Sinteza, karakterizacija i procena antioksidativne i antimikrobne aktivnosti tri nova n-heteroaromatična hidrazonil-tiazola

(Thiazolyl-2-yl)hydrazones (THs) are a group of organic compounds containing both hydrazone and 1,3-thiazole pharmacophores present in many approved drugs. They have been investigated greatly in recent years due to potent anticancer, antibacterial, antifungal, antituberculosis, anti-inflammatory, and antiparasitic activities. In this study, one pyridine-based and two quinoline-based, novel THs were synthesized and characterized by elemental analysis, Fourier-transform infrared spectroscopy (FTIR), and nuclear magnetic resonance spectroscopy (NMR). The antimicrobial activity of the compounds was tested against five Gram-positive and five Gram-negative bacteria, as well as against three fungi. The antioxidant capacity of the compounds was tested in six antioxidative assays. The results showed that quinoline-based THs were more active against tested Gram-negative bacteria and fungi strains than pyridine-based compounds. All the compounds showed excellent antioxidative activity comparable to or greater than the used standards (vitamin C and Trolox). Absorption, distribution, metabolism, excretion, and toxicity (ADMET) parameters were calculated in-silico. Results pointed to promising good pharmacokinetics profiles of investigated compounds, especially 2-quinoline carboxaldehyde-based compound, which can be a lead drug candidate.(Tiazolil-2-il)-hidrazoni (TH) su grupa organskih jedinjenja koja sadrže i hidrazon i 1,3-tiazol farmakofore koje su prisutne u mnogim odobrenim lekovima. Poslednjih godina se u velikoj meri istražuju zbog jakih antikancerogenih, antibakterijskih, antifungalnih, antituberkuloznih, antiinflamatornih i antiparazitskih aktivnosti. U ovoj studiji, sintetisan je jedan novi TH na bazi piridina i dva na bazi hinolina, koji su okarakterisani elementalnom analizom, infracrvenom spektroskopijom sa Furijeovom transformacijom (FTIR) i spektroskopijom nuklearne magnetne rezonancije (NMR). Antimikrobna aktivnost jedinjenja je testirana na pet Gram-pozitivnih i pet Gram-negativnih bakterijskih sojeva, kao i na tri soja gljivica. Šest antioksidativnih testova je korišćeno za određivanje antioksidativnog kapaciteta sintetisanih jedinjenja. Rezultati su pokazali da su TH na bazi hinolina aktivniji prema testiranim Gram-negativnim sojevima bakterija i prema gljivicama, nego jedinjenja na bazi piridina. Sva jedinjenja su pokazala odlično antioksidativno dejstvo, uporedivo ili veće od korišćenih standarda (vitamin C i troloks). Parametri apsorpcije, distribucije, metabolizma, izlučivanja i toksičnosti (ADME) izračunati su in-silico. Rezultati ukazuju na dobre farmakokinetičke profile ispitivanih jedinjenja, posebno jedinjenja na bazi 2-hinolinkarboksaldehida koje ima potencijal da bude kandidat za osnovno jedinjenje (engl. lead compound)

Chloroquine Analogues as Leads against Pneumocystis Lung Pathogens

The impact of Pneumocystis pneumonia (PcP) on morbidity and mortality remains substantial for immunocompromised individuals, including those afflicted by HIV infection, organ transplantation, cancer, autoimmune diseases, or subject to chemotherapy or corticosteroid-based therapies. Previous work from our group has shown that repurposing antimalarial compounds for PcP holds promise for treatment of this opportunistic infection. Following our previous discovery of chloroquine analogues with dual-stage antimalarial action both in vitro and in vivo, we now report the potent action of these compounds on Pneumocystis carinii in vitro Identification of chloroquine analogues as anti-PcP leads is an unprecedented finding.info:eu-repo/semantics/publishedVersio

