A mini-review of the anti-SARS-CoV-2 potency of Amaryllidaceae alkaloids.

BACKGROUND: Nature has perennially served as an infinite reservoir of diverse chemicals with numerous applications benefiting humankind. In recent years, due to the emerging COVID-19 pandemic, there has been a surge in studies on repurposing natural products as anti-SARS-CoV-2 agents, including plant-derived substances. Among all types of natural products, alkaloids remain one of the most important groups with various known medicinal values. The current investigation focuses on Amaryllidaceae alkaloids (AAs) since AAs have drawn significant scientific attention as anti-SARS-CoV-2 agents over the past few years. PURPOSE AND STUDY DESIGN: This study serves as a mini-review, summarizing recent advances in studying the anti-SARS-CoV-2 potency of AAs, covering two aspects: structure-activity relationship and mechanism of action (MOA). METHODS: The study covers the period from 2019 to 2023. The information in this review were retrieved from common databases including Web of Science, ScienceDirect, PubMed and Google scholar. Reported anti-SARS-CoV-2 potency, cytotoxicity and possible biological targets of AAs were summarized and classified into different skeletal subclasses. Then, the structure-activity relationship (SAR) was explored, pinpointing the key pharmacophore-related structural moieties. To study the mechanism of action of anti-SARS-CoV-2 AAs, possible biological targets were discussed. RESULTS: In total, fourteen research articles about anti-SARS-CoV-2 was selected. From the SAR point of view, four skeletal subclasses of AAs (lycorine-, galanthamine-, crinine- and homolycorine-types) appear to be promising for further investigation as anti-SARS-CoV-2 agents despite experimental inconsistencies in determining in vitro half maximal inhibitory effective concentration (EC50). Narciclasine, haemanthamine- and montanine-type skeletons were cytotoxic and devoid of anti-SARS-CoV-2 activity. The lycorine-type scaffold was the most structurally diverse in this study and preliminary structure-activity relationships revealed the crucial role of ring C and substituents on rings A, C and D in its anti-SARS-CoV-2 activity. It also appears that two enantiomeric skeletons (haemanthamine- and crinine-types) displayed opposite activity/toxicity profiles regarding anti-SARS-CoV-2 activity. Pharmacophore-related moieties of the haemanthamine/crinine-type skeletons were the substituents on rings B, C and the dioxymethylene moiety. All galanthamine-type alkaloids in this study were devoid of cytotoxicity and it appears that varying substituents on rings C and D could enhance the anti-SARS-CoV-2 potency. Regarding MOAs, initial experimental results suggested Mpro and RdRp as possible viral targets. Dual functionality between anti-inflammatory activity on host cells and anti-SARS-CoV-2 activity on the SARS-CoV-2 virus of isoquinoline alkaloids, including AAs, were suggested as the possible MOAs to alleviate severe complications in COVID-19 patients. This dual functionality was proposed to be related to the p38 MAPK signaling pathway. CONCLUSION: Overall, Amaryllidaceae alkaloids appear to be promising for further investigation as anti-SARS-CoV-2 agents. The skeletal subclasses holding the premise for further investigation are lycorine-, crinine-, galanthamine- and homolycorine-types.

Tackling assay interference associated with small molecules.

Biochemical and cell-based assays are essential to discovering and optimizing efficacious and safe drugs, agrochemicals and cosmetics. However, false assay readouts stemming from colloidal aggregation, chemical reactivity, chelation, light signal attenuation and emission, membrane disruption, and other interference mechanisms remain a considerable challenge in screening synthetic compounds and natural products. To address assay interference, a range of powerful experimental approaches are available and in silico methods are now gaining traction. This Review begins with an overview of the scope and limitations of experimental approaches for tackling assay interference. It then focuses on theoretical methods, discusses strategies for their integration with experimental approaches, and provides recommendations for best practices. The Review closes with a summary of the critical facts and an outlook on potential future developments.

Targeted metabolite profiling of Salvia rosmarinus Italian local ecotypes and cultivars and inhibitory activity against Pectobacterium carotovorum subsp. carotovorum.

