Application of parallel artificial membrane permeability assay technique and chemometric modeling for blood-brain barrier permeability prediction of protein kinase inhibitors.

Aim: This study aims to investigate the passive diffusion of protein kinase inhibitors through the blood-brain barrier (BBB) and to develop a model for their permeability prediction. Materials & methods: We used the parallel artificial membrane permeability assay to obtain logPe values of each of 34 compounds and calculated descriptors for these structures to perform quantitative structure-property relationship modeling, creating different regression models. Results: The logPe values have been calculated for all 34 compounds. Support vector machine regression was considered the most reliable, and CATS2D_09_DA, CATS2D_04_AA, B04[N-S] and F07[C-N] descriptors were identified as the most influential to passive BBB permeability. Conclusion: The quantitative structure-property relationship-support vector machine regression model that has been generated can serve as an efficient method for preliminary screening of BBB permeability of new analogs.

[Box: see text].

Machine Learning-Based Prediction of Glioma IDH Gene Mutation Status Using Physio-Metabolic MRI of Oxygen Metabolism and Neovascularization (A Bicenter Study).

The mutational status of the isocitrate dehydrogenase (IDH) gene plays a key role in the treatment of glioma patients because it is known to affect energy metabolism pathways relevant to glioma. Physio-metabolic magnetic resonance imaging (MRI) enables the non-invasive analysis of oxygen metabolism and tissue hypoxia as well as associated neovascularization and microvascular architecture. However, evaluating such complex neuroimaging data requires computational support. Traditional machine learning algorithms and simple deep learning models were trained with radiomic features from clinical MRI (cMRI) or physio-metabolic MRI data. A total of 215 patients (first center: 166 participants + 16 participants for independent internal testing of the algorithms versus second site: 33 participants for independent external testing) were enrolled using two different physio-metabolic MRI protocols. The algorithms trained with physio-metabolic data demonstrated the best classification performance in independent internal testing: precision, 91.7%; accuracy, 87.5%; area under the receiver operating curve (AUROC), 0.979. In external testing, traditional machine learning models trained with cMRI data exhibited the best IDH classification results: precision, 84.9%; accuracy, 81.8%; and AUROC, 0.879. The poor performance for the physio-metabolic MRI approach appears to be explainable by site-dependent differences in data acquisition methodologies. The physio-metabolic MRI approach potentially supports reliable classification of IDH gene status in the presurgical stage of glioma patients. However, non-standardized protocols limit the level of evidence and underlie the need for a reproducible framework of data acquisition techniques.

Targeting Histone Deacetylases 6 in Dual-Target Therapy of Cancer.

Histone deacetylases (HDACs) are the major regulators of the balance of acetylation of histone and non-histone proteins. In contrast to other HDAC isoforms, HDAC6 is mainly involved in maintaining the acetylation balance of many non-histone proteins. Therefore, the overexpression of HDAC6 is associated with tumorigenesis, invasion, migration, survival, apoptosis and growth of various malignancies. As a result, HDAC6 is considered a promising target for cancer treatment. However, none of selective HDAC6 inhibitors are in clinical use, mainly because of the low efficacy and high concentrations used to show anticancer properties, which may lead to off-target effects. Therefore, HDAC6 inhibitors with dual-target capabilities represent a new trend in cancer treatment, aiming to overcome the above problems. In this review, we summarize the advances in tumor treatment with dual-target HDAC6 inhibitors.

Preclinical Evaluation of an Imidazole-Linked Heterocycle for Alzheimer's Disease.

Humanity is facing a vast prevalence of neurodegenerative diseases, with Alzheimer's disease (AD) being the most dominant, without efficacious drugs, and with only a few therapeutic targets identified. In this scenario, we aim to find molecular entities that modulate imidazoline I2 receptors (I2-IRs) that have been pointed out as relevant targets in AD. In this work, we explored structural modifications of well-established I2-IR ligands, giving access to derivatives with an imidazole-linked heterocycle as a common key feature. We report the synthesis, the affinity in human I2-IRs, the brain penetration capabilities, the in silico ADMET studies, and the three-dimensional quantitative structure-activity relationship (3D-QSAR) studies of this new bunch of I2-IR ligands. Selected compounds showed neuroprotective properties and beneficial effects in an in vitro model of Parkinson's disease, rescued the human dopaminergic cell line SH-SY5Y from death after treatment with 6-hydroxydopamine, and showed crucial anti-inflammatory effects in a cellular model of neuroinflammation. After a preliminary pharmacokinetic study, we explored the action of our representative 2-(benzo[b]thiophen-2-yl)-1H-imidazole LSL33 in a mouse model of AD (5xFAD). Oral administration of LSL33 at 2 mg/Kg for 4 weeks ameliorated 5XFAD cognitive impairment and synaptic plasticity, as well as reduced neuroinflammation markers. In summary, this new I2-IR ligand that promoted beneficial effects in a well-established AD mouse model should be considered a promising therapeutic strategy for neurodegeneration.

