Abstract Background Brain-derived neurotrophic factor (BDNF) is essential for antidepressant treatment of major depressive disorder (MDD). Our repeated studies suggest that DNA methylation of a specific CpG site in the promoter region of exon IV of the BDNF gene (CpG -87) might be predictive of the efficacy of monoaminergic antidepressants such as selective serotonin reuptake inhibitors (SSRIs), serotonin-norepinephrine reuptake inhibitors (SNRIs), and others. This trial aims to evaluate whether knowing the biomarker is non-inferior to treatment-as-usual (TAU) regarding remission rates while exhibiting significantly fewer adverse events (AE). Methods The BDNF trial is a prospective, randomized, rater-blinded diagnostic study conducted at five university hospitals in Germany. The study’s main hypothesis is that {1} knowing the methylation status of CpG -87 is non-inferior to not knowing it with respect to the remission rate while it significantly reduces the AE rate in patients experiencing at least one AE. The baseline assessment will occur upon hospitalization and a follow-up assessment on day 49 (± 3). A telephone follow-up will be conducted on day 70 (± 3). A total of 256 patients will be recruited, and methylation will be evaluated in all participants. They will be randomly assigned to either the marker or the TAU group. In the marker group, the methylation results will be shared with both the patient and their treating physician. In the TAU group, neither the patients nor their treating physicians will receive the marker status. The primary endpoints include the rate of patients achieving remission on day 49 (± 3), defined as a score of ≤ 10 on the Hamilton Depression Rating Scale (HDRS-24), and the occurrence of AE. Ethics and dissemination The trial protocol has received approval from the Institutional Review Boards at the five participating universities. This trial holds significance in generating valuable data on a predictive biomarker for antidepressant treatment in patients with MDD. The findings will be shared with study participants, disseminated through professional society meetings, and published in peer-reviewed journals. Trial registration German Clinical Trial Register DRKS00032503. Registered on 17 August 2023.

Lipid composition is conserved within sub-cellular compartments to maintain cell function. Lipidomic analyses of liver, muscle, white and brown adipose tissue (BAT) mitochondria revealed substantial differences in their glycerophospholipid (GPL) and free cholesterol (FC) contents. The GPL to FC ratio was 50-fold higher in brown than white adipose tissue mitochondria. Their purity was verified by comparison of proteomes with ER and mitochondria-associated membranes. A lipid signature containing PC and FC, calculated from the lipidomic profiles, allowed differentiation of mitochondria from BAT of mice housed at different temperatures. Elevating FC in BAT mitochondria prevented uncoupling protein (UCP) 1 function, whereas increasing GPL boosted it. Similarly,STARD3overexpression facilitating mitochondrial FC import inhibited UCP1 function in primary brown adipocytes, whereas a knockdown promoted it. We conclude that the mitochondrial GPL/FC ratio is key for BAT function and propose that targeting it might be a promising strategy to promote UCP1 activity.

Omics applications in nephrology may have relevance in the future to improve clinical care of kidney disease patients. In a short term, patients will benefit from specific measurement and computational analyses around biomarkers identified at various omics-levels. In mid term and long term, these approaches will need to be integrated into a holistic representation of the kidney and all its influencing factors for individualized patient care. Research demonstrates robust data to justify the application of omics for better understanding, risk stratification, and individualized treatment of kidney disease patients. Despite these advances in the research setting, there is still a lack of evidence showing the combination of omics technologies with artificial intelligence and its application in clinical diagnostics and care of patients with kidney disease.

