Pharmacological Data Sets Research Articles

Medication use and risk of amyotrophic lateral sclerosis: using machine learning for an exposome-wide screen of a large clinical database

Background Accumulating evidence suggests that non-genetic factors have important etiologic roles in amyotrophic lateral sclerosis (ALS), yet identification of specific culprit factors has been challenging. Many medications target biological pathways implicated in ALS pathogenesis, and screening large pharmacologic datasets for signals could greatly accelerate the identification of risk-modulating pharmacologic factors for ALS. Method We conducted a high-dimensional screening of patients’ history of medication use and ALS risk using an advanced machine learning approach based on gradient-boosted decision trees coupled with Bayesian model optimization and repeated data sampling. Clinical and medication dispensing data were obtained from a large Israeli health fund for 501 ALS cases and 4,998 matched controls using a lag period of 3 or 5 years prior to ALS diagnosis for ascertaining medication exposure. Results Of over 1,000 different medication classes, we identified 8 classes that were consistently associated with increased ALS risk across independently trained models, where most are indicated for control of symptoms implicated in ALS. Some suggestive protective effects were also observed, notably for vitamin E. Discussion Our results indicate that use of certain medications well before the typically recognized prodromal period was associated with ALS risk. This could result because these medications increase ALS risk or could indicate that ALS symptoms can manifest well before suggested prodromal periods. The results also provide further evidence that vitamin E may be a protective factor for ALS. Targeted studies should be performed to elucidate the possible pathophysiological mechanisms while providing insights for therapeutics design.

WED-246 - Novel Pathways implicated in the seladelpar-mediated reductions of established liver fibrosis are identified from RNA-SEQ data using plex search and two independent mouse pharmacology datasets

WED-246 - Novel Pathways implicated in the seladelpar-mediated reductions of established liver fibrosis are identified from RNA-SEQ data using plex search and two independent mouse pharmacology datasets

Abstract 5776: Advancing a screening platform with panels of patient-derived organoid models for drug discovery and development

Abstract High-throughput screening (HTS) offers the potential to rapidly evaluate the activities of anticancer single agents and combinations in many types of cell-based tumor models; however, successful translation of the findings to patients requires tumor models with high biological relevance. The National Cancer Institute’s Patient-Derived Models Repository (PDMR, https://pdmr.cancer.gov) continues to develop a collection of highly characterized patient-derived organoid models representing a variety of cancer types including rare and recalcitrant malignancies and tumors from patients of diverse ancestry. More than 230 publicly available organoid models are undergoing characterization of their suitability for incorporation into HTS panels based on their recovery from cryopreservation, reproducible growth and expansion capacity, and assay performance metrics in a 384-well format with a CellTiter-Glo 3D viability endpoint. Organoid models with suitable characteristics are being screened with a library of 166 FDA-approved oncology drugs (https://dtp.cancer.gov/organization/dscb/obtaining/default.htm). Over a 7-day period, the organoids are exposed to five concentrations of each drug with a 1-log dilution series ranging from 0.01 µM to 100 µM. The concentration-response profiles of patient-derived organoids will provide reference data for the organoid screen and will eventually include data from all approved and most investigational oncology agents (approximately 1,000 small molecules). NCI-60 cell lines were screened as monolayers in a 384-well format with a CellTiter-Glo viability endpoint after a 3-day exposure to the same oncology drug concentrations. Both monolayer and 3D tumor models were tested using a custom designed fully automated screening system and data analysis software platform. A comparison of the FDA-approved drug responses of a panel of 3D colon cancer organoids and a corresponding panel of 9 colon carcinoma NCI-60 cell lines elucidated similarities and differences attributable to the assay methodologies as well as the tumor model characteristics, such as the presence of a BRAF V600E variant or other clinically relevant genetic alterations (APC, ARID1A, BRCA2, etc.). As the characterization of PDMR organoid models for HTS continues, these pharmacological datasets will be made available as a resource for the cancer research community. This project was funded in part with federal funds from the NCI, NIH, under contract no. HHSN261201500003I. Citation Format: Annamaria Rapisarda, Eric Jones, Curtis Hose, Erik Harris, Petreena S. Campbell, Mariaestela Ortiz, John Connelly, Nanda Mahashetty, Thomas Silvers, Zahra Davoudi, Penny Sellers Brady, Tiffany Nikirk Rohrer, Julie Grams, Karen Martin, Patricia Ramsey, Lori Bowles, John R. Britt, Ralph E. Parchment, Thomas S. Dexheimer, Beverly A. Teicher, James H. Doroshow, Nathan P. Coussens. Advancing a screening platform with panels of patient-derived organoid models for drug discovery and development. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 5776.

