Nuevas aportaciones al desarrollo de modelos QSAR/QSPR para la predicción de la mutagenicidad de contaminantes ambientales y su interacción con sustancias activas presentes en el medio

Se estudió mediante modelos QSAR, la posible mutagenicidad de sustancias presentes en el medio ambiente como los ácidos haloacéticos (derivados de la cloración del agua) y los carbonilos alfa, beta insaturados (sobre todo los empleados como monómeros para la preparación de materiales dentales de restauración) y su posible interacción con la beta ciclodextrina, la cual está presente como excipiente en productos farmacéuticos y como estabilizador de aromas, colorantes y algunas vitaminas en alimentos. Como resultado de este estudio pudimos destacar: -El ácido fluoroiodoacético y difluoroiodoacético podrían ser mutagénicos debido a los valores de potencia mutagénica obtenidos con los modelos desarrollados. Sustancias que podrían encontrarse en aguas fluoradas ricas en ioduro/bromuro. Además es posible que estén presentes en aguas fluoradas ricas en bromuro/ioduro hecho que pondría en duda la necesidad de fluorar el agua potable. - Sustancias comúnmente empleadas como monómeros dentales presentaron predicciones negativas para el ensayo de Ames y un carácter mutagénico para el ensayo con células de mamífero, a excepción del UDMA (Uretil dimetacrilato). - Respecto a la posible interacción de estas sustancias con la beta-ciclodextrina, los ácidos haloacéticos presentan valores de complejación inferiores a los que normalmente presentan fármacos o componentes de los alimentos, por lo que es de esperar que la interacción entre los ácidos haloacéticos y la beta-CD sea de escasa importancia. En cuanto a los monómeros dentales hay que resaltar que sustancias como el TEGDMA, 1,6-ADMA, 1,8-ADMA, GMR, MEPC y 6-HHMA, predichos como mutagénicos, presentan valores de complejación superiores a los que presentan fármacos o componentes de los alimentos. Por lo tanto, estas sustancias podrían desplazar de sus complejos a fármacos o componentes de los alimentos pudiéndose llegar a algún tipo de interacción.Farmaci

Disain ja modelleerimine HIV-1 pöördtranskriptaasi ja Malaaria ravimite väljatöötamise varajases faasis

