Quantitative analysis of active pharmaceutical ingredients (APIs) using a potentiometric electronic tongue in a SIA flow system

Research funding: National Science Centre. Grant Numbers: DEC-2013/09/B/ST4/00957, LIDER/17/202/L-1/09/NCBiR/2010An advanced potentiometric electronic tongue and Sequential Injection Analysis (SIA) measurement system was applied for the quantitative analysis of mixtures containing three active pharmaceutical ingredients (APIs): acetaminophen, ascorbic acid and acetylsalicylic acid, in the presence of various amounts of caffeine as interferent. The flow-through sensor array was composed of miniaturized classical ion-selective electrodes based on plasticized PVC membranes containing only ion exchangers. Partial Least Squares (PLS) analysis of the steady-state sensor array responses, measured in API mixtures prepared by the SIA system permitted a correct quantitative analysis of acetylsalicylic acid and ascorbic acid. Further optimization using multiway PLS fed by dynamic responses without additional feature extraction did not improve significantly the resolution of acetaminophen. Lastly, the chemometric treatment, involving the extraction of dynamic components of the transient response employing the Wavelet transform, the removal of less-significant coefficients by means of Causal Index pruning and training of an Artificial Neural Network (ANN) with the selected coefficients, allowed the simultaneous determination of all the three studied APIs, while counterbalancing any interference due to caffeine

Independent comparison study of six different electronic tongues applied for pharmaceutical analysis

Electronic tongue technology based on arrays of cross-sensitive chemical sensors and chemometric data processing has attracted a lot of researchers' attention through the last years. Several so far reported applications dealing with pharmaceutical related tasks employed different e-tongue systems to address different objectives. In this situation, it is hard to judge on the benefits and drawbacks of particular e-tongue implementations for R&D in pharmaceutics. The objective of this study was to compare the performance of six different e-tongues applied to the same set of pharmaceutical samples. For this purpose, two commercially available systems (from Insent and AlphaMOS) and four laboratory prototype systems (two potentiometric systems from Warsaw operating in flow and static modes, one potentiometric system from St. Petersburg, one voltammetric system from Barcelona) were employed. The sample set addressed in the study comprised nine different formulations based on caffeine citrate, lactose monohydrate, maltodextrine, saccharin sodium and citric acid in various combinations. To provide for the fair and unbiased comparison, samples were evaluated under blind conditions and data processing from all the systems was performed in a uniform way. Different mathematical methods were applied to judge on similarity of the e-tongues response from the samples. These were principal component analysis (PCA), RV' matrix correlation coefficients and Tuckeŕs congruency coefficients

Early detection of Fusarium basal rot infection in onions and shallots based on VOC profiles analysis

Gas chromatography ion-mobility spectrometry (GC-IMS) technology is drawing increasing attention due to its high sensitivity, low drift, and capability for the identification of compounds. The noninvasive detection of plant pests and pathogens is an application area well suited to this technology. In this work, we employed GC-IMS technology for early detection of Fusarium basal rot in brown onion, red onion, and shallot bulbs and for tracking disease progression during storage. The volatile profiles of the infected and healthy control bulbs were characterized using GC-IMS and gas chromatography-time-of-flight mass spectrometry (GC-TOF-MS). GC-IMS data combined with principal component analysis and supervised methods provided discrimination between infected and healthy control bulbs as early as 1 day after incubation with the pathogen, classification regarding the proportion of infected to healthy bulbs in a sample, and prediction of the infection’s duration with an average R2 = 0.92. Furthermore, GC-TOF-MS revealed several compounds, mostly sulfides and disulfides, that could be uniquely related to Fusarium basal rot infection

Genome-wide inhibition of pro-atherogenic gene expression by multi-STAT targeting compounds as a novel treatment strategy of CVDs

