Three million images and morphological profiles of cells treated with matched chemical and genetic perturbations.

The identification of genetic and chemical perturbations with similar impacts on cell morphology can elucidate compounds' mechanisms of action or novel regulators of genetic pathways. Research on methods for identifying such similarities has lagged due to a lack of carefully designed and well-annotated image sets of cells treated with chemical and genetic perturbations. Here we create such a Resource dataset, CPJUMP1, in which each perturbed gene's product is a known target of at least two chemical compounds in the dataset. We systematically explore the directionality of correlations among perturbations that target the same protein encoded by a given gene, and we find that identifying matches between chemical and genetic perturbations is a challenging task. Our dataset and baseline analyses provide a benchmark for evaluating methods that measure perturbation similarities and impact, and more generally, learn effective representations of cellular state from microscopy images. Such advancements would accelerate the applications of image-based profiling of cellular states, such as uncovering drug mode of action or probing functional genomics.

Machine Learning Assisted Hit Prioritization for High Throughput Screening in Drug Discovery.

Efficient prioritization of bioactive compounds from high throughput screening campaigns is a fundamental challenge for accelerating drug development efforts. In this study, we present the first data-driven approach to simultaneously detect assay interferents and prioritize true bioactive compounds. By analyzing the learning dynamics during training of a gradient boosting model on noisy high throughput screening data using a novel formulation of sample influence, we are able to distinguish between compounds exhibiting the desired biological response and those producing assay artifacts. Therefore, our method enables false positive and true positive detection without relying on prior screens or assay interference mechanisms, making it applicable to any high throughput screening campaign. We demonstrate that our approach consistently excludes assay interferents with different mechanisms and prioritizes biologically relevant compounds more efficiently than all tested baselines, including a retrospective case study simulating its use in a real drug discovery campaign. Finally, our tool is extremely computationally efficient, requiring less than 30 s per assay on low-resource hardware. As such, our findings show that our method is an ideal addition to existing false positive detection tools and can be used to guide further pharmacological optimization after high throughput screening campaigns.

AIDDISON: Empowering Drug Discovery with AI/ML and CADD Tools in a Secure, Web-Based SaaS Platform.

The widespread proliferation of artificial intelligence (AI) and machine learning (ML) methods has a profound effect on the drug discovery process. However, many scientists are reluctant to utilize these powerful tools due to the steep learning curve typically associated with them. AIDDISON offers a convenient, secure, web-based platform for drug discovery, addressing the reluctance of scientists to adopt AI and ML methods due to the steep learning curve. By seamlessly integrating generative models, ADMET property predictions, searches in vast chemical spaces, and molecular docking, AIDDISON provides a sophisticated platform for modern drug discovery. It enables less computer-savvy scientists to utilize these powerful tools in their daily activities, as demonstrated by an example of identifying a valuable set of molecules for lead optimization. With AIDDISON, the benefits of AI/ML in drug discovery are accessible to all.

Practical guidelines for the use of gradient boosting for molecular property prediction.

Decision tree ensembles are among the most robust, high-performing and computationally efficient machine learning approaches for quantitative structure-activity relationship (QSAR) modeling. Among them, gradient boosting has recently garnered particular attention, for its performance in data science competitions, virtual screening campaigns, and bioactivity prediction. However, different variants of gradient boosting exist, the most popular being XGBoost, LightGBM and CatBoost. Our study provides the first comprehensive comparison of these approaches for QSAR. To this end, we trained 157,590 gradient boosting models, which were evaluated on 16 datasets and 94 endpoints, comprising 1.4 million compounds in total. Our results show that XGBoost generally achieves the best predictive performance, while LightGBM requires the least training time, especially for larger datasets. In terms of feature importance, the models surprisingly rank molecular features differently, reflecting differences in regularization techniques and decision tree structures. Thus, expert knowledge must always be employed when evaluating data-driven explanations of bioactivity. Furthermore, our results show that the relevance of each hyperparameter varies greatly across datasets and that it is crucial to optimize as many hyperparameters as possible to maximize the predictive performance. In conclusion, our study provides the first set of guidelines for cheminformatics practitioners to effectively train, optimize and evaluate gradient boosting models for virtual screening and QSAR applications.

SUMO: In Silico Sequence Assessment Using Multiple Optimization Parameters.

To select the most promising screening hits from antibody and VHH display campaigns for subsequent in-depth profiling and optimization, it is highly desirable to assess and select sequences on properties beyond only their binding signals from the sorting process. In addition, developability risk criteria, sequence diversity, and the anticipated complexity for sequence optimization are relevant attributes for hit selection and optimization. Here, we describe an approach for the in silico developability assessment of antibody and VHH sequences. This method not only allows for ranking and filtering multiple sequences with regard to their predicted developability properties and diversity, but also visualizes relevant sequence and structural features of potentially problematic regions and thereby provides rationales and starting points for multi-parameter sequence optimization.