Electrochemical and theoretical study on interaction between erlotinib and DNA.

A comprehensive investigation of tyrosine kinase inhibitor erlotinib (ERL) electrochemical behavior and interaction with DNA was performed with the aim to clarify its redox mechanism and to determine the mode of binding. Irreversible oxidation and reduction processes of ERL on glassy carbon electrode were investigated using three voltammetric techniques CV, DPV, SWV in pH range between 2.0 and 9.0. Oxidation was established as an adsorption-controlled process, while the reduction manifested diffusion-adsorption mixed controlled process in acidic medium and adsorption became predominant in the neutral solutions. According to the determined number of transferred electrons and protons, oxidation and reduction mechanism of ERL are proposed. To follow the interaction between ERL and DNA, the multilayer ct-DNA electrochemical biosensor was incubated in ERL solutions concentrations ranged from 2 × 10-7 M to 5 × 10-5 M (pH 4.6) for 30 min. SWV measurements have shown the decrease in deoxyadenosine peak current as a consequence of ERL increased concentration and binding to ct-DNA. The calculated value of binding constant was K = 8.25 × 104 M-1. Molecular docking showed that ERL forms hydrophobic interactions when docked into minor groove, as well as when intercalated, and molecular dynamics analysis predicted the stability of obtained complexes. These results together with voltammetric studies imply that the intercalation could be more dominant way ERL binding to DNA compared to minor groove binding.

Reversible Control of RNA Splicing by Photoswitchable Small Molecules.

Dynamics are intrinsic to both RNA function and structure. Yet, the available means to precisely provide RNA-based processes with spatiotemporal resolution are scarce. Here, our work pioneers a reversible approach to regulate RNA splicing within primary patient-derived cells by synthetic photoswitches. Our small molecule enables conditional real-time control at mRNA and protein levels. NMR experiments, together with theoretical calculations, photochemical characterization, fluorescence polarization measurements, and living cell-based assays, confirmed light-dependent exon inclusion as well as an increase in the target functional protein. Therefore, we first demonstrated the potential of photopharmacology modulation in splicing, tweaking the current optochemical toolkit. The timeliness on the consolidation of RNA research as the driving force toward therapeutical innovation holds the promise that our approach will contribute to redrawing the vision of RNA.

Correlating Basal Gene Expression across Chemical Sensitivity Data to Screen for Novel Synergistic Interactors of HDAC Inhibitors in Pancreatic Carcinoma.

Pancreatic ductal adenocarcinoma (PDAC) is one of the most aggressive and lethal malignancies. Development of the chemoresistance in the PDAC is one of the key contributors to the poor survival outcomes and the major reason for urgent development of novel pharmacological approaches in a treatment of PDAC. Systematically tailored combination therapy holds the promise for advancing the treatment of PDAC. However, the number of possible combinations of pharmacological agents is too large to be explored experimentally. In respect to the many epigenetic alterations in PDAC, epigenetic drugs including histone deacetylase inhibitors (HDACi) could be seen as the game changers especially in combined therapy settings. In this work, we explored a possibility of using drug-sensitivity data together with the basal gene expression of pancreatic cell lines to predict combinatorial options available for HDACi. Developed bioinformatics screening protocol for predictions of synergistic drug combinations in PDAC identified the sphingolipid signaling pathway with associated downstream effectors as a promising novel targets for future development of multi-target therapeutics or combined therapy with HDACi. Through the experimental validation, we have characterized novel synergism between HDACi and a Rho-associated protein kinase (ROCK) inhibitor RKI-1447, and between HDACi and a sphingosine 1-phosphate (S1P) receptor agonist fingolimod.

