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Andreas Bender

Publications and source records attributed to Andreas Bender.

At least 19 recordsLinked to original sources
PubMed2026

Single-cell and spatial transcriptomic technologies for lung cancer tumor microenvironment analysis.

Lung cancer remains one of the leading causes of cancer-related mortality worldwide; beyond its rising incidence, its marked molecular heterogeneity and complex tumor microenvironment (TME) hinder treatment response and drive resistance, contributing directly to its high mortality rate. Single-cell RNA sequencing (scRNA-seq) and spatial transcriptomics (ST) provide complementary approaches for dissecting these features. scRNA-seq enables high-resolution analysis of cellular diversity and transcriptional states but requires tissue dissociation and therefore loses spatial context. In contrast, ST preserves tissue architecture and provides insights into how gene-expression programs within the TME are organized, although no currently available spatial platform combines whole-transcriptome coverage with true single-cell resolution over large tissue areas. Together, these technologies have enabled detailed mapping of tumor, immune and stromal populations and of their spatial interactions, revealing functionally distinct cellular niches that contribute to immune evasion, metastasis and response to therapy. In this narrative review we organize the primary literature around a single question, how spatially structured cellular ecosystems, rather than individual cell types, determine therapeutic response and resistance in lung cancer - and we explicitly separate observations that are reproducible across independent cohorts and platforms from those that remain confined to single studies. We further summarize the technical, analytical and logistic barriers that currently prevent spatially resolved signatures from entering routine diagnostic pathology. Understanding dysregulated pathways and spatially constrained intercellular communication within the TME helps identify candidate biomarkers and may support the identification of therapeutic approaches directed at tumor-intrinsic programs as well as at microenvironment-driven resistance mechanisms.

Cell-cell communication↗
PubMed2006

Bridging chemical and biological space: "target fishing" using 2D and 3D molecular descriptors.

Bridging chemical and biological space is the key to drug discovery and development. Typically, cheminformatics methods operate under the assumption that similar chemicals have similar biological activity. Ideally then, one could predict a drug's biological function(s) given only its chemical structure by similarity searching in libraries of compounds with known activities. In practice, effectively choosing a similarity metric is case dependent. This work compares both 2D and 3D chemical descriptors as tools for predicting the biological targets of ligand probes, on the basis of their similarity to reference molecules in a 46,000 compound, biologically annotated chemical database. Overall, we found that the 2D methods employed here outperform the 3D (88% vs 67% success) in correct target prediction. However, the 3D descriptors proved superior in cases of probes with low structural similarity to other compounds in the database (singletons). Additionally, the 3D method (FEPOPS) shows promise for providing pharmacophoric alignment of the small molecules' chemical features consistent with those seen in experimental ligand/ receptor complexes. These results suggest that querying annotated chemical databases with a systematic combination of both 2D and 3D descriptors will prove more effective than employing single methods.

Adenosine Triphosphate↗
PubMed2006

Systematic, standardized and comprehensive neurological phenotyping of inbred mice strains in the German Mouse Clinic.

Neurological and psychiatric disorders are among the most common and most serious health problems in developed countries. Transgenic mouse models mimicking human neurological diseases have provided new insights into development and function of the nervous system. One of the prominent goals of the German National Genome Research Network is the understanding of the in vivo function of single genes and the pathophysiological and clinical consequences of respective mutations. The German Mouse Clinic (GMC) offers a high-throughput primary screen of genetically modified mouse models as well as an in-depth analysis in secondary and tertiary screens covering various fields of mouse physiology. Here we describe the phenotyping methods of the Neurological Screen in the GMC, exemplified in the four inbred mouse lines C57BL/6J, C3HeB/FeJ, BALB/cByJ, and 129S2/SvPas. For our primary screen, we generated "standard operating procedures" that were validated between different laboratories. The phenotyping of inbred strains already showed significant differences in various parameters, thus being a prerequisite for the examination of mutant mouse lines.

Analysis of Variance↗
PubMed2006

High levels of mitochondrial DNA deletions in substantia nigra neurons in aging and Parkinson disease.

