Department of Pharmaceutical Biosciences, Uppsala University, Uppsala 75124, Sweden.
Inhalation PD Unit, Pharmaceutical Technology & Development, Operations, AstraZeneca, Gothenburg, Sweden.
Eur J Pharm Biopharm. 2020 Nov;156:191-202. doi: 10.1016/j.ejpb.2020.09.004. Epub 2020 Sep 15.
Systemic exposure of inhaled drugs is used to estimate the local lung exposure and assess systemic side effects for drugs with local pharmacological targets. Predicting systemic exposure is therefore central for successful development of drugs intended to be inhaled. Currently, these predictions are based mainly on data from in vitro experiments, but the accuracy of these predictions might be improved if they were based on data with higher physiological relevance. In this study, systemic exposure was simulated by applying biopharmaceutics input parameters from isolated perfused rat lung (IPL) data to a lung model developed in MoBi® as an extension to the full physiologically-based pharmacokinetic (PBPK) model in PK-Sim®. These simulations were performed for a set of APIs with a variety of physicochemical properties and formulation types. Simulations based on rat IPL data were also compared to simulations based on in vitro data. The predictive performances of the simulations were evaluated by comparing simulated plasma concentration-time profiles to clinical observations after pulmonary administration. Simulations using IPL-based input parameters predicted systemic exposure well, with predicted AUCs within two-fold of the observed value for nine out of ten drug compounds/formulations, and predicted C values within two-fold for eight out of ten drug compounds/formulations. Simulations using input parameters based on IPL data performed generally better than simulations based on in vitro input parameters. These results suggest that the developed model in combination with IPL data can be used to predict human lung absorption for compounds with different physicochemical properties and types of inhalation formulations.
全身暴露于吸入药物用于估计局部肺部暴露，并评估具有局部药理作用靶点的药物的全身副作用。因此，预测全身暴露是成功开发拟吸入药物的关键。目前，这些预测主要基于体外实验数据，但如果基于具有更高生理相关性的数据进行预测，则可以提高预测的准确性。在这项研究中，通过将离体灌流大鼠肺（IPL）数据的生物药剂学输入参数应用于在 MoBi®中开发的肺模型，来模拟全身暴露，作为在 PK-Sim®中完整生理相关药代动力学（PBPK）模型的扩展。对一组具有多种物理化学性质和制剂类型的 API 进行了模拟。基于大鼠 IPL 数据的模拟也与基于体外数据的模拟进行了比较。通过将模拟的血浆浓度-时间曲线与肺部给药后的临床观察结果进行比较，评估了模拟的预测性能。使用基于 IPL 的输入参数进行的模拟很好地预测了全身暴露，十种药物化合物/制剂中有九种的预测 AUC 值在观察值的两倍以内，十种药物化合物/制剂中有八种的预测 C 值在两倍以内。基于 IPL 数据的输入参数进行的模拟通常比基于体外输入参数的模拟效果更好。这些结果表明，所开发的模型与 IPL 数据相结合，可用于预测具有不同物理化学性质和吸入制剂类型的化合物的人体肺部吸收。
