Paolo Vicini

Pharmacokinetic/Pharmacodynamic Modeling of Crizotinib for Anaplastic Lymphoma Kinase Inhibition and Antitumor Efficacy in Human Tumor Xenograft Mouse Models

Crizotinib [Xalkori; PF02341066; (R)-3-[1-(2,6-dichloro-3-fluoro-phenyl)-ethoxy]-5-(1-piperidin-4-yl-1H-pyrazol-4-yl)-pyridin-2-ylamine] is an orally available dual inhibitor of anaplastic lymphoma kinase (ALK) and hepatocyte growth factor receptor. The objectives of the present studies were to characterize: 1) the pharmacokinetic/pharmacodynamic relationship of crizotinib plasma concentrations to the inhibition of ALK phosphorylation in tumors, and 2) the relationship of ALK inhibition to antitumor efficacy in human tumor xenograft models. Crizotinib was orally administered to athymic nu/nu mice implanted with H3122 non–small-cell lung carcinomas or severe combined immunodeficient/beige mice implanted with Karpas299 anaplastic large-cell lymphomas. Plasma concentration-time courses of crizotinib were adequately described by a one-compartment pharmacokinetic model. A pharmacodynamic link model reasonably fit the time courses of ALK inhibition in both H3122 and Karpas299 models with EC50 values of 233 and 666 ng/ml, respectively. A tumor growth inhibition model also reasonably fit the time course of individual tumor growth curves with EC50 values of 255 and 875 ng/ml, respectively. Thus, the EC50 for ALK inhibition approximately corresponded to the EC50 for tumor growth inhibition in both xenograft models, suggesting that >50% ALK inhibition would be required for significant antitumor efficacy (>50%). Furthermore, based on the observed clinical pharmacokinetic data coupled with the pharmacodynamic parameters obtained from the present nonclinical xenograft mouse model, >70% ALK inhibition was projected in patients with non–small-cell lung cancer who were administered the clinically recommended dosage of crizotinib, twice-daily doses of 250 mg (500 mg/day). The result suggests that crizotinib could sufficiently inhibit ALK phosphorylation for significant antitumor efficacy in patients.

The development of a fully-integrated immune response model (FIRM) simulator of the immune response through integration of multiple subset models

BackgroundThe complexity and multiscale nature of the mammalian immune response provides an excellent test bed for the potential of mathematical modeling and simulation to facilitate mechanistic understanding. Historically, mathematical models of the immune response focused on subsets of the immune system and/or specific aspects of the response. Mathematical models have been developed for the humoral side of the immune response, or for the cellular side, or for cytokine kinetics, but rarely have they been proposed to encompass the overall system complexity. We propose here a framework for integration of subset models, based on a system biology approach.ResultsA dynamic simulator, the Fully-integrated Immune Response Model (FIRM), was built in a stepwise fashion by integrating published subset models and adding novel features. The approach used to build the model includes the formulation of the network of interacting species and the subsequent introduction of rate laws to describe each biological process. The resulting model represents a multi-organ structure, comprised of the target organ where the immune response takes place, circulating blood, lymphoid T, and lymphoid B tissue. The cell types accounted for include macrophages, a few T-cell lineages (cytotoxic, regulatory, helper 1, and helper 2), and B-cell activation to plasma cells. Four different cytokines were accounted for: IFN-γ, IL-4, IL-10 and IL-12. In addition, generic inflammatory signals are used to represent the kinetics of IL-1, IL-2, and TGF-β. Cell recruitment, differentiation, replication, apoptosis and migration are described as appropriate for the different cell types. The model is a hybrid structure containing information from several mammalian species. The structure of the network was built to be physiologically and biochemically consistent. Rate laws for all the cellular fate processes, growth factor production rates and half-lives, together with antibody production rates and half-lives, are provided. The results demonstrate how this framework can be used to integrate mathematical models of the immune response from several published sources and describe qualitative predictions of global immune system response arising from the integrated, hybrid model. In addition, we show how the model can be expanded to include novel biological findings. Case studies were carried out to simulate TB infection, tumor rejection, response to a blood borne pathogen and the consequences of accounting for regulatory T-cells.ConclusionsThe final result of this work is a postulated and increasingly comprehensive representation of the mammalian immune system, based on physiological knowledge and susceptible to further experimental testing and validation. We believe that the integrated nature of FIRM has the potential to simulate a range of responses under a variety of conditions, from modeling of immune responses after tuberculosis (TB) infection to tumor formation in tissues. FIRM also has the flexibility to be expanded to include both complex and novel immunological response features as our knowledge of the immune system advances.