Here we show that in substantia nigra neurons from both aged controls and individuals with Parkinson disease, there is a high level of deleted mitochondrial DNA (mtDNA) (controls, 43.3% +/- 9.3%; individuals with Parkinson disease, 52.3% +/- 9.3%). These mtDNA mutations are somatic, with different clonally expanded deletions in individual cells, and high levels of these mutations are associated with respiratory chain deficiency. Our studies suggest that somatic mtDNA deletions are important in the selective neuronal loss observed in brain aging and in Parkinson disease.

Aging↗
PubMed2006

A pharmacological model for psychosis based on N-methyl-D-aspartate receptor hypofunction: molecular, cellular, functional and behavioral abnormalities.

BACKGROUND: The psychotomimetic effects of N-methyl-D-aspartate (NMDA) receptor antagonists such as phencyclidine (PCP) in healthy humans and their ability to exacerbate psychotic symptoms in schizophrenic patients have promoted a view of schizophrenia as being related to altered glutamatergic neurotransmission. METHODS: This prompted us and others to develop animal models for psychosis based on a glutamatergic approach. Pharmacological induction of a state of impaired glutamatergic neurotransmission based on chronic, low-dose application of MK-801, a highly selective noncompetitive NMDA antagonist, revealed marked parallels between schizophrenia and our animal model. RESULTS: MK-801 altered the expression of NR1 splice variants and NR2 subunits of the NMDA receptor in a pattern partially resembling the alterations detected in schizophrenia. Ultrastructurally, the number of gamma-aminobutyric-acid (GABA)ergic parvalbumin-positive interneurons was relatively decreased, a finding which again parallels observations in post mortem brain from schizophrenic patients. As a functional consequence, local inhibition of pyramidal cells which is largely mediated by recurrent axon collaterals, originating from GABAergic interneurons, was altered. Not unexpectedly, these animals showed cognitive deficits resembling findings in schizophrenic humans. CONCLUSIONS: These convergent lines of evidence suggest that our approach has a significant potential of serving as a model of the pathobiology of several aspects of psychosis and consequently could contribute to the development of new therapeutic strategies.

Animals↗
PubMed2006

Circular fingerprints: flexible molecular descriptors with applications from physical chemistry to ADME.

Circular fingerprints -- the representation of molecular structures by atom neighborhoods -- have been applied to a wide range of applications, such as similarity searching and the prediction of absorption, distribution, metabolism, excretion and toxicity properties. In recent years there has been a surge in applications resulting from the superior performance of circular fingerprints in comparative studies. This feature examines the nature of circular fingerprints as well as their applications, including virtual screening, metabolism prediction and the estimation of pK((a)) constants.

Algorithms↗
PubMed2005

Screening for dihydrofolate reductase inhibitors using MOLPRINT 2D, a fast fragment-based method employing the naïve Bayesian classifier: limitations of the descriptor and the importance of balanced chemistry in training and test sets.

A fragment-based similarity searching method, MOLPRINT 2D, was employed for virtual screening of Escherichia coli dihydrofolate reductase inhibitors. Using the original training set of 50,000 compounds, only marginal enrichment factors (between 1 and 3) could be achieved on the test library. The active structures contained in the training and test libraries represented different types of "chemistry", that is, different substructural features associated with activity. Training and test sets were pooled in a 2nd step and randomly split into training and test of equal size, with the objective of smoothing out the different chemical characteristics of both libraries. In a 10-fold cross-validation study on the new training and test sets, typically 10-fold enrichment could be found in the first 96 positions, 4-fold enrichment in the first 384 positions, and 3-fold enrichment in the first 1536 positions, corresponding to 6, 10, and 28 hits, respectively (out of a total of 307; activity defined as average residual activity of less than 80%). The conclusions are 2-fold. On one hand, the exact fragment-matching similarity searching method employed here is not capable of finding completely novel hit structures. On the other hand, this study emphasizes the requirement for a comparable distribution of chemical features of the training and test sets. MOLPRINT 2D is freely downloadable from http://www.cheminformatics.org.

Bayes Theorem↗
PubMed2005

Increasing use of intravenous rt-PA does not affect safety in acute stroke.

