Man vs. machine: comparison of pharmacogenetic expert counselling with a clinical medication support system in a study with 200 genotyped patients

Background: Medication problems such as strong side effects or inefficacy occur frequently. At our university hospital, a consultation group of specialists takes care of patients suffering from medication problems. Nevertheless, the counselling of poly-treated patients is complex, as it requires the consideration of a large network of interactions between drugs and their targets, their metabolizing enzymes, and their transporters, etc. Purpose This study aims to check whether a score-based decision-support system (1) reduces the time and effort and (2) suggests solutions at the same quality level. Patients and methods: A total of 200 multimorbid, poly-treated patients with medication problems were included. All patients were considered twice: manually, as clinically established, and using the Drug-PIN decision-support system. Besides diagnoses, lab data (kidney, liver), phenotype (age, gender, BMI, habits), and genotype (genetic variants with actionable clinical evidence I or IIa) were considered, to eliminate potentially inappropriate medications and to select individually favourable drugs from existing medication classes. The algorithm is connected to automatically updated knowledge resources to provide reproducible up-to-date decision support. Results: The average turnaround time for manual poly-therapy counselling per patient ranges from 3 to 6 working hours, while it can be reduced to ten minutes using Drug-PIN. At the same time, the results of the novel computerized approach coincide with the manual approach at a level of > 90%. The holistic medication score can be used to find favourable drugs within a class of drugs and also to judge the severity of medication problems, to identify critical cases early and automatically. Conclusion: With the computerized version of this approach, it became possible to score all combinations of all alternative drugs from each class of drugs administered ("personalized medication landscape ") and to identify critical patients even before problems are reported ("medication alert"). Careful comparison of manual and score-based results shows that the incomplete manual consideration of genetic specialties and pharmacokinetic conflicts is responsible for most of the (minor) deviations between the two approaches. The meaning of the reduction of working time for experts by about 2 orders of magnitude should not be underestimated, as it enables practical application of personalized medicine in clinical routine

Chalcone-based Selective Inhibitors of a C4 Plant Key Enzyme as Novel Potential Herbicides

Weeds are a challenge for global food production due to their rapidly evolving resistance against herbicides. We have identified chalcones as selective inhibitors of phosphoenolpyruvate carboxylase (PEPC), a key enzyme for carbon fixation and biomass increase in the C4 photosynthetic pathway of many of the world’s most damaging weeds. In contrast, many of the most important crop plants use C3 photosynthesis. Here, we show that 2′,3′,4′,3,4-Pentahydroxychalcone (IC50 = 600 nM) and 2′,3′,4′-Trihydroxychalcone (IC50 = 4.2 μM) are potent inhibitors of C4 PEPC but do not affect C3 PEPC at a same concentration range (selectivity factor: 15–45). Binding and modeling studies indicate that the active compounds bind at the same site as malate/aspartate, the natural feedback inhibitors of the C4 pathway. At the whole plant level, both substances showed pronounced growth-inhibitory effects on the C4 weed Amaranthus retroflexus, while there were no measurable effects on oilseed rape, a C3 plant. Growth of selected soil bacteria was not affected by these substances. Our chalcone compounds are the most potent and selective C4 PEPC inhibitors known to date. They offer a novel approach to combat C4 weeds based on a hitherto unexplored mode of allosteric inhibition of a C4 plant key enzyme

Country-wide HIV incidence study complementing HIV surveillance in Germany

Serological methods exist that allow differentiating between recent and long-standing infections in persons infected with HIV. During a pilot study in Berlin between 2005 and 2007 methodologies have been evaluated. In a cross-sectional study blood samples, demographic, laboratory, clinical and behavioural data based on a KABP survey were collected from patients with newly diagnosed HIV infections. The BED-CEIA was used to determine recency of infection. Recent HIV infections contributed 54% (CI [95%]: 45; 64) in MSM and 16% (CI [95%]: 0; 39) in patients with other transmission risks (p=0.041). Proportions of recent infections were significantly higher in MSM ≤30 years (p=0.019). The mean age was 33.9 (median 34 years) in recent compared with 38.6 years (median: 38 years) in long-standing infections (p=0.011). High-risk behaviour indicated through very low condom use in recently HIV infected MSM could be identified. The results of the pilot study support expectations that the modified application of the method may contribute to improving HIV prevention efforts in Germany. On this basis the Robert Koch Institute implemented a countrywide HIV incidence study to complement HIV surveillance in early 2008. The study is funded by the German Ministry of Health. Data on recent HIV infections and current HIV transmission risks are collected. Design, methods and impact are described in detail

Association of COMT genotypes with S-COMT promoter methylation in growth-discordant monozygotic twins and healthy adults

<p>Abstract</p> <p>Background</p> <p>Catechol-O-Methyltransferase (COMT) plays a key role in dopamine and estrogen metabolism. Recently, COMT haplotypes rather than the single polymorphism Val158Met have been reported to underlie differences in protein expression by modulating mRNA secondary structure. So far, studies investigating the epigenetic variability of the S-COMT (soluble COMT) promoter region mainly focused on phenotypical aspects, and results have been controversial.</p> <p>Methods</p> <p>We assessed S-COMT promoter methylation in saliva and blood derived DNA with regard to early pre- and postnatal growth as well as to genotype for polymorphisms rs6269, rs4633, and rs4680 (Val158Met) in 20 monozygotic twin pairs (mean age 4 years), who were discordant for intrauterine development due to severe feto-fetal-transfusion syndrome. Methylation levels of two previously reported partially methylated cytosines were determined by the quantitative SIRPH (SNuPE- IP RP HPLC) assay.</p> <p>Results</p> <p>Overall, we observed a high variability of S-COMT promoter methylation, which did not correlate with individual differences in the pre- or postnatal growth pattern. Within the twin pairs however we noted a distinct similarity that could be linked to underlying COMT genotypes. This association was subsequently confirmed in a cohort of 93 unrelated adult controls. Interestingly, 158Val-alleles were found at both ends of the epigenotypical range, which is in accordance with a recently proposed model of COMT haplotypes corresponding to a continuum of phenotypical variability.</p> <p>Conclusion</p> <p>The strong heritable component of S-COMT promoter methylation found in our study needs to be considered in future approaches that focus on interactions between COMT epigenotype and phenotype.</p

