Deep Learning-Based Conformal Prediction of Toxicity

Predictive modeling for toxicity can help reduce risks in a range of applications and potentially serve as the basis for regulatory decisions. However, the utility of these predictions can be limited if the associated uncertainty is not adequately quantified. With recent studies showing great promise for deep learning-based models also for toxicity predictions, we investigate the combination of deep learning-based predictors with the conformal prediction framework to generate highly predictive models with well-defined uncertainties. We use a range of deep feedforward neural networks and graph neural networks in a conformal prediction setting and evaluate their performance on data from the Tox21 challenge. We also compare the results from the conformal predictors to those of the underlying machine learning models. The results indicate that highly predictive models can be obtained that result in very efficient conformal predictors even at high confidence levels. Taken together, our results highlight the utility of conformal predictors as a convenient way to deliver toxicity predictions with confidence, adding both statistical guarantees on the model performance as well as better predictions of the minority class compared to the underlying models

LightGBM: An Effective and Scalable Algorithm for Prediction of Chemical Toxicity – Application to the Tox21 and Mutagenicity Datasets

Machine learning algorithms have attained widespread use in assessing the potential toxicities of pharmaceuticals and industrial chemicals because of their faster-speed and lower-cost compared to experimental bioassays. Gradient boosting is an effective algorithm that often achieves high predictivity, but historically the relative long computational time limited its applications in predicting large compound libraries or developing in silico predictive models that require frequent retraining. LightGBM, a recent improvement of the gradient boosting algorithm inherited its high predictivity but resolved its scalability and long computational time by adopting leaf-wise tree growth strategy and introducing novel techniques. In this study, we compared the predictive performance and the computational time of LightGBM to deep neural networks, random forests, support vector machines, and XGBoost. All algorithms were rigorously evaluated on publicly available Tox21 and mutagenicity datasets using a Bayesian optimization integrated nested 10-fold cross-validation scheme that performs hyperparameter optimization while examining model generalizability and transferability to new data. The evaluation results demonstrated that LightGBM is an effective and highly scalable algorithm offering the best predictive performance while consuming significantly shorter computational time than the other investigated algorithms across all Tox21 and mutagenicity datasets. We recommend LightGBM for applications in in silico safety assessment and also in other areas of cheminformatics to fulfill the ever-growing demand for accurate and rapid prediction of various toxicity or activity related endpoints of large compound libraries present in the pharmaceutical and chemical industry

A one-year study of foodborne illnesses in the municipality of Uppsala, Sweden.

Surveillance was enhanced and a retrospective interview study performed in 1998-99 to determine incidence, causes, and costs of foodborne illnesses in Uppsala, Sweden. Sixty-eight percent of the detected foodborne illness incidents were single cases, and 32% were outbreaks. Most (85%) of the incidents came to the attention of the municipal authorities through telephone calls from affected persons. Calicivirus, Campylobacter spp., and Staphyloccocus aureus were the most common etiological agents; meat, meat products, and mixed dishes were the most implicated food categories. The incidence of foodborne illness was estimated to be 38 cases per 1,000 inhabitants per year. The estimated average costs per illness were 2,164 Swedish Krona (SEK) ($246) to society and 500 SEK ($57) to the patient. The annual cost of foodborne illnesses in Sweden was estimated to be 1,082 million SEK ($123 million)

Mode shifting in school travel mode: examining the prevalence and correlates of active school transport in Ontario, Canada

