Tools for predicting the PK/PD of therapeutic proteins

Diao, Lei

doi:10.1517/17425255.2015.1041917

Cited by 22 publications

(14 citation statements)

References 62 publications

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“…While a number of PBPK models have been used to accurately predict the kinetics of monoclonal antibodies (Baxter et al, 1995;Ferl et al, 2005;Shah and Betts, 2012;Cao and Jusko, 2014;Elmeliegy et al, 2014;Li et al, 2014a;Chetty et al, 2015;Zhao et al, 2015), model structures are inconsistent (Chetty et al, 2012;Jones et al, 2015). Limited data on target expression and changes in disease populations result in the risk for overparameterization with PBPK models, and thus there is an effort to move toward reduced PBPK models for therapeutic proteins (Elmeliegy et al, 2014;Li et al, 2014a;Chetty et al, 2015;Diao and Meibohm, 2015).…”

Section: Introductionmentioning

confidence: 99%

Physiologically Based Pharmacokinetic (PBPK) Modeling and Simulation Approaches: A Systematic Review of Published Models, Applications, and Model Verification

et al. 2015

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Modeling and simulation of drug disposition has emerged as an important tool in drug development, clinical study design and regulatory review, and the number of physiologically based pharmacokinetic (PBPK) modeling related publications and regulatory submissions have risen dramatically in recent years. However, the extent of use of PBPK modeling by researchers, and the public availability of models has not been systematically evaluated. This review evaluates PBPK-related publications to 1) identify the common applications of PBPK modeling; 2) determine ways in which models are developed; 3) establish how model quality is assessed; and 4) provide a list of publically available PBPK models for sensitive P450 and transporter substrates as well as selective inhibitors and inducers. PubMed searches were conducted using the terms "PBPK" and "physiologically based pharmacokinetic model" to collect published models. Only papers on PBPK modeling of pharmaceutical agents in humans published in English between 2008 and May 2015 were reviewed. A total of 366 PBPK-related articles met the search criteria, with the number of articles published per year rising steadily. Published models were most commonly used for drug-drug interaction predictions (28%), followed by interindividual variability and general clinical pharmacokinetic predictions (23%), formulation or absorption modeling (12%), and predicting age-related changes in pharmacokinetics and disposition (10%). In total, 106 models of sensitive substrates, inhibitors, and inducers were identified. An in-depth analysis of the model development and verification revealed a lack of consistency in model development and quality assessment practices, demonstrating a need for development of best-practice guidelines.

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Section: Introductionmentioning

confidence: 99%

Physiologically Based Pharmacokinetic (PBPK) Modeling and Simulation Approaches: A Systematic Review of Published Models, Applications, and Model Verification

et al. 2015

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“…1,2 Non-specific degradation of antibodies occurs by pinocytosis following cellular endosomal uptake and subsequent lysosomal proteolytic degradation into amino acids or smaller peptides. 3 Cellular uptake of antibodies is thought to occur mostly in endothelial and haematopoietic cells. [4][5][6][7][8][9] Once antibodies are taken up into the endosome, they can be protected from degradation by binding to the neonatal Fc receptor (FcRn).…”

Section: Introductionmentioning

confidence: 99%

Quantification of IgG monoclonal antibody clearance in tissues

Eigenmann

Fronton

Grimm

et al. 2017

mAbs

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Monoclonal antibodies are an important therapeutic entity, and knowledge of antibody pharmacokinetics has steadily increased over the years. Despite this effort, little is known about the extent of IgG antibody degradation in different tissues of the body. While studies have been published identifying sites of degradation with the use of residualizing and non-residualizing radiolabels, quantitative tissue clearances have not yet been derived. Here, we show that in physiologically-based pharmacokinetic (PBPK) models we can combine mouse data of Indium-111 and Iodine-125 labeled antibodies with prior physiologic knowledge to determine tissue-specific intrinsic clearances. Unspecific total tissue clearance (mL/day) in the mouse was estimated to be: liver = 4.75; brain = 0.02; gut = 0.40; heart = 0.07; kidney = 0.97; lung = 0.20; muscle = 3.02; skin = 3.89; spleen = 0.45; rest of body = 2.16. The highest catabolic activity (per g tissue) was in spleen for an FcRn wild-type antibody, but shifts to the liver for an antibody with reduced FcRn affinity. In the model developed, this shift can be explained by the liver having a greater FcRn-mediated protection capacity than the spleen. The quantification of tissue intrinsic clearances and FcRn salvage capacity increases our understanding of quantitative processes that drive the therapeutic responses of antibodies. This knowledge is critical, for instance to estimate the non-specific cellular uptake and degradation of antibodies used for targeted delivery of payloads.

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“…In healthy volunteers the starting dose should be lower than the pharmacologically active dose (PAD). Also the importance of pharmacokinetic/pharmacodynamic models is increasing (Yu et al 2011;Diao and Meibohm 2015). Whenever possible, alternatives to the use of live animals should be employed, e.g., in vitro studies using human-derived materials.…”

Section: Immunological Safety Assessment: First-in-human Studiesmentioning

confidence: 99%