The cytochrome P450 (CYP) enzymes are major players in drug metabolism. More than 2,000 mutations have been described, and certain single nucleotide polymorphisms (SNPs) have been shown to have a ...large impact on CYP activity. Therefore, CYPs play an important role in inter-individual drug response and their genetic variability should be factored into personalized medicine. To identify the most relevant polymorphisms in human CYPs, a text mining approach was used. We investigated their frequencies in different ethnic groups, the number of drugs that are metabolized by each CYP, the impact of CYP SNPs, as well as CYP expression patterns in different tissues. The most important polymorphic CYPs were found to be 1A2, 2D6, 2C9 and 2C19. Thirty-four common allele variants in Caucasians led to altered enzyme activity. To compare the relevant Caucasian SNPs with those of other ethnicities a search in 1,000 individual genomes was undertaken. We found 199 non-synonymous SNPs with frequencies over one percent in the 1,000 genomes, many of them not described so far. With knowledge of frequent mutations and their impact on CYP activities, it may be possible to predict patient response to certain drugs, as well as adverse side effects. With improved availability of genotyping, our data may provide a resource for an understanding of the effects of specific SNPs in CYPs, enabling the selection of a more personalized treatment regimen.
The real-world evidence data from multiple sources which includes information on patient health status and medical behavior in routine clinical setup can give deeper insights into drugs 'safety and ...efficacy. The RWE-based analysis in this study revealed a statistically significant link between biologics usage and hepatotoxicity in patients. To the best of our knowledge, this study is the first to conduct a large-scale multi-cohort analysis on the hepatotoxic profiles of biologics. Biologics are among the most prescribed medicines for several chronic inflammatory diseases. These agents target critical pathogenic pathways, but they may also have serious side effects. It is important to analyze whether biologics agents are an added concern or therapeutic opportunity. Real-world evidence (RWE) data were extracted for patients using biologics to monitor the safety and effectiveness of the biologics. All six biologics included in this analysis-are mostly highly prescribed biologics. The aim of the study was to assess the hepatotoxic profiles of subjects using different biologics. We evaluated the safety of current treatment regimens for patients in a large real-world cohort from multiple health care centers. Total number of eligible patients retrieved from the database is 38,112,285. Of these 38 million patients, 2.3 million take biologics. The primary objective was to assess the potential adverse hepatotoxic effects of the six biologics; adalimumab, trastuzumab, prevnar13, pegfilgrastim, interferon-beta1a and insulin glargine across different indications like diabetes mellitus, encounter for immunization, malignant neoplasm of breast, multiple sclerosis, malignant neoplasm of kidney, aplastic anaemias, radiation sickness, Crohn's disease, psoriasis, rheumatoid arthritis, spondylopathies. Data from patients using the six most-used biologics-adalimumab, trastuzumab, prevnar13, pegfilgrastim, interferon-beta1a and insulin glargine were retrieved from a global research network covering 250 million patients' data from 19 countries, and assigned to the cohorts 1 and 2, respectively. The cohorts were propensity score matched for age and sex. After defining the primary outcome as "hepatotoxicity" (endpoint defined as ICD-10 code: K71 (hepatotoxic liver disease), a Kaplan-Meier survival analysis was performed, and risk ratios (RR), odds ratios (OR), and hazard ratios (HR) were determined. A total number of 2,312,655 subjects were eligible who take biologics, and after matching total cohorts accounted for 2,303,445. We have considered the clinical data as a 1:1 matched-study design, using propensity score-matched sub-cohorts to better control for confounding associations that might stem from different distributions of age and gender between the whole dataset and the subset of patients. We discovered evidence supporting the hepatotoxic-causing effect of biologic drugs: (i) all biologics considered together had an OR of 1.9 (95% CI, 1.67-2.35), with (ii) Adalimumab 1.9 (95% CI, 1.72-2.20), Trastuzumab 1.7 (95% CI, 1.2-2.3), Prevnar13 2.3 (95% CI, 2.16-2.60), Pegfilgrastim 2.3 (95% CI, 2.0-2.50), Interferon-Beta1a 1.7 (95% CI, 1.18-2.51), and Insulin glargine 1.9 (95% CI, 1.8-1.99). Our findings indicate that clinicians should consider evaluating hepatic profiles of patients undergoing treatment with biologic drugs and counsel them regarding the risk of developing hepatic injury. Strengths of the study includes a large sample size and robust statistical techniques. Limitations of this study include lack of detailed information regarding clinical severity. Major biologics are associated with hepatotoxicity. We discovered evidence supporting the hepatotoxicity-causing effects of biologics: all biologics considered together had an OR of 1.9 (95% CI, 1.67-2.35).
Postoperative complications following mandibular fracture treatment vary from local wound infections to severe conditions including osteomyelitis and impaired fracture healing. Several risk factors ...have been associated with the development healing disorders, including fracture localisation, treatment modality and substance abuse. However, limited research on the sex-specific influence of these complications exists. A total of about 300,000 female and male patients with mandibular fractures were examined in two cohorts. After matching for confounders (age, nicotine and alcohol dependence, malnutrition, overweight, anaemia, diabetes, osteoporosis and vitamin D deficiency), two cohorts were compared with propensity-score-matched patients according to outcomes (osteomyelitis, pseudoarthrosis and disruption of the wound) within 1 year after fracture. There were significant differences between female and male patients regarding the occurrence of osteomyelitis (odds ratio OR 95% confidence interval: 0.621 0.563; 0.686) and disruption of the wound (OR 95% confidence interval: 0.703 0.632; 0.782). Surprisingly, matching for the expected confounders did not change the results substantially. Sex plays a dominant role in determining the risk stratification for postoperative osteomyelitis and disruption of the wound, after accounting for other potential confounding factors. Additional research is needed to understand the underlying mechanisms and to develop sex-specific strategies to prevent these complications.
