External validation of the GRACE risk score in patients with myocardial infarction in Hungary.

Background: Literature confirms that the Global Registry of Acute Coronary Events (GRACE) risk score provides a better risk evaluation than clinical judgment in patients with acute myocardial infarction. We aimed to externally validate the GRACE risk score in unselected patients with myocardial infarction in Hungary. Methods: Data from the comprehensive Hungarian Myocardial Infarction Registry (HUMIR), a national registry that collects data on consecutive acute myocardial infarction (AMI) patients, were used. Hospitals registered 102,939 infarction events in the HUMIR between January 1, 2014, and December 31, 2020. The data required to calculate GRACE risk score were available for 75,199 events. We studied the 6-months, 1-year, and 3-year outcomes. We calculated widely used metrics to characterise calibration (calibration curve, calibration intercept and slope, Eavg, Emax, and E90) and discrimination (c-score, equivalent to AUC, and Somer's Dxy). Results: The risk of low-risk patients was underestimated, and the risk of high-risk patients was overestimated. However, the deviation was small, especially for the three-year survival (E90 was 0.15, 0.22, and 0.08). Discrimination was good, with an AUC of approximately 0.8, and was very similar in all the periods. Conclusions: These data confirmed the usefulness of GRACE risk score in selecting high-risk patients with myocardial infarction in the Hungarian population.

Management of patients treated for myocardial infarction in different regions of Hungary and patient survival for 10 years / Szívinfarktus miatt kezelt betegek ellátása Magyarország különbözo régióiban és a betegek 10 éves túlélése.

Összefoglaló. Elozmény: A szívinfarktus miatt kezelt betegek ellátásának regionális adataira és a betegek hosszú távú kórlefolyására vonatkozó hazai kutatás eddig nem történt. Célkituzés: A vizsgálat célja a Magyar Infarktus Regiszter pilotidoszakában rögzített betegeknél az ellátás és a 10 éves túlélés elemzése a magyarországi nagyrégiókban. Módszer: A Magyar Infarktus Regiszter (késobbi neve: Nemzeti Szívinfarktus Regiszter) 2010. január 1. és 2013. december 31. között a centrumok önkéntes részvételével 23 142 beteg adatait rögzítette, akik írásban hozzájárultak egészségügyi és klinikai adataik kezeléséhez. Az adatgyujtés a Kutatásetikai Bizottság engedélyével rendelkezett. A vizsgált populációban 12 104, ST-elevációval járó myocardialis infarctuson (STEMI) és 10 768, ST-elevációval nem járó myocardialis infarctuson (NSTEMI) átesett beteg szerepelt. A feldolgozott adatok 128 220 betegévre vonatkoznak, amelyeket nagyrégiók szerint (Nyugat-, Közép- és Kelet-Magyarország) hasonlítottunk össze. Eredmények: A STEMI-betegek 78,4%-ánál, az NSTEMI-betegek 51,6%-ánál történt katéteres érmegnyitás (PCI). NSTEMI esetén a Közép-Magyarország és Nyugat-Magyarország régiókban a beavatkozás gyakoribb volt, mint a Kelet-Magyarország régióban (p<0,01). Az utánkövetés során a PCI a Nyugat-Magyarország régióban, a revascularisatiós szívmutét (CABG) a Nyugat-Magyarország és a Kelet-Magyarország régióban szignifikánsan gyakoribb volt, mint a Közép-Magyarország régióban (p<0,01). A STEMI-betegek között a 10 év alatt a férfiak 49,2%-a, a nok 46,6%-a halt meg, az NSTEMI-csoportban 63%, illetve 57,6%. Az akut szakban elvégzett PCI mindkét betegcsoportban, nemben, az utánkövetés minden idopontjában és a vizsgált régiókban csökkentette a halálozást (p<0,01). A STEMI-betegek esetén a túlélés a régiók között nem különbözött (p = 0,72), míg az NSTEMI után a 10 éves túlélés a Nyugat-Magyarország régióban jobb volt (p<0,01). Következtetés: A magyarországi nagyrégiók között az infarktusos betegek ellátásában és prognózisában regionális különbségek vannak. Orv Hetil. 2021; 162(36): 1438-1450. HISTORY: Regional data on patients' care for myocardial infarction and the long-term follow up of patients have not yet been studied in Hungary. OBJECTIVE: The study aims to analyze the care and 10-year survival of patients recorded during the Hungarian Myocardial Infarction Registry's pilot period in large regions of Hungary. METHOD: Between Jan 1, 2010 and Dec 31, 2013, the Hungarian Myocardial Infarction Registry recorded data on 23 142 patients with voluntary participation. The Research Ethics Committee approved the program. The study included 12 104 patients with ST-elevation myocardial infarction (STEMI) and 10 768 patients with non-ST-elevation myocardial infarction (NSTEMI). The data processed refer to 128 220 patient years based on large regions (West, Central and East Hungary). RESULTS: Percutaneous coronary intervention occurred in 78.4% of STEMI patients and 51.6% of NSTEMI patients. In the NSTEMI group, percutaneous coronary interventions (PCIs) in the Central-Hungary and West-Hungary regions were significantly more common than in the East-Hungary region (p<0.01). During follow-up, PCI in the West-Hungary region, revascularization surgery in the West-Hungary and East-Hungary regions were significantly more common than in the Central-Hungary region (p<0.01). Among STEMI patients, 49.2% of men and 46.6% of women died within 10 years, while in the NSTEMI group 63% and 57.6%, respectively. PCI reduced mortality in both patient groups, sex, at all times of follow-up and in the regions studied (p<0.01). As for STEMI patients, survival was similar in all regions (p = 0.72), while after NSTEMI, 10-year survival in the West-Hungary region was better (p<0.01). CONCLUSION: There are regional differences in the care and prognosis of patients with myocardial infarction. Orv Hetil. 2021; 162(36): 1438-1450.