Introduction: The development of agriculture in terms of sustainability and low environmental impact is, at present, a great challenge, mainly in underdeveloped and marginal geographical areas. The Salvia rosmarinus "Eretto Liguria" ecotype is widespread in Liguria (Northwest Italy), and farmers commonly use it by for cuttings and for marketing. In the present study, this ecotype was characterized in comparison with other cultivars from the same geographical region and Campania (Southern Italy), with a view to application and registration processes for the designation of protected geographical indications. Moreover, the possibility of using the resulting biomass after removing cuttings or fronds as a source of extracts and pure compounds to be used as phytosanitary products in organic farming was evaluated. Specifically, the potential of rosemary extracts and pure compounds to prevent soft rot damage was then tested. Methods: A targeted NMR metabolomic approach was employed, followed by multivariate analysis, to characterize the rosemary accessions. Bacterial soft rot assay and disk diffusion test were carried out to evaluate the activity of extracts and isolated compounds against Pectobacterium carotovorum subsp. carotovorum. Enzymatic assay was performed to measure the in vitro inhibition of the pectinase activity produced by the selected pathogen. Molecular docking simulations were used to explore the possible interaction of the selected compounds with the pectinase enzymes. Results and Discussion: The targeted metabolomic analysis highlighted those different geographical locations can influence the composition and abundance of bioactive metabolites in rosemary extracts. At the same time, genetic factors are important when a single geographical area is considered. Self-organizing maps (SOMs) showed that the accessions of "Eretto Liguria" appeared well characterized when compared to the others and had a good content in specialized metabolites, particularly carnosic acid. Soft rotting Enterobacteriaceae belonging to the Pectobacterium genus represent a serious problem in potato culture. Even though rosemary methanolic extracts showed a low antibacterial activity against a strain of Pectobacterium carotovorum subsp. carotovorum in the disk diffusion test, they showed ability in reducing the soft rot damage induced by the bacterium on potato tissue. 7-O-methylrosmanol, carnosol and isorosmanol appeared to be the most active components. In silico studies indicated that these abietane diterpenoids may interact with P. carotovorum subsp. carotovorum pectate lyase 1 and endo-polygalacturonase, thus highlighting these rosemary components as starting points for the development of agents able to prevent soft rot progression.

Active Learning Approach for Guiding Site-of-Metabolism Measurement and Annotation.

The ability to determine and predict metabolically labile atom positions in a molecule (also called "sites of metabolism" or "SoMs") is of high interest to the design and optimization of bioactive compounds, such as drugs, agrochemicals, and cosmetics. In recent years, several in silico models for SoM prediction have become available, many of which include a machine-learning component. The bottleneck in advancing these approaches is the coverage of distinct atom environments and rare and complex biotransformation events with high-quality experimental data. Pharmaceutical companies typically have measured metabolism data available for several hundred to several thousand compounds. However, even for metabolism experts, interpreting these data and assigning SoMs are challenging and time-consuming. Therefore, a significant proportion of the potential of the existing metabolism data, particularly in machine learning, remains dormant. Here, we report on the development and validation of an active learning approach that identifies the most informative atoms across molecular data sets for SoM annotation. The active learning approach, built on a highly efficient reimplementation of SoM predictor FAME 3, enables experts to prioritize their SoM experimental measurements and annotation efforts on the most rewarding atom environments. We show that this active learning approach yields competitive SoM predictors while requiring the annotation of only 20% of the atom positions required by FAME 3. The source code of the approach presented in this work is publicly available.

Epstein-Barr virus-driven B cell lymphoma mediated by a direct LMP1-TRAF6 complex.

Epstein-Barr virus (EBV) latent membrane protein 1 (LMP1) drives viral B cell transformation and oncogenesis. LMP1's transforming activity depends on its C-terminal activation region 2 (CTAR2), which induces NF-κB and JNK by engaging TNF receptor-associated factor 6 (TRAF6). The mechanism of TRAF6 recruitment to LMP1 and its role in LMP1 signalling remains elusive. Here we demonstrate that TRAF6 interacts directly with a viral TRAF6 binding motif within CTAR2. Functional and NMR studies supported by molecular modeling provide insight into the architecture of the LMP1-TRAF6 complex, which differs from that of CD40-TRAF6. The direct recruitment of TRAF6 to LMP1 is essential for NF-κB activation by CTAR2 and the survival of LMP1-driven lymphoma. Disruption of the LMP1-TRAF6 complex by inhibitory peptides interferes with the survival of EBV-transformed B cells. In this work, we identify LMP1-TRAF6 as a critical virus-host interface and validate this interaction as a potential therapeutic target in EBV-associated cancer.

