Welcome to the working week. We hope the weekend was refreshing and restful. Now, of course, the time has come to resume the usual routine of meetings and deadlines and what-not. Are you ready? If not, grab a cup of something stimulating and scan the news of the world. What better way to get started? So dig in and have a good day, everyone. Catch you soon...
Human Genome Concedes Hep C Drug Approval Is Unlikely (TheStreet)
Bayer's Nexavar Fails Phase III Lung Cancer Trial (Reuters)
Glaxo Gets Complete Response Letter For MenHibrix (PharmaTimes)
Debating Anti-Counterfeiting Technology In Nigeria (234Next)
Dr. Reddy's Blocked From Launching Version Of Allegra (Business Standard)
Abbott Lays Off 120 Workers In California (North County Times)
New KV Pharma Board Ousts CEO (St. Louis Business Journal)
Teva Increases Stake In Andromeda (domain-b)
1 Comment
In regards to the Nexavar failing Phase III lung cancer trial, the importance of mechanistic work around targets as a starting point could have been downplayed in favor of a systems biology approach were compounds are first screened in cell-based assays, with mechanistic understanding of the target coming after validation of its impact on the biology of the cancer cells.
Many of these drugs cry out for validated clinical biomarkers to help set dosage and select patients likely to respond. Optimal and reproducible targeted testing continues to evade the diagnostics of the disease. Numerous genes, tumor, and patient factors contribute to the risk of the cancer coming back and the effectiveness of chemotherapy.
It could be vastly more beneficial to measure the net effect of all processes (systems) instead of just individual molecular targets. The cell is a system, an integrated, interacting network of genes, proteins, and other cellular constituents that produce functions. One needs to analyze the systems’ response to drug treatments, not just one or a few targets (pathways/mechanisms).
There are many pathways/mechanisms to the altered cellular (forest) function, hence all the different “trees” which correlate in different situations. Improvement can be made by measuring what happens at the end (the effects on the forest), rather than the status of the indivudal trees.