A widely distributed gene cluster compensates for uricase loss in hominids.

Approximately 15% of US adults have circulating levels of uric acid above its solubility limit, which is causally linked to the disease gout. In most mammals, uric acid elimination is facilitated by the enzyme uricase. However, human uricase is a pseudogene, having been inactivated early in hominid evolution. Though it has long been known that uric acid is eliminated in the gut, the role of the gut microbiota in hyperuricemia has not been studied. Here, we identify a widely distributed bacterial gene cluster that encodes a pathway for uric acid degradation. Stable isotope tracing demonstrates that gut bacteria metabolize uric acid to xanthine or short chain fatty acids. Ablation of the microbiota in uricase-deficient mice causes severe hyperuricemia, and anaerobe-targeted antibiotics increase the risk of gout in humans. These data reveal a role for the gut microbiota in uric acid excretion and highlight the potential for microbiome-targeted therapeutics in hyperuricemia.

Chemistry-Wide Association Studies (CWAS): A Novel Framework for Identifying and Interpreting Structure-Activity Relationships.

Quantitative structure-activity relationships (QSAR) models are often seen as a "black box" because they are considered difficult to interpret. Meanwhile, qualitative approaches, e.g., structural alerts (SA) or read-across, provide mechanistic insight, which is preferred for regulatory purposes, but predictive accuracy of such approaches is often low. Herein, we introduce the chemistry-wide association study (CWAS) approach, a novel framework that both addresses such deficiencies and combines advantages of statistical QSAR and alert-based approaches. The CWAS framework consists of the following steps: (i) QSAR model building for an end point of interest, (ii) identification of key chemical features, (iii) determination of communities of such features disproportionately co-occurring more frequently in the active than in the inactive class, and (iv) assembling these communities to form larger (and not necessarily chemically connected) novel structural alerts with high specificity. As a proof-of-concept, we have applied CWAS to model Ames mutagenicity and Stevens-Johnson Syndrome (SJS). For the well-studied Ames mutagenicity data set, we identified 76 important individual fragments and assembled co-occurring fragments into SA both replicative of known as well as representing novel mutagenicity alerts. For the SJS data set, we identified 29 important fragments and assembled co-occurring communities into SA including both known and novel alerts. In summary, we demonstrate that CWAS provides a new framework to interpret predictive QSAR models and derive refined structural alerts for more effective design and safety assessment of drugs and drug candidates.

Synergistic drug combinations from electronic health records and gene expression.

OBJECTIVE: Using electronic health records (EHRs) and biomolecular data, we sought to discover drug pairs with synergistic repurposing potential. EHRs provide real-world treatment and outcome patterns, while complementary biomolecular data, including disease-specific gene expression and drug-protein interactions, provide mechanistic understanding. METHOD: We applied Group Lasso INTERaction NETwork (glinternet), an overlap group lasso penalty on a logistic regression model, with pairwise interactions to identify variables and interacting drug pairs associated with reduced 5-year mortality using EHRs of 9945 breast cancer patients. We identified differentially expressed genes from 14 case-control human breast cancer gene expression datasets and integrated them with drug-protein networks. Drugs in the network were scored according to their association with breast cancer individually or in pairs. Lastly, we determined whether synergistic drug pairs found in the EHRs were enriched among synergistic drug pairs from gene-expression data using a method similar to gene set enrichment analysis. RESULTS: From EHRs, we discovered 3 drug-class pairs associated with lower mortality: anti-inflammatories and hormone antagonists, anti-inflammatories and lipid modifiers, and lipid modifiers and obstructive airway drugs. The first 2 pairs were also enriched among pairs discovered using gene expression data and are supported by molecular interactions in drug-protein networks and preclinical and epidemiologic evidence. CONCLUSIONS: This is a proof-of-concept study demonstrating that a combination of complementary data sources, such as EHRs and gene expression, can corroborate discoveries and provide mechanistic insight into drug synergism for repurposing.

Cheminformatics-aided pharmacovigilance: application to Stevens-Johnson Syndrome.

OBJECTIVE: Quantitative Structure-Activity Relationship (QSAR) models can predict adverse drug reactions (ADRs), and thus provide early warnings of potential hazards. Timely identification of potential safety concerns could protect patients and aid early diagnosis of ADRs among the exposed. Our objective was to determine whether global spontaneous reporting patterns might allow chemical substructures associated with Stevens-Johnson Syndrome (SJS) to be identified and utilized for ADR prediction by QSAR models. MATERIALS AND METHODS: Using a reference set of 364 drugs having positive or negative reporting correlations with SJS in the VigiBase global repository of individual case safety reports (Uppsala Monitoring Center, Uppsala, Sweden), chemical descriptors were computed from drug molecular structures. Random Forest and Support Vector Machines methods were used to develop QSAR models, which were validated by external 5-fold cross validation. Models were employed for virtual screening of DrugBank to predict SJS actives and inactives, which were corroborated using knowledge bases like VigiBase, ChemoText, and MicroMedex (Truven Health Analytics Inc, Ann Arbor, Michigan). RESULTS: We developed QSAR models that could accurately predict if drugs were associated with SJS (area under the curve of 75%-81%). Our 10 most active and inactive predictions were substantiated by SJS reports (or lack thereof) in the literature. DISCUSSION: Interpretation of QSAR models in terms of significant chemical descriptors suggested novel SJS structural alerts. CONCLUSIONS: We have demonstrated that QSAR models can accurately identify SJS active and inactive drugs. Requiring chemical structures only, QSAR models provide effective computational means to flag potentially harmful drugs for subsequent targeted surveillance and pharmacoepidemiologic investigations.

Repurposing cAMP-modulating medications to promote ß-cell replication.

Loss of ß-cell mass is a cardinal feature of diabetes. Consequently, developing medications to promote ß-cell regeneration is a priority. cAMP is an intracellular second messenger that modulates ß-cell replication. We investigated whether medications that increase cAMP stability or synthesis selectively stimulate ß-cell growth. To identify cAMP-stabilizing medications that promote ß-cell replication, we performed high-content screening of a phosphodiesterase (PDE) inhibitor library. PDE3, -4, and -10 inhibitors, including dipyridamole, were found to promote ß-cell replication in an adenosine receptor-dependent manner. Dipyridamole's action is specific for ß-cells and not α-cells. Next we demonstrated that norepinephrine (NE), a physiologic suppressor of cAMP synthesis in ß-cells, impairs ß-cell replication via activation of α(2)-adrenergic receptors. Accordingly, mirtazapine, an α(2)-adrenergic receptor antagonist and antidepressant, prevents NE-dependent suppression of ß-cell replication. Interestingly, NE's growth-suppressive effect is modulated by endogenously expressed catecholamine-inactivating enzymes (catechol-O-methyltransferase and l-monoamine oxidase) and is dominant over the growth-promoting effects of PDE inhibitors. Treatment with dipyridamole and/or mirtazapine promote ß-cell replication in mice, and treatment with dipyridamole is associated with reduced glucose levels in humans. This work provides new mechanistic insights into cAMP-dependent growth regulation of ß-cells and highlights the potential of commonly prescribed medications to influence ß-cell growth.