Therapeutic outcomes, assessments, risk factors and mitigation efforts of immunogenicity of therapeutic protein products

Therapeutic protein products (TPPs) are of considerable value in the treatment of a variety of diseases, including cancer, hemophilia, and autoimmune diseases. The success of TPP mainly results from prolonged half-life, increased target specificity and decreased intrinsic toxicity compared with small molecule drugs. However, unwanted immune responses against TPP, such as generation of anti-drug antibody, can impact both drug efficacy and patient safety, which has led to requirements for increased monitoring in regulatory studies and clinical practice, termination of drug development, or even withdrawal of marketed products. We present an overview of current knowledge on immunogenicity of TPP and its impact on efficacy and safety. We also discuss methods for measurement and prediction of immunogenicity and review both product-related and patient-related risk factors that affect its development, and efforts that may be taken to mitigate it. Lastly, we discuss gaps in knowledge and technology and what is needed to fill these.

A Mechanistic, Multiscale Mathematical Model of Immunogenicity for Therapeutic Proteins: Part 1—Theoretical Model

A mechanistic, multiscale mathematical model of immunogenicity for therapeutic proteins was formulated by recapitulating key biological mechanisms, including antigen presentation, activation, proliferation, and differentiation of immune cells, secretion of antidrug antibodies (ADA), as well as in vivo disposition of ADA and therapeutic proteins. This system‐level model contains three scales: a subcellular level representing antigen presentation processes by dendritic cells; a cellular level accounting for cell kinetics during humoral immune response; and a whole‐body level accounting for therapeutic protein in vivo disposition. The model simulations for in vivo responses against antigenic protein challenge are consistent with many known immunological observations. By simulating immune responses under various initial parameter conditions, the model suggests hypotheses for future experimental investigation and contributes to the mechanistic understanding of immunogenicity. With future experimental validation, this model may potentially provide a platform to generate and test hypotheses about immunogenicity risk assessment and ultimately aid in immunogenicity prediction.

A Mathematical Model of the Effect of Immunogenicity on Therapeutic Protein Pharmacokinetics

A mathematical pharmacokinetic/anti-drug-antibody (PK/ADA) model was constructed for quantitatively assessing immunogenicity for therapeutic proteins. The model is inspired by traditional pharmacokinetic/pharmacodynamic (PK/PD) models, and is based on the observed impact of ADA on protein drug clearance. The hypothesis for this work is that altered drug PK contains information about the extent and timing of ADA generation. By fitting drug PK profiles while accounting for ADA-mediated drug clearance, the model provides an approach to characterize ADA generation during the study, including the maximum ADA response, sensitivity of ADA response to drug dose level, affinity maturation rate, time lag to observe an ADA response, and the elimination rate for ADA–drug complex. The model also provides a mean to estimate putative concentration–time profiles for ADA, ADA–drug complex, and ADA binding affinity-time profile. When simulating ADA responses to various drug dose levels, bell-shaped dose–response curves were generated. The model contains simultaneous quantitative modeling and provides estimation of the characteristics of therapeutic protein drug PK and ADA responses in vivo. With further experimental validation, the model may be applied to the simulation of ADA response to therapeutic protein drugs in silico, or be applied in subsequent PK/PD models.

