| 1. |
- Harrison, Philip John, 1977-
(author)
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Deep learning approaches for image cytometry: assessing cellular morphological responses to drug perturbations
- 2023
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Doctoral thesis (other academic/artistic)abstract
- Image cytometry is the analysis of cell properties from microscopy image data and is used ubiquitously in basic cell biology, medical diagnosis and drug development. In recent years deep learning has shown impressive results for many image cytometry tasks, including image processing, segmentation, classification and detection. Deep learning enables a more data-driven and end-to-end approach than was previously possible with conventional methods. This thesis investigates deep learning-based approaches for assessing cellular morphological responses to drug perturbations. In paper I we demonstrated the benefit of combining convolutional neural networks and transfer learning for predicting mechanism of action and nucleus translocation. In paper II we showed, using convolutional and recurrent neural networks applied to time-lapse microscopy data, that it is possible to predict if mRNA delivery via nanoparticles has been effective based on cell morphology changes at time points prior to the protein production evidence of successful delivery. In paper III we used convolutional neural networks, adversarial training and privileged information to faithfully generate fluorescence imaging channels of adipocyte cells from their corresponding z-stack of brightfield images. Our models were both faithful at the fluorescence image level and at the level of the features extracted from these images, features that are commonly used for downstream analysis, including the design of effective drug therapies. In paper IV we showed that convolutional neural networks trained on brightfield image data provide similar, and in some cases superior, performance to models trained on fluorescence image data for predicting mechanism of action, due to the brightfield images possessing additional information not available in the fluorescence images. In paper V we applied deep learning models to brightfield time-lapse image data to explore the evolution of cellular morphological changes after drug administration for a diverse set of compounds, compounds that are often used as positive controls in image-based assays.
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| 2. |
- Arvidsson McShane, Staffan, 1990-
(author)
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Confidence Predictions in Pharmaceutical Sciences
- 2023
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Doctoral thesis (other academic/artistic)abstract
- The main focus of this thesis has been on Quantitative Structure Activity Relationship (QSAR) modeling using methods producing valid measures of uncertainty. The goal of QSAR is to prospectively predict the outcome from assays, such as ADMET (Absorption, Distribution, Metabolism, Excretion), toxicity and on- and off-target interactions, for novel compounds. QSAR modeling offers an appealing alternative to laboratory work, which is both costly and time-consuming, and can be applied earlier in the development process as candidate drugs can be tested in silico without requiring to synthesize them first. A common theme across the presented papers is the application of conformal and probabilistic prediction models, which are used in order to associate predictions with a level of their reliability – a desirable property that is essential in the stage of decision making. In Paper I we studied approaches on how to utilize biological assay data from legacy systems, in order to improve predictive models. This is otherwise problematic since mixing data from separate systems will cause issues for most machine learning algorithms. We demonstrated that old data could be used to augment the proper training set of a conformal predictor to yield more efficient predictions while preserving model calibration. In Paper II we studied a new approach of predicting metabolic transformations of small molecules based on transformations encoded in SMIRKS format. In this work use used the probabilistic Cross-Venn-ABERS predictor which overall worked well, but had difficulty in modeling the minority class of imbalanced datasets. In Paper III we studied metabolomics data from patients diagnosed with Multiple Sclerosis and found a set of 15 discriminatory metabolites that could be used to classify patients from a validation cohort into one of two sub types of the disease with high accuracy. We further demonstrated that conformal prediction could be useful for tracking the progression of the disease for individual patients, which we exemplified using data from a clinical trial. In Paper IV we introduced CPSign – a software for cheminformatics modeling using conformal and probabilistic methods. CPSign was compared against other regularly used methods for this task, using 32 benchmark datasets, demonstrating that CPSign produces predictive accuracy on par with the best performing methods.