The Hybrid Excess and Decay (HED) model: an automated approach to characterising changes in the photoplethysmography pulse waveform

Photoplethysmography offers a widely used, convenient and non-invasive approach to monitoring basic indices of cardiovascular function, such as heart rate and blood oxygenation. Systematic analysis of the shape of the waveform generated by photoplethysmography might be useful to extract estimates of several physiological and psychological factors influencing the waveform. Here, we developed a robust and automated method for such a systematic analysis across individuals and across different physiological and psychological contexts. We describe a psychophysiologically-relevant model, the Hybrid Excess and Decay (HED) model, which characterises pulse wave morphology in terms of three underlying pressure waves and a decay function. We present the theoretical and practical basis for the model and demonstrate its performance when applied to a pharmacological dataset of 105 participants receiving intravenous administrations of the sympathomimetic drug isoproterenol (isoprenaline). We show that these parameters capture photoplethysmography data with a high degree of precision and, moreover, are sensitive to experimentally-induced changes in interoceptive arousal within individuals. We conclude by discussing the possible value in using the HED model as a complement to standard measures of photoplethysmography signals.

Graph2MDA: a multi-modal variational graph embedding model for predicting microbe-drug associations.

Accumulated clinical studies show that microbes living in humans interact closely with human hosts, and get involved in modulating drug efficacy and drug toxicity. Microbes have become novel targets for the development of antibacterial agents. Therefore, screening of microbe-drug associations can benefit greatly drug research and development. With the increase of microbial genomic and pharmacological datasets, we are greatly motivated to develop an effective computational method to identify new microbe-drug associations. In this article, we proposed a novel method, Graph2MDA, to predict microbe-drug associations by using variational graph autoencoder (VGAE). We constructed multi-modal attributed graphs based on multiple features of microbes and drugs, such as molecular structures, microbe genetic sequences and function annotations. Taking as input the multi-modal attribute graphs, VGAE was trained to learn the informative and interpretable latent representations of each node and the whole graph, and then a deep neural network classifier was used to predict microbe-drug associations. The hyperparameter analysis and model ablation studies showed the sensitivity and robustness of our model. We evaluated our method on three independent datasets and the experimental results showed that our proposed method outperformed six existing state-of-the-art methods. We also explored the meaning of the learned latent representations of drugs and found that the drugs show obvious clustering patterns that are significantly consistent with drug ATC classification. Moreover, we conducted case studies on two microbes and two drugs and found 75-95% predicted associations have been reported in PubMed literature. Our extensive performance evaluations validated the effectiveness of our proposed method. Source codes and preprocessed data are available at https://github.com/moen-hyb/Graph2MDA. Supplementary data are available at Bioinformatics online.

Comparison of Invasive and Non-invasive Estimation of [11C]PBR28 Binding in Non-human Primates.

To identify a reliable alternative to the full blood [11C]PBR28 quantification method that would be easily replicated in multiple research and clinical settings. Ten [11C]PBR28 scans were acquired from 7 healthy non-human primates (NHP). Arterial input functions (AIFs) were averaged to create a population template input function (TIF). Population-based input functions were created by scaling the TIF with injected activity per body weight (PBIF) or unmetabolized tracer activity in blood at 15-,30-, and 60-min post-injection (PBIF15, PBIF30, and PBIF60). Two additional input functions were used: the native unmetabolized total plasma activity (Totals) and the Totals curve metabolite corrected by a scaled template parent fraction from a 30-min sample (TPF30-IF). Total distribution volumes (VTs) were calculated using PBIF, PBIF30, PBIF15, PBIF60, Totals, TPF30-IF, and the individual AIF (VTAIF). Distribution volume ratios (DVR) were computed using the cerebellum and the centrum semiovale (CSO), as pseudo-reference regions (DVRCereb, DVRCSO). Results obtained with each method were compared to VTAIF. Applicability of these alternative methods was tested on an independent pharmacological challenge dataset of microglial activation and depletion. Evaluation was carried at baseline, immediately after intervention (acute), and weeks post-intervention (post-recovery). VTs computed using PBIF15 and PBIF30 showed the best correlation to VTAIF (r > 0.90), while VT derived from the blood-free-scaled PBIF showed poor correlation (r = 0.46) and DVRCSO correlated the least (r = 0.26). In the pharmacological challenge study, most population-derived VT values were comparable to VTAIF at baseline and showed varied sensitivity to challenges at acute and post-recovery evaluation. DVR values did not detect relevant changes. Population-based input functions scaled with a single blood sample might be a useful alternative to using AIF to compute [11C]PBR28 binding in healthy NHPs or animals with comparable metabolism and overall perform better than pseudo-reference regions approaches.