Longitudinal data on humoral response and neutralizing antibodies against SARS-CoV-2 Omicron BA.1 and subvariants BA.4/5 and BQ.1.1 after COVID-19 vaccination in cancer patients.

PURPOSE: The SARS-CoV-2 Omicron variant of concern (VOC) and subvariants like BQ.1.1 demonstrate immune evasive potential. Little is known about the efficacy of booster vaccinations regarding this VOC and subvariants in cancer patients. This study is among the first to provide data on neutralizing antibodies (nAb) against BQ.1.1. METHODS: Cancer patients at our center were prospectively enrolled between 01/2021 and 02/2022. Medical data and blood samples were collected at enrollment and before and after every SARS-CoV-2 vaccination, at 3 and 6 months. RESULTS: We analyzed 408 samples from 148 patients (41% female), mainly with solid tumors (85%) on active therapy (92%; 80% chemotherapy). SARS-CoV-2 IgG and nAb titers decreased over time, however, significantly increased following third vaccination (p < 0.0001). NAb (ND50) against Omicron BA.1 was minimal prior and increased significantly after the third vaccination (p < 0.0001). ND50 titers against BQ.1.1 after the third vaccination were significantly lower than against BA.1 and BA.4/5 (p < 0.0001) and undetectable in half of the patients (48%). Factors associated with impaired immune response were hematologic malignancies, B cell depleting therapy and higher age. Choice of vaccine, sex and treatment with chemo-/immunotherapy did not influence antibody response. Patients with breakthrough infections had significantly lower nAb titers after both 6 months (p < 0.001) and the third vaccination (p = 0.018). CONCLUSION: We present the first data on nAb against BQ.1.1 following the third vaccination in cancer patients. Our results highlight the threat that new emerging SARS-CoV-2 variants pose to cancer patients and support efforts to apply repeated vaccines. Since a considerable number of patients did not display an adequate immune response, continuing to exhibit caution remains reasonable.

3D zero-echo time and 3D T1-weighted gradient-echo MRI sequences as an alternative to CT for the evaluation of the lumbar facet joints and lumbosacral transitional vertebrae.

BACKGROUND: Computed tomography (CT) is the reference standard for assessment of the bone. Magnetic resonance imaging (MRI) developments enable a CT-like visualization of the osseous structures. PURPOSE: To assess the diagnostic performance of 3D zero-echo time (3D-ZTE) and 3D T1-weighted gradient-echo (3D-T1GRE) MRI sequences for the evaluation of lumbar facet joints (LFJs) and the detection of lumbosacral transitional vertebrae (LSTV) using CT as the reference standard. MATERIAL AND METHODS: In total, 87 adult patients were included in this prospective study. Evaluation of degenerative changes of the facet joints at the L3/L4, L4/L5, and L5/S1 levels on both sides was performed by two readers using a 4-point Likert scale. LSTV were classified according to Castelvi et al. Image quality was quantitatively measured using the signal-to-noise (SNR) and contrast-to-noise (CNR) ratios. Intra-reader, inter-reader, and inter-modality reliability were calculated using Cohen's kappa statistic. RESULTS: Intra-reader agreement for 3D-ZTE, 3D-T1GRE, and CT was 0.607, 0.751, and 0.856 and inter-reader agreement was 0.535, 0.563, and 0.599, respectively. The inter-modality agreement between 3D-ZTE and CT was 0.631 and between 3D-T1GRE and CT 0.665. A total of LSTV were identified in both MR sequences with overall comparable accuracy compared to CT. Mean SNR for bone, muscle, and fat was highest for 3D-T1GRE and mean CNR was highest for CT. CONCLUSION: 3D-ZTE and 3D-T1GRE MRI sequences can assess the LFJs and LSTV and may serve as potential alternatives to CT.

Tuning gradient boosting for imbalanced bioassay modelling with custom loss functions.

While in the last years there has been a dramatic increase in the number of available bioassay datasets, many of them suffer from extremely imbalanced distribution between active and inactive compounds. Thus, there is an urgent need for novel approaches to tackle class imbalance in drug discovery. Inspired by recent advances in computer vision, we investigated a panel of alternative loss functions for imbalanced classification in the context of Gradient Boosting and benchmarked them on six datasets from public and proprietary sources, for a total of 42 tasks and 2 million compounds. Our findings show that with these modifications, we achieve statistically significant improvements over the conventional cross-entropy loss function on five out of six datasets. Furthermore, by employing these bespoke loss functions we are able to push Gradient Boosting to match or outperform a wide variety of previously reported classifiers and neural networks. We also investigate the impact of changing the loss function on training time and find that it increases convergence speed up to 8 times faster. As such, these results show that tuning the loss function for Gradient Boosting is a straightforward and computationally efficient method to achieve state-of-the-art performance on imbalanced bioassay datasets without compromising on interpretability and scalability.

