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Feasibility study on integrating morphological and transcriptomic data for identifying teratogenic molecular markers in zebrafish embryo toxicity testing

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compUTATIONAL TOXICOLOGY 2025年 34卷

作者： Wehr, Matthias M. Witters, Hilda Remy, Silvie Schultz, Bruce Jacobs, Marc Escher, Sylvia E. Fraunhofer Inst Toxicol & Expt Med ITEM Hannover Germany VITO Flemish Inst Technol Res NV HLTH Mol Belgium Fraunhofer Inst Algorithms & Sci Comp SCAI St Augustin Germany

To create a resource for the integration of developmental toxicity new approach methodologies (NAM) data, zebrafish embryo toxicity test (ZET) data were curated and the zeTera database was created. To capture observations of the morphological alteration potential of chemicals, the zeTera database contains experimental study designs and morphological observation data from the literature. Observations of alterations recorded in zeTera were mapped to ontologies and terms were harmonized. In addition, public transcriptomics repositories were mined for data on zebrafish embryos under chemically induced stress. The re-analyzed datasets were compiled into the zetOmics database for the identification of biomarkers of teratogenicity. To identify data-rich compounds, an overlap of both databases was formed, and compounds were grouped based on structural similarities. To identify the molecular drivers of teratogenic toxicants, Triadimefon was chosen as model compound for its well-documented teratogenic effects and known mode of action (MOA). We have compiled existing data about Triadimefon from zeTera and conducted additional testing using the ZET, with additional gene expression measurements for data gap filling. From the literature search we identified the adverse outcome pathway (AOP) of triadimefon leading to craniofacial malformations by disruption of retinoic acid metabolism. Transcriptomic response in a concentration dependent manner was observed as early as 24 h post fertilization (hpf) with consistent, statistically significant, differential expression spanning the later timepoints. A set of 5 genes (cyp26a1, dhrs3b, cyp26b1, cthrc1a, and cd248b) were selected for their differential expression pattern across time and concentration. These biomarkers were further confirmed using read across approach including data from related structures.

关键词： Zebrafish embryo toxicity Morphological alteration Teratogenicity Transcriptomics Data mining

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comparing scientific Machine Learning With Population Pharmacokinetic and Classical Machine Learning Approaches for Prediction of Drug Concentrations

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CPT-PHARMACOMETRICS & SYSTEMS PHARMACOLOGY 2025年第4期14卷 759-769页

作者： Valderrama, Diego Teplytska, Olga Koltermann, Luca Marie Trunz, Elena Schmulenson, Eduard Fritsch, Achim Jaehde, Ulrich Froehlich, Holger Fraunhofer Inst Algorithms & Sci Comp SCAI Dept Bioinformat St Augustin Germany Univ Bonn Inst Pharm Dept Clin Pharm Bonn Germany Univ Bonn Inst Comp Sci 2 Visual Comp Bonn Germany Univ Bonn Bonn Aachen Int Ctr Informat Technol B IT Bonn Germany

A variety of classical machine learning (ML) approaches has been developed over the past decade aiming to individualize drug dosages based on measured plasma concentrations. However, the interpretability of these models is challenging as they do not incorporate information on pharmacokinetic (PK) drug disposition. In this work we compare drug plasma concentraton predictions of well-known population PK (PopPK) modeling with classical machine learning models and a newly proposed scientific machine learning (MMPK-sciML) framework. MMPK-sciML allows to estimate PopPK parameters and their inter-individual variability (IIV) using multimodal covariate data of each patient and does not require assumptions about the underlying covariate relationships. A dataset of 541 fluorouracil (5FU) plasma concentrations as example for an intravenously administered drug and a dataset of 302 sunitinib and its active metabolite concentrations each as example for an orally administered drug were used for analysis. Whereas classical ML models were not able to describe the data sufficiently, MMPK-sciML allowed us to obtain accurate drug plasma concentration predictions for test patients. In case of 5FU, goodness-of-fit shows that the MMPK-sciML approach predicts drug plasma concentrations more accurately than PopPK models. For sunitinib, we observed slightly less accurate drug concentration predictions compared to PopPK. Overall, MMPK-sciML has shown promising results and should therefore be further investigated as a valuable alternative to classical PopPK modeling, provided there is sufficient training data.

