Modeling Of Antibody In Vitroin Vivo Correlations

Throughout most of the currently available literature, IVIVC for mAbs are generally qualitative. Quantitative correlations of in vitro and in vivo data in a statistical sense have not been established in most cases. The primary reasons for the general lack of IVIVCs are insufficient data and the limited mechanistic understanding of the relationship between in vitro and in vivo data. Accordingly, most examples of IVIVC for Abs are primarily focused on the assessment of in vitro binding behavior to qualitatively support in vivo binding, targeting, and the related impact on PK. Such qualitative or semiquantitative comparisons with few or even single compounds relating in vitro to in vivo information do hold some value; however, for consistency, evaluations assessing the validity of either the in vitro or the in vivo assay provides more reliability. In this sense, a more systematic approach providing IVIVC driven toward mechanism-based quantitative assessments would provide far greater benefits including mechanistic insight and predictive power to estimate the outcome of future experiments. One must also realize that modeling and simulation (M&S) as a data-driven approach is a continuously evolving process in drug development; learning from prior knowledge is extracted from data and used to predict future experiments, followed by the next learning cycle. Since IVIVCs are not yet established for many Abs, most current M&S approaches are primarily focused on estimating drug specific PK/PD, efficacy, safety parameters (e.g., PK parameters,

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ED50, Kd, kon, koff), and biological system information (e.g., target expression, turnover constants of binding targets) by using in vivo data from experiments with animal species or from clinical trials in healthy volunteers or patients. The information residing in in vitro data is only rarely used in these M&S evaluations. Retrospectively, in vivo parameter estimates are sometimes compared with those obtained from in vitro data. One commonly used modeling approach called target-mediated drug disposition modeling (TDDM) can capitalize on in vitro data by capturing the binding of a mAb to its target antigen in addition to other nonspecific distribution and clearance processes (3). In cases where relevant portions of the mAb are bound with high affinity to their cognate target antigen, the PK becomes very dependent on the binding and the downstream processing of the antigen-mAb complex (e.g., internalization into target cells and metabolism). TDDM models describe binding to the target antigen by estimating in vivo binding kinetics on the basis of the following parameters: the dissociation constant Kd, the association constant Ka, the second-order association rate constant kon, and the first-order dissociation rate constant koff. If bioassays allow measurement of occupied and free cell surface target antigen in addition to the unbound mAb as part of PK/PD studies, these parameters can be estimated from in vivo experiments. Consistency between these in vivo parameter estimates and binding parameters obtained from in vitro studies provides further confidence of physiologically meaningful parameters, providing a sound basis to use such models to optimize dose and dosing regimens to obtain optimal safety and efficacy in clinical settings. Excellent examples for TDDM modeling have been demonstrated with several mAbs including efaluzimab (anti-CD11a Ab) (151) and muromonab (anti-CD3 Ab) (152).

In the case of anti-CD11a in the treatment of psoriasis, mechanism-based modeling assisted the optimal clinical dose regimen selection (31). In vitro binding studies of anti-CD11a to CD11a on the surface of human T cells were conducted, and the binding affinity constant was measured. It was observed in the early clinical trials that intravenous doses of anti-CD11a higher than 0.3 mg/ kg saturated CD11a binding sites, and the receptor clearance was dependent on the plasma concentration of Ab (40). A two-compartmental TDDM model was proposed to model the clearance of anti-CD11a coupled with a feedback loop of CD11a to the clearance of anti-CD11a (153). From this model, the affinity of anti-CD11a to CD11a was estimated on the basis of in vivo data and was very similar to the in vitro estimates. This model was subsequently studied both in vitro and in vivo (39,40). Collectively, these studies and modeling exercises provided a sound basis for further optimal dose selection to guide future clinical trials, eventually leading to an approved dose regimen for clinical use. Ideally, in vitro studies and modeling can be designed in such a way that more relevant in vivo studies may be conducted and the resulting data may then be related back to the in vitro studies. Combination of in vitro and in vivo studies can provide more insight to future study design, especially clinical designs, through mechanistic modeling. After the first clinical trial, more information is gathered, and the mechanical model can be modified and further refined to guide the next clinical trial design, including patient and dose selection.

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