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Showing 1031 to 1034 of 5186 entries
Published on January 18, 2022
A community challenge for a pancancer drug mechanism of action inference from perturbational profile data.
Abstract: The Columbia Cancer Target Discovery and Development (CTD2) Center is developing PANACEA, a resource comprising dose-responses and RNA sequencing (RNA-seq) profiles of 25 cell lines perturbed with approximately 400 clinical oncology drugs, to study a tumor-specific drug mechanism of action. Here, this resource serves as the basis for a DREAM Challenge assessing the accuracy and sensitivity of computational algorithms for de novo drug polypharmacology predictions. Dose-response and perturbational profiles for 32 kinase inhibitors are provided to 21 teams who are blind to the identity of the compounds. The teams are asked to predict high-affinity binding targets of each compound among approximately 1,300 targets cataloged in DrugBank. The best performing methods leverage gene expression profile similarity analysis as well as deep-learning methodologies trained on individual datasets. This study lays the foundation for future integrative analyses of pharmacogenomic data, reconciliation of polypharmacology effects in different tumor contexts, and insights into network-based assessments of drug mechanisms of action.
Published on January 18, 2022
Population Pharmacokinetics of Vancomycin in Critically Ill Adult Patients Receiving Extracorporeal Membrane Oxygenation (an ASAP ECMO Study).
Abstract: Our study aimed to describe the population pharmacokinetics (PK) of vancomycin in critically ill patients receiving extracorporeal membrane oxygenation (ECMO), including those receiving concomitant renal replacement therapy (RRT). Dosing simulations were used to recommend maximally effective and safe dosing regimens. Serial vancomycin plasma concentrations were measured and analyzed using a population PK approach on Pmetrics. The final model was used to identify dosing regimens that achieved target exposures of area under the curve (AUC0-24) of 400-700 mg . h/liter at steady state. Twenty-two patients were enrolled, of which 11 patients received concomitant RRT. In the non-RRT patients, the median creatinine clearance (CrCL) was 75 ml/min and the mean daily dose of vancomycin was 25.5 mg/kg. Vancomycin was well described in a two-compartment model with CrCL, the presence of RRT, and total body weight found as significant predictors of clearance and central volume of distribution (Vc). The mean vancomycin renal clearance and Vc were 3.20 liters/h and 29.7 liters respectively, while the clearance for patients on RRT was 0.15 liters/h. ECMO variables did not improve the final covariate model. We found that recommended dosing regimens for critically ill adult patients not on ECMO can be safely and effectively used in those on ECMO. Loading doses of at least 25 mg/kg followed by maintenance doses of 12.5-20 mg/kg every 12 h are associated with a 97-98% probability of efficacy and 11-12% probability of toxicity, in patients with normal renal function. Therapeutic drug monitoring along with reductions in dosing are warranted for patients with renal impairment and those with concomitant RRT. (This study is registered with the Australian New Zealand Clinical Trials Registry [ANZCTR] under number ACTRN12612000559819.).
Published on January 18, 2022
Assessment of FDA-Approved Drugs as a Therapeutic Approach for Niemann-Pick Disease Type C1 Using Patient-Specific iPSC-Based Model Systems.
Abstract: Niemann-Pick type C1 (NP-C1) is a fatal, progressive neurodegenerative disease caused by mutations in the NPC1 gene. Mutations of NPC1 can result in a misfolded protein that is subsequently marked for proteasomal degradation. Such loss-of-function mutations lead to cholesterol accumulation in late endosomes and lysosomes. Pharmacological chaperones (PCs) are described to protect misfolded proteins from proteasomal degradation and are being discussed as a treatment strategy for NP-C1. Here, we used a combinatorial approach of high-throughput in silico screening of FDA-approved drugs and in vitro biochemical assays to identify potential PCs. The effects of the hit compounds identified by molecular docking were compared in vitro with 25-hydroxycholesterol (25-HC), which is known to act as a PC for NP-C1. We analyzed cholesterol accumulation, NPC1 protein content, and lysosomal localization in patient-specific fibroblasts, as well as in neural differentiated and hepatocyte-like cells derived from patient-specific induced pluripotent stem cells (iPSCs). One compound, namely abiraterone acetate, showed comparable results to 25-HC and restored NPC1 protein level, corrected the intracellular localization of NPC1, and consequently decreased cholesterol accumulation in NPC1-mutated fibroblasts and iPSC-derived neural differentiated and hepatocyte-like cells. The discovered PC altered not only the pathophysiological phenotype of cells carrying the I1061T mutation- known to be responsive to treatment with PCs-but an effect was also observed in cells carrying other NPC1 missense mutations. Therefore, we hypothesize that the PCs studied here may serve as an effective treatment strategy for a large group of NP-C1 patients.