BACKGROUND: Intravenous thrombolysis with rt-PA improves outcome in acute ischemic stroke. In a prospective study we analyzed the annual frequency of rt-PA treatment, its safety, and early clinical outcome. METHODS: All patients admitted to our stroke unit (SU) from 1998 to 2003 were registered in a prospective data base. Documented data included patient age, sex, time interval until admission, initial therapy (e. g., thrombolysis), death, intracerebral hemorrhage, other complications, and score on the National Institute of Health Stroke Scale (NIHSS). RESULTS: From 1998 to 2003, a total of 112 patients were treated with systemic thrombolysis. The number of acute stroke patients admitted within 2.5 hours and therefore eligible for thrombolysis did not substantially change between 1998 and 2003. From 1998 to 2001 the percentage of acute stroke patients that received rt-PA was stable (12.6-16.9 %). This percentage increased in 2002 (29.6%, p<0.05) and, again, in 2003 (42.1%, p<0.01). Of all treated patients, two developed symptomatic intracerebral hemorrhage (1.8%) and five died three to seven days after thrombolysis (4.5 %). The NIHSS score of patients receiving rt-PA significantly decreased during the acute treatment phase (14.2+/-5.1 to 8.0+/-5.9, p<0.001). A comparison of single years revealed that this NIHSS score reduction was stable. CONCLUSION: In our selected patients, the proportion of acute stroke patients treated with systemic thrombolysis increased almost three-fold from 1998 to 2003. This may be explained by protocol modifications and growing experience with the use of rt-PA. Our data demonstrate that increased use of rt-PA in acute stroke patients can be achieved without adversely affecting safety or clinical benefit.

Adolescent↗
PubMed2005

Creatine supplementation lowers brain glutamate levels in Huntington's disease.

There is evidence from in vitro and animal experiments that oral creatine (Cr) supplementation might prevent or slow down neurodegeneration in Huntington's disease (HD). However, this neuroprotective effect could not be replicated in clinical trials, possibly owing to treatment periods being too short to impact on clinical endpoints. We used proton magnetic resonance spectroscopy ((1)H-MRS) as a surrogate marker to evaluate the effect of Cr supplementation on brain metabolite levels in HD.Twenty patients (age 46+/-7.3 years, mean duration of symptoms 4.0+/-2.1 years, number of CAG repeats 44.5+/-2.7) were included. The primary endpoint was metabolic alteration as measured by (1)H-MRS in the parieto-occipital cortex before (t1) and after 8-10 weeks (t2) of Cr administration. Secondary measures comprised the motor section of the Unified Huntington's Disease Rating Scale and the Mini Mental State Examination. (1)H-MRS showed a 15.6% decrease of unresolved glutamate (Glu)+glutamine (Gln; Glu+Gln=Glx; p<0.001) and a 7.8% decrease of Glu (p<0.027) after Cr treatment. N-acetylaspartate trended to fall (p=0.073) whereas total Cr, choline-containing compounds, glucose, and lactate remained unchanged. There was no effect on clinical rating scales. This cortical Glx and Glu decrease may be explained by Cr enhancing the energy-dependent conversion of Glu to Gln via the Glu-Gln cycle, a pathway known to be impaired in HD. Since Glu-mediated excitotoxicity is presumably pivotal in HD pathogenesis, these results indicate a therapeutic potential of Cr in HD. Thus, longterm clinical trials are warranted.

Administration, Oral↗
PubMed2005

Assessment of structural diversity in combinatorial synthesis.

This article covers the combinatorial synthesis of small molecules with maximal structural diversity to generate a collection of pure compounds that are attractive for lead generation in a phenotypic, high-throughput screening approach. Nature synthesises diverse small molecules, but there are disadvantages with using natural product sources. The efficient chemical synthesis of structural diversity (and complexity) is the aim of diversity-oriented synthesis, and recent progress is reviewed. Specific highlights include a discussion of strategies to obtain structural diversity and an analysis of molecular descriptors used to classify compounds. The assessment of how successful one synthesis is versus another is subjective, therefore we test-drive software to assess structural diversity in combinatorial synthesis, which is freely available via a web interface.

Combinatorial Chemistry Techniques↗
PubMed2004

Molecular surface point environments for virtual screening and the elucidation of binding patterns (MOLPRINT 3D).