关键词： concentration prediction drug therapy machine learning oncology pharmacokinetics scientific machine learning

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Estimating the causal impact of non-pharmaceutical interventions on COVID-19 spread in seven EU countries via machine learning

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sciENTIFIC REPORTS 2025年第1期15卷 1-16页

作者： Guski, Jannis Botz, Jonas Froehlich, Holger Fraunhofer Inst Algorithms & Sci Comp SCAI Dept Bioinformat D-53757 St Augustin Germany Univ Bonn Bonn Aachen Int Ctr Informat Technol B IT D-53115 Bonn Germany

During the COVID-19 pandemic, Non-Pharmaceutical Interventions (NPIs) were imposed all over Europe with the intent to reduce infection spread. However, reports on the effectiveness of those measures across different European countries are inconclusive up to now. Moreover, attempts to predict the effect of NPIs in a prospective and dynamical manner with the aim to support decision makers in future global health emergencies are largely lacking. Here, we explore causal machine learning to isolate causal effects of NPIs in observational public health data from seven EU countries, taking into account specific challenges like their sequential nature, effect heterogeneity, time-dependent confounding and lack of robustness due to violated assumptions. In a pseudo-prospective scenario planning analysis, we investigate which recommendations our model would have made during the second wave of the pandemic in Germany, demonstrating its capacity to generalize to the near future and identifying effective NPIs. In retrospect, our approach indicates that a wide range of response measures curbed COVID-19 across countries, especially in the early phases of the pandemic. Interestingly, this includes controversial interventions like strict school and border closures, but also recommendation-based policies in Sweden. Finally, we discuss important data- and modeling-related considerations that may optimize causal effect estimation in future pandemics.

关键词： Causal machine learning Policy recommendation Time-dependent confounding

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Thermodynamical and electrochemical model of a PEM electrolyzer plant in the megawatt range with a literature analysis of the fitting parameters

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INTERNATIONAL JOURNAL OF HYDROGEN ENERGY 2025年 104卷 567-583页

作者： Pfennig, Malte Schiffer, Barbara Clees, Tanja Univ Appl Sci Hsch Bonn Rhein Sieg Inst Technol Resource & Energy Efficient Engn TREE Dept Engn & Commun Grantham Allee 20 D-53757 St Augustin Germany Fraunhofer Inst Algorithms & Sci Comp SCAI D-53757 St Augustin Germany

In the coming years, the European Union plans to establish Proton Exchange Membrane (PEM) electrolyzers, each with a 100 MW capacity. However, the selection of their locations has not been systematically optimized to leverage potential benefits, such as utilizing waste heat from large facilities for district heating. Presently, there are hardly any corresponding system models in the literature dynamically simulating a PEM electrolyzer of this size. This paper introduces a first model approach for such systems, drawing on parameters from existing literature. It addresses the inconsistency found in the literature regarding the use of the exchange current density, which varies by a factor of 109. A novel optimization process is developed by using an auxiliary parameter to fit the exchange current density with a newfound condition between the anode and cathode side. The outcome is a comprehensive model of a PEM electrolyzer plant, exemplarily adapted to the Siemens Silyzer 300.