School of cheminformatics in Latin America.

We report the major highlights of the School of Cheminformatics in Latin America, Mexico City, November 24-25, 2022. Six lectures, one workshop, and one roundtable with four editors were presented during an online public event with speakers from academia, big pharma, and public research institutions. One thousand one hundred eighty-one students and academics from seventy-nine countries registered for the meeting. As part of the meeting, advances in enumeration and visualization of chemical space, applications in natural product-based drug discovery, drug discovery for neglected diseases, toxicity prediction, and general guidelines for data analysis were discussed. Experts from ChEMBL presented a workshop on how to use the resources of this major compounds database used in cheminformatics. The school also included a round table with editors of cheminformatics journals. The full program of the meeting and the recordings of the sessions are publicly available at https://www.youtube.com/@SchoolChemInfLA/featured .

Flindissone, a Limonoid Isolated from Trichilia prieuriana, Is an LXR Agonist.

In this study, the ability of six limonoids from Trichilia prieuriana (Meliaceae) to activate the liver X receptor (LXR) was assessed. One of these limonoids, flindissone, was shown to activate LXR by reporter-gene assays. Flindissone is a ring-intact limonoid, structurally similar to sterol-like LXR ligands. In endogenous cellular settings, flindissone showed an activity profile that is characteristic of LXR agonists. It induced cholesterol efflux in THP-1 macrophages by increasing the cholesterol transporter ABCA1 and ABCG1 gene expression. In HepG2 cells, flindissone induced the expression of IDOL, an LXR-target gene that is associated with the downregulation of the LDL receptor. However, unlike synthetic and similarly to sterol-based LXR agonists, flindissone did not induce the expression of the SREBP1c gene, a major transcription factor regulating de novo lipogenesis. Additionally, flindissone also appeared to be able to inhibit post-translational activation of SREBP1c. The results presented here reveal a natural product as a new LXR agonist and point to an additional property of T. prieuriana and other plant extracts containing flindissone.

Computational prediction of the metabolites of agrochemicals formed in rats.

Today, computational tools for the prediction of the metabolite structures of xenobiotics are widely available and employed in small-molecule research. Reflecting the availability of measured data, these in silico tools are trained and validated primarily on drug metabolism data. In this work, we assessed the capacity of five leading metabolite structure predictors to represent the metabolism of agrochemicals observed in rats. More specifically, we tested the ability of SyGMa, GLORY, GLORYx, BioTransformer 3.0, and MetaTrans to correctly predict and rank the experimentally observed metabolites of a set of 85 parent compounds. We found that the models were able to recover about one to two-thirds of the experimentally observed first-generation, second-generation and third-generation metabolites, confirming their value in applications such as metabolite identification. However, precision was low for all investigated tools and did not exceed approximately 18 % for the pool of first-generation metabolites and 2 % for the pool of compounds representing the first three generations of metabolites. The variance in prediction success rates was high across the individual metabolic maps, meaning that outcomes depend strongly on the specific compound under investigation. We also found that the predictions for individual parent compounds differed strongly between the tools, particularly between those built on orthogonal technologies (e.g., rule-based and end-to-end machine learning approaches). This renders ensemble model strategies promising for improving success rates. Overall, the results of this benchmark study show that there is still considerable room for the improvement of metabolite structure predictors left. Our discussion points out several avenues to progress. The bottleneck in method development certainly has been, and will remain, for the foreseeable future, the limited quantity and quality of available measured data on small-molecule metabolism.

Toxicity prediction using target, interactome, and pathway profiles as descriptors.

