Assessment of area and structural irregularity of retinal layers in diabetic retinopathy using machine learning and image processing techniques.

Diabetes retinopathy prevention necessitates early detection, monitoring, and treatment. Non-invasive optical coherence tomography (OCT) shows structural changes in the retinal layer. OCT image evaluation necessitates retinal layer segmentation. The ability of our automated retinal layer segmentation to distinguish between normal, non-proliferative (NPDR), and proliferative diabetic retinopathy (PDR) was investigated in this study using quantifiable biomarkers such as retina layer smoothness index (SI) and area (S) in horizontal and vertical OCT images for each zone (fovea, superior, inferior, nasal, and temporal). This research includes 84 eyes from 57 individuals. The study shows a significant difference in the Area (S) of inner nuclear layer (INL) and outer nuclear layer (ONL) in the horizontal foveal zone across the three groups (p < 0.001). In the horizontal scan, there is a significant difference in the smoothness index (SI) of the inner plexiform layer (IPL) and the upper border of the outer plexiform layer (OPL) among three groups (p < 0.05). There is also a significant difference in the area (S) of the OPL in the foveal zone among the three groups (p = 0.003). The area (S) of the INL in the foveal region of horizontal slabs performed best for distinguishing diabetic patients (NPDR and PDR) from normal individuals, with an accuracy of 87.6%. The smoothness index (SI) of IPL in the nasal zone of horizontal foveal slabs was the most accurate at 97.2% in distinguishing PDR from NPDR. The smoothness index of the top border of the OPL in the nasal zone of horizontal slabs was 84.1% accurate in distinguishing NPDR from PDR. Smoothness index of IPL in the temporal zone of horizontal slabs was 89.8% accurate in identifying NPDR from PDR patients. In conclusion, optical coherence tomography can assess the smoothness index and irregularity of the inner and outer plexiform layers, particularly in the nasal and temporal regions of horizontal foveal slabs, to distinguish non-proliferative from proliferative diabetic retinopathy. The evolution of diabetic retinopathy throughout severity levels and its effects on retinal layer irregularity need more study.

Vessel Density Features of Optical Coherence Tomography Angiography for Classification of Optic Neuropathies Using Machine Learning.

BACKGROUND: To evaluate the classification performance of machine learning based on the 4 vessel density features of peripapillary optical coherence tomography angiography (OCT-A) for classifying healthy, nonarteritic anterior ischemic optic neuropathy (NAION), and optic neuritis (ON) eyes. METHODS: Forty-five eyes of 45 NAION patients, 32 eyes of 32 ON patients, and 76 eyes of 76 healthy individuals with optic nerve head OCT-A were included. Four vessel density features of OCT-A images were developed using a threshold-based segmentation method and were integrated in 3 models of machine learning classifiers. Classification performances of support vector machine (SVM), random forest, and Gaussian Naive Bayes (GNB) models were evaluated with the area under the receiver-operating-characteristic curve (AUC) and accuracy. RESULTS: We divided 121 images into a 70% training set and 30% test set. For ON-NAION classification, best results were achieved with 50% threshold, in which 3 classifiers (SVM, RF, and GNB) discriminated ON from NAION with an AUC of 1 and accuracy of 1. For ON-Normal classification, with 100% threshold, SVM and RF classifiers were able to discriminate normal from ON with AUCs of 1 and accuracies of 1. For NAION-normal classification, with 50% threshold, the SVM and RF classified the NAION from normal with AUC and accuracy of 1. CONCLUSIONS: ML based on the combined peripapillary vessel density features of total vessels and capillaries in the whole image and ring image could provide excellent performance for NAION and ON distinction.

Vessel Density Features of Optical Coherence Tomography Angiography for Classification of Glaucoma Using Machine Learning.

