Data Quality in Predictive Toxicology: Identification of Chemical Structures and Calculation of Chemical Descriptors

Every technique for toxicity prediction and for the detection of structure–activity relationships relies on the accurate estimation and representation of chemical and toxicologic properties. In this paper we discuss the potential sources of errors associated with the identification of compounds, the representation of their structures, and the calculation of chemical descriptors. It is based on a case study where machine learning techniques were applied to data from noncongeneric compounds and a complex toxicologic end point (carcinogenicity). We propose methods applicable to the routine quality control of large chemical datasets, but our main intention is to raise awareness about this topic and to open a discussion about quality assurance in predictive toxicology. The accuracy and reproducibility of toxicity data will be reported in another paper

The selective PI3Kα inhibitor BYL719 as a novel therapeutic option for neuroendocrine tumors: Results from multiple cell line models

BACKGROUND/AIMS The therapeutic options for metastatic neuroendocrine tumors (NETs) are limited. As PI3K signaling is often activated in NETs, we have assessed the effects of selective PI3Kp110\textgreeka inhibition by the novel agent BYL719 on cell viability, colony formation, apoptosis, cell cycle, signaling pathways, differentiation and secretion in pancreatic (BON-1, QGP-1) and pulmonary (H727) NET cell lines. METHODS Cell viability was investigated by WST-1 assay, colony formation by clonogenic assay, apoptosis by caspase3/7 assay, the cell cycle by FACS, cell signaling by Western blot analysis, expression of chromogranin A and somatostatin receptors 1/2/5 by RT-qPCR, and chromogranin A secretion by ELISA. RESULTS BYL719 dose-dependently decreased cell viability and colony formation with the highest sensitivity in BON-1, followed by H727, and lowest sensitivity in QGP-1 cells. BYL719 induced apoptosis and G0/G1 cell cycle arrest associated with increased p27 expression. Western blots showed inhibition of PI3K downstream targets to a varying degree in the different cell lines, but IGF1R activation. The most sensitive BON-1 cells displayed a significant, and H727 cells a non-significant, GSK3 inhibition after BYL719 treatment, but these effects do not appear to be mediated through the IGF1R. In contrast, the most resistant QGP-1 cells showed no GSK3 inhibition, but a modest activation, which would partially counteract the other anti-proliferative effects. Accordingly, BYL719 enhanced neuroendocrine differentiation with the strongest effect in BON-1, followed by H727 cells indicated by induction of chromogranin A and somatostatin receptor 1/2 mRNA-synthesis, but not in QGP-1 cells. In BON-1 and QGP-1 cells, the BYL719/everolimus combination was synergistic through simultaneous AKT/mTORC1 inhibition, and significantly increased somatostatin receptor 2 transcription compared to each drug separately. CONCLUSION Our results suggest that the agent BYL719 could be a novel therapeutic approach to the treatment of NETs that may sensitize NET cells to somatostatin analogs, and that if there is resistance to its action this may be overcome by combination with everolimus

Collaborative development of predictive toxicology applications

OpenTox provides an interoperable, standards-based Framework for the support of predictive toxicology data management, algorithms, modelling, validation and reporting. It is relevant to satisfying the chemical safety assessment requirements of the REACH legislation as it supports access to experimental data, (Quantitative) Structure-Activity Relationship models, and toxicological information through an integrating platform that adheres to regulatory requirements and OECD validation principles. Initial research defined the essential components of the Framework including the approach to data access, schema and management, use of controlled vocabularies and ontologies, architecture, web service and communications protocols, and selection and integration of algorithms for predictive modelling. OpenTox provides end-user oriented tools to non-computational specialists, risk assessors, and toxicological experts in addition to Application Programming Interfaces (APIs) for developers of new applications. OpenTox actively supports public standards for data representation, interfaces, vocabularies and ontologies, Open Source approaches to core platform components, and community-based collaboration approaches, so as to progress system interoperability goals

A Comparison of Nine Machine Learning Mutagenicity Models and Their Application for Predicting Pyrrolizidine Alkaloids

Random forest, support vector machine, logistic regression, neural networks and k-nearest neighbor (lazar) algorithms, were applied to a new Salmonella mutagenicity dataset with 8,290 unique chemical structures utilizing MolPrint2D and Chemistry Development Kit (CDK) descriptors. Crossvalidation accuracies of all investigated models ranged from 80 to 85% which is comparable with the interlaboratory variability of the Salmonella mutagenicity assay. Pyrrolizidine alkaloid predictions showed a clear distinction between chemical groups, where otonecines had the highest proportion of positive mutagenicity predictions and monoesters the lowest

Machine learning and data mining

The present situation in biological and medical sciences is characterized by the availability of massive amounts of experimental data. This has two main reasons, which are both related to the developments in computer hard- and software. First it is possible to generate massive amounts of data with the help of modern automated laboratory techniques. And second it becomes cheaper and easier to store and retrieve this data. In most cases these databases are used to retrieve experimental data in a convenient way and to circumvent the consultation of the original literature. But these databases also provide the empirical evidence, which may be the basis for the discovery of new scientific theories. The derivation of theories from experimental data is traditionally a process depending on the experience, skill and intuition of the individual researcher. But even the best expert works under limitations: Humans cannot handle large amount of data, have troubles detecting complex relationships, cannot keep working for a long time and tend to make errors in an unpredictable way. Therefore many efforts have been made to use computational methods in this process. In Artificial Intelligence research Data Mining techniques were developed for the identification of useful and understandable patterns in databases. This review will focus on the application of Knowledge Discovery in Databases (KDD), Data Mining (DM) and Machine Learning (ML) in toxicology. The emphasis will be on methods that are capable of providing new insights and theories