Abstract: In light of increased co-prescription of multiple drugs, the ability to discern and predict drug-drug interactions (DDI) has become crucial to guarantee the safety of patients undergoing treatment with multiple drugs. However, information on DDI profiles is incomplete and the experimental determination of DDIs is labor-intensive and time-consuming. Although previous studies have explored various feature spaces for in silico screening of interacting drug pairs, their use of conventional cross-validation prevents them from achieving generalizable performance on drug pairs where neither drug is seen during training. Here we demonstrate for the first time targets of adversely interacting drug pairs are significantly more likely to have synergistic genetic interactions than non-interacting drug pairs. Leveraging genetic interaction features and a novel training scheme, we construct a gradient boosting-based classifier that achieves robust DDI prediction even for drugs whose interaction profiles are completely unseen during training. We demonstrate that in addition to classification power-including the prediction of 432 novel DDIs-our genetic interaction approach offers interpretability by providing plausible mechanistic insights into the mode of action of DDIs.

Abstract: Background: Ischemic heart disorders and accumulation of lipids in blood vessels could contribute to angina pectoris. Therefore, the aim of this study was to formulate sublingual tablets containing a novel combination of Atorvastatin calcium (ATOR) and Trimetazidine HCl (TMZ) for efficient treatment of coronary heart disorders. Methods: The dissolution rate of water-insoluble ATOR was enhanced via complexation with sulfobutyl ether-beta-cyclodextrin (SBE-beta-CD) and addition of soluplus as a polymeric solubilizer excipient. The solubilized ATOR and TMZ were compressed into a sublingual tablets by direct compression technique and evaluated for their tableting characteristics. In addition, a new validated method based on High Performance Thin Layer Chromatography (HPTLC) was developed for simultaneous determination of both drugs in pure forms and sublingual tablets. Results: The developed HPTLC method showed LODs of 0.056 and 0.013mug/band and LOQs of 0.17, 0.040mug/band for TMZ and ATOR, respectively and proved to be linear, accurate, precise and robust. The optimum formulation containing mixture of superdisintegrants; Ac-Di-Sol and crospovidone (4.8% w/w, each) showed the shortest disintegration time (65s) and enhanced release profiles of both drugs. Conclusions: The prepared sublingual tablets combining ATOR and TMZ will be a promising dosage form for coronary heart disease patients with an instant action and improved patient compliance.

Abstract: BACKGROUND: Metabolic rewiring allows cancer cells to sustain high proliferation rates. Thus, targeting only the cancer-specific cellular metabolism will safeguard healthy tissues. METHODS: We developed the very efficient FASTCORMICS RNA-seq workflow (rFASTCORMICS) to build 10,005 high-resolution metabolic models from the TCGA dataset to capture metabolic rewiring strategies in cancer cells. Colorectal cancer (CRC) was used as a test case for a repurposing workflow based on rFASTCORMICS. FINDINGS: Alternative pathways that are not required for proliferation or survival tend to be shut down and, therefore, tumours display cancer-specific essential genes that are significantly enriched for known drug targets. We identified naftifine, ketoconazole, and mimosine as new potential CRC drugs, which were experimentally validated. INTERPRETATION: The here presented rFASTCORMICS workflow successfully reconstructs a metabolic model based on RNA-seq data and successfully predicted drug targets and drugs not yet indicted for colorectal cancer. FUND: This study was supported by the University of Luxembourg (IRP grant scheme; R-AGR-0755-12), the Luxembourg National Research Fund (FNR PRIDE PRIDE15/10675146/CANBIO), the Fondation Cancer (Luxembourg), the European Union's Horizon2020 research and innovation programme under the Marie Sklodowska- Curie grant agreement No 642295 (MEL-PLEX), and the German Federal Ministry of Education and Research (BMBF) within the project MelanomSensitivity (BMBF/BM/7643621).

Abstract: Childhood critical periods of experience-dependent plasticity are essential for the development of environmentally appropriate behavior and cognition. Disruption of critical periods can alter development of normal function and confer risk for neurodevelopmental disorders. While genes and their expression relevant to neurodevelopment are associated with schizophrenia, the molecular relationship between schizophrenia and critical periods has not been assessed systematically. Here, we apply a transcriptome-based bioinformatics approach to assess whether genes associated with the human critical period for visual cortex plasticity, a well-studied model of cortical critical periods, are aberrantly expressed in schizophrenia and bipolar disorder. Across two dozen datasets encompassing 522 cases and 374 controls, we find that the majority show aberrations in expression of genes associated with the critical period. We observed both hyper- and hypo-critical period plasticity phenotypes at the transcriptome level, which partially mapped to drug candidates that reverse the disorder signatures in silico. Our findings indicate plasticity aberrations in schizophrenia and their treatment may need to be considered in the context of subpopulations with elevated and others reduced plasticity. Future work should leverage ongoing consortia RNA-sequencing efforts to tease out the sources of plasticity-related transcriptional aberrations seen in schizophrenia, including true biological heterogeneity, interaction between normal development/aging and the disorder, and medication history. Our study also urges innovation towards direct assessment of visual cortex plasticity in humans with schizophrenia to precisely deconstruct the role of plasticity in this disorder.

Abstract: Chemicals interact with genes in the process of disease development and treatment. Although much biomedical research has been performed to understand relationships among genes, chemicals, and diseases, which have been reported in biomedical articles in Medline, there are few studies that extract disease-gene-chemical relationships from biomedical literature at a PubMed scale. In this study, we propose a deep learning model based on bidirectional long short-term memory to identify the evidence sentences of relationships among genes, chemicals, and diseases from Medline abstracts. Then, we develop the search engine DigChem to enable disease-gene-chemical relationship searches for 35,124 genes, 56,382 chemicals, and 5,675 diseases. We show that the identified relationships are reliable by comparing them with manual curation and existing databases. DigChem is available at http://gcancer.org/digchem.

