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Density of the Pearson correlation scores of the drug combinations: (A) the blue graph is the correlation scores of efficacious drug combination and the red graph is that of non-efficacious drug combinations. (B) The blue, green, and blue-green graphs express the correlation scores of efficacious, additive and synergistic drug combinations, respectively.
Source publication
Recently, the productivity of drug discovery has gradually decreased as the limitations of single-target-based drugs for various and complex diseases become exposed. To overcome these limitations, drug combinations have been proposed, and great efforts have been made to predict efficacious drug combinations by statistical methods using drug databas...
Contexts in source publication
Context 1
... the density graph of efficacious combinations was weighted with a score of 0.2 and that of non-efficacious combinations ranged widely from -0.2 to 1. On average, the cor- relation score of efficacious combinations was 0.24, and that of non-efficacious combinations was 0.35. Thus, in general, effi- cacious combinations had lower correlation scores ( Figure 4A). The difference between the shapes of graph of two groups was remarkable. ...
Context 2
... drug combinations showed an almost parallel distri- bution to efficacious drug combinations. However, synergistic combinations interestingly showed a narrower distribution graph than efficacious combinations ( Figure 4B). The average score of synergistic combinations (0.23) was lower than that of efficacious combinations (0.24), and the ratio of the score which Figure 3. Target-enzyme interaction networks between drug combinations: (A) network of the acetaminophen and diclofenac combination, and (B) network of the acetaminophen and oxycodone combination. ...
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Citations
... To this date, clinical developments of drug combinations are typically through trial and error or guided by insight into the dysregulated signaling pathways in specific diseases. In this direction, computational prediction and high-throughput screening of potentially beneficial drug combinations made notable progresses (9,26,(37)(38)(39). For example, Miller et al. (40) performed a combinatorial drug screen in a dedifferentiated liposarcoma (DDLS)derived cell line, and identified cyclin-dependent kinase 4 (CDK4) and insulin-like growth factor 1 receptor (IGF1R) as synergistic drug targets. ...
Experience in clinical practice and research in systems pharmacology suggested the limitations of the current one-drug-one-target paradigm in new drug discovery. Single-target drugs may not always produce desired physiological effects on the entire biological system, even if they have successfully regulated the activities of their designated targets. On the other hand, multicomponent therapy, in which two or more agents simultaneously interact with multiple targets, has attracted growing attention. Many drug combinations consisting of multiple agents have already entered clinical practice, especially in treating complex and refractory diseases. Drug combination database (DCDB), launched in 2010, is the first available database that collects and organizes information on drug combinations, with an aim to facilitate systems-oriented new drug discovery. Here, we report the second major release of DCDB (Version 2.0), which includes 866 new drug combinations (1363 in total), consisting of 904 distinctive components. These drug combinations are curated from ∼140 000 clinical studies and the food and drug administration (FDA) electronic orange book. In this update, DCDB collects 237 unsuccessful drug combinations, which may provide a contrast for systematic discovery of the patterns in successful drug combinations. Database URL: http://www.cls.zju.edu.cn/dcdb/
© The Author(s) 2014. Published by Oxford University Press.
The use of fixed dose combination (FDC) drug therapies has been world-widely accepted for long years due to providing better disease treatment with enhanced therapeutic efficacy and safety as well as improved patient compliance and adherence, and reduced cost to patients than single drug therapies. From many different perspectives, the development of FDC products is likely a promising approach to achieving clinical benefits and business advantages in many classes of drugs. The rationale for drug combinations can be well established only when the potential benefits are based on valid therapeutic principles and substantiated by clinical evidences. Herein, how combination products can be rationalized, individually or combinedly, with respect to category of therapeutic benefits, class of pharmacokinetic and pharmacodynamics interactions, and type of combination effects is first discussed. Potential limitations of FDC products are to be minimized through a careful assessment of benefits to risks by selecting rational component drugs and their doses as well as by either taking their efficacious interactions and/or avoiding their non-efficacious interactions. A series of step-wise product development strategies are necessary to attain target product profiles of prospective oral FDC products set based on their intended clinical use. This review gives an overview of strategies for formulation development of oral FDC products to be optimized differently depending upon prior knowledge of single products and designated dosage regimens of the FDC products, along with highlighting the current issues and challenges arising in formulation development and evaluation on the performance of FDC products.