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| 3. |
- Gupta, Ankit
(author)
-
Adapting Deep Learning for Microscopy: Interaction, Application, and Validation
- 2023
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Doctoral thesis (other academic/artistic)abstract
- Microscopy is an integral technique in biology to study the fundamental components of life visually. Digital microscopy and automation have enabled biologists to conduct faster and larger-scale experiments with a sharp increase in the data generated. Microscopy images contain rich but sparse information, as typically, only small regions in the images are relevant for further study. Image analysis is a crucial tool for biologists in the objective interpretation and extraction of quantitative measurements from microscopy data. Recently, deep learning techniques have shown superior performance in various image analysis tasks. The models learn feature representations from the data by optimizing for a task. However, the techniques require a significant amount of annotated data to perform well. Domain experts are required to annotate microscopy data, making it expensive and time-consuming. The models offer no insight into their prediction, and the learned features are not directly interpretable. This poses challenges to the reliable utilization of the technique in high-trust applications such as drug discovery or disease detection. High data variability in microscopy and poor generalization performance of deep learning models further increase the difficulty in general usage of the technique. The work in this thesis presents frameworks and methods to solve the practical challenges of applying deep learning in microscopy. The application-specific evaluation approaches were presented to validate the approaches, aiming to increase trust in the system. The major contributions of this work are as follows. Papers I and III present human-in-the-loop frameworks for quick adaption of deep learning to new data and for improving models' performance based on human input in visual explanations provided by the model, respectively. Paper II proposes a template-matching approach to improve user interactions in the framework proposed in Paper I. Papers III and IV present architectural modifications in the deep learning models proposed for better visual explanation and image-to-image translation, respectively. Papers IV and V present biologically relevant evaluations of approaches, i.e., analysis of the deep learning models in relation to the biological task.This thesis is aimed towards better utilization and adaptation of the DL methods and techniques to the microscopy data. We show that the annotation burden for the user can be significantly reduced by intuitive annotation frameworks and using contemporary deep-learning paradigms. We further propose architectural modifications in the models to adapt to the requirements and demonstrate the utility of application-specific analysis in microscopy.
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| 4. |
- Herman, Stephanie
(author)
-
Towards an Earlier Detection of Progressive Multiple Sclerosis using Metabolomics and Machine Learning
- 2020
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Doctoral thesis (other academic/artistic)abstract
- Decision-making guided by advanced analytics is becoming increasingly common in many fields. Implementing computationally driven healthcare solutions does, however, pose ethical dilemmas as it involves human health. Therefore, augmenting clinical expertise with advanced analytical insights to support decision-making in healthcare is probably a more feasible strategy.Multiple sclerosis is a debilitating neurological disease with two subtypes; relapsing-remitting multiple sclerosis (RRMS) and the typically late-stage progressive subtype (PMS). Progressive multiple sclerosis is a neurodegenerative phenotype, with a vague functional definition, that currently is diagnosed retrospectively. The challenge of diagnosing PMS earlier is a great example where data-driven insights might prove useful.This thesis addresses the need for an earlier detection of patients developing the progressive and neurodegenerative subtype of multiple sclerosis, using primarily metabolomics and machine learning approaches. In Paper I, the biochemical differences in cerebrospinal fluid (CSF) from RRMS and PMS patients were characterised, leading to the conclusion that it is possible to distinguish PMS patients based on biochemical alterations. In addition, pathway analysis revealed several metabolic pathways that were affected in the transition to PMS, including tryptophan metabolism and pyrimidine metabolism. In Paper II and III, the possibility of generating a concise PMS signature based on solely low-molecular measurements (III) or in combination with radiological and protein measures (II) was explored. In both cases, it was concluded that it is plausible to generate a condensed set of highly informative markers that can distinguish PMS patients from RRMS patients. In Paper III, the classifier was complemented with conformal prediction that enabled an estimate of confidence in single patient predictions and a personalised evaluation of current disease state. Finally, in Paper IV, the extracted low-molecular marker candidates were characterised in isolation, revealing that several metabolites were distinctively altered in the CSF of PMS patients, including increased levels of 4-acetamidobutanoate, 4-hydroxybenzoate and thymine.Overall, the results from this work indicate that it is possible to detect PMS at an earlier stage and that advanced analytical algorithms can support healthcare.
