Motivation: Many drug combinations are routinely assessed to identify synergistic interactions in the attempt to develop novel treatment strategies. Appropriate software is required to analyze the results of these studies.Results: We present Combenefit, new free software tool that enables the visualization, analysis and quantification of drug combination effects in terms of synergy and/or antagonism. Data from combinations assays can be processed using classical Synergy models (Loewe, Bliss, HSA), as single experiments or in batch for High Throughput Screens. This user-friendly tool provides laboratory scientists with an easy and systematic way to analyze their data. The companion package provides bioinformaticians with critical implementations of routines enabling the processing of combination data.Availability and Implementation: Combenefit is provided as a Matlab package but also as standalone software for Windows (http://sourceforge.net/projects/combenefit/).Contact: Giovanni.DiVeroli@cruk.cam.ac.ukSupplementary information: Supplementary data are available at Bioinformatics online.

The effectiveness of most cancer targeted therapies is short-lived. Tumors often develop resistance that might be overcome with drug combinations. However, the number of possible combinations is vast, necessitating data-driven approaches to find optimal patient-specific treatments. Here we report AstraZeneca’s large drug combination dataset, consisting of 11,576 experiments from 910 combinations across 85 molecularly characterized cancer cell lines, and results of a DREAM Challenge to evaluate computational strategies for predicting synergistic drug pairs and biomarkers. 160 teams participated to provide a comprehensive methodological development and benchmarking. Winning methods incorporate prior knowledge of drug-target interactions. Synergy is predicted with an accuracy matching biological replicates for >60% of combinations. However, 20% of drug combinations are poorly predicted by all methods. Genomic rationale for synergy predictions are identified, including ADAM17 inhibitor antagonism when combined with PIK3CB/D inhibition contrasting to synergy when combined with other PI3K-pathway inhibitors in PIK3CA mutant cells.

In cancer pharmacology (and many other areas), most dose-response curves are satisfactorily described by a classical Hill equation (i.e. 4 parameters logistical). Nevertheless, there are instances where the marked presence of more than one point of inflection, or the presence of combined agonist and antagonist effects, prevents straight-forward modelling of the data via a standard Hill equation. Here we propose a modified model and automated fitting procedure to describe dose-response curves with multiphasic features. The resulting general model enables interpreting each phase of the dose-response as an independent dose-dependent process. We developed an algorithm which automatically generates and ranks dose-response models with varying degrees of multiphasic features. The algorithm was implemented in new freely available Dr Fit software (sourceforge.net/projects/drfit/). We show how our approach is successful in describing dose-response curves with multiphasic features. Additionally, we analysed a large cancer cell viability screen involving 11650 dose-response curves. Based on our algorithm, we found that 28% of cases were better described by a multiphasic model than by the Hill model. We thus provide a robust approach to fit dose-response curves with various degrees of complexity, which, together with the provided software implementation, should enable a wide audience to easily process their own data.

The use of computational models to predict drug-induced changes in the action potential (AP) is a promising approach to reduce drug safety attrition but requires a better representation of more complex drug-target interactions to improve the quantitative prediction. The blockade of the human ether-a-go-go-related gene (HERG) channel is a major concern for QT prolongation and Torsade de Pointes risk. We aim to develop quantitative in-silico AP predictions based on a new electrophysiological protocol (suitable for high-throughput HERG screening) and mathematical modeling of ionic currents. Electrophysiological recordings using the IonWorks device were made from HERG channels stably expressed in Chinese hamster ovary cells. A new protocol that delineates inhibition over time was applied to assess dofetilide, cisapride, and almokalant effects. Dynamic effects displayed distinct profiles for these drugs compared with concentration-effects curves. Binding kinetics to specific states were identified using a new HERG Markov model. The model was then modified to represent the canine rapid delayed rectifier K(+) current at 37°C and carry out AP predictions. Predictions were compared with a simpler model based on conductance reduction and were found to be much closer to experimental data. Improved sensitivity to concentration and pacing frequency variables was obtained when including binding kinetics. Our new electrophysiological protocol is suitable for high-throughput screening and is able to distinguish drug-binding kinetics. The association of this protocol with our modeling approach indicates that quantitative predictions of AP modulation can be obtained, which is a significant improvement compared with traditional conductance reduction methods.

Our study demonstrates the need for screening for hERG binding configurations rather than potency alone. It also demonstrates the potential link between hERG, drug mode of action and TdP, and the need to question the current regulatory guidance.

There is currently a strong interest in using high-throughput in-vitro ion-channel screening data to make predictions regarding the cardiac toxicity potential of a new compound in both animal and human studies. A recent FDA think tank encourages the use of biophysical mathematical models of cardiac myocytes for this prediction task. However, it remains unclear whether this approach is the most appropriate. Here we examine five literature data-sets that have been used to support the use of four different biophysical models and one statistical model for predicting cardiac toxicity in numerous species using various endpoints. We propose a simple model that represents the balance between repolarisation and depolarisation forces and compare the predictive power of the model against the original results (leave-one-out cross-validation). Our model showed equivalent performance when compared to the four biophysical models and one statistical model. We therefore conclude that this approach should be further investigated in the context of early cardiac safety screening when in-vitro potency data is generated.

Processes involving particle formation in turbulent flows feature complex interactions between turbulence and the various physicochemical processes involved. An example of such a process is aerosol formation in a turbulent jet, a process investigated experimentally by Lesniewski and Friedlander [Proc. R. Soc. London, Ser. A 454, 2477 (1998)]. Polydispersed particle formation can be described mathematically by a population balance (also called general dynamic) equation, but its formulation and use within a turbulent flow are riddled with problems, as straightforward averaging results in unknown correlations. In this paper we employ a probability density function formalism in conjunction with the population balance equation (the PBE-PDF method) to simulate and study the experiments of Lesniewski and Friedlander. The approach allows studying the effects of turbulence-particle formation interaction, as well as the prediction of the particle size distribution and the incorporation of kinetics of arbitrary complexity in the population balance equation. It is found that turbulence critically affects the first stages of the process, while it seems to have a secondary effect downstream. While Lesniewski and Friedlander argued that the bulk of the nucleation arises in the initial mixing layer, our results indicate that most of the particles nucleate downstream. The full particle size distributions are obtained via our method and can be compared to the experimental results showing good agreement. The sources of uncertainties in the experiments and the kinetic expressions are analyzed, and the underlying mechanisms that affect the evolution of particle size distribution are discussed.

Turbulent precipitation is a complex problem, whose mathematical description of precipitation requires a coupling of fluid dynamics with the population balance equation (PBE). In the case of turbulent flow, this coupling results in unclosed equations due to the nonlinear nature of precipitation kinetics. In this article, we present a methodology for modeling turbulent precipitation using the concept of the transported probability density function (PDF) in conjunction with a discretized PBE, simulated via a Lagrangian stochastic method. The transported PBE-PDF approach resolves the closure problem of turbulent precipitation for arbitrarily complex precipitation kinetics, while retrieving the full particle size distribution (PSD). The method is applied to the precipitation of BaSO 4 in a turbulent pipe flow and comparisons are made with the experimental results of Baldyga and Orciuch (Chem Eng Sci. 2001;56:2435-2444 showing excellent agreement, while insight is drawn into the mechanisms that determine the evolution of the product PSD. V