Therapeutic selectivity and the multi-node drug target.

Drug combinations are an increasingly favored strategy for increasing therapeutic windows for potential drugs, but enthusiasm for this approach is tempered by concerns that therapeutic synergy will too often be mirrored by synergistic toxicity. Here we review our recent experimental results and numerical simulations that establish the context-specificity of synergistic combinations. Thus systematic testing of chemical combinations in cell-based disease models can preferentially discover synergies with beneficial therapeutic selectivity. For an anti-inflammatory combination, we demonstrate how such selective synergy is achieved through differential expression of its targets in cells associated with therapeutic and toxic effects, and validate the combination's therapeutic relevance in animals. The narrower context specificity of synergistic combinations creates many new opportunities for such therapeutically relevant selectivity, and reinforces the realization that the most useful paradigm for a drug target is often a set of biomolecules that cooperate to produce a therapeutic response with reduced side effects.

Synergistic drug combinations tend to improve therapeutically relevant selectivity.

Drug combinations are a promising strategy to overcome the compensatory mechanisms and unwanted off-target effects that limit the utility of many potential drugs. However, enthusiasm for this approach is tempered by concerns that the therapeutic synergy of a combination will be accompanied by synergistic side effects. Using large scale simulations of bacterial metabolism and 94,110 multi-dose experiments relevant to diverse diseases, we provide evidence that synergistic drug combinations are generally more specific to particular cellular contexts than are single agent activities. We highlight six combinations whose selective synergy depends on multitarget drug activity. For one anti-inflammatory example, we show how such selectivity is achieved through differential expression of the drugs' targets in cell types associated with therapeutic, but not toxic, effects and validate its therapeutic relevance in a rat model of asthma. The context specificity of synergistic combinations creates many opportunities for therapeutically relevant selectivity and enables improved control of complex biological systems.

NMR chemical exchange as a probe for ligand-binding kinetics in a theophylline-binding RNA aptamer.

The apparent on and off rate constants for binding of theophylline to its RNA aptamer in the absence of Mg(2+) were determined here by 2D (1)H-(1)H ZZ-exchange NMR spectroscopy. Analysis of the buildup rate of the exchange cross peaks for several base-paired imino protons in the RNA yielded an apparent k(on) of 600 M(-1) s(-1). This small apparent k(on) results because the free RNA exist as a dynamic equilibrium of inactive states rapidly interconverting with a low population of active species. The data found here indicate that the RNA aptamer employs a conformational selection mechanism for binding theophylline in the absence of Mg(2+). The kinetic data found here also explain a very unusual property of this RNA-theophylline system: slow exchange on the NMR chemical shift time scale for a weakly binding complex. To our knowledge, it is unprecedented to have such a weakly binding complex (K(d) approximately 3.0 mM at 15 degrees C) show slow exchange on the NMR chemical shift time scale, but the results clearly demonstrate that slow exchange and weak binding are readily rationalized by a small k(on). Comparisons with other ligand-receptor interactions are presented.

Selective amplification of glucocorticoid anti-inflammatory activity through synergistic multi-target action of a combination drug.