In silico methods are essential to the safety evaluation of chemicals. Computational risk assessment offers several approaches, with data science and knowledge-based methods becoming an increasingly important sub-group. One of the substantial attributes of data science is that it allows using existing data to find correlations, build strong hypotheses, and create new, valuable knowledge that may help to reduce the number of resource intensive experiments. In choosing a suitable method for toxicity prediction, the available data and desired toxicity endpoint are two essential factors to consider. The complexity of the endpoint can impact the success rate of the in silico models. For highly complex endpoints such as hepatotoxicity, it can be beneficial to decipher the toxic event from a more systemic point of view. We propose a data science-based modelling pipeline that uses compounds` connections to tissue-specific biological targets, interactome, and biological pathways as descriptors of compounds. Models trained on different combinations of the collected, compound-target, compound-interactor, and compound-pathway profiles, were used to predict the hepatotoxicity of drug-like compounds. Several tree-based models were trained, utilizing separate and combined target, interactome and pathway level variables. The model using combined descriptors of all levels and the random forest algorithm was further optimized. Descriptor importance for model performance was addressed and examined for a biological explanation to define which targets or pathways can have a crucial role in toxicity. Descriptors connected to cytochromes P450 enzymes, heme degradation and biological oxidation received high weights. Furthermore, the involvement of other, less discussed processes in connection with toxicity, such as the involvement of RHO GTPase effectors in hepatotoxicity, were marked as fundamental. The optimized combined model using only the selected descriptors yielded the best performance with an accuracy of 0.766. The same dataset using classical Morgan fingerprints for compound representation yielded models with similar performance measures, as well as the combination of systems biology-based descriptors and Morgan fingerprints. Consequently, adding the structural information of compounds did not enhance the predictive value of the models. The developed systems biology-based pipeline comprises a valuable tool in predicting toxicity, while providing novel insights about the possible mechanisms of the unwanted events.

Structural Insights into the Ligand-LsrK Kinase Binding Mode: A Step Forward in the Discovery of Novel Antimicrobial Agents.

LsrK is a bacterial kinase that triggers the quorum sensing, and it represents a druggable target for the identification of new agents for fighting antimicrobial resistance. Herein, we exploited tryptophan fluorescence spectroscopy (TFS) as a suitable technique for the identification of potential LsrK ligands from an in-house library of chemicals comprising synthetic compounds as well as secondary metabolites. Three secondary metabolites (Hib-ester, Hib-carbaldehyde and (R)-ASME) showed effective binding to LsrK, with KD values in the sub-micromolar range. The conformational changes were confirmed via circular dichroism and molecular docking results further validated the findings and displayed the specific mode of interaction. The activity of the identified compounds on the biofilm formation by some Staphylococcus spp. was investigated. Hib-carbaldehyde and (R)-ASME were able to reduce the production of biofilm, with (R)-ASME resulting in the most effective compound with an EC50 of 14 mg/well. The successful application of TFS highlights its usefulness in searching for promising LsrK inhibitor candidates with inhibitor efficacy against biofilm formation.

Identification of Natural Products Inhibiting SARS-CoV-2 by Targeting Viral Proteases: A Combined in Silico and in Vitro Approach.

In this study, an integrated in silico-in vitro approach was employed to discover natural products (NPs) active against SARS-CoV-2. The two SARS-CoV-2 viral proteases, i.e., main protease (Mpro) and papain-like protease (PLpro), were selected as targets for the in silico study. Virtual hits were obtained by docking more than 140,000 NPs and NP derivatives available in-house and from commercial sources, and 38 virtual hits were experimentally validated in vitro using two enzyme-based assays. Five inhibited the enzyme activity of SARS-CoV-2 Mpro by more than 60% at a concentration of 20 µM, and four of them with high potency (IC50 < 10 µM). These hit compounds were further evaluated for their antiviral activity against SARS-CoV-2 in Calu-3 cells. The results from the cell-based assay revealed three mulberry Diels-Alder-type adducts (MDAAs) from Morus alba with pronounced anti-SARS-CoV-2 activities. Sanggenons C (12), O (13), and G (15) showed IC50 values of 4.6, 8.0, and 7.6 µM and selectivity index values of 5.1, 3.1 and 6.5, respectively. The docking poses of MDAAs in SARS-CoV-2 Mpro proposed a butterfly-shaped binding conformation, which was supported by the results of saturation transfer difference NMR experiments and competitive 1H relaxation dispersion NMR spectroscopy.

Reduced expression and activity of patient-derived SHIP1 phosphatase domain mutants.

