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Pharmacogenomics is the basis of personalized medicine which will be the medicine of the future.Through both reducing the numbers of adverse drug reactions and improving the use of existing drugs in targeted populations, pharmacogenomics represents a real advance on traditional therapeutic drug monitoring.

Modern research in the biomedical sciences is data-driven utilizing high-throughput technologies to generate big genomic data. The Library of Integrated Network-based Cellular Signatures (LINCS) is an example for a large-scale genomic data repository providing hundred thousands of high-dimensional gene expression measurements for thousands of drugs and dozens of cell lines. However, the remaining challenge is how to use these data effectively for pharmacogenomics. In this paper, we use LINCS data to construct drug association networks (DANs) representing the relationships between drugs. By using the Anatomical Therapeutic Chemical (ATC) classification of drugs we demonstrate that the DANs represent a systems pharmacogenomic landscape of drugs summarizing the entire LINCS repository on a genomic scale meaningfully. Here we identify the modules of the DANs as therapeutic attractors of the ATC drug classes.

In this paper we analyze the impact of the stochastic fluctuation of genes between their ON and OFF states on the pharmacodynamics of a potentially large class of drugs. We focus on basic mechanisms underlying the onset of in vitro experimental dose-response curves, by investigating two elementary molecular circuits. Both circuits consist in the transcription of a gene and in the successive translation into the corresponding protein. Whereas in the first the activation/deactivation rates of the single gene copy are constant, in the second the protein, now a transcription factor, amplifies the deactivation rate, so introducing a negative feedback. The drug is assumed to enhance the elimination of the protein, and in both cases the success of therapy is assured by keeping the level of the given protein under a threshold for a fixed time. Our numerical simulations suggests that the gene switching plays a primary role in determining the sigmoidal shape of dose-response curves. Moreover, the simulations show interesting phenomena related to the magnitude of the average gene switching time and to the drug concentration. In particular, for slow gene switching a significant fraction of cells can respond also in the absence of drug or with drug concentrations insufficient for the response in a deterministic setting. For higher drug concentrations, the non-responding fraction exhibits a maximum at intermediate values of the gene switching rates. For fast gene switching, instead, the stochastic prediction follows the prediction of the deterministic approximation, with all the cells responding or non-responding according to the drug dose.

Molecular inputs to chromatin via cellular metabolism are modifiers of the epigenome. These inputs — which include both nutrient availability as a result of diet and growth factor signalling — are implicated in linking the environment to the maintenance of cellular homeostasis and cell identity. Recent studies have demonstrated that these inputs are much broader than had previously been known, encompassing metabolism from a wide variety of sources, including alcohol and microbiotal metabolism. These factors modify DNA and histones and exert specific effects on cell biology, systemic physiology and pathology. In this Review, we discuss the nature of these molecular networks, highlight their role in mediating cellular responses and explore their modifiability through dietary and pharmacological interventions.Various cellular metabolites provide the chemical moieties for DNA and histone modifications, resulting in a complex interplay between metabolism and epigenetics. In this Review, Dai, Ramesh and Locasale discuss the metabolic regulation of diverse types of chromatin modifications and the functional consequences of these modifications at the molecular, cellular and organismal levels, as well as influences from diet and microbiota.

Targeted mutant models are common in mechanistic toxicology experiments investigating the absorption, metabolism, distribution, or elimination (ADME) of chemicals from individuals. Key models include those for xenosensing transcription factors and cytochrome P450s (CYP). Here we investigated changes in transcript levels, protein expression, and steroid hydroxylation of several xenobiotic detoxifying CYPs in constitutive androstane receptor (CAR)-null and two CYP-null mouse models that have subfamily members regulated by CAR; the Cyp3a-null and a newly described Cyp2b9/10/13-null mouse model. Compensatory changes in CYP expression that occur in these models may also occur in polymorphic humans, or may complicate interpretation of ADME studies performed using these models. The loss of CAR causes significant changes in several CYPs probably due to loss of CAR-mediated constitutive regulation of these CYPs. Expression and activity changes include significant repression of Cyp2a and Cyp2b members with corresponding drops in 6α- and 16β-testosterone hydroxylase activity. Further, the ratio of 6α-/15α-hydroxylase activity, a biomarker of sexual dimorphism in the liver, indicates masculinization of female CAR-null mice, suggesting a role for CAR in the regulation of sexually dimorphic liver CYP profiles. The loss of Cyp3a causes fewer changes than CAR. Nevertheless, there are compensatory changes including gender-specific increases in Cyp2a and Cyp2b. Cyp2a and Cyp2b were down-regulated in CAR-null mice, suggesting activation of CAR and potentially PXR following loss of the Cyp3a members. However, the loss of Cyp2b causes few changes in hepatic CYP transcript levels and almost no significant compensatory changes in protein expression or activity with the possible exception of 6α-hydroxylase activity. This lack of a compensatory response in the Cyp2b9/10/13-null mice is probably due to low CYP2B hepatic expression, especially in male mice. Overall, compensatory and regulatory CYP changes followed the order CAR-null > Cyp3a-null > Cyp2b-null mice.

