scholarly journals The Geometric Sparse Matrix Completion Model for Predicting Drug Side effects

2019 ◽  
Author(s):  
Diego Galeano ◽  
Alberto Paccanaro
Keyword(s):  
Clinical Trials ◽  
Side Effects ◽  
Side Effect ◽  
Large Scale ◽  
Learning Algorithm ◽  
Sparse Matrix ◽  
Matrix Completion ◽  
Optimal Solution ◽  
Drug Side Effects ◽  
Chemical Structures

AbstractPair-input associations for drug-side effects are obtained through expensive placebo-controlled experiments in human clinical trials. An important challenge in computational pharmacology is to predict missing associations given a few entries in the drug-side effect matrix, as these predictions can be used to direct further clinical trials. Here we introduce the Geometric Sparse Matrix Completion (GSMC) model for predicting drug side effects. Our high-rank matrix completion model learns non-negative sparse matrices of coefficients for drugs and side effects by imposing smoothness priors that exploit a set of pharmacological side information graphs, including information about drug chemical structures, drug interactions, molecular targets, and disease indications. Our learning algorithm is based on the diagonally rescaled gradient descend principle of non-negative matrix factorization. We prove that it converges to a globally optimal solution with a first-order rate of convergence. Experiments on large-scale side effect data from human clinical trials show that our method achieves better prediction performance than six state-of-the-art methods for side effect prediction while offering biological interpretability and favouring explainable predictions.

Investigating side effect modules in the interactome and their use in drug adverse effect discovery

2016 ◽  
Author(s):  
Emre Guney
Keyword(s):  
Side Effects ◽  
Side Effect ◽  
Drug Targets ◽  
Large Scale ◽  
New Drugs ◽  
Drug Side Effects ◽  
Data Set ◽  
Drug Adverse Effect ◽  
Phase Ii And Iii

One of the biggest challenges in drug development is increasing costs of bringing new drugs to the market. Many candidate drugs fail during phase II and III trials due to unexpected side effects and experimental methods remain cost ineffective for large scale discovery of adverse effects. Alternatively, computational methods are used to characterize drug side effects, but they often rely on training predictors based on drug and side effect similarity. Moreover, these methods are typically tailored to the underlying data set and provide little mechanistic insights on the predicted associations. In this study, we investigate the role of network topology in explaining observed side effects of drugs. We find that drug targets are closer in the interactome to the proteins inducing the known side effects of the drug compared to the proteins associated with the rest of the side effects. We show that the interactome based proximity can be used to identify side effects and we highlight a use case in which interactome-based side effect prediction can give insights on drug side effects observed in the clinic.

scholarly journals Thrombolytic Therapy in the Acute Period of Aortic Thromboembolism in Cats

2019 ◽  
Vol 4 (2) ◽  
Author(s):  
Joshua Billy Hannabuss
Keyword(s):  
Clinical Trials ◽  
Side Effects ◽  
Open Access ◽  
Thrombolytic Therapy ◽  
Acute Phase ◽  
Large Scale ◽  
Supportive Therapy ◽  
Controlled Clinical Trials ◽  
Bottom Line ◽  
Adverse Side Effects

<strong>PICO question</strong><br /><p>Of cats that present with aortic thromboembolism, do patients that receive thrombolytic therapy in the acute phase have improved survival as compared to those who do not?</p><strong>Clinical bottom line</strong><br /><p>Based on the current available evidence, the use of thrombolytic therapy in the acute phase of aortic thromboembolism (ATE) does not appear to improve survival when compared to conventional supportive therapy. Frequently reported adverse side effects further questions its merits, and large scale controlled clinical trials would be required to further evaluate any benefit in the use of this therapy.</p><br /> <img src="https://www.veterinaryevidence.org/rcvskmod/icons/oa-icon.jpg" alt="Open Access" /> <img src="https://www.veterinaryevidence.org/rcvskmod/icons/pr-icon.jpg" alt="Peer Reviewed" />

scholarly journals Side Effects of mRNA-Based COVID-19 Vaccine: Nationwide Phase IV Study among Healthcare Workers in Slovakia

