Validation of the predictive value of BDNF -87 methylation for antidepressant treatment success in severely depressed patients-a randomized rater-blinded trial.

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.

Omics and Artificial Intelligence in Kidney Diseases.

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.

A new promising oncogenic target (p.C382R) for treatment with pemigatinib in patients with cholangiocarcinoma.

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.

Rare SARS-CoV-2 antibody development in cancer patients.

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.

SARS-CoV-2 infections in cancer outpatients-Most infected patients are asymptomatic carriers without impact on chemotherapy.

Oncologic patients are regarded as the population most at risk of developing a severe course of COVID-19 due to the fact that malignant diseases and chemotherapy often weaken the immune system. In the face of the ongoing SARS-CoV-2 pandemic, how particular patients deal with this infection remains an important question. In the period between the 15 and 26 April 2020, a total of 1227 patients were tested in one of seven oncologic outpatient clinics for SARS-CoV-2, regardless of symptoms, employing RT-qPCR. Of 1227 patients, 78 (6.4%) were tested positive of SARS-CoV-2. Only one of the patients who tested positive developed a severe form of COVID-19 with pneumonia (CURB-65 score of 2), and two patients showed mild symptoms. Fourteen of 75 asymptomatic but positively tested patients received chemotherapy or chemo-immunotherapy according to their regular therapy algorithm (±4 weeks of SARS-CoV-2 test), and 48 of 78 (61.5%) positive-tested patients received glucocorticoids as co-medication. None of the asymptomatic infected patients showed unexpected complications due to the SARS-CoV-2 infection during the cancer treatment. These data clearly contrast the view that patients with an oncologic disease are particularly vulnerable to SARS-CoV-2 and suggest that compromising therapies could be continued or started despite the ongoing pandemic. Moreover the relatively low appearance of symptoms due to COVID-19 among patients on chemotherapy and other immunosuppressive co-medication like glucocorticoids indicate that suppressing the response capacity of the immune system reduces disease severity.

xMaP-An Interpretable Alignment-Free Four-Dimensional Quantitative Structure-Activity Relationship Technique Based on Molecular Surface Properties and Conformer Ensembles.

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.

Backtranslating clinical knowledge for use in cheminformatics--what is the potential?

'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.

How can we enable drug discovery informatics for personalized healthcare?

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.

Interaction of (benzylidene-hydrazono)-1,4-dihydropyridines with beta-amyloid, acetylcholine, and butyrylcholine esterases.

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 (Abeta) 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 Abeta fibril formation completely and destruct the already formed fibrils significantly.

Chemogenomic analysis of safety profiling data.

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 are 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 approximately 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 on 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.

Plate-based diversity selection based on empirical HTS data to enhance the number of hits and their chemical diversity.

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.

Gaining insight into off-target mediated effects of drug candidates with a comprehensive systems chemical biology analysis.

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.

Mapping adverse drug reactions in chemical space.

We present a novel method to better investigate adverse drug reactions in chemical space. By integrating data sources about adverse drug reactions of drugs with an established cheminformatics modeling method, we generate a data set that is then visualized with a systems biology tool. Thereby new insights into undesired drug effects are gained. In this work, we present a global analysis linking chemical features to adverse drug reactions.

Use of ligand based models for protein domains to predict novel molecular targets and applications to triage affinity chromatography data.

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.

How similar are similarity searching methods? A principal component analysis of molecular descriptor space.

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.

Which aspects of HTS are empirically correlated with downstream success?

High-throughput screening (HTS) is a well-established hit-finding approach used in the pharmaceutical industry. In this article, recent experience at Novartis with respect to factors influencing the success of HTS campaigns is discussed. An inherent measure of HTS quality could be defined by the assay Z and Z' factors, the number of hits and their biological potencies; however, such measures of quality do not always correlate with the advancement of hits to the later stages of drug discovery. Also, for many target classes, such as kinases, it is easy to identify hits, but, as a result of selectivity, intellectual property and other issues, the projects do not result in lead declarations. In this article, HTS success is defined as the fraction of HTS campaigns that advance into the later stages of drug discovery, and the major influencing factors are examined. Interestingly, screening compounds in individual wells or in mixtures did not have a major impact on the HTS success and, equally interesting, there was no difference in the progression rates of biochemical and cell-based assays. Particular target types, assay technologies, structure-activity relationships and powder availability had a much greater impact on success as defined above. In addition, significant mutual dependencies can be observed - while one assay format works well with one target type, this situation might be completely reversed for a combination of the same readout technology with a different target type. The results and opinions presented here should be regarded as groundwork, and a plethora of factors that influence the fate of a project, such as biophysical measurements, chemical attractiveness of the hits, strategic reasons and safety pharmacology, are not covered here. Nonetheless, it is hoped that this information will be used industry-wide to improve success rates in terms of hits progressing into exploratory chemistry and beyond. The support that can be obtained from new in silico approaches to phase transitions are also described, along with the gaps they are designed to fill.

"Virtual fragment linking": an approach to identify potent binders from low affinity fragment hits.

