Biology subjects

Spjuth, O.

Publications and source records attributed to Spjuth, O..

At least 37 records · Page 2Linked to original sources

bioRxiv2022

Predicting Gastrointestinal Absorption of Prodrugs and their Drugs with the ANDROMEDA by Prosilico Software

AO_SCPLOWBSTRACTC_SCPLOWO_ST_ABSIntroductionC_ST_ABSSome prodrugs are developed in order to improve gastrointestinal absorption properties such as permeability and solubility/dissolution. Prediction of the uptake of prodrugs and their drugs is challening for reasons including gastrointestinal hydrolysis and active transport. Objective and MethodologyThe objective was to use the ANDROMEDA software by Prosilico to predict absorption characteristics - passive fraction absorbed (fa,passive), dose-adjusted dissolution potential (fdiss) and total fa (fa) - of prodrugs and their drugs (including drugs and their active metabolites), and to evaluate how they differ between prodrugs and drugs and the predictive accuracy of the software. Results70 prodrug-drug pairs were found and selected for the study. The mean predicted fa,passive and fdiss for the prodrugs were 0.74 and 0.94, respectively. Corresponding estimates for the drugs were 0.72 and 0.98, respectively. For non-hydrolyzed prodrugs, the median relative and absolute prediction errors for fa were 1.17-fold and 0.08, respectively. Corresponding values for drugs were 1.11-fold and 0.07, respectively. The correlation between predicted and observed fa for non-hydrolyzed ester prodrugs and drugs combined (predictive accuracy) was 0.6. ConclusionProdrugs and drugs had similar average predicted fa,passive and fdiss, and most had or were predicted to have at least 50 % fa. The fa for about 1/3 of non-hydrolyzed prodrugs was higher than for corresponding drugs, showing successful prodrug design. Adequate prediction accuracy validates ANDROMEDA for prediction of prodrug and drug absorption in man.

pharmacology and toxicology↗

bioRxiv2022

Validation of predicted conformal intervals for prediction of human clinical pharmacokinetics

IntroductionConformal prediction (CP) methodology sits on top of machine learning methods and produces prediction confidence intervals that depend on how "strange" (non-conforming) test compounds are compared to training set compounds. CP has previously been successfully applied for prediction of steady-state volume of distribution (Vss) in humans, with 69 % of observations within the prediction interval at a 70 % confidence level. We have developed CP models for a variety of human pharmacokinetic (PK) parameters and validated their predictive accuracy (predicted vs observed estimates), but not validated prediction confidence intervals for them. The main objective of this study was to predict 70 % confidence intervals for Vss, unbound fraction in plasma (fu), intrinsic metabolic clearance (CLint), fraction absorbed passively (fa,passive) and maximum fraction dissolved (fdiss) for a variety of compounds in man and investigate the consistency between prediction intervals and observed/measured values. MethodologyCP models featured in the ANDROMEDA software by Prosilico were used for prediction of 70 % confidence intervals of Vss, fu, CLint, fa,passive and fdiss for compounds from different chemical classes and with broad physicochemical variety and for small drugs marketed in 2021. Results70 % prediction confidence intervals for 217, 117, 117, 89 and 89 compounds were produced for Vss, fu, CLint, fa,passive and fdiss, respectively. 78 % (expected 70 %) of observed data were within 70 % confidence intervals for the parameters. 70 % of predictions of Vss, fu, CLint fa,passive and fdiss are expected to have errors of maximally 2-, 4- and 6-fold and 7 and 12 %, respectively, which is in line with prediction errors. These findings validate the CP methodology. ConclusionIn conclusion, the results further validate CP models and confidence intervals of ANDROMEDA for prediction of human PK.

pharmacology and toxicology↗

bioRxiv2022

Is brightfield all you need for mechanism of action prediction?