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| 5. |
- Wieslander, Håkan
(author)
-
Application, Optimisation and Evaluation of Deep Learning for Biomedical Imaging
- 2022
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Doctoral thesis (other academic/artistic)abstract
- Microscopy imaging is a powerful technique when studying biology at a cellular and sub-cellular level. When combined with digital image analysis it creates an invaluable tool for investigating complex biological processes and phenomena. However, imaging at the cell and sub-cellular level tends to generate large amounts of data which can be difficult to analyse, navigate and store. Despite these difficulties, large data volumes mean more information content which is beneficial for computational methods like machine learning, especially deep learning. The union of microscopy imaging and deep learning thus provides numerous opportunities for advancing our scientific understanding and uncovering interesting and useful biological insights.The work in this thesis explores various means for optimising information extraction from microscopy data utilising image analysis with deep learning. The focus is on three different imaging modalities: bright-field; fluorescence; and transmission electron microscopy. Within these modalities different learning-based image analysis and processing techniques are explored, ranging from image classification and detection to image restoration and translation. The main contributions are: (i) a computational method for diagnosing oral and cervical cancer based on smear samples and bright-field microscopy; (ii) a hierarchical analysis of whole-slide tissue images from fluorescence microscopy and introducing a confidence based measure for pixel classifications; (iii) an image restoration model for motion-degraded images from transmission electron microscopy with an evaluation of model overfitting on underlying textures; and (iv) an image-to-image translation (virtual staining) of cell images from bright-field to fluorescence microscopy, optimised for biological feature relevance. A common theme underlying all the investigations in this thesis is that the evaluation of the methods used is in relation to the biological question at hand.
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| 6. |
- Blamey, Ben, et al.
(author)
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Rapid development of cloud-native intelligent data pipelines for scientific data streams using the HASTE Toolkit
- 2021
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In: GigaScience. - : Oxford University Press. - 2047-217X. ; 10:3, s. 1-14
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Journal article (peer-reviewed)abstract
- BACKGROUND: Large streamed datasets, characteristic of life science applications, are often resource-intensive to process, transport and store. We propose a pipeline model, a design pattern for scientific pipelines, where an incoming stream of scientific data is organized into a tiered or ordered "data hierarchy". We introduce the HASTE Toolkit, a proof-of-concept cloud-native software toolkit based on this pipeline model, to partition and prioritize data streams to optimize use of limited computing resources.FINDINGS: In our pipeline model, an "interestingness function" assigns an interestingness score to data objects in the stream, inducing a data hierarchy. From this score, a "policy" guides decisions on how to prioritize computational resource use for a given object. The HASTE Toolkit is a collection of tools to adopt this approach. We evaluate with 2 microscopy imaging case studies. The first is a high content screening experiment, where images are analyzed in an on-premise container cloud to prioritize storage and subsequent computation. The second considers edge processing of images for upload into the public cloud for real-time control of a transmission electron microscope.CONCLUSIONS: Through our evaluation, we created smart data pipelines capable of effective use of storage, compute, and network resources, enabling more efficient data-intensive experiments. We note a beneficial separation between scientific concerns of data priority, and the implementation of this behaviour for different resources in different deployment contexts. The toolkit allows intelligent prioritization to be `bolted on' to new and existing systems - and is intended for use with a range of technologies in different deployment scenarios.
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| 7. |
- Dahlö, Martin
(author)
-
Approaches for Distributing Large Scale Bioinformatic Analyses
- 2021
-
Doctoral thesis (other academic/artistic)abstract
- Ever since high-throughput DNA sequencing became economically feasible, the amount of biological data has grown exponentially. This has been one of the biggest drivers in introducing high-performance computing (HPC) to the field of biology. Unlike physics and mathematics, biology education has not had a strong focus on programming or algorithmic development. This has forced many biology researchers to start learning a whole new skill set, and introduced new challenges for those managing the HPC clusters.The aim of this thesis is to investigate the problems that arise when novice users are using an HPC cluster for bioinformatics data analysis, and exploring approaches for how these can be mitigated. In paper 1 we quantify and visualise these problems and contrast them with the more computer experienced user groups already using the HPC cluster. In paper 2 we introduce a new workflow system (SciPipe), implemented as a Go library, as a way to organise and manage analysis steps. Paper 3 is aimed at cloud computing and how containerised tools can be used to run workflows without having to worry about software installations. In paper 4 we demonstrate a fully automated cloud-based system for image-based cell profiling. Starting with a robotic arm in a lab, it covers all the steps from cell culture and microscope to having the cell profiling results stored in a database and visualised in a web interface.
