Haste Makes Waste - Putting Quality First
There is, however, a strong caveat to this that has a lot to do with another maxim of human behavior in the post Microsoft Office era. Once a number appears, it is cut and pasted into Excel spreadsheets for analysis and decision making, into Powerpoint presentations for discussion and group-think, into Word documents for reports and publications, and into Outlook emails for broad dissemination.
So, data that appears once can very easily become separated from its context, documentized and broadly distributed until it becomes part of the organizational urban legend. I call this the database-document-dogma paradigm.
Undoing the effect of dogmaticized errant data is far more difficult than avoiding it in the first place.
What does this have to do with ADME? Well, later-stage ADME/Tox data is carefully created, providing a high-degree of analytical acuity and controlling for the potential artifacts due to low-solubility and high-non-specific binding. The earlier data, on the other hand, has been generated with reasonable analytical aquity and no explicit regard for these physicochemical artifacts.
The prevailing view is that the economics of the situation, i.e., the number of samples, requires scaled down assays to support throughput goals and as previously argued, turn-around time goals.
The potential harm here comes from the database-document-dogma paradigm. It runs like this: A project team receives a number of lead compounds from the hit to lead group, has them all profiled and decides to pursue a lead compound series where the metabolic stability looks pretty high, completely unaware that the results were really reflective of an apparent stability without specific information about the unbound fraction.
Furthermore, the CYP inhibition profile appeared clean, but the solubility data, on further inspection, show a solubility that is significantly lower than the analyte concentration in the early CYP assay. The project continues to optimize potency, adding lipophilicity, and spot-checks the ADME properties.
Excitement builds and the team nominates some compounds for Tier II ADME profiling, where the experiments give a much clearer insight and control for solubility and non-specific binding. The results are shocking to the project team. CYP 3A4 inhibition is high, solubility is low, plasma protein binding is high, and intrinsic clearance is high. Essentially the entire project has ground to a halt because its ADME properties are awry.
Meanwhile, there was a second chemical class presented by lead discovery, which showed moderate metabolic stability and moderate CYP inhibition, liabilities that could be overcome with appropriate synthetic steps.
What was not appreciated at the time of selection, however, was that this series had much higher solubility and much lower non-specific binding, so that the real liabilities were actually less than the series that was chosen.
In our example, the project team went back to this second class, put them directly into the Tier II assays, saw the relative advantage of these experiments, and made progress toward the clinic.
There were two costs to the drug discovery organization, however, about six months of lost time as well as a significant increase in demand for the Tier II experiments, and these were almost never generated within the timeframe of the potency data.