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Showing 1391 to 1394 of 5186 entries
Published on November 8, 2021
Drug triggered pruritus, rash, papules, and blisters - is AGEP a clash of an altered sphingolipid-metabolism and lysosomotropism of drugs accumulating in the skin?
Abstract: Rash, photosensitivity, erythema multiforme, and the acute generalized exanthematous pustulosis (AGEP) are relatively uncommon adverse reactions of drugs. To date, the etiology is not well understood and individual susceptibility still remains unknown. Amiodarone, chlorpromazine, amitriptyline, and trimipramine are classified lysosomotropic as well as photosensitizing, however, they fail to trigger rash and pruritic papules in all individuals. Lysosomotropism is a common charcteristic of various drugs, but independent of individuals. There is evidence that the individual ability to respond to external oxidative stress is crosslinked with the elongation of long-chain fatty acids to very long-chain fatty acids by ELOVLs. ELOVL6 and ELOVL7 are sensitive to ROS induced depletion of cellular NADPH and insufficient regeneration via the pentose phosphate pathway and mitochondrial fatty acid oxidation. Deficiency of NADPH in presence of lysosomotropic drugs promotes the synthesis of C16-ceramide in lysosomes and may contribute to emerging pruritic papules of AGEP. However, independently from a lysosomomotropic drug, severe depletion of ATP and NAD(P)H, e.g., by UV radiation or a potent photosensitizer can trigger likewise the collapse of the lysosomal transmembrane proton gradient resulting in lysosomal C16-ceramide synthesis and pruritic papules. This kind of papules are equally present in polymorphous light eruption (PMLE/PLE) and acne aestivalis (Mallorca acne). The suggested model of a compartmentalized ceramide metabolism provides a more sophisticated explanation of cutaneous drug adverse effects and the individual sensitivity to UV radiation. Parameters such as pKa and ClogP of the triggering drug, cutaneous fatty acid profile, and ceramide profile enables new concepts in risk assessment and scoring of AGEP as well as prophylaxis outcome.
Published on November 8, 2021
Drug Interactions for Patients with Respiratory Diseases Receiving COVID-19 Emerged Treatments.
Abstract: Pandemic of coronavirus disease (COVID-19) is still pressing the healthcare systems worldwide. Thus far, the lack of available COVID-19-targeted treatments has led scientists to look through drug repositioning practices and exploitation of available scientific evidence for potential efficient drugs that may block biological pathways of SARS-CoV-2. Till today, several molecules have emerged as promising pharmacological agents, and more than a few medication protocols are applied during hospitalization. On the other hand, given the criticality of the disease, it is important for healthcare providers, especially those in COVID-19 clinics (i.e., nursing personnel and treating physicians), to recognize potential drug interactions that may lead to adverse drug reactions that may negatively impact the therapeutic outcome. In this review, focusing on patients with respiratory diseases (i.e., asthma or chronic obstructive pulmonary disease) that are treated also for COVID-19, we discuss possible drug interactions, their underlying pharmacological mechanisms, and possible clinical signs that healthcare providers in COVID-19 clinics may need to acknowledge as adverse drug reactions due to drug-drug interactions.
Published on November 8, 2021
Integrative analysis of the plasma proteome and polygenic risk of cardiometabolic diseases.
Abstract: Cardiometabolic diseases are frequently polygenic in architecture, comprising a large number of risk alleles with small effects spread across the genome(1-3). Polygenic scores (PGS) aggregate these into a metric representing an individual's genetic predisposition to disease. PGS have shown promise for early risk prediction(4-7) and there is an open question as to whether PGS can also be used to understand disease biology(8). Here, we demonstrate that cardiometabolic disease PGS can be used to elucidate the proteins underlying disease pathogenesis. In 3,087 healthy individuals, we found that PGS for coronary artery disease, type 2 diabetes, chronic kidney disease and ischaemic stroke are associated with the levels of 49 plasma proteins. Associations were polygenic in architecture, largely independent of cis and trans protein quantitative trait loci and present for proteins without quantitative trait loci. Over a follow-up of 7.7 years, 28 of these proteins associated with future myocardial infarction or type 2 diabetes events, 16 of which were mediators between polygenic risk and incident disease. Twelve of these were druggable targets with therapeutic potential. Our results demonstrate the potential for PGS to uncover causal disease biology and targets with therapeutic potential, including those that may be missed by approaches utilizing information at a single locus.
