Design, quality and validation of the EU-OPENSCREEN fragment library poised to a high-throughput screening collection.

The EU-OPENSCREEN (EU-OS) European Research Infrastructure Consortium (ERIC) is a multinational, not-for-profit initiative that integrates high-capacity screening platforms and chemistry groups across Europe to facilitate research in chemical biology and early drug discovery. Over the years, the EU-OS has assembled a high-throughput screening compound collection, the European Chemical Biology Library (ECBL), that contains approximately 100 000 commercially available small molecules and a growing number of thousands of academic compounds crowdsourced through our network of European and non-European chemists. As an extension of the ECBL, here we describe the computational design, quality control and use case screenings of the European Fragment Screening Library (EFSL) composed of 1056 mini and small chemical fragments selected from a substructure analysis of the ECBL. Access to the EFSL is open to researchers from both academia and industry. Using EFSL, eight fragment screening campaigns using different structural and biophysical methods have successfully identified fragment hits in the last two years. As one of the highlighted projects for antibiotics, we describe the screening by Bio-Layer Interferometry (BLI) of the EFSL, the identification of a 35 µM fragment hit targeting the beta-ketoacyl-ACP synthase 2 (FabF), its binding confirmation to the protein by X-ray crystallography (PDB 8PJ0), its subsequent rapid exploration of its surrounding chemical space through hit-picking of ECBL compounds that contain the fragment hit as a core substructure, and the final binding confirmation of two follow-up hits by X-ray crystallography (PDB 8R0I and 8R1V).

NMR characterization and ligand binding site of the stem loop 2 motif (s2m) from the Delta variant of SARS-CoV-2.

The stem loop 2 motif (s2m) in SARS-CoV-2 (SCoV-2) is located in the 3'-UTR. Although s2m has been reported to display characteristics of a mobile genomic element that might lead to an evolutionary advantage, its function has remained unknown. The secondary structure of the original SCoV-2 RNA sequence (Wuhan-Hu-1) was determined by NMR in late 2020, delineating the base pairing pattern and revealing substantial differences in secondary structure compared to SARS-CoV-1 (SCoV-1). The existence of a single G29742-A29756 mismatch in the upper stem of s2m leads to its destabilization and impedes a complete NMR analysis. With Delta, a variant of concern has evolved with one mutation compared to the original sequence that replaces G29742 by U29742. We show here that this mutation results in a more defined structure at ambient temperature accompanied by a rise in melting temperature. Consequently, we were able to identify over 90 % of the relevant NMR resonances using a combination of selective RNA labeling and filtered 2D NOESY as well as 4D NMR experiments. We present a comprehensive NMR analysis of the secondary structure, (sub-) nanosecond dynamics and ribose conformation of s2m Delta based on heteronuclear 13C NOE and T1 measurements and ribose carbon chemical shift-derived canonical coordinates. We further show that the G29742U mutation in Delta has no influence on the druggability of s2m compared to the Wuhan-Hu-1 sequence. With the assignment at hand, we identify the flexible regions of s2m as primary site for small molecule binding.

Biophysical Investigation of RNA ⋠DNA : DNA Triple Helix and RNA : DNA Heteroduplex Formation by the lncRNAs MEG3 and Fendrr.

Long non-coding RNAs (lncRNAs) are important regulators of gene expression and can associate with DNA as RNA : DNA heteroduplexes or RNA ⋅ DNA : DNA triple helix structures. Here, we review in vitro biochemical and biophysical experiments including electromobility shift assays (EMSA), circular dichroism (CD) spectroscopy, thermal melting analysis, microscale thermophoresis (MST), single-molecule Förster resonance energy transfer (smFRET) and nuclear magnetic resonance (NMR) spectroscopy to investigate RNA ⋅ DNA : DNA triple helix and RNA : DNA heteroduplex formation. We present the investigations of the antiparallel triplex-forming lncRNA MEG3 targeting the gene TGFB2 and the parallel triplex-forming lncRNA Fendrr with its target gene Emp2. The thermodynamic properties of these oligonucleotides lead to concentration-dependent heterogeneous mixtures, where a DNA duplex, an RNA : DNA heteroduplex and an RNA ⋅ DNA : DNA triplex coexist and their relative populations are modulated in a temperature-dependent manner. The in vitro data provide a reliable readout of triplex structures, as RNA ⋅ DNA : DNA triplexes show distinct features compared to DNA duplexes and RNA : DNA heteroduplexes. Our experimental results can be used to validate computationally predicted triple helix formation between novel disease-relevant lncRNAs and their DNA target genes.