关键词： PEM Electrolyzer Hydrogen Thermal energy Modeling

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Predictive modeling to uncover Parkinson's disease characteristics that delay diagnosis

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NPJ PARKINSONS DISEASE 2025年第1期11卷 1-7页

作者： Haehnel, Tom Raschka, Tamara Klucken, Jochen Glaab, Enrico Corvol, Jean-Christophe Falkenburger, Bjoern H. Froehlich, Holger Fraunhofer Inst Algorithms & Sci Comp SCAI Dept Bioinformat St Augustin Germany TUD Dresden Univ Technol Med Fac Dept Neurol Dresden Germany TUD Dresden Univ Technol Univ Hosp Carl Gustav Carus Dresden Germany Univ Bonn Bonn Aachen Int Ctr IT Bonn Germany Univ Luxembourg Luxembourg Ctr Syst Biomed LCSB Biomed Data Sci Esch Sur Alzette Luxembourg Luxembourg Inst Hlth LIH Strassen Luxembourg Ctr Hosp Luxembourg CHL Esch Sur Alzette Luxembourg Sorbonne Univ Pitie Salpetriere Hosp AP HP Paris Brain Inst ICMCNRSInsermDept Neurol Paris France German Ctr Neurodegenerat Dis DZNE Dresden Germany

PD patients present with diverse symptoms, complicating timely diagnosis. We analyzed 1124 PD trajectories using a novel model-based approach to estimate whether diagnosis was early or late compared to cohort averages. Higher age, specific non-motor symptoms, and fast disease progression were linked to later diagnosis, while gait impairment led to earlier diagnosis. Our findings are in line with a biological definition of PD that extends beyond classical motor symptoms.

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Quantum chemical investigation of initial reactions between the molecular precursor TADB and ammonia. 1. Gas-phase reactions

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JOURNAL OF PHYSICAL CHEMISTRY A 2002年第16期106卷 4205-4216页

作者： Reinhardt, S Gastreich, M Marian, CM Fraunhofer Inst Sci Comp & Algorithms D-53754 St Augustin Germany

Cl3Si(NH)BCl2, i.e., [(trichlorosilyl)amino]dichloroborane or TADB may act as a molecular precursor in syntheses to high-demand Si/B/N ceramics. To build a ceramic such as Si3B3N7. one may use liquid ammonia as the network forming agent. The actual processes which lead to such amorphous material are not yet fully understood. With this work. we aim at a quantification of product formation probabilities during initial reactions of TADB with NH3. For this reason, we have investigated the less complicated case of various gas-phase reactions with perturbation theory, and the situation in the liquid phase Will be published separately, In more detail, the reactions comprise (a) the first and second ammonolysis of TADB, (b) the decomposition of adducts (H3N --> SiCl3NHBCl2 and H3N --> BCl2NHSiCl3.). (c) the dimerization of TADB, and finally (d) a continuing reaction of TADB with the most probable product of the first ammonolysis of TADB (which we term TACBA, trichlorosilylaminochloroboryl amine). Besides these initial steps. and because of the significance of their products, reactions of TACBA with other TACBA molecules and formations of borazine derivates have also been looked into. Our results confirm qualitative chemical intuition: The major primary process in the gas phase is a substitution at the boron center of TADB to yield TACBA. Also. it can be seen that the dissociation of the H3N --> BCl2NHSiCl3 adduct into BCl2NH2 and SiCl3NH2 is a side reaction. Albeit, in the continuing stage, the trimerization of TACBA under formation of a borazane six-membered ring releases much more energy than competing processes and is therewith important. As further reactions to be taken into consideration, we find a branching of the molecular backbone accompanied by a break-up of the Si-N-B unit in TADB and a second ammonolysis at the boron side of TACBA. Linking monomer units becomes energetically less and less probable as the ammonolyses of Si-Cl and B-Cl bonds proceed.