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| 8. |
- Ekmefjord, Morgan, et al.
(author)
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Scalable federated machine learning with FEDn
- 2022
-
In: 2022 22nd IEEE/ACM International Symposium on Cluster, Cloud and Internet Computing (CCGrid 2022). - : Institute of Electrical and Electronics Engineers (IEEE). - 9781665499569 - 9781665499576 ; , s. 555-564
-
Conference paper (peer-reviewed)abstract
- Federated machine learning promises to overcome the input privacy challenge in machine learning. By iteratively updating a model on private clients and aggregating these local model updates into a global federated model, private data is incorporated in the federated model without needing to share and expose that data. Several open software projects for federated learning have appeared. Most of them focuses on supporting flexible experimentation with different model aggregation schemes and with different privacy-enhancing technologies. However, there is a lack of open frameworks that focuses on critical distributed computing aspects of the problem such as scalability and resilience. It is a big step to take for a data scientist to go from an experimental sandbox to testing their federated schemes at scale in real-world geographically distributed settings. To bridge this gap we have designed and developed a production-grade hierarchical federated learning framework, FEDn. The framework is specifically designed to make it easy to go from local development in pseudo-distributed mode to horizontally scalable distributed deployments. FEDn both aims to be production grade for industrial applications and a flexible research tool to explore real-world performance of novel federated algorithms and the framework has been used in number of industrial and academic R&D projects. In this paper we present the architecture and implementation of FEDn. We demonstrate the framework's scalability and efficiency in evaluations based on two case-studies representative for a cross-silo and a cross-device use-case respectively.
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| 9. |
- Gupta, Ankit, et al.
(author)
-
Is brightfield all you need for MoA prediction?
- 2022
-
Conference paper (peer-reviewed)abstract
- 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.
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| 10. |
- Harrison, Philip J., et al.
(author)
-
Deep-learning models for lipid nanoparticle-based drug delivery
- 2021
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In: Nanomedicine. - : Future Medicine. - 1743-5889 .- 1748-6963. ; 16:13, s. 1097-1110
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Journal article (peer-reviewed)abstract
- Background: Early prediction of time-lapse microscopy experiments enables intelligent data management and decision-making. Aim: Using time-lapse data of HepG2 cells exposed to lipid nanoparticles loaded with mRNA for expression of GFP, the authors hypothesized that it is possible to predict in advance whether a cell will express GFP. Methods: The first modeling approach used a convolutional neural network extracting per-cell features at early time points. These features were then combined and explored using either a long short-term memory network (approach 2) or time series feature extraction and gradient boosting machines (approach 3). Results: Accounting for the temporal dynamics significantly improved performance. Conclusion: The results highlight the benefit of accounting for temporal dynamics when studying drug delivery using high-content imaging.
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| 11. |
- Harrison, Philip John, et al.
(author)
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Evaluating the utility of brightfield image data for mechanism of action prediction
- 2023
-
In: PloS Computational Biology. - : Public Library of Science (PLoS). - 1553-734X .- 1553-7358. ; 19:7
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Journal article (peer-reviewed)abstract
- 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, 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 largely correlated predictive performance for the two imaging modalities. This is promising for future studies of MoAs in time-lapse experiments for which using fluorescence images is problematic. Explorations based on explainable AI techniques also provided valuable insights regarding compounds that were better predicted by one modality over the other.
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| 12. |
- Raykova, Doroteya, 1986-, et al.
