An Ensemble Model of QSAR Tools for Regulatory Risk Assessment

Quantitative structure activity relationships (QSARs) are theoretical models that relate a quantitative measure of chemical structure to a physical property or a biological effect. QSAR predictions can be used for chemical risk assessment for protection of human and environmental health, which makes them interesting to regulators, especially in the absence of experimental data. For compatibility with regulatory use, QSAR models should be transparent, reproducible and optimized to minimize the number of false negatives. In silico QSAR tools are gaining wide acceptance as a faster alternative to otherwise time-consuming clinical and animal testing methods. However, different QSAR tools often make conflicting predictions for a given chemical and may also vary in their predictive performance across different chemical datasets. In a regulatory context, conflicting predictions raise interpretation, validation and adequacy concerns. To address these concerns, ensemble learning techniques in the machine learning paradigm can be used to integrate predictions from multiple tools. By leveraging various underlying QSAR algorithms and training datasets, the resulting consensus prediction should yield better overall predictive ability. We present a novel ensemble QSAR model using Bayesian classification. The model allows for varying a cut-off parameter that allows for a selection in the desirable trade-off between model sensitivity and specificity. The predictive performance of the ensemble model is compared with four in silico tools (Toxtree, Lazar, OECD Toolbox, and Danish QSAR) to predict carcinogenicity for a dataset of air toxins (332 chemicals) and a subset of the gold carcinogenic potency database (480 chemicals). Leave-one-out cross validation results show that the ensemble model achieves the best trade-off between sensitivity and specificity (accuracy: 83.8 % and 80.4 %, and balanced accuracy: 80.6 % and 80.8 %) and highest inter-rater agreement [kappa (κ): 0.63 and 0.62] for both the datasets. The ROC curves demonstrate the utility of the cut-off feature in the predictive ability of the ensemble model. This feature provides an additional control to the regulators in grading a chemical based on the severity of the toxic endpoint under study

The benefits of in silico modeling to identify possible small-molecule drugs and their off-target interactions

Accepted for publication in a future issue of Future Medicinal Chemistry.The research into the use of small molecules as drugs continues to be a key driver in the development of molecular databases, computer-aided drug design software and collaborative platforms. The evolution of computational approaches is driven by the essential criteria that a drug molecule has to fulfill, from the affinity to targets to minimal side effects while having adequate absorption, distribution, metabolism, and excretion (ADME) properties. A combination of ligand- and structure-based drug development approaches is already used to obtain consensus predictions of small molecule activities and their off-target interactions. Further integration of these methods into easy-to-use workflows informed by systems biology could realize the full potential of available data in the drug discovery and reduce the attrition of drug candidates.Peer reviewe

A rapid stability-indicating, fused-core HPLC method for simultaneous determination of β-artemether and lumefantrine in anti-malarial fixed dose combination products

Background: Artemisinin-based fixed dose combination (FDC) products are recommended by World Health Organization (WHO) as a first-line treatment. However, the current artemisinin FDC products, such as beta-artemether and lumefantrine, are inherently unstable and require controlled distribution and storage conditions, which are not always available in resource-limited settings. Moreover, quality control is hampered by lack of suitable analytical methods. Thus, there is a need for a rapid and simple, but stability-indicating method for the simultaneous assay of beta-artemether and lumefantrine FDC products. Methods: Three reversed-phase fused-core HPLC columns (Halo RP-Amide, Halo C18 and Halo Phenyl-hexyl), all thermostated at 30 degrees C, were evaluated. beta-artemether and lumefantrine (unstressed and stressed), and reference-related impurities were injected and chromatographic parameters were assessed. Optimal chromatographic parameters were obtained using Halo RP-Amide column and an isocratic mobile phase composed of acetonitrile and 1mM phosphate buffer pH 3.0 (52:48; V/V) at a flow of 1.0 ml/min and 3 mu l injection volume. Quantification was performed at 210 nm and 335 nm for beta-artemether and for lumefantrine, respectively. In-silico toxicological evaluation of the related impurities was made using Derek Nexus v2.0 (R). Results: Both beta-artemether and lumefantrine were separated from each other as well as from the specified and unspecified related impurities including degradants. A complete chromatographic run only took four minutes. Evaluation of the method, including a Plackett-Burman robustness verification within analytical QbD-principles, and real-life samples showed the method is suitable for quantitative assay purposes of both active pharmaceutical ingredients, with a mean recovery relative standard deviation (+/- RSD) of 99.7 % (+/- 0.7%) for beta-artemether and 99.7 % (+/- 0.6%) for lumefantrine. All identified beta-artemether-related impurities were predicted in Derek Nexus v2.0 (R) to have toxicity risks similar to beta-artemether active pharmaceutical ingredient (API) itself. Conclusions: A rapid, robust, precise and accurate stability-indicating, quantitative fused-core isocratic HPLC method was developed for simultaneous assay of beta-artemether and lumefantrine. This method can be applied in the routine regulatory quality control of FDC products. The in-silico toxicological investigation using Derek Nexus (R) indicated that the overall toxicity risk for beta-artemether-related impurities is comparable to that of beta-artemether API

