Title |
Application of PBPK modelling in drug discovery and development at Pfizer
|
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Published in |
Xenobiotica, October 2011
|
DOI | 10.3109/00498254.2011.627477 |
Pubmed ID | |
Authors |
Hannah M Jones, Maurice Dickins, Kuresh Youdim, James R Gosset, Neil J Attkins, Tanya L Hay, Ian K Gurrell, Y Raj Logan, Peter J Bungay, Barry C Jones, Iain B Gardner |
Abstract |
Early prediction of human pharmacokinetics (PK) and drug-drug interactions (DDI) in drug discovery and development allows for more informed decision making. Physiologically based pharmacokinetic (PBPK) modelling can be used to answer a number of questions throughout the process of drug discovery and development and is thus becoming a very popular tool. PBPK models provide the opportunity to integrate key input parameters from different sources to not only estimate PK parameters and plasma concentration-time profiles, but also to gain mechanistic insight into compound properties. Using examples from the literature and our own company, we have shown how PBPK techniques can be utilized through the stages of drug discovery and development to increase efficiency, reduce the need for animal studies, replace clinical trials and to increase PK understanding. Given the mechanistic nature of these models, the future use of PBPK modelling in drug discovery and development is promising, however, some limitations need to be addressed to realize its application and utility more broadly. |
X Demographics
Geographical breakdown
Country | Count | As % |
---|---|---|
Unknown | 1 | 100% |
Demographic breakdown
Type | Count | As % |
---|---|---|
Members of the public | 1 | 100% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
---|---|---|
Netherlands | 2 | 2% |
United States | 2 | 2% |
United Kingdom | 1 | <1% |
China | 1 | <1% |
Unknown | 112 | 95% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Researcher | 29 | 25% |
Student > Ph. D. Student | 18 | 15% |
Student > Master | 13 | 11% |
Student > Bachelor | 8 | 7% |
Other | 7 | 6% |
Other | 16 | 14% |
Unknown | 27 | 23% |
Readers by discipline | Count | As % |
---|---|---|
Pharmacology, Toxicology and Pharmaceutical Science | 31 | 26% |
Agricultural and Biological Sciences | 22 | 19% |
Medicine and Dentistry | 16 | 14% |
Chemistry | 6 | 5% |
Social Sciences | 5 | 4% |
Other | 10 | 8% |
Unknown | 28 | 24% |