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.

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.

ABSTRACT:Drug-induced changes in expression of cytochrome P450 (P450) genes are a significant issue in the preclinical development of pharmaceuticals. For example, preclinically, P450 induction can affect safety studies by reducing the systemic exposure of a compound undergoing toxicological evaluation, thus limiting the exposure that can be safely investigated in patients. Therefore, the induction potential of candidate drugs has been studied as part of the drug development process, typically using protein and/or catalytic end points. However, measuring changes in the levels of mRNA using TaqMan technology offers the opportunity to investigate this issue with the advantages of better dynamic range and specific enzyme identification. Here, we describe the TaqMan application to study ex vivo the P450 gene induction in the rat. Initially, livers from rats dosed with the prototypic P450 inducers ␤-napthoflavone (BNF), phenobarbital (PB), dexamethasone (DEX), and clofibric acid (CLO) were analyzed for mRNA levels of CYP1A1, 1A2, 2B1, 2B2, 2E1, 3A2, 3A23, and 4A1 and compared with control animals. The maximum fold induction of mRNA varied: 2500-fold for CYP1A1 with BNF, 680-fold for CYP2B1 with PB, 59-fold for CYP3A23 with DEX, and 16-fold for CYP4A1 with CLO. This method was then applied to estimate the inductive potential of putative drug candidates undergoing rodent toxicological evaluation. We present a summary of these data that demonstrates the sensitivity and specificity of the TaqMan assay to distinguish between inducers and noninducers and that offers a highly specific alternative to the quantification of drug effects on P450 expression using immunodetection and substrate metabolism.

Background:Amplification of aurora kinase A (AK-A) overrides the mitotic spindle assembly checkpoint, inducing resistance to taxanes. RNA interference targeting AK-A in human pancreatic cancer cell lines enhanced taxane chemosensitivity. In this study, a novel AK-A inhibitor, CYC3, was investigated in pancreatic cancer cell lines, in combination with paclitaxel.Methods:Western blot, flow cytometry and immunostaining were used to investigate the specificity of CYC3. Sulforhodamine B staining, time-lapse microscopy and colony-formation assays were employed to evaluate the cytotoxic effect of CYC3 and paclitaxel. Human colony-forming unit of granulocyte and macrophage (CFU-GM) cells were used to compare the effect in tumour and normal tissue.Results:CYC3 was shown to be a specific AK-A inhibitor. Three nanomolar paclitaxel (growth inhibition 50% (GI50) 3 nℳ in PANC-1, 5.1 nℳ in MIA PaCa-2) in combination with 1 μℳ CYC3 (GI50 1.1 μℳ in MIA PaCa2 and 2 μℳ in PANC-1) was synergistic in inhibiting pancreatic cell growth and causing mitotic arrest, achieving similar effects to 10-fold higher concentrations of paclitaxel (30 nℳ). In CFU-GM cells, the effect of the combination was simply additive, displaying significantly less myelotoxicity compared with high concentrations of paclitaxel (30 nℳ; 60–70% vs 100% inhibition).Conclusion:The combination of lower doses of paclitaxel and CYC3 merits further investigation with the potential for an improved therapeutic index in vivo.