The characterization of dysregulated proteins in cell signaling pathways is important for the development of therapeutic approaches. The PI3K/AKT/mTOR pathway is frequently upregulated in cancer cells and the SH2-containing inositol 5-phosphatase SHIP1 can act as a negative regulator of the PI3K/AKT pathway. In this study, we investigated different patient-derived mutations within the conserved phosphatase domain of SHIP1. We could demonstrate that 2 out of 7 SHIP1-phosphatase domain mutations (G585K and R673Q) possessed reduced protein expression and reduced enzymatic activity in comparison to SHIP1 wild type (WT) protein and two additional mutations (E452K, R551Q) possessed reduced enzymatic activity at a comparable expression level compared to SHIP1 WT in the cell line H1299. The investigated mutations resulted in protein expression levels that were up to 93% lower than those of the SHIP1 WT for SHIP1 mutant R673Q and the enzymatic activity was below the detection limit of the performed phosphatase assay. Whereas the protein level of the R673Q mutant was reduced in comparison to SHIP1 WT the mRNA level was comparable indicating a post-transcriptional regulation. SHIP1 R673Q was rapidly degraded, with a calculated half-life of l.5 h. In addition, SHIP1 R673Q levels were significantly increased by the treatment with the proteasome inhibitor MG-132 in comparison to the DMSO control. Therefore, SHIP1 was confirmed as the target of enhanced proteasomal degradation. Computational analysis of the wild type and mutant protein structures revealed that the loss of the positively charged arginine residue R673 is associated with the loss of two salt bridges to the negatively charged amino acids D617 and E634 leading to an intramolecular instability of the mutated SHIP1 R673Q protein. Six out of seven SHIP1 mutants significantly affected the PI3K/AKT/mTOR pathway in the three cancer cell lines H1299, Reh and Sem. Four out of seven SHIP1 mutants affected phosphorylation of AKT and its target GSK3ß positively compared to SHIP1 WT, whereas a negative effect on the phosphorylation of S6 was found in five out of seven mutants. In general, SHIP1 mutants impacting signal transduction were either associated with decreased SHIP1 activity or SHIP1 expression or both. Overall, the presented results indicate a regulation of the protein expression and activity of SHIP1 by patient-derived mutations in its phosphatase domain.

Discovery of N-quinazolinone-4-hydroxy-2-quinolone-3-carboxamides as DNA gyrase B-targeted antibacterial agents.

Emerging drug resistance is generating an urgent need for novel and effective antibiotics. A promising target that has not yet been addressed by approved antibiotics is the bacterial DNA gyrase subunit B (GyrB), and GyrB inhibitors could be effective against drug-resistant bacteria, such as methicillin-resistant S. aureus (MRSA). Here, we used the 4-hydroxy-2-quinolone fragment to search the Specs database of purchasable compounds for potential inhibitors of GyrB and identified AG-690/11765367, or f1, as a novel and potent inhibitor of the target protein (IC50: 1.21 µM). Structural modification was used to further identify two more potent GyrB inhibitors: f4 (IC50: 0.31 µM) and f14 (IC50: 0.28 µM). Additional experiments indicated that compound f1 is more potent than the others in terms of antibacterial activity against MRSA (MICs: 4-8 µg/mL), non-toxic to HUVEC and HepG2 (CC50: approximately 50 µM), and metabolically stable (t1/2: > 372.8 min for plasma; 24.5 min for liver microsomes). In summary, this study showed that the discovered N-quinazolinone-4-hydroxy-2-quinolone-3-carboxamides are novel GyrB-targeted antibacterial agents; compound f1 is promising for further development.

Identification of the Natural Steroid Sapogenin Diosgenin as a Direct Dual-Specific RORα/γ Inverse Agonist.

The steroid sapogenin diosgenin is a well-known natural product with a plethora of described pharmacological activities including the amelioration of T helper 17 (Th17)-driven pathologies. However, the exact underlying mode of action of diosgenin leading to a dampened Th17 response is still largely unknown and specific molecular targets have yet to be identified. Here, we show that diosgenin acts as a direct ligand and inverse agonist of the nuclear receptor retinoic acid receptor (RAR)-related orphan receptor (ROR)α and RORγ, which are key transcription factors involved in Th17 cell differentiation and metabolism. IC50 values determined by luciferase reporter gene assays, employing constructs for either RORγ-Gal4 fusion proteins or full length receptors, were in the low micromolar range at around 2 µM. To highlight the functional consequences of this RORα/γ inverse agonism, we determined gene expression levels of important ROR target genes, i.e., IL-17A and glucose-6-phosphatase, in relevant cellular in vitro models of Jurkat T and HepG2 cells, respectively, by RT-qPCR (reverse transcription quantitative PCR). Thereby, it was shown that diosgenin leads to a dose-dependent decrease in target gene expressions consistent with its potent cellular ROR inverse agonistic activity. Additionally, in silico dockings of diosgenin to the ROR ligand-binding domain were performed to determine the underlying binding mode. Taken together, our results establish diosgenin as a novel, direct and dual-selective RORα/γ inverse agonist. This finding establishes a direct molecular target for diosgenin for the first time, which can further explain reported amendments in Th17-driven diseases by this compound.