Targeting the Main Protease (Mpro, nsp5) by Growth of Fragment Scaffolds Exploiting Structure-Based Methodologies.

The main protease Mpro, nsp5, of SARS-CoV-2 (SCoV2) is one of its most attractive drug targets. Here, we report primary screening data using nuclear magnetic resonance spectroscopy (NMR) of four different libraries and detailed follow-up synthesis on the promising uracil-containing fragment Z604 derived from these libraries. Z604 shows time-dependent binding. Its inhibitory effect is sensitive to reducing conditions. Starting with Z604, we synthesized and characterized 13 compounds designed by fragment growth strategies. Each compound was characterized by NMR and/or activity assays to investigate their interaction with Mpro. These investigations resulted in the four-armed compound 35b that binds directly to Mpro. 35b could be cocrystallized with Mpro revealing its noncovalent binding mode, which fills all four active site subpockets. Herein, we describe the NMR-derived fragment-to-hit pipeline and its application for the development of promising starting points for inhibitors of the main protease of SCoV2.

NMR 1H,19F-based screening of the four stem-looped structure 5_SL1-SL4 located in the 5'-untranslated region of SARS-CoV 2 RNA.

Development of new antiviral medication against the beta-coronavirus SARS-CoV-2 (SCoV2) is actively being pursued. Both NMR spectroscopy and crystallography as structural screening technologies have been utilised to screen the viral proteome for binding to fragment libraries. Here, we report on NMR screening of elements of the viral RNA genome with two different ligand libraries using 1H-NMR-screening experiments and 1H and 19F NMR-screening experiments for fluorinated compounds. We screened against the 5'-terminal 119 nucleotides located in the 5'-untranslated region of the RNA genome of SCoV2 and further dissected the four stem-loops into its constituent RNA elements to test specificity of binding of ligands to shorter and longer viral RNA stretches. The first library (DRTL-F library) is enriched in ligands binding to RNA motifs, while the second library (DSI-poised library) represents a fragment library originally designed for protein screening. Conducting screens with two different libraries allows us to compare different NMR screening methodologies, describe NMR screening workflows, validate the two different fragment libraries, and derive initial leads for further downstream medicinal chemistry optimisation.

The DRESDEN PLATFORM is a research hub for ultra-high dose rate radiobiology.

The recently observed FLASH effect describes the observation of normal tissue protection by ultra-high dose rates (UHDR), or dose delivery in a fraction of a second, at similar tumor-killing efficacy of conventional dose delivery and promises great benefits for radiotherapy patients. Dedicated studies are now necessary to define a robust set of dose application parameters for FLASH radiotherapy and to identify underlying mechanisms. These studies require particle accelerators with variable temporal dose application characteristics for numerous radiation qualities, equipped for preclinical radiobiological research. Here we present the DRESDEN PLATFORM, a research hub for ultra-high dose rate radiobiology. By uniting clinical and research accelerators with radiobiology infrastructure and know-how, the DRESDEN PLATFORM offers a unique environment for studying the FLASH effect. We introduce its experimental capabilities and demonstrate the platform's suitability for systematic investigation of FLASH by presenting results from a concerted in vivo radiobiology study with zebrafish embryos. The comparative pre-clinical study was conducted across one electron and two proton accelerator facilities, including an advanced laser-driven proton source applied for FLASH-relevant in vivo irradiations for the first time. The data show a protective effect of UHDR irradiation up to [Formula: see text] and suggests consistency of the protective effect even at escalated dose rates of [Formula: see text]. With the first clinical FLASH studies underway, research facilities like the DRESDEN PLATFORM, addressing the open questions surrounding FLASH, are essential to accelerate FLASH's translation into clinical practice.

Multitask Learning with Convolutional Neural Networks and Vision Transformers Can Improve Outcome Prediction for Head and Neck Cancer Patients.