Adding Stochastic Negative Examples into Machine Learning Improves Molecular Bioactivity Prediction.

Multitask deep neural networks learn to predict ligand-target binding by example, yet public pharmacological data sets are sparse, imbalanced, and approximate. We constructed two hold-out benchmarks to approximate temporal and drug-screening test scenarios, whose characteristics differ from a random split of conventional training data sets. We developed a pharmacological data set augmentation procedure, Stochastic Negative Addition (SNA), which randomly assigns untested molecule-target pairs as transient negative examples during training. Under the SNA procedure, drug-screening benchmark performance increases from R2 = 0.1926 ± 0.0186 to 0.4269 ± 0.0272 (122%). This gain was accompanied by a modest decrease in the temporal benchmark (13%). SNA increases in drug-screening performance were consistent for classification and regression tasks and outperformed y-randomized controls. Our results highlight where data and feature uncertainty may be problematic and how leveraging uncertainty into training improves predictions of drug-target relationships.

Pharmacological hypothesis: TPC2 antagonist tetrandrine as a potential therapeutic agent for COVID-19.

More than ten million patients worldwide have been diagnosed with coronavirus disease 19 (COVID‐19) to date (WHO situation report, 1st July 2020). There is no vaccine to prevent infection with the causative organism, severe acute respiratory syndrome coronavirus 2 (SARS‐CoV‐2), nor a cure. In the struggle to devise potentially useful therapeutics in record time, the repurposing of existing compounds is a key route of action. In this hypothesis paper, we argue that the bisbenzylisoquinoline and calcium channel blocker tetrandrine, originally extracted from the plant Stephania tetrandra and utilized in traditional Chinese medicine, may have potential in the treatment of COVID‐19 and should be further investigated. We collate and review evidence for tetrandrine's putative mechanism of action in viral infection, specifically its recently discovered antagonism of the two‐pore channel 2 (TPC2). While tetrandrine's particular history of use provides a very limited pharmacological dataset, there is a suggestion from the available evidence that it could be effective at doses used in clinical practice. We suggest that further research to investigate this possibility should be conducted.

Abstract 2090: Machine learning-derived gene signature predicts strong sensitivity of several solid tumors to the alkylating agent LP-184

