More On Combination Drug Therapy For Cancer

Combination drug therapy for cancer has been thought to always involve the drugs working together in an additive or even synergistic fashion.  However, one study shows that in some cases, the efficacy of drug combinations is solely due to patient-to-patient variability in response and the independent action of the drugs.  So, assume you have two patients, Bill and Mary, both with the same type of cancer, and they are given combination therapy with drugs A and B.  The traditional idea was that success would be due to A and B both working equally in both patients, or working synergistically; this new study suggests that in some cases, if you don’t know in advance which drug is best for what patient, you give both, and maybe Bill responds to A and Mary to B, and you see a success that you would not have seen if you had given both patients either A or B – so you get 100% success instead of 50%. Keep in mind that there are of course cases where combination therapy does wok additively or synergistically.  Abstract:

Combination cancer therapies aim to improve the probability and magnitude of therapeutic responses and reduce the likelihood of acquired resistance in an individual patient. However, drugs are tested in clinical trials on genetically diverse patient populations. We show here that patient-to-patient variability and independent drug action are sufficient to explain the superiority of many FDA-approved drug combinations in the absence of drug synergy or additivity. This is also true for combinations tested in patient-derived tumor xenografts. In a combination exhibiting independent drug action, each patient benefits solely from the drug to which his or her tumor is most sensitive, with no added benefit from other drugs. Even when drug combinations exhibit additivity or synergy in pre-clinical models, patient-to-patient variability and low cross-resistance make independent action the dominant mechanism in clinical populations. This insight represents a different way to interpret trial data and a different way to design combination therapies.