(author)
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A method for Boolean analysis of protein interactions at a molecular level
- 2022
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In: Nature Communications. - : Springer Nature. - 2041-1723. ; 13:1
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Journal article (peer-reviewed)abstract
- Determination of interactions between native proteins in cells is important for understanding function. Here the authors report MolBoolean as a method to detect interactions between endogenous proteins in subcellular compartments, using antibody-DNA conjugates for identification and signal amplification. Determining the levels of protein-protein interactions is essential for the analysis of signaling within the cell, characterization of mutation effects, protein function and activation in health and disease, among others. Herein, we describe MolBoolean - a method to detect interactions between endogenous proteins in various subcellular compartments, utilizing antibody-DNA conjugates for identification and signal amplification. In contrast to proximity ligation assays, MolBoolean simultaneously indicates the relative abundances of protein A and B not interacting with each other, as well as the pool of A and B proteins that are proximal enough to be considered an AB complex. MolBoolean is applicable both in fixed cells and tissue sections. The specific and quantifiable data that the method generates provide opportunities for both diagnostic use and medical research.
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| 13. |
- Wieslander, Håkan, et al.
(author)
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Deep learning and conformal prediction for hierarchical analysis of large-scale whole-slide tissue images
- 2021
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In: IEEE journal of biomedical and health informatics. - : Institute of Electrical and Electronics Engineers (IEEE). - 2168-2194 .- 2168-2208. ; 25:2, s. 371-380
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Journal article (peer-reviewed)abstract
- With the increasing amount of image data collected from biomedical experiments there is an urgent need for smarter and more effective analysis methods. Many scientific questions require analysis of image subregions related to some specific biology. Finding such regions of interest (ROIs) at low resolution and limiting the data subjected to final quantification at high resolution can reduce computational requirements and save time. In this paper we propose a three-step pipeline: First, bounding boxes for ROIs are located at low resolution. Next, ROIs are subjected to semantic segmentation into sub-regions at mid-resolution. We also estimate the confidence of the segmented sub-regions. Finally, quantitative measurements are extracted at high resolution. We use deep learning for the first two steps in the pipeline and conformal prediction for confidence assessment. We show that limiting final quantitative analysis to sub regions with high confidence reduces noise and increases separability of observed biological effects.
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| 14. |
- Zhang, Tianru, et al.
(author)
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Data management of scientific applications in a reinforcement learning-based hierarchical storage system
- 2024
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In: Expert systems with applications. - : Elsevier. - 0957-4174 .- 1873-6793. ; 237
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Journal article (peer-reviewed)abstract
- In many areas of data-driven science, large datasets are generated where the individual data objects are images, matrices, or otherwise have a clear structure. However, these objects can be information-sparse, and a challenge is to efficiently find and work with the most interesting data as early as possible in an analysis pipeline. We have recently proposed a new model for big data management where the internal structure and information of the data are associated with each data object (as opposed to simple metadata). There is then an opportunity for comprehensive data management solutions to account for data-specific internal structure as well as access patterns. In this article, we explore this idea together with our recently proposed hierarchical storage management framework that uses reinforcement learning (RL) for autonomous and dynamic data placement in different tiers in a storage hierarchy. Our case-study is based on four scientific datasets: Protein translocation microscopy images, Airfoil angle of attack meshes, 1000 Genomes sequences, and Phenotypic screening images. The presented results highlight that our framework is optimal and can quickly adapt to new data access requirements. It overall reduces the data processing time, and the proposed autonomous data placement is superior compared to any static or semi-static data placement policies.
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| 15. |
- Alvarsson, Jonathan, 1981-, et al.