Quality assurance of national internet-based patient register data: Experiences during the operation of the Hungarian Myocardial Infarction Registry, 2010­2020 / Internetalapú, országos betegségregiszter adatainak minoségbiztosítása: Tapasztalatok a Nemzeti Szívinfarktus Regiszter muködtetése során, 2010­2020

Összefoglaló. Bevezetés: A Nemzeti Szívinfarktus Regiszterben 111 788 beteg 122 351 infarktusos eseményéhez kapcsolódó 145 292 kezelés adatai szerepelnek. Módszer: A rögzített adatokat az üzemeltetok folyamatosan kontrollálják, bemutatják azokat a minoségbiztosítási módszereket, amelyekkel az adatbázis teljességét és megfeleloségét biztosítják. Az online informatikai rendszerben az adatbevitel során 119 automatikus ellenorzési algoritmust muködtetnek. Az automatikus ellenorzési algoritmussal nem kezelheto adatok ellenorzését 5 részállású, egészségügyi képzettségu kontroller és 2 foállású munkatárs végzi. A regiszter muködése során folyamatosan fejlesztették az ellenorzés módszereit, ennek során 2018-tól a kontrollerek által ellenorzött adatlapok utóellenorzésére is sor kerül. Az utóellenorzés során a már ellenorzött adatlapok 2,4%-ában további javításra volt szükség. Eredmények: Az utóellenorzés eredménye, hogy a kontrolleri munkát hatékonyabbá sikerült tenni, mivel egyre kevesebb az utóellenorzés során hibásnak talált adatlapok száma. Megvizsgálták, hogy az adatlap kérdéseire milyen arányban kaptak értékelheto választ. Az értékelheto válaszok aránya a legtöbb esetben meghaladta a 90%-ot, azonban a panaszok kezdetének ideje az adatlapok 39%-ában volt megadva, míg a dohányzási szokásokkal kapcsolatos válasz az esetek 59%-ában volt megfelelo. Megbeszélés: A szerzok rámutatnak arra, hogy a Nemzeti Egészségbiztosítási Alapkezelo és a Nemzeti Szívinfarktus Regiszter adatbázisának folyamatos egyeztetése hozzájárul a regisztráció teljességének biztosításához, lehetové teszi a betegek állapotának hosszú távú követését. Miután a program kötelezo jelleguvé vált 2014. 01. 01-jén, az elso évben a szívinfarktus-diagnózissal finanszírozott betegek kétharmada (67%) szerepelt a regiszter adatbázisában; ez az arány a 2017-2019-es években meghaladta a 90%-ot (91,7-93,6-91,3%). Következtetés: Vizsgálatukból a szerzok azt a következtetést vonják le, hogy a betegségregiszter muködése során szükséges az adatok teljességének és megfeleloségének folyamatos ellenorzése. A regiszter adatbázisának 90% feletti teljessége az ellátórendszer minoségi paramétereinek folyamatos követését teszi lehetové. Orv Hetil. 2021; 162(2): 61-68. INTRODUCTION: The Hungarian Myocardial Infarction Registry contains data on 145 592 treatments related to the 111 788 patients and the 122 351 myocardial infarctions. METHOD: The recorded information is continuously monitored, and the quality assurance methods used to ensure the completeness and adequacy of the database are presented. In the online IT system, 119 automatic verification algorithms are operated during data entry. Data that cannot be handled by the automated verification algorithm is checked by five part-time health-qualified controllers and two full-time employees. During the operation of the register, the control methods were continuously developed, during which the data sheets checked by the controllers