Neural-network-based outcome predictions may enable further treatment personalization of patients with head and neck cancer. The development of neural networks can prove challenging when a limited number of cases is available. Therefore, we investigated whether multitask learning strategies, implemented through the simultaneous optimization of two distinct outcome objectives (multi-outcome) and combined with a tumor segmentation task, can lead to improved performance of convolutional neural networks (CNNs) and vision transformers (ViTs). Model training was conducted on two distinct multicenter datasets for the endpoints loco-regional control (LRC) and progression-free survival (PFS), respectively. The first dataset consisted of pre-treatment computed tomography (CT) imaging for 290 patients and the second dataset contained combined positron emission tomography (PET)/CT data of 224 patients. Discriminative performance was assessed by the concordance index (C-index). Risk stratification was evaluated using log-rank tests. Across both datasets, CNN and ViT model ensembles achieved similar results. Multitask approaches showed favorable performance in most investigations. Multi-outcome CNN models trained with segmentation loss were identified as the optimal strategy across cohorts. On the PET/CT dataset, an ensemble of multi-outcome CNNs trained with segmentation loss achieved the best discrimination (C-index: 0.29, 95% confidence interval (CI): 0.22-0.36) and successfully stratified patients into groups with low and high risk of disease progression (p=0.003). On the CT dataset, ensembles of multi-outcome CNNs and of single-outcome ViTs trained with segmentation loss performed best (C-index: 0.26 and 0.26, CI: 0.18-0.34 and 0.18-0.35, respectively), both with significant risk stratification for LRC in independent validation (p=0.002 and p=0.011). Further validation of the developed multitask-learning models is planned based on a prospective validation study, which has recently completed recruitment.

Comparison of 3D and 4D robustly optimized proton treatment plans for non-small cell lung cancer patients with tumour motion amplitudes larger than 5 mm.

Background and purpose: There is no consensus about an ideal robust optimization (RO) strategy for proton therapy of targets with large intrafractional motion. We investigated the plan robustness of 3D and different 4D RO strategies. Materials and methods: For eight non-small cell lung cancer patients with clinical target volume (CTV) motion >5 mm, different RO approaches were investigated: 3DRO considering the average CT (AvgCT) with a target density override, 4DRO considering three/all 4DCT phases, and 4DRO considering the AvgCT and three/all 4DCT phases. Robustness against setup/range errors, interplay effects based on breathing and machine log file data for deliveries with/without rescanning, and interfractional anatomical changes were analyzed for target coverage and OAR sparing. Results: All nominal plans fulfilled the clinical requirements with individual CTV coverage differences <2pp; 4DRO without AvgCT generated the most conformal dose distributions. Robustness against setup/range errors was best for 4DRO with AvgCT (18% more passed error scenarios than 3DRO). Interplay effects caused fraction-wise median CTV coverage loss of 3pp and missed maximum dose constraints for heart and esophagus in 18% of scenarios. CTV coverage and OAR sparing fulfilled requirements in all cases when accumulating four interplay scenarios. Interfractional changes caused less target misses for RO with AvgCT compared to 4DRO without AvgCT (≤42%/33% vs. ≥56%/44% failed single/accumulated scenarios). Conclusions: All RO strategies provided acceptable plans with equally low robustness against interplay effects demanding other mitigation than rescanning to ensure fraction-wise target coverage. 4DRO considering three phases and the AvgCT provided best compromise on planning effort and robustness.

Integrated NMR/Molecular Dynamics Determination of the Ensemble Conformation of a Thermodynamically Stable CUUG RNA Tetraloop.

Both experimental and theoretical structure determinations of RNAs have remained challenging due to the intrinsic dynamics of RNAs. We report here an integrated nuclear magnetic resonance/molecular dynamics (NMR/MD) structure determination approach to describe the dynamic structure of the CUUG tetraloop. We show that the tetraloop undergoes substantial dynamics, leading to averaging of the experimental data. These dynamics are particularly linked to the temperature-dependent presence of a hydrogen bond within the tetraloop. Interpreting the NMR data by a single structure represents the low-temperature structure well but fails to capture all conformational states occurring at a higher temperature. We integrate MD simulations, starting from structures of CUUG tetraloops within the Protein Data Bank, with an extensive set of NMR data, and provide a structural ensemble that describes the dynamic nature of the tetraloop and the experimental NMR data well. We thus show that one of the most stable and frequently found RNA tetraloops displays substantial dynamics, warranting such an integrated structural approach.

Potential margin reduction in prostate cancer proton therapy with prompt gamma imaging for online treatment verification.