Väitekirja elektrooniline versioon ei sisalda publikatsiooneKäesolev uurimus keskendub kahele ohtlikule infektsioonhaigusele: inimese immuunpuudulikkuse viirus tüüp 1 (HIV-1) ja malaaria. Uue ravimi väljatöötamine algusest lõppuni on aega nõudev ning kulukas protsess, mis jaotatakse viieks etapiks: baas uurimistöö, põhi sihtmärgi ja baas ühendi(te) leidmine, eelkliiniline arendus, kliiniline arendus ja vajalike dokumentide esitamine ravimiametisse. Antud väitekirjas keskendutakse kahele esimesele etappidele, mida tuntakse ka varajase ravimiarenduse faasina. HIV-1 uurimisel oli kaks põhisuunda. Esmalt tuginedes eelnevalt tehtud virtuaalsõelumise tulemustele teostati uudsete s-triasiini derivaatide avastamine, disainimine, ja süntees, mille tulemused valideeriti eksperimentaalselt ning analüüsiti valk-ligand interaktsioonimudelite abil. Kõige tõhusam HIV-1 mitte-nukleosiidne pöördtranskriptaasi inhibiitor oli madala molekulmassiga, heade ligandi efektiivsust näitavate parameetritega, ja madala toksilisusega, võimaldades edasist modifitseerimist ja arendamist. Tehtud aktiivse keemilise struktuuri avastus motiveeris HIV-1 inhibiitorite keemilise struktuuriruumi laiemat uurimist, et kindlaks teha kas uudsed s-triasiinid moodustavad ka unikaalsed keemiliste ühendite grupi HIV-1 mitte-nukleosiidsete pöördtranskriptaasi inhibiitorite maastikul. Selle läbiviimiseks koostati, korrastati ja kureeriti ChEMBL-i andmebaasist saadud andmetest fokusseeritud andmeseeriad HIV-1 mitte-nukleosiidne ja nukelosiidsete pöördtranskriptaasi inhibiitorite jaoks, kuhu lisati ka avastatud s-triasiini derivaadid. Andmeseeriate struktuuride analüüs hierarhilise klassifitseerimise meetodil grupeeris ühendid keemiliste struktuuritüüpide (nn. vanematüüp) järgi. Selgus, et avastatud s-triasiinid moodustasid eraldiseisva struktuuritüübi grupi. Leitud struktuuritüüpe analüüsiti, lisades juurde ka vastavad mõõdetud seondumise afiinsuse tasakaalukonstandid (Ki). Selle analüüsi käigus toodi välja struktuurifragmendid, mis omavad olulist rolli afiinsuse ning stabiilsuse seisukohast. Lisaks võimaldasid struktuurselt mitmekesised ja unikaalsed HIV-1 mitte-nukleosiidne ja nukelosiidsete pöördtranskriptaasi inhibiitorite andmeseeriad esmakordselt arendada kirjeldavaid kvantitatiivsete struktuur-aktiivsus sõltuvuste prognoosmudeleid, mida on võimalik kasutada järgnevas uurimustöös uute aktiivsete keemiliste ühendite avastamisel. Selleks et leida uudseid malaaria ravimikanditaate koostati ja kureeriti süsteemselt andmebaas eksperimentaalsete anti-Plasmodium andmetega kasutades nii asutusesisesed, kui ka ChEMBL-i andmebaasis olevad andmed. Saadud andmete ulatusliku kureerimise, filtreerimise ning ühendamise tulemusena saadi kolmkümmend modelleeritavat andmeseeriat, millele koostati klassifitseerimise mudelid, eesmärgiga eristada aktiivsed ja mitteaktiivsed ühendid. Nendest seitsmeteistkümnele andmeseeriale saadi ennustusvõimelised nn. üksmeele (inglise keeles consensus) mudelid. Loodud mudelitega teostati ennustusi asutusesiseselt olemasolevatele curcuminoidide seerjale ning nende analoogidele, millest parima ennustusvõimega ühenditele teostati eksperimentaalne valideerimine in vitro katsetega, kus aktiivseks osutusid seitseteist ühendit, mida saab edasistes uuringutes täpsemini uurida. Samuti tehti kindaks, et arvutuslikult tuvastatud mitteaktiivsed ühendid jäid mitteaktiivseks ka eksperimentaalse valideerimise käigus, mis näitas süsteemselt kureeritud ja koostatud andmeseeriate ning prognoosmudelite jätkusuutlikust.Current thesis focused on study of two highly prevalent infections affecting many regions in the world: alaria and human immunodeficiency virus 1 (HIV-1). Developing a new drug from scratch is time consuming and costly process. This could be divided into five stages: basic research, lead target and lead compound(s) discovery, preclinical development, clinical development and filing to drug administration agency. Present thesis focused on basic research and lead compound discovery stages, i.e. to the early drug discovery. For the HIV-1, the focus was two-fold. First, based on the earlier multi-objective in silico screening, novel s-triazine derivatives were designed, discovered, synthesized, and findings where supported by the modelling tasks and validated with biological evaluation. The most potent compound is with small molecular size, potent ligand efficiencies, and measured low toxicity permitting further exploration and modifications. Second, the discovered new bioactive s-triazines motivated to analyse the chemical landscape of HIV-1 RT inhibitors. For this the dataset was systematically created and curated for HIV-1 NNRT (non-nucleoside reverse transcriptase) and NRT (nucleoside reverse transcriptase) inhibitors based on data from ChEMBL database. The hierarchical classification of scaffold structures of curated datasets revealed common chemical parent types for the compounds, hierarchy in chemical structures and showed that discovered s-triazines formed a separate structural parent type group. Each group of compounds related to the parent type was analysed and examined together with corresponding binding affinity equilibrium constants (Ki). The structural fragments affecting the potency and stability of compounds were highlighted. The structurally diverse datasets for the HIV-1 NNRTIs and NRTIs with binding affinity equilibrium constants allowed development of novel descriptive and predictive QSAR models for log Ki, that in future will help in design of new compounds. In order to discover new promising antimalarial compounds, the experimental anti-Plasmodium data was gathered and systematically curated from in-house experimental studies and expanded with data from ChEMBL database. Extracted data was carefully extensively curated, fused, filtered, and grouped into thirty data sets for the modelling. The consensus models for each dataset for the classification of active/inactive compounds were established and seventeen models with promising prediction ability were used in consensus predictions and in identifying the series of curcuminoids and their structural analogues as potential inhibitors for the malaria. The selection of compounds was experimentally validated, i.e. tested in vitro, revealing seventeen potentially active compounds for further testing and modifications. The validation showed that computationally predicted inactive compounds were also inactive in experiment, being additional proof for the quality of data curation and dataset assembly process forming the ground for the modelling task