Protolytic equilibria of ACE inhibitors in micellar solution of nonionic surfactant Brij 35.

The acid-base equilibria of six ACE inhibitors (ACEIs), captopril, cilazapril, enalapril, lisinopril, quinapril, and ramipril, were investigated in the presence of micelles of nonionic surfactant Brij 35. The pKa values were potentiometrically determined at 25 °C and at a constant ionic strength (0.1 M NaCl). The obtained potentiometric data were evaluated in the computer program Hyperquad. On the basis of the shift in the pKa values (ΔpKa) determined in micellar media in relation to the pKa values previously determined in "pure" water, the effect of Brij 35 micelles on ACEIs ionization was estimated. The presence of nonionic Brij 35 micelles caused a shift in the pKa values of all ionizable groups of the investigated ACEIs (ΔpKa from - 3.44 to + 1.9) while shifting the protolytic equilibria of both acidic and basic groups toward the molecular form. The Brij 35 micelles expressed the most pronounced effect on the ionization of captopril among the investigated ACEIs and stronger effect on the ionization of amino than on the ionization of carboxyl groups. The obtained results suggest that ionizable functional groups of ACEIs are involved in interactions with palisade layer of nonionic Brij 35 micelles, which potentially can be considered in physiological conditions. Distribution diagrams of the investigated ACEIs equilibrium forms as a function of pH indicate that the change in distribution is most strongly expressed in pH range 4-8, which includes biopharmaceutically important pH values.

SIRT2i_Predictor: A Machine Learning-Based Tool to Facilitate the Discovery of Novel SIRT2 Inhibitors.

A growing body of preclinical evidence recognized selective sirtuin 2 (SIRT2) inhibitors as novel therapeutics for treatment of age-related diseases. However, none of the SIRT2 inhibitors have reached clinical trials yet. Transformative potential of machine learning (ML) in early stages of drug discovery has been witnessed by widespread adoption of these techniques in recent years. Despite great potential, there is a lack of robust and large-scale ML models for discovery of novel SIRT2 inhibitors. In order to support virtual screening (VS), lead optimization, or facilitate the selection of SIRT2 inhibitors for experimental evaluation, a machine-learning-based tool titled SIRT2i_Predictor was developed. The tool was built on a panel of high-quality ML regression and classification-based models for prediction of inhibitor potency and SIRT1-3 isoform selectivity. State-of-the-art ML algorithms were used to train the models on a large and diverse dataset containing 1797 compounds. Benchmarking against structure-based VS protocol indicated comparable coverage of chemical space with great gain in speed. The tool was applied to screen the in-house database of compounds, corroborating the utility in the prioritization of compounds for costly in vitro screening campaigns. The easy-to-use web-based interface makes SIRT2i_Predictor a convenient tool for the wider community. The SIRT2i_Predictor's source code is made available online.

Dual LSD1 and HDAC6 Inhibition Induces Doxorubicin Sensitivity in Acute Myeloid Leukemia Cells.

Defects in epigenetic pathways are key drivers of oncogenic cell proliferation. We developed a LSD1/HDAC6 multitargeting inhibitor (iDual), a hydroxamic acid analogue of the clinical candidate LSD1 inhibitor GSK2879552. iDual inhibits both targets with IC50 values of 540, 110, and 290 nM, respectively, against LSD1, HDAC6, and HDAC8. We compared its activity to structurally similar control probes that act by HDAC or LSD1 inhibition alone, as well as an inactive null compound. iDual inhibited the growth of leukemia cell lines at a higher level than GSK2879552 with micromolar IC50 values. Dual engagement with LSD1 and HDAC6 was supported by dose dependent increases in substrate levels, biomarkers, and cellular thermal shift assay. Both histone methylation and acetylation of tubulin were increased, while acetylated histone levels were only mildly affected, indicating selectivity for HDAC6. Downstream gene expression (CD11b, CD86, p21) was also elevated in response to iDual treatment. Remarkably, iDual synergized with doxorubicin, triggering significant levels of apoptosis with a sublethal concentration of the drug. While mechanistic studies did not reveal changes in DNA repair or drug efflux pathways, the expression of AGPAT9, ALOX5, BTG1, HIPK2, IFI44L, and LRP1, previously implicated in doxorubicin sensitivity, was significantly elevated.