CACHE (Critical Assessment of Computational Hit-finding Experiments): A public-private partnership benchmarking initiative to enable the development of computational methods for hit-finding.

One aspirational goal of computational chemistry is to predict potent and drug-like binders for any protein, such that only those that bind are synthesized. In this Roadmap, we describe the launch of Critical Assessment of Computational Hit-finding Experiments (CACHE), a public benchmarking project to compare and improve small molecule hit-finding algorithms through cycles of prediction and experimental testing. Participants will predict small molecule binders for new and biologically relevant protein targets representing different prediction scenarios. Predicted compounds will be tested rigorously in an experimental hub, and all predicted binders as well as all experimental screening data, including the chemical structures of experimentally tested compounds, will be made publicly available, and not subject to any intellectual property restrictions. The ability of a range of computational approaches to find novel binders will be evaluated, compared, and openly published. CACHE will launch 3 new benchmarking exercises every year. The outcomes will be better prediction methods, new small molecule binders for target proteins of importance for fundamental biology or drug discovery, and a major technological step towards achieving the goal of Target 2035, a global initiative to identify pharmacological probes for all human proteins.

Patient-Derived Organoids of Cholangiocarcinoma.

Cholangiocarcinoma (CC) is an aggressive malignancy with an inferior prognosis due to limited systemic treatment options. As preclinical models such as CC cell lines are extremely rare, this manuscript reports a protocol of cholangiocarcinoma patient-derived organoid culture as well as a protocol for the transition of 3D organoid lines to 2D cell lines. Tissue samples of non-cancer bile duct and cholangiocarcinoma were obtained during surgical resection. Organoid lines were generated following a standardized protocol. 2D cell lines were generated from established organoid lines following a novel protocol. Subcutaneous and orthotopic patient-derived xenografts were generated from CC organoid lines, histologically examined, and treated using standard CC protocols. Therapeutic responses of organoids and 2D cell lines were examined using standard CC agents. Next-generation exome and RNA sequencing was performed on primary tumors and CC organoid lines. Patient-derived organoids closely recapitulated the original features of the primary tumors on multiple levels. Treatment experiments demonstrated that patient-derived organoids of cholangiocarcinoma and organoid-derived xenografts can be used for the evaluation of novel treatments and may therefore be used in personalized oncology approaches. In summary, this study establishes cholangiocarcinoma organoids and organoid-derived cell lines, thus expanding translational research resources of cholangiocarcinoma.

Optimization of a Screening Hit toward M2912, an Oral Tankyrase Inhibitor with Antitumor Activity in Colorectal Cancer Models.

Constitutive activation of the canonical Wnt signaling pathway, in most cases driven by inactivation of the tumor suppressor APC, is a hallmark of colorectal cancer. Tankyrases are druggable key regulators in these malignancies and are considered as attractive targets for therapeutic interventions, although no inhibitor has been progressed to clinical development yet. We continued our efforts to develop tankyrase inhibitors targeting the nicotinamide pocket with suitable drug-like properties for investigating effects of Wnt pathway inhibition on tumor growth. Herein, the identification of a screening hit series and its optimization through scaffold hopping and SAR exploration is described. The systematic assessment delivered M2912, a compound with an optimal balance between excellent TNKS potency, exquisite PARP selectivity, and a predicted human PK compatible with once daily oral dosing. Modulation of cellular Wnt pathway activity and significant tumor growth inhibition was demonstrated with this compound in colorectal xenograft models in vivo.

Degradation of micropollutant cephalexin by ultraviolet (UV) and assessment of residual antimicrobial activity of transformation products.

Cephalexin (CEX) is an antibiotic commonly used to treat bacterial infections in humans and animals. However, it is also a micropollutant. Thus, this study evaluated the degradation of CEX using ultraviolet irradiation (UV-C) and analyzed the by-products as well as their residual antimicrobial activity. A reactor with a mercury vapor lamp was used for the degradation. Irradiated CEX solutions were collected over a period of 4 hours and analyzed using high-performance liquid chromatography coupled with mass spectrometry. For the residual antimicrobial activity the susceptibility test was performed using Staphylococcus aureus and Escherichia coli microorganisms by broth microdilution. It was found that CEX, after treatment, generated a metabolite with a mass of 150 m/z in 15 min. A four- and eightfold increase in the minimum inhibitory concentration of the drug against S. aureus and E. coli could be observed, respectively, after 20 min. Therefore, this treatment proved to be effective in the degradation of CEX, being able to degrade 81% of the initial molecule of the drug in 20 min. Furthermore, the antimicrobial activity of the CEX solution decreased as the irradiation time increased, indicating loss of antimicrobial function of the initial CEX molecule and the resulting by-products.

