The Complex Structure of the Pharmacological Drug–Disease Network

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).

A multiplex analysis of phonological and orthographic networks

“…According to statistical tests, most of the degree distribution networks of each layer (either orthographic or phonological) can be well described by the Weibull distribution, while for the remaining distributions the best fits correspond to the Loglogistic and Lognormal, although the Weibull remains the second best fit in most of these cases (see Supplementary Material [ 44 ]). The corresponding survival cumulative distribution of the Weibull is represented by a stretched exponential function which have been used to describe a variety of phenomena [ 51 – 56 ]. We note that this distribution is more skewed than a single exponential distribution but less skewed than a power law distribution.…”

Network modeling helps to tackle the complexity of drug–disease systems

“…Finally, a more basic question to be investigated regards, in our opinion, the assessment of the complexity of the drug-disease system. An attempt in this sense has been done by L opez-Rodríguez et al (2021), who explored a comprehensive drug-disease network by means of two measures of complexity, that is, Shannon entropy and algorithmic complexity. This work provides an initial look at the complex drug-disease system, even though it has some limitations, particularly regarding the completeness of the database on which the analysis is based.…”