Point mutations of the fibroblast growth factor receptor (FGFR)2 receptor in intrahepatic cholangiocarcinoma (iCC) are mainly of unknown functional significance compared to FGFR2 fusions. Pemigatinib, a tyrosine kinase inhibitor, is approved for the treatment of cholangiocarcinoma with FGFR2 fusion/rearrangement. Although it is hypothesized that FGFR2 mutations may cause uncontrolled activation of the signaling pathway, the data for targeted therapies for FGFR2 mutations remain unclear. In vitro analyses demonstrated the importance of the p.C382R mutation for ligand-independent constitutive activation of FGFR2 with transforming potential. The following report describes the clinical case of a patient diagnosed with an iCC carrying a FGFR2 p.C382R point mutation which was detected in liquid, as well as in tissue-based biopsies. The patient was treated with pemigatinib, resulting in a sustained complete functional remission in fluorodeoxyglucose-positron emission tomography/computed tomography over 10 months to date. The reported case is the first description of a complete functional remission under the treatment with pemigatinib in a patient with p.C383R mutation.

In the era of personalized medicine, the identification of driver mutations has paved the way towards targeted therapy. With the identification of anaplastic lymphoma kinase (ALK) as an oncogenic driver mutation, ALK rearrangements became druggable by tyrosine kinase inhibitors and, thus, have improved the prognosis for patients. Nevertheless, these approaches are limited by resistances occurring within the first or second year of administering ALK inhibitors. Among the different ALK resistant mutations, G1202R is the most common mutation, located in the kinase domain of the ALK protein resulting in resistance to treatment with the first- and second-generation kinase inhibitors (e.g., crizotinib, ceritinib, brigatenib and alectinib). Conflicting reports exist regarding the efficacy of lorlatinib, a next generation ALK inhibitor. The aim of this study is to access the potential impact of lorlatinib as a second-line treatment for a metastatic progressive NSCLC disease harboring genomic alteration of ALK G1202R, an AKLi-resistant mutation. The case of a patient with advanced lung cancer and the mentioned mutation is described.

SARS-CoV-2 antibody development and immunity will be crucial for the further course of the pandemic. Until now, it has been assumed that patients who are infected with SARS-CoV-2 will develop antibodies as has been the case with other coronaviruses, like MERS-CoV and SARS-CoV. In the present study, we analyzed the development of antibodies in 77 patients with an oncologic diagnosis 26 days after positive RT-qPCR testing for SARS-CoV2. RT-qPCR and anti-SARS-CoV2-antibody methods from BGI (MGIEasy Magnetic Beads Virus DNA/RNA Extraction Kit) and Roche (Elecsys Anti-SARS-CoV-2 immunoassay) were used, respectively, according to the manufacturers' specifications. Surprisingly, antibody development was detected in only 6 of 77 individuals with a confirmed history of COVID-19. Despite multiple testing, the remaining patients did not show measurable antibody concentrations in subsequent tests. These results undermine the previous hypothesis that SARS-CoV2 infections are regularly associated with antibody development and cast doubt on the provided immunity to COVID-19. Understanding the adaptive and humoral response to SARS-CoV2 will play a key role in vaccine development and gaining further knowledge on the pathogenesis.

(1) Background: With vaccination and new variants of SARS-CoV-2 on the horizon, efficient testing in schools may enable prevention of mass infection outbreaks, keeping schools safe places and buying time until decisions on feasibility and the necessity of vaccination in children and youth are made. We established, in the course of the WICOVIR (Where Is the COrona VIRus) study, that gargle-based pool-PCR testing offers a feasible, efficient, and safe testing system for schools in Germany when applied by central university laboratories. (2) Objectives: We evaluated whether this approach can be implemented in different rural and urban settings. (3) Methods: We assessed the arrangements required for successful implementation of the WICOVIR approach in a variety of settings in terms of transport logistics, data transfer and pre-existing laboratory set-up, as well as the time required to establish the set-up. (4) Results: We found that once regulatory issues have been overcome, all challenges pertaining to logistics, data transfer, and laboratory testing on different platforms can be solved within one month. Pooling and depooling of samples down to the individual test result were achievable within one working day in all settings. Local involvement of the community and decentralized set-ups were keys for success. (5) Conclusion: The WICOVIR gargle-based pool-PCR system is so robust and simple that it can be implemented within one month in all settings now or in future pandemics.