In Silico and In Vitro Approach to Assess Direct Allosteric AMPK Activators from Nature.

The 5'-adenosine monophosphate-activated protein kinase (AMPK) is an important metabolic regulator. Its allosteric drug and metabolite binding (ADaM) site was identified as an attractive target for direct AMPK activation and holds promise as a novel mechanism for the treatment of metabolic diseases. With the exception of lusianthridin and salicylic acid, no natural product (NP) is reported so far to directly target the ADaM site. For the streamlined assessment of direct AMPK activators from the pool of NPs, an integrated workflow using in silico and in vitro methods was applied. Virtual screening combining a 3D shape-based approach and docking identified 21 NPs and NP-like molecules that could potentially activate AMPK. The compounds were purchased and tested in an in vitro AMPK α 1 ß 1 γ 1 kinase assay. Two NP-like virtual hits were identified, which, at 30 µM concentration, caused a 1.65-fold (± 0.24) and a 1.58-fold (± 0.17) activation of AMPK, respectively. Intriguingly, using two different evaluation methods, we could not confirm the bioactivity of the supposed AMPK activator lusianthridin, which rebuts earlier reports.

Maximizing the Performance of Similarity-Based Virtual Screening Methods by Generating Synergy from the Integration of 2D and 3D Approaches.

Methods for the pairwise comparison of 2D and 3D molecular structures are established approaches in virtual screening. In this work, we explored three strategies for maximizing the virtual screening performance of these methods: (i) the merging of hit lists obtained from multi-compound screening using a single screening method, (ii) the merging of the hit lists obtained from 2D and 3D screening by parallel selection, and (iii) the combination of both of these strategies in an integrated approach. We found that any of these strategies led to a boost in virtual screening performance, with the clearest advantages observed for the integrated approach. On test sets for virtual screening, covering 50 pharmaceutically relevant proteins, the integrated approach, using sets of five query molecules, yielded, on average, an area under the receiver operating characteristic curve (AUC) of 0.84, an early enrichment among the top 1% of ranked compounds (EF1%) of 53.82 and a scaffold recovery rate among the top 1% of ranked compounds (SRR1%) of 0.50. In comparison, the 2D and 3D methods on their own (when using a single query molecule) yielded AUC values of 0.68 and 0.54, EF1% values of 19.96 and 17.52, and SRR1% values of 0.20 and 0.17, respectively. In conclusion, based on these results, the integration of 2D and 3D methods, via a (balanced) parallel selection strategy, is recommended, and, in particular, when combined with multi-query screening.

Ring systems in natural products: structural diversity, physicochemical properties, and coverage by synthetic compounds.

Covering: up to 2021The structural core of most small-molecule drugs is formed by a ring system, often derived from natural products. However, despite the importance of natural product ring systems in bioactive small molecules, there is still a lack of a comprehensive overview and understanding of natural product ring systems and how their full potential can be harnessed in drug discovery and related fields. Herein, we present a comprehensive cheminformatic analysis of the structural and physicochemical properties of 38 662 natural product ring systems, and the coverage of natural product ring systems by readily purchasable, synthetic compounds that are commonly explored in virtual screening and high-throughput screening. The analysis stands out by the use of comprehensive, curated data sets, the careful consideration of stereochemical information, and a robust analysis of the 3D molecular shape and electrostatic properties of ring systems. Among the key findings of this study are the facts that only about 2% of the ring systems observed in NPs are present in approved drugs but that approximately one in two NP ring systems are represented by ring systems with identical or related 3D shape and electrostatic properties in compounds that are typically used in (high-throughput) screening.

