Ex vivo drug sensitivity screening in multiple myeloma identifies drug combinations that act synergistically.

The management of multiple myeloma (MM) is challenging: An assortment of available drug combinations adds complexity to treatment selection, and treatment resistance frequently develops. Given the heterogeneous nature of MM, personalized testing tools are required to identify drug sensitivities. To identify drug sensitivities in MM cells, we established a drug testing pipeline to examine ex vivo drug responses. MM cells from 44 patients were screened against 30 clinically relevant single agents and 44 double- and triple-drug combinations. We observed variability in responses across samples. The presence of gain(1q21) was associated with low sensitivity to venetoclax, and decreased ex vivo responses to dexamethasone reflected the drug resistance observed in patients. Less heterogeneity and higher efficacy was detected with many combinations compared to the corresponding single agents. We identified new synergistic effects of melflufen plus panobinostat using low concentrations (0.1-10 nm and 8 nm, respectively). In agreement with clinical studies, clinically approved combinations, such as triple combination of selinexor plus bortezomib plus dexamethasone, acted synergistically, and synergies required low drug concentrations (0.1 nm bortezomib, 10 nm selinexor and 4 nm dexamethasone). In summary, our drug screening provided results within a clinically actionable 5-day time frame and identified synergistic drug efficacies in patient-derived MM cells that may aid future therapy choices.

bayesynergy: flexible Bayesian modelling of synergistic interaction effects in in vitro drug combination experiments.

The effect of cancer therapies is often tested pre-clinically via in vitro experiments, where the post-treatment viability of the cancer cell population is measured through assays estimating the number of viable cells. In this way, large libraries of compounds can be tested, comparing the efficacy of each treatment. Drug interaction studies focus on the quantification of the additional effect encountered when two drugs are combined, as opposed to using the treatments separately. In the bayesynergy R package, we implement a probabilistic approach for the description of the drug combination experiment, where the observed dose response curve is modelled as a sum of the expected response under a zero-interaction model and an additional interaction effect (synergistic or antagonistic). Although the model formulation makes use of the Bliss independence assumption, we note that the posterior estimates of the dose-response surface can also be used to extract synergy scores based on other reference models, which we illustrate for the Highest Single Agent model. The interaction is modelled in a flexible manner, using a Gaussian process formulation. Since the proposed approach is based on a statistical model, it allows the natural inclusion of replicates, handles missing data and uneven concentration grids, and provides uncertainty quantification around the results. The model is implemented in the open-source Stan programming language providing a computationally efficient sampler, a fast approximation of the posterior through variational inference, and features parallel processing for working with large drug combination screens.