2021 ◽  
Vol 14 (9) ◽  
pp. 873
Author(s):  
Abanoub Riad ◽  
Barbora Hocková ◽  
Lucia Kantorová ◽  
Rastislav Slávik ◽  
Lucia Spurná ◽  
...  
Keyword(s):  
Side Effects ◽  
Side Effect ◽  
Healthcare Workers ◽  
Large Scale ◽  
Large Population ◽  
Medical Attention ◽  
Cross Sectional Survey ◽  
Cross Sectional ◽  
Post Vaccination ◽  
Phase Iv

mRNA-based COVID-19 vaccines such as BNT162b2 have recently been a target of anti-vaccination campaigns due to their novelty in the healthcare industry; nevertheless, these vaccines have exhibited excellent results in terms of efficacy and safety. As a consequence, they acquired the first approvals from drug regulators and were deployed at a large scale among priority groups, including healthcare workers. This phase IV study was designed as a nationwide cross-sectional survey to evaluate the post-vaccination side effects among healthcare workers in Slovakia. The study used a validated self-administered questionnaire that inquired about participants’ demographic information, medical anamneses, COVID-19-related anamnesis, and local, systemic, oral, and skin-related side effects following receiving the BNT162b2 vaccine. A total of 522 participants were included in this study, of whom 77% were females, 55.7% were aged between 31 and 54 years, and 41.6% were from Banska Bystrica. Most of the participants (91.6%) reported at least one side effect. Injection site pain (85.2%) was the most common local side effect, while fatigue (54.2%), headache (34.3%), muscle pain (28.4%), and chills (26.4%) were the most common systemic side effects. The reported side effects were of a mild nature (99.6%) that did not require medical attention and a short duration, as most of them (90.4%) were resolved within three days. Females and young adults were more likely to report post-vaccination side effects; such a finding is also consistent with what was previously reported by other phase IV studies worldwide. The role of chronic illnesses and medical treatments in post-vaccination side effect incidence and intensity requires further robust investigation among large population groups.

scholarly journals MONITORING OF ANTICONVULSANT DRUG SIDE EFFECTS IN OUTPATIENTS WITH EPILEPSY

2018 ◽  
Vol 10 (1) ◽  
pp. 303
Author(s):  
Santi Purna Sari ◽  
Natasha Kurnia Salma S ◽  
Alfina Rianti
Keyword(s):  
Valproic Acid ◽  
Side Effects ◽  
Medical Records ◽  
Side Effect ◽  
Anticonvulsant Drug ◽  
Secondary Data ◽  
Drug Side Effects ◽  
Inclusion Criteria ◽  
Descriptive Data ◽  
Effect Monitoring

Objective: This study aimed to monitor the side effects of carbamazepine, phenytoin, and valproic acid, and combinations of these drugs in adultpatients with epilepsy, to raise awareness of the importance of drug side effect monitoring in hospitals.Methods: In this prospective study, descriptive data were collected from patients who met the inclusion criteria of complete samples. Primary datawere obtained using questionnaires, secondary data were collected from medical records, and analyses were performed using the Naranjo algorithm.Results: Among the 54 included patients, 38 (70.37%) of them experienced drug side effects, and the most frequently observed side effect occurredin 48.15% of study subjects.Conclusion: No correlation was identified between side effects and age (p=0.903) or gender (p=1.000).

scholarly journals Large Scale Conversion of Trilobolide into the Payload of Mipsagargin: 8-O-(12-Aminododecanoyl)-8-O-Debutanoylthapsigargin

Biomolecules ◽  
2020 ◽  
Vol 10 (12) ◽  
pp. 1640
Author(s):  
Tomáš Zimmermann ◽  
Pavel Drašar ◽  
Silvie Rimpelová ◽  
Søren Brøgger Christensen ◽  
Vladimir A. Khripach ◽  
...  
Keyword(s):  
Clinical Trials ◽  
Side Effects ◽  
Sesquiterpene Lactone ◽  
Large Scale ◽  
Active Principle ◽  
Chemotherapeutic Drug ◽  
Limited Access ◽  
General Toxicity ◽  
Clinical Potential