A novel alignment-free molecular descriptor called xMaP (flexible MaP descriptor) is introduced. The descriptor is the advancement of the previously published translationally and rotationally invariant three-dimensional (3D) descriptor MaP (mapping property distributions onto the molecular surface) to the fourth dimension (4D). In addition to MaP, xMaP is independent of the chosen starting conformation of the encoded molecules and is therefore entirely alignment-free. This is achieved by using ensembles of conformers, which are generated by conformational searches. This step of the procedure is similar to Hopfinger's 4D quantitative structure-activity relationship (QSAR). A five-step procedure is used to compute the xMaP descriptor. First, a conformational search for each molecule is carried out. Next, for each of the conformers an approximation to the molecular surface with equally distributed surface points is computed. Third, molecular properties are projected onto this surface. Fourth, areas of identical properties are clustered to so-called patches. Fifth, the spatial distribution of the patches is converted into an alignment-free descriptor that is based on the entire conformer ensemble. The resulting descriptor can be interpreted by superimposing the most important descriptor variables and the molecules of the data set. The most important descriptor variables are identified with chemometric regression tools. The novel descriptor was applied to several benchmark data sets and was compared to other descriptors and QSAR techniques comprising a binary fingerprint, a topological pharmacophore descriptor (Cats2D), and the field-based 3D-QSAR technique GRID/PLS which is alignment-dependent. The use of conformer ensembles renders xMaP very robust. It turns out that xMaP performs very well on (almost) all data sets and that the statistical results are comparable to GRID/PLS. In addition to that, xMaP can also be used to efficiently visualize the derived quantitative structure-activity relationships.

There is little information about age or maturation specific dosing for many frequently used drugs in pediatrics because of an inadequate number of clinical trials. In clinical practice doses for children are usually based on adult doses and adapted to weight or body surface area. Also off-label drug use is common in pediatrics.

‘From bench to bedside’ is seeing a very strong focus in current Drug Discovery. However, often overlooked are the advantages that turn out if data is used ‘from bedside to bench’, the fact one can also make beneficial use of clinical information in early Drug Discovery. By leveraging the wealth of clinical data carried by each marketed drug, down to the level of a single person, one can gain a deep insight that can be leveraged in conjunction with chemical structure information and therefore within all kinds of cheminformatics analyses. This supports the design of drugs that better fit the requirements of a well-defined subpopulation. Within this contribution I am going to focus on the realm of cheminformatics applications and how this data can thereby used to better impact the decisions of medicinal chemists.

Individualized treatment selection based on scientific results is set to be the future of healthcare. It will not only have a significant favorable impact on the health of patients suffering from various diseases, but also on how drug discovery is performed. Previously unobserved information will be generated, facilitating much deeper disease insight on an individual level than was feasible before. Without a doubt, this will also lead to major consequences for informatics as it is necessary to deal with numerous novel and constantly changing information types and requirements. One central concern will be addressing the scale of data flooding in, but much more important will be bringing together the complexity of available data enabling scientists to successfully generate meaningful hypotheses and results. This will then help in aiming for an understanding of disease phenomena as a whole, and not only fragments within drug discovery. Informatics needs to be the key enabler for the entire process. This contribution aims to show a possible route for approaching this in a future-proof way, leveraging and adapting knowledge-sharing approaches.

This chapter contains sections titled: Introduction A Cautionary Tale in Data Provenance From Data to Knowledge Semantic Integration and the Need for Curation Knowledge Curators Commercial Chemical Databases Biological Databases for Federation and Annotation Big Pharma Chemical and Biology Integration Efforts The NDFI Kinase Pilot NDFI: Federation vs. Warehousing Lessons Learned Summary References