Animal trials are currently the major method for determining the possible toxic effects of drug candidates and cosmetics. In silico prediction methods represent an alternative approach and aim to ...rationalize the preclinical drug development, thus enabling the reduction of the associated time, costs and animal experiments. Here, we present ProTox, a web server for the prediction of rodent oral toxicity. The prediction method is based on the analysis of the similarity of compounds with known median lethal doses (LD50) and incorporates the identification of toxic fragments, therefore representing a novel approach in toxicity prediction. In addition, the web server includes an indication of possible toxicity targets which is based on an in-house collection of protein-ligand-based pharmacophore models ('toxicophores') for targets associated with adverse drug reactions. The ProTox web server is open to all users and can be accessed without registration at: http://tox.charite.de/tox. The only requirement for the prediction is the two-dimensional structure of the input compounds. All ProTox methods have been evaluated based on a diverse external validation set and displayed strong performance (sensitivity, specificity and precision of 76, 95 and 75%, respectively) and superiority over other toxicity prediction tools, indicating their possible applicability for other compound classes.
Natural products play a significant role in drug discovery and development. Many topological pharmacophore patterns are common between natural products and commercial drugs. A better understanding of ...the specific physicochemical and structural features of natural products is important for corresponding drug development. Several encyclopedias of natural compounds have been composed, but the information remains scattered or not freely available. The first version of the Supernatural database containing ∼ 50,000 compounds was published in 2006 to face these challenges. Here we present a new, updated and expanded version of natural product database, Super Natural II (http://bioinformatics.charite.de/supernatural), comprising ∼ 326,000 molecules. It provides all corresponding 2D structures, the most important structural and physicochemical properties, the predicted toxicity class for ∼ 170,000 compounds and the vendor information for the vast majority of compounds. The new version allows a template-based search for similar compounds as well as a search for compound names, vendors, specific physical properties or any substructures. Super Natural II also provides information about the pathways associated with synthesis and degradation of the natural products, as well as their mechanism of action with respect to structurally similar drugs and their target proteins.
As the number of prescribed drugs is constantly rising, drug-drug interactions are an important issue. The simultaneous administration of several drugs can cause severe adverse effects based on ...interactions with the same metabolizing enzyme(s). The Transformer database (http://bioinformatics.charite.de/transformer) contains integrated information on the three phases of biotransformation (modification, conjugation and excretion) of 3000 drugs and >350 relevant food ingredients (e.g. grapefruit juice) and herbs, which are catalyzed by 400 proteins. A total of 100,000 interactions were found through text mining and manual validation. The 3D structures of 200 relevant proteins are included. The database enables users to search for drugs with a visual display of known interactions with phase I (Cytochrome P450) and phase II enzymes, transporters, food and herbs. For each interaction, PubMed references are given. To detect mutual impairments of drugs, the drug-cocktail tool displays interactions between selected drugs. By choosing the indication for a drug, the tool offers suggestions for alternative medications to avoid metabolic conflicts. Drug interactions can also be visualized in an interactive network view. Additionally, prodrugs, including their mechanisms of activation, and further information on enzymes of biotransformation, including 3D models, can be viewed.
Increase in the number of new chemicals synthesized in past decades has resulted in constant growth in the development and application of computational models for prediction of activity as well as ...safety profiles of the chemicals. Most of the time, such computational models and its application must deal with imbalanced chemical data. It is indeed a challenge to construct a classifier using imbalanced data set. In this study, we analyzed and validated the importance of different sampling methods over non-sampling method, to achieve a well-balanced sensitivity and specificity of a machine learning model trained on imbalanced chemical data. Additionally, this study has achieved an accuracy of 93.00%, an AUC of 0.94, F1 measure of 0.90, sensitivity of 96.00% and specificity of 91.00% using SMOTE sampling and Random Forest classifier for the prediction of Drug Induced Liver Injury (DILI). Our results suggest that, irrespective of data set used, sampling methods can have major influence on reducing the gap between sensitivity and specificity of a model. This study demonstrates the efficacy of different sampling methods for class imbalanced problem using binary chemical data sets.
Kinase inhibitors are important cancer therapeutics. Polypharmacology is commonly observed, requiring thorough target deconvolution to understand drug mechanism of action. Using chemical proteomics, ...we analyzed the target spectrum of 243 clinically evaluated kinase drugs. The data revealed previously unknown targets for established drugs, offered a perspective on the "druggable" kinome, highlighted (non)kinase off-targets, and suggested potential therapeutic applications. Integration of phosphoproteomic data refined drug-affected pathways, identified response markers, and strengthened rationale for combination treatments. We exemplify translational value by discovering SIK2 (salt-inducible kinase 2) inhibitors that modulate cytokine production in primary cells, by identifying drugs against the lung cancer survival marker MELK (maternal embryonic leucine zipper kinase), and by repurposing cabozantinib to treat FLT3-ITD-positive acute myeloid leukemia. This resource, available via the ProteomicsDB database, should facilitate basic, clinical, and drug discovery research and aid clinical decision-making.
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