Discovery of 1-Benzhydryl-Piperazine-Based HDAC Inhibitors with Anti-Breast Cancer Activity: Synthesis, Molecular Modeling, In Vitro and In Vivo Biological Evaluation.

Isoform-selective histone deacetylase (HDAC) inhibition is promoted as a rational strategy to develop safer anti-cancer drugs compared to non-selective HDAC inhibitors. Despite this presumed benefit, considerably more non-selective HDAC inhibitors have undergone clinical trials. In this report, we detail the design and discovery of potent HDAC inhibitors, with 1-benzhydryl piperazine as a surface recognition group, that differ in hydrocarbon linker. In vitro HDAC screening identified two selective HDAC6 inhibitors with nanomolar IC50 values, as well as two non-selective nanomolar HDAC inhibitors. Structure-based molecular modeling was employed to study the influence of linker chemistry of synthesized inhibitors on HDAC6 potency. The breast cancer cell lines (MDA-MB-231 and MCF-7) were used to evaluate compound-mediated in vitro anti-cancer, anti-migratory, and anti-invasive activities. Experiments on the zebrafish MDA-MB-231 xenograft model revealed that a novel non-selective HDAC inhibitor with a seven-carbon-atom linker exhibits potent anti-tumor, anti-metastatic, and anti-angiogenic effects when tested at low micromolar concentrations.

Electrochemical Determination of Morphine in Urine Samples by Tailoring FeWO4/CPE Sensor.

Morphine (MORPH) is natural alkaloid and mainly used as a pain reliever. Its monitoring in human body fluids is crucial for modern medicine. In this paper, we have developed an electrochemical sensor for submicromolar detection of MORPH. The sensor is based on modified carbon paste electrode (CPE) by investigating the FexW1-xO4 ratio in iron tungstate (FeWO4), as well as the ratio of this material in CPE. For the first time, the effect of the iron-tungsten ratio in terms of achieving the best possible electrochemical characteristics for the detection of an important molecule for humans was examined. Morphological and electrochemical characteristics of materials were studied. The best results were obtained using Fe1W3 and 7.5% of modifier in CPE. For MORPH detection, square wave voltammetry (SWV) was optimized. Under the optimized conditions, Fe1W3@CPE resulted in limit of detection (LOD) of the method of 0.58 µM and limit of quantification (LOQ) of 1.94 µM. The linear operating range between 5 and 85 µM of MORPH in the Britton-Robinson buffer solution (BRBS) at pH 8 as supporting electrolyte was obtained. The Fe1W3@CPE sensor resulted in good selectivity and excellent repeatability with relative standard deviation (RSD) and was applied in real-world samples of human urine. Application for direct MORPH detection, without tedious sample pretreatment procedures, suggests that developed electrochemical sensor has appeared to be a suitable competitor for efficient, precise, and accurate monitoring of the MORPH in biological fluids.

Discovery of new chemotypes of dual 5-HT2A/D2 receptor antagonists with a strategy of drug design methodologies.

Aim: Through the application of structure- and ligand-based methods, the authors aimed to create an integrative approach to developing a computational protocol for the rational drug design of potent dual 5-HT2A/D2 receptor antagonists without off-target activities on H1 receptors. Materials & methods: Molecular dynamics and virtual docking methods were used to identify key interactions of the structurally diverse antagonists in the binding sites of the studied targets, and to generate their bioactive conformations for further 3D-quantitative structure-activity relationship modeling. Results & conclusion: Toward the goal of finding multi-potent drugs with a more effective and safer profile, the obtained results led to the design of a new set of dual antagonists and opened a new perspective on the therapy for complex brain diseases.

Expanding the Accessible Chemical Space of SIRT2 Inhibitors through Exploration of Binding Pocket Dynamics.