Novel Amphiphilic Cyclobutene and Cyclobutane \u3ci\u3ecis\u3c/i\u3e-C18 Fatty Acid Derivatives Inhibit \u3ci\u3eMycobacterium avium\u3c/i\u3e subsp. \u3ci\u3eparatuberculosis\u3c/i\u3e Growth

Mycobacterium avium subspecies paratuberculosis (Map) is the etiologic agent of Johne’s disease in ruminants and has been associated with Crohn’s disease in humans. An effective control of Map by either vaccines or chemoprophylaxis is a paramount need for veterinary and possibly human medicine. Given the importance of fatty acids in the biosynthesis of mycolic acids and the mycobacterial cell wall, we tested novel amphiphilic C10 and C18 cyclobutene and cyclobutane fatty acid derivatives for Map inhibition. Microdilution minimal inhibitory concentrations (MIC) with 5 or 7 week endpoints were measured in Middlebrook 7H9 base broth media. We compared the Map MIC results with those obtained previously with Mycobacterium tuberculosis and Mycobacterium smegmatis. Several of the C18 compounds showed moderate effcacy (MICs 392 to 824 μM) against Map, while a higher level of inhibition (MICs 6 to 82 μM) was observed for M. tuberculosis for select analogs from both the C10 and C18 groups. For most of these analogs tested in M. smegmatis, their effcacy decreased in the presence of bovine or human serum albumin. Compound 5 (OA-CB, 1-(octanoic acid-8-yl)-2-octylcyclobutene) was identified as the best chemical lead against Map, which suggests derivatives with better pharmacodynamics may be of interest for evaluation in animal models

Analysis of Multitarget Activities and Assay Interference Characteristics of Pharmaceutically Relevant Compounds

The availability of large amounts of data in public repositories provide a useful source of knowledge in the field of drug discovery. Given the increasing sizes of compound databases and volumes of activity data, computational data mining can be used to study different characteristics and properties of compounds on a large scale. One of the major source of identification of new compounds in early phase of drug discovery is high-throughput screening where millions of compounds are tested against many targets. The screening data provides opportunities to assess activity profiles of compounds. This thesis aims at systematically mining activity data from publicly available sources in order to study the nature of growth of bioactive compounds, analyze multitarget activities and assay interference characteristics of pharmaceutically relevant compounds in context of polypharmacology. In the first study, growth of bioactive compounds against five major target families is monitored over time and compound-scaffold-CSK (cyclic skeleton) hierarchy is applied to investigate structural diversity of active compounds and topological diversity of their scaffolds. The next part of the thesis is based on the analysis of screening data. Initially, extensively assayed compounds are mined from the PubChem database and promiscuity of these compounds is assessed by taking assay frequencies into account. Next, DCM (dark chemical matter) or consistently inactive compounds that have been extensively tested are systematically extracted and their analog relationships with bioactive compounds are determined in order to derive target hypotheses for DCM. Further, PAINS (pan-assay interference compounds) are identified in the extensively tested set of compounds using substructure filters and their assay interference characteristics are studied. Finally, the limitations of PAINS filters are addressed using machine learning models that can distinguish between promiscuous and DCM PAINS. Structural context dependence of PAINS activities is studied by assessing predictions through feature weighting and mapping

Statistical models for identifying frequent hitters in high throughput screening

High throughput screening (HTS) interrogates compound libraries to find those that are “active” in an assay. To better understand compound behavior in HTS, we assessed an existing binomial survivor function (BSF) model of “frequent hitters” using 872 publicly available HTS data sets. We found large numbers of “infrequent hitters” using this model leading us to reject the BSF for identifying “frequent hitters.” As alternatives, we investigated generalized logistic, gamma, and negative binomial distributions as models for compound behavior. The gamma model reduced the proportion of both frequent and infrequent hitters relative to the BSF. Within this data set, conclusions about individual compound behavior were limited by the number of times individual compounds were tested (1–1613 times) and disproportionate testing of some compounds. Specifically, most tests (78%) were on a 309,847-compound subset (17.6% of compounds) each tested ≥ 300 times. We concluded that the disproportionate retesting of some compounds represents compound repurposing at scale rather than drug discovery. The approach to drug discovery represented by these 872 data sets characterizes the assays well by challenging them with many compounds while each compound is characterized poorly with a single assay. Aggregating the testing information from each compound across the multiple screens yielded a continuum with no clear boundary between normal and frequent hitting compounds