Fluorescence staining techniques, such as Cell Painting, together with fluorescence microscopy have proven invaluable for visualizing and quantifying the effects that drugs and other perturbations have on cultured cells. However, fluorescence microscopy is expensive, time-consuming, and labor-intensive, and the stains applied can be cytotoxic, interfering with the activity under study. The simplest form of microscopy, brightfield microscopy, lacks these downsides, but the images produced have low contrast and the cellular compartments are difficult to discern. Nevertheless, by harnessing deep learning, these brightfield images may still be sufficient for various predictive purposes. In this study, we compared the predictive performance of models trained on fluorescence images to those trained on brightfield images for predicting the mechanism of action (MoA) of different drugs. We also extracted CellProfiler features from the fluorescence images and used them to benchmark the performance. Overall, we found comparable and correlated predictive performance for the two imaging modalities. This is promising for future studies of MoAs in time-lapse experiments.

bioinformatics↗

bioRxiv2022

Combining molecular and cell painting image data for mechanism of action prediction

The mechanism of action (MoA) of a compound describes the biological interaction through which it produces a pharmacological effect. Multiple data sources can be used for the purpose of predicting MoA, including compound structural information, and various assays, such as those based on cell morphology, transcriptomics and metabolomics. In the present study we explored the benefits and potential additive/synergistic effects of combining structural information, in the form of Morgan fingerprints, and morphological information, in the form of five-channel Cell Painting image data. For a set of 10 well represented MoA classes, we compared the performance of deep learning models trained on the two datasets separately versus a model trained on both datasets simultaneously. On a held-out test set we obtained a macro-averaged F1 score of 0.58 when training on only the structural data, 0.81 when training on only the image data, and 0.92 when training on both together. Thus indicating clear additive/synergistic effects and highlighting the benefit of integrating multiple data sources for MoA prediction.

bioinformatics↗

bioRxiv2022

ANDROMEDA by Prosilico Software Successfully Predicts Human Clinical Pharmacokinetics of 70 Drugs Out of Reach for In Vitro Methods

AO_SCPLOWBSTRACTC_SCPLOWO_ST_ABSIntroductionC_ST_ABSIn vitro measurements and predictions of human clinical pharmacokinetics (PK) are sometimes hindered and made impossible due to factors such as extensive binding to materials, low methodological sensitivity and large variability. MethodsThe objective was to find compounds out of reach for in vitro PK-methods and (if possible) predict corresponding human clinical estimates using the ANDROMEDA by Prosilico software. In vitro methods selected for the investigation were human microsomes and hepatocytes for measuring and predicting intrinsic hepatic metabolic clearance (CLint), Caco-2 and Ralph Russ canine kidney cells (RRCK) cells for measuring apparent intestinal permeability (Papp) for prediction of fraction absorbed (fa), plasma for measurement and estimation of unbound fraction (fu), and water and buffers for measuring solubility (S) for prediction of in vivo dissolution potential (fdiss). Results and ConclusionAs many as 329 non-quantifiable in vitro PK-measurements for 300 compounds were found in the literature: 191 for CLint, 101 for Papp, 11 for fu and 26 for S. ANDROMEDA was successful in predicting all corresponding clinical PK-estimates for the selection of compounds with non-quantifiable in vitro PK, and predicted estimates (1.6-fold median prediction error; n=159) were generally in line with observed in vivo data and results/problems at in vitro laboratories. Thus, ANDROMEDA is applicable for predicting human clinical PK for compounds out of reach for laboratory methods.

pharmacology and toxicology↗

bioRxiv2022

Prediction and Classification of the Uptake and Disposition of Antidepressants and New CNS-Active Drugs in the Human Brain using the ANDROMEDA by Prosilico Software and Brainavailability-Matrix

AO_SCPLOWBSTRACTC_SCPLOWO_ST_ABSBackgroundC_ST_ABSPassive blood-brain barrier permeability (BBB Pe), fraction bound to brain tissue (fb,brain) and efflux by transport proteins MDR-1 and BCRP are essential determinants for the brain uptake and disposition of drugs. MethodsThe main objective of the study was to use the software ANDROMEDA by Prosilico to predict passive BBB Pe- and fb,brain-classes and MDR-1- and BCRP-specificities for various classes of antidepressants and for CNS-active small drugs marketed during 2020 and 2021, and then to position them according to a new 2-dimensional Brainavailability-Matrix (8 passive BBB Pe x 4 fb,brain classes, where class 11 has highest and 84 lowest values/brainavailability). Predicted estimates were used, except for cases where measured values were available. Results and ConclusionResults for 53 drugs show that adequate CNS uptake and disposition are achieved for compounds placed in the zones for low, moderate and high brainavailability, despite efflux. They also show that high brainavailability and efflux are common for CNS-active drugs and that modern CNS-active drugs generally have lower brainavailability than older antidepressive drugs. Furthermore, they demonstrate that ANDROMEDA by Prosilico and the new Brainavailability-Matrix are applicable for prediction, optimization and positioning of CNS uptake and disposition of drugs and drug candidates in man.