A novel method (MOLPRINT 3D) for virtual screening and the elucidation of ligand-receptor binding patterns is introduced that is based on environments of molecular surface points. The descriptor uses points relative to the molecular coordinates, thus it is translationally and rotationally invariant. Due to its local nature, conformational variations cause only minor changes in the descriptor. If surface point environments are combined with the Tanimoto coefficient and applied to virtual screening, they achieve retrieval rates comparable to that of two-dimensional (2D) fingerprints. The identification of active structures with minimal 2D similarity ("scaffold hopping") is facilitated. In combination with information-gain-based feature selection and a naive Bayesian classifier, information from multiple molecules can be combined and classification performance can be improved. Selected features are consistent with experimentally determined binding patterns. Examples are given for angiotensin-converting enzyme inhibitors, 3-hydroxy-3-methylglutaryl-coenzyme A reductase inhibitors, and thromboxane A2 antagonists.

Angiotensin-Converting Enzyme Inhibitors↗
PubMed2004

Molecular similarity: a key technique in molecular informatics.

Molecular Informatics utilises many ideas and concepts to find relationships between molecules. The concept of similarity, where molecules may be grouped according to their biological effects or physicochemical properties has found extensive use in drug discovery. Some areas of particular interest have been in lead discovery and compound optimisation. For example, in designing libraries of compounds for lead generation, one approach is to design sets of compounds "similar" to known active compounds in the hope that alternative molecular structures are found that maintain the properties required while enhancing e.g. patentability, medicinal chemistry opportunities or even in achieving optimised pharmacokinetic profiles. Thus the practical importance of the concept of molecular similarity has grown dramatically in recent years. The predominant users are pharmaceutical companies, employing similarity methods in a wide range of applications e.g. virtual screening, estimation of absorption, distribution, metabolism, excretion and toxicity (ADME/Tox) and prediction of physicochemical properties (solubility, partitioning etc.). In this perspective, we discuss the representation of molecular structure (descriptors), methods of comparing structures and how these relate to measured properties. This leads to the concept of molecular similarity, its various definitions and uses and how these have evolved in recent years. Here, we wish to evaluate and in some cases challenge accepted views and uses of molecular similarity. Molecular similarity, as a paradigm, contains many implicit and explicit assumptions in particular with respect to the prediction of the binding and efficacy of molecules at biological receptors. The fundamental observation is that molecular similarity has a context which both defines and limits its use. The key issues of solvation effects, heterogeneity of binding sites and the fundamental problem of the form of similarity measure to use are addressed.

Algorithms↗
PubMed2004

Severe symptomatic aseptic chemical meningitis following myelography: the role of procalcitonin.

Symptomatic aseptic, chemical meningitis is a rare complication of myelography. Its acute clinical course and standard laboratory findings are indistinguishable from those of bacterial meningitis. The authors present a case of severe postmyelographic chemical meningitis and compare CSF and serum inflammatory markers to a group of seven patients with proven bacterial meningitis. As in viral meningitis, procalcitonin might be able to discriminate between bacterial and chemical causes of CNS inflammation.

Adult↗
PubMed2003

Properties and prediction of mitochondrial transit peptides from Plasmodium falciparum.

A neural network approach for the prediction of mitochondrial transit peptides (mTPs) from the malaria-causing parasite Plasmodium falciparum is presented. Nuclear-encoded mitochondrial protein precursors of P. falciparum were analyzed by statistical methods, principal component analysis and supervised neural networks, and were compared to those of other eukaryotes. A distinct amino acid usage pattern has been found in protein encoding regions of P. falciparum: glycine, alanine, tryptophan and arginine are under-represented, whereas isoleucine, tyrosine, asparagine and lysine are over-represented compared to the SwissProt average. Similar patterns were observed in mTPs of P. falciparum. Using principal component analysis (PCA), mTPs from P. falciparum were shown to differ considerably from those of other organisms. A neural network system (PlasMit) for prediction of mTPs in P. falciparum sequences was developed, based on the relative amino acid frequency in the first 24 N-terminal amino acids, yielding a Matthews correlation coefficient of 0.74 (90% correct prediction) in a 20-fold cross-validation study. This system predicted 1177 (22%) mitochondrial genes, based on 5334 annotated genes in the P. falciparum genome. A second network with the same topology was trained to give more conservative estimate. This more stringent network yielded a Matthews correlation coefficient of 0.51 (84% correct prediction) in a 10-fold cross-validation study. It predicted 381 (7.1%) mitochondrial genes, based on 5334 annotated genes in the P. falciparum genome.