XML in Motion from Genome to Drug

Information technology (IT) has emerged as a central to the solution of contemporary genomics and drug discovery problems. Researchers involved in genomics, proteomics, transcriptional profiling, high throughput structure determination, and in other sub-disciplines of bioinformatics have direct impact on this IT revolution. As the full genome sequences of many species, data from structural genomics, micro-arrays, and proteomics became available, integration of these data to a common platform require sophisticated bioinformatics tools. Organizing these data into knowledgeable databases and developing appropriate software tools for analyzing the same are going to be major challenges. XML (eXtensible Markup Language) forms the backbone of biological data representation and exchange over the internet, enabling researchers to aggregate data from various heterogeneous data resources. The present article covers a comprehensive idea of the integration of XML on particular type of biological databases mainly dealing with sequence-structure-function relationship and its application towards drug discovery. This e-medical science approach should be applied to other scientific domains and the latest trend in semantic web applications is also highlighted

New Synthetic Cannabinoids Metabolism and Strategies to Best Identify Optimal Marker Metabolites.

Synthetic cannabinoids (SCs) were initially developed as pharmacological tools to probe the endocannabinoid system and as novel pharmacotherapies, but are now highly abused. This is a serious public health and social problem throughout the world and it is highly challenging to identify which SC was consumed by the drug abusers, a necessary step to tie adverse health effects to the new drug\u27s toxicity. Two intrinsic properties complicate SC identification, their often rapid and extensive metabolism, and their generally high potency relative to the natural psychoactive Δ 9 -tetrahydrocannabinol in cannabis. Additional challenges are the lack of reference standards for the major urinary metabolites needed for forensic verification, and the sometimes differing illicit and licit status and, in some cases, identical metabolites produced by closely related SC pairs, i.e., JWH-018/AM-2201, THJ-018/THJ-2201, and BB-22/MDMB-CHMICA/ADB-CHMICA. We review current SC prevalence, establish the necessity for SC metabolism investigation and contrast the advantages and disadvantages of multiple metabolic approaches. The human hepatocyte incubation model for determining a new SC\u27s metabolism is highly recommended after comparison to human liver microsomes incubation, in silico prediction, rat in vivo, zebrafish, and fungus Cunninghamella elegans models. We evaluate SC metabolic patterns, and devise a practical strategy to select optimal urinary marker metabolites for SCs. New SCs are incubated first with human hepatocytes and major metabolites are then identified by high-resolution mass spectrometry. Although initially difficult to obtain, authentic human urine samples following the specified SC exposure are hydrolyzed and analyzed by high-resolution mass spectrometry to verify identified major metabolites. Since some SCs produce the same major urinary metabolites, documentation of the specific SC consumed may require identification of the SC parent itself in either blood or oral fluid. An encouraging trend is the recent reduction in the number of new SC introduced per year. With global collaboration and communication, we can improve education of the public about the toxicity of new SC and our response to their introduction

Identifying and Prioritizing Chemicals with Uncertain Burden of Exposure: Opportunities for Biomonitoring and Health-Related Research.

BackgroundThe National Institutes of Health's Environmental influences on Child Health Outcomes (ECHO) initiative aims to understand the impact of environmental factors on childhood disease. Over 40,000 chemicals are approved for commercial use. The challenge is to prioritize chemicals for biomonitoring that may present health risk concerns.ObjectivesOur aim was to prioritize chemicals that may elicit child health effects of interest to ECHO but that have not been biomonitored nationwide and to identify gaps needing additional research.MethodsWe searched databases and the literature for chemicals in environmental media and in consumer products that were potentially toxic. We selected chemicals that were not measured in the National Health and Nutrition Examination Survey. From over 700 chemicals, we chose 155 chemicals and created eight chemical panels. For each chemical, we compiled biomonitoring and toxicity data, U.S. Environmental Protection Agency exposure predictions, and annual production usage. We also applied predictive modeling to estimate toxicity. Using these data, we recommended chemicals either for biomonitoring, to be deferred pending additional data, or as low priority for biomonitoring.ResultsFor the 155 chemicals, 97 were measured in food or water, 67 in air or house dust, and 52 in biospecimens. We found in vivo endocrine, developmental, reproductive, and neurotoxic effects for 61, 74, 47, and 32 chemicals, respectively. Eighty-six had data from high-throughput in vitro assays. Positive results for endocrine, developmental, neurotoxicity, and obesity were observed for 32, 11, 35, and 60 chemicals, respectively. Predictive modeling results suggested 90% are toxicants. Biomarkers were reported for 76 chemicals. Thirty-six were recommended for biomonitoring, 108 deferred pending additional research, and 11 as low priority for biomonitoring.DiscussionThe 108 deferred chemicals included those lacking biomonitoring methods or toxicity data, representing an opportunity for future research. Our evaluation was, in general, limited by the large number of unmeasured or untested chemicals. https://doi.org/10.1289/EHP5133

In silico and in vitro drug screening identifies new therapeutic approaches for Ewing sarcoma.

