Improved Bevirimat resistance prediction by combination of structural and sequence-based classifiers

<p>Abstract</p> <p>Background</p> <p>Maturation inhibitors such as Bevirimat are a new class of antiretroviral drugs that hamper the cleavage of HIV-1 proteins into their functional active forms. They bind to these preproteins and inhibit their cleavage by the HIV-1 protease, resulting in non-functional virus particles. Nevertheless, there exist mutations in this region leading to resistance against Bevirimat. Highly specific and accurate tools to predict resistance to maturation inhibitors can help to identify patients, who might benefit from the usage of these new drugs.</p> <p>Results</p> <p>We tested several methods to improve Bevirimat resistance prediction in HIV-1. It turned out that combining structural and sequence-based information in classifier ensembles led to accurate and reliable predictions. Moreover, we were able to identify the most crucial regions for Bevirimat resistance computationally, which are in line with experimental results from other studies.</p> <p>Conclusions</p> <p>Our analysis demonstrated the use of machine learning techniques to predict HIV-1 resistance against maturation inhibitors such as Bevirimat. New maturation inhibitors are already under development and might enlarge the arsenal of antiretroviral drugs in the future. Thus, accurate prediction tools are very useful to enable a personalized therapy.</p

Отбор информативных геометрических признаков ядер клеток на люминесцентных изображениях раковых клеток

The methods of geometric informative features selection of nuclei on fluorescent images of cancer cells are considered. During the survey, a review of existing geometric features was carried out, including both the signs of rotation resisted shape and displacement of the image, as well as signs of location in space. For the selection of characteristics, the methods were used: median, correlation with calculation of the Pearson correlation coefficient, correlation with calculation of the Spearman correlation coefficient, logistic regression model, random forest with CART trees and Gini criterion, random forest with CART trees and error minimization criterion. As a result of the investigation 11 characteristics were selected from 59 features, the quality of classification and time costs were calculated depending on the number of features for describing the objects. The use of 11 features is sufficient for the accuracy of classification as it allows to reduce time costs in 2,3 times.Рассмотрены методы отбора информативных признаков для выделения геометрических признаков при описании ядер на люминесцентных изображениях раковых клеток. Выполнен обзор существующих геометрических признаков, который включает в себя как признаки формы, устойчивые к повороту и перемещению изображения, так и признаки расположения в пространстве. Для отбора наиболее информативных признаков использованы шесть методов: медианный, корреляционный с расчетом коэффициента корреляции по Пирсону, корреляционный с расчетом коэффициента корреляции по Спирмену, метод логистической регрессии, случайного леса с CART-деревьями и критерием Gini, случайного леса с CART-деревьями и критерием минимизации ошибки. В результате исследования из 59 признаков отобраны 11 наиболее информативных, выполнен анализ качества классификации с помощью метода случайного леса и рассчитаны временные затраты в зависимости от количества признаков для описания объектов. Для метода случайного леса использование 11 признаков является достаточным по точности классификации и позволяет снизить временные затраты в 2,3 раза

Exploiting HIV-1 protease and reverse transcriptase cross-resistance information for improved drug resistance prediction by means of multi-label classification

Gini impurity PIs. (PDF 8 kb

QSAR model development for early stage screening of monoclonal antibody therapeutics to facilitate rapid developability

PhD ThesisMonoclonal antibodies (mAbs) and related therapeutics are highly desirable from a biopharmaceutical perspective as they are highly target specific and well tolerated within the human system. Nevertheless, several mAbs have been discontinued or withdrawn based either on their inability to demonstrate efficacy and/or due to adverse effects. With nearly 80% of drugs failing in clinical development mainly due to lack of efficacy and safety there arises an urgent need for better understanding of biological activity, affinity, pharmacology, toxicity, immunogenicity etc. thus leading to early prediction of success/failure. In this study a hybrid modelling framework was developed that enabled early stage screening of mAbs. The applicability of the experimental methods was first tested on chemical compounds to assess the assay quality following which they were used to assess potential off target adverse effects of mAbs. Furthermore, hypersensitivity reactions were assessed using Skimune™, a non-artificial human skin explants based assay for safety and efficacy assessment of novel compounds and drugs, developed by Alcyomics Ltd. The suitability of Skimune™ for assessing the immune related adverse effects of aggregated mAbs was studied where aggregation was induced using a heat stress protocol. The aggregates were characterised by protein analysis techniques such as analytical ultra-centrifugation following which the immunogenicity tested using Skimune™ assay. Numerical features (descriptors) of mAbs were identified and generated using ProtDCal, EMBOSS Pepstat software as well as amino acid scales for different. Five independent and novel X block datasets consisting of these descriptors were generated based on the physicochemical, electronic, thermodynamic, electronic and topological properties of amino acids: Domain, Window, Substructure, Single Amino Acid, and Running Sum. This study describes the development of a hybrid QSAR based model with a structured workflow and clear evaluation metrics, with several optimisation steps, that could be beneficial for broader and more generic PLS modelling. Based on the results and observation from this study, it was demonstrated incremental improvement via selection of datasets and variables help in further optimisation of these hybrid models. Furthermore, using hypersensitivity and cross reactivity as responses and physicochemical characteristics of mAbs as descriptors, the QSAR models generated for different applicability domains allow for rapid early stage screening and developability. These models were validated with external test set comprising of proprietary compounds from industrial partners, thus paving way for enhanced developability that tackles manufacturing failures as well as attrition rates.European Union’s Horizon 2020 research and innovation program under the Marie Skłodowska-Curie actions grant agreemen