Pharmacokinetic-Pharmacodynamic Modeling of Biomarker Response and Tumor Growth Inhibition to an Orally Available Heat Shock Protein 90 Inhibitor in a Human Tumor Xenograft Mouse Model

PF04942847 [2-amino-4-{4-chloro-2-[2-(4-fluoro-1H-pyrazol-1-yl)ethoxy]-6-methylphenyl}-N-(2,2-difluoropropyl)-5,7-dihydro-6H-pyrrolo[3,4-d]pyrimidine-6-carboxamide] was identified as an orally available, ATP-competitive, small-molecule inhibitor of heat shock protein 90 (HSP90). The objectives of the present study were: 1) to characterize the pharmacokinetic-pharmacodynamic relationship of the plasma concentrations of PF04942847 to the inhibition of HSP90-dependent protein kinase, AKT, as a biomarker and 2) to characterize the relationship of AKT degradation to tumor growth inhibition as a pharmacological response (antitumor efficacy). Athymic mice implanted with MDA-MB-231 human breast cancer cells were treated with PF04942847 once daily at doses selected to encompass ED50 values. Plasma concentrations of PF04942847 were adequately described by a two-compartment pharmacokinetic model. A time delay (hysteresis) was observed between the plasma concentrations of PF04942847 and AKT degradation; therefore, a link model was used to account for the hysteresis. The model reasonably fit the time courses of AKT degradation with the estimated EC50 of 18 ng/ml. For tumor growth inhibition, the signal transduction model reasonably fit the inhibition of individual tumor growth curves with the estimated EC50 of 7.3 ng/ml. Thus, the EC50 for AKT degradation approximately corresponded to the EC50 to EC80 for tumor growth inhibition, suggesting that 50% AKT degradation was required for significant antitumor efficacy (50–80%). The consistent relationship between AKT degradation and antitumor efficacy was also demonstrated by applying an integrated signal transduction model for linking AKT degradation to tumor growth inhibition. The present results will be helpful in determining the appropriate dosing regimen and guiding dose escalation to achieve efficacious systemic exposure in the clinic.

The status of pharmacometrics in pregnancy: highlights from the 3rd American conference on pharmacometrics

Physiological changes during pregnancy may alter drug pharmacokinetics. Therefore, mechanistic understanding of these changes and, ultimately, clinical studies in pregnant women are necessary to determine if and how dosing regimens should be adjusted. Because of the typically limited number of patients who can be recruited in this patient group, efficient design and analysis of these studies is of special relevance. This paper is a summary of a conference session organized at the American Conference of Pharmacometrics in April 2011, around the topic of applying pharmacometric methodology to this important problem. The discussion included both design and analysis of clinical studies during pregnancy and in silico predictions. An overview of different pharmacometric methods relevant to this subject was given. The impact of pharmacometrics was illustrated using a range of case examples of studies around pregnancy.

Translational Pharmacokinetic-Pharmacodynamic Modeling for An Orally Available Novel Inhibitor of Anaplastic Lymphoma Kinase and c-Ros Oncogene 1

An orally available macrocyclic small molecule, PF06463922 [(10R)-7-amino-12-fluoro-2,10,16-trimethyl-15-oxo-10,15,16,17-tetrahydro-2H-8,4-(metheno)pyrazolo[4,3-h][2,5,11]benzoxadiazacyclotetradecine-3-carbonitrile], is a selective inhibitor of anaplastic lymphoma kinase (ALK) and c-Ros oncogene 1 (ROS1). The objectives of the present study were to characterize the pharmacokinetic-pharmacodynamic relationships of PF06463922 between its systemic exposures, pharmacodynamic biomarker (target modulation), and pharmacologic response (antitumor efficacy) in athymic mice implanted with H3122 non–small cell lung carcinomas expressing echinoderm microtubule-associated protein-like 4 (EML4)-ALK mutation (EML4-ALKL1196M) and with NIH3T3 cells expressing CD74-ROS1. In these nonclinical tumor models, PF06463922 was orally administered to animals with EML4-ALKL1196M and CD74-ROS1 at twice daily doses of 0.3–20 and 0.01–3 mg/kg per dose, respectively. Plasma concentration-time profiles of PF06463922 were adequately described by a one-compartment pharmacokinetic model. Using the model-simulated plasma concentrations, a pharmacodynamic indirect response model with a modulator sufficiently fit the time courses of target modulation (i.e., ALK phosphorylation) in tumors of EML4-ALKL1196M–driven models with EC50,in vivo of 36 nM free. A drug-disease model based on an indirect response model reasonably fit individual tumor growth curves in both EML4-ALKL1196M– and CD74-ROS1–driven models with the estimated tumor stasis concentrations of 51 and 6.2 nM free, respectively. Thus, the EC60,in vivo (52 nM free) for ALK inhibition roughly corresponded to the tumor stasis concentration in an EML4-ALKL1196M–driven model, suggesting that 60% ALK inhibition would be required for tumor stasis. Accordingly, we proposed that the EC60,in vivo for ALK inhibition corresponding to the tumor stasis could be considered a minimum target efficacious concentration of PF06463922 for cancer patients in a phase I trial.