Studying and mitigating the effects of data drifts on ML model performance at the example of chemical toxicity data.

Machine learning models are widely applied to predict molecular properties or the biological activity of small molecules on a specific protein. Models can be integrated in a conformal prediction (CP) framework which adds a calibration step to estimate the confidence of the predictions. CP models present the advantage of ensuring a predefined error rate under the assumption that test and calibration set are exchangeable. In cases where the test data have drifted away from the descriptor space of the training data, or where assay setups have changed, this assumption might not be fulfilled and the models are not guaranteed to be valid. In this study, the performance of internally valid CP models when applied to either newer time-split data or to external data was evaluated. In detail, temporal data drifts were analysed based on twelve datasets from the ChEMBL database. In addition, discrepancies between models trained on publicly-available data and applied to proprietary data for the liver toxicity and MNT in vivo endpoints were investigated. In most cases, a drastic decrease in the validity of the models was observed when applied to the time-split or external (holdout) test sets. To overcome the decrease in model validity, a strategy for updating the calibration set with data more similar to the holdout set was investigated. Updating the calibration set generally improved the validity, restoring it completely to its expected value in many cases. The restored validity is the first requisite for applying the CP models with confidence. However, the increased validity comes at the cost of a decrease in model efficiency, as more predictions are identified as inconclusive. This study presents a strategy to recalibrate CP models to mitigate the effects of data drifts. Updating the calibration sets without having to retrain the model has proven to be a useful approach to restore the validity of most models.

Development and Experimental Validation of Regularized Machine Learning Models Detecting New, Structurally Distinct Activators of PXR.

The pregnane X receptor (PXR) regulates the metabolism of many xenobiotic and endobiotic substances. In consequence, PXR decreases the efficacy of many small-molecule drugs and induces drug-drug interactions. The prediction of PXR activators with theoretical approaches such as machine learning (ML) proves challenging due to the ligand promiscuity of PXR, which is related to its large and flexible binding pocket. In this work we demonstrate, by the example of random forest models and support vector machines, that classifiers generated following classical training procedures often fail to predict PXR activity for compounds that are dissimilar from those in the training set. We present a novel regularization technique that penalizes the gap between a model's training and validation performance. On a challenging test set, this technique led to improvements in Matthew correlation coefficients (MCCs) by up to 0.21. Using these regularized ML models, we selected 31 compounds that are structurally distinct from known PXR ligands for experimental validation. Twelve of them were confirmed as active in the cellular PXR ligand-binding domain assembly assay and more hits were identified during follow-up studies. Comprehensive analysis of key features of PXR biology conducted for three representative hits confirmed their ability to activate the PXR.

Azepine-Indole Alkaloids From Psychotria nemorosa Modulate 5-HT2A Receptors and Prevent in vivo Protein Toxicity in Transgenic Caenorhabditis elegans.

Nemorosine A (1) and fargesine (2), the main azepine-indole alkaloids of Psychotria nemorosa, were explored for their pharmacological profile on neurodegenerative disorders (NDs) applying a combined in silico-in vitro-in vivo approach. By using 1 and 2 as queries for similarity-based searches of the ChEMBL database, structurally related compounds were identified to modulate the 5-HT2A receptor; in vitro experiments confirmed an agonistic effect for 1 and 2 (24 and 36% at 10 µM, respectively), which might be linked to cognition-enhancing properties. This and the previously reported target profile of 1 and 2, which also includes BuChE and MAO-A inhibition, prompted the evaluation of these compounds in several Caenorhabditis elegans models linked to 5-HT modulation and proteotoxicity. On C. elegans transgenic strain CL4659, which expresses amyloid beta (Aß) in muscle cells leading to a phenotypic paralysis, 1 and 2 reduced Aß proteotoxicity by reducing the percentage of paralyzed worms to 51%. Treatment of the NL5901 strain, in which α-synuclein is yellow fluorescent protein (YFP)-tagged, with 1 and 2 (10 µM) significantly reduced the α-synuclein expression. Both alkaloids were further able to significantly extend the time of metallothionein induction, which is associated with reduced neurodegeneration of aged brain tissue. These results add to the multitarget profiles of 1 and 2 and corroborate their potential in the treatment of NDs.