Published on November 5, 2021
Artificial intelligence for the discovery of novel antimicrobial agents for emerging infectious diseases.
Abstract: The search for effective drugs to treat new and existing diseases is a laborious one requiring a large investment of capital, resources, and time. The coronavirus 2019 (COVID-19) pandemic has been a painful reminder of the lack of development of new antimicrobial agents to treat emerging infectious diseases. Artificial intelligence (AI) and other in silico techniques can drive a more efficient, cost-friendly approach to drug discovery by helping move potential candidates with better clinical tolerance forward in the pipeline. Several research teams have developed successful AI platforms for hit identification, lead generation, and lead optimization. In this review, we investigate the technologies at the forefront of spearheading an AI revolution in drug discovery and pharmaceutical sciences.
Published on November 5, 2021
A multiple network-based bioinformatics pipeline for the study of molecular mechanisms in oncological diseases for personalized medicine.
Abstract: MOTIVATION: Assessment of genetic mutations is an essential element in the modern era of personalized cancer treatment. Our strategy is focused on 'multiple network analysis' in which we try to improve cancer diagnostics by using biological networks. Genetic alterations in some important hubs or in driver genes such as BRAF and TP53 play a critical role in regulating many important molecular processes. Most of the studies are focused on the analysis of the effects of single mutations, while tumors often carry mutations of multiple driver genes. The aim of this work is to define an innovative bioinformatics pipeline focused on the design and analysis of networks (such as biomedical and molecular networks), in order to: (1) improve the disease diagnosis; (2) identify the patients that could better respond to a given drug treatment; and (3) predict what are the primary and secondary effects of gene mutations involved in human diseases. RESULTS: By using our pipeline based on a multiple network approach, it has been possible to demonstrate and validate what are the joint effects and changes of the molecular profile that occur in patients with metastatic colorectal carcinoma (mCRC) carrying mutations in multiple genes. In this way, we can identify the most suitable drugs for the therapy for the individual patient. This information is useful to improve precision medicine in cancer patients. As an application of our pipeline, the clinically significant case studies of a cohort of mCRC patients with the BRAF V600E-TP53 I195N missense combined mutation were considered. AVAILABILITY: The procedures used in this paper are part of the Cytoscape Core, available at (www.cytoscape.org). Data used here on mCRC patients have been published in [55]. SUPPLEMENTARY INFORMATION: A supplementary file containing a more detailed discussion of this case study and other cases is available at the journal site as Supplementary Data.
Published on November 5, 2021
Knowledge bases and software support for variant interpretation in precision oncology.
Abstract: Precision oncology is a rapidly evolving interdisciplinary medical specialty. Comprehensive cancer panels are becoming increasingly available at pathology departments worldwide, creating the urgent need for scalable cancer variant annotation and molecularly informed treatment recommendations. A wealth of mainly academia-driven knowledge bases calls for software tools supporting the multi-step diagnostic process. We derive a comprehensive list of knowledge bases relevant for variant interpretation by a review of existing literature followed by a survey among medical experts from university hospitals in Germany. In addition, we review cancer variant interpretation tools, which integrate multiple knowledge bases. We categorize the knowledge bases along the diagnostic process in precision oncology and analyze programmatic access options as well as the integration of knowledge bases into software tools. The most commonly used knowledge bases provide good programmatic access options and have been integrated into a range of software tools. For the wider set of knowledge bases, access options vary across different parts of the diagnostic process. Programmatic access is limited for information regarding clinical classifications of variants and for therapy recommendations. The main issue for databases used for biological classification of pathogenic variants and pathway context information is the lack of standardized interfaces. There is no single cancer variant interpretation tool that integrates all identified knowledge bases. Specialized tools are available and need to be further developed for different steps in the diagnostic process.
Published on November 5, 2021
Utilizing graph machine learning within drug discovery and development.
Abstract: Graph machine learning (GML) is receiving growing interest within the pharmaceutical and biotechnology industries for its ability to model biomolecular structures, the functional relationships between them, and integrate multi-omic datasets - amongst other data types. Herein, we present a multidisciplinary academic-industrial review of the topic within the context of drug discovery and development. After introducing key terms and modelling approaches, we move chronologically through the drug development pipeline to identify and summarize work incorporating: target identification, design of small molecules and biologics, and drug repurposing. Whilst the field is still emerging, key milestones including repurposed drugs entering in vivo studies, suggest GML will become a modelling framework of choice within biomedical machine learning.
