Literature DB >> 35650328

Opportunities and Challenges for PBPK Model of mAbs in Paediatrics and Pregnancy.

Katherine L Gill1, Hannah M Jones2.   

Abstract

New drugs may in some cases need to be tested in paediatric and pregnant patients. However, it is difficult to recruit such patients and there are many ethical issues around their inclusion in clinical trials. Modelling and simulation can help to plan well-designed clinical trials with a reduced number of participants and to bridge gaps where recruitment is difficult. Physiologically based pharmacokinetic (PBPK) models for small molecule drugs have been used to aid study design and dose adjustments in paediatrics and pregnancy, with several publications in the literature. However, published PBPK models for monoclonal antibodies (mAb) in these populations are scarce. Here, the current status of mAb PBPK models in paediatrics and pregnancy is discussed. Seven mAb PBPK models published for paediatrics were found, which report good prediction accuracy across a wide age range. No mAb PBPK models for pregnant women have been published to date. Current challenges to the development of such PBPK models are discussed, including gaps in our knowledge of relevant physiological processes and availability of clinical data to verify models. As the availability of such data increases, it will help to improve our confidence in the PBPK model predictive ability. Advantages for using PBPK models to predict mAb PK in paediatrics and pregnancy are discussed. For example, the ability to incorporate ontogeny and gestational changes in physiology, prediction of maternal, placental and foetal exposure and the ability to make predictions from in vitro and preclinical data prior to clinical data being available.
© 2022. The Author(s).

Entities:  

Keywords:  PBPK; monoclonal antibodies; paediatric; pharmacokinetics; pregnancy

Mesh:

Substances:

Year:  2022        PMID: 35650328     DOI: 10.1208/s12248-022-00722-0

Source DB:  PubMed          Journal:  AAPS J        ISSN: 1550-7416            Impact factor:   3.603


  100 in total

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