pharmacology and toxicology↗

bioRxiv2022

Prediction of Biopharmaceutical Characteristics of PROTACs using the ANDROMEDA by Prosilico Software

BackgroundPROTACs are comparably large and flexible compounds with limited solubility (S) and permeability (Pe). It is crucial to better understand, predict and optimize their human clinical pharmacokinetics (PK). MethodsThe main objective was to use the ANDROMEDA by Prosilico software to predict the human clinical in vivo dissolution potential (fdiss) and fraction absorbed (fa) of 23 PROTACs at a dose level of 50 mg and to explore whether there is any relationship between in vitro S and in silico predicted in vivo fdiss. ResultsIn silico predictions showed that the PROTACs are effluxed by intestinal transporters and have limited fdiss (34 to 98 %), permeability and fa (13 to 58 %) in man. For some PROTACs this may be a major obstacle and jeopardize the clinical development programs, especially in cases of required high oral dose. A modest relationship between in vitro S and predicted in vivo fdiss was demonstrated (R2=0.26). Predicted human fa (27 %) and oral bioavailability (20 %) of ARV-110 (a PROTAC with some available in vivo PK data in rodents and man) were consistent with data obtained in rodents (estimated fa approximately 30-40 %; measured oral bioavailability 27-38 %). Laboratories were unable to quantify S for 7 (30 %) of the PROTACs. In contrast, ANDROMEDA could predict parameters for all. ConclusionANDROMEDA predicted fdiss and fa for all the chosen PROTACs and showed limited fdiss, Pe and fa and dose-dependent fdiss and fa. One available example shows promise for the applicability of ANDROMEDA for predicting biopharmaceutics of PROTACs in vivo in man. Weak to modest correlations between S and fdiss and a considerable portion of compounds with non-quantifiable S limit the use of S-data to predict the uptake of PROTACs.

pharmacology and toxicology↗

bioRxiv2022

Using the ANDROMEDA by Prosilico Software for Prediction of the Human Pharmacokinetics of 4 Compounds of Natural Origin - Colistin, Curucumin, UCN-01 and Voclosporin

AO_SCPLOWBSTRACTC_SCPLOWO_ST_ABSBackgroundC_ST_ABSIt is important that pharmacokinetic (PK) prediction methods are validated, and also for compounds with varying physicochemical properties, molecular weights and PK characteristics. MethodsThe objective was to investigate how well the ANDROMEDA by Prosilico software predicts the clinical PK of four compounds of natural origin and with PK obstacles, not yet fully characterized PK, and/or inaccurate lab method-based predictions - colistin (negligible absorption, good metabolic stability, significant excretion), curucumin (low solubility, apparently poor bioavailability), UCN-01 (extremely high degree of plasma protein binding, metabolic stability, long half-life and poor PK prediction) and voclosporin (poorly understood PK). ResultsAll categorial predictions except one were correct, and the median prediction error was 2.5-fold. Largest prediction errors were found for the unbound fraction in plasma (>24-fold), clearance (178-fold) and half-life (90-fold) of UCN-01. Corresponding errors for clearance and half-life obtained with allometry were greater, 5800- and 145-fold, respectively. Extremely high affinity for alpha1-acid glycoprotein could explain these large prediction errors for this compound. A substantial amount of data and knowledge was added with the predictions. ConclusionDespite challenging compounds and PK, predictions were comparably good. The results further validated ANDROMEDA by Prosilico for human clinical PK-predictions.

pharmacology and toxicology↗

bioRxiv2022

Merging Bioactivity Predictions from Cell Morphology and Chemical Fingerprint Models by Leveraging Similarity to Training Data