(author)
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Predicting With Confidence : Using Conformal Prediction in Drug Discovery
- 2021
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In: Journal of Pharmaceutical Sciences. - : Elsevier. - 0022-3549 .- 1520-6017. ; 110:1, s. 42-49
-
Research review (peer-reviewed)abstract
- One of the challenges with predictive modeling is how to quantify the reliability of the models' predictions on new objects. In this work we give an introduction to conformal prediction, a framework that sits on top of traditional machine learning algorithms and which outputs valid confidence estimates to predictions from QSAR models in the form of prediction intervals that are specific to each predicted object. For regression, a prediction interval consists of an upper and a lower bound. For classification, a prediction interval is a set that contains none, one, or many of the potential classes. The size of the prediction interval is affected by a user-specified confidence/significance level, and by the nonconformity of the predicted object; i.e., the strangeness as defined by a nonconformity function. Conformal prediction provides a rigorous and mathematically proven framework for in silico modeling with guarantees on error rates as well as a consistent handling of the models' applicability domain intrinsically linked to the underlying machine learning model. Apart from introducing the concepts and types of conformal prediction, we also provide an example application for modeling ABC transporters using conformal prediction, as well as a discussion on general implications for drug discovery.
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| 16. |
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| 17. |
- Arvidsson McShane, Staffan, et al.
(author)
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Machine Learning Strategies When Transitioning between Biological Assays
- 2021
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In: Journal of Chemical Information and Modeling. - : American Chemical Society (ACS). - 1549-9596 .- 1549-960X. ; 61:7, s. 3722-3733
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Journal article (peer-reviewed)abstract
- Machine learning is widely used in drug development to predict activity in biological assays based on chemical structure. However, the process of transitioning from one experimental setup to another for the same biological endpoint has not been extensively studied. In a retrospective study, we here explore different modeling strategies of how to combine data from the old and new assays when training conformal prediction models using data from hERG and Na-v assays. We suggest to continuously monitor the validity and efficiency of models as more data is accumulated from the new assay and select a modeling strategy based on these metrics. In order to maximize the utility of data from the old assay, we propose a strategy that augments the proper training set of an inductive conformal predictor by adding data from the old assay but only having data from the new assay in the calibration set, which results in valid (well-calibrated) models with improved efficiency compared to other strategies. We study the results for varying sizes of new and old assays, allowing for discussion of different practical scenarios. We also conclude that our proposed assay transition strategy is more beneficial, and the value of data from the new assay is higher, for the harder case of regression compared to classification problems.
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| 18. |
- Ashrafian, Hutan, et al.
(author)
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Metabolomics : The Stethoscope for the Twenty-First Century
- 2021
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In: Medical principles and practice. - : S. Karger. - 1011-7571 .- 1423-0151. ; 30:4, s. 301-310
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Journal article (peer-reviewed)abstract
- Metabolomics encompasses the systematic identification and quantification of all metabolic products in the human body. This field could provide clinicians with novel sets of diagnostic biomarkers for disease states in addition to quantifying treatment response to medications at an individualized level. This literature review aims to highlight the technology underpinning metabolic profiling, identify potential applications of metabolomics in clinical practice, and discuss the translational challenges that the field faces. We searched PubMed, MEDLINE, and EMBASE for primary and secondary research articles regarding clinical applications of metabolomics. Metabolic profiling can be performed using mass spectrometry and nuclear magnetic resonance-based techniques using a variety of biological samples. This is carried out in vivo or in vitro following careful sample collection, preparation, and analysis. The potential clinical applications constitute disruptive innovations in their respective specialities, particularly oncology and metabolic medicine. Outstanding issues currently preventing widespread clinical use are scalability of data interpretation, standardization of sample handling practice, and e-infrastructure. Routine utilization of metabolomics at a patient and population level will constitute an integral part of future healthcare provision.
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| 19. |
- Braeuning, Albert, et al.
