Targeted therapies - drugs that attack or bind to a specific molecule or part of a molecule such as one that triggers tumor growth or controls blood flow - have been around for several years. The biggest problem with these new drugs if we can predict who will respond to them.

The much touted success of Gleevec for the rare liquid cancer CML is not generalizable to solid tumors. Resistance to Gleevec in CML develops rapidly. Many of the initial responders to Gleevec in blast crisis relapse within months and the growing consensus is that Gleevec is an exception, rather than a new paradigm.

Gleevec should be seen in its proper clinical perspective. It is a treatment that largely involves single cells amenable to attack because of their presence in the circulation. Metastatic tumors - which cause 90% of all deaths - by contrast have hundreds to thousands of surgically inaccessible growths dispersed throughout an organ. They cannot be attacked out in the open as is the case with tumor cells in the circulation.

And I've often wondered if the underlying science of Herceptin is sound? Her2 just happens to be one molecule which has been implicated in the process but there may be more. If it were the only protein involved, then one would expect that Her2 expression would correlate with Herceptin activity 100% of the time but it actually does so only about 20% of the time.

Monoclonal antibodies, which are usually water soluble and large, target extracellular (outside) components of these pathways. In contrast, small molecule inhibitors can enter cells, thereby blocking receptor signaling and interfering with downstream intracellular molecules.

Many of these drugs cry out for validated clinical biomarkers to help set dosage and select people likely to respond. And optimal and reproducible Her2 testing continues to evade the diagnositcs of the disease. Numerous other genes, tumor, and patient factors contribute to the risk of the cancer coming back and the effectiveness of chemotherapy for breast cancer.

Instead of "blindly" mixing and matching drugs to individual cancer patients, what would be more beneficial is to sort out what's the best profile in terms of which patients benefit from this drug or any other drug. Can they be combined? What's the proper way to work with these new drugs?

If a drug works extremely well for a certain percentage of cancer patients, identify which ones. If one drug or another is working for some people (not average populations) then obviously there are others out there who would also benefit.

What's good for the group (population) may not be good for the individual, affirms that in the tactic of using "fresh" biopsied cells to predict which cancer treatments will work best for the individual patient, these "smart" drugs have to get inside the cells in order to "target" anything.

If the "targeted" drug either won't "get in" in the first place or if it gets pumped out/extruded or if it gets immediately metabolized inside the cell, it just isn't going to work. Each of these new "targeted" drugs are not for everybody. Even when the disease is the same type, different patients' tumors respond differently to the same agent.

Upgrading clinical therapy by using functional tumor cell assays measuring "cell death" of three dimensional microclusters of live "fresh" tumor cells, can improve the situation by allowing more drugs to be considered. The more drug types there are in the selective arsenal, the more likely the system is to prove beneficial.

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).

What would be more beneficial is to test those pharmacodynamic endpoints with the ability to measure multiple parameters in cellular screens now in hand using flow cytometry. Using a systems biology approach where compounds are first screened in cell-based assays, with mechanistic understanding of the target coming only after validation of its impact on the biology.

Unlike a test for the presence of receptors to a specific antigen, which only "implies" dependence upon that antigen, a functional assay actually assesses the direct or indirect effect of the drug upon the whole cell, whether it is a tumor cell or an endothelial cell.

A "functional" assay doesn't just focus on Her2 or any one protein or mechanism. Whether it's Her2 alone (unlikely) or in combination with other proteins and other mechanical factors, the assay works by assessing the net effect of all those factors.

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 individual trees.