The applicability domain of machine learning models trained on structural fingerprints for the prediction of biological endpoints is often limited by the lack of diversity of chemical space of the training data. In this work, we developed similarity-based merger models which combined the outputs of individual models trained on cell morphology (based on Cell Painting) and chemical structure (based on chemical fingerprints) and the structural and morphological similarities of the compounds in the test dataset to compounds in the training dataset. We applied these similarity-based merger models using logistic regression models on the predictions and similarities as features and predicted assay hit calls of 177 assays from ChEMBL, PubChem and the Broad Institute (where the required Cell Painting annotations were available). We found that the similarity-based merger models outperformed other models with an additional 20% assays (79 out of 177 assays) with an AUC>0.70 compared with 65 out of 177 assays using structural models and 50 out of 177 assays using Cell Painting models. Our results demonstrated that similarity-based merger models combining structure and cell morphology models can more accurately predict a wide range of biological assay outcomes and further expanded the applicability domain by better extrapolating to new structural and morphology spaces. O_FIG_DISPLAY_L [Figure 1] M_FIG_DISPLAY O_FLOATNOFigure:C_FLOATNO For TOC Only C_FIG_DISPLAY

bioinformatics↗

bioRxiv2022

Designing microplate layouts using artificial intelligence

Microplates are indispensable in large-scale biomedical experiments but the physical location of samples and controls on the microplate can significantly affect the resulting data and quality metric values. We introduce a new method based on constraint programming for designing microplate layouts that reduces unwanted bias and limits the impact of batch effects after error correction and normalisation. We demonstrate that our method applied to dose-response experiments leads to more accurate regression curves and lower errors when estimating IC50/EC50, and for drug screening leads to increased sensitivity, when compared to random layouts. It also reduces the risk of inflated scores from common microplate quality assessment metrics such as Z factor and SSMD. We make our method available via a suite of tools (PLAID) including a reference constraint model, a web application, and Python notebooks to evaluate and compare designs when planning microplate experiments.

bioinformatics↗

bioRxiv2022

Morphological profiling of environmental chemicals enables efficient and untargeted exploration of combination effects

Environmental chemicals are commonly studied one at a time, and there is a need to advance our understanding of the effect of exposure to their combinations. Here we apply high-content microscopy imaging of cells stained with multiplexed dyes (Cell Painting) to profile the effects of Cetyltrimethylammonium bromide (CTAB), Bisphenol A (BPA), and Dibutyltin dilaurate (DBTDL) exposure on four human cell lines; both individually and in all combinations. We show that morphological features can be used with multivariate data analysis to discern between exposures from individual compounds, concentrations, and combinations. CTAB and DBTDL induced concentration-dependent morphological changes across the four cell lines, and BPA exacerbated morphological effects when combined with CTAB and DBTDL. Combined exposure to CTAB and BPA induced changes on the ER, Golgi apparatus, nucleoli and cytoplasmic RNA in one of the cell lines. Different responses between cell lines indicate that multiple cell types are needed when assessing combination effects. The rapid and relatively low-cost experiments combined with high information content makes Cell Painting an attractive methodology for future studies of combination effects. All data in the study is made publicly available on Figshare. HighlightsO_LIAssessment of combination effects of BPA, CTAB and DBTDL on four human cell lines C_LIO_LIMorphological profiling/Cell Painting captures dose and combination dependent effects C_LIO_LIBPA exacerbated morphological effects when combined with CTAB and DBTDL. C_LIO_LICell models of diverse origin are needed when profiling environmental chemicals C_LI

pharmacology and toxicology↗

bioRxiv2022

Integrating Cell Morphology with Gene Expression and Chemical Structure to Aid Mitochondrial Toxicity Detection

Mitochondrial toxicity is an important safety endpoint in drug discovery. Models based solely on chemical structure for predicting mitochondrial toxicity are currently limited in accuracy and applicability domain to the chemical space of the training compounds. In this work, we aimed to utilize both -omics and chemical data to push beyond the state-of-the-art. We combined Cell Painting and Gene Expression data with chemical structural information from Morgan fingerprints for 382 chemical perturbants tested in the Tox21 mitochondrial membrane depolarization assay. We observed that mitochondrial toxicants differ from non-toxic compounds in morphological space and identified compound clusters having similar mechanisms of mitochondrial toxicity, thereby indicating that morphological space provides biological insights related to mechanisms of action of this endpoint. We further showed that models combining Cell Painting, Gene Expression features and Morgan fingerprints improved model performance on an external test set of 244 compounds by 60% (in terms of F1 score) and improved extrapolation to new chemical space. The performance of our combined models was comparable with dedicated in vitro assays for mitochondrial toxicity. Our results suggest that combining chemical descriptors with biological readouts enhances the detection of mitochondrial toxicants, with practical implications in drug discovery. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=114 SRC="FIGDIR/small/475326v2_ufig1.gif" ALT="Figure 1"> View larger version (28K): org.highwire.dtl.DTLVardef@f29341org.highwire.dtl.DTLVardef@1a1970borg.highwire.dtl.DTLVardef@1c3581dorg.highwire.dtl.DTLVardef@1ce00d4_HPS_FORMAT_FIGEXP M_FIG O_FLOATNOGraphical AbstractC_FLOATNO C_FIG