Amino Acids↗
PubMedDate not supplied

Molecular similarity searching using atom environments, information-based feature selection, and a naïve Bayesian classifier.

A novel technique for similarity searching is introduced. Molecules are represented by atom environments, which are fed into an information-gain-based feature selection. A naïve Bayesian classifier is then employed for compound classification. The new method is tested by its ability to retrieve five sets of active molecules seeded in the MDL Drug Data Report (MDDR). In comparison experiments, the algorithm outperforms all current retrieval methods assessed here using two- and three-dimensional descriptors and offers insight into the significance of structural components for binding.

Journal Article↗
PubMedDate not supplied

Encoding and decoding graphical chemical structures as two-dimensional (PDF417) barcodes.

A wide range of molecular representations exist today, ranging from human-readable structural diagrams over line notations such as Wiswesser Line Notation (WLN) and SMILES to several dozen computer-readable file formats. Still, to encode molecular structures in a computer-readable way for inputting structures in computer systems those formats are not the method of choice since they are not easily and faultlessly readable via optical recognition. In the present study a two-dimensional (PDF417) barcode representation of molecular structures in SMILES format is explored that enables the user to read and input molecular structures into computer systems in a fully automated fashion. A Lempel-Ziv-Welch (LZW) based compressed version of SMILES is suggested for cases where the size of the structure exceeds the storage capacity of PDF417 barcodes. Alternatively, the compact ACS format may be employed as a structural representation. The input via barcodes is fast, practically error free due to the 2D barcodes used which employ error correction and fully automatic. A Web application interface is developed which is able to interpret these barcodes and export them as optimized 3D chemical structures. Applications of this representation range from keeping automated storage systems to Web-based tracking systems of molecular samples. The National Chemical Laboratory, Pune, employs 2D barcode encoded structures for in-house repository management, where barcodes can also be used for querying the database for similar or substructures of the query structure.

Journal Article↗
PubMedDate not supplied

Similarity searching of chemical databases using atom environment descriptors (MOLPRINT 2D): evaluation of performance.

A molecular similarity searching technique based on atom environments, information-gain-based feature selection, and the naive Bayesian classifier has been applied to a series of diverse datasets and its performance compared to those of alternative searching methods. Atom environments are count vectors of heavy atoms present at a topological distance from each heavy atom of a molecular structure. In this application, using a recently published dataset of more than 100000 molecules from the MDL Drug Data Report database, the atom environment approach appears to outperform fusion of ranking scores as well as binary kernel discrimination, which are both used in combination with Unity fingerprints. Overall retrieval rates among the top 5% of the sorted library are nearly 10% better (more than 14% better in relative numbers) than those of the second best method, Unity fingerprints and binary kernel discrimination. In 10 out of 11 sets of active compounds the combination of atom environments and the naive Bayesian classifier appears to be the superior method, while in the remaining dataset, data fusion and binary kernel discrimination in combination with Unity fingerprints is the method of choice. Binary kernel discrimination in combination with Unity fingerprints generally comes second in performance overall. The difference in performance can largely be attributed to the different molecular descriptors used. Atom environments outperform Unity fingerprints by a large margin if the combination of these descriptors with the Tanimoto coefficient is compared. The naive Bayesian classifier in combination with information-gain-based feature selection and selection of a sensible number of features performs about as well as binary kernel discrimination in experiments where these classification methods are compared. When used on a monoaminooxidase dataset, atom environments and the naive Bayesian classifier perform as well as binary kernel discrimination in the case of a 50/50 split of training and test compounds. In the case of sparse training data, binary kernel discrimination is found to be superior on this particular dataset. On a third dataset, the atom environment descriptor shows higher retrieval rates than other 2D fingerprints tested here when used in combination with the Tanimoto similarity coefficient. Feature selection is shown to be a crucial step in determining the performance of the algorithm. The representation of molecules by atom environments is found to be more effective than Unity fingerprints for the type of biological receptor similarity calculations examined here. Combining information prior to scoring and including information about inactive compounds, as in the Bayesian classifier and binary kernel discrimination, is found to be superior to posterior data fusion (in the datasets tested here).

Journal Article↗