PRCIS: Machine learning (ML) based on the optical coherence tomography angiography vessel density features with different thresholds using a support vector machine (SVM) model provides excellent performance for glaucoma detection. BACKGROUND: To assess the classification performance of ML based on the 4 vessel density features of peripapillary optical coherence tomography angiography for glaucoma detection. METHODS: Images from 119 eyes of 119 glaucoma patients and 76 eyes of 76 healthy individuals were included. Four vessel density features of optical coherence tomography angiography images were developed using a threshold-based segmentation method and were integrated into 3 models of machine learning classifiers. Images were divided into 70% training set and 30% test set. Classification performances of SVM, random forest, and Gaussian Naive Bayes models were evaluated with the area under the receiver operating characteristic curve (AUC) and accuracy. RESULTS: Glaucoma eyes had lower vessel densities at different thresholds. For differentiating glaucoma eyes, the best results were achieved with 70% and 100% thresholds, in which SVM classifier discriminated glaucoma from healthy eyes with an AUC of 1 and accuracy of 1. After SVM, the random forest classifier with 100% thresholds showed an AUC of 0.993 and an accuracy of 0.994. Furthermore, the AUC of our ML performance (SVM) was 0.96 in a subgroup analysis of mild and moderate glaucoma eyes. CONCLUSIONS: ML based on the combined peripapillary vessel density features of total vessels and capillaries in the whole image and ring image could provide excellent performance for glaucoma detection.

Assessment of retinal and choroidal vessel density and nerve fibre layer thickness changes after orbitotomy in patients with severe non-active thyroid orbitopathy: a prospective study.

PURPOSE: To evaluate the optical coherence tomography angiogram changes in non-active severe thyroid-related ophthalmopathy patients after cosmetic bone decompression. METHODS: Eighteen patients (25 eyes) with severe not active not compressive (NANC) TED who were candidates for decompression surgery for cosmetic reasons were included in this study, and a 3 × 3 mm macular scan was used to measure vessel density and RNFL thickness. Whole macular vessel density in its superficial, deep and choriocapillaris layers was evaluated. The following data were extracted for each of layers: superior and inferior hemispheres, fovea, parafoveal vessel density, its superior and inferior hemispheres, and temporal, superior, nasal and inferior quadrant. RESULTS: The mean RPC increased postoperatively, which was statistically significant in small vessels of peripapillary area (p-value = 0.045). The mean RNFL thickness decreased after surgery and it was statistically significant in the peripapillary (p-value = 0.032) and Inferior-Hemifield area (p-value = 0.036). The choriocapillaris changes were significant in Superior-Hemifield (p-value = 0.031) and Fovea (p-value = 0.03). CONCLUSION: Thyroid-associated orbitopathy patients have a tendency to decrease vascular density and correlated with disease activity more than stage of orbitopathy. There was not a strong and even discrepant result in linkage of RNFL thickness and other optic nerve function tests and TED patient status and it is needed to do studies with more epidemiologic power and same methodology of study to be more comparable.

In vitro and in silico scolicidal effect of sanguinarine on the hydatid cyst protoscoleces.

We aimed to investigate the scolicidal effects of sanguinarine on hydatid cyst protoscoleces (PSCs) in vitro and in silico. Different targets were docked into the active sites of sanguinarine. Molecular docking processes and visualization of interactions were performed using AutoDock Vina and Discovery Studio Visualizer. Binding energy was calculated and compared (kcal/mol). PSCs were aspirated from the hydatid cysts and washed. The sediments of PSCs were then exposed to various concentrations (50, 25, 12, 6, 3, and 1 µg/mL) of sanguinarine. The viability test was finally evaluated by the Trypan blue solution 4%. Levels of malondialdehyde (MDA), superoxide dismutase (SOD), glutathione (GSH), glutathione peroxidase (GPX), and catalase were analyzed to assess the level of oxidative stress-treated PSCs. Caspase-3 activity rate was determined to evaluate cell apoptosis in treated PSCs. Among the receptors, acetylcholinesterase was identified as the excellent target, with Vina score of -11.8. Sanguinarine showed high scolicidal effects after 12, 24, and 48 h. Also, in the first hour of exposure to the drug, caspase-3 activity and MDA level significantly increased, but the levels of GSH and GPx had a significant reduction after 12, 24, and 48 h (P < 0.05). The findings of this study revealed that sanguinarine have potent scolicidal effects in vitro and in silico and could be considered an opportunity for the introduction of a novel and safe therapeutic agent for the treatment of cystic echinococcosis. However, supplementary studies will be desired to prove the current findings by examining sanguinarine in a clinical setting.

