Abstract
The complexity of drug–disease interactions is a process that has been explained in terms of the need for new drugs and the increasing cost of drug development, among other factors. Over the last years, diverse approaches have been explored to understand drug–disease relationships. Here, we construct a bipartite graph in terms of active ingredients and diseases based on thoroughly classified data from a recognized pharmacological website. We find that the connectivities between drugs (outgoing links) and diseases (incoming links) follow approximately a stretched-exponential function with different fitting parameters; for drugs, it is between exponential and power law functions, while for diseases, the behavior is purely exponential. The network projections, onto either drugs or diseases, reveal that the co-ocurrence of drugs (diseases) in common target diseases (drugs) lead to the appearance of connected components, which varies as the threshold number of common target diseases (drugs) is increased. The corresponding projections built from randomized versions of the original bipartite networks are considered to evaluate the differences. The heterogeneity of association at group level between active ingredients and diseases is evaluated in terms of the Shannon entropy and algorithmic complexity, revealing that higher levels of diversity are present for diseases compared to drugs. Finally, the robustness of the original bipartite network is evaluated in terms of most-connected nodes removal (direct attack) and random removal (random failures).
Highlights
The complexity of drug–disease interactions is a process that has been explained in terms of the need for new drugs and the increasing cost of drug development, among other factors
We proceed to construct a bipartite network in which an active ingredient and a disease are linked if the disease is listed as a target of the active ingredient
The need for new drugs together with their cost implications have contributed to the formation of a complex drug–disease interaction, the understanding of which is important for evaluating indirect interactions between active substances and the proximity between diseases
Summary
The need for the development of new drugs, together with the scalating (rising) cost and the time consumed to make new treatments available are the current challenges being faced by modern medicine and the pharmaceutical industry [1,2]. Despite the fact that various studies have explored the complex association between drugs and diseases, it is important to note that this characterization requires large-scale analyses that cover the various scales, from the molecular level to the information of comorbidities in patients [7,8,9,10,18,19,20,21,22,23]. We find that a higher diversity is present for diseases, compared to drugs, and the complexity of the bipartite structure changes as the threshold degree is increased. We perform an evaluation of the robustness of the bipartite network by considering two strategies of eliminating either fractions of the most connected or randomly selected nodes, and monitor the fragmentation of the projected networks.
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