Drug perturbations of human cells lead to complex responses upon target binding. One of the known mechanisms is a (positive or negative) feedback loop that adjusts the expression level of the respective target protein. To quantify this mechanism systems-wide in an unbiased way, drug-induced differential expression of drug target mRNA was examined in three cell lines using the Connectivity Map. To overcome various biases in this valuable resource, we have developed a computational normalization and scoring procedure that is applicable to gene expression recording upon heterogeneous drug treatments. In 1290 drug-target relations, corresponding to 466 drugs acting on 167 drug targets studied, 8% of the targets are subject to regulation at the mRNA level. We confirmed systematically that in particular G-protein coupled receptors, when serving as known targets, are regulated upon drug treatment. We further newly identified drug-induced differential regulation of Lanosterol 14-alpha demethylase, Endoplasmin, DNA topoisomerase 2-alpha and Calmodulin 1. The feedback regulation in these and other targets is likely to be relevant for the success or failure of the molecular intervention.

Celastrus and Tripterygium species, which are used in traditional Chinese medicine, have attracted much attention due to their anti-tumor promoting and neuroprotective activities, in addition to their applications in autoimmune disorders. However, systematic relationships between them and among species are unclear, and it may disturb their further medicinal utilization. In the present study, the molecular analysis of combined chloroplast and nuclear markers of all Chinese Celastrus and Tripterygium was performed, and clear inter- and intra-genus relationships were presented. The result suggests that Tripterygium constitute a natural monophyletic clade within Celastrus with strong support value. Fruit and seed type are better than inflorescence in subgeneric classification. Chinese Celastrus are classified for three sections: Sect. Sempervirentes (Maxim.) CY Cheng & TC Kao, Sect. Lunatus XY Mu & ZX Zhang, sect. nov., and Sect. Ellipticus XY Mu & ZX Zhang, sect. nov. The phylogenetic data was consistent with their chemical components reported previously. Owing to the close relationship, several evergreen Celastrus species are recommended for chemical and pharmacological studies. Our results also provide reference for molecular identification of Chinese Celastrus and Tripterygium.

The PharmacoGenomic Mutation Database (PGMD) is a comprehensive manually curated pharmacogenomics database. Two major sources of PGMD data are peer-reviewed literature and Food and Drug Administration (FDA) and European Medicines Agency (EMA) drug labels. PGMD curators capture information on exact genomic location and sequence changes, on resulting phenotype, drugs administered, patient population, study design, disease context, statistical significance and other properties of reported pharmacogenomic variants. Variants are annotated into functional categories on the basis of their influence on pharmacokinetics, pharmacodynamics, efficacy or clinical outcome. The current release of PGMD includes over 117 000 unique pharmacogenomic observations, covering all 24 disease superclasses and nearly 1400 drugs. Over 2800 genes have associated pharmacogenomic variants, including genes in proximity to intergenic variants. PGMD is optimized for use in annotating next-generation sequencing data by providing genomic coordinates for all covered variants, including Single Nucleotide Polymorphisms (SNPs), insertions, deletions, haplotypes, diplotypes, Variable Number Tandem Repeats (VNTR), copy number variations and structural variations.

One of the challenges in antibiotic lead discovery is the difficulty and time-consuming task of determining the mechanism of action (MOA) of antibacterial compounds. In this report, we describe the development and validation of a facile and inexpensive assay system utilizing disk diffusion of inhibitors on solid agar medium embedded with mixed pools of a comprehensive collection of Escherichia coli clones each containing a plasmid-borne inducible essential gene from E. coli. From individual clones, pilot small-scale (48 or 50 clones) assays, to full-scale target identification platform for antibacterials (TIPA) system, involving a variety of assay formats (liquid vs solid media, individual vs mix clones), we demonstrate that elevated resistance phenotypes of relevant cell clones were highly specific. In particular, the TIPA system was able to reveal cellular targets of several known antibacterial inhibitors: cerulenin, diazaborine, indolmycin, phosphomycin, and triclosan. Complementary to several existing MOA profiling schemes, the TIPA system offers a simple and low-cost method for elucidating the target proteins of antibacterial inhibitors, thus will facilitate discovery and development of novel antibacterial compounds to combat multidrug-resistant bacterial pathogens.

There is a strong association between genetic polymorphisms and the acenocoumarol dosage requirements. Genotyping the polymorphisms involved in the pharmacokinetics and pharmacodynamics of acenocoumarol before starting anticoagulant therapy would result in a better quality of life and a more efficient use of healthcare resources. The objective of this study is to develop a new algorithm that includes clinical and genetic variables to predict the most appropriate acenocoumarol dosage for stable anticoagulation in a wide range of patients. We recruited 685 patients from 2 Spanish hospitals and 1 primary healthcare center. We randomly chose 80% of the patients (n = 556), considering an equitable distribution of genotypes to form the generation cohort. The remaining 20% (n = 129) formed the validation cohort. Multiple linear regression was used to generate the algorithm using the acenocoumarol stable dosage as the dependent variable and the clinical and genotypic variables as the independent variables. The variables included in the algorithm were age, weight, amiodarone use, enzyme inducer status, international normalized ratio target range and the presence of CYP2C9*2 (rs1799853), CYP2C9*3 (rs1057910), VKORC1 (rs9923231) and CYP4F2 (rs2108622). The coefficient of determination (R2) explained by the algorithm was 52.8% in the generation cohort and 64% in the validation cohort. The following R2 values were evaluated by pathology: atrial fibrillation, 57.4%; valve replacement, 56.3%; and venous thromboembolic disease, 51.5%. When the patients were classified into 3 dosage groups according to the stable dosage (<11 mg/week, 11-21 mg/week, >21 mg/week), the percentage of correctly classified patients was higher in the intermediate group, whereas differences between pharmacogenetic and clinical algorithms increased in the extreme dosage groups. Our algorithm could improve acenocoumarol dosage selection for patients who will begin treatment with this drug, especially in extreme-dosage patients. The predictability of the pharmacogenetic algorithm did not vary significantly between diseases.