<p>Abstract</p> <p>Background</p> <p>Studies examining the correlates of school transport commonly fail to make the distinction between morning and afternoon school trips. The purpose of this study was to examine the prevalence and correlates of mode shift from passive in the morning to active in the afternoon among elementary and secondary school students in Ontario, Canada.</p> <p>Methods</p> <p>Data were derived from the 2009 cycle of the Ontario Student Drug Use and Health Survey (OSDUHS). 3,633 students in grades 7 through 12 completed self-administered questionnaires. Socio-demographic, behavioural, psychological, and environmental predictors of active school transport (AST) were assessed using logistic regression.</p> <p>Results</p> <p>Overall, 47% and 38% of elementary school students reported AST to and from school, respectively. The corresponding figures were 23% and 32% for secondary school students. The prevalence of AST varied temporarily and spatially. There was a higher prevalence of walking/biking found for elementary school students than for secondary school students, and there was an approximate 10% increase in AST in the afternoon. Different correlates of active school transport were also found across elementary and secondary school students. For all ages, students living in urban areas, with a shorter travel time between home and school, and having some input to the decision making process, were more likely to walk to and from school.</p> <p>Conclusions</p> <p>Future research examining AST should continue to make the analytic distinction between the morning and afternoon trip, and control for the moderating effect of age and geography in predicting mode choice. In terms of practice, these variations highlight the need for school-specific travel plans rather than 'one size fits all' interventions in promoting active school transport.</p

Classification of Inhibitors of Hepatic Organic Anion Transporting Polypeptides (OATPs): Influence of Protein Expression on Drug–Drug Interactions

ABSTRACT: The hepatic organic anion transporting poly-peptides (OATPs) influence the pharmacokinetics of several drug classes and are involved in many clinical drug−drug interactions. Predicting potential interactions with OATPs is, therefore, of value. Here, we developed in vitro and in silico models for identification and prediction of specific and general inhibitors of OATP1B1, OATP1B3, and OATP2B1. The maximal transport activity (MTA) of each OATP in human liver was predicted from transport kinetics and protein quantification. We then used MTA to predict the effects of a subset of inhibitors on atorvastatin uptake in vivo. Using a data set of 225 drug-like compounds, 91 OATP inhibitors were identified. In silico models indicated that lipophilicity and polar surface area are key molecular features of OATP inhibition. MTA predictions identified OATP1B1 and OATP1B3 as major determinants of atorvastatin uptake in vivo. The relative contributions to overall hepatic uptake varied with isoform specificities of the inhibitors

At the Biological Modeling and Simulation Frontier

We provide a rationale for and describe examples of synthetic modeling and simulation (M&S) of biological systems. We explain how synthetic methods are distinct from familiar inductive methods. Synthetic M&S is a means to better understand the mechanisms that generate normal and disease-related phenomena observed in research, and how compounds of interest interact with them to alter phenomena. An objective is to build better, working hypotheses of plausible mechanisms. A synthetic model is an extant hypothesis: execution produces an observable mechanism and phenomena. Mobile objects representing compounds carry information enabling components to distinguish between them and react accordingly when different compounds are studied simultaneously. We argue that the familiar inductive approaches contribute to the general inefficiencies being experienced by pharmaceutical R&D, and that use of synthetic approaches accelerates and improves R&D decision-making and thus the drug development process. A reason is that synthetic models encourage and facilitate abductive scientific reasoning, a primary means of knowledge creation and creative cognition. When synthetic models are executed, we observe different aspects of knowledge in action from different perspectives. These models can be tuned to reflect differences in experimental conditions and individuals, making translational research more concrete while moving us closer to personalized medicine

Qualitative prediction of blood–brain barrier permeability on a large and refined dataset

The prediction of blood–brain barrier permeation is vitally important for the optimization of drugs targeting the central nervous system as well as for avoiding side effects of peripheral drugs. Following a previously proposed model on blood–brain barrier penetration, we calculated the cross-sectional area perpendicular to the amphiphilic axis. We obtained a high correlation between calculated and experimental cross-sectional area (r = 0.898, n = 32). Based on these results, we examined a correlation of the calculated cross-sectional area with blood–brain barrier penetration given by logBB values. We combined various literature data sets to form a large-scale logBB dataset with 362 experimental logBB values. Quantitative models were calculated using bootstrap validated multiple linear regression. Qualitative models were built by a bootstrapped random forest algorithm. Both methods found similar descriptors such as polar surface area, pKa, logP, charges and number of positive ionisable groups to be predictive for logBB. In contrast to our initial assumption, we were not able to obtain models with the cross-sectional area chosen as relevant parameter for both approaches. Comparing those two different techniques, qualitative random forest models are better suited for blood-brain barrier permeability prediction, especially when reducing the number of descriptors and using a large dataset. A random forest prediction system (ntrees = 5) based on only four descriptors yields a validated accuracy of 88%

