An age-progressive platelet differentiation path from hematopoietic stem cells causes exacerbated thrombosis.

Platelet dysregulation is drastically increased with advanced age and contributes to making cardiovascular disorders the leading cause of death of elderly humans. Here, we reveal a direct differentiation pathway from hematopoietic stem cells into platelets that is progressively propagated upon aging. Remarkably, the aging-enriched platelet path is decoupled from all other hematopoietic lineages, including erythropoiesis, and operates as an additional layer in parallel with canonical platelet production. This results in two molecularly and functionally distinct populations of megakaryocyte progenitors. The age-induced megakaryocyte progenitors have a profoundly enhanced capacity to engraft, expand, restore, and reconstitute platelets in situ and upon transplantation and produce an additional platelet population in old mice. The two pools of co-existing platelets cause age-related thrombocytosis and dramatically increased thrombosis in vivo. Strikingly, aging-enriched platelets are functionally hyper-reactive compared with the canonical platelet populations. These findings reveal stem cell-based aging as a mechanism for platelet dysregulation and age-induced thrombosis.

Tissue-resident macrophages self-maintain locally throughout adult life with minimal contribution from circulating monocytes.

Despite accumulating evidence suggesting local self-maintenance of tissue macrophages in the steady state, the dogma remains that tissue macrophages derive from monocytes. Using parabiosis and fate-mapping approaches, we confirmed that monocytes do not show significant contribution to tissue macrophages in the steady state. Similarly, we found that after depletion of lung macrophages, the majority of repopulation occurred by stochastic cellular proliferation in situ in a macrophage colony-stimulating factor (M-Csf)- and granulocyte macrophage (GM)-CSF-dependent manner but independently of interleukin-4. We also found that after bone marrow transplantation, host macrophages retained the capacity to expand when the development of donor macrophages was compromised. Expansion of host macrophages was functional and prevented the development of alveolar proteinosis in mice transplanted with GM-Csf-receptor-deficient progenitors. Collectively, these results indicate that tissue-resident macrophages and circulating monocytes should be classified as mononuclear phagocyte lineages that are independently maintained in the steady state.

Conformal Prediction Classification of a Large Data Set of Environmental Chemicals from ToxCast and Tox21 Estrogen Receptor Assays.

Quantitative structure-activity relationships (QSAR) are critical to exploitation of the chemical information in toxicology databases. Exploitation can be extraction of chemical knowledge from the data but also making predictions of new chemicals based on quantitative analysis of past findings. In this study, we analyzed the ToxCast and Tox21 estrogen receptor data sets using Conformal Prediction to enhance the full exploitation of the information in these data sets. We applied aggregated conformal prediction (ACP) to the ToxCast and Tox21 estrogen receptor data sets using support vector machine classifiers to compare overall performance of the models but, more importantly, to explore the performance of ACP on data sets that are significantly enriched in one class without employing sampling strategies of the training set. ACP was also used to investigate the problem of applicability domain using both data sets. Comparison of ACP to previous results obtained on the same data sets using traditional QSAR approaches indicated similar overall balanced performance to methods in which careful training set selections were made, e.g., sensitivity and specificity for the external Tox21 data set of 70-75% and far superior results to those obtained using traditional methods without training set sampling where the corresponding results showed a clear imbalance of 50 and 96%, respectively. Application of conformal prediction to imbalanced data sets facilitates an unambiguous analysis of all data, allows accurate predictive models to be built which display similar accuracy in external validation to external validation, and, most importantly, allows an unambiguous treatment of the applicability domain.

Flagging Drugs That Inhibit the Bile Salt Export Pump.

The bile salt export pump (BSEP) is an ABC-transporter expressed at the canalicular membrane of hepatocytes. Its physiological role is to expel bile salts into the canaliculi from where they drain into the bile duct. Inhibition of this transporter may lead to intrahepatic cholestasis. Predictive computational models of BSEP inhibition may allow for fast identification of potentially harmful compounds in large databases. This article presents a predictive in silico model based on physicochemical descriptors that is able to flag compounds as potential BSEP inhibitors. This model was built using a training set of 670 compounds with available BSEP inhibition potencies. It successfully predicted BSEP inhibition for two independent test sets and was in a further step used for a virtual screening experiment. After in vitro testing of selected candidates, a marketed drug, bromocriptin, was identified for the first time as BSEP inhibitor. This demonstrates the usefulness of the model to identify new BSEP inhibitors and therefore potential cholestasis perpetrators.