will be post-checked from 2018 onwards. During the post-checked process, 2.4% of the datasheets required further correction. RESULTS: The number of data sheets found to be incorrect during the post-audit was decreasing. The authors examined the proportion of evaluable answers to the questionnaire. The rate of evaluable responses was over 90% in most cases; however, the time of the onset of symptoms was given in 39% of the datasheets, while the answer to smoking habits was adequate in 59% of cases. DISCUSSION: The authors point out that the continuous consultation of the database of the National Health Fund Management Centre and the Hungarian Myocardial Infarction Registry contributes to ensuring the completeness of registration, enabling long-term monitoring of the condition of patients. In the first year of the mandatory period of the program, two-thirds (67%) of patients treated with a diagnosis of myocardial infarction were included in the registry database, and this proportion exceeded 90% in the years 2017-2019 (91.7-93.6-91.3%). CONCLUSION: The study of the authors concludes that the completeness and adequacy of the data need to be constantly monitored during the operation of the patient registry. The integrity of the register database above 90% enables the continuous monitoring of the quality parameters of the system. Orv Hetil. 2021; 162(2): 61-68.

Identification of PPARγ ligands with One-dimensional Drug Profile Matching.

INTRODUCTION: Computational molecular database screening helps to decrease the time and resources needed for drug development. Reintroduction of generic drugs by second medical use patents also contributes to cheaper and faster drug development processes. We screened, in silico, the Food and Drug Administration-approved generic drug database by means of the One-dimensional Drug Profile Matching (oDPM) method in order to find potential peroxisome proliferator-activated receptor gamma (PPARγ) agonists. The PPARγ action of the selected generics was also investigated by in vitro and in vivo experiments. MATERIALS AND METHODS: The in silico oDPM method was used to determine the binding potency of 1,255 generics to 149 proteins collected. In vitro PPARγ activation was determined by measuring fatty acid-binding protein 4/adipocyte protein gene expression in a Mono Mac 6 cell line. The in vivo insulin sensitizing effect of the selected compound (nitazoxanide; 50-200 mg/kg/day over 8 days; n = 8) was established in type 2 diabetic rats by hyperinsulinemic euglycemic glucose clamping. RESULTS: After examining the closest neighbors of each of the reference set's members and counting their most abundant neighbors, ten generic drugs were selected with oDPM. Among them, four enhanced fatty acid-binding protein/adipocyte protein gene expression in the Mono Mac 6 cell line, but only bromfenac and nitazoxanide showed dose-dependent actions. Induction by nitazoxanide was higher than by bromfenac. Nitazoxanide lowered fasting blood glucose levels and improved insulin sensitivity in type 2 diabetic rats. CONCLUSION: We demonstrated that the oDPM method can predict previously unknown therapeutic effects of generic drugs. Nitazoxanide can be the prototype chemical structure of the new generation of insulin sensitizers.