Hit Dexter 2.0: Machine-Learning Models for the Prediction of Frequent Hitters

Assay interference caused by small molecules continues to pose a significant challenge for early drug discovery. A number of rule-based and similarity-based approaches have been derived that allow the flagging of potentially “badly behaving compounds”, “bad actors”, or “nuisance compounds”. These compounds are typically aggregators, reactive compounds, and/or pan-assay interference compounds (PAINS), and many of them are frequent hitters. Hit Dexter is a recently introduced machine learning approach that predicts frequent hitters independent of the underlying physicochemical mechanisms (including also the binding of compounds based on “privileged scaffolds” to multiple binding sites). Here we report on the development of a second generation of machine learning models which now covers both primary screening assays and confirmatory dose–response assays. Protein sequence clustering was newly introduced to minimize the overrepresentation of structurally and functionally related proteins. The models correctly classified compounds of large independent test sets as (highly) promiscuous or nonpromiscuous with Matthews correlation coefficient (MCC) values of up to 0.64 and area under the receiver operating characteristic curve (AUC) values of up to 0.96. The models were also utilized to characterize sets of compounds with specific biological and physicochemical properties, such as dark chemical matter, aggregators, compounds from a high-throughput screening library, drug-like compounds, approved drugs, potential PAINS, and natural products. Among the most interesting outcomes is that the new Hit Dexter models predict the presence of large fractions of (highly) promiscuous compounds among approved drugs. Importantly, predictions of the individual Hit Dexter models are generally in good agreement and consistent with those of Badapple, an established statistical model for the prediction of frequent hitters. The new Hit Dexter 2.0 web service, available at http://hitdexter2.zbh.uni-hamburg.de, not only provides user-friendly access to all machine learning models presented in this work but also to similarity-based methods for the prediction of aggregators and dark chemical matter as well as a comprehensive collection of available rule sets for flagging frequent hitters and compounds including undesired substructures.acceptedVersio

Computational advances in combating colloidal aggregation in drug discovery.

Small molecule effectors are essential for drug discovery. Specific molecular recognition, reversible binding and dose-dependency are usually key requirements to ensure utility of a novel chemical entity. However, artefactual frequent-hitter and assay interference compounds may divert lead optimization and screening programmes towards attrition-prone chemical matter. Colloidal aggregates are the prime source of false positive readouts, either through protein sequestration or protein-scaffold mimicry. Nevertheless, assessment of colloidal aggregation remains somewhat overlooked and under-appreciated. In this Review, we discuss the impact of aggregation in drug discovery by analysing select examples from the literature and publicly-available datasets. We also examine and comment on technologies used to experimentally identify these potentially problematic entities. We focus on evidence-based computational filters and machine learning algorithms that may be swiftly deployed to flag chemical matter and mitigate the impact of aggregates in discovery programmes. We highlight the tools that can be used to scrutinize libraries, and identify and eliminate these problematic compounds.D.R. is a Swiss National Science Foundation Fellow (Grants P2EZP3_168827 and P300P2_177833). G.J.L.B. is a Royal Society URF (UF110046 and URF/R/180019), an iFCT Investigator (IF/00624/2015), and the recipient of an ERC StG (TagIt, Grant Agreement 676832). T.R. and G.J.L.B. acknowledge Marie Sklodowska-Curie ITN Protein Conjugates (Grant Agreement 675007) for funding. T.R. is a Marie Curie Fellow (Grant Agreement 743640). T.R. acknowledges the H2020 (TWINN-2017 ACORN, Grant Agreement 807281) and POR Lisboa 2020/FEDER (02/SAICT/2017, Grant Agreement Lisboa-01-0145-FEDER-028333) for funding. D.R. acknowledges the MIT-IBM Watson AI Lab and the MIT SenseTime coalition for funding

