The discovery of new pharmaceutical drugs is one of the preeminent tasks—scientifically, economically, and socially—in biomedical research. Advances in informatics and computational biology have increased productivity at many stages of the drug discovery pipeline. Nevertheless, drug discovery has slowed, largely due to the reliance on small molecules as the primary source of novel hypotheses. Natural products (such as plant metabolites, animal toxins, and immunological components) comprise a vast and diverse source of bioactive compounds, some of which are supported by thousands of years of traditional medicine, and are largely disjoint from the set of small molecules used commonly for discovery. However, natural products possess unique characteristics that distinguish them from traditional small molecule drug candidates, requiring new methods and approaches for assessing their therapeutic potential. In this review, we investigate a number of state-of-the-art techniques in bioinformatics, cheminformatics, and knowledge engineering for data-driven drug discovery from natural products. We focus on methods that aim to bridge the gap between traditional small-molecule drug candidates and different classes of natural products. We also explore the current informatics knowledge gaps and other barriers that need to be overcome to fully leverage these compounds for drug discovery. Finally, we conclude with a “road map” of research priorities that seeks to realize this goal.

Small molecules are indispensable to modern medical therapy. However, their use may lead to unintended, negative medical outcomes commonly referred to as adverse drug reactions (ADRs). These effects vary widely in mechanism, severity, and populations affected, making ADR prediction and identification important public health concerns. Current methods rely on clinical trials and post-market surveillance programs to find novel ADRs; however, clinical trials are limited by small sample size, while post-market surveillance methods may be biased and inherently leave patients at risk until sufficient clinical evidence has been gathered. Systems pharmacology, an emerging interdisciplinary field combining network and chemical biology, provides important tools to uncover and understand ADRs and may mitigate the drawbacks of traditional methods. In particular, network analysis allows researchers to integrate heterogeneous data sources and quantify the interactions between biological and chemical entities. Recent work in this area has combined chemical, biological, and large-scale observational health data to predict ADRs in both individual patients and global populations. In this review, we explore the rapid expansion of systems pharmacology in the study of ADRs. We enumerate the existing methods and strategies and illustrate progress in the field with a model framework that incorporates crucial data elements, such as diet and comorbidities, known to modulate ADR risk. Using this framework, we highlight avenues of research that may currently be underexplored, representing opportunities for future work.

Background/Aims: A previous study found a high prevalence of headaches in persons with familial Alzheimer’s disease (FAD) due to a PSEN1 mutation. In our study we compared the prevalence of headaches between nondemented FAD mutation carriers (MCs) and non-mutation-carrying controls (NCs). Methods: A headache questionnaire that assessed the prevalence of significant headaches and diagnosis of migraine and aura by ICHD-2 criteria was administered to 27 individuals at risk for FAD. Frequency of significant headaches, migraine, and aura were compared between MCs and NCs by χ² or Fisher’s exact tests. Results: Twenty-three subjects were at risk for PSEN1 mutations and 4 for an APP substitution. The majority of subjects were female (23/27). MCs were more likely to report significant recurrent headache than NCs (67 vs. 25%, p = 0.031). Forty percent of MCs had headaches that met criteria for migraine whereas 17% of NCs met such criteria. The tendency for a higher prevalence of headaches in MCs held for different PSEN1 and APP mutations but was not significant unless all families were combined. Conclusions: In this population, headache was more common in nondemented FAD MCs than NCs. Possible mechanisms for this include cerebral inflammation, aberrant processing of Notch3, or disrupted intracellular calcium regulation.

No abstract

No abstract