Advancing algorithmic drug product development: Recommendations for machine learning approaches in drug formulation

Jack D. Murray, Justus J. Lange, Harriet Bennett-Lenane, René Holm, Martin Kuentz, Patrick J. O'Dwyer, Brendan T. Griffin*

*Kontaktforfatter

Publikation: Bidrag til tidsskriftTidsskriftartikelForskningpeer review

11 Downloads (Pure)

Abstract

Artificial intelligence is a rapidly expanding area of research, with the disruptive potential to transform traditional approaches in the pharmaceutical industry, from drug discovery and development to clinical practice. Machine learning, a subfield of artificial intelligence, has fundamentally transformed in silico modelling and has the capacity to streamline clinical translation. This paper reviews data-driven modelling methodologies with a focus on drug formulation development. Despite recent advances, there is limited modelling guidance specific to drug product development and a trend towards suboptimal modelling practices, resulting in models that may not give reliable predictions in practice. There is an overwhelming focus on benchtop experimental outcomes obtained for a specific modelling aim, leaving the capabilities of data scraping or the use of combined modelling approaches yet to be fully explored. Moreover, the preference for high accuracy can lead to a reliance on black box methods over interpretable models. This further limits the widespread adoption of machine learning as black boxes yield models that cannot be easily understood for the purposes of enhancing product performance. In this review, recommendations for conducting machine learning research for drug product development to ensure trustworthiness, transparency, and reliability of the models produced are presented. Finally, possible future directions on how research in this area might develop are discussed to aim for models that provide useful and robust guidance to formulators.

OriginalsprogEngelsk
Artikelnummer106562
TidsskriftEuropean Journal of Pharmaceutical Sciences
Vol/bind191
Antal sider13
ISSN0928-0987
DOI
StatusUdgivet - 1. dec. 2023

Bibliografisk note

Funding Information:
JDM is funded by the Irish Research Council Government of Ireland Postgraduate Scholarship Programme grant number GOIPG/2022/1580.

Funding Information:
PJOD, MK, JJL, RH, and BTG are part of the InPharma European Training network, which has received funding under the Marie Skłodowska-Curie grant agreement No 955756.

Publisher Copyright:
© 2023

Fingeraftryk

Dyk ned i forskningsemnerne om 'Advancing algorithmic drug product development: Recommendations for machine learning approaches in drug formulation'. Sammen danner de et unikt fingeraftryk.

Citationsformater