"A pharmacodynamic model of clinical synergy in multiple myeloma"
Most anti-cancer therapies involve combinations of three or more agents, with the rationale that combining drugs with different mechanisms of action could maximize efficacy by targeting several subpopulations of a heterogeneous tumor. Synergy cannot be investigated in a clinical trial setting as the same patient cannot simultaneously receive single agents and their combination to quantify synergistic effect, while pre-clinical studies successfully quantify this effect, they do so in homogeneous cell lines. For this reason, clinical synergy remains difficult to investigate and translate into clinical utility. We focus our study on Multiple Myeloma (MM), an incurable hematological malignancy, due to its widespread use of combination therapy and the importance of timely therapeutic decisions in extending patient survival. We developed a mathematical framework that employs a second-order drug response model to fit patient-specific ex vivo responses of 203 MM patients to inform a novel pharmacodynamic model that accounts for two-way combination effects for 130 two-drug combinations. We have demonstrated that this model is sufficiently parameterized by single-agent and fixed-ratio combination responses, by validating model estimates with ex vivo combination responses for different concentration ratios, using a checkerboard assay. This novel model reconciles ex vivo observations from both Loewe and BLISS synergy models, by accounting for the dimension of time, as opposed to focusing on arbitrary time-points or drug effect. Clinical outcomes of patients were simulated by coupling patient-specific drug combination models with pharmacokinetic data from phase I clinical trials. Combination screening showed 1 in 5 combinations (21.43% by LD50, 18.42% by AUC) were synergistic ex vivo with statistical significance (P<0.05), but clinical synergy was predicted for only 1 in 10 combinations (8.69%), which was attributed to the role of pharmacokinetics and dosing schedules. Pre-clinical model predictions were used to accurately classify patients’ responses with statistically significant (P<0.05) accuracy as per International Myeloma Working Group response stratification criteria. This high-throughput combination screening framework identified drug combinations that are putatively clinically synergistic, and thus could potentially be used to screen for combinations that are likely candidates for a phase-III clinical trial. This could greatly benefit patients enrolling in these trials by improving the response on the experimental arm. The combinations shown to be most synergistic could be investigated to identify molecular pathways that govern their synergistic interaction.