Feasibility of physiologically based pharmacokinetic simulations for assessing pediatric patients after accidental drug ingestion: A case study of a 1.4-year-old girl who ingested alprazolam.

The accidental ingestion of drugs is a common concern, especially in the case of young children. A physiologically based pharmacokinetic (PBPK) model that implements the age-dependent size growth and ontogeny of organ functions can be used to predict the concentration-time profiles of drugs in the pediatric population. In this study, the feasibility of using a PBPK model for predicting the amount of drug accidentally swallowed by a child was assessed based on a case study in an infant. Alprazolam was the drug involved in the current case. The developed PBPK model of alprazolam was first evaluated using pharmacokinetic data obtained in healthy adult male volunteers. Then, it was adapted for application to virtual Japanese pediatric subjects having the same demographic information as the infant of interest. The pharmacokinetic data observed in the infant fell within the range of the 5th and 95th percentiles of the pharmacokinetic simulations after administration of 0.4 mg alprazolam (equivalent to one tablet) in the panel of virtual infants. PBPK simulations could provide estimates of the amount accidentally ingested by a child and also give mechanistic insights into the observed drug concentrations. The current study demonstrates the potential clinical utility of PBPK modeling.

A review on symptoms, treatments protocols, and proteomic profile in sulfur mustard-exposed victims.

Sulfur mustard (SM) as an alkylating and vesicating agent was used for 100 years as a chemical weapon. SM as bi-functional mustard can attacks and alkylates lots of biomolecules. Different cellular mechanism and molecular pathways are responsible for damages to body tissues. Such as DNA damages, oxidative stress, Apoptosis, and inflammation. Sulfur mustard penetrated body organs and induces long term eye, skin, lung, gastrointestinal, urogenital damages and can cause carcinogenic and mutagenic consequences. Currently there is no definitive treatment protocol for SM exposed patients. The goal of treatment is relieving the symptoms with fast healing rate and retrieval of damaged tissues to normal function and appearance in short period of time. Evaluation of proteomics profile in SM-exposed victims has been performed in animal model and human patients. These studies revealed that different protein were involved in the patients with SM damages to skin and lungs. Apolipoprotein A1, type I cytokeratins K14, K16 and K17, S100 calcium-binding protein A8, α1 haptoglobin isoforms, Amyloid A1, albumin, haptoglobin, and keratin isoforms, immunoglobulin kappa chain are defined expressed proteins in the damaged tissues.

Sieve-based coreference resolution enhances semi-supervised learning model for chemical-induced disease relation extraction.

The BioCreative V chemical-disease relation (CDR) track was proposed to accelerate the progress of text mining in facilitating integrative understanding of chemicals, diseases and their relations. In this article, we describe an extension of our system (namely UET-CAM) that participated in the BioCreative V CDR. The original UET-CAM system's performance was ranked fourth among 18 participating systems by the BioCreative CDR track committee. In the Disease Named Entity Recognition and Normalization (DNER) phase, our system employed joint inference (decoding) with a perceptron-based named entity recognizer (NER) and a back-off model with Semantic Supervised Indexing and Skip-gram for named entity normalization. In the chemical-induced disease (CID) relation extraction phase, we proposed a pipeline that includes a coreference resolution module and a Support Vector Machine relation extraction model. The former module utilized a multi-pass sieve to extend entity recall. In this article, the UET-CAM system was improved by adding a 'silver' CID corpus to train the prediction model. This silver standard corpus of more than 50 thousand sentences was automatically built based on the Comparative Toxicogenomics Database (CTD) database. We evaluated our method on the CDR test set. Results showed that our system could reach the state of the art performance with F1 of 82.44 for the DNER task and 58.90 for the CID task. Analysis demonstrated substantial benefits of both the multi-pass sieve coreference resolution method (F1 + 4.13%) and the silver CID corpus (F1 +7.3%).Database URL: SilverCID-The silver-standard corpus for CID relation extraction is freely online available at: https://zenodo.org/record/34530 (doi:10.5281/zenodo.34530).

Combining machine learning, crowdsourcing and expert knowledge to detect chemical-induced diseases in text.

Drug toxicity is a major concern for both regulatory agencies and the pharmaceutical industry. In this context, text-mining methods for the identification of drug side effects from free text are key for the development of up-to-date knowledge sources on drug adverse reactions. We present a new system for identification of drug side effects from the literature that combines three approaches: machine learning, rule- and knowledge-based approaches. This system has been developed to address the Task 3.B of Biocreative V challenge (BC5) dealing with Chemical-induced Disease (CID) relations. The first two approaches focus on identifying relations at the sentence-level, while the knowledge-based approach is applied both at sentence and abstract levels. The machine learning method is based on the BeFree system using two corpora as training data: the annotated data provided by the CID task organizers and a new CID corpus developed by crowdsourcing. Different combinations of results from the three strategies were selected for each run of the challenge. In the final evaluation setting, the system achieved the highest Recall of the challenge (63%). By performing an error analysis, we identified the main causes of misclassifications and areas for improving of our system, and highlighted the need of consistent gold standard data sets for advancing the state of the art in text mining of drug side effects.Database URL: https://zenodo.org/record/29887?ln»en#.VsL3yDLWR_V.