In spite of the impressing cytotoxicity of thapsigargin (Tg), this compound cannot be used as a chemotherapeutic drug because of general toxicity, causing unacceptable side effects. Instead, a prodrug targeted towards tumors, mipsagargin, was brought into clinical trials. What substantially reduces the clinical potential is the limited access to Tg and its derivatives and cost-inefficient syntheses with unacceptably low yields. Laser trilobum, which contains a structurally related sesquiterpene lactone, trilobolide (Tb), is successfully cultivated. Here, we report scalable isolation of Tb from L. trilobum and a transformation of Tb to 8-O-(12-aminododecanoyl)-8-O-debutanoylthapsigargin in seven steps. The use of cultivated L. trilobum offers an unlimited source of the active principle in mipsagargin.

scholarly journals Prediction of Side Effects Using Comprehensive Similarity Measures

2020 ◽  
Vol 2020 ◽  
pp. 1-10 ◽  
Author(s):  
Sukyung Seo ◽  
Taekeon Lee ◽  
Mi-hyun Kim ◽  
Youngmi Yoon
Keyword(s):  
Side Effects ◽  
Drug Interactions ◽  
Side Effect ◽  
Drug Repositioning ◽  
Phenotypic Expression ◽  
Area Under The Curve ◽  
Similarity Measures ◽  
Biological Properties ◽  
Nucleotide Polymorphisms ◽  
Chemical Structures

Identifying the potential side effects of drugs is crucial in clinical trials in the pharmaceutical industry. The existing side effect prediction methods mainly focus on the chemical and biological properties of drugs. This study proposes a method that uses diverse information such as drug-drug interactions from DrugBank, drug-drug interactions from network, single nucleotide polymorphisms, and side effect anatomical hierarchy as well as chemical structures, indications, and targets. The proposed method is based on the assumption that properties used in drug repositioning studies could be utilized to predict side effects because the phenotypic expression of a side effect is similar to that of the disease. The prediction results using the proposed method showed a 3.5% improvement in the area under the curve (AUC) over that obtained when only chemical, indication, and target features were used. The random forest model delivered outstanding results for all combinations of feature types. Finally, after identifying candidate side effects of drugs using the proposed method, the following four popular drugs were discussed: (1) dasatinib, (2) sitagliptin, (3) vorinostat, and (4) clonidine.

scholarly journals Chemical Impurities: An Epistemological Riddle with Serious Side Effects

2020 ◽  
Vol 17 (3) ◽  
pp. 1030
Author(s):  
Ahmad Yaman Abdin ◽  
Prince Yeboah ◽  
Claus Jacob
Keyword(s):  
Side Effects ◽  
Large Scale ◽  
Biological Activities ◽  
Pharmaceutical Research ◽  
Pharmaceutical Products ◽  
Regulatory Agencies ◽  
Chemical Structures ◽  
Chemical Impurities ◽  
Large Scale Manufacture ◽  
Modern Analytical

Chemical synthesis is a science and an art. Rooted in laboratory or large-scale manufacture, it results in certain side products, eventually compromising the integrity of the final products. Such “impurities” occur in small amounts and, within chemistry itself, are of little concern. In pharmacy, in contrast, impurities increase the potential for toxicity, side effects, and serious implications for human health and the environment. The pharmaceutical regulatory agencies have therefore developed regulatory and strategic systems to minimize the chemical presence or biological impact of such substances. Here, pharmaceuticals are turned from impure into more defined materials as part of a complex socio-technological system revolving around and constantly evolving its specific rules and regulations. Whilst modern analytical methods indicate the presence of impurities, the interpretations of corresponding results are gated by risk management and agreed thresholds. Ironically, this allows for entities with no identified chemical structures, and hence epistemologically outside chemistry, to be regulated in pharmaceutical products. We will refer to such substances which are not, epistemologically speaking, “chemicals” as Xpurities, in order to distinguish them from recognized and identified impurities. The presence of such Xpurities is surprisingly common and constitutes a major issue in pharmaceutical research and practice. We propose a Space of Information to deal with such impurities based on values regarding the presence, chemical identities, and biological activities. It is anticipated that this may enable pharmacists to handle such Xpurities more efficiently.

