Literature DB >> 17046236

In silico prediction of clinical efficacy.

Seth Michelson1, Anil Sehgal, Christina Friedrich.   

Abstract

Drug development is a high risk and costly process, and the ability to predict clinical efficacy in silico (in a computer) can save the pharmaceutical industry time and resources. Additionally, such an approach will result in more targeted, personalized therapies. To date, a number of in silico strategies have been developed to provide better information about the human response to novel therapies earlier in the drug development process. Some of the most prominent include physiological modeling of disease and disease processes, analytical tools for population pharmacodynamics, tools for the analysis of genomic expression data, Monte Carlo simulation technologies, and predictive biosimulation. These strategies are likely to contribute significantly to reducing the failure rate of drugs entering clinical trials.

Entities:  

Mesh:

Year:  2006        PMID: 17046236     DOI: 10.1016/j.copbio.2006.09.004

Source DB:  PubMed          Journal:  Curr Opin Biotechnol        ISSN: 0958-1669            Impact factor:   9.740


  10 in total

Review 1.  Harnessing systems biology approaches to engineer functional microvascular networks.

Authors:  Lauren S Sefcik; Jennifer L Wilson; Jason A Papin; Edward A Botchwey
Journal:  Tissue Eng Part B Rev       Date:  2010-06       Impact factor: 6.389

Review 2.  Mechanistic systems modeling to guide drug discovery and development.

Authors:  Brian J Schmidt; Jason A Papin; Cynthia J Musante
Journal:  Drug Discov Today       Date:  2012-09-19       Impact factor: 7.851

3.  In vitro to in vivo extrapolation and species response comparisons for drug-induced liver injury (DILI) using DILIsym™: a mechanistic, mathematical model of DILI.

Authors:  Brett A Howell; Yuching Yang; Rukmini Kumar; Jeffrey L Woodhead; Alison H Harrill; Harvey J Clewell; Melvin E Andersen; Scott Q Siler; Paul B Watkins
Journal:  J Pharmacokinet Pharmacodyn       Date:  2012-08-09       Impact factor: 2.745

Review 4.  Cardiac models in drug discovery and development: a review.

Authors:  Robert K Amanfu; Jeffrey J Saucerman
Journal:  Crit Rev Biomed Eng       Date:  2011

5.  In Silico Augmentation of the Drug Development Pipeline: Examples from the study of Acute Inflammation.

Authors:  Gary An; John Bartels; Yoram Vodovotz
Journal:  Drug Dev Res       Date:  2011-03-01       Impact factor: 4.360

6.  Development of an optimized dose for coformulation of zidovudine with drugs that select for the K65R mutation using a population pharmacokinetic and enzyme kinetic simulation model.

Authors:  Selwyn J Hurwitz; Ghazia Asif; Nancy M Kivel; Raymond F Schinazi
Journal:  Antimicrob Agents Chemother       Date:  2008-10-06       Impact factor: 5.191

7.  Population-Specific Models of Glycemic Control in Intensive Care: Towards a Simulation-Based Methodology for Protocol Optimization.

Authors:  Stephen D Patek; E Andy Ortiz; Leon S Farhy; Jennifer Mason Lobo; James Isbell; Jennifer L Kirby; Anthony McCall
Journal:  Proc Am Control Conf       Date:  2015-07-30

Review 8.  Facilitating compound progression of antiretroviral agents via modeling and simulation.

Authors:  Jeffrey S Barrett
Journal:  J Neuroimmune Pharmacol       Date:  2007-01-17       Impact factor: 4.147

9.  Confidence from uncertainty--a multi-target drug screening method from robust control theory.

Authors:  Camilla Luni; Jason E Shoemaker; Kevin R Sanft; Linda R Petzold; Francis J Doyle
Journal:  BMC Syst Biol       Date:  2010-11-24

10.  The role of tumor tissue architecture in treatment penetration and efficacy: an integrative study.

Authors:  Katarzyna A Rejniak; Veronica Estrella; Tingan Chen; Allison S Cohen; Mark C Lloyd; David L Morse
Journal:  Front Oncol       Date:  2013-05-10       Impact factor: 6.244

  10 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.