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The unexpected occurrence of idiosyncratic drug reactions during late clinical trials or after a drug has been released can lead to a severe restriction in its use or failure to release/withdrawal. This leads to considerable uncertainty in drug development and has led to attempts to try to predict a drug's potential to cause such reactions. The biotransformation of relatively inert drugs to highly reactive metabolites, commonly referred to as "bioactivation", is now recognized to be an obligatory step in several kinds of drug-induced adverse reactions. Reactive metabolites can be formed by most, if not all, of the enzymes that are involved in drug metabolism. A major theme explored in this review includes the diversity of oxidative bioactivation reactions on nitrogen-containing xenobiotics including drugs. A variety of Phase I enzymes including P450, MAO, and peroxidases bioactivate nitrogen-containing xenobiotics via direct oxidations on the nitrogen atom leading to reactive intermediates or by oxidation at an alternate site in the molecule; for the metabolite to be reactive via the latter sequence nitrogen participation in required. Examples of direct oxidations on nitrogen include the N-oxidation of aromatic amines (e.g. procainamide), single electron N-oxidation of imides (e.g. phenytoin), or alpha-carbon oxidations of arylalkyl- or alkylamines (e.g. mianserin), to reactive nitroso, nitrogen free radical and iminium species, respectively. Examples of indirect bioactivation are highlighted with aromatic amines (e.g. diclofenac) that undergo p-hydroxylation resulting in the formation of p-aminophenols, two-electron oxidation of which results in the formation of reactive quinoneimines. Potential strategies that could be utilized in the screening of novel bioactivation pathways are also discussed.

In this paper we present a model describing susceptible-infected-susceptible-type epidemics spreading on a dynamic contact network with random link activation and deletion where link activation can be locally constrained. We use and adapt an improved effective degree compartmental modeling framework recently proposed by Lindquist et al. [J. Math Biol. 62, 143 (2010)] and Marceau et al. [Phys. Rev. E 82, 036116 (2010)]. The resulting set of ordinary differential equations (ODEs) is solved numerically, and results are compared to those obtained using individual-based stochastic network simulation. We show that the ODEs display excellent agreement with simulation for the evolution of both the disease and the network and are able to accurately capture the epidemic threshold for a wide range of parameters. We also present an analytical R 0 calculation for the dynamic network model and show that, depending on the relative time scales of the network evolution and disease transmission, two limiting cases are recovered: (i) the static network case when network evolution is slow and (ii) homogeneous random mixing when the network evolution is rapid. We also use our threshold calculation to highlight the dangers of relying on local stability analysis when predicting epidemic outbreaks on evolving networks.