INTRODUCTION: Glucocorticoids are a mainstay of anti-inflammatory therapy, but significant adverse effects ultimately limit their utility. Previous efforts to design glucocorticoid structures with an increased therapeutic window have focused on dissociating anti-inflammatory transcriptional repression from adverse effects primarily driven by transcriptional activation. An alternative to this medicinal chemistry approach is a systems biology based strategy that seeks to amplify selectively the anti-inflammatory activity of very low dose glucocorticoid in immune cells without modulating alternative cellular networks that mediate glucocorticoid toxicity. METHODS: The combination of prednisolone and the antithrombotic drug dipyridamole was profiled using in vitro and in vivo models of anti-inflammatory activity and glucocorticoid-induced adverse effects to demonstrate a dissociated activity profile. RESULTS: The combination synergistically suppresses release of proinflammatory mediators, including tumour necrosis factor-alpha, IL-6, chemokine (C-C motif) ligand 5 (RANTES), matrix metalloproteinase-9, and others, from human peripheral blood mononuclear cells and mouse macrophages. In rat models of acute lipopolysaccharide-induced endotoxemia and delayed-type hypersensitivity, and in chronic models of collagen-induced and adjuvant-induced arthritis, the combination produced anti-inflammatory activity that required only a subtherapeutic dose of prednisolone. The immune-specific amplification of prednisolone anti-inflammatory activity by dipyridamole did not extend to glucocorticoid-mediated adverse effects, including corticosterone suppression or increased expression of tyrosine aminotransferase, in vivo after repeat dosing in rats. After 8 weeks of oral dosing in mice, treatment with the combination did not alter prednisolone-induced reduction in osteocalcin and mid-femur bone density, which are markers of steroid-induced osteoporosis. Additionally, amplification was not observed in the cellular network of corticotroph AtT-20/D16v-F2 cells in vitro, as measured by pro-opiomelanocortin expression and adrenocorticotropic hormone secretion. CONCLUSIONS: These data suggest that the multi-target mechanism of low-dose prednisolone and dipyridamole creates a dissociated activity profile with an increased therapeutic window through cellular network selective amplification of glucocorticoid-mediated anti-inflammatory signaling.

Chemical combination effects predict connectivity in biological systems.

Efforts to construct therapeutically useful models of biological systems require large and diverse sets of data on functional connections between their components. Here we show that cellular responses to combinations of chemicals reveal how their biological targets are connected. Simulations of pathways with pairs of inhibitors at varying doses predict distinct response surface shapes that are reproduced in a yeast experiment, with further support from a larger screen using human tumour cells. The response morphology yields detailed connectivity constraints between nearby targets, and synergy profiles across many combinations show relatedness between targets in the whole network. Constraints from chemical combinations complement genetic studies, because they probe different cellular components and can be applied to disease models that are not amenable to mutagenesis. Chemical probes also offer increased flexibility, as they can be continuously dosed, temporally controlled, and readily combined. After extending this initial study to cover a wider range of combination effects and pathway topologies, chemical combinations may be used to refine network models or to identify novel targets. This response surface methodology may even apply to non-biological systems where responses to targeted perturbations can be measured.

Multi-target therapeutics: when the whole is greater than the sum of the parts.

Drugs designed to act against individual molecular targets cannot usually combat multigenic diseases such as cancer, or diseases that affect multiple tissues or cell types such as diabetes and immunoinflammatory disorders. Combination drugs that impact multiple targets simultaneously are better at controlling complex disease systems, are less prone to drug resistance and are the standard of care in many important therapeutic areas. The combination drugs currently employed are primarily of rational design, but the increased efficacy they provide justifies in vitro discovery efforts for identifying novel multi-target mechanisms. In this review, we discuss the biological rationale for combination therapeutics, review some existing combination drugs and present a systematic approach to identify interactions between molecular pathways that could be leveraged for therapeutic benefit.

Systematic discovery of multicomponent therapeutics.

Multicomponent therapies, originating through deliberate mixing of drugs in a clinical setting, through happenstance, and through rational design, have a successful history in a number of areas of medicine, including cancer, infectious diseases, and CNS disorders. We have developed a high-throughput screening method for identifying effective combinations of therapeutic compounds. We report here that systematic screening of combinations of small molecules reveals unexpected interactions between compounds, presumably due to interactions between the pathways on which they act. Through systematic screening of approximately 120,000 different two-component combinations of reference-listed drugs, we identified potential multicomponent therapeutics, including (i) fungistatic and analgesic agents that together generate fungicidal activity in drug-resistant Candida albicans, yet do not significantly affect human cells, (ii) glucocorticoid and antiplatelet agents that together suppress the production of tumor necrosis factor-alpha in human primary peripheral blood mononu-clear cells, and (iii) antipsychotic and antiprotozoal agents that do not exhibit significant antitumor activity alone, yet together prevent the growth of tumors in mice. Systematic combination screening may ultimately be useful for exploring the connectivity of biological pathways and, when performed with reference-listed drugs, may result in the discovery of new combination drug regimens.