Using random forest and decision tree models for a new vehicle prediction approach in computational toxicology

yesDrug vehicles are chemical carriers that provide beneficial aid to the drugs they bear. Taking advantage of their favourable properties can potentially allow the safer use of drugs that are considered highly toxic. A means for vehicle selection without experimental trial would therefore be of benefit in saving time and money for the industry. Although machine learning is increasingly used in predictive toxicology, to our knowledge there is no reported work in using machine learning techniques to model drug-vehicle relationships for vehicle selection to minimise toxicity. In this paper we demonstrate the use of data mining and machine learning techniques to process, extract and build models based on classifiers (decision trees and random forests) that allow us to predict which vehicle would be most suited to reduce a drug’s toxicity. Using data acquired from the National Institute of Health’s (NIH) Developmental Therapeutics Program (DTP) we propose a methodology using an area under a curve (AUC) approach that allows us to distinguish which vehicle provides the best toxicity profile for a drug and build classification models based on this knowledge. Our results show that we can achieve prediction accuracies of 80 % using random forest models whilst the decision tree models produce accuracies in the 70 % region. We consider our methodology widely applicable within the scientific domain and beyond for comprehensively building classification models for the comparison of functional relationships between two variables

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

A Knowledge Based Approach of Toxicity Prediction for Drug Formulation. Modelling Drug Vehicle Relationships Using Soft Computing Techniques

This multidisciplinary thesis is concerned with the prediction of drug formulations for the reduction of drug toxicity. Both scientific and computational approaches are utilised to make original contributions to the field of predictive toxicology. The first part of this thesis provides a detailed scientific discussion on all aspects of drug formulation and toxicity. Discussions are focused around the principal mechanisms of drug toxicity and how drug toxicity is studied and reported in the literature. Furthermore, a review of the current technologies available for formulating drugs for toxicity reduction is provided. Examples of studies reported in the literature that have used these technologies to reduce drug toxicity are also reported. The thesis also provides an overview of the computational approaches currently employed in the field of in silico predictive toxicology. This overview focuses on the machine learning approaches used to build predictive QSAR classification models, with examples discovered from the literature provided. Two methodologies have been developed as part of the main work of this thesis. The first is focused on use of directed bipartite graphs and Venn diagrams for the visualisation and extraction of drug-vehicle relationships from large un-curated datasets which show changes in the patterns of toxicity. These relationships can be rapidly extracted and visualised using the methodology proposed in chapter 4. The second methodology proposed, involves mining large datasets for the extraction of drug-vehicle toxicity data. The methodology uses an area-under-the-curve principle to make pairwise comparisons of vehicles which are classified according to the toxicity protection they offer, from which predictive classification models based on random forests and decisions trees are built. The results of this methodology are reported in chapter 6

Using Ensemble Technique to Improve Multiclass Classification

Many real world applications inevitably contain datasets that have multiclass structure characterized by imbalance classes, redundant and irrelevant features that degrade performance of classifiers. Minority classes in the datasets are treated as outliers’ classes. The research aimed at establishing the role of ensemble technique in improving performance of multiclass classification. Multiclass datasets were transformed to binary and the datasets resampled using Synthetic minority oversampling technique (SMOTE) algorithm.  Relevant features of the datasets were selected by use of an ensemble filter method developed using Correlation, Information Gain, Gain-Ratio and ReliefF filter selection methods. Adaboost and Random subspace learning algorithms were combined using Voting methodology utilizing random forest as the base classifier. The classifiers were evaluated using 10 fold stratified cross validation. The model showed better performance in terms of outlier detection and classification prediction for multiclass problem. The model outperformed other well-known existing classification and outlier detection algorithms such as Naïve bayes, KNN, Bagging, JRipper, Decision trees, RandomTree and Random forest. The study findings established that ensemble technique, resampling datasets and decomposing multiclass results in an improved classification performance as well as enhanced detection of minority outlier (rare) classes. Keywords: Multiclass, Classification, Outliers, Ensemble, Learning Algorithm DOI: 10.7176/JIEA/9-5-04 Publication date: August 31st 201