Introducing conformal prediction in predictive modeling for regulatory purposes. A transparent and flexible alternative to applicability domain determination.

Conformal prediction is presented as a framework which fulfills the OECD principles on (Q)SAR. It offers an intuitive extension to the application of machine-learning methods to structure-activity data where focus is on predictions with pre-defined confidence levels. A conformal predictor will make correct predictions on new compounds corresponding to a user defined confidence level. The confidence level can be altered depending on the situation the predictor is being used in, which allows for flexibility and adaption to risks that the user is willing to take. We demonstrate the usefulness of conformal prediction by applying it to 2 publicly available CAESAR binary classification datasets.

Computational derivation of structural alerts from large toxicology data sets.

Structural alerts have been one of the backbones of computational toxicology and have applications in many areas including cosmetic, environmental, and pharmaceutical toxicology. The development of structural alerts has always involved a manual analysis of existing data related to a relevant end point followed by the determination of substructures that appear to be related to a specific outcome. The substructures are then analyzed for their utility in posterior validation studies, which at times have stretched over years or even decades. With higher throughput methods now being employed in many areas of toxicology, data sets are growing at an unprecedented rate. This growth has made manual analysis of data sets impractical in many cases. This report outlines a fully automatic method that highlights significant substructures for toxicologically important data sets. The method identifies important substructures by computationally breaking chemical structures into fragments and analyzing those fragments for their contribution to the given activity by the calculation of a p-value and a substructure accuracy. The method is intended to aid the expert in locating and analyzing alerts by automatic retrieval of alerts or by enhancing existing alerts. The method has been applied to a data set of AMES mutagenicity results and compared to the substructures generated by manual curation of this same data set as well as another computationally based substructure identification method. The results show that this method can retrieve significant substructures quickly, that the substructures are comparable and in some cases superior to those derived from manual curation, that the substructures found covers all previously known substructures, and that they can be used to make reasonably accurate predictions of AMES activity.

Choosing feature selection and learning algorithms in QSAR.

Feature selection is an important part of contemporary QSAR analysis. In a recently published paper, we investigated the performance of different feature selection methods in a large number of in silico experiments conducted using real QSAR datasets. However, an interesting question that we did not address is whether certain feature selection methods are better than others in combination with certain learning methods, in terms of producing models with high prediction accuracy. In this report we extend our work from the previous investigation by using four different feature selection methods (wrapper, ReliefF, MARS, and elastic nets), together with eight learners (MARS, elastic net, random forest, SVM, neural networks, multiple linear regression, PLS, kNN) in an empirical investigation to address this question. The results indicate that state-of-the-art learners (random forest, SVM, and neural networks) do not gain prediction accuracy from feature selection, and we found no evidence that a certain feature selection is particularly well-suited for use in combination with a certain learner.

Introducing conformal prediction in predictive modeling. A transparent and flexible alternative to applicability domain determination.

Conformal prediction is introduced as an alternative approach to domain applicability estimation. The advantages of using conformal prediction are as follows: First, the approach is based on a consistent and well-defined mathematical framework. Second, the understanding of the confidence level concept in conformal predictions is straightforward, e.g. a confidence level of 0.8 means that the conformal predictor will commit, at most, 20% errors (i.e., true values outside the assigned prediction range). Third, the confidence level can be varied depending on the situation where the model is to be applied and the consequences of such changes are readily understandable, i.e. prediction ranges are increased or decreased, and the changes can immediately be inspected. We demonstrate the usefulness of conformal prediction by applying it to 10 publicly available data sets.

Assessment of machine learning reliability methods for quantifying the applicability domain of QSAR regression models.

The vastness of chemical space and the relatively small coverage by experimental data recording molecular properties require us to identify subspaces, or domains, for which we can confidently apply QSAR models. The prediction of QSAR models in these domains is reliable, and potential subsequent investigations of such compounds would find that the predictions closely match the experimental values. Standard approaches in QSAR assume that predictions are more reliable for compounds that are "similar" to those in subspaces with denser experimental data. Here, we report on a study of an alternative set of techniques recently proposed in the machine learning community. These methods quantify prediction confidence through estimation of the prediction error at the point of interest. Our study includes 20 public QSAR data sets with continuous response and assesses the quality of 10 reliability scoring methods by observing their correlation with prediction error. We show that these new alternative approaches can outperform standard reliability scores that rely only on similarity to compounds in the training set. The results also indicate that the quality of reliability scoring methods is sensitive to data set characteristics and to the regression method used in QSAR. We demonstrate that at the cost of increased computational complexity these dependencies can be leveraged by integration of scores from various reliability estimation approaches. The reliability estimation techniques described in this paper have been implemented in an open source add-on package ( https://bitbucket.org/biolab/orange-reliability ) to the Orange data mining suite.