关键词： Molecules Substitution reactions Boron Silicon Elimination reactions

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Focused ultrasonic therapy planning: Metamodeling, optimization, visualization

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JOURNAL OF compUTATIONAL sciENCE 2014年第6期5卷 891-897页

作者： Clees, T. Hornung, N. Nikitin, I. Nikitina, L. Steffes-lai, D. Klimenko, S. Fraunhofer Inst Algorithms & Sci Comp St Augustin Germany Inst Comp Phys & Technol Protvino Russia

We present a generic approach for focused ultrasonic therapy planning on the basis of numerical simulation, multi-objective optimization, stochastic analysis and visualization in virtual environments. A realistic test case is used to demonstrate the approach. RBF metamodeling of simulation results is performed for continuous representation of two optimization objectives. The non-convex Pareto front of the objectives is determined by means of non-dominated set and local improvement algorithms. Uncertainties of metamodeling are estimated by means of a cross-validation procedure. The 3D visualization in virtual environment framework Avango allows detailed inspection of MRT images, the corresponding material model and spatial distribution of the resulting thermal dose. (C) 2014 Elsevier B.V. All rights reserved.

关键词： Healthcare Stochastic modeling and simulation Multi-objective optimization Virtual environments

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computational drug repurposing: approaches, evaluation of in silico resources and case studies

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NATURE REVIEWS DRUG DISCOVERY 2025年 1-22页

作者： Tanoli, Ziaurrehman Fernandez-Torras, Adria Ozcan, Umut Onur Kushnir, Aleksandr Nader, Kristen Michelle Gadiya, Yojana Fiorenza, Laura Ianevski, Aleksandr Vaha-Koskela, Markus Miihkinen, Mitro Seemab, Umair Leinonen, Henri Seashore-Ludlow, Brinton Tampere, Marianna Kalman, Adelinn Ballante, Flavio Benfenati, Emilio Saunders, Gary Potdar, Swapnil Garcia, Ismael Gomez Garcia-Serna, Ricard Talarico, Carmine Beccari, Andrea Rosario Schaal, Wesley Polo, Andrea Costantini, Susan Cabri, Enrico Jacobs, Marc Saarela, Jani Budillon, Alfredo Spjuth, Ola Ostling, Paivi Xhaard, Henri Quintana, Jordi Mestres, Jordi Gribbon, Philip Ussi, Anton E. Lo, Donald C. de Kort, Martin Wennerberg, Krister Fratelli, Maddalena Carreras-Puigvert, Jordi Aittokallio, Tero Univ Helsinki Inst Mol Med Finland FIMM HiLIFE Helsinki Finland Univ Helsinki ICAN Digital Precis Canc Med Flagship Helsinki Finland Helsinki Univ Hosp Helsinki Finland Bioctr Finland Drug Discovery & Chem Biol DDCB Consortium Helsinki Finland Chemotargets SL Parc Cientif Barcelona Barcelona 08028 Catalonia Spain Fraunhofer Inst Translat Med & Pharmacol ITMP D-22525 Hamburg Germany Fraunhofer Frankfurt Germany Univ Bonn Bonn Aachen Int Ctr Informat Technol B IT Bonn Germany Politecn Milan Dipartimento Elettron Informaz & Bioingn DEIB I-20133 Milan Italy IRCCS Ist Ric Farmacolog Mario Negri Milan Italy Univ Eastern Finland Fac Hlth Sci Sch Pharm Kuopio Finland Karolinska Inst Dept Oncol Pathol Sci Life Lab SciLifeLab Stockholm Sweden Karolinska Inst Chem Biol Consortium Sweden CBCS Dept Med Biochem & Biophys Stockholm Sweden European Infrastruct Translat Med EATRIS ERIC NL-1081 HZ Amsterdam Netherlands Dompe Farmaceut SpA EXSCALATE Naples Italy Uppsala Univ Dept Pharmaceut Biosci Uppsala Sweden Uppsala Univ Sci Life Lab Uppsala Sweden IRCCS Fdn G Pascale Ist Nazl Tumori Naples Italy Fraunhofer Inst Algorithms & Sci Comp SCAI D-53754 St Augustin Augustin Germany Univ Helsinki Fac Pharm Helsinki Finland Univ Girona Inst Quim Computac & Catalisi Girona 17071 Catalonia Spain Univ Copenhagen Biotech Res & Innovat Ctr Copenhagen Denmark Oslo Univ Hosp Inst Canc Res Dept Canc Genet Oslo Norway Univ Oslo Fac Med Oslo Ctr Biostat & Epidemiol OCBE Oslo Norway