(author)
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Development of new approach methods for the identification and characterization of endocrine metabolic disruptors : a PARC project
- 2023
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In: Frontiers in Toxicology. - : Frontiers Media SA. - 2673-3080. ; 5
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Journal article (peer-reviewed)abstract
- In past times, the analysis of endocrine disrupting properties of chemicals has mainly been focused on (anti-)estrogenic or (anti-)androgenic properties, as well as on aspects of steroidogenesis and the modulation of thyroid signaling. More recently, disruption of energy metabolism and related signaling pathways by exogenous substances, so-called metabolism-disrupting chemicals (MDCs) have come into focus. While general effects such as body and organ weight changes are routinely monitored in animal studies, there is a clear lack of mechanistic test systems to determine and characterize the metabolism-disrupting potential of chemicals. In order to contribute to filling this gap, one of the project within EU-funded Partnership for the Assessment of Risks of Chemicals (PARC) aims at developing novel in vitro methods for the detection of endocrine metabolic disruptors. Efforts will comprise projects related to specific signaling pathways, for example, involving mTOR or xenobiotic-sensing nuclear receptors, studies on hepatocytes, adipocytes and pancreatic beta cells covering metabolic and morphological endpoints, as well as metabolism-related zebrafish-based tests as an alternative to classic rodent bioassays. This paper provides an overview of the approaches and methods of these PARC projects and how this will contribute to the improvement of the toxicological toolbox to identify substances with endocrine disrupting properties and to decipher their mechanisms of action.
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| 20. |
- Carreras-Puigvert, Jordi, et al.
(author)
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Artificial intelligence for high content imaging in drug discovery
- 2024
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In: Current opinion in structural biology. - : Elsevier. - 0959-440X .- 1879-033X. ; 87
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Journal article (peer-reviewed)abstract
- Artificial intelligence (AI) and high-content imaging (HCI) are contributing to advancements in drug discovery, propelled by the recent progress in deep neural networks. This review highlights AI's role in analysis of HCI data from fixed and livecell imaging, enabling novel label-free and multi-channel fluorescent screening methods, and improving compound profiling. HCI experiments are rapid and cost-effective, facilitating large data set accumulation for AI model training. However, the success of AI in drug discovery also depends on highquality data, reproducible experiments, and robust validation to ensure model performance. Despite challenges like the need for annotated compounds and managing vast image data, AI's potential in phenotypic screening and drug profiling is significant. Future improvements in AI, including increased interpretability and integration of multiple modalities, are expected to solidify AI and HCI's role in drug discovery.
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| 21. |
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| 22. |
- Fagerholm, Urban, et al.
(author)
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Advances in Predictions of Oral Bioavailability of Candidate Drugs in Man with New Machine Learning Methodology
- 2021
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In: Molecules. - : MDPI. - 1431-5157 .- 1420-3049. ; 26:9
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Journal article (peer-reviewed)abstract
- Oral bioavailability (F) is an essential determinant for the systemic exposure and dosing regimens of drug candidates. F is determined by numerous processes, and computational predictions of human estimates have so far shown limited results. We describe a new methodology where F in humans is predicted directly from chemical structure using an integrated strategy combining 9 machine learning models, 3 sets of structural alerts, and 2 physiologically-based pharmacokinetic models. We evaluate the model on a benchmark dataset consisting of 184 compounds, obtaining a predictive accuracy (Q2) of 0.50, which is successful according to a pharmaceutical industry proposal. Twenty-seven compounds were found (beforehand) to be outside the main applicability domain for the model. We compare our results with interspecies correlations (rat, mouse and dog vs. human) using the same dataset, where animal vs. human-correlations (R2) were found to be 0.21 to 0.40 and maximum prediction errors were smaller than maximum interspecies differences. We conclude that our method has sufficient predictive accuracy to be practically useful with applications in human exposure and dose predictions, compound optimization and decision making, with potential to rationalize drug discovery and development and decrease failures and overexposures in early clinical trials with candidate drugs.
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| 23. |
- Fagerholm, Urban, et al.