Abstract A novel clinical agent, LP-184 is being developed in conjunction with a dedicated machine learning-guided response signature, to allow optimal benefit of LP-184 through genomics-guided therapy. To define correlates of tumor genomics with sensitivity to LP-184, we used RADR™ (Response Algorithm for Drug Positioning and Rescue), a proprietary artificial intelligence (AI)-driven platform, and CellMinerCDB (cross database)™, a systems biology platform integrating molecular and pharmacological datasets on cancer cell lines. The input for our correlational analyses, include LP-184 IC50 data on NCI-60 cell line panel representing drug response, and multi-omics data on these cell lines. CellMinerCDB™ based analysis of LP-184 using a Lasso regression model generated a 38 gene response signature that included expression of APP, NEK6, EGFR, SQSTM1, SLC25A42, SLC16A10, POLD1, SMARCC1, POLG2, CHEK1. In building the model, the signature demonstrates a sensitivity R2 = 0.98 between observed and predicted IC50 values. Additional omics data including methylation and protein levels validate the relevance of several signature genes: APP, NEK6, EGFR, SQSTM1. Concurrently, a RADR™ based analysis of LP-184 using an Artificial Neural Network (ANN) classifier model generated a 10 gene response signature with PTGR1 and PTPN14 as the top weighted genes by relative importance. To establish a signature with highest possible sensitivity, we integrated these 2 additional genes in the 38 gene signature, creating a modified 40 gene signature now having an R2 = 0.99. We next used laboratory experimental studies to measure the performance of this signature. Using an independent set of 18 cell lines not used in signature development, we obtained IC50 values and categorized cell lines as sensitive or resistant. Our in silico results matched in vitro experimental results for 13/18 cell lines (72 percent accuracy). We further applied this 40 gene signature to interrogate 1036 cell lines representing a spectrum of tumor types in the Cancer Cell Line Encyclopedia (CCLE) and obtain a signature-derived IC50. The predicted IC50 ranges from 2.7nM to 114.6uM. The least responsive tumors represent hematological cancers, reproducing the observations made in the NCI-60 panel. 92 of 116 (79%) hematologic cancers in the CCLE database showed a predicted IC50 above 1µM with a median of 3.4uM. The top 279 cell line records with a predicted IC50 below 100nM represented solid tumors with NSCLC, renal, CNS and head & neck cancers as the most sensitive. In conclusion our results demonstrate that the development of LP-184 guided by tumor gene expression patterns modeled using a combination of algorithms and signatures provides a valuable component to the armamentarium of drugs in diverse solid tumors. Citation Format: Umesh Kathad, Aditya Kulkarni, Jean Philippe Richard, Terri Lehman, Rama Modali, Kishor Bhatia, Panna Sharma, Fathi Elloumi, Yves Pommier, William C. Reinhold. Machine learning-derived gene signature predicts strong sensitivity of several solid tumors to the alkylating agent LP-184 [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 2090.

HNet-DNN: Inferring New Drug-Disease Associations with Deep Neural Network Based on Heterogeneous Network Features.

Drug research and development is a time-consuming and high-cost task, pressing an urgent demand to identify novel indications of approved drugs, referred to as drug repositioning, which provides an economical and efficient way for drug discovery. With increasing volumes of large-scale chemical, genomic, and pharmacological data sets generated by the high-throughput technique, it is crucial to develop systematic and rational computational approaches to identify new indications of approved drugs. In this paper, we introduce HNet-DNN, which utilizes a deep neural network (DNN), to predict new drug-disease associations based on the features extracted from the drug-disease heterogeneous network. Instead of the straightforward concatenation of chemical and phenotypic features as the input of DNN, we used these raw features of drugs and diseases to construct a drug-drug similarity network and a disease-disease similarity network, and then built a drug-disease heterogeneous network by integrating known drug-disease associations. Subsequently, we extracted topological features for drug-disease associations from the heterogeneous network and used them to train a DNN model. Our intensive performance evaluations demonstrated that HNet-DNN effectively exploits the features of the heterogeneous network to boost the predictive performance of drug-disease associations. Compared with a couple of typical classifiers and competitive approaches, our method not only achieved state-of-the-art performance but also effectively alleviated the overfitting problem. Moreover, we ran HNet-DNN to predict new drug-disease associations and carried out case studies to verify the effectiveness of our method.

Abstract B070: LOCUS database enables rapid query and targeted selection of preclinical tumor models based on cancer genomics and pharmacologic response