The potential of proton therapy is currently limited due to large safety margins. We estimated the potential reduction of clinical margins when using prompt gamma imaging (PGI) for online treatment verification of prostate cancer. For two adaptive scenarios a potential reduction relative to clinical practice was evaluated. The use of a trolley-mounted PGI system for online treatment verification to trigger an adaptation reduced the current range margins from 7 mm to 3 mm. In a case example, the dose reduction due to reduced range margins was substantially larger compared to reduced setup margins when using pre-treatment volumetric imaging.

Detectability of Anatomical Changes With Prompt-Gamma Imaging: First Systematic Evaluation of Clinical Application During Prostate-Cancer Proton Therapy.

PURPOSE: The development of online-adaptive proton therapy (PT) is essential to overcome limitations encountered by day-to-day variations of the patient's anatomy. Range verification could play an essential role in an online feedback loop for the detection of treatment deviations such as anatomical changes. Here, we present the results of the first systematic patient study regarding the detectability of anatomical changes by a prompt-gamma imaging (PGI) slit-camera system. METHODS AND MATERIALS: For 15 patients with prostate cancer, PGI measurements were performed during 105 fractions (201 fields) with in-room control computed tomography (CT)acquisitions. Field-wise doses on control CT scans were manually classified as whether showing relevant or non-relevant anatomical changes. This manual classification of the treatment fields was then used to establish an automatic field-wise ground truth based on spot-wise dosimetric range shifts, which were retrieved from integrated depth-dose (IDD) profiles. To determine the detection capability of anatomical changes with PGI, spot-wise PGI-based range shifts were initially compared with corresponding dosimetric IDD range shifts. As final endpoint, the agreement of a developed field-wise PGI classification model with the field-wise ground truth was determined. Therefore, the PGI model was optimized and tested for a subcohort of 131 and 70 treatment fields, respectively. RESULTS: The correlation between PGI and IDD range shifts was high, ρpearson = 0.67 (p < 0.01). Field-wise binary PGI classification resulted in an area under the curve of 0.72 and 0.80 for training and test cohorts, respectively. The model detected relevant anatomical changes in the independent test cohort, with a sensitivity and specificity of 74% and 79%, respectively. CONCLUSIONS: For the first time, evidence of the detection capability of anatomical changes in prostate-cancer PT from clinically acquired PGI data is shown. This emphasizes the benefit of PGI-based range verification and demonstrates its potential for online-adaptive PT.

Chemiluminescence Biosensor for the Determination of Cardiac Troponin I (cTnI).

Cardiac vascular diseases, especially acute myocardial infarction (AMI), are one of the leading causes of death worldwide. Therefore cardio-specific biomarkers such as cardiac troponin I (cTnI) play an essential role in the field of diagnostics. In order to enable rapid and accurate measurement of cTnI with the potential of online measurements, a chemiluminescence-based immunosensor is presented as a proof of concept. A flow cell was designed and combined with a sensitive CMOS camera allowing sensitive optical readout. In addition, a microfluidic setup was established, which achieved selective and quasi-online cTnI determination within ten minutes. The sensor was tested with recombinant cTnI in phosphate buffer and demonstrated cTnI measurements in the concentration range of 2-25 µg/L. With the optimized system, a limit of detection (LoD) of 0.6 µg/L (23 pmol/L) was achieved. Furthermore, the selectivity of the immunosensor was investigated with other recombinant proteins, such as cTnT, and cTnC, at a level of 16 µg/L. No cross-reactivity could be observed. Measurements with diluted blood plasma and serum resulted in an LoD of 60 µg/L (2.4 nmol/L) and 70 µg/L (2.9 nmol/L), respectively.

A survey of practice patterns for adaptive particle therapy for interfractional changes.

Background and purpose: Anatomical changes may compromise the planned target coverage and organs-at-risk dose in particle therapy. This study reports on the practice patterns for adaptive particle therapy (APT) to evaluate current clinical practice and wishes and barriers to further implementation. Materials and methods: An institutional questionnaire was distributed to PT centres worldwide (7/2020-6/2021) asking which type of APT was used, details of the workflow, and what the wishes and barriers to implementation were. Seventy centres from 17 countries participated. A three-round Delphi consensus analysis (10/2022) among the authors followed to define recommendations on required actions and future vision. Results: Out of the 68 clinically operational centres, 84% were users of APT for at least one treatment site with head and neck being most common. APT was mostly performed offline with only two online APT users (plan-library). No centre used online daily re-planning. Daily 3D imaging was used for APT by 19% of users. Sixty-eight percent of users had plans to increase their use or change their technique for APT. The main barrier was "lack of integrated and efficient workflows". Automation and speed, reliable dose deformation for dose accumulation and higher quality of in-room volumetric imaging were identified as the most urgent task for clinical implementation of online daily APT. Conclusion: Offline APT was implemented by the majority of PT centres. Joint efforts between industry research and clinics are needed to translate innovations into efficient and clinically feasible workflows for broad-scale implementation of online APT.