Automatic filtering and substantiation of drug safety signals.

Drug safety issues pose serious health threats to the population and constitute a major cause of mortality worldwide. Due to the prominent implications to both public health and the pharmaceutical industry, it is of great importance to unravel the molecular mechanisms by which an adverse drug reaction can be potentially elicited. These mechanisms can be investigated by placing the pharmaco-epidemiologically detected adverse drug reaction in an information-rich context and by exploiting all currently available biomedical knowledge to substantiate it. We present a computational framework for the biological annotation of potential adverse drug reactions. First, the proposed framework investigates previous evidences on the drug-event association in the context of biomedical literature (signal filtering). Then, it seeks to provide a biological explanation (signal substantiation) by exploring mechanistic connections that might explain why a drug produces a specific adverse reaction. The mechanistic connections include the activity of the drug, related compounds and drug metabolites on protein targets, the association of protein targets to clinical events, and the annotation of proteins (both protein targets and proteins associated with clinical events) to biological pathways. Hence, the workflows for signal filtering and substantiation integrate modules for literature and database mining, in silico drug-target profiling, and analyses based on gene-disease networks and biological pathways. Application examples of these workflows carried out on selected cases of drug safety signals are discussed. The methodology and workflows presented offer a novel approach to explore the molecular mechanisms underlying adverse drug reactions.

Localized heuristic inverse quantitative structure activity relationship with bulk descriptors using numerical gradients.

State-of-the-art quantitative structure-activity relationship (QSAR) models are often based on nonlinear machine learning algorithms, which are difficult to interpret. From a pharmaceutical perspective, QSARs are used to enhance the chemical design process. Ultimately, they should not only provide a prediction but also contribute to a mechanistic understanding and guide modifications to the chemical structure, promoting compounds with desirable biological activity profiles. Global ranking of descriptor importance and inverse QSAR have been used for these purposes. This paper introduces localized heuristic inverse QSAR, which provides an assessment of the relative ability of the descriptors to influence the biological response in an area localized around the predicted compound. The method is based on numerical gradients with parameters optimized using data sets sampled from analytical functions. The heuristic character of the method reduces the computational requirements and makes it applicable not only to fragment based methods but also to QSARs based on bulk descriptors. The application of the method is illustrated on congeneric QSAR data sets, and it is shown that the predicted influential descriptors can be used to guide structural modifications that affect the biological response in the desired direction. The method is implemented into the AZOrange Open Source QSAR package. The current implementation of localized heuristic inverse QSAR is a step toward a generally applicable method for elucidating the structure activity relationship specifically for a congeneric region of chemical space when using QSARs based on bulk properties. Consequently, this method could contribute to accelerating the chemical design process in pharmaceutical projects, as well as provide information that could enhance the mechanistic understanding for individual scaffolds.

The EU-ADR Web Platform: delivering advanced pharmacovigilance tools.

PURPOSE: Pharmacovigilance methods have advanced greatly during the last decades, making post-market drug assessment an essential drug evaluation component. These methods mainly rely on the use of spontaneous reporting systems and health information databases to collect expertise from huge amounts of real-world reports. The EU-ADR Web Platform was built to further facilitate accessing, monitoring and exploring these data, enabling an in-depth analysis of adverse drug reactions risks. METHODS: The EU-ADR Web Platform exploits the wealth of data collected within a large-scale European initiative, the EU-ADR project. Millions of electronic health records, provided by national health agencies, are mined for specific drug events, which are correlated with literature, protein and pathway data, resulting in a rich drug-event dataset. Next, advanced distributed computing methods are tailored to coordinate the execution of data-mining and statistical analysis tasks. This permits obtaining a ranked drug-event list, removing spurious entries and highlighting relationships with high risk potential. RESULTS: The EU-ADR Web Platform is an open workspace for the integrated analysis of pharmacovigilance datasets. Using this software, researchers can access a variety of tools provided by distinct partners in a single centralized environment. Besides performing standalone drug-event assessments, they can also control the pipeline for an improved batch analysis of custom datasets. Drug-event pairs can be substantiated and statistically analysed within the platform's innovative working environment. CONCLUSIONS: A pioneering workspace that helps in explaining the biological path of adverse drug reactions was developed within the EU-ADR project consortium. This tool, targeted at the pharmacovigilance community, is available online at https://bioinformatics.ua.pt/euadr/.