Deciphering anti-biofilm property of Arthrospira platensis-origin peptides against Staphylococcusaureus.

Arthrospira platensis is a valuable natural health supplement consisting of various types of vitamins, dietary minerals, and antioxidants. Although different studies have been conducted to explore the hidden benefits of this bacterium, its antimicrobial property has been poorly understood. To decipher this important feature, here, we extended our recently introduced optimization algorithm (Trader) for aligning amino acid sequences associated with the antimicrobial peptides (AMPs) of Staphylococcus aureus and A.platensis. As a result, similar amino acid sequences were identified, and several candidate peptides were generated accordingly. The obtained peptides were then filtered based on their potential biochemical and biophysical properties, and their 3D structures were simulated based on homology modeling techniques. Next, to investigate how the generated peptides can interact with S. aureus proteins (i.e., heptameric state of the hly and homodimeric form of the arsB), molecular docking approaches were used. The results indicated that four peptides included better molecular interactions relative to the other generated ones in terms of the number/average length of hydrogen bonds and hydrophobic interactions. Based on the outcomes, it can be concluded that the antimicrobial property of A.platensis might be associated with its capability in disturbing the membrane of pathogens and their functions.

Drug Repurposing for Identification of S1P1 Agonists with Potential Application in Multiple Sclerosis Using In Silico Drug Design Approaches.

Purpose: Drug repurposing is an approach successfully used for discovery of new therapeutic applications for the existing drugs. The current study was aimed to use the combination of in silico methods to identify FDA-approved drugs with possible S1P1 agonistic activity useful in multiple sclerosis (MS). Methods: For this, a 3D-QSAR model for the known 21 S1P1 agonists were generated based on 3D-QSAR approach and used to predict the possible S1P1 agonistic activity of FDA-approved drugs. Then, the selected compounds were screened by docking into S1P1 and S1P3 receptors to select the S1P1 potent and selective compounds. Further evaluation was carried out by molecular dynamics (MD) simulation studies where the S1P1 binding energies of selected compounds were calculated. Results: The analyses resulted in identification of cobicistat, benzonatate and brigatinib as the selective and potent S1P1 agonists with the binding energies of -85.93, -69.77 and -67.44 kcal. mol-1, calculated using MM-GBSA algorithm based on 50 ns MD simulation trajectories. These values are better than that of siponimod (-59.35 kcal mol-1), an FDA approved S1P1 agonist indicated for MS treatment. Furthermore, similarity network analysis revealed that cobicistat and brigatinib are the most structurally favorable compounds to interact with S1P1. Conclusion: The findings in this study revealed that cobicistat and brigatinib can be evaluated in experimental studies as potential S1P1 agonist candidates useful in the treatment of MS.

Automated assessment of the smoothness of retinal layers in optical coherence tomography images using a machine learning algorithm.

Quantifying the smoothness of different layers of the retina can potentially be an important and practical biomarker in various pathologic conditions like diabetic retinopathy. The purpose of this study is to develop an automated machine learning algorithm which uses support vector regression method with wavelet kernel and automatically segments two hyperreflective retinal layers (inner plexiform layer (IPL) and outer plexiform layer (OPL)) in 50 optical coherence tomography (OCT) slabs and calculates the smoothness index (SI). The Bland-Altman plots, mean absolute error, root mean square error and signed error calculations revealed a modest discrepancy between the manual approach, used as the ground truth, and the corresponding automated segmentation of IPL/ OPL, as well as SI measurements in OCT slabs. It was concluded that the constructed algorithm may be employed as a reliable, rapid and convenient approach for segmenting IPL/OPL and calculating SI in the appropriate layers.