A survey of practice patterns for real-time intrafractional motion-management in particle therapy.

Background and purpose: Organ motion compromises accurate particle therapy delivery. This study reports on the practice patterns for real-time intrafractional motion-management in particle therapy to evaluate current clinical practice and wishes and barriers to implementation. Materials and methods: An institutional questionnaire was distributed to particle therapy centres worldwide (7/2020-6/2021) asking which type(s) of real-time respiratory motion management (RRMM) methods were used, for which treatment sites, and what were the wishes and barriers to implementation. This was followed by a three-round DELPHI consensus analysis (10/2022) to define recommendations on required actions and future vision. With 70 responses from 17 countries, response rate was 100% for Europe (23/23 centres), 96% for Japan (22/23) and 53% for USA (20/38). Results: Of the 68 clinically operational centres, 85% used RRMM, with 41% using both rescanning and active methods. Sixty-four percent used active-RRMM for at least one treatment site, mostly with gating guided by an external marker. Forty-eight percent of active-RRMM users wished to expand or change their RRMM technique. The main barriers were technical limitations and limited resources. From the DELPHI analysis, optimisation of rescanning parameters, improvement of motion models, and pre-treatment 4D evaluation were unanimously considered clinically important future focus. 4D dose calculation was identified as the top requirement for future commercial treatment planning software. Conclusion:  A majority of particle therapy centres have implemented RRMM. Still, further development and clinical integration were desired by most centres. Joint industry, clinical and research efforts are needed to translate innovation into efficient workflows for broad-scale implementation.

NMR resonance assignment of a fibroblast growth factor 8 splicing isoform b.

The splicing isoform b of human fibroblast growth factor 8 (FGF8b) is an important regulator of brain embryonic development. Here, we report the almost complete NMR chemical shift assignment of the backbone and aliphatic side chains of FGF8b. Obtained chemical shifts are in good agreement with the previously reported X-ray data, excluding the N-terminal gN helix, which apparently forms only in complex with the receptor. The reported data provide an NMR starting point for the investigation of FGF8b interaction with its receptors and with potential drugs or inhibitors.

Consensus guide on CT-based prediction of stopping-power ratio using a Hounsfield look-up table for proton therapy.

BACKGROUND AND PURPOSE: Studies have shown large variations in stopping-power ratio (SPR) prediction from computed tomography (CT) across European proton centres. To standardise this process, a step-by-step guide on specifying a Hounsfield look-up table (HLUT) is presented here. MATERIALS AND METHODS: The HLUT specification process is divided into six steps: Phantom setup, CT acquisition, CT number extraction, SPR determination, HLUT specification, and HLUT validation. Appropriate CT phantoms have a head- and body-sized part, with tissue-equivalent inserts in regard to X-ray and proton interactions. CT numbers are extracted from a region-of-interest covering the inner 70% of each insert in-plane and several axial CT slices in scan direction. For optimal HLUT specification, the SPR of phantom inserts is measured in a proton beam and the SPR of tabulated human tissues is computed stoichiometrically at 100 MeV. Including both phantom inserts and tabulated human tissues increases HLUT stability. Piecewise linear regressions are performed between CT numbers and SPRs for four tissue groups (lung, adipose, soft tissue, and bone) and then connected with straight lines. Finally, a thorough but simple validation is performed. RESULTS: The best practices and individual challenges are explained comprehensively for each step. A well-defined strategy for specifying the connection points between the individual line segments of the HLUT is presented. The guide was tested exemplarily on three CT scanners from different vendors, proving its feasibility. CONCLUSION: The presented step-by-step guide for CT-based HLUT specification with recommendations and examples can contribute to reduce inter-centre variations in SPR prediction.

The COVID19-NMR Consortium: A Public Report on the Impact of this New Global Collaboration.