Abstract Preclincal drug testing in oncology research has benefited from the development of thousands of tumors models—both in vitro and in vivo. Within BioDuro laboratories alone, there is a collection of >500 models, including traditional 2D human cell lines grown in vitro, primary 3D lines, cell-derived xenograft (CDX), patient derived xenograft (PDX) and syngeneic murine models (SYN). Identifying the most relevant models to support cancer drug discovery is a challenge. In an era of precision medicine, the need to understand tumor model characteristics, such as functional drug response profiles or genomic status, has become a critical path. In this report, we present LOCUS, a SQL backed database with graphical user interface, populated with functional pharmacologic response and genomic data for hundreds of tumor models including in vitro 2D, 3D, CDX, PDX and SYN. Models are readily searched by any number of conditions, including cancer type, model type, specific mutation, or target expression level. LOCUS combines relational databases for pharmacology and genomics data with scientific statistical models through a spring integrator. A scientific data centered domain architecture allows for pharmacogenomic queries and analysis. Pharmacogenomic data is pushed to dynamic scientific statistical models running on a python environment, the results of which are visualized to a Java EE UI Framework. All tumor models are genomically characterized, with searchable fields for single nucleotide variations, insertion deletions, and transcript expression levels. Extracted RNA was profiled using next generation sequencing performed on Illumina HiSeq using a paired end protocol. For xenograft samples, reads were first aligned and filtered by the mouse reference genome using the Bowtie2 algorithm. The remaining reads were aligned to the GRCh38 human reference genome using the MapSplice2 algorithm. Transcript- and Gene-level expression were determined by counting the number of sequence alignments per exon according to the RefSeq gene model, and computing Fragments Per Killobase Million (FPKM) and Transcripts Per Million (TPM) values using best practices. Opossum software was used to further prepare alignments produced by MapSplice2 algorithm for small variant calling. The Platypus variant caller was used to identify single nucleotide variant, small insertion, and small deletion events. The variation events were annotated using SnpEff and custom perl scripts to overlay known somatic mutations with oncogenic significance. LOCUS provides users with rapid access to hundreds of pharmacology and genomic data sets that enable rational selection of tumor models for preclinical oncology drug testing. The database and structure are readily scaled, supporting future growth of thousands of more tumor models and characterization. Citation Format: Armand Amin, Sandeep Sanga, Shanshan Gan, Ruyi Li, Todd Blewett, Zhuyao Wang, Thomas B Broudy. LOCUS database enables rapid query and targeted selection of preclinical tumor models based on cancer genomics and pharmacologic response [abstract]. In: Proceedings of the AACR-NCI-EORTC International Conference on Molecular Targets and Cancer Therapeutics; 2019 Oct 26-30; Boston, MA. Philadelphia (PA): AACR; Mol Cancer Ther 2019;18(12 Suppl):Abstract nr B070. doi:10.1158/1535-7163.TARG-19-B070

Abstract 2472: LOCUS: Queryable database of cancer genomics and pharmacologic response enables rapid selection of in vitro and in vivo preclinical tumor models

Abstract Preclincal drug testing in oncology research has benefited from the development of thousands of tumors models—both in vitro and in vivo. Within BioDuro laboratories alone, there is a collection of >500 models, including traditional 2D human cell lines grown in vitro, primary 3D lines, cell-derived xenograft (CDX), patient derived xenograft (PDX) and syngeneic murine models (SYN). Identifying the most relevant models to support cancer drug discovery is a challenge. In an era of precision medicine, the need to understand tumor model characteristics, such as functional drug response profiles or genomic status, has become a critical path. In this report, we present LOCUS, a SQL backed database with graphical user interface, populated with functional pharmacologic response and genomic data for hundreds of tumor models including in vitro 2D, 3D, CDX, PDX and SYN. Models are readily searched by any number of conditions, including cancer type, model type, specific mutation, or target expression level. LOCUS combines relational databases for pharmacology and genomics data with scientific statistical models through a spring integrator. A scientific data centered domain architecture allows for pharmacogenomic queries and analysis. Pharmacogenomic data is pushed to dynamic scientific statistical models running on a python environment, the results of which are visualized to a Java EE UI Framework. All tumor models are genomically characterized, with searchable fields for single nucleotide variations, insertion deletions, and transcript expression levels. Extracted RNA was profiled using next generation sequencing performed on Illumina HiSeq using a paired end protocol. For xenograft samples, reads were first aligned and filtered by the mouse reference genome using the Bowtie2 algorithm. The remaining reads were aligned to the GRCh38 human reference genome using the MapSplice2 algorithm. Transcript- and Gene-level expression were determined by counting the number of sequence alignments per exon according to the RefSeq gene model, and computing Fragments Per Killobase Million (FPKM) and Transcripts Per Million (TPM) values using best practices. Opossum software was used to further prepare alignments produced by MapSplice2 algorithm for small variant calling. The Platypus variant caller was used to identify single nucleotide variant, small insertion, and small deletion events. The variation events were annotated using SnpEff and custom perl scripts to overlay known somatic mutations with oncogenic significance. LOCUS provides users with rapid access to hundreds of pharmacology and genomic data sets that enable rational selection of tumor models for preclinical oncology drug testing. The database and structure are readily scaled, supporting future growth of thousands of more tumor models and characterization. Citation Format: Armand Amin, Sandeep Sanga, Shanshan Gan, Yong Hu, Thomas Broudy. LOCUS: Queryable database of cancer genomics and pharmacologic response enables rapid selection of in vitro and in vivo preclinical tumor models [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 2472.