A Limited Sampling Schedule to Estimate Individual Pharmacokinetic Parameters of Fludarabine in Hematopoietic Cell Transplant Patients

Purpose: Fludarabine monophosphate (fludarabine) is frequently administered to patients receiving a reduced-intensity conditioning regimen for allogeneic hematopoietic cell transplant (HCT) in an ambulatory care setting. These patients experience significant interpatient variability in clinical outcomes, potentially due to pharmacokinetic variability in 2-fluoroadenine (F-ara-A) plasma concentrations. To test such hypotheses, patient compliance with the blood sampling should be optimized by the development of a minimally intrusive limited sampling schedule (LSS) to characterize F-ara-A pharmacokinetics. To this end, we sought to create the first F-ara-A population pharmacokinetic model and subsequently a LSS. Experimental Design: A retrospective evaluation of F-ara-A pharmacokinetics was conducted after one or more doses of daily i.v. fludarabine in 42 adult HCT recipients. NONMEM software was used to estimate the population pharmacokinetic parameters and compute the area under the concentration-time curve. Results: A two-compartment model best fits the data. A LSS was constructed using a simulation approach, seeking to minimize the scaled mean squared error for the area under the concentration-time curve for each simulated individual. The LSS times chosen were 0.583, 1.5, 6.5, and 24 hours after the start of the 30-minute fludarabine infusion. Discussion: The pharmacokinetics of F-ara-A in an individual HCT patient can be accurately estimated by obtaining four blood samples (using the LSS) and maximum a posteriori Bayesian estimation. Conclusion: These are essential tools for prospective pharmacodynamic studies seeking to determine if clinical outcomes are related to F-ara-A pharmacokinetics in patients receiving i.v. fludarabine in the ambulatory clinic. (Clin Cancer Res 2009;15(16):5280–7)

A Mechanistic, Multiscale Mathematical Model of Immunogenicity for Therapeutic Proteins: Part 2—Model Applications

A mechanistic, multiscale mathematical model of immunogenicity for therapeutic proteins was built by recapitulating key underlying known biological processes for immunogenicity. The model is able to simulate immune responses based on protein‐specific antigenic properties (e.g., number of T‐epitopes and their major histocompatibility complex (MHC)‐II binding affinities) and host‐specific immunological/physiological characteristics (e.g., MHC‐II allele genotype, drug clearance rate). Preliminary validation was performed using mouse studies with antigens such as ovalbumin (OVA) or OVA‐derived peptide. Further, using adalimumab as an example therapeutic protein, the model is able to simulate immune responses against adalimumab in individual subjects and in a population, and also provides estimations of immunogenicity incidence and drug exposure reduction that can be validated experimentally. This is a first attempt at modeling immunogenicity of biologics, so the model simulations should be used to help understand the immunogenicity mechanisms and impacting factors, rather than making direct predictions. This prototype model needs to be subjected to extensive experimental validation and refinement before fulfilling its ultimate mission of predicting immunogenicity. Nevertheless, the current model could potentially set up the starting framework to integrate various in silico, in vitro, in vivo, and clinical immunogenicity assessment results to help meet the challenge of immunogenicity prediction.