Automated machine learning-based classification of proliferative and non-proliferative diabetic retinopathy using optical coherence tomography angiography vascular density maps.

PURPOSE: The study aims to classify the eyes with proliferative diabetic retinopathy (PDR) and non-proliferative diabetic retinopathy (NPDR) based on the optical coherence tomography angiography (OCTA) vascular density maps using a supervised machine learning algorithm. METHODS: OCTA vascular density maps (at superficial capillary plexus (SCP), deep capillary plexus (DCP), and total retina (R) levels) of 148 eyes from 78 patients with diabetic retinopathy (45 PDR and 103 NPDR) was used to classify the images to NPDR and PDR groups based on a supervised machine learning algorithm known as the support vector machine (SVM) classifier optimized by a genetic evolutionary algorithm. RESULTS: The implemented algorithm in three different models reached up to 85% accuracy in classifying PDR and NPDR in all three levels of vascular density maps. The deep retinal layer vascular density map demonstrated the best performance with a 90% accuracy in discriminating between PDR and NPDR. CONCLUSIONS: The current study on a limited number of patients with diabetic retinopathy demonstrated that a supervised machine learning-based method known as SVM can be used to differentiate PDR and NPDR patients using OCTA vascular density maps.

Infectious keratitis caused by Klebsiella spp.: predisposing factors, presentation, and management.

PURPOSE: To study predisposing factors, clinical presentation and management strategies for Klebsiella keratitis. METHODS: A retrospective case review was performed on clinical records of culture-proven Klebsiella keratitis cases in a tertiary referral center over an 8-year period (from 2012 to 2020). RESULTS: Thirty eight episodes of culture-proven Klebsiella keratitis were identified in 37 patients. The mean age of the patients was 62.9 years (range, 24-101). Multiple predisposing factors were identified in 33 eyes including history of previous keratoplasty (n = 11) history of ocular trauma (n = 7), preexisting ocular surface disease (n = 7) and diabetes (n = 6). Corrected distance visual acuity (CDVA) at presentation was light perception (LP) in 16 patients, hand motion (HM) in 12, counting fingers (CF) at 50 cm in 5, CF at 1 m in 1, CF at 2 m in 2. One patient had a CDVA of 3/10. On initial examination Hypopyon was detected in 21 eyes. Descemet's membrane folds were present in 1 eye. Corneal thinning was identified in 20 eyes and perforation occurred in 4 patients. Corneal ulcer progressed to endophthalmitis in one patient. Microbiologic sensitivity testing showed that 89.5% isolates were sensitive to amikacin (34/38),88.9%sensitive to ceftazidime (32/36),94.4% were sensitive to gentamicin (34/36),97.2% sensitive to ciprofloxacin (35/36), and 100% to levofloxacin (26/26).Ultimately, one or more surgical procedures was needed in 21 patients. CONCLUSION: Previous keratoplasty, history of ocular trauma, ocular surface disease and systemic disease such as diabetes are major risk factors for Klebsiella keratitis. In most of the patients, surgical and tectonic procedures were necessary to control the infection.

A novel multi-objective metaheuristic algorithm for protein-peptide docking and benchmarking on the LEADS-PEP dataset.