The long-term overall survival of Ewing sarcoma (EWS) patients remains poor; less than 30% of patients with metastatic or recurrent disease survive despite aggressive combinations of chemotherapy, radiation and surgery. To identify new therapeutic options, we employed a multi-pronged approach using in silico predictions of drug activity via an integrated bioinformatics approach in parallel with an in vitro screen of FDA-approved drugs. Twenty-seven drugs and forty-six drugs were identified, respectively, to have anti-proliferative effects for EWS, including several classes of drugs in both screening approaches. Among these drugs, 30 were extensively validated as mono-therapeutic agents and 9 in 14 various combinations in vitro. Two drugs, auranofin, a thioredoxin reductase inhibitor, and ganetespib, an HSP90 inhibitor, were predicted to have anti-cancer activities in silico and were confirmed active across a panel of genetically diverse EWS cells. When given in combination, the survival rate in vivo was superior compared to auranofin or ganetespib alone. Importantly, extensive formulations, dose tolerance, and pharmacokinetics studies demonstrated that auranofin requires alternative delivery routes to achieve therapeutically effective levels of the gold compound. These combined screening approaches provide a rapid means to identify new treatment options for patients with a rare and often-fatal disease

Predicting Skin Permeability by means of Computational Approaches : Reliability and Caveats in Pharmaceutical Studies

© 2019 American Chemical Society.The skin is the main barrier between the internal body environment and the external one. The characteristics of this barrier and its properties are able to modify and affect drug delivery and chemical toxicity parameters. Therefore, it is not surprising that permeability of many different compounds has been measured through several in vitro and in vivo techniques. Moreover, many different in silico approaches have been used to identify the correlation between the structure of the permeants and their permeability, to reproduce the skin behavior, and to predict the ability of specific chemicals to permeate this barrier. A significant number of issues, like interlaboratory variability, experimental conditions, data set building rationales, and skin site of origin and hydration, still prevent us from obtaining a definitive predictive skin permeability model. This review wants to show the main advances and the principal approaches in computational methods used to predict this property, to enlighten the main issues that have arisen, and to address the challenges to develop in future research.Peer reviewedFinal Accepted Versio

QUANTITATIVE STRUCTURE-ACTIVITY RELATIONSHIP (QSAR) OF N’-ETHYL-N’-PHENYL-N-BENZOYLTHIOUREA AND ITS DERIVATIVES AS ANTICANCER COMPOUNDS BY IN SILICO STUDY

Quantitative Structure Activity Relationship (QSAR) has important role in drug development that is improving efficiency on next research to determine new derivatives which are more potent, safer, and have good absorption when consumed. In this research we used N’-Ethyl-N’-Phenyl-N-Benzoylthiourea and 12 derivatives which have anticancer activity based on in silico test. Then, we conducted their relationship analysis of physicochemical properties (lipophilic, electronic, and steric) to in silico prediction of activity, toxicity, and bioavailability to obtain the best QSAR equation. QSAR equation was determined by linear and non linier regression using statistic program of SPSS 20.0. The result showed that activity prediction (Log 1/RS, from docking on RR receptor PDB ID: 2EUD) with the best QSAR equation: Log 1/RS = 0,118 Mw + 22,994 pKa + 0,022 tPSA2 – 2,590 tPSA -270,960 (n = 13; R = 0,949; SE = 2,054; F = 18,150; Sig = 0,000), toxicity prediction (Log 1/LD-50, ACD/I-Lab prediction) with the best QSAR equation: Log 1/(LD-50 Mouse oral) = - 4,527 Mw – 0,496 tPSA2 + 57,150 tPSA + 744,724 (n = 13; R = 0,925; SE = 61,569; F = 17,846; Sig = 0,000), and bioavailability prediction (Log1/F, ACD/I-Lab prediction) with the best QSAR equation: Log 1/F = - 0,006 Mw - 0,003 tPSA – 2,554 (n = 13; R = 0,802; SE = 0,132; F = 9,006; Sig = 0,006). Furthermore, all of the best equation can be used to develop new compounds as anticancer agent