The outbreak of COVID-19 in December 2019 required the formation of international consortia for a coordinated scientific effort to understand and combat the virus. In this Viewpoint Article, we discuss how the NMR community has gathered to investigate the genome and proteome of SARS-CoV-2 and tested them for binding to low-molecular-weight binders. External factors including extended lockdowns due to the global pandemic character of the viral infection triggered the transition from locally focused collaborative research conducted within individual research groups to digital exchange formats for immediate discussion of unpublished results and data analysis, sample sharing, and coordinated research between more than 50 groups from 18 countries simultaneously. We discuss key lessons that might pertain after the end of the pandemic and challenges that we need to address.

Modulation of Aß42 Aggregation Kinetics and Pathway by Low-Molecular-Weight Inhibitors.

The aggregation of amyloid-ß 42 (Aß42) is directly related to the pathogenesis of Alzheimer's disease. Here, we have investigated the early stages of the aggregation process, during which most of the cytotoxic species are formed. Aß42 aggregation kinetics, characterized by the quantification of Aß42 monomer consumption, were tracked by real-time solution NMR spectroscopy (RT-NMR) allowing the impact that low-molecular-weight (LMW) inhibitors and modulators exert on the aggregation process to be analysed. Distinct differences in the Aß42 kinetic profiles were apparent and were further investigated kinetically and structurally by using thioflavin T (ThT) and transmission electron microscopy (TEM), respectively. LMW inhibitors were shown to have a differential impact on early-state aggregation. Insight provided here could direct future therapeutic design based on kinetic profiling of the process of fibril formation.

Automatic detection and classification of treatment deviations in proton therapy from realistically simulated prompt gamma imaging data.

BACKGROUND: A clinical study regarding the potential of range verification in proton therapy (PT) by prompt gamma imaging (PGI) is carried out at our institution. Manual interpretation of the detected spot-wise range shift information is time-consuming, highly complex, and therefore not feasible in a broad routine application. PURPOSE: Here, we present an approach to automatically detect and classify treatment deviations in realistically simulated PGI data for head-and-neck cancer (HNC) treatments using convolutional neural networks (CNNs) and conventional machine learning (ML) approaches. METHODS: For 12 HNC patients and 1 anthropomorphic head phantom (n = 13), pencil beam scanning (PBS) treatment plans were generated, and 1 field per plan was assumed to be monitored with a PGI slit camera system. In total, 386 scenarios resembling different relevant or non-relevant treatment deviations were simulated on planning and control CTs and manually classified into 7 classes: non-relevant changes (NR) and relevant changes (RE) triggering treatment intervention due to range prediction errors (±RP), setup errors in beam direction (±SE), anatomical changes (AC), or a combination of such errors (CB). PBS spots with reliable PGI information were considered with their nominal Bragg peak position for the generation of two 3D spatial maps of 16 × 16 × 16 voxels containing PGI-determined range shift and proton number information. Three complexity levels of simulated PGI data were investigated: (I) optimal PGI data, (II) realistic PGI data with simulated Poisson noise based on the locally delivered proton number, and (III) realistic PGI data with an additional positioning uncertainty of the slit camera following an experimentally determined distribution. For each complexity level, 3D-CNNs were trained on a data subset (n = 9) using patient-wise leave-one-out cross-validation and tested on an independent test cohort (n = 4). Both the binary task of detecting RE and the multi-class task of classifying the underlying error source were investigated. Similarly, four different conventional ML classifiers (logistic regression, multilayer perceptron, random forest, and support vector machine) were trained using five previously established handcrafted features extracted from the PGI data and used for performance comparison. RESULTS: On the test data, the CNN ensemble achieved a binary accuracy of 0.95, 0.96, and 0.93 and a multi-class accuracy of 0.83, 0.81, and 0.76 for the complexity levels (I), (II), and (III), respectively. In the case of binary classification, the CNN ensemble detected treatment deviations in the most realistic scenario with a sensitivity of 0.95 and a specificity of 0.88. The best performing ML classifiers showed a similar test performance. CONCLUSIONS: This study demonstrates that CNNs can reliably detect relevant changes in realistically simulated PGI data and classify most of the underlying sources of treatment deviations. The CNNs extracted meaningful features from the PGI data with a performance comparable to ML classifiers trained on previously established handcrafted features. These results highlight the potential of a reliable, automatic interpretation of PGI data for treatment verification, which is highly desired for a broad clinical application and a prerequisite for the inclusion of PGI in an automated feedback loop for online adaptive PT.