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iBCS: 2. Mechanistic Modeling of Pulmonary Availability of Inhaled Drugs versus Critical Product Attributes.

Per BäckmanAntonio CabalAndy ClarkCarsten EhrhardtBen ForbesJayne E HastedtAnthony HickeyGuenther HochhausWenlei JiangStavros KassinosPhilip J KuehlDavid PrimeYoen-Ju SonSimon P TeagueUlrika TehlerJennifer Wylie
Published in: Molecular pharmaceutics (2022)
This work is the second in a series of publications outlining the fundamental principles and proposed design of a biopharmaceutics classifications system for inhaled drugs and drug products (the iBCS). Here, a mechanistic computer-based model has been used to explore the sensitivity of the primary biopharmaceutics functional output parameters: (i) pulmonary fraction dose absorbed ( F abs ) and (ii) drug half-life in lumen ( t 1/2 ) to biopharmaceutics-relevant input attributes including dose number (Do) and effective permeability ( P eff ). Results show the nonlinear sensitivity of primary functional outputs to variations in these attributes. Drugs with Do < 1 and P eff > 1 × 10 -6 cm/s show rapid ( t 1/2 < 20 min) and complete ( F abs > 85%) absorption from lung lumen into lung tissue. At Do > 1, dissolution becomes a critical drug product attribute and F abs becomes dependent on regional lung deposition. The input attributes used here, Do and P eff , thus enabled the classification of inhaled drugs into parameter spaces with distinctly different biopharmaceutic risks. The implications of these findings with respect to the design of an inhalation-based biopharmaceutics classification system (iBCS) and to the need for experimental methodologies to classify drugs need to be further explored.
Keyphrases
  • drug induced
  • pulmonary hypertension
  • deep learning
  • machine learning
  • climate change