RNA Sequencing of the NCI-60: Integration into CellMiner and CellMiner CDB.

CellMiner (http://discover.nci.nih.gov/cellminer) and CellMinerCDB (https://discover.nci.nih.gov/cellminercdb/) are web-based applications for mining publicly available genomic, molecular, and pharmacologic datasets of human cancer cell lines including the NCI-60, Cancer Cell Line Encyclopedia, Genomics of Drug Sensitivity in Cancer, Cancer Therapeutics Response Portal, NCI/DTP small cell lung cancer, and NCI Almanac cell line sets. Here, we introduce our RNA sequencing (RNA-seq) data for the NCI-60 and their access and integration with the other databases. Correlation to transcript microarray expression levels for identical genes and identical cell lines across CellMinerCDB demonstrates the high quality of these new RNA-seq data. We provide composite and isoform transcript expression data and demonstrate diversity in isoform composition for individual cancer- and pharmacologically relevant genes, including HRAS, PTEN, EGFR, RAD51, ALKBH2, BRCA1, ERBB2, TP53, FGFR2, and CTNND1. We reveal cell-specific differences in the overall levels of isoforms and show their linkage to expression of RNA processing and splicing genes as well as resultant alterations in cancer and pharmacologic gene sets. Gene-drug pairings linked by pathways or functions show specific correlations to isoforms compared with composite gene expression, including ALKBH2-benzaldehyde, AKT3-vandetanib, BCR-imatinib, CDK1 and 20-palbociclib, CASP1-imexon, and FGFR3-pazopanib. Loss of MUC1 20 amino acid variable number tandem repeats, which is used to elicit immune response, and the presence of the androgen receptor AR-V4 and -V7 isoforms in all NCI-60 tissue of origin types demonstrate translational relevance. In summary, we introduce RNA-seq data to our CellMiner and CellMinerCDB web applications, allowing their exploration for both research and translational purposes. SIGNIFICANCE: The current study provides RNA sequencing data for the NCI-60 cell lines made accessible through both CellMiner and CellMinerCDB and is an important pharmacogenomics resource for the field.

PTPRD: neurobiology, genetics, and initial pharmacology of a pleiotropic contributor to brain phenotypes.

Receptor-type protein tyrosine phosphatase, receptor type D (PTPRD) has likely roles as a neuronal cell adhesion molecule and synaptic specifier. Interest in its neurobiology and genomics has been stimulated by results from human genetics and mouse models for phenotypes related to addiction, restless leg syndrome, neurofibrillary pathology in Alzheimer's disease, cognitive impairment/intellectual disability, mood lability, and obsessive-compulsive disorder. We review PTPRD's discovery, gene family, candidate homomeric and heteromeric binding partners, phosphatase activities, brain distribution, human genetic associations with nervous system phenotypes, and mouse model data relevant to these phenotypes. We discuss the recently reported discovery of the first small molecule inhibitor of PTPRD phosphatase, the identification of its addiction-related effects, and the implications of these findings for the PTPRD-associated brain phenotypes. In assembling PTPRD neurobiology, human genetics, and mouse genetic and pharmacological datasets, we provide a compelling picture of the roles played by PTPRD, its variation, and its potential as a target for novel therapeutics.

Integration of transcriptomics, proteomics, metabolomics and systems pharmacology data to reveal the therapeutic mechanism underlying Chinese herbal Bufei Yishen formula for the treatment of chronic obstructive pulmonary disease.

Bufei Yishen formula (BYF) is a traditional Chinese medicine formula, which has long been used as a therapeutic agent for the treatment of chronic obstructive pulmonary disease (COPD). Systems pharmacology has previously been used to identify the potential targets of BYF, and an experimental study has demonstrated that BYF is able to prevent COPD. In addition, the transcriptomic and metabolomic profiles of lung tissues from rats with COPD and BYF-treated rats have been characterized. The present study aimed to determine the therapeutic mechanisms underlying the effects of BYF on COPD treatment by integrating transcriptomics, proteomics and metabolomics, together with systems pharmacology datasets. Initially, the proteomic profiles of rats with COPD and BYF-treated rats were analyzed. Subsequently, pathway and network analyses were conducted to integrate three-omics data; the results demonstrated that the genes, proteins and metabolites were predominantly associated with oxidoreductase activity, antioxidant activity, focal adhesion and lipid metabolism. Finally, a comprehensive analysis of systems pharmacology, transcriptomic, proteomic and metabolomic datasets was performed, and numerous genes, proteins and metabolites were found to be regulated in BYF-treated rats; the potential target proteins of BYF were involved in lipid metabolism, inflammatory response, oxidative stress and focal adhesion. In conclusion, BYF exerted beneficial effects against COPD, potentially by modulating lipid metabolism, the inflammatory response, oxidative stress and cell junction pathways at the system level.