Use of in silico systems and expert knowledge for structure-based assessment of potentially mutagenic impurities.

Genotoxicity hazard identification is part of the impurity qualification process for drug substances and products, the first step of which being the prediction of their potential DNA reactivity using in silico (quantitative) structure-activity relationship (Q)SAR models/systems. This white paper provides information relevant to the development of the draft harmonized tripartite guideline ICH M7 on potentially DNA-reactive/mutagenic impurities in pharmaceuticals and their application in practice. It explains relevant (Q)SAR methodologies as well as the added value of expert knowledge. Moreover, the predictive value of the different methodologies analyzed in two surveys conveyed in the US and European pharmaceutical industry is compared: most pharmaceutical companies used a rule-based expert system as their primary methodology, yielding negative predictivity values of ⩾78% in all participating companies. A further increase (>90%) was often achieved by an additional expert review and/or a second QSAR methodology. Also in the latter case, an expert review was mandatory, especially when conflicting results were obtained. Based on the available data, we concluded that a rule-based expert system complemented by either expert knowledge or a second (Q)SAR model is appropriate. A maximal transparency of the assessment process (e.g. methods, results, arguments of weight-of-evidence approach) achieved by e.g. data sharing initiatives and the use of standards for reporting will enable regulators to fully understand the results of the analysis. Overall, the procedures presented here for structure-based assessment are considered appropriate for regulatory submissions in the scope of ICH M7.

Decreased levels of discomfort in repeatedly handled mice during experimental procedures, assessed by facial expressions.

Mice are the most commonly used laboratory animal, yet there are limited studies which investigate the effects of repeated handling on their welfare and scientific outcomes. Furthermore, simple methods to evaluate distress in mice are lacking, and specialized behavioral or biochemical tests are often required. Here, two groups of CD1 mice were exposed to either traditional laboratory handling methods or a training protocol with cup lifting for 3 and 5 weeks. The training protocol was designed to habituate the mice to the procedures involved in subcutaneous injection, e.g., removal from the cage, skin pinch. This protocol was followed by two common research procedures: subcutaneous injection and tail vein blood sampling. Two training sessions and the procedures (subcutaneous injection and blood sampling) were video recorded. The mouse facial expressions were then scored, focusing on the ear and eye categories of the mouse grimace scale. Using this assessment method, trained mice expressed less distress than the control mice during subcutaneous injection. Mice trained for subcutaneous injection also had reduced facial scores during blood sampling. We found a clear sex difference as female mice responded to training faster than the male mice, they also had lower facial scores than the male mice when trained. The ear score appeared to be a more sensitive measure of distress than the eye score, which may be more indicative of pain. In conclusion, training is an important refinement method to reduce distress in mice during common laboratory procedures and this can best be assessed using the ear score of the mouse grimace scale.

New transgenic mouse models enabling pan-hematopoietic or selective hematopoietic stem cell depletion in vivo.

Hematopoietic stem cell (HSC) multipotency and self-renewal are typically defined through serial transplantation experiments. Host conditioning is necessary for robust HSC engraftment, likely by reducing immune-mediated rejection and by clearing limited HSC niche space. Because irradiation of the recipient mouse is non-specific and broadly damaging, there is a need to develop alternative models to study HSC performance at steady-state and in the absence of radiation-induced stress. We have generated and characterized two new mouse models where either all hematopoietic cells or only HSCs can be specifically induced to die in vivo or in vitro. Hematopoietic-specific Vav1-mediated expression of a loxP-flanked diphtheria-toxin receptor (DTR) renders all hematopoietic cells sensitive to diphtheria toxin (DT) in "Vav-DTR" mice. Crossing these mice to Flk2-Cre mice results in "HSC-DTR" mice which exhibit HSC-selective DT sensitivity. We demonstrate robust, rapid, and highly selective cell ablation in these models. These new mouse models provide a platform to test whether HSCs are required for long-term hematopoiesis in vivo, for understanding the mechanisms regulating HSC engraftment, and interrogating in vivo hematopoietic differentiation pathways and mechanisms regulating hematopoietic homeostasis.

