Chara Litou1, Ibtihel Hammami1, Swati Jaiswal1, Poonam Thakur1, Yan Yeap1, Hannah M. Jones1
1Simcyp Division, Certara UK, Ltd
Introduction: Predicting drug-drug interactions (DDIs) involving acid-reducing agents (ARAs) is crucial for ensuring drug efficacy and patient safety, as well as for supporting drug development and clinical practice. ARAs, defined as antacids, histamine-2 receptor antagonists, and proton pump inhibitors, are widely prescribed and can alter the absorption and systemic exposure of co-administered drugs with pH-dependent solubility. This interaction may lead to reduced drug efficacy or treatment failure for basic drugs or increased exposures, thus safety risks, for acidic drugs, making it essential to evaluate these risks during drug development. Physiologically based pharmacokinetic modelling has emerged as an effective tool for assessing pH-dependent DDIs (Mitra et al. 2020, Dodd et al., 2019). Nowadays, such models are mainly used to guide formulation strategies. However, there is minimal application in informing clinical study designs, and regulatory decisions/labelling recommendations. Furthermore, a best-practice approach on how to develop, apply and interpret the results of such models at the different stages of drug development is currently missing. Methods: Case studies available inhouse and from the literature, of pH-dependent DDIs assessed with PBPK models, were reviewed. Current approaches and the respective regulatory feedback, when available, were used to inform the development of a best practice framework for applying PBPK to predict pH-dependent DDIs. The drug development process was divided into three major stages: Discovery/ Preclinical, Early Clinical Development and Late Clinical Development. Results: Basic model input was defined for all three phases. Information around physicochemical properties, solubility, dissolution, permeability and disposition of the compound are crucial at all stages. However, the amount of detail that is needed as well as the type of measurement required at each stage are different and can have a major impact on the predicted PK as well as on the result interpretation. Available solubility and dissolution information should be used with caution and the respective drug substance and drug product information should be taken into consideration. Furthermore, modelled pH physiology changes should reflect the actual pH changes observed in vivo in co-administration scenarios with ARAs (Litou et al., 2016, Dodd et al., 2019). To this end, static pH-DDI predictions are recommended in the first two stages, where a DDI-risk assessment is required, while mechanistic-DDI predictions are recommended after Phase 1 and generation of mass balance data to allow for a mechanistic representation of the DDI and inform clinical design and regulatory decisions. Conclusions: A best practice framework for developing, applying and interpreting results of PBPK models to predict pH-dependent DDIs was created. This framework can allow for harmonization and consistency around the pH-DDI PBPK predictions, thus paving the way forward to an established use of such models in informing clinical and regulatory decisions.
Mitra A, Parrott N, Miller N, Lloyd R, Tistaert C, Heimbach T, Ji Y, Kesisoglou F. Prediction of pH-Dependent Drug-Drug Interactions for Basic Drugs Using Physiologically Based Biopharmaceutics Modeling: Industry Case Studies. J Pharm Sci. 2020 Mar;109(3):1380-1394. doi: 10.1016/j.xphs.2019.11.017. Dodd S, Kollipara S, Sanchez-Felix M, Kim H, Meng Q, Beato S, Heimbach T. Prediction of ARA/PPI Drug-Drug Interactions at the Drug Discovery and Development Interface. J Pharm Sci. 2019 Jan;108(1):87-101. doi: 10.1016/j.xphs.2018.10.032. Litou C, Vertzoni M, Goumas C, Vasdekis V, Xu W, Kesisoglou F, Reppas C. Characteristics of the Human Upper Gastrointestinal Contents in the Fasted State Under Hypo- and A-chlorhydric Gastric Conditions Under Conditions of Typical Drug – Drug Interaction Studies. Pharm Res. 2016 Jun;33(6):1399-412. doi: 10.1007/s11095-016-1882-8.
Reference: PAGE 33 (2025) Abstr 11544 [www.page-meeting.org/?abstract=11544]
Poster: Drug/Disease Modelling - Absorption & PBPK