Abstract LB-106: Using a data-driven Bayesian approach to predict the targets of orphan small molecules and ways to overcome drug resistance

Abstract For natural products and phenotypic screen derived small molecules, one of the greatest bottlenecks is target identification. Since most experimental approaches are failure-prone and require a large investment of time and resources, computational methods have the potential to substantially improve target identification efforts. Recently there has been an explosion of genomic, chemical, clinical, and pharmacological datasets that characterize a small molecule's mechanism of action, but no method has been invented to integrate the multiple, independent pieces of evidence provided by each data type into a cohesive prediction framework To address this problem we developed BANDIT: a Bayesian ANalysis to determine Drug Interaction Targets. BANDIT integrates over 20,000,000 pieces of unique information from seven distinct data types to predict drug targets. By integrating these data types within a Bayesian network we outperformed other target prediction methods and achieved a target prediction accuracy of over 90%. Furthermore, we observed that BANDIT was able to predict the precise targeting mechanism as well as the protein target. We further validated BANDIT's accuracy by using it to reproduce the results of a previously published experimental kinase inhibitor screen without any additional data. We observed that our target predictions for the tested small molecules were significantly more likely to be the targets with the highest levels of inhibition in the experimental screen (P = 3e-06). We next used BANDIT to predict targets and mechanisms for over 50,000 small molecules with no known targets. We identified 24 molecules with varying structures and efficacies that we predicted to inhibit microtubule polymerization. Using immunofluorescence and tubulin binding assays we were experimentally validated microtubules as targets for 18 of these compounds - a success rate much higher than expected by random chance. Moreover, one of the greatest challenges with current microtubule chemotherapy is the development of drug resistance, which is lethal for the patients. Remarkably, we identified a subset of our compounds to be active against tumor cells resistant to Eribulin, a microtubule depolymerizing agent FDA approved for breast cancer, and cross-resistant to Vincristine and Colchicine, known depolymerizing drugs. This demonstrated that BANDIT could not only rapidly identify drug mechanisms and targets, but also determine novel molecules with the potential to act on refractory tumors. Finally we used BANDIT to observe how different drug types could interact with one another. We observed many unexpected and exciting results. For instance we observed a tight clustering of the seemingly unrelated classes of beta-blockers with anti-Parkinson's medications. This interaction potentially indicates a shared protein target interaction and provides support to studies proposing the clinical use of beta-blockers in the treatment of tremors in Parkinson's patients. Examples such as this indicate the validity of BANDIT's clustering approach and reveal how previously unknown shared target interactions could cause the unexplained phenotypic effects. This approach could further be expanded to determine undiscovered relationships between classes of drugs, discover which drugs could be repositioned for new uses, and predict clinical drug synergies. Citation Format: Neel S. Madhukar, Prashant Khade, Linda Huang, Kaitlyn Gayvert, Giuseppe Galletti, Paraskevi Giannakakou, Olivier Elemento. Using a data-driven Bayesian approach to predict the targets of orphan small molecules and ways to overcome drug resistance. [abstract]. In: Proceedings of the 107th Annual Meeting of the American Association for Cancer Research; 2016 Apr 16-20; New Orleans, LA. Philadelphia (PA): AACR; Cancer Res 2016;76(14 Suppl):Abstract nr LB-106.

Long non-coding RNA expression profiling in the NCI60 cancer cell line panel using high-throughput RT-qPCR.

Long non-coding RNAs (lncRNAs) form a new class of RNA molecules implicated in various aspects of protein coding gene expression regulation. To study lncRNAs in cancer, we generated expression profiles for 1707 human lncRNAs in the NCI60 cancer cell line panel using a high-throughput nanowell RT-qPCR platform. We describe how qPCR assays were designed and validated and provide processed and normalized expression data for further analysis. Data quality is demonstrated by matching the lncRNA expression profiles with phenotypic and genomic characteristics of the cancer cell lines. This data set can be integrated with publicly available omics and pharmacological data sets to uncover novel associations between lncRNA expression and mRNA expression, miRNA expression, DNA copy number, protein coding gene mutation status or drug response

Combining systems pharmacology, transcriptomics, proteomics, and metabolomics to dissect the therapeutic mechanism of Chinese herbal Bufei Jianpi formula for application to COPD.

