|Be Careful What You Ask for: Challenges of Predicting Human Clearance for a Low Metabolic Turnover Compound, ELND006|
Kevin Quinn, David Nakamura, Heather Zhang, Shawn Gauby, Colin Lorentzen, Erich Goldbach, Amanda Moore, Salman Khetani, Earvin Liang, John-Michael Sauer and George Tonn,
A study of in vitro clearance of ELND006, a low turn-over compound.
|Global Gene Expression Changes Induced In Primary Human Hepatocytes By Thiazolidinediones Upon Repeat Dosing of HepatoPac™ Cultures|
Michael McVay and Salman R. Khetani
An assessment of global gene expression changes in HepatoPac, a micropatterned co-culture of hepatocytes and stromal cells.
|A Long Term Culture Model for Primary Hepatocytes from Cynomolgus Monkeys|
Simon Aoyama, Sara Lambirth, Chitra Kanchagar and Salman R. Khetani
The Macaca fascicularis or cynomolgus monkey is a non-human primate often used in pre-clinical animal studies. In this study, we applied microtechnology and tissue engineering techniques to cynomologus monkey hepatocytes in order to determine if these cells could be stabilized in micropatterned co-cultures similar to their human and rat counterparts.
|Live Cell Beating Assay Using Human iPSC-derived Cardiomyocytes for Evaluation of Drug Efficacy and Toxicity|
Oksana Sirenko, Carole Crittenden, Blake Anson, Jayne Hesley, Yen-Wen Chen, Nick Callamaras and Evan F. Cromwell
A large percentage of new drugs fail in clinical studies due to cardiac toxicity. Development of highly predictive in vitro assays suitable for screening, safety assessment or other environments is therefore extremely important for drug development. Human cardiomyocytes derived from stem cell sources can greatly accelerate the discovery of cardiac drugs and improve drug safety by offering more clinically relevant cell-based models than those presently available.
|Quantification of cytokines on the SpectraMax® Paradigm® Multi-Mode Microplate Detection Platform using Alpha Technology|
Caroline Cardonnel, Cathleen Salomo, Michael Katzlinger, Yvonne Fitzgerald, Cathy Olsen and Harald Hundsberger
Inflammation is accompanied by increased endothelial chemokine production and adhesion molecule expression, which may result in an extensive neutrophil infiltration. As such, the search for novel anti-inflammatory substances able to downregulate these parameters, as well as tissue damage, holds therapeutic promise.
|GALAS Modeling Methodology Applications In The Prediction Of Drug Metabolism Related Properties|
Remigijus Didziapetris, Justas Dapkunas, Andrius Sazonovas and Pranas Japertas
Analytical identification of metabolites for a drug candidate is usually a time consuming and low-throughput task and is performed only at the later phases of drug development. Therefore the possibility to predict possible sites of human liver microsomal (HLM) metabolism using in silico techniques would be a very attractive feature for any medicinal chemist.
|Effective Use of In-Silico Tools in Lead Optimization|
Pranas Japertas, Andrius Sazonovas and Kiril Lanevskij
Of all the challenges facing medicinal chemists in general, one of the most significant must be transforming an active molecule into a viable drug. Lead optimization efforts are guided by a combination of factors, such as potency, ease of synthesis, patentability concerns, specific synthetic constrains of the interaction with the target, as well as the lead’s toxicity and ADME properties.
|A Weight-of-Evidence Approach to Prioritisation based on Consensus across Multiple Sources of Information|
Roman Affentranger (1), Barry Hardy (1), Glenn Myatt (2), Nina Jeliazkova (3), Matthew Clark (4), Jeffrey Wiseman (4)
We present the results of initial work carried out within the OpenToxLink Virtual Organization, applying a Weight-of-Evidence (WoE) approach based on consensus across multiple sources of information for the prediction of adverse effects of a large set of potential antimalarial compounds. The work was carried out as part of the EU FP7 project SYNERGY, evaluating the support of decision dashboards and event-driven collaborative research of software developed within SYNERGY.
|Three-dimensional quantitative structure-activity relationship analysis and ADME predictions of guanylhydrazone coactivator binding inhibitors of estrogen receptors|
Sergey Shityakov, Thomas Dandekar
The estrogen receptors (ER) refer to a group of the nuclear hormone receptor superfamily of ligand-mediated transcriptional factors. Over expression of this type of receptors leads to a breast cancer progression. Hormone-responsive breast cancer develops resistance to conventional anti-cancer therapy, and this becomes a major problem in a breast cancer therapy.