In this work we explore the possibilities of using fragment-based screening data to prioritize compounds from a full HTS library, a method we call virtual fragment linking (VFL). The ability of VFL to identify compounds of nanomolar potency based on micromolar fragment binding data was tested on 75 target classes from the WOMBAT database and succeeded in 57 cases. Further, the method was demonstrated for seven drug targets from in-house screening programs that performed both FBS of 8800 fragments and screens of the full library. VFL captured between 28% and 67% of the hits (IC 50 < 10microM) in the top 5% of the ranked library for four of the targets (enrichment between 5-fold and 13-fold). Our findings lead us to conclude that proper coverage of chemical space by the fragment library is crucial for the VFL methodology to be successful in prioritizing HTS libraries from fragment-based screening data.

Cis-Configured aziridines are new pseudo-irreversible dual-mode inhibitors of Candida albicans secreted aspartic protease 2.

A series of cis-configured epoxides and aziridines containing hydrophobic moieties and amino acid esters were synthesized as new potential inhibitors of the secreted aspartic protease 2 (SAP2) of Candida albicans. Enzyme assays revealed the N-benzyl-3-phenyl-substituted aziridines 11 and 17 as the most potent inhibitors, with second-order inhibition rate constants (k(2)) between 56,000 and 121,000 M(-1) min(-1). The compounds were shown to be pseudo-irreversible dual-mode inhibitors: the intermediate esterified enzyme resulting from nucleophilic ring opening was hydrolyzed and yielded amino alcohols as transition-state-mimetic reversible inhibitors. The results of docking studies with the ring-closed aziridine forms of the inhibitors suggest binding modes mainly dominated by hydrophobic interactions with the S1, S1', S2, and S2' subsites of the protease, and docking studies with the processed amino alcohol forms predict additional hydrogen bonds of the new hydroxy group to the active site Asp residues. C. albicans growth assays showed the compounds to decrease SAP2-dependent growth while not affecting SAP2-independent growth.

Understanding false positives in reporter gene assays: in silico chemogenomics approaches to prioritize cell-based HTS data.

High throughput screening (HTS) data is often noisy, containing both false positives and negatives. Thus, careful triaging and prioritization of the primary hit list can save time and money by identifying potential false positives before incurring the expense of followup. Of particular concern are cell-based reporter gene assays (RGAs) where the number of hits may be prohibitively high to be scrutinized manually for weeding out erroneous data. Based on statistical models built from chemical structures of 650 000 compounds tested in RGAs, we created "frequent hitter" models that make it possible to prioritize potential false positives. Furthermore, we followed up the frequent hitter evaluation with chemical structure based in silico target predictions to hypothesize a mechanism for the observed "off target" response. It was observed that the predicted cellular targets for the frequent hitters were known to be associated with undesirable effects such as cytotoxicity. More specifically, the most frequently predicted targets relate to apoptosis and cell differentiation, including kinases, topoisomerases, and protein phosphatases. The mechanism-based frequent hitter hypothesis was tested using 160 additional druglike compounds predicted by the model to be nonspecific actives in RGAs. This validation was successful (showing a 50% hit rate compared to a normal hit rate as low as 2%), and it demonstrates the power of computational models toward understanding complex relations between chemical structure and biological function.

Analysis of pharmacology data and the prediction of adverse drug reactions and off-target effects from chemical structure.

Preclinical Safety Pharmacology (PSP) attempts to anticipate adverse drug reactions (ADRs) during early phases of drug discovery by testing compounds in simple, in vitro binding assays (that is, preclinical profiling). The selection of PSP targets is based largely on circumstantial evidence of their contribution to known clinical ADRs, inferred from findings in clinical trials, animal experiments, and molecular studies going back more than forty years. In this work we explore PSP chemical space and its relevance for the prediction of adverse drug reactions. Firstly, in silico (computational) Bayesian models for 70 PSP-related targets were built, which are able to detect 93% of the ligands binding at IC(50) < or = 10 microM at an overall correct classification rate of about 94%. Secondly, employing the World Drug Index (WDI), a model for adverse drug reactions was built directly based on normalized side-effect annotations in the WDI, which does not require any underlying functional knowledge. This is, to our knowledge, the first attempt to predict adverse drug reactions across hundreds of categories from chemical structure alone. On average 90% of the adverse drug reactions observed with known, clinically used compounds were detected, an overall correct classification rate of 92%. Drugs withdrawn from the market (Rapacuronium, Suprofen) were tested in the model and their predicted ADRs align well with known ADRs. The analysis was repeated for acetylsalicylic acid and Benperidol which are still on the market. Importantly, features of the models are interpretable and back-projectable to chemical structure, raising the possibility of rationally engineering out adverse effects. By combining PSP and ADR models new hypotheses linking targets and adverse effects can be proposed and examples for the opioid mu and the muscarinic M2 receptors, as well as for cyclooxygenase-1 are presented. It is hoped that the generation of predictive models for adverse drug reactions is able to help support early SAR to accelerate drug discovery and decrease late stage attrition in drug discovery projects. In addition, models such as the ones presented here can be used for compound profiling in all development stages.