Considerations of binding pocket dynamics are one of the crucial aspects of the rational design of binders. Identification of alternative conformational states or cryptic subpockets could lead to the discovery of completely novel groups of the ligands. However, experimental characterization of pocket dynamics, besides being expensive, may not be able to elucidate all of the conformational states relevant for drug discovery projects. In this study, we propose the protocol for computational simulations of sirtuin 2 (SIRT2) binding pocket dynamics and its integration into the structure-based virtual screening (SBVS) pipeline. Initially, unbiased molecular dynamics simulations of SIRT2:inhibitor complexes were performed using optimized force field parameters of SIRT2 inhibitors. Time-lagged independent component analysis (tICA) was used to design pocket-related collective variables (CVs) for enhanced sampling of SIRT2 pocket dynamics. Metadynamics simulations in the tICA eigenvector space revealed alternative conformational states of the SIRT2 binding pocket and the existence of a cryptic subpocket. Newly identified SIRT2 conformational states outperformed experimentally resolved states in retrospective SBVS validation. After performing prospective SBVS, compounds from the under-represented portions of the SIRT2 inhibitor chemical space were selected for in vitro evaluation. Two compounds, NDJ18 and NDJ85, were identified as potent and selective SIRT2 inhibitors, which validated the in silico protocol and opened up the possibility for generalization and broadening of its application. The anticancer effects of the most potent compound NDJ18 were examined on the triple-negative breast cancer cell line. Results indicated that NDJ18 represents a promising structure suitable for further evaluation.

Quinazoline-based analog of adenine as an antidote against MLL-rearranged leukemia cells: synthesis, inhibition assays and docking studies.

Background: Post-translational modifications of histones constitute a dynamic process impacting gene expression. A well-studied modification is lysine methylation. Among the lysine histone methyltransferases, DOT1L is implicated in various diseases, making it a very interesting target for drug discovery. DOT1L has two substrates, the SAM cofactor that gives the methyl group and the lysine H3K79 substrate. Results: Using molecular docking, the authors explored new bisubstrate analogs to enlarge the chemical landscape of DOT1L inhibitors. The authors showed that quinazoline can successfully replace the adenine in the design of bisubstrate inhibitors of DOT1L, showing similar activity compared with the adenine derivative but with diminished cytotoxicity. Conclusion: The docking model is validated together with the use of quinazoline in the design of bisubstrate inhibitors.

Thoracic Epidural Analgesia Use in Large Recurrent Desmoid Fibromatosis Resection: A Case Report.

We present a case report of the successful use of thoracic epidural analgesia for the surgical resection of a large recurrent desmoid tumor and forequarter amputation in an adolescent male. Spinal anesthesia has been reported for intra-operative management of desmoid tumor resection, however, there are no reported cases of thoracic epidural analgesia for this tumor. Thoracic epidural should be used with caution in this patient population due to risk of de novo tumor creation but can be useful adjuvant to multi-modal analgesia to decrease post-operative opioid requirement.

Bistable Photoswitch Allows in Vivo Control of Hematopoiesis.

Optical control has enabled functional modulation in cell culture with unparalleled spatiotemporal resolution. However, current tools for in vivo manipulation are scarce. Here, we design and implement a genuine on-off optochemical probe capable of achieving hematopoietic control in zebrafish. Our photopharmacological approach first developed conformationally strained visible light photoswitches (CS-VIPs) as inhibitors of the histone methyltransferase MLL1 (KMT2A). In blood homeostasis MLL1 plays a crucial yet controversial role. CS-VIP 8 optimally fulfils the requirements of a true bistable functional system in vivo under visible-light irradiation, and with unprecedented stability. These properties are exemplified via hematopoiesis photoinhibition with a single isomer in zebrafish. The present interdisciplinary study uncovers the mechanism of action of CS-VIPs. Upon WDR5 binding, CS-VIP 8 causes MLL1 release with concomitant allosteric rearrangements in the WDR5/RbBP5 interface. Since our tool provides on-demand reversible control without genetic intervention or continuous irradiation, it will foster hematopathology and epigenetic investigations. Furthermore, our workflow will enable exquisite photocontrol over other targets inhibited by macrocycles.

Targeting Histone Deacetylases: Opportunities for Cancer Treatment and Chemoprevention.