Protein-peptide interactions have attracted the attention of many drug discovery scientists due to their possible druggability features on most key biological activities such as regulating disease-related signaling pathways and enhancing the immune system's responses. Different studies have utilized some protein-peptide-specific docking algorithms/methods to predict protein-peptide interactions. However, the existing algorithms/methods suffer from two serious limitations which make them unsuitable for protein-peptide docking problems. First, it seems that the prevalent approaches require to be modified and remodeled for weighting the unbounded forces between a protein and a peptide. Second, they do not employ state-of-the-art search algorithms for detecting the 3D pose of a peptide relative to a protein. To address these restrictions, the present study aims to introduce a novel multi-objective algorithm, which first generates some potential 3D poses of a peptide, and then, improves them through its operators. The candidate solutions are further evaluated using Multi-Objective Pareto Front (MOPF) optimization concepts. To this end, van der Waals, electrostatic, solvation, and hydrogen bond energies between the atoms of a protein and designated peptide are computed. To evaluate the algorithm, it is first applied to the LEADS-PEP dataset containing 53 protein-peptide complexes with up to 53 rotatable branches/bonds and then compared with three popular/efficient algorithms. The obtained results indicate that the MOPF-based approaches which reduce the backbone RMSD between the original and predicted states, achieve significantly better results in terms of the success rate in predicting the near-native conditions. Besides, a comparison between the different types of search algorithms reveals that efficient ones like the multi-objective Trader/differential evolution algorithm can predict protein-peptide interactions better than the popular algorithms such as the multi-objective genetic/particle swarm optimization algorithms.

Structure-based drug repurposing against COVID-19 and emerging infectious diseases: methods, resources and discoveries.

To attain promising pharmacotherapies, researchers have applied drug repurposing (DR) techniques to discover the candidate medicines to combat the coronavirus disease 2019 (COVID-19) outbreak. Although many DR approaches have been introduced for treating different diseases, only structure-based DR (SBDR) methods can be employed as the first therapeutic option against the COVID-19 pandemic because they rely on the rudimentary information about the diseases such as the sequence of the severe acute respiratory syndrome coronavirus 2 genome. Hence, to try out new treatments for the disease, the first attempts have been made based on the SBDR methods which seem to be among the proper choices for discovering the potential medications against the emerging and re-emerging infectious diseases. Given the importance of SBDR approaches, in the present review, well-known SBDR methods are summarized, and their merits are investigated. Then, the databases and software applications, utilized for repurposing the drugs against COVID-19, are introduced. Besides, the identified drugs are categorized based on their targets. Finally, a comparison is made between the SBDR approaches and other DR methods, and some possible future directions are proposed.

Role of cellulose family in fibril organization of collagen for forming 3D cancer spheroids: In vitro and in silico approach.

Introduction: Cell aggregation of three-dimensional (3D) culture systems (the so-called spheroids) are designed as in vitro platform to represent more accurately the in vivo environment for drug discovery by using semi-solid media. The uniform multicellular tumor spheroids can be generated based on the interaction of cells with extracellular matrix (ECM) macromolecules such as collagen and integrin. This study aimed to investigate the possible interactions between the cellulose family and collagen using both in vitro and in silico approaches. Methods: The 3D microtissue of JIMT-1 cells was generated using hanging drop method to study the effects of charge and viscosity of the medium containing cellulose family. To determine the mode of interaction between cellulose derivatives (CDs) and collagen-integrin, docking analysis and molecular simulation were further performed using open source web servers and chemical simulations (GROMACS), respectively. Results: The results confirmed that the addition of CDs into the 3D medium can promote the formation of solid spheroids, where methylcellulose (MC) yielded uniform spheroids compared to carboxymethyl cellulose (CMC). Moreover, the computational analysis showed that MC interacted with both integrin and collagen, while sodium carboxymethyl cellulose (NaCMC) only interacted with collagen residues. The stated different behaviors in the 3D culture formation and collagen interaction were found in the physicochemical properties of CDs. Conclusion: Based on in vitro and in silico findings, MC is suggested as an important ECM-mimicking entity that can support the semi-solid medium and promote the formation of the uniform spheroid in the 3D culture.

A domain-based vaccine construct against SARS-CoV-2, the causative agent of COVID-19 pandemic: development of self-amplifying mRNA and peptide vaccines.

