| Literature DB >> 33609523 |
Martin Kuentz1, René Holm2, Christian Kronseder3, Christoph Saal4, Brendan T Griffin5.
Abstract
New drug candidates often require bio-enabling formation technologies such as lipid-based formulations, solid dispersions, or nanosized drug formulations. Development of such more sophisticated delivery systems generally requires higher resource investment compared to a conventional oral dosage form, which might slow down clinical development. To achieve the biopharmaceutical objectives while enabling rapid cost effective development, it is imperative to identify a suitable formulation technique for a given drug candidate as early as possible. Hence many companies have developed internal decision trees based mostly on prior organizational experience, though they also contain some arbitrary elements. As part of the EU funded PEARRL project, a number of new decision trees are here proposed that reflect both the current scientific state of the art and a consensus among the industrial project partners. This commentary presents and discusses these, while also going beyond this classical expert approach with a pilot study using emerging machine learning, where the computer suggests formulation strategy based on the physicochemical and biopharmaceutical properties of a molecule. Current limitations are discussed and an outlook is provided for likely future developments in this emerging field of pharmaceutics.Entities:
Keywords: Bioavailability; Dissolution; Formulation; In silico modeling; Lipid-based formulation(s); Physicochemical properties; Solid dosage forms
Year: 2021 PMID: 33609523 DOI: 10.1016/j.xphs.2021.02.004
Source DB: PubMed Journal: J Pharm Sci ISSN: 0022-3549 Impact factor: 3.534