Benchmarking of Human Dose Prediction for Inhaled Medicines from Preclinical In Vivo Data.

PURPOSE: A scientifically robust prediction of human dose is important in determining whether to progress a candidate drug into clinical development. A particular challenge for inhaled medicines is that unbound drug concentrations at the pharmacological target site cannot be easily measured or predicted. In the absence of such data, alternative empirical methods can be useful. This work is a post hoc analysis based on preclinical in vivo pharmacokinetic/pharmacodynamic (PK/PD) data with the aim to evaluate such approaches and provide guidance on clinically effective dose prediction for inhaled medicines.

METHODS: Five empirically based methodologies were applied on a diverse set of marketed inhaled therapeutics (inhaled corticosteroids and bronchodilators). The approaches include scaling of dose based on body weight or body surface area and variants of PK/PD approaches aiming to predict the therapeutic dose based on having efficacious concentrations of drug in the lung over the dosing interval.

RESULTS: The most robust predictions of dose were made by body weight adjustment (90% within 3-fold) and by a specific PK/PD approach aiming for an average predicted 75% effect level during the dosing interval (80% within 3-fold). Scaling of dose based on body surface area consistently under predicted the therapeutic dose.

CONCLUSIONS: Preclinical in vivo data and empirical scaling to man can be used as a baseline method for clinical dose predictions of inhaled medicines. The development of more sophisticated translational models utilizing free drug concentration and target engagement data is a desirable build.