CSAR Benchmark Exercise of 2010: Selection of the Protein–Ligand Complexes

ABSTRACT: A major goal in drug design is the improvement of computational methods for docking and scoring. The Community Structure Activity Resource (CSAR) aims to collect available data from industry and academia which may be used for this purpose (www.csardock.org). Also, CSAR is charged with organizing community-wide exercises based on the collected data. The first of these exercises was aimed to gauge the overall state of docking and scoring, using a large and diverse data set of protein ligand complexes. Participants were asked to calculate the affinity of the complexes as provided and then recalculate with changes which may improve their specific method. This first data set was selected from existing PDB entries which had binding data (Kd or Ki) in Binding MOAD, augmented with entries from PDBbind. The final data set contains 343 diverse protein ligand complexes and spans 14 pKd. Sixteen proteins have three or more complexes in the data set, from which a user could start an inspection of congeneric series. Inherent experimental error limits the possible correlation between scores and measured affinity; R 2 is limited to ∼0.9 when fitting to the data set without over parametrizing. R 2 is limited to ∼0.8 when scoring the data set with a method trained on outside data. The details of how the data set was initially selected, and the process by which it matured t

SynSysNet:integration of experimental data on synaptic protein-protein interactions with drug-target relations

We created SynSysNet, available online at http://bioinformatics.charite.de/ synsysnet, to provide a platform that creates a comprehensive 4D network of synaptic interactions. Neuronal synapses are fundamental structures linking nerve cells in the brain and they are responsible for neuronal communication and information processing. These processes are dynamically regulated by a network of proteins. New developments in interaction prote-omics and yeast two-hybrid methods allow unbiased detection of interactors. The consolidation of data from different resources and methods is important to understand the relation to human behaviour and disease and to identify new therapeutic approaches. To this end, we established SynSysNet from a set of ∼1000 synapse specific proteins, their structures and small-molecule interactions. For two-thirds of these, 3D structures are provided (from Protein Data Bank and homology modelling). Drug-target interactions for 750 approved drugs and 50000 compounds, as well as 5000 experimentally validated protein-protein interactions, are included. The resulting interaction network and user-selected parts can be viewed interactively and exported in XGMML. Approximately 200 involved pathways can be explored regarding drug-target interactions. Homology-modelled structures are downloadable in Protein Data Bank format, and drugs are available as MOL-files. Protein-protein interactions and drug-target interactions can be viewed as networks; corresponding PubMed IDs or sources are given. © The Author(s) 2012

Structure-Based Development of Small Molecule PFKFB3 Inhibitors: A Framework for Potential Cancer Therapeutic Agents Targeting the Warburg Effect

Cancer cells adopt glycolysis as the major source of metabolic energy production for fast cell growth. The HIF-1-induced PFKFB3 plays a key role in this adaptation by elevating the concentration of Fru-2,6-BP, the most potent glycolysis stimulator. As this metabolic conversion has been suggested to be a hallmark of cancer, PFKFB3 has emerged as a novel target for cancer chemotherapy. Here, we report that a small molecular inhibitor, N4A, was identified as an initial lead compound for PFKFB3 inhibitor with therapeutic potential. In an attempt to improve its potency, we determined the crystal structure of the PFKFB3•N4A complex to 2.4 Å resolution and, exploiting the resulting molecular information, attained the more potent YN1. When tested on cultured cancer cells, both N4A and YN1 inhibited PFKFB3, suppressing the Fru-2,6-BP level, which in turn suppressed glycolysis and, ultimately, led to cell death. This study validates PFKFB3 as a target for new cancer therapies and provides a framework for future development efforts

Prediction of Ligand Binding Using an Approach Designed to Accommodate Diversity in Protein-Ligand Interactions

Computational determination of protein-ligand interaction potential is important for many biological applications including virtual screening for therapeutic drugs. The novel internal consensus scoring strategy is an empirical approach with an extended set of 9 binding terms combined with a neural network capable of analysis of diverse complexes. Like conventional consensus methods, internal consensus is capable of maintaining multiple distinct representations of protein-ligand interactions. In a typical use the method was trained using ligand classification data (binding/no binding) for a single receptor. The internal consensus analyses successfully distinguished protein-ligand complexes from decoys (r2, 0.895 for a series of typical proteins). Results are superior to other tested empirical methods. In virtual screening experiments, internal consensus analyses provide consistent enrichment as determined by ROC-AUC and pROC metrics

Solvent accessible surface area approximations for rapid and accurate protein structure prediction

The burial of hydrophobic amino acids in the protein core is a driving force in protein folding. The extent to which an amino acid interacts with the solvent and the protein core is naturally proportional to the surface area exposed to these environments. However, an accurate calculation of the solvent-accessible surface area (SASA), a geometric measure of this exposure, is numerically demanding as it is not pair-wise decomposable. Furthermore, it depends on a full-atom representation of the molecule. This manuscript introduces a series of four SASA approximations of increasing computational complexity and accuracy as well as knowledge-based environment free energy potentials based on these SASA approximations. Their ability to distinguish correctly from incorrectly folded protein models is assessed to balance speed and accuracy for protein structure prediction. We find the newly developed “Neighbor Vector” algorithm provides the most optimal balance of accurate yet rapid exposure measures