Investigation of 7-dehydrocholesterol reductase pathway to elucidate off-target prenatal effects of pharmaceuticals: a systematic review.

Mendelian diseases contain important biological information regarding developmental effects of gene mutations that can guide drug discovery and toxicity efforts. In this review, we focus on Smith-Lemli-Opitz syndrome (SLOS), a rare Mendelian disease characterized by compound heterozygous mutations in 7-dehydrocholesterol reductase (DHCR7) resulting in severe fetal deformities. We present a compilation of SLOS-inducing DHCR7 mutations and the geographic distribution of those mutations in healthy and diseased populations. We observed that several mutations thought to be disease causing occur in healthy populations, indicating an incomplete understanding of the condition and highlighting new research opportunities. We describe the functional environment around DHCR7, including pharmacological DHCR7 inhibitors and cholesterol and vitamin D synthesis. Using PubMed, we investigated the fetal outcomes following prenatal exposure to DHCR7 modulators. First-trimester exposure to DHCR7 inhibitors resulted in outcomes similar to those of known teratogens (50 vs 48% born-healthy). DHCR7 activity should be considered during drug development and prenatal toxicity assessment.

Systems pharmacology augments drug safety surveillance.

Small molecule drugs are the foundation of modern medical practice, yet their use is limited by the onset of unexpected and severe adverse events (AEs). Regulatory agencies rely on postmarketing surveillance to monitor safety once drugs are approved for clinical use. Despite advances in pharmacovigilance methods that address issues of confounding bias, clinical data of AEs are inherently noisy. Systems pharmacology-the integration of systems biology and chemical genomics-can illuminate drug mechanisms of action. We hypothesize that these data can improve drug safety surveillance by highlighting drugs with a mechanistic connection to the target phenotype (enriching true positives) and filtering those that do not (depleting false positives). We present an algorithm, the modular assembly of drug safety subnetworks (MADSS), to combine systems pharmacology and pharmacovigilance data and significantly improve drug safety monitoring for four clinically relevant adverse drug reactions.

Similarity-based modeling applied to signal detection in pharmacovigilance.

One of the main objectives in pharmacovigilance is the detection of adverse drug events (ADEs) through mining of healthcare databases, such as electronic health records or administrative claims data. Although different approaches have been shown to be of great value, research is still focusing on the enhancement of signal detection to gain efficiency in further assessment and follow-up. We applied similarity-based modeling techniques, using 2D and 3D molecular structure, ADE, target, and ATC (anatomical therapeutic chemical) similarity measures, to the candidate associations selected previously in a medication-wide association study for four ADE outcomes. Our results showed an improvement in the precision when we ranked the subset of ADE candidates using similarity scorings. This method is simple, useful to strengthen or prioritize signals generated from healthcare databases, and facilitates ADE detection through the identification of the most similar drugs for which ADE information is available.

Connecting the dots: applications of network medicine in pharmacology and disease.

In 2011, >2.5 million people died from only 15 causes in the United States. Ten of these involved complex or infectious diseases for which there is insufficient knowledge or treatment, such as heart disease, influenza, and Alzheimer's disease.(1) Complex diseases have been difficult to understand due to their multifarious genetic and molecular fingerprints, while certain infectious agents have evolved to elude treatment and prophylaxis. Network medicine provides a macroscopic approach to understanding and treating such illnesses. It integrates experimental data on gene, protein, and metabolic interactions with clinical knowledge of disease and pharmacology in order to extend the understanding of diseases and their treatments. The resulting "big picture" allows for the development of computational and mathematical methods to identify novel disease pathways and predict patient drug response, among others. In this review, we discuss recent advances in network medicine.

Detecting drug interactions from adverse-event reports: interaction between paroxetine and pravastatin increases blood glucose levels.

The lipid-lowering agent pravastatin and the antidepressant paroxetine are among the most widely prescribed drugs in the world. Unexpected interactions between them could have important public health implications. We mined the US Food and Drug Administration's (FDA's) Adverse Event Reporting System (AERS) for side-effect profiles involving glucose homeostasis and found a surprisingly strong signal for comedication with pravastatin and paroxetine. We retrospectively evaluated changes in blood glucose in 104 patients with diabetes and 135 without diabetes who had received comedication with these two drugs, using data in electronic medical record (EMR) systems of three geographically distinct sites. We assessed the mean random blood glucose levels before and after treatment with the drugs. We found that pravastatin and paroxetine, when administered together, had a synergistic effect on blood glucose. The average increase was 19 mg/dl (1.0 mmol/l) overall, and in those with diabetes it was 48 mg/dl (2.7 mmol/l). In contrast, neither drug administered singly was associated with such changes in glucose levels. An increase in glucose levels is not a general effect of combined therapy with selective serotonin reuptake inhibitors (SSRIs) and statins.