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29th June to 30th June 2015, London, United Kingdom



The poor success rate in developing a new drug from discovery to market is a major problem faced by the pharmaceutical industry today.
The goal of early ADME is to determine whether a candidate drug forms reactive metabolites as early as possible and mitigate attrition rates.
Reactive drug metabolites are known to be one of the factors behind unexpected drug-induced toxicity and therefore their identification early in the drug discovery process is of big importance.

Furthermore, cardiovascular toxicity is often to blame, accounting for approximately 27% of drug failures due to toxicity in the preclinical phase:
The overall attrition rate due to cardiovascular events in clinical development is 21%, indicating that several cardiovascular effects occur in Phase II and III clinical trials which are not detected in the preclinical studies or earlier clinical trials. Thus, the conference will feature a dedicated session on toxicity and highlight emerging technology to aid better prediction.


ADME parameters obtained from in vitro and in vivo models, which aid in the prediction of drug behaviors in patients, are important for the decision to advance, hold or terminate a drug candidate. However, incomplete ADME studies or misinterpretation of ADME data may cause failures in drug development. ADME studies are conducted with in vitro, in vivo or in silico models.

In vitro models generate many ADME parameters, including apparent permeability, metabolic stability, protein binding, blood-to-plasma partitioning, drug–drug interaction potentials (e.g., inhibition and induction of cytochrome P450 (CYP) and transporters), cell proliferation and cytotoxicity, and hERG inhibition. In vivo models of animals and healthy human subjects provide information such as drug oral bioavailability, exposures, distribution, clearance, and duration of exposure for a drug and its metabolites.

Finally, in silico models predict drug behaviors based on physicochemical properties of drug candidates in combination with crystal structures of a protein (an enzyme or a transporter) and database of ADME properties generated in laboratories. With the numerous models available, proper experimental model selection is essential for ADME property optimization.



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