Published on January 18, 2022
Identification of putative genetic variants in major depressive disorder patients in Pakistan.
Abstract: BACKGROUND: Major depressive disorder (MDD) is a polygenic, and highly prevalent disorder affecting 322 million people globally. It results in several psychological changes which adversely affect different dimensions of life and may lead to suicide. METHODS: Whole exome sequencing of 15 MDD patients, enrolled at the Dr. A. Q. Khan Institute of Behavioral Sciences, Karachi, was performed using NextSeq500. Different bioinformatics tools and databases like ANNOVAR, ALoFT, and GWAS were used to identify both common and rare variants associated with the pathogenesis of MDD. RESULTS: A total of 1985 variations were identified in 479 MDD-related genes. Several SNPs including rs1079610, rs11750538, rs1799913, rs1801131, rs2230267, rs2231187, rs3819976, rs4314963, rs56265970, rs587780434, rs6330, rs75111588, rs7596487, and rs9624909 were prioritized due to their deleteriousness and frequency difference between the patients and the South Asian population. A non-synonymous variation rs56265970 (BCR) had 26% frequency in patients and was not found in the South Asian population; a multiallelic UTR-5' insertion rs587780434 (RELN) was present with an allelic frequency of 70% in patients whereas 22% in the SAS population. Genetic alterations in PABPC1 genes, a stress-associated gene also had higher allele frequency in the cases than in the normal population. CONCLUSION: This present study identifies both common and rare variants in the genes associated with the pathogenesis of MDD in Pakistani patients. Genetic variations in BCR, RELN, and stress-associated PABPC1 suggest potential roles in the pathogenesis of MDD.
Published on January 17, 2022
Integrated bioinformatics based subtractive genomics approach to decipher the therapeutic function of hypothetical proteins from Salmonella typhi XDR H-58 strain.
Abstract: PURPOSE: The efficacy of drugs against Salmonella infection have compromised due to emerging XDR H58 strain. There is a dire need to find novel antimicrobial drug targets as well as drug candidates to cure by the XDR strain of Salmonella. It is observed that the complete genome sequence of the XDR H58 strain contains a large number of hypothetical proteins with unknown cellular and biological functions. Hence, it is indispensable to annotate these proteins functionally as well as structurally to identify novel drug targets. METHODS: In the current study, a comparative genomics and proteomics based approach was applied to find the novel drug targets in XDR strain while comparing the MDR and NR strains of Salmonella typhi. RESULTS: The characterization of ~ 350 hypothetical proteins were performed through determination of their physio-chemical properties, sub-cellular localization, functional annotation, and structure-based studies. As a result, only five proteins were prioritized as essential, druggable, and virulent proteins. Moreover, only one protein i.e. WP_000916613.1 was functionally annotated with high confidence and subjected to further structure-based analysis. CONCLUSION: The current study presents a hypothetical protein from the XDR S. typhi proteome as a potential pharmacological target against which novel therapeutic candidates may be predicted. The outcome of the current study may lead to formulate a general set of pipelines for better understanding of the role of hypothetical proteins in pathogenesis of not only Salmonella but also for other pathogens.
Published on January 17, 2022
Machine learning methods, databases and tools for drug combination prediction.
Abstract: Combination therapy has shown an obvious efficacy on complex diseases and can greatly reduce the development of drug resistance. However, even with high-throughput screens, experimental methods are insufficient to explore novel drug combinations. In order to reduce the search space of drug combinations, there is an urgent need to develop more efficient computational methods to predict novel drug combinations. In recent decades, more and more machine learning (ML) algorithms have been applied to improve the predictive performance. The object of this study is to introduce and discuss the recent applications of ML methods and the widely used databases in drug combination prediction. In this study, we first describe the concept and controversy of synergism between drug combinations. Then, we investigate various publicly available data resources and tools for prediction tasks. Next, ML methods including classic ML and deep learning methods applied in drug combination prediction are introduced. Finally, we summarize the challenges to ML methods in prediction tasks and provide a discussion on future work.
Published on January 17, 2022
Computationally prioritized drugs inhibit SARS-CoV-2 infection and syncytia formation.