Repurposing of existing drugs for new indications has attracted substantial attention owing to its potential to accelerate drug development and reduce costs. Hundreds of computational resources such as databases and predictive platforms have been developed that can be applied for drug repurposing, making it challenging to select the right resource for a specific drug repurposing project. With the aim of helping to address this challenge, here we overview computational approaches to drug repurposing based on a comprehensive survey of available in silico resources using a purpose-built drug repurposing ontology that classifies the resources into hierarchical categories and provides application-specific information. We also present an expert evaluation of selected resources and three drug repurposing case studies implemented within the Horizon Europe REMEDi4ALL project to demonstrate the practical use of the resources. This comprehensive Review with expert evaluations and case studies provides guidelines and recommendations on the best use of various in silico resources for drug repurposing and establishes a basis for a sustainable and extendable drug repurposing web catalogue.

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STATISTICAL ANALYSIS AND ROBUST OPTIMIZATION OF FORMING PROCESSES AND FORMING-TO-CRASH PROCESS CHAINS

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INTERNATIONAL JOURNAL OF MATERIAL FORMING 2010年第1-Sup期3卷 45-48页

作者： Clees, T. Steffes-Iai, D. Helbig, M. Sun, D. -Z. Fraunhofer Inst Algorithms & Sci Comp SCAI Aachen Germany Fraunhofer Inst Mech Mat IWM Aachen Germany

In this paper, a novel strategy for statistical analysis and multi-objective robust design-parameter optimization of forming processes as well as forming-to-crash process chains is discussed and results presented for a ZStE340 metal blank of a B-pillar. The strategy comprises both physical experiments and numerical simulation and is built upon a novel material and damage model as well as several software tools which allow for an efficient sensitivity, stability and robustness analysis, even for simulation results on highly resolved grids.

关键词： simulation material modeling process chains random fields robust design sensitivity optimization

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Confidence measures for protein fold recognition

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BIOINFORMATICS 2002年第6期18卷 802-812页

作者： Sommer, I Zien, A von Öhsen, N Zimmer, R Lengauer, T Fraunhofer Inst Algorithms & Sci Comp D-53754 St Augustin Germany

Motivation: We present an extensive evaluation of different methods and criteria to detect remote homologs of a given protein sequence. We investigate two associated problems: first, to develop a sensitive searching method to identify possible candidates and, second, to assign a confidence to the putative candidates in order to select the best one. For searching methods where the score distributions are known, p-values are used as confidence measure with great success. For the cases where such theoretical backing is absent, we propose empirical approximations to p-values for searching procedures. Results: As a baseline, we review the performances of different methods for detecting remote protein folds (sequence alignment and threading, with and without sequence profiles, global and local). The analysis is performed on a large representative set of protein structures. For fold recognition, we find that methods using sequence profiles generally perform better than methods using plain sequences, and that threading methods perform better than sequence alignment methods. In order to assess the quality of the predictions made, we establish and compare several confidence measures, including raw scores, Z-scores, raw score gaps, z-score gaps, and different methods of p-value estimation. We work our way from the theoretically well backed local scores towards more explorative global and threading scores. The methods for assessing the statistical significance of predictions are compared using specificity-sensitivity plots. For local alignment techniques we find that p-value methods work best, albeit computationally cheaper methods such as those based on score gaps achieve similar performance. For global methods where no theory is available methods based on score gaps work best. By using the score gap functions as the measure of confidence we improve the more powerful fold recognition methods for which p-values are unavailable.

关键词： Amino Acid Sequence computational Biology Confidence Intervals Protein Folding* Protein Structure, Tertiary Sequence Alignment/statistics & numerical data

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