Approved drugs for the treatment of Alzheimer's disease belong to the group of inhibitors of the acetylcholinesterase (AChE) and NMDA receptor inhibitors. However none of the drugs is able to combat or reverse the progression of the disease. Thus, the recently reported promising multitarget-directed molecule approach was applied here. Using the lead compound DUO3, which was found to be a potent inhibitor of the AChE and butyrylcholinesterase (BuChE) as well as an inhibitor of the formation of the amyloid ( Ab) plaque, new non-permanently positively charged derivatives were synthesized and biologically characterized. In contrast to DUO3 the new bisphenyl-substituted pyridinylidene hydrazones 5 are appropriate to cross the blood-brain barrier due to their pK(a) values and lipophilicity, and to inhibit both the AChE and BuChE. More important some of the pyridinylidene hydrazones inhibit the Ab fibril formation completely and destruct the already formed fibrils significantly. (C) 2010 Elsevier Ltd. All rights reserved.

Understanding the safety of newly developed compounds is a key task in each early drug discovery project. In early stages, pharmaceutical companies address this task by using so-called preclinical safety profiling, in which compounds arc screened in inexpensive large-scale assays to understand possible liabilities. This process generates a large amount of binding data on various compounds against a panel of targets - usually thousands or tens of thousands of compounds profiled against similar to 100 different targets. This data matrix is highly valuable and elicits further analysis. After briefly introducing the nature of safety profiling data, we describe several computational methods used internally at Novartis to analyze it. We showcase protocols that can be used to understand compound promiscuity oil a chemical structure level and protocols to evaluate the promiscuity of targets used in safety profiling. We also describe a method to quickly determine the chemical similarity of compounds active against different targets. Next, it is shown what protocols can be used to evaluate global chemical similarity of targets. The above approaches can be used either to optimize the composition of a panel of targets or to better understand certain toxicities. Finally, we will explain a simple method to elucidate hidden patterns in safety profiling data.

Typically, screening collections of pharmaceutical companies contain more than a million compounds today. However, for certain high-throughput screening (HTS) campaigns, constraints posed by the assay throughput and/or the reagent costs make it impractical to screen the entire deck. Therefore, it is desirable to effectively screen subsets of the collection based on a hypothesis or a diversity selection. How to select compound subsets is a subject of ongoing debate. The authors present an approach based on extended connectivity fingerprints to carry out diversity selection on a per plate basis (instead of a per compound basis). HTS data from 35 Novartis screens spanning 5 target classes were investigated to assess the performance of this approach. The analysis shows that selecting a fingerprint-diverse subset of 250K compounds, representing 20% of the screening deck, would have achieved significantly higher hit rates for 86% of the screens. This measure also outperforms the Murcko scaffold-based plate selection described previously, where only 49% of the screens showed similar improvements. Strikingly, the 2-fold improvement in average hit rates observed for 3 of 5 target classes in the data set indicates a target bias of the plate (and thus compound) selection method. Even though the diverse subset selection lacks any target hypothesis, its application shows significantly better results for some targets-namely, G-protein-coupled receptors, proteases, and protein-protein interactions-but not for kinase and pathway screens. The synthetic origin of the compounds in the diverse subset appears to influence the screening hit rates. Natural products were the most diverse compound class, with significantly higher hit rates compared to the compounds from the traditional synthetic and combinatorial libraries. These results offer empirical guidelines for plate-based diversity selection to enhance hit rates, based on target class and the library type being screened.

We present a workflow that leverages data from chemogenomics based target predictions with Systems Biology databases to better understand off-target related toxicities. By analyzing a set of compounds that share a common toxic phenotype and by comparing the pathways they affect with pathways modulated by nontoxic compounds we are able to establish links between pathways and particular adverse effects. We further link these predictive results with literature data in order to explain why a certain pathway is predicted. Specifically, relevant pathways are elucidated for the side effects rhabdomyolysis and hypotension. Prospectively, our approach is valuable not only to better understand toxicities of novel compounds early on but also for drug repurposing exercises to find novel uses for known drugs.