Abstract: The pharmacological arsenal against the COVID-19 pandemic is largely based on generic anti-inflammatory strategies or poorly scalable solutions. Moreover, as the ongoing vaccination campaign is rolling slower than wished, affordable and effective therapeutics are needed. To this end, there is increasing attention toward computational methods for drug repositioning and de novo drug design. Here, multiple data-driven computational approaches are systematically integrated to perform a virtual screening and prioritize candidate drugs for the treatment of COVID-19. From the list of prioritized drugs, a subset of representative candidates to test in human cells is selected. Two compounds, 7-hydroxystaurosporine and bafetinib, show synergistic antiviral effects in vitro and strongly inhibit viral-induced syncytia formation. Moreover, since existing drug repositioning methods provide limited usable information for de novo drug design, the relevant chemical substructures of the identified drugs are extracted to provide a chemical vocabulary that may help to design new effective drugs.
Published on January 17, 2022
Minimal information for chemosensitivity assays (MICHA): a next-generation pipeline to enable the FAIRification of drug screening experiments.
Abstract: Chemosensitivity assays are commonly used for preclinical drug discovery and clinical trial optimization. However, data from independent assays are often discordant, largely attributed to uncharacterized variation in the experimental materials and protocols. We report here the launching of Minimal Information for Chemosensitivity Assays (MICHA), accessed via https://micha-protocol.org. Distinguished from existing efforts that are often lacking support from data integration tools, MICHA can automatically extract publicly available information to facilitate the assay annotation including: 1) compounds, 2) samples, 3) reagents and 4) data processing methods. For example, MICHA provides an integrative web server and database to obtain compound annotation including chemical structures, targets and disease indications. In addition, the annotation of cell line samples, assay protocols and literature references can be greatly eased by retrieving manually curated catalogues. Once the annotation is complete, MICHA can export a report that conforms to the FAIR principle (Findable, Accessible, Interoperable and Reusable) of drug screening studies. To consolidate the utility of MICHA, we provide FAIRified protocols from five major cancer drug screening studies as well as six recently conducted COVID-19 studies. With the MICHA web server and database, we envisage a wider adoption of a community-driven effort to improve the open access of drug sensitivity assays.
Published on January 17, 2022
Artificial intelligence in clinical research of cancers.
Abstract: Several factors, including advances in computational algorithms, the availability of high-performance computing hardware, and the assembly of large community-based databases, have led to the extensive application of Artificial Intelligence (AI) in the biomedical domain for nearly 20 years. AI algorithms have attained expert-level performance in cancer research. However, only a few AI-based applications have been approved for use in the real world. Whether AI will eventually be capable of replacing medical experts has been a hot topic. In this article, we first summarize the cancer research status using AI in the past two decades, including the consensus on the procedure of AI based on an ideal paradigm and current efforts of the expertise and domain knowledge. Next, the available data of AI process in the biomedical domain are surveyed. Then, we review the methods and applications of AI in cancer clinical research categorized by the data types including radiographic imaging, cancer genome, medical records, drug information and biomedical literatures. At last, we discuss challenges in moving AI from theoretical research to real-world cancer research applications and the perspectives toward the future realization of AI participating cancer treatment.
Published on January 15, 2022
Core promoter mutation contributes to abnormal gene expression in bladder cancer.
Abstract: BACKGROUND: Bladder cancer is one of the most mortal cancers. Bladder cancer has distinct gene expression signature, highlighting altered gene expression plays important roles in bladder cancer etiology. However, the mechanism for how the regulatory disorder causes the altered expression in bladder cancer remains elusive. Core promoter controls transcriptional initiation. We hypothesized that mutation in core promoter abnormality could cause abnormal transcriptional initiation thereby the altered gene expression in bladder cancer. METHODS: In this study, we performed a genome-wide characterization of core promoter mutation in 77 Spanish bladder cancer cases. RESULTS: We identified 69 recurrent somatic mutations in 61 core promoters of 62 genes and 28 recurrent germline mutations in 20 core promoters of 21 genes, including TERT, the only gene known with core promoter mutation in bladder cancer, and many oncogenes and tumor suppressors. From the RNA-seq data from bladder cancer, we observed altered expression of the core promoter-mutated genes. We further validated the effects of core promoter mutation on gene expression by using luciferase reporter gene assay. We also identified potential drugs targeting the core promoter-mutated genes. CONCLUSIONS: Data from our study highlights that core promoter mutation contributes to bladder cancer development through altering gene expression.
Showing 1031 to 1034 of 5186 entries