Abstract: Genome-wide association studies (GWAS) have identified more than 100 schizophrenia (SCZ)-associated loci, but using these findings to illuminate disease biology remains a challenge. Here we present integrative risk gene selector (iRIGS), a Bayesian framework that integrates multi-omics data and gene networks to infer risk genes in GWAS loci. By applying iRIGS to SCZ GWAS data, we predicted a set of high-confidence risk genes, most of which are not the nearest genes to the GWAS index variants. High-confidence risk genes account for a significantly enriched heritability, as estimated by stratified linkage disequilibrium score regression. Moreover, high-confidence risk genes are predominantly expressed in brain tissues, especially prenatally, and are enriched for targets of approved drugs, suggesting opportunities to reposition existing drugs for SCZ. Thus, iRIGS can leverage accumulating functional genomics and GWAS data to advance our understanding of SCZ etiology and potential therapeutics.

Abstract: AIM: The lag time between initial human studies of oncology agents and the first-in-child clinical trials of these agents has not been defined. METHODS: We conducted a systematic analysis of time from first-in-human trials to first-in-child trials (age of eligibility <18 years) of agents first approved by the US Food and Drug Administration (FDA) for any oncology indication from 1997 to 2017. We used clinical trial registry data, published literature and oncology abstracts to identify relevant trials and start dates. RESULTS: From 1997 to 2017, 126 drugs received initial FDA approval for an oncology indication. Of these, 117 were non-hormonal agents used in subsequent analyses. Fifteen of 117 drugs (12.8%) did not yet have a paediatric trial, and six of 117 drugs (5.1%) had an initial approval that included children. The median time between the first-in-human trial and first-in-child trial was 6.5 years (range 0-27.7 years). The median time from initial FDA approval to the first-in-child clinical trial was -0.66 years (range -43 to +19 years). These values were stable regardless of year of initial FDA approval, drug class and initial approved disease indication. CONCLUSION: The median lag time from first-in-human to first-in-child trials of oncology agents that were ultimately approved by FDA was 6.5 years. These results provide a benchmark against which to evaluate recent initiatives designed to hasten drug development relevant to children with cancer.

Abstract: High-throughput drug screening has facilitated the discovery of drug combinations in cancer. Many existing studies adopted a full matrix design, aiming for the characterization of drug pair effects for cancer cells. However, the full matrix design may be suboptimal as it requires a drug pair to be combined at multiple concentrations in a full factorial manner. Furthermore, many of the computational tools assess only the synergy but not the sensitivity of drug combinations, which might lead to false positive discoveries. We proposed a novel cross design to enable a more cost-effective and simultaneous testing of drug combination sensitivity and synergy. We developed a drug combination sensitivity score (CSS) to determine the sensitivity of a drug pair, and showed that the CSS is highly reproducible between the replicates and thus supported its usage as a robust metric. We further showed that CSS can be predicted using machine learning approaches which determined the top pharmaco-features to cluster cancer cell lines based on their drug combination sensitivity profiles. To assess the degree of drug interactions using the cross design, we developed an S synergy score based on the difference between the drug combination and the single drug dose-response curves. We showed that the S score is able to detect true synergistic and antagonistic drug combinations at an accuracy level comparable to that using the full matrix design. Taken together, we showed that the cross design coupled with the CSS sensitivity and S synergy scoring methods may provide a robust and accurate characterization of both drug combination sensitivity and synergy levels, with minimal experimental materials required. Our experimental-computational approach could be utilized as an efficient pipeline for improving the discovery rate in high-throughput drug combination screening, particularly for primary patient samples which are difficult to obtain.

Abstract: Cancer is a complex disease that relies on both oncogenic mutations and non-mutated genes for survival, and therefore coined as oncogene and non-oncogene addictions. The need for more effective combination therapies to overcome drug resistance in oncology has been increasingly recognized, but the identification of potentially synergistic drugs at scale remains challenging. Here we propose a gene-expression-based approach, which uses the recurrent perturbation-transcript regulatory relationships inferred from a large compendium of chemical and genetic perturbation experiments across multiple cell lines, to engender a testable hypothesis for combination therapies. These transcript-level recurrences were distinct from known compound-protein target counterparts, were reproducible in external datasets, and correlated with small-molecule sensitivity. We applied these recurrent relationships to predict synergistic drug pairs for cancer and experimentally confirmed two unexpected drug combinations in vitro. Our results corroborate a gene-expression-based strategy for combinatorial drug screening as a way to target non-mutated genes in complex diseases.

Abstract: Identifying the viability of protein targets is one of the preliminary steps of drug discovery. Determining the ability of a protein to bind drugs in order to modulate its function, termed the druggability, requires a non-trivial amount of time and resources. Inability to properly measure druggability has accounted for a significant portion of failures in drug discovery. This problem is only further exacerbated by the large sample space of proteins involved in human diseases. With these barriers, the druggability space within the human proteome remains unexplored and has made it difficult to develop drugs for numerous diseases. Hence, we present a new feature developed in eFindSite that employs supervised machine learning to predict the druggability of a given protein. Benchmarking calculations against the Non-Redundant data set of Druggable and Less Druggable binding sites demonstrate that an AUC for druggability prediction with eFindSite is as high as 0.88. With eFindSite, we elucidated the human druggability space to be 10,191 proteins. Considering the disease space from the Open Targets Platform and excluding already known targets from the predicted data set reveal 2731 potentially novel therapeutic targets. eFindSite is freely available as a stand-alone software at https://github.com/michal-brylinski/efindsite .