Virtual affinity fingerprints for target fishing: a new application of Drug Profile Matching.

We recently introduced Drug Profile Matching (DPM), a novel virtual affinity fingerprinting bioactivity prediction method. DPM is based on the docking profiles of ca. 1200 FDA-approved small-molecule drugs against a set of nontarget proteins and creates bioactivity predictions based on this pattern. The effectiveness of this approach was previously demonstrated for therapeutic effect prediction of drug molecules. In the current work, we investigated the applicability of DPM for target fishing, i.e. for the prediction of biological targets for compounds. Predictions were made for 77 targets, and their accuracy was measured by Receiver Operating Characteristic (ROC) analysis. Robustness was tested by a rigorous 10-fold cross-validation procedure. This procedure identified targets (N = 45) with high reliability based on DPM performance. These 45 categories were used in a subsequent study which aimed at predicting the off-target profiles of currently approved FDA drugs. In this data set, 79% of the known drug-target interactions were correctly predicted by DPM, and additionally 1074 new drug-target interactions were suggested. We focused our further investigation on the suggested interactions of antipsychotic molecules and confirmed several interactions by a review of the literature.

Contribution of 2D and 3D structural features of drug molecules in the prediction of Drug Profile Matching.

Drug Profile Matching (DPM), a novel virtual affinity fingerprinting method capable of predicting the medical effect profiles of small molecules, was introduced by our group recently. The method exploits the information content of interaction patterns generated by flexible docking to a series of rigidly kept nontarget protein active sites. We presented the ability of DPM to classify molecules excellently, and the question arose, what the contribution of 2D and 3D structural features of the small molecules is to the intriguingly high prediction power of DPM. The present study compared the prediction powers for effect profiles of 1163 FDA-approved drug compounds determined by DPM and ChemAxon 2D and 3D similarity fingerprinting approaches. We found that DPM outperformed the 2D and 3D approaches in almost all therapeutic categories for drug classification except for mechanically rigid structural categories where high accuracy was obtained by all three methods. Moreover, we also tested the predictive power of DPM on external data by reducing the parent data set and demonstrated that DPM can overcome the common screening problems of 2D and 3D similarity methods arising from the presence of structurally diverse molecules in certain effect categories.

A novel actin binding site of myosin required for effective muscle contraction.

F-actin serves as a track for myosin's motor functions and activates its ATPase activity by several orders of magnitude, enabling actomyosin to produce effective force against load. Although actin activation is a ubiquitous property of all myosin isoforms, the molecular mechanism and physiological role of this activation are unclear. Here we describe a conserved actin-binding region of myosin named the 'activation loop', which interacts with the N-terminal segment of actin. We demonstrate by biochemical, biophysical and in vivo approaches using transgenic Caenorhabditis elegans strains that the interaction between the activation loop and actin accelerates the movement of the relay, stimulating myosin's ATPase activity. This interaction results in efficient force generation, but it is not essential for the unloaded motility. We conclude that the binding of actin to myosin's activation loop specifically increases the ratio of mechanically productive to futile myosin heads, leading to efficient muscle contraction.

Drug effect prediction by polypharmacology-based interaction profiling.

