tumors (GEP-NETs), to quantify the magnitude of inter-patient variability (IPV), and to identify those patient characteristics that impact on pharmacokinetics. Methods Analyses were based on pooled data from clinical trials. A total of 1541 serum concentrations from 290 patients were analyzed simultaneously by the population approach using NONMEM® version 7.2. Covariates evaluated included demographics, renal and hepatic function markers, and disease-related parameters. Results Serum profiles were described by a one-compartment disposition model in which the absorption process was characterized by two parallel pathways following first- and zero-order kinetics. The estimated apparent volume of distribution was 18.3 L. The estimated apparent total serum clearance for a typical 74 kg patient was 513 L/day, representing a substantial difference in clearance in this population of patients with respect to healthy volunteers that could not be explained by any of the covariates tested. Body weight was the only covariate to show a statistically significant effect on the pharmacokinetic profile, but due to the overlap between the pharmacokinetic profiles of patients with lower or higher body weights the effect of body weight on clearance was not considered clinically relevant. The IPV was low for clearance (27 %) and moderate to high for volume of distribution (150 %) and the absorption constant (61 %). Conclusions Using two mechanisms of absorption, the pharmacokinetics of lanreotide Autogel® were well-described in patients with GEP-NET. None of the patient characteristics tested were of clinical relevance to potential dose adjustment in clinical practice

Disease pharmacokinetic–pharmacodynamic modelling in acute intermittent porphyria to support the development of mRNA-based therapies

Background and Purpose Acute intermittent porphyria (AIP) results from haplo-insufficiency of the porphobilinogen deaminase (PBGD) gene encoding the third enzyme in the haem biosynthesis pathway. As liver is the main organ of pathology for AIP, emerging therapies that restore enzyme hepatic levels are appealing. The objective of this work was to develop a mechanistic-based computational framework to describe the effects of novel PBGD mRNA therapy on the accumulation of neurotoxic haem precursors in small and large animal models. Experimental Approach Liver PBGD activity data and/or 24-hr urinary haem precursors were obtained from genetic AIP mice and wild-type mice, rats, rabbits, and macaques. To mimic acute attacks, porphyrogenic drugs were administered over one or multiple challenges, and animals were used as controls or treated with different PBGD mRNA products. Available experimental data were sequentially used to build and validate a semi-mechanistic mathematical model using non-linear mixed-effects approach. Key Results The developed framework accounts for the different biological processes involved (i.e., mRNA sequence, release from lipid nanoparticle and degradation, mRNA translation, increased PBGD activity in liver, and haem precursor metabolism) in a simplified mechanistic fashion. The model, validated using external data, shows robustness in the extrapolation of PBGD activity data in rat, rabbit, and non-human primate species. Conclusion and Implications This quantitative framework provides a valuable tool to compare PBGD mRNA drug products during early preclinical stages, optimize the amount of experimental data required, and project results to humans, thus supporting drug development and clinical dose and dosing regimen selection