Published on November 5, 2021
Artificial intelligence and machine learning methods in predicting anti-cancer drug combination effects.
Abstract: Drug combinations have exhibited promising therapeutic effects in treating cancer patients with less toxicity and adverse side effects. However, it is infeasible to experimentally screen the enormous search space of all possible drug combinations. Therefore, developing computational models to efficiently and accurately identify potential anti-cancer synergistic drug combinations has attracted a lot of attention from the scientific community. Hypothesis-driven explicit mathematical methods or network pharmacology models have been popular in the last decade and have been comprehensively reviewed in previous surveys. With the surge of artificial intelligence and greater availability of large-scale datasets, machine learning especially deep learning methods are gaining popularity in the field of computational models for anti-cancer drug synergy prediction. Machine learning-based methods can be derived without strong assumptions about underlying mechanisms and have achieved state-of-the-art prediction performances, promoting much greater growth of the field. Here, we present a structured overview of available large-scale databases and machine learning especially deep learning methods in computational predictive models for anti-cancer drug synergy prediction. We provide a unified framework for machine learning models and detail existing model architectures as well as their contributions and limitations, shedding light into the future design of computational models. Besides, unbiased experiments are conducted to provide in-depth comparisons between reviewed papers in terms of their prediction performance.
Published on November 4, 2021
Learning from low-rank multimodal representations for predicting disease-drug associations.
Abstract: BACKGROUND: Disease-drug associations provide essential information for drug discovery and disease treatment. Many disease-drug associations remain unobserved or unknown, and trials to confirm these associations are time-consuming and expensive. To better understand and explore these valuable associations, it would be useful to develop computational methods for predicting unobserved disease-drug associations. With the advent of various datasets describing diseases and drugs, it has become more feasible to build a model describing the potential correlation between disease and drugs. RESULTS: In this work, we propose a new prediction method, called LMFDA, which works in several stages. First, it studies the drug chemical structure, disease MeSH descriptors, disease-related phenotypic terms, and drug-drug interactions. On this basis, similarity networks of different sources are constructed to enrich the representation of drugs and diseases. Based on the fused disease similarity network and drug similarity network, LMFDA calculated the association score of each pair of diseases and drugs in the database. This method achieves good performance on Fdataset and Cdataset, AUROCs were 91.6% and 92.1% respectively, higher than many of the existing computational models. CONCLUSIONS: The novelty of LMFDA lies in the introduction of multimodal fusion using low-rank tensors to fuse multiple similar networks and combine matrix complement technology to predict potential association. We have demonstrated that LMFDA can display excellent network integration ability for accurate disease-drug association inferring and achieve substantial improvement over the advanced approach. Overall, experimental results on two real-world networks dataset demonstrate that LMFDA able to delivers an excellent detecting performance. Results also suggest that perfecting similar networks with as much domain knowledge as possible is a promising direction for drug repositioning.
Published on November 3, 2021
Towards Biomanufacturing of Cell-Derived Matrices.
Abstract: Cell-derived matrices (CDM) are the decellularised extracellular matrices (ECM) of tissues obtained by the laboratory culture process. CDM is developed to mimic, to a certain extent, the properties of the needed natural tissue and thus to obviate the use of animals. The composition of CDM can be tailored for intended applications by carefully optimising the cell sources, culturing conditions and decellularising methods. This unique advantage has inspired the increasing use of CDM for biomedical research, ranging from stem cell niches to disease modelling and regenerative medicine. However, while much effort is spent on extracting different types of CDM and exploring their utilisation, little is spent on the scale-up aspect of CDM production. The ability to scale up CDM production is essential, as the materials are due for clinical trials and regulatory approval, and in fact, this ability to scale up should be an important factor from the early stages. In this review, we first introduce the current CDM production and characterisation methods. We then describe the existing scale-up technologies for cell culture and highlight the key considerations in scaling-up CDM manufacturing. Finally, we discuss the considerations and challenges faced while converting a laboratory protocol into a full industrial process. Scaling-up CDM manufacturing is a challenging task since it may be hindered by technologies that are not yet available. The early identification of these gaps will not only quicken CDM based product development but also help drive the advancement in scale-up cell culture and ECM extraction.
Showing 1391 to 1394 of 5186 entries