Review of Data Sources, QSARs and Integrated Testing Strategies for Skin Sensitisation

This review collects information on sources of skin sensitisation data and computational tools for the estimation of skin sensitisation potential, such as expert systems and (quantitative) structure-activity relationship (QSAR) models. The review also captures current thinking of what constitutes an integrated testing strategy (ITS) for this endpoint. The emphasis of the review is on the usefulness of the models for the regulatory assessment of chemicals, particularly for the purposes of the new European legislation for the Registration, Evaluation, Authorisation and Restriction of CHemicals (REACH), which entered into force on 1 June 2007. Since there are no specific databases for skin sensitisation currently available, a description of experimental data found in various literature sources is provided. General (global) models, models for specific chemical classes and mechanisms of action and expert systems are summarised. This review was prepared as a contribution to the EU funded Integrated Project, OSIRIS.JRC.I.3-Consumer products safety and qualit

An Analysis of Global Gene Expression Resulting from Exposure to Energetic Materials

AN ANALYSIS OF GLOBAL GENE EXPRESSION RESULTING FROM EXPOSURE TO ENERGETIC MATERIALS A Dissertation Presented for the Doctor of Philosophy Degree University of Tennessee, Knoxville VERNON LASHAWN MCINTOSH JR. August 2010 Dedication This dissertation is dedicated to my family. My mother and father Debra and Vernon McIntosh instilled in me the respect for academic excellence and the drive maximize my potential. Early on, my younger brother Kyle started showing signs of a shared interest in biology thus my desire to be a positive role model for him kept me motivated. Last but certainly not least, my loving wife and best friend Nichole has been there to offer love and support throughout my entire undergraduate and graduate degrees. It’s difficult to imagine making it this far without her (and that’s not just because she paid the bills). Abstract Characteristic transcriptional biomarkers have been identified for microbial cultures exposed to 2, 4, 6-trinitrotoluene (TNT), 2, 6-dinitrotoluene (DNT), or triacetone-triperoxide (TATP). This study describes the generation of expression profiles for exposure to each compound, the functional significance of each response, and the identification of the characteristic alterations in gene expression associated with exposure to each compound. Expression profiles were generated from a total of three different candidate organisms: Escherichia coli, Saccharomyces cerevisiae, and Pseudomonas putida. Common to all three organisms, TNT exposure resulted in increased expression of genes involved in toxin resistance and drug efflux systems. The S.cerevisiae and E.coli expression profiles were both characterized by increased expression of genes involved in iron-sulfur cluster assembly, sulfur containing amino acids, sulfate transport and assimilation and the metabolism of nitrogen compounds. Only E.coli and Saccharomyces were used to generate DNT induced expression profiles; both profiles exhibited high degrees of similarity with each organism’s respective TNT profiles. This was especially true of the E.coli profile where 25 of the 30 alterations were also observed after exposure to TNT. A computational discriminant functional analysis was performed to identify characteristic biomarkers for each exposure. For each compound a set of transcriptional biomarkers (10 or less) was developed. An additional set of biomarkers was developed encompassing both TNT and DNT exposure. These sets of genes serve as a transcriptional fingerprint for exposure to each respective compound. The sensitivity and specificity of each transcriptional fingerprint is sufficient to correctly identify exposure to energetic materials against a background of non-energetic compound exposures. This study makes several novel contributions to the greater body of scientific knowledge: • This is the first documented study of the interactions of TATP in any biological system. • This is the first comprehensive gene expression study of the TNT response by P. putida, E.coli or E.coli. • This is the first application of computational class prediction in the development of biomarkers for exposure to energetic material

Characterisation of data resources for in silico modelling: benchmark datasets for ADME properties.