Modified aqueous mobile phases: A way to improve retention behavior of active pharmaceutical compounds and their impurities in liquid chromatography

Most commonly used analytical technique for determination of active pharmaceutical ingredients and their impurities in quality control throughout all phases of drug research, development and manufacture is definitely reversed-phase high performance liquid chromatography (RP-HPLC). However, pharmaceutical industry professionals are often faced with various challenges in RP mode, which cannot be resolved with common variations in the composition of the mobile phase. These challenges often occur when analyzing compounds that contain basic ionizable groups, possess large differences in polarities and require consumption of high amounts of toxic organic solvents. Among available strategies for addressing the aforementioned issues, the most convenient one includes RP-HPLC mobile phase modifications by an addition of the proper chemical compounds. In that respect, RP-HPLC method can be easily adapted to the needs of the analysis without time-consuming and expensive equipment procurement. In this review the chaotropic chromatography, micellar liquid chromatography, and cyclodextrin modified RP-HPLC systems are presented and discussed in details. Special attention is devoted to the theoretical background, the possibility of retention modeling and applications in various fields of pharmacy, as well as their prospective in further research

Virtual compound screening and SAR analysis: method development and practical applications in the design of new serine and cysteine protease inhibitors

Virtual screening is an important tool in drug discovery that uses different computational methods to screen chemical databases for the identification of possible drug candidates. Most virtual screening methodologies are knowledge driven where the availability of information on either the nature of the target binding pocket or the type of ligand that is expect to bind is essential. In this regard, the information contained in X-ray crystal structures of protein-ligand complexes provides a detailed insight into the interactions between the protein and the ligand and opens the opportunity for further understanding of drug action and structure activity relationships at molecular level. Protein-ligand interaction information can be utilized to introduce target-specific interaction-based constraints in the design of focused combinatorial libraries. It can also be directly transformed into structural interaction fingerprints and can be applied in virtual screening to analyze docking studies or filter compounds. However, the integration of protein-ligand interaction information into two-dimensional compound similarity searching is not fully explored. Therefore, novel methods are still required to efficiently utilize protein-ligand interaction information in two-dimensional ligand similarity searching. Furthermore, application of protein-ligand interaction information in the interpretation of SARs at the ligand level needs further exploration. Thus, utilization of three-dimensional protein ligand interaction information in virtual screening and SAR analysis was the major aim of this thesis. The thesis is presented in two major parts. In the first part, utilization of three-dimensional protein-ligand interaction information for the development of a new hybrid virtual screening method and analysis of the nature of SARs in analog series at molecular level is presented. The second part of the thesis is focused on the application of different virtual screening methods for the identification of new cysteine and membrane-bound serine proteases inhibitors. In addition, molecular modeling studies were also applied to analyze the binding mode of structurally complex cyclic peptide inhibitors

Tuning hERG Out: Antitarget QSAR Models for Drug Development

Several non-cardiovascular drugs have been withdrawn from the market due to their inhibition of hERG K+ channels that can potentially lead to severe heart arrhythmia and death. As hERG safety testing is a mandatory FDA-required procedure, there is a considerable interest for developing predictive computational tools to identify and filter out potential hERG blockers early in the drug discovery process. In this study, we aimed to generate predictive and well-characterized quantitative structure–activity relationship (QSAR) models for hERG blockage using the largest publicly available dataset of 11,958 compounds from the ChEMBL database. The models have been developed and validated according to OECD guidelines using four types of descriptors and four different machine-learning techniques. The classification accuracies discriminating blockers from non-blockers were as high as 0.83–0.93 on external set. Model interpretation revealed several SAR rules, which can guide structural optimization of some hERG blockers into non-blockers. We have also applied the generated models for screening the World Drug Index (WDI) database and identify putative hERG blockers and non-blockers among currently marketed drugs. The developed models can reliably identify blockers and non-blockers, which could be useful for the scientific community. A freely accessible web server has been developed allowing users to identify putative hERG blockers and non-blockers in chemical libraries of their interest (http://labmol.farmacia.ufg.br/predherg)

In Silico Mining for Antimalarial Structure-Activity Knowledge and Discovery of Novel Antimalarial Curcuminoids.