bioinformatics↗

bioRxiv2021

An Open-Source Modular Framework for Automated Pipetting and Imaging Applications

The number of samples in biological experiments are continuously increasing, but complex protocols and human experimentation in many cases lead to suboptimal data quality and hence difficulties in reproducing scientific findings. Laboratory automation can alleviate many of these problems by precisely reproducing machine-readable protocols. These instruments generally require high up-front investments and due to lack of open APIs they are notoriously difficult for scientists to customize and control outside of the vendor-supplied software. Here, we demonstrate automated, high-throughput experiments for interdisciplinary research in life science that can be replicated on a modest budget, using open tools to ensure reproducibility by combining the tools Openflexure, Opentrons, ImJoy and UC2. Our automated sample preparation and imaging pipeline can easily be replicated and established in many laboratories as well as in educational contexts through easy-to-understand algorithms and easy-to-build microscopes. Additionally, the creation of feedback loops, with later pipetting or imaging steps depending on analysis of previously acquired images, enables the realization of smart microscopy experiments, featuring completely autonomously performed experiments. All documents and source-files are publicly available (https://beniroquai.github.io/Hi2) to prove the concept of smart lab automation using inexpensive, open tools. We believe this democratizes access to the power and repeatability of automated experiments.

cell biology↗

bioRxiv2021

A rapid phenomics workflow for the in vitro identification of antiviral drugs

BackgroundThe current COVID-19 pandemic has highlighted the need for new and fast methods to identify novel or repurposed therapeutic drugs. Here we present a method for untargeted phenotypic drug screening of virus-infected cells, combining Cell Painting with antibody-based detection of viral infection in a single assay. We designed an image analysis pipeline for segmentation and classification of virus-infected and non-infected cells, followed by extraction of morphological properties. ResultsWe show that the methodology can successfully capture virus-induced phenotypic signatures of MRC-5 human lung fibroblasts infected with Human coronavirus 229E (CoV-229E). Moreover, we demonstrate that our method can be used in phenotypic drug screening using a panel of nine host- and virus-targeting antivirals. Treatment with effective antiviral compounds reversed the morphological profile of the host cells towards a non-infected state. ConclusionsThe method can be used in drug discovery for morphological profiling of novel antiviral compounds on both infected and non-infected cells.

pharmacology and toxicology↗

bioRxiv2020

Rapid development of cloud-native intelligent data pipelines for scientific data streams using the HASTE Toolkit

This paper introduces the HASTE Toolkit, a cloud-native software toolkit capable of partitioning data streams in order to prioritize usage of limited resources. This in turn enables more efficient data-intensive experiments. We propose a model that introduces automated, autonomous decision making in data pipelines, such that a stream of data can be partitioned into a tiered or ordered data hierarchy. Importantly, the partitioning is online and based on data content rather than a priori metadata. At the core of the model are interestingness functions and policies. Interestingness functions assign a quantitative measure of interestingness to a single data object in the stream, an interestingness score. Based on this score, a policy guides decisions on how to prioritize computational resource usage for a given object. The HASTE Toolkit is a collection of tools to adapt data stream processing to this pipeline model. The result is smart data pipelines capable of effective or even optimal use of e.g. storage, compute and network bandwidth, to support experiments involving rapid processing of scientific data characterized by large individual data object sizes. We demonstrate the proposed model and our toolkit through two microscopy imaging case studies, each with their own interestingness functions, policies, and data hierarchies. The first deals with a high content screening experiment, where images are analyzed in an on-premise container cloud with the goal of prioritizing the images for storage and subsequent computation. The second considers edge processing of images for upload into the public cloud for a real-time control loop for a transmission electron microscope. Key PointsO_LIWe propose a pipeline model for building intelligent pipelines for streams, accounting for actual information content in data rather than a priori metadata, and present the HASTE Toolkit, a cloud-native software toolkit for supporting rapid development according to the proposed model. C_LIO_LIWe demonstrate how the HASTE Toolkit enables intelligent resource optimization in two image analysis case studies based on a) high-content imaging and b) transmission electron microscopy. C_LIO_LIWe highlight the challenges of storage, processing and transfer in streamed high volume, high velocity scientific data for both cloud and cloud-edge use cases. C_LI

