Methodologies for quantitative systems pharmacology (QSP) models : Design and estimation

With the increased interest in the application of quantitative systems pharmacology (QSP) models within medicine research and development, there is an increasing need to formalize model development and verification aspects. In February 2016, a workshop was held at Roche Pharma Research and Early Development to focus discussions on two critical methodological aspects of QSP model development: optimal structural granularity and parameter estimation. We here report in a perspective article a summary of presentations and discussions

Use of Quantitative Pharmacology in the Development of HAE1, a High-Affinity Anti-IgE Monoclonal Antibody

HAE1, a high-affinity anti-IgE monoclonal antibody, is discussed here as a case study in the use of quantitative pharmacology in the development of a second-generation molecule. In vitro, preclinical, and clinical data from the first-generation molecule, omalizumab, were heavily leveraged in the HAE1 program. A preliminary mechanism-based pharmacokinetic/pharmacodynamic (PK/PD) model for HAE1 was developed using an existing model for omalizumab, together with in vitro binding data for HAE1 and omalizumab. When phase I data were available, the model was refined by simultaneously modeling PK/PD data from omalizumab studies with the available HAE1 phase I data. The HAE1 clinical program was based on knowledge of the quantitative relationship between a pharmacodynamic biomarker, suppression of free IgE, and clinical response (e.g., lower exacerbation rates) obtained in pivotal studies with omalizumab. A clinical trial simulation platform was developed to predict free IgE levels and clinical responses following attainment of a target free IgE level (≤10 IU/ml). The simulation platform enabled selection of four doses for the phase II dose-ranging trial by two independent methods: dose-response non-linear fitting and linear mixed modeling. Agreement between the two methods provided confidence in the doses selected. Modeling and simulation played a large role in supporting acceleration of the HAE1 program by enabling data-driven decision-making, often based on confirmation of projections and/or learning from incoming new data

Clinical Relevance of Dissolution Testing in Quality by Design

Quality by design (QbD) has recently been introduced in pharmaceutical product development in a regulatory context and the process of implementing such concepts in the drug approval process is presently on-going. This has the potential to allow for a more flexible regulatory approach based on understanding and optimisation of how design of a product and its manufacturing process may affect product quality. Thus, adding restrictions to manufacturing beyond what can be motivated by clinical quality brings no benefits but only additional costs. This leads to a challenge for biopharmaceutical scientists to link clinical product performance to critical manufacturing attributes. In vitro dissolution testing is clearly a key tool for this purpose and the present bioequivalence guidelines and biopharmaceutical classification system (BCS) provides a platform for regulatory applications of in vitro dissolution as a marker for consistency in clinical outcomes. However, the application of these concepts might need to be further developed in the context of QbD to take advantage of the higher level of understanding that is implied and displayed in regulatory documentation utilising QbD concepts. Aspects that should be considered include identification of rate limiting steps in the absorption process that can be linked to pharmacokinetic variables and used for prediction of bioavailability variables, in vivo relevance of in vitro dissolution test conditions and performance/interpretation of specific bioavailability studies on critical formulation/process variables. This article will give some examples and suggestions how clinical relevance of dissolution testing can be achieved in the context of QbD derived from a specific case study for a BCS II compound

Predicting the F(ab)-mediated effect of monoclonal antibodies in vivo by combining cell-level kinetic and pharmacokinetic modelling

Cell-level kinetic models for therapeutically relevant processes increasingly benefit the early stages of drug development. Later stages of the drug development processes, however, rely on pharmacokinetic compartment models while cell-level dynamics are typically neglected. We here present a systematic approach to integrate cell-level kinetic models and pharmacokinetic compartment models. Incorporating target dynamics into pharmacokinetic models is especially useful for the development of therapeutic antibodies because their effect and pharmacokinetics are inherently interdependent. The approach is illustrated by analysing the F(ab)-mediated inhibitory effect of therapeutic antibodies targeting the epidermal growth factor receptor. We build a multi-level model for anti-EGFR antibodies by combining a systems biology model with in vitro determined parameters and a pharmacokinetic model based on in vivo pharmacokinetic data. Using this model, we investigated in silico the impact of biochemical properties of anti-EGFR antibodies on their F(ab)-mediated inhibitory effect. The multi-level model suggests that the F(ab)-mediated inhibitory effect saturates with increasing drug-receptor affinity, thereby limiting the impact of increasing antibody affinity on improving the effect. This indicates that observed differences in the therapeutic effects of high affinity antibodies in the market and in clinical development may result mainly from Fc-mediated indirect mechanisms such as antibody-dependent cell cytotoxicity

Drug Absorption Modeling as a Tool to Define the Strategy in Clinical Formulation Development

The purpose of this mini review is to discuss the use of physiologically-based drug absorption modeling to guide the formulation development. Following an introduction to drug absorption modeling, this article focuses on the preclinical formulation development. Case studies are presented, where the emphasis is not only the prediction of absolute exposure values, but also their change with altered input values. Sensitivity analysis of technologically relevant parameters, like the drug’s particle size, dose and solubility, is presented as the basis to define the clinical formulation strategy. Taking the concept even one step further, the article shows how the entire design space for drug absorption can be constructed. This most accurate prediction level is mainly foreseen once clinical data is available and an example is provided using mefenamic acid as a model drug. Physiologically-based modeling is expected to be more often used by formulators in the future. It has the potential to become an indispensable tool to guide the formulation development of challenging drugs, which will help minimize both risks and costs of formulation development

Exposure–response relationship of AMG 386 in combination with weekly paclitaxel in recurrent ovarian cancer and its implication for dose selection

To characterize exposure-response relationships of AMG 386 in a phase 2 study in advanced ovarian cancer for the facilitation of dose selection in future studies.A population pharmacokinetic model of AMG 386 (N = 141) was developed and applied in an exposure-response analysis using data from patients (N = 160) with recurrent ovarian cancer who received paclitaxel plus AMG 386 (3 or 10 mg/kg once weekly) or placebo. Reduction in the risk of progression or death with increasing exposure (steady-state area under the concentration-versus-time curve [AUC(ss)]) was assessed using Cox regression analyses. Confounding factors were tested in multivariate analysis. Alternative AMG 386 doses were explored with Monte Carlo simulations using population pharmacokinetic and parametric survival models.There was a trend toward increased PFS with increased AUC(ss) (hazard ratio [HR] for each one-unit increment in AUC(ss), 0.97; P = 0.097), suggesting that the maximum effect on prolonging PFS was not achieved at the highest dose tested (10 mg/kg). Among patients with AUC(ss) ≥ 9.6 mg h/mL, PFS was 8.1 months versus 5.7 months for AUC(ss) < 9.6 mg h/mL and 4.6 months for placebo. No relationship between AUC(ss) and grade ≥ 3 adverse events was observed. Simulations predicted that AMG 386 15 mg/kg once weekly would result in an AUC(ss) ≥ 9.6 mg h/mL in > 90% of patients with median PFS of 8.2 months versus 5.0 months for placebo (HR [15 mg/kg vs. placebo], 0.56).Increased exposure to AMG 386 was associated with improved clinical outcomes in recurrent ovarian cancer, supporting the evaluation of a higher dose in future studies