Most drugs exert their effects via multitarget interactions, as hypothesized by polypharmacology. While these multitarget interactions are responsible for the clinical effect profiles of drugs, current methods have failed to uncover the complex relationships between them. Here, we introduce an approach which is able to relate complex drug-protein interaction profiles with effect profiles. Structural data and registered effect profiles of all small-molecule drugs were collected, and interactions to a series of nontarget protein binding sites of each drug were calculated. Statistical analyses confirmed a close relationship between the studied 177 major effect categories and interaction profiles of ca. 1200 FDA-approved small-molecule drugs. On the basis of this relationship, the effect profiles of drugs were revealed in their entirety, and hitherto uncovered effects could be predicted in a systematic manner. Our results show that the prediction power is independent of the composition of the protein set used for interaction profile generation.

Predicting P-glycoprotein-mediated drug transport based on support vector machine and three-dimensional crystal structure of P-glycoprotein.

Human P-glycoprotein (P-gp) is an ATP-binding cassette multidrug transporter that confers resistance to a wide range of chemotherapeutic agents in cancer cells by active efflux of the drugs from cells. P-gp also plays a key role in limiting oral absorption and brain penetration and in facilitating biliary and renal elimination of structurally diverse drugs. Thus, identification of drugs or new molecular entities to be P-gp substrates is of vital importance for predicting the pharmacokinetics, efficacy, safety, or tissue levels of drugs or drug candidates. At present, publicly available, reliable in silico models predicting P-gp substrates are scarce. In this study, a support vector machine (SVM) method was developed to predict P-gp substrates and P-gp-substrate interactions, based on a training data set of 197 known P-gp substrates and non-substrates collected from the literature. We showed that the SVM method had a prediction accuracy of approximately 80% on an independent external validation data set of 32 compounds. A homology model of human P-gp based on the X-ray structure of mouse P-gp as a template has been constructed. We showed that molecular docking to the P-gp structures successfully predicted the geometry of P-gp-ligand complexes. Our SVM prediction and the molecular docking methods have been integrated into a free web server (http://pgp.althotas.com), which allows the users to predict whether a given compound is a P-gp substrate and how it binds to and interacts with P-gp. Utilization of such a web server may prove valuable for both rational drug design and screening.

Evaluation of drug-human serum albumin binding interactions with support vector machine aided online automated docking.

MOTIVATION: Human serum albumin (HSA), the most abundant plasma protein is well known for its extraordinary binding capacity for both endogenous and exogenous substances, including a wide range of drugs. Interaction with the two principal binding sites of HSA in subdomain IIA (site 1) and in subdomain IIIA (site 2) controls the free, active concentration of a drug, provides a reservoir for a long duration of action and ultimately affects the ADME (absorption, distribution, metabolism, and excretion) profile. Due to the continuous demand to investigate HSA binding properties of novel drugs, drug candidates and drug-like compounds, a support vector machine (SVM) model was developed that efficiently predicts albumin binding. Our SVM model was integrated to a free, web-based prediction platform (http://albumin.althotas.com). Automated molecular docking calculations for prediction of complex geometry are also integrated into the web service. The platform enables the users (i) to predict if albumin binds the query ligand, (ii) to determine the probable ligand binding site (site 1 or site 2), (iii) to select the albumin X-ray structure which is complexed with the most similar ligand and (iv) to calculate complex geometry using molecular docking calculations. Our SVM model and the potential offered by the combined use of in silico calculation methods and experimental binding data is illustrated.

Relating the shape of protein binding sites to binding affinity profiles: is there an association?