This chapter contains sections titled: Introduction “Meta Analysis” of Safety Pharmacology Data: Predicting Compound Promiscuity Prediction of Off-Target Effects of Molecules Based on Chemical Structure Future Directions References

We present a novel generic method to better understand the divergent activities of molecules that often occur in orthogonal assays. The newly developed simple prediction of activity differences (SPREAD) method directly aims to model and understand the differences compounds exhibit when tested in two or more assays. By transforming the activity values from the assays into meta-categories (specifically defined for datasets under scrutiny), statistical models can be trained directly on the qualitative differences between assays. This contributes heavily toward a tangible understanding of molecular assay selectivity. Although ensembles of models could be used alternatively to predict compounds that score highly in one assay and low in another, the advantage of the SPREAD approach is that the chemical features influencing assay differences are parsed out immediately as a consequence of training the model on the coincident assay differences. By training the model that describes the difference between two or more assays, molecular substructures that are responsible for assay selectivity can be parsed out. The method was validated by using four challenging datasets. Copyright © 2009 Wiley Periodicals, Inc., Statistical Analysis and Data Mining 2: 115-122, 2009

The elucidation of drug targets is important both to optimize desired compound action and to understand drug side-effects. In this study, we created statistical models which link chemical substructures of ligands to protein domains in a probabilistic manner and employ the model to triage the results of affinity chromatography experiments. By annotating targets with their InterPro domains, general rules of ligand-protein domain associations were derived and successfully employed to predict protein targets outside the scope of the training set. This methodology was then tested on a proteomics affinity chromatography data set containing 699 compounds. The domain prediction model correctly detected 31.6% of the experimental targets at a specificity of 46.8%. This is striking since 86% of the predicted targets are not part of them (but share InterPro domains with them), and thus could not have been predicted by conventional target prediction approaches. Target predictions improve drastically when significance (FDR) scores for target pulldowns are employed, emphasizing their importance for eliminating artifacts. Filament proteins (such as actin and tubulin) are detected to be 'frequent hitters' in proteomics experiments and their presence in pulldowns is not supported by the target predictions. On the other hand, membrane-bound receptors such as serotonin and dopamine receptors are noticeably absent in the affinity chromatography sets, although their presence would be expected from the predicted targets of compounds. While this can partly be explained by the experimental setup, we suggest the computational methods employed here as a complementary step of identifying protein targets of small molecules. Affinity chromatography results for gefitinib are discussed in detail and while two out of the three kinases with the highest affinity to gefitinib in biochemical assays are detected by affinity chromatography, also the possible involvement of NSF as a target for modulating cancer progressions via beta-arrestin can be proposed by this method.

Different molecular descriptors capture different aspects of molecular structures, but this effect has not yet been quantified systematically on a large scale. In this work, we calculate the similarity of 37 descriptors by repeatedly selecting query compounds and ranking the rest of the database. Euclidean distances between the rank-ordering of different descriptors are calculated to determine descriptor (as opposed to compound) similarity, followed by PCA for visualization. Four broad descriptor classes are identified, which are circular fingerprints; circular fingerprints considering counts; path-based and keyed fingerprints; and pharmacophoric descriptors. Descriptor behavior is much more defined by those four classes than the particular parametrization. Using counts instead of the presence/absence of fingerprints significantly changes descriptor behavior, which is crucial for performance of topological autocorrelation vectors, but not circular fingerprints. Four-point pharmacophores (piDAPH4) surprisingly lead to much higher retrieval rates than three-point pharmacophores (28.21% vs 19.15%) but still similar rank-ordering of compounds (retrieval of similar actives). Looking into individual rankings, circular fingerprints seem more appropriate than path-based fingerprints if complex ring systems or branching patterns are present; count-based fingerprints could be more suitable in databases with a large number of repeated subunits (amide bonds, sugar rings, terpenes). Information-based selection of diverse fingerprints for consensus scoring (ECFP4/TGD fingerprints) led only to marginal improvement over single fingerprint results. While it seems to be nontrivial to exploit orthogonal descriptor behavior to improve retrieval rates in consensus virtual screening, those descriptors still each retrieve different actives which corroborates the strategy of employing diverse descriptors individually in prospective virtual screening settings.