Malaria is a parasitic tropical disease that kills around 600,000 patients every year. The emergence of resistant Plasmodium falciparum parasites to artemisinin-based combination therapies (ACTs) represents a significant public health threat, indicating the urgent need for new effective compounds to reverse ACT resistance and cure the disease. For this, extensive curation and homogenization of experimental anti-Plasmodium screening data from both in-house and ChEMBL sources were conducted. As a result, a coherent strategy was established that allowed compiling coherent training sets that associate compound structures to the respective antimalarial activity measurements. Seventeen of these training sets led to the successful generation of classification models discriminating whether a compound has a significant probability to be active under the specific conditions of the antimalarial test associated with each set. These models were used in consensus prediction of the most likely active from a series of curcuminoids available in-house. Positive predictions together with a few predicted as inactive were then submitted to experimental in vitro antimalarial testing. A large majority from predicted compounds showed antimalarial activity, but not those predicted as inactive, thus experimentally validating the in silico screening approach. The herein proposed consensus machine learning approach showed its potential to reduce the cost and duration of antimalarial drug discovery

Computational approaches in supramolecular chemistry with a special focus on virtual screening

Within this thesis novel computational tools for the rational design of synthetic host-guest complexes (SHGC) were developed and applied that employ the concepts of efficient virtual screening (VS) approaches. The first part describes the development of a fast structure prediction tool for flexible SHGC. The tool was validated on a test dataset comprising crystallographically determined SHGC. In nine of ten cases near-native solutions were generated. The tool can be applied for VS. In the second part of the thesis computational techniques were applied for designing SHGC based on ß-cyclodextrins (ß-CD). We performed a structure-based inverse virtual screening for identifying modified ß-CDs as receptors for the anticancer drug camptothecin (CPT). Six of the proposed receptors exhibited binding affinities which were significantly higher than for any other CPT-receptor. Furthermore, we applied a combination of a similarity-based virtual screening technique with a regression model (RM) for identifying novel high affinity guest molecules of ß-CD. Ten of the proposed guest molecules exhibited a binding free energy of lower than -20 kJ/mol. The last chapter describes a comparison of regression methods regarding their ability to generate predictive RM for thermodynamical parameters (dG, dH and dS) of ß-CD-guest complexes. dG could be predicted in good agreement with experimental values, none of the methods led to comparably good predictive models for dH. dS appears almost unpredictable.Im Rahmen dieser Arbeit wurden rechnergestützte Verfahren (RGV) zum gezielten Entwurf von synthetischen Wirt-Gast Komplexen (SWGK) entwickelt und eingesetzt. Dabei wurde ein Fokus auf schnelle virtuelle Screening (VS) Verfahren gelegt. Der erste Teil beschreibt die Entwicklung eines Programms zur schnellen Strukturvorhersage von flexiblen SWGK. Das Programm wurde auf einem Testdatensatz an kristallographisch vermessenen SWGK validiert. Für neun von zehn SWGK wurden nativ-ähnliche Lösungen gefunden. Das Programm kann für VS eingesetzt werden. Der zweite Teil der Arbeit behandelt RGV zum gezielten Entwurf von ß-Cyclodextrin (ß-CD) Komplexen. Mit Hilfe eines strukturbasierten inversen VS wurden sechs modifizierte ß-CD-Rezeptoren für den Krebsarzneistoff Camptothecin (CPT) gefunden, die deutlich höhere Bindungsaffinitäten zu CPT aufwiesen als alle bislang bekannten CPT-Rezeptoren. Zur Identifizierung neuer hochaffiner Gäste von ß-CD wurde ein ähnlichkeitsbasiertes VS Verfahren in Kombination mit einem Regressionsmodell (RM) eingesetzt. Zehn der mit Hilfe dieses Verfahrens vorgeschlagenen Moleküle wiesen eine Bindungsenergie von unter -20 kJ/mol auf. Das letzte Kapitel beschreibt einen Vergleich von drei Regressionsverfahren. Es wurde die Fähigkeit untersucht, vorhersagekräftige RM für thermodynamische Parameter (dG, dH und dS) von ß-CD-Gast-Komplexen zu generieren. dG konnte mit allen Methoden sehr gut vorhergesagt werden, während dH nur begrenzt und dS unzureichend vorhersagbar war

Chemical Information Bulletin

Periodic supplement for "the regular journals of the American Chemical Society," containing annotated bibliographies of chemical documentation literature as well as information about meetings, conferences, awards, scholarships, and other news from the American Chemical Society (ACS) Division of Chemical Literature