bioinformatics↗

bioRxiv2020

Deep learning models for lipid-nanoparticle-based drug delivery

Large-scale time-lapse microscopy experiments are useful to understand delivery and expression in RNA-based therapeutics. The resulting data has high dimensionality and high (but sparse) information content, making it challenging and costly to store and process. Early prediction of experimental outcome enables intelligent data management and decision making. We start from time-lapse data of HepG2 cells exposed to lipid-nanoparticles loaded with mRNA for expression of green fluorescent protein (GFP). We hypothesize that it is possible to predict if a cell will express GFP or not based on cell morphology at time-points prior to GFP expression. Here we present results on per-cell classification (GFP expression/no GFP expression) and regression (level of GFP expression) using three different approaches. In the first approach we use a convolutional neural network extracting per-cell features at each time point. We then utilize the same features combined with: a long-short-term memory (LSTM) network encoding temporal dynamics (approach 2); and time-series feature extraction using the python package tsfresh followed by principal component analysis and gradient boosting machines (approach 3), to reach a final classification or regression result. Application of the three approaches to a previously unanalyzed test set of cells showed good predictive performance of all three approaches but that accounting for the temporal dynamics via LSTMs or tsfresh led to significantly improved performance. The predictions made by the LSTM and tsfresh applications were not significantly different. The results highlight the benefit of accounting for temporal dynamics when studying drug delivery using high content imaging.

cell biology↗

bioRxiv2019

Evaluation of gene expression and phenotypic profiling data as quantitative descriptors for predicting drug targets and mechanisms of action

Profiling drug leads by means of in silico and in vitro assays as well as omics is widely used in drug discovery for safety and efficacy predictions. In this study, we evaluate the performance of machine learning models trained on data from gene expression and phenotypic profiling assays, with models trained on chemical structure descriptors, for prediction of various drug mechanisms of action and target proteins. Models for several hundred mechanisms of actions and targets were trained using data on 1484 compounds characterized in both gene expression using L1000 profiles, and phenotypic profiling with cell painting assay. The results indicate that the accuracy of the three profiling technologies varies for different endpoints, and indicate a clear potential synergistic effect if these methods are combined. We also study the effect of predictive accuracy of data from different cell lines for L1000 profiles, showing that the choice of cell line has a non-negligible effect on the predictive accuracy. The results strengthen the idea of integrated approaches for predicting drug targets and mechanisms of action in preclinical drug discovery.

bioinformatics↗

bioRxiv2018

Galaxy-Kubernetes integration: scaling bioinformatics workflows in the cloud

Making reproducible, auditable and scalable data-processing analysis workflows is an important challenge in the field of bioinformatics. Recently, software containers and cloud computing introduced a novel solution to address these challenges. They simplify software installation, management and reproducibility by packaging tools and their dependencies. In this work we implemented a cloud provider agnostic and scalable container orchestration setup for the popular Galaxy workflow environment. This solution enables Galaxy to run on and offload jobs to most cloud providers (e.g. Amazon Web Services, Google Cloud or OpenStack, among others) through the Kubernetes container orchestrator. AvailabilityAll code has been contributed to the Galaxy Project and is available (since Galaxy 17.05) at https://github.com/galaxyproject/ in the galaxy and galaxy-kubernetes repositories. https://public.phenomenal-h2020.eu/ is an example deployment. Suppl. InformationSupplementary Files are available online. Contactpmoreno@ebi.ac.uk, European Molecular Biology Laboratory, EMBL-EBI, Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SD, UK, Tel: +44-1223-494267, Fax: +44-1223-484696.

bioinformatics↗