Introduction: Coronavirus disease 2019 (COVID-19) is undoubtedly the most challenging pandemic in the current century with more than 293,241 deaths worldwide since its emergence in late 2019 (updated May 13, 2020). COVID-19 is caused by a novel emerged coronavirus named severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Today, the world needs crucially to develop a prophylactic vaccine scheme for such emerged and emerging infectious pathogens. Methods: In this study, we have targeted spike (S) glycoprotein, as an important surface antigen to identify its B- and T-cell immunodominant regions. We have conducted a multi-method B-cell epitope (BCE) prediction approach using different predictor algorithms to discover the most potential BCEs. Besides, we sought among a pool of MHC class I and II-associated peptide binders provided by the IEDB server through the strict cut-off values. To design a broad-coverage vaccine, we carried out a population coverage analysis for a set of candidate T-cell epitopes and based on the HLA allele frequency in the top most-affected countries by COVID-19 (update 02 April 2020). Results: The final determined B- and T-cell epitopes were mapped on the S glycoprotein sequence, and three potential hub regions covering the largest number of overlapping epitopes were identified for the vaccine designing (I531-N711; T717-C877; and V883-E973). Here, we have designed two domain-based constructs to be produced and delivered through the recombinant protein- and gene-based approaches, including (i) an adjuvanted domain-based protein vaccine construct (DPVC), and (ii) a self-amplifying mRNA vaccine (SAMV) construct. The safety, stability, and immunogenicity of the DPVC were validated using the integrated sequential (i.e. allergenicity, autoimmunity, and physicochemical features) and structural (i.e. molecular docking between the vaccine and human Toll-like receptors (TLRs) 4 and 5) analysis. The stability of the docked complexes was evaluated using the molecular dynamics (MD) simulations. Conclusion: These rigorous in silico validations supported the potential of the DPVC and SAMV to promote both innate and specific immune responses in preclinical studies.

Structure-based virtual screening of dipeptidyl peptidase 4 inhibitors and their in vitro analysis.

Type 2 diabetes mellitus (T2DM) is one of the most widely prevalent metabolic disorders with no cure to date thus remains the most challenging task in the current drug discovery. Therefore, the only strategy to control diabetes prevalence is to develop novel efficacious therapeutics. Dipeptidyl Peptidase 4 (DPP-4) inhibitors are currently used as anti-diabetic drugs for the inhibition of incretins. This study aims to construct the chemical feature based on pharmacophore models for dipeptidyl peptidase IV. The structure-based pharmacophore modeling has been employed to evaluate new inhibitors of DPP-4. A four-featured pharmacophore model was developed from crystal structure of DPP-4 enzyme with 4-(2-aminoethyl) benzenesulfonyl fluoride in its active site via pharmacophore constructing tool of Molecular Operating Environment (MOE) consisting F1 Hyd (hydrophobic region), F2 Hyd|Cat|Don (hydrophobic cationic and donor region), F3 Acc (acceptor region) and F4 Hyd (hydrophobic region). The generated pharmacophore model was used for virtual screening of in-house compound library (the available compounds which were used for initial screening to get the few compounds for the current studies). The resultant selected compounds, after virtual screening were further validated using in vitro assay. Furthermore, structure-activity relationship was carried out for the compounds possessing significant inhibition potential after docking studies. The binding free energy of analogs was evaluated via molecular mechanics generalized Born surface area (MM-GBSA) and Poisson-Boltzmann surface area (MM-PBSA) methods using AMBER 16 as a molecular dynamics (MD) simulation package. Based on potential findings, we report that selected candidates are more likely to be used as DPP-4 inhibitors or as starting leads for the development of novel and potent DPP-4 inhibitors.

Nanobody-based therapeutics against colorectal cancer: Precision therapies based on the personal mutanome profile and tumor neoantigens.

Monoclonal antibodies and vaccines have widely been studied for the immunotherapy of cancer, while their large size appears to limit their functionality in solid tumors, in large part due to unique properties of tumor microenvironment such as high pressure of tumor interstitial fluid. To tackle such limitations, smaller formats of antibodies have been developed, including antigen-binding fragments, single-chain variable fragments, single variable domain of camelid antibody (so-called nanobody (Nb) or VHH). Of these, Nbs offer great immunotherapy potentials because of their advantageous physicochemical and pharmacological features, including small size, high stability, and excellent tissue penetration. Besides, the therapeutic impacts of Nbs can be improved by their modifications and functionalizations (e.g., PEGylation and conjugation to the Fc domain, peptide tags, drugs, toxins, aptamers, and radionuclides). This review aims to provide comprehensive insights into key signaling networks of colorectal cancer and discuss Nb-based precision immunotherapy of colorectal cancer.