Bufei Jianpi formula (BJF) has long been used as a therapeutic agent in the treatment of COPD. Systems pharmacology identified 145 active compounds and 175 potential targets of BJF in a previous study. Additionally, BJF was previously shown to effectively prevent COPD and its comorbidities, such as ventricular hypertrophy, by inhibition of inflammatory cytokine production, matrix metalloproteinases expression, and other cytokine production, in vivo. However, the system-level mechanism of BJF for the treatment of COPD is still unclear. The aim of this study was to gain insight into its system-level mechanisms by integrating transcriptomics, proteomics, and metabolomics together with systems pharmacology datasets. Using molecular function, pathway, and network analyses, the genes and proteins regulated in COPD rats and BJF-treated rats could be mainly attributed to oxidoreductase activity, antioxidant activity, focal adhesion, tight junction, or adherens junction. Furthermore, a comprehensive analysis of systems pharmacology, transcript, protein, and metabolite datasets is performed. The results showed that a number of genes, proteins, metabolites regulated in BJF-treated rats and potential target proteins of BJF were involved in lipid metabolism, cell junction, oxidative stress, and inflammatory response, which might be the system-level therapeutic mechanism of BJF treatment.

Prioritization of pharmaceuticals for potential environmental hazard through leveraging a large-scale mammalian pharmacological dataset.

The potential for pharmaceuticals in the environment to cause adverse ecological effects is of increasing concern. Given the thousands of active pharmaceutical ingredients (APIs) that can enter the aquatic environment through human and/or animal (e.g., livestock) waste, a current challenge in aquatic toxicology is identifying those that pose the greatest risk. Because empirical toxicity information for aquatic species is generally lacking for pharmaceuticals, an important data source for prioritization is that generated during the mammalian drug development process. Applying concepts of species read-across, mammalian pharmacokinetic data were used to systematically prioritize APIs by estimating their potential to cause adverse biological consequences to aquatic organisms, using fish as an example. Mammalian absorption, distribution, metabolism, and excretion (ADME) data (e.g., peak plasma concentration, apparent volume of distribution, clearance rate, and half-life) were collected and curated, creating the Mammalian Pharmacokinetic Prioritization For Aquatic Species Targeting (MaPPFAST) database representing 1070 APIs. From these data, a probabilistic model and scoring system were developed and evaluated. Individual APIs and therapeutic classes were ranked based on clearly defined read-across assumptions for translating mammalian-derived ADME parameters to estimate potential hazard in fish (i.e., greatest predicted hazard associated with lowest mammalian peak plasma concentrations, total clearance and highest volume of distribution, half-life). It is anticipated that the MaPPFAST database and the associated API prioritization approach will help guide research and/or inform ecological risk assessment.

GLASS: a comprehensive database for experimentally validated GPCR-ligand associations.

G protein-coupled receptors (GPCRs) are probably the most attractive drug target membrane proteins, which constitute nearly half of drug targets in the contemporary drug discovery industry. While the majority of drug discovery studies employ existing GPCR and ligand interactions to identify new compounds, there remains a shortage of specific databases with precisely annotated GPCR-ligand associations. We have developed a new database, GLASS, which aims to provide a comprehensive, manually curated resource for experimentally validated GPCR-ligand associations. A new text-mining algorithm was proposed to collect GPCR-ligand interactions from the biomedical literature, which is then crosschecked with five primary pharmacological datasets, to enhance the coverage and accuracy of GPCR-ligand association data identifications. A special architecture has been designed to allow users for making homologous ligand search with flexible bioactivity parameters. The current database contains ∼500 000 unique entries, of which the vast majority stems from ligand associations with rhodopsin- and secretin-like receptors. The GLASS database should find its most useful application in various in silico GPCR screening and functional annotation studies. The website of GLASS database is freely available at http://zhanglab.ccmb.med.umich.edu/GLASS/. zhng@umich.edu Supplementary data are available at Bioinformatics online.