scholarly journals Projective Quadratic Regression for Online Learning

2020 ◽  
Vol 34 (04) ◽  
pp. 5093-5100
Author(s):  
Wenye Ma
Keyword(s):  
Online Learning ◽  
Large Scale ◽  
Learning Algorithm ◽  
Optimal Solution ◽  
Streaming Data ◽  
Low Rank ◽  
High Dimensional ◽  
Quadratic Regression ◽  
Convex Model ◽  
Real World Data

This paper considers online convex optimization (OCO) problems - the paramount framework for online learning algorithm design. The loss function of learning task in OCO setting is based on streaming data so that OCO is a powerful tool to model large scale applications such as online recommender systems. Meanwhile, real-world data are usually of extreme high-dimensional due to modern feature engineering techniques so that the quadratic regression is impractical. Factorization Machine as well as its variants are efficient models for capturing feature interactions with low-rank matrix model but they can't fulfill the OCO setting due to their non-convexity. In this paper, We propose a projective quadratic regression (PQR) model. First, it can capture the import second-order feature information. Second, it is a convex model, so the requirements of OCO are fulfilled and the global optimal solution can be achieved. Moreover, existing modern online optimization methods such as Online Gradient Descent (OGD) or Follow-The-Regularized-Leader (FTRL) can be applied directly. In addition, by choosing a proper hyper-parameter, we show that it has the same order of space and time complexity as the linear model and thus can handle high-dimensional data. Experimental results demonstrate the performance of the proposed PQR model in terms of accuracy and efficiency by comparing with the state-of-the-art methods.

scholarly journals Patient-Reported Outcomes in Online Communications on Statins, Memory, and Cognition: Qualitative Analysis Using Online Communities (Preprint)

2019 ◽  
Author(s):  
Farris Timimi ◽  
Sara Ray ◽  
Erik Jones ◽  
Lee Aase ◽  
Kathleen Hoffman
Keyword(s):  
Clinical Trials ◽  
Side Effects ◽  
Online Communities ◽  
Side Effect ◽  
Online Communication ◽  
Linguistic Analysis ◽  
Control Group ◽  
Patient Reported ◽  
Hands On ◽  
The Impact

BACKGROUND In drug development clinical trials, there is a need for balance between restricting variables by setting eligibility criteria and representing the broader patient population that may use a product once it is approved. Similarly, although recent policy initiatives focusing on the inclusion of historically underrepresented groups are being implemented, barriers still remain. These limitations of clinical trials may mask potential product benefits and side effects. To bridge these gaps, online communication in health communities may serve as an additional population signal for drug side effects. OBJECTIVE The aim of this study was to employ a nontraditional dataset to identify drug side-effect signals. The study was designed to apply both natural language processing (NLP) technology and hands-on linguistic analysis to a set of online posts from known statin users to (1) identify any underlying crossover between the use of statins and impairment of memory or cognition and (2) obtain patient lexicon in their descriptions of experiences with statin medications and memory changes. METHODS Researchers utilized user-generated content on Inspire, looking at over 11 million posts across Inspire. Posts were written by patients and caregivers belonging to a variety of communities on Inspire. After identifying these posts, researchers used NLP and hands-on linguistic analysis to draw and expand upon correlations among statin use, memory, and cognition. RESULTS NLP analysis of posts identified statistical correlations between statin users and the discussion of memory impairment, which were not observed in control groups. NLP found that, out of all members on Inspire, 3.1% had posted about memory or cognition. In a control group of those who had posted about TNF inhibitors, 6.2% had also posted about memory and cognition. In comparison, of all those who had posted about a statin medication, 22.6% (<italic>P</italic>&lt;.001) also posted about memory and cognition. Furthermore, linguistic analysis of a sample of posts provided themes and context to these statistical findings. By looking at posts from statin users about memory, four key themes were found and described in detail in the data: memory loss, aphasia, cognitive impairment, and emotional change. CONCLUSIONS Correlations from this study point to a need for further research on the impact of statins on memory and cognition. Furthermore, when using nontraditional datasets, such as online communities, NLP and linguistic methodologies broaden the population for identifying side-effect signals. For side effects such as those on memory and cognition, where self-reporting may be unreliable, these methods can provide another avenue to inform patients, providers, and the Food and Drug Administration.

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