Predictive Design and Analysis of Drug Transport by MultiScale Computational Models Under Uncertainty

AKALIN A. A., DEDEKARGINOĞLU B., Choi S. R., Han B., ÖZÇELİKKALE A.

Pharmaceutical Research, cilt.40, sa.2, ss.501-523, 2023 (SCI-Expanded) identifier identifier identifier

  • Yayın Türü: Makale / Derleme
  • Cilt numarası: 40 Sayı: 2
  • Basım Tarihi: 2023
  • Doi Numarası: 10.1007/s11095-022-03298-8
  • Dergi Adı: Pharmaceutical Research
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, BIOSIS, CAB Abstracts, Chemical Abstracts Core, Chimica, EMBASE, INSPEC, International Pharmaceutical Abstracts, MEDLINE, Veterinary Science Database
  • Sayfa Sayıları: ss.501-523
  • Anahtar Kelimeler: continuum modeling, discrete modeling, nanomedicine, sensitivity analysis, SMEARED FINITE-ELEMENT, PHARMACOKINETIC MODEL, MICROVESSEL DENSITY, MOLECULAR-DYNAMICS, MASS-TRANSPORT, BLOOD-VESSELS, FE MODEL, TUMOR, SIMULATION, DELIVERY
  • Orta Doğu Teknik Üniversitesi Adresli: Evet

Özet

© 2022, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.Computational modeling of drug delivery is becoming an indispensable tool for advancing drug development pipeline, particularly in nanomedicine where a rational design strategy is ultimately sought. While numerous in silico models have been developed that can accurately describe nanoparticle interactions with the bioenvironment within prescribed length and time scales, predictive design of these drug carriers, dosages and treatment schemes will require advanced models that can simulate transport processes across multiple length and time scales from genomic to population levels. In order to address this problem, multiscale modeling efforts that integrate existing discrete and continuum modeling strategies have recently emerged. These multiscale approaches provide a promising direction for bottom-up in silico pipelines of drug design for delivery. However, there are remaining challenges in terms of model parametrization and validation in the presence of variability, introduced by multiple levels of heterogeneities in disease state. Parametrization based on physiologically relevant in vitro data from microphysiological systems as well as widespread adoption of uncertainty quantification and sensitivity analysis will help address these challenges.