A proposed framework for the systematic review and integrated assessment (SYRINA) of endocrine disrupting chemicals

Background - The issue of endocrine disrupting chemicals (EDCs) is receiving wide attention from both the scientific and regulatory communities. Recent analyses of the EDC literature have been criticized for failing to use transparent and objective approaches to draw conclusions about the strength of evidence linking EDC exposures to adverse health or environmental outcomes. Systematic review methodologies are ideal for addressing this issue as they provide transparent and consistent approaches to study selection and evaluation. Objective methods are needed for integrating the multiple streams of evidence (epidemiology, wildlife, laboratory animal, in vitro, and in silico data) that are relevant in assessing EDCs. Methods - We have developed a framework for the systematic review and integrated assessment (SYRINA) of EDC studies. The framework was designed for use with the International Program on Chemical Safety (IPCS) and World Health Organization (WHO) definition of an EDC, which requires appraisal of evidence regarding 1) association between exposure and an adverse effect, 2) association between exposure and endocrine disrupting activity, and 3) a plausible link between the adverse effect and the endocrine disrupting activity. Results - Building from existing methodologies for evaluating and synthesizing evidence, the SYRINA framework includes seven steps: 1) Formulate the problem; 2) Develop the review protocol; 3) Identify relevant evidence; 4) Evaluate evidence from individual studies; 5) Summarize and evaluate each stream of evidence; 6) Integrate evidence across all streams; 7) Draw conclusions, make recommendations, and evaluate uncertainties. The proposed method is tailored to the IPCS/WHO definition of an EDC but offers flexibility for use in the context of other definitions of EDCs. Conclusions - When using the SYRINA framework, the overall objective is to provide the evidence base needed to support decision making, including any action to avoid/minimise potential adverse effects of exposures. This framework allows for the evaluation and synthesis of evidence from multiple evidence streams. Finally, a decision regarding regulatory action is not only dependent on the strength of evidence, but also the consequences of action/inaction, e.g. limited or weak evidence may be sufficient to justify action if consequences are serious or irreversible.The workshops that supported the writing of this manuscript were funded by the Swedish Foundation for Strategic Environmental Research “Mistra”. LNV was funded by Award Number K22ES025811 from the National Institute of Environmental Health Sciences of the National Institutes of Health. TJW was funded by The Clarence Heller Foundation (A123547), the Passport Foundation, the Forsythia Foundation, the National Institute of Environmental Health Sciences (grants ES018135 and ESO22841), and U.S. EPA STAR grants (RD83467801 and RD83543301). JT was funded by the Academy of Finland and Sigrid Juselius. UH was funded by the Danish EPA. KAK was funded by the Canada Research Chairs program grant number 950–230607

Identification of Novel Functional Inhibitors of Acid Sphingomyelinase

We describe a hitherto unknown feature for 27 small drug-like molecules, namely functional inhibition of acid sphingomyelinase (ASM). These entities named FIASMAs (Functional Inhibitors of Acid SphingoMyelinAse), therefore, can be potentially used to treat diseases associated with enhanced activity of ASM, such as Alzheimer's disease, major depression, radiation- and chemotherapy-induced apoptosis and endotoxic shock syndrome. Residual activity of ASM measured in the presence of 10 µM drug concentration shows a bimodal distribution; thus the tested drugs can be classified into two groups with lower and higher inhibitory activity. All FIASMAs share distinct physicochemical properties in showing lipophilic and weakly basic properties. Hierarchical clustering of Tanimoto coefficients revealed that FIASMAs occur among drugs of various chemical scaffolds. Moreover, FIASMAs more frequently violate Lipinski's Rule-of-Five than compounds without effect on ASM. Inhibition of ASM appears to be associated with good permeability across the blood-brain barrier. In the present investigation, we developed a novel structure-property-activity relationship by using a random forest-based binary classification learner. Virtual screening revealed that only six out of 768 (0.78%) compounds of natural products functionally inhibit ASM, whereas this inhibitory activity occurs in 135 out of 2028 (6.66%) drugs licensed for medical use in humans