scholarly journals In silico models in drug development: where we are

2018 ◽  
Vol 42 ◽  
pp. 111-121 ◽  
Author(s):  
Janet Piñero ◽  
Laura I Furlong ◽  
Ferran Sanz
2020 ◽  
Vol 9 (4) ◽  
pp. 195-197
Author(s):  
Flora T. Musuamba ◽  
Roberta Bursi ◽  
Efthymios Manolis ◽  
Kristin Karlsson ◽  
Alexander Kulesza ◽  
...  

Author(s):  
Pierre Morissette ◽  
Jeffrey Travis ◽  
Pamela Gerenser ◽  
Patrick Fanelli ◽  
Anne Chain ◽  
...  

Pharmaceutics ◽  
2021 ◽  
Vol 13 (5) ◽  
pp. 704
Author(s):  
Zhengying Zhou ◽  
Jinwei Zhu ◽  
Muhan Jiang ◽  
Lan Sang ◽  
Kun Hao ◽  
...  

Human-derived in vitro models can provide high-throughput efficacy and toxicity data without a species gap in drug development. Challenges are still encountered regarding the full utilisation of massive data in clinical settings. The lack of translated methods hinders the reliable prediction of clinical outcomes. Therefore, in this study, in silico models were proposed to tackle these obstacles from in vitro to in vivo translation, and the current major cell culture methods were introduced, such as human-induced pluripotent stem cells (hiPSCs), 3D cells, organoids, and microphysiological systems (MPS). Furthermore, the role and applications of several in silico models were summarised, including the physiologically based pharmacokinetic model (PBPK), pharmacokinetic/pharmacodynamic model (PK/PD), quantitative systems pharmacology model (QSP), and virtual clinical trials. These credible translation cases will provide templates for subsequent in vitro to in vivo translation. We believe that synergising high-quality in vitro data with existing models can better guide drug development and clinical use.


2014 ◽  
Vol 14 (16) ◽  
pp. 1913-1922 ◽  
Author(s):  
Dimitar Dobchev ◽  
Girinath Pillai ◽  
Mati Karelson

2021 ◽  
Vol 350 ◽  
pp. S64-S65
Author(s):  
K. Kopanska ◽  
J.C. Gómez-Tamayo ◽  
J. Llopis-Lorente ◽  
B.A. Trenor-Gomis ◽  
J. Sáiz ◽  
...  

Author(s):  
Juri A. Steiner ◽  
Urs A.T. Hofmann ◽  
Patrik Christen ◽  
Jean M. Favre ◽  
Stephen J. Ferguson ◽  
...  

2016 ◽  
Vol 22 (10) ◽  
Author(s):  
Carlyle Ribeiro Lima ◽  
Nicolas Carels ◽  
Ana Carolina Ramos Guimaraes ◽  
Pierre Tufféry ◽  
Philippe Derreumaux

2010 ◽  
Vol 7 (3) ◽  
Author(s):  
Simon J Cockell ◽  
Jochen Weile ◽  
Phillip Lord ◽  
Claire Wipat ◽  
Dmytro Andriychenko ◽  
...  

SummaryDrug development is expensive and prone to failure. It is potentially much less risky and expensive to reuse a drug developed for one condition for treating a second disease, than it is to develop an entirely new compound. Systematic approaches to drug repositioning are needed to increase throughput and find candidates more reliably. Here we address this need with an integrated systems biology dataset, developed using the Ondex data integration platform, for the in silico discovery of new drug repositioning candidates. We demonstrate that the information in this dataset allows known repositioning examples to be discovered. We also propose a means of automating the search for new treatment indications of existing compounds.


Sign in / Sign up

Export Citation Format

Share Document