A crowdsourcing evaluation of the NIH chemical probes.

Between 2004 and 2008, the US National Institutes of Health Molecular Libraries and Imaging initiative pilot phase funded 10 high-throughput screening centers, resulting in the deposition of 691 assays into PubChem and the nomination of 64 chemical probes. We crowdsourced the Molecular Libraries and Imaging initiative output to 11 experts, who expressed medium or high levels of confidence in 48 of these 64 probes.

The incidence of positive results in the mouse lymphoma TK assay (MLA) in pharmaceutical screening and their prediction by MultiCase MC4PC.

There are published data indicating that the mouse lymphoma TK assay (MLA) has an unacceptably high incidence of positive results, hence it was decided to review the MLA data generated in this laboratory for potential drug candidates. Of the 355 compounds tested, only 52 (15%) gave positive results so, even if it is assumed that all of these are non-carcinogens, the incidence of 'false positive' predictions of carcinogenicity is much lower than the 61% apparent from analysis of the literature. Furthermore, only 19 compounds (5%) were positive by a mechanism that could not be associated with the compounds primary pharmacological activity or positive responses in other genotoxicity assays. It should be noted that the majority of these compounds were not bacterial mutagens so, in most cases, the positive results were an additional indicator of genotoxicity. However, data are not available to assess any risk they might present. At least for pharmaceuticals, it appears that the MLA does not generate as many positive results as is commonly believed, and it is against this incidence that the performance of other in vitro genotoxicity tests should be compared. The predictive accuracy of the program MultiCase MC4PC was also examined using these results. The sensitivity and specificity were found to be 62 and 38%, respectively; in fact, 62% of all compounds were predicted to be positive irrespective of whether they were actually positive or negative. It was concluded that, in its current state of development, M4PC cannot be considered sufficiently accurate to be used to predict the activity of pharmaceuticals in the MLA.

Integrated decision support for assessing chemical liabilities.

Chemical liabilities, such as adverse effects and toxicity, have a major impact on today's drug discovery process. In silico prediction of chemical liabilities is an important approach which can reduce costs and animal testing by complementing or replacing in vitro and in vivo liability models. There is a lack of integrated, extensible decision support systems for chemical liability assessment which run quickly and have easily interpretable results. Here we present a method which integrates similarity searches, structural alerts, and QSAR models which all are available from the Bioclipse workbench. Emphasis has been placed on interpretation of results, and substructures which are important for predictions are highlighted in the original chemical structures. This allows for interactively changing chemical structures with instant visual feedback and can be used for hypothesis testing of single chemical structures as well as compound collections. The system has a clear separation between methods and data, and the extensible architecture enables straightforward extension via addition of more plugins (such as new data sets and computational models). We demonstrate our method on three important safety end points: mutagenicity, carcinogenicity, and aryl hydrocarbon receptor (AhR) activation. Bioclipse and the decision support implementation are free, open source, and available from http://www.bioclipse.net/decision-support .

Use of historic metabolic biotransformation data as a means of anticipating metabolic sites using MetaPrint2D and Bioclipse.

BACKGROUND: Predicting metabolic sites is important in the drug discovery process to aid in rapid compound optimisation. No interactive tool exists and most of the useful tools are quite expensive. RESULTS: Here a fast and reliable method to analyse ligands and visualise potential metabolic sites is presented which is based on annotated metabolic data, described by circular fingerprints. The method is available via the graphical workbench Bioclipse, which is equipped with advanced features in cheminformatics. CONCLUSIONS: Due to the speed of predictions (less than 50 ms per molecule), scientists can get real time decision support when editing chemical structures. Bioclipse is a rich client, which means that all calculations are performed on the local computer and do not require network connection. Bioclipse and MetaPrint2D are free for all users, released under open source licenses, and available from http://www.bioclipse.net.

Evaluation of quantitative structure-activity relationship modeling strategies: local and global models.

A thorough comparison between different QSAR modeling strategies is presented. The comparison is conducted for local versus global modeling strategies, risk assessment, and computational cost. The strategies are implemented using random forests, support vector machines, and partial least squares. Results are presented for simulated data, as well as for real data, generally indicating that a global modeling strategy is preferred over a local strategy. Furthermore, the results also show that there is an pronounced risk and a comparatively high computational cost when using the local modeling strategies.