Non-parametric synergy modeling with Gaussian processes

Abstract

Background: Understanding the synergetic and antagonistic effects of combinations of drugs and toxins is vital for many applications, including treatment of multifactorial diseases and ecotoxicological monitoring. Synergy is usually assessed by comparing the response of drug combinations to a predicted non-interactive response from reference (null) models. Possible choices of null models are Loewe additivity, Bliss independence and the recently rediscovered Hand model. A different approach is taken by the MuSyC model, which directly fits a generalization of the Hill model to the data. All of these models, however, fit the dose-response relationship with a parametric model. Results: We propose the Hand-GP model, a non-parametric model based on the combination of the Hand model with Gaussian processes. We introduce a new logarithmic squared exponential kernel for the Gaussian process which captures the logarithmic dependence of response on dose. From the monotherapeutic response and the Hand principle, we construct a null reference response and synergy is assessed from the difference between this null reference and the Gaussian process fitted response. We evaluated performance of our model on a simulated data from Greco, two simulated data sets of our own design and two benchmark data sets from Chou and Talalay. We compare the Hand-GP model to standard synergy models and show that our model performs better than these standards. We also compare our model to the MuSyC model as example of a recent method which also fits a complete dose-response surface. Also in this case, the Hand-GP model performs better. Conclusion: The Hand-GP model is a flexible model to capture synergy. Its non-parametric natures allows it to model a wide variety of response patterns.

SynBa: Improved estimation of drug combination synergies with uncertainty quantification

“…In the current literature, there are examples that use a probabilistic model to incorporate uncertainty in their outputs. Shapovalova et al developed Hand-GP [13], a non-parametric model based on the combination of the Hand model with Gaussian processes, providing more believable uncertainty estimation than MuSyC in some cases. However, Hand-GP does not incorporate the 1D Hill equation that imposed biological constraints useful for providing interpretability of the model outputs.…”