Cardiovascular diseases (CVDs), including atherosclerosis, are globally the leading cause of death. Key factors contributing to onset and progression of atherosclerosis include the pro-inflammatory cytokines Interferon (IFN)a and IFN? and the Pattern Recognition Receptor (PRR) Toll-like receptor 4 (TLR4). Together, they trigger activation of Signal Transducer and Activator of Transcription (STAT)s. Searches for compounds targeting the pTyr-SH2 interaction area of STAT3, yielded many small molecules, including STATTIC and STX-0119. However, many of these inhibitors do not seem STAT3-specific. We hypothesized that multi-STAT-inhibitors that simultaneously block STAT1, STAT2, and STAT3 activity and pro-inflammatory target gene expression may be a promising strategy to treat CVDs. Using comparative in silico docking of multiple STAT-SH2 models on multi-million compound libraries, we identified the novel multi-STAT inhibitor, C01L-F03. This compound targets the SH2 domain of STAT1, STAT2, and STAT3 with the same affinity and simultaneously blocks their activity and expression of multiple STAT-target genes in HMECs in response to IFNa. The same in silico and in vitro multi-STAT inhibiting capacity was shown for STATTIC and STX-0119. Moreover, C01L-F03, STATTIC and STX-0119 were also able to affect genome-wide interactions between IFN? and TLR4 by commonly inhibiting pro-inflammatory and pro-atherogenic gene expression directed by cooperative involvement of STATs with IRFs and/or NF-κB. Moreover, we observed that multi-STAT inhibitors could be used to inhibit IFN?+LPS-induced HMECs migration, leukocyte adhesion to ECs as well as impairment of mesenteric artery contractility. Together, this implicates that application of a multi-STAT inhibitory strategy could provide great promise for the treatment of CVDsThis publication was supported by grants UMO-2015/17/B/NZ2/00967 (HB) and UMO-2015/16/T/NZ2/00055 (MS) from National Science Centre Poland. This work was supported by the KNOW RNA Research Centre in Poznan (No. 01/KNOW2/2014) and in part by PL-Grid Infrastructure (MS

Direct Inhibition of IRF-Dependent Transcriptional Regulatory Mechanisms Associated With Disease

Interferon regulatory factors (IRFs) are a family of homologous proteins that regulate the transcription of interferons (IFNs) and IFN-induced gene expression. As such they are important modulating proteins in the Toll-like receptor (TLR) and IFN signaling pathways, which are vital elements of the innate immune system. IRFs have a multi-domain structure, with the N-terminal part acting as a DNA binding domain (DBD) that recognizes a DNA-binding motif similar to the IFN-stimulated response element (ISRE). The C-terminal part contains the IRF-association domain (IAD), with which they can self-associate, bind to IRF family members or interact with other transcription factors. This complex formation is crucial for DNA binding and the commencing of target-gene expression. IRFs bind DNA and exert their activating potential as homo or heterodimers with other IRFs. Moreover, they can form complexes (e.g., with Signal transducers and activators of transcription, STATs) and collaborate with other co-acting transcription factors such as Nuclear factor-κB (NF-κB) and PU.1. In time, more of these IRF co-activating mechanisms have been discovered, which may play a key role in the pathogenesis of many diseases, such as acute and chronic inflammation, autoimmune diseases, and cancer. Detailed knowledge of IRFs structure and activating mechanisms predisposes IRFs as potential targets for inhibition in therapeutic strategies connected to numerous immune system-originated diseases. Until now only indirect IRF modulation has been studied in terms of antiviral response regulation and cancer treatment, using mainly antisense oligonucleotides and siRNA knockdown strategies. However, none of these approaches so far entered clinical trials. Moreover, no direct IRF-inhibitory strategies have been reported. In this review, we summarize current knowledge of the different IRF-mediated transcriptional regulatory mechanisms and how they reflect the diverse functions of IRFs in homeostasis and in TLR and IFN signaling. Moreover, we present IRFs as promising inhibitory targets and propose a novel direct IRF-modulating strategy employing a pipeline approach that combines comparative in silico docking to the IRF-DBD with in vitro validation of IRF inhibition. We hypothesize that our methodology will enable the efficient identification of IRF-specific and pan-IRF inhibitors that can be used for the treatment of IRF-dependent disorders and malignancies

Genome-Wide Inhibition of Pro-atherogenic Gene Expression by Multi-STAT Targeting Compounds as a Novel Treatment Strategy of CVDs