Large-Scale Assessment of Binding Free Energy Calculations in Active Drug Discovery Projects.

Accurate ranking of compounds with regards to their binding affinity to a protein using computational methods is of great interest to pharmaceutical research. Physics-based free energy calculations are regarded as the most rigorous way to estimate binding affinity. In recent years, many retrospective studies carried out both in academia and industry have demonstrated its potential. Here, we present the results of large-scale prospective application of the FEP+ method in active drug discovery projects in an industry setting at Merck KGaA, Darmstadt, Germany. We compare these prospective data to results obtained on a new diverse, public benchmark of eight pharmaceutically relevant targets. Our results offer insights into the challenges faced when using free energy calculations in real-life drug discovery projects and identify limitations that could be tackled by future method development. The new public data set we provide to the community can support further method development and comparative benchmarking of free energy calculations.

Discovery and Optimization of 2-Arylquinazolin-4-ones into a Potent and Selective Tankyrase Inhibitor Modulating Wnt Pathway Activity.

Tankyrases 1 and 2 (TNKS1/2) are promising pharmacological targets that recently gained interest for anticancer therapy in Wnt pathway dependent tumors. 2-Aryl-quinazolinones were identified and optimized into potent tankyrase inhibitors through SAR exploration around the quinazolinone core and the 4'-position of the phenyl residue. These efforts were supported by analysis of TNKS X-ray and WaterMap structures and resulted in compound 5k, a potent, selective tankyrase inhibitor with favorable pharmacokinetic properties. The X-ray structure of 5k in complex with TNKS1 was solved and confirmed the design hypothesis. Modulation of Wnt pathway activity was demonstrated with this compound in a colorectal xenograft model in vivo.

The Back College for nurses - an evaluation of intermediate effects.

BACKGROUND: Nursing staff and care workers run an increased risk of work related musculoskeletal disorders such as low back pain. The Institution for Statutory Accident Insurance and Prevention in the Health and Welfare Services (BGW) offers its insured persons the opportunity to participate in a three-week Back College with the aim of preventing them having to abandon their profession due to back problems. The aim of the study was to record the effectiveness and sustainability of the Back College on an intermediate basis (6 months). METHODS: As part of a single-group pre-post measurement on three survey dates - at the start (T0) and end (T1) of rehabilitation and 6 months later (T2) - in 2013 all participants in the Back College at three locations were surveyed using a standard questionnaire. Wilcoxon signed-rank tests were performed to evaluate statistically significant changes. RESULTS: For measurement dates T0 to T2 we had 570 complete datasets (response rate 70.81%). There was a significant decrease in reported back pain and the general state of health and quality of life index improved. Participants' emotional strain decreased and they showed an improved understanding of illness as well as of having acquired knowledge-based abilities and skills for dealing with the disease. After training, they recorded back-friendly behaviour in everyday life and opportunities to relieve strain on the spinal column were utilised at work more often. Participants' subjective assessment of their ability to work (Work Ability Index) improved. CONCLUSION: The present study proved the intermediate effectiveness of the Back College curriculum. Whether these effects remain stable in the long term will be tested on the subsequent measurement date (T3, after 24 months).

Assessment of selenium bioaccumulation in lactic acid bacteria.

Selenium is an essential micronutrient for living beings, as it helps to maintain the normal physiological functions of the organism. The numerous discoveries involving the importance of this element to the health of human beings have fostered interest in research to develop enriched and functional foods. The present study evaluated the potential for bacterial strains of Enterococcus faecalis (CH121 and CH124), Lactobacillus parabuchneri (ML4), Lactobacillus paracasei (ML13, ML33, CH135, and CH139), and Lactobacillus plantarum (CH131) to bioaccumulate Se in their biomass by adding different concentrations of sodium selenite (30 to 200 mg/L) to the culture medium. Quantification of Se with UV and visible molecular absorption spectroscopy showed that the investigated bacteria were able to bioaccumulate this micromineral into their biomass. Two of the L. paracasei strains (ML13 and CH135) bioaccumulated the highest Se concentrations (38.1 ± 1.7 mg/g and 40.7 ± 1.1 mg/g, respectively) after culture in the presence of 150 mg/L of Se. This bioaccumulation potential has applications in the development of dairy products and may be an alternative Se source in the diets of humans and other animals.