BACKGROUND: Various pattern-based methods exist that use in vitro or in silico affinity profiles for classification and functional examination of proteins. Nevertheless, the connection between the protein affinity profiles and the structural characteristics of the binding sites is still unclear. Our aim was to investigate the association between virtual drug screening results (calculated binding free energy values) and the geometry of protein binding sites. Molecular Affinity Fingerprints (MAFs) were determined for 154 proteins based on their molecular docking energy results for 1,255 FDA-approved drugs. Protein binding site geometries were characterized by 420 PocketPicker descriptors. The basic underlying component structure of MAFs and binding site geometries, respectively, were examined by principal component analysis; association between principal components extracted from these two sets of variables was then investigated by canonical correlation and redundancy analyses. RESULTS: PCA analysis of the MAF variables provided 30 factors which explained 71.4% of the total variance of the energy values while 13 factors were obtained from the PocketPicker descriptors which cumulatively explained 94.1% of the total variance. Canonical correlation analysis resulted in 3 statistically significant canonical factor pairs with correlation values of 0.87, 0.84 and 0.77, respectively. Redundancy analysis indicated that PocketPicker descriptor factors explain 6.9% of the variance of the MAF factor set while MAF factors explain 15.9% of the total variance of PocketPicker descriptor factors. Based on the salient structures of the factor pairs, we identified a clear-cut association between the shape and bulkiness of the drug molecules and the protein binding site descriptors. CONCLUSIONS: This is the first study to investigate complex multivariate associations between affinity profiles and the geometric properties of protein binding sites. We found that, except for few specific cases, the shapes of the binding pockets have relatively low weights in the determination of the affinity profiles of proteins. Since the MAF profile is closely related to the target specificity of ligand binding sites we can conclude that the shape of the binding site is not a pivotal factor in selecting drug targets. Nonetheless, based on strong specific associations between certain MAF profiles and specific geometric descriptors we identified, the shapes of the binding sites do have a crucial role in virtual drug design for certain drug categories, including morphine derivatives, benzodiazepines, barbiturates and antihistamines.

Cyclodextrin KnowledgeBase a web-based service managing CD-ligand complexation data.

Cyclodextrins are cyclic oligosaccharides that are able to form water-soluble inclusion complexes with small molecules. Because of their complexing ability, they are widely applied in food, pharmaceutical and chemical industries. In this paper we describe the development of a free web-service, Cyclodextrin KnowledgeBase: ( http://www.cyclodextrin.net ). The database contains four modules: the Publication, Interaction, Chirality and Analysis Modules. In the Publication Module, almost 50,000 publication details are collected that can be retrieved by text search. In the Interaction and Chirality Modules relevant literature data on cyclodextrin complexation and chiral recognition are collected that can be retrieved by both text and structural searches. Moreover, in the Analysis Module, the geometries of small molecule-cyclodextrin complexes can be predicted using molecular docking tools in order to explore the structures and interaction energies of the inclusion complexes. Complex geometry prediction is made possible by the built-in database of 95 cyclodextrin derivatives, where the 3D structures as well as the partial charges are calculated and stored for further utilization. The use of the database is demonstrated by several examples.

Breaking the hierarchy--a new cluster selection mechanism for hierarchical clustering methods.

BACKGROUND: Hierarchical clustering methods like Ward's method have been used since decades to understand biological and chemical data sets. In order to get a partition of the data set, it is necessary to choose an optimal level of the hierarchy by a so-called level selection algorithm. In 2005, a new kind of hierarchical clustering method was introduced by Palla et al. that differs in two ways from Ward's method: it can be used on data on which no full similarity matrix is defined and it can produce overlapping clusters, i.e., allow for multiple membership of items in clusters. These features are optimal for biological and chemical data sets but until now no level selection algorithm has been published for this method. RESULTS: In this article we provide a general selection scheme, the level independent clustering selection method, called LInCS. With it, clusters can be selected from any level in quadratic time with respect to the number of clusters. Since hierarchically clustered data is not necessarily associated with a similarity measure, the selection is based on a graph theoretic notion of cohesive clusters. We present results of our method on two data sets, a set of drug like molecules and set of protein-protein interaction (PPI) data. In both cases the method provides a clustering with very good sensitivity and specificity values according to a given reference clustering. Moreover, we can show for the PPI data set that our graph theoretic cohesiveness measure indeed chooses biologically homogeneous clusters and disregards inhomogeneous ones in most cases. We finally discuss how the method can be generalized to other hierarchical clustering methods to allow for a level independent cluster selection. CONCLUSION: Using our new cluster selection method together with the method by Palla et al. provides a new interesting clustering mechanism that allows to compute overlapping clusters, which is especially valuable for biological and chemical data sets.