Introduction: The cost of in vivo and in vitro screening of ADME properties of compounds has motivated efforts to develop a range of in silico models. At the heart of the development of any computational model are the data; high quality data are essential for developing robust and accurate models. The characteristics of a dataset, such as its availability, size, format and type of chemical identifiers used, influence the modelability of the data. Areas covered: This review explores the usefulness of publicly available ADME datasets for researchers to use in the development of predictive models. More than 140 ADME datasets were collated from publicly available resources and the modelability of 31selected datasets were assessed using specific criteria derived in this study. Expert opinion: Publicly available datasets differ significantly in information content and presentation. From a modelling perspective, datasets should be of adequate size, available in a user-friendly format with all chemical structures associated with one or more chemical identifiers suitable for automated processing (e.g. CAS number, SMILES string or InChIKey). Recommendations for assessing dataset suitability for modelling and publishing data in an appropriate format are discussed

More than just hormones: H295R cells as predictors of reproductive toxicity

AbstractMany of the commonly observed reproductive toxicities associated with therapeutic compounds can be traced to a disruption of the steroidogenic pathway. We sought to develop an in vitro assay that would predict reproductive toxicity and be high throughput in nature. H295R cells, previously validated as having an intact and functional steroidogenic pathway, were treated with 83 known-positive and 79 known-negative proprietary and public-domain compounds. The assay measured the expression of the key enzymes STAR, 3βHSD2, CYP17A1, CYP11B2, CYP19A1, CYP21A2, and CYP11A1 and the hormones DHEA, progesterone, testosterone, and cortisol. We found that a Random Forest model yielded a receiver operating characteristic area under the curve (ROC AUC) of 0.845, with sensitivity of 0.724 and specificity of 0.758 for predicting in vivo reproductive toxicity with this in vitro assay system

Language modelling for clinical natural language understanding and generation

One of the long-standing objectives of Artificial Intelligence (AI) is to design and develop algorithms for social good including tackling public health challenges. In the era of digitisation, with an unprecedented amount of healthcare data being captured in digital form, the analysis of the healthcare data at scale can lead to better research of diseases, better monitoring patient conditions and more importantly improving patient outcomes. However, many AI-based analytic algorithms rely solely on structured healthcare data such as bedside measurements and test results which only account for 20% of all healthcare data, whereas the remaining 80% of healthcare data is unstructured including textual data such as clinical notes and discharge summaries which is still underexplored. Conventional Natural Language Processing (NLP) algorithms that are designed for clinical applications rely on the shallow matching, templates and non-contextualised word embeddings which lead to limited understanding of contextual semantics. Though recent advances in NLP algorithms have demonstrated promising performance on a variety of NLP tasks in the general domain with contextualised language models, most of these generic NLP algorithms struggle at specific clinical NLP tasks which require biomedical knowledge and reasoning. Besides, there is limited research to study generative NLP algorithms to generate clinical reports and summaries automatically by considering salient clinical information. This thesis aims to design and develop novel NLP algorithms especially clinical-driven contextualised language models to understand textual healthcare data and generate clinical narratives which can potentially support clinicians, medical scientists and patients. The first contribution of this thesis focuses on capturing phenotypic information of patients from clinical notes which is important to profile patient situation and improve patient outcomes. The thesis proposes a novel self-supervised language model, named Phenotypic Intelligence Extraction (PIE), to annotate phenotypes from clinical notes with the detection of contextual synonyms and the enhancement to reason with numerical values. The second contribution is to demonstrate the utility and benefits of using phenotypic features of patients in clinical use cases by predicting patient outcomes in Intensive Care Units (ICU) and identifying patients at risk of specific diseases with better accuracy and model interpretability. The third contribution is to propose generative models to generate clinical narratives to automate and accelerate the process of report writing and summarisation by clinicians. This thesis first proposes a novel summarisation language model named PEGASUS which surpasses or is on par with the state-of-the-art performance on 12 downstream datasets including biomedical literature from PubMed. PEGASUS is further extended to generate medical scientific documents from input tabular data.Open Acces