Cardiovascular diseases (CVDs), including atherosclerosis, are globally the leading cause of death. Key factors contributing to onset and progression of atherosclerosis include the pro-inflammatory cytokines Interferon (IFN)α and IFNγ and the Pattern Recognition Receptor (PRR) Toll-like receptor 4 (TLR4). Together, they trigger activation of Signal Transducer and Activator of Transcription (STAT)s. Searches for compounds targeting the pTyr-SH2 interaction area of STAT3, yielded many small molecules, including STATTIC and STX-0119. However, many of these inhibitors do not seem STAT3-specific. We hypothesized that multi-STAT-inhibitors that simultaneously block STAT1, STAT2, and STAT3 activity and pro-inflammatory target gene expression may be a promising strategy to treat CVDs. Using comparative in silico docking of multiple STAT-SH2 models on multi-million compound libraries, we identified the novel multi-STAT inhibitor, C01L_F03. This compound targets the SH2 domain of STAT1, STAT2, and STAT3 with the same affinity and simultaneously blocks their activity and expression of multiple STAT-target genes in HMECs in response to IFNα. The same in silico and in vitro multi-STAT inhibiting capacity was shown for STATTIC and STX-0119. Moreover, C01L_F03, STATTIC and STX-0119 were also able to affect genome-wide interactions between IFNγ and TLR4 by commonly inhibiting pro-inflammatory and pro-atherogenic gene expression directed by cooperative involvement of STATs with IRFs and/or NF-κB. Moreover, we observed that multi-STAT inhibitors could be used to inhibit IFNγ+LPS-induced HMECs migration, leukocyte adhesion to ECs as well as impairment of mesenteric artery contractility. Together, this implicates that application of a multi-STAT inhibitory strategy could provide great promise for the treatment of CVDs

Identification of STAT1 and STAT3 specific inhibitors using comparative virtual screening and docking validation.

Signal transducers and activators of transcription (STATs) facilitate action of cytokines, growth factors and pathogens. STAT activation is mediated by a highly conserved SH2 domain, which interacts with phosphotyrosine motifs for specific STAT-receptor contacts and STAT dimerization. The active dimers induce gene transcription in the nucleus by binding to a specific DNA-response element in the promoter of target genes. Abnormal activation of STAT signaling pathways is implicated in many human diseases, like cancer, inflammation and auto-immunity. Searches for STAT-targeting compounds, exploring the phosphotyrosine (pTyr)-SH2 interaction site, yielded many small molecules for STAT3 but sparsely for other STATs. However, many of these inhibitors seem not STAT3-specific, thereby questioning the present modeling and selection strategies of SH2 domain-based STAT inhibitors. We generated new 3D structure models for all human (h)STATs and developed a comparative in silico docking strategy to obtain further insight into STAT-SH2 cross-binding specificity of a selection of previously identified STAT3 inhibitors. Indeed, by primarily targeting the highly conserved pTyr-SH2 binding pocket the majority of these compounds exhibited similar binding affinity and tendency scores for all STATs. By comparative screening of a natural product library we provided initial proof for the possibility to identify STAT1 as well as STAT3-specific inhibitors, introducing the 'STAT-comparative binding affinity value' and 'ligand binding pose variation' as selection criteria. In silico screening of a multi-million clean leads (CL) compound library for binding of all STATs, likewise identified potential specific inhibitors for STAT1 and STAT3 after docking validation. Based on comparative virtual screening and docking validation, we developed a novel STAT inhibitor screening tool that allows identification of specific STAT1 and STAT3 inhibitory compounds. This could increase our understanding of the functional role of these STATs in different diseases and benefit the clinical need for more drugable STAT inhibitors with high specificity, potency and excellent bioavailability

Quantitative analysis of active pharmaceutical ingredients (APIs) using a potentiometric electronic tongue in a SIA flow system

An advanced potentiometric electronic tongue and Sequential Injection Analysis (SIA) measurement system was applied for the quantitative analysis of mixtures containing three active pharmaceutical ingredients (APIs): acetaminophen, ascorbic acid and acetylsalicylic acid, in the presence of various amounts of caffeine as interferent. The flow-through sensor array was composed of miniaturized classical ion-selective electrodes based on plasticized PVC membranes containing only ion exchangers. Partial Least Squares (PLS) analysis of the steady-state sensor array responses, measured in API mixtures prepared by the SIA system permitted a correct quantitative analysis of acetylsalicylic acid and ascorbic acid. Further optimization using multiway PLS fed by dynamic responses without additional feature extraction did not improve significantly the resolution of acetaminophen. Lastly, the chemometric treatment, involving the extraction of dynamic components of the transient response employing the Wavelet transform, the removal of less-significant coefficients by means of Causal Index pruning and training of an Artificial Neural Network (ANN) with the selected coefficients, allowed the simultaneous determination of all the three studied APIs, while counterbalancing any interference due to caffeine

STAT3-CBAVs of STAT3-specific inhibitors.

<p>Graph presents comparative binding affinity values of a selection of STAT3-specific inhibitors docked to models of all hSTAT monomers.</p