Identification of novel small-molecule inhibitors of α-methylacyl-CoA racemase (AMACR; P504S) and structure activity relationships

α-Methylacyl-CoA racemase (AMACR; P504S; EC 5.1.99.4) catalyses epimerization of 2-methylacyl-CoAs and is important for the degradation of branched-chain fatty acids and the pharmacological activation of ibuprofen and related drugs. It is also a novel drug target for prostate and other cancers. However, development of AMACR as a drug target has been hampered by the difficulties in assaying enzyme activity. Consequently, reported inhibitors have been rationally designed acyl-CoA esters, which are delivered as their carboxylate prodrugs. The novel colorimetric assay for AMACR based on the elimination of 2,4-dinitrophenolate was developed for highthroughput screening and 20,387 ‘drug-like compounds’ were screened, with a throughput of 768 compounds assayed per day. Pyrazoloquinolines and pyrazolopyrimidines were identified as novel scaffolds and investigated as AMACR inhibitors. The most potent inhibitors have IC50 values of ~2 μM. The pyrazoloquinoline inhibitor 10a displayed uncompetitive inhibition, whilst 10j displayed mixed competitive inhibition. The pyrazolopyrimidine inhibitor 11k displayed uncompetitive inhibition. This is the first report of the identification of specific drug-like small-molecule AMACR inhibitors by high-throughput screening. Pyrazoloquinolines and pyrazolopyrimidines may also be useful as inhibitors of other CoA-utilizing enzymes

Identifying SARS-CoV-2 antiviral compounds by screening for small molecule inhibitors of nsp13 helicase

The coronavirus disease 2019 (COVID-19) pandemic, which is caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), is a global public health challenge. While the efficacy of vaccines against emerging and future virus variants remains unclear, there is a need for therapeutics. Repurposing existing drugs represents a promising and potentially rapid opportunity to find novel antivirals against SARS-CoV-2. The virus encodes at least nine enzymatic activities that are potential drug targets. Here, we have expressed, purified and developed enzymatic assays for SARS-CoV-2 nsp13 helicase, a viral replication protein that is essential for the coronavirus life cycle. We screened a custom chemical library of over 5000 previously characterized pharmaceuticals for nsp13 inhibitors using a fluorescence resonance energy transfer-based high-throughput screening approach. From this, we have identified FPA-124 and several suramin-related compounds as novel inhibitors of nsp13 helicase activity in vitro. We describe the efficacy of these drugs using assays we developed to monitor SARS-CoV-2 growth in Vero E6 cells

The distribution of standard deviations applied to high throughput screening

High throughput screening (HTS) assesses compound libraries for “activity” using target assays. A subset of HTS data contains a large number of sample measurements replicated a small number of times providing an opportunity to introduce the distribution of standard deviations (DSD). Applying the DSD to some HTS data sets revealed signs of bias in some of the data and discovered a sub-population of compounds exhibiting high variability which may be difficult to screen. In the data examined, 21% of 1189 such compounds were pan-assay interference compounds. This proportion reached 57% for the most closely related compounds within the sub-population. Using the DSD, large HTS data sets can be modelled in many cases as two distributions: a large group of nearly normally distributed “inactive” compounds and a residual distribution of “active” compounds. The latter were not normally distributed, overlapped inactive distributions – on both sides –, and were larger than typically assumed. As such, a large number of compounds are being misclassified as “inactive” or are invisible to current methods which could become the next generation of drugs. Although applied here to HTS, it is applicable to data sets with a large number of samples measured a small number of times