(author)
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Comparison between lab variability and in silico prediction errors for the unbound fraction of drugs in human plasma
- 2021
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In: Xenobiotica. - : Taylor & Francis. - 0049-8254 .- 1366-5928. ; 51:10, s. 1095-1100
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Journal article (peer-reviewed)abstract
- Variability of the unbound fraction in plasma (f(u)) between labs, methods and conditions is known to exist. Variability and uncertainty of this parameter influence predictions of the overall pharmacokinetics of drug candidates and might jeopardise safety in early clinical trials. Objectives of this study were to evaluate the variability of human in vitro f(u)-estimates between labs for a range of different drugs, and to develop and validate an in silico f(u)-prediction method and compare the results to the lab variability. A new in silico method with prediction accuracy (Q(2)) of 0.69 for log f(u) was developed. The median and maximum prediction errors were 1.9- and 92-fold, respectively. Corresponding estimates for lab variability (ratio between max and min f(u) for each compound) were 2.0- and 185-fold, respectively. Greater than 10-fold lab variability was found for 14 of 117 selected compounds. Comparisons demonstrate that in silico predictions were about as reliable as lab estimates when these have been generated during different conditions. Results propose that the new validated in silico prediction method is valuable not only for predictions at the drug design stage, but also for reducing uncertainties of f(u)-estimations and improving safety of drug candidates entering the clinical phase.
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| 24. |
- Fagerholm, Urban, et al.
(author)
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In Silico Prediction of Human Clinical Pharmacokinetics with ANDROMEDA by Prosilico : Predictions for an Established Benchmarking Data Set, a Modern Small Drug Data Set, and a Comparison with Laboratory Methods
- 2023
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In: ATLA (Alternatives to Laboratory Animals). - : SAGE Publications. - 0261-1929. ; 51:1, s. 39-54
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Journal article (peer-reviewed)abstract
- There is an ongoing aim to replace animal and in vitro laboratory models with in silico methods. Such replacement requires the successful validation and comparably good performance of the alternative methods. We have developed an in silico prediction system for human clinical pharmacokinetics, based on machine learning, conformal prediction and a new physiologically-based pharmacokinetic model, i.e. ANDROMEDA. The objectives of this study were: a) to evaluate how well ANDROMEDA predicts the human clinical pharmacokinetics of a previously proposed benchmarking data set comprising 24 physicochemically diverse drugs and 28 small drug molecules new to the market in 2021; b) to compare its predictive performance with that of laboratory methods; and c) to investigate and describe the pharmacokinetic characteristics of the modern drugs. Median and maximum prediction errors for the selected major parameters were ca 1.2 to 2.5-fold and 16-fold for both data sets, respectively. Prediction accuracy was on par with, or better than, the best laboratory-based prediction methods (superior performance for a vast majority of the comparisons), and the prediction range was considerably broader. The modern drugs have higher average molecular weight than those in the benchmarking set from 15 years earlier (ca 200 g/mol higher), and were predicted to (generally) have relatively complex pharmacokinetics, including permeability and dissolution limitations and significant renal, biliary and/or gut-wall elimination. In conclusion, the results were overall better than those obtained with laboratory methods, and thus serve to further validate the ANDROMEDA in silico system for the prediction of human clinical pharmacokinetics of modern and physicochemically diverse drugs.
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| 25. |
- Fagerholm, Urban, et al.
(author)
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In silico prediction of volume of distribution of drugs in man using conformal prediction performs on par with animal data-based models
- 2021
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In: Xenobiotica. - : Taylor & Francis. - 0049-8254 .- 1366-5928. ; 51:12, s. 1366-1371
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Journal article (peer-reviewed)abstract
- Volume of distribution at steady state (Vss) is an important pharmacokinetic endpoint. In this study we apply machine learning and conformal prediction for human Vss prediction, and make a head-to-head comparison with rat-to-man scaling, allometric scaling and the Rodgers-Lukova method on combined in silico and in vitro data, using a test set of 105 compounds with experimentally observed Vss.The mean prediction error and % with <2-fold prediction error for our method were 2.4-fold and 64%, respectively. 69% of test compounds had an observed Vss within the prediction interval at a 70% confidence level. In comparison, 2.2-, 2.9- and 3.1-fold mean errors and 69, 64 and 61% of predictions with <2-fold error was reached with rat-to-man and allometric scaling and Rodgers-Lukova method, respectively.We conclude that our method has theoretically proven validity that was empirically confirmed, and showing predictive accuracy on par with animal models and superior to an alternative widely used in silico-based method. The option for the user to select the level of confidence in predictions offers better guidance on how to optimise Vss in drug discovery applications.
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