After analyzing hundreds of interactions between cancer drugs and cancer cells using information theory and advanced modeling techniques, Harvard Medical School researchers have found that a standard model for predicting drug effectiveness is incomplete and potentially misleading.
The findings, published recently in Nature Chemical Biology, could have implications for directing billions of dollars of drug research in a way that will rule out drugs unlikely to be effective in the clinic and highlight potentially useful drugs that the traditional standard would miss. The techniques suggested by the findings could also potentially help identify combination therapies that would boost the performance of under-achieving drugs and help clinicians maximize effectiveness without undue side effects.
“The results of this study are a small but significant step toward a new understanding of therapeutics,” said senior author Peter Sorger, the Otto Krayer Professor of Systems Pharmacology and head of the HMS Program in Therapeutic Science.
Trying to predict the interaction of thousands of cells in a body with thousands of drug molecules is like watching a dozen games of billiards all at once and trying to predict where the balls will all end up. Each single ball bouncing off another follows simple, precise physical rules that play out in unpredictable ways in an enormously complex environment.
In biochemistry, researchers use mathematical relationships to predict how a given drug will perform in a particular set of circumstances. One standard rule states that for any drug, the relationship between effectiveness and dosage can be drawn as a sinuous sigmoidal curve in the characteristic shape of an elongated italic S.
In a typical graph comparing a variety of drugs, the good ones—the ones that kill lots of cancer cells at doses with few toxic side effects, say—cluster on the left, and less effective drugs cluster on the right (meaning that more drug is needed to achieve similar effect).
“The shape of this curve and the particular mathematical relationship between dosage and effectiveness that the curve represents have been held as a fundamental principle of biochemistry for more than a century,” said Sorger. The relationship is used to measure all kinds of natural and therapeutic biochemical reactions, from drugs killing pathogens to signals binding with receptors.
“It’s like the hydrogen ion in physics—that most basic particle upon which we build our science. Except it turns out we have focused on one variable to the exclusion of other, equally important ones,” Sorger said.