The dysregulation of gene expression is a critical event involved in all steps of tumorigenesis. Aberrant histone and non-histone acetylation modifications of gene expression due to the abnormal activation of histone deacetylases (HDAC) have been reported in hematologic and solid types of cancer. In this sense, the cancer-associated epigenetic alterations are promising targets for anticancer therapy and chemoprevention. HDAC inhibitors (HDACi) induce histone hyperacetylation within target proteins, altering cell cycle and proliferation, cell differentiation, and the regulation of cell death programs. Over the last three decades, an increasing number of synthetic and naturally derived compounds, such as dietary-derived products, have been demonstrated to act as HDACi and have provided biological and molecular insights with regard to the role of HDAC in cancer. The first part of this review is focused on the biological roles of the Zinc-dependent HDAC family in malignant diseases. Accordingly, the small-molecules and natural products such as HDACi are described in terms of cancer therapy and chemoprevention. Furthermore, structural considerations are included to improve the HDACi selectivity and combinatory potential with other specific targeting agents in bifunctional inhibitors and proteolysis targeting chimeras. Additionally, clinical trials that combine HDACi with current therapies are discussed, which may open new avenues in terms of the feasibility of HDACi's future clinical applications in precision cancer therapies.

The evaluation of drug-plasma protein binding interaction on immobilized human serum albumin stationary phase, aided by different computational approaches.

The drug-human serum albumin binding interaction was evaluated on a stationary phase immobilized with human serum albumin using a mixture of phosphate buffer (pH 7.0) and acetonitrile modifier as mobile phase. The 33 compounds that have a wide structural and therapeutic diversity were analyzed by performing a large number of experiments. The interaction mechanism was interpreted based on: i) retention characteristics of structurally related compounds, ii) retention modeling, iii) quantitative structure retention relationship (QSRR), and iv) molecular docking. Small structural differences of related compounds (e.g., reflected in different lipophilicity and polarity) have been found to affect their different binding to human serum albumin. It was found that drug retention in HSA column can be successfully described by using the quadratic function. The isocratic (logk(14%)) and extrapolated (b0(LSS)) retention factors showed the highest correlation (r > 0.76) with the constant that defines the binding affinity for human serum albumin (ACD/I-Lab). Therefore, selected chromatographic parameters can demonstrate reliable applicability for rapid screening of drug-plasma protein binding in drug discovery. In QSRR study, the resulting SVM/logk(14%) and MLR/b0(LSS) models display high internal and external predictive power. The constitutional properties (double bonds, aromatic rings, benzyl, allyl, -amino and -sulfur containing functional groups) supported by the charged parts of surface area had a significant impact on human serum albumin-binding affinity, which was also confirmed with molecular docking study. The high structural diversity of the data set provides wide applicability of tested chromatographic conditions and constructed models for defining the pharmacokinetic profile and possible structural modifications that can increase plasma protein binding of newly synthesized, pharmaceutically important compounds.

Application of in vitro PAMPA technique and in silico computational methods for blood-brain barrier permeability prediction of novel CNS drug candidates.

Permeability assessment of small molecules through the blood-brain barrier (BBB) plays a significant role in the development of effective central nervous system (CNS) drug candidates. Since in vivo methods for BBB permeability estimation require a lot of time and resources, in silico and in vitro approaches are becoming increasingly popular nowadays for faster and more economical predictions in early phases of drug discovery. In this work, through application of in vitro parallel artificial membrane permeability assay (PAMPA-BBB) and in silico computational methods we aimed to examine the passive permeability of eighteen compounds, which affect serotonin and dopamine levels in the CNS. The data set was consisted of novel six human dopamine transporter (hDAT) substrates that were previously identified as the most promising lead compounds for further optimisation to achieve neuroprotective effect, twelve approved CNS drugs, and their related compounds. Firstly, PAMPA methods was used to experimentally determine effective BBB permeability (Pe) for all studied compounds and obtained results were further submitted for quantitative structure permeability relationship (QSPR) analysis. QSPR models were built by using three different statistical methods: stepwise multiple linear regression (MLR), partial least square (PLS), and support-vector machine (SVM), while their predictive capability was tested through internal and external validation. Obtained statistical parameters (MLR- R2pred=-0.10; PLS- R2pred=0.64, r2m=0.69, r/2m=0.44; SVM- R2pred=0.57, r2m=0.72, r/2m=0.55) indicated that the SVM model is superior over others. The most important molecular descriptors (H0p and SolvEMt_3D) were identified and used to propose structural modifications of the examined compounds in order to improve their BBB permeability. Moreover, steered molecular dynamics (SMD) simulation was employed to comprehensively investigate the permeability pathway of compounds through a lipid bilayer. Taken together, the created QSPR model could be used as a reliable and fast pre-screening tool for BBB permeability prediction of structurally related CNS compounds, while performed MD simulations provide a good foundation for future in silico examination.