Medicinal plants used in the treatment of Malaria: A key emphasis to Artemisia, Cinchona, Cryptolepis, and Tabebuia genera.

Malaria is one of the life-threatening parasitic diseases that is endemic in tropical areas. The increased prevalence of malaria due to drug resistance leads to a high incidence of mortality. Drug discovery based on natural products and secondary metabolites is considered as alternative approaches for antimalarial therapy. Herbal medicines have advantages over modern medicines, including fewer side effects, cost-effectiveness, and affordability encouraging the herbal-based drug discovery. Several naturally occurring, semisynthetic, and synthetic antimalarial medications are on the market. For example, chloroquine is a synthetic medication for antimalarial therapy derived from quinine. Moreover, artemisinin, and its derivative, artesunate with sesquiterpene lactone backbone, is an antimalarial agent originated from Artemisia annua L. A. annua traditionally has been used to detoxify blood and eliminate fever in China. Although the artemisinin-based combination therapy against malaria has shown exceptional responses, the limited medicinal options demand novel therapeutics. Furthermore, drug resistance is the cause in most cases, and new medications are proposed to overcome the resistance. In addition to conventional therapeutics, this review covers some important genera in this area, including Artemisia, Cinchona, Cryptolepis, and Tabebuia, whose antimalarial activities are finely verified.

Application of bioinformatics and molecular dynamics simulation approaches for identification of fibroblast growth factor 10 analogues with potentially improved thermostability.

Fibroblast growth factor 10 functions as a paracrine mesenchymal molecule to initiate signalling pathways regarding to cellular development and health. However, the low thermal stability restricts it's functionality in the human body and the shelf-life of FGF10-based formulations. The current study aimed to employ rational design and bioinformatics approaches to identify some point mutations which may improve the thermal stability of FGF10. Bioinformatics analyses resulted in N105D, C106F, K144R, K153M and I156R as the potential stability conferring mutations. The identified mutants were subjected to MD simulation indicating that all mutations are both structurally and energetically favoured. Finally, the effects of the identified mutations on receptor binding of FGF10 were predicted and the results showed that K144R and K153M mutations may increase the binding affinity relative to the wild type. The findings of the current study propose potentially improved FGF10 analogues for further experimental investigations.

Highly Potent and Selective Ectonucleoside Triphosphate Diphosphohydrolase (ENTPDase1, 2, 3 and 8) Inhibitors Having 2-substituted-7- trifluoromethyl-thiadiazolopyrimidones Scaffold.

BACKGROUND: The ecto-nucleoside triphosphate diphosphohydrolases (NTPDases) terminate nucleotide signaling via the hydrolysis of extracellular nucleoside-5'-triphosphate and nucleoside- 5'-diphosphate, to nucleoside-5'-monophosphate and composed of eight Ca2+/Mg2+ dependent ectonucleotidases (NTPDase1-8). Extracellular nucleotides are involved in a variety of physiological mechanisms. However, they are rapidly inactivated by ectonucleotidases that are involved in the sequential removal of phosphate group from nucleotides with the release of inorganic phosphate and their respective nucleoside. Ectonucleoside triphosphate diphosphohydrolases (NTPDases) represent the key enzymes responsible for nucleotides hydrolysis and their overexpression has been related to certain pathological conditions. Therefore, the inhibitors of NTPDases are of particular importance in order to investigate their potential to treat various diseases e.g., cancer, ischemia and other disorders of the cardiovascular and immune system. METHODS: Keeping in view the importance of NTPDase inhibitors, a series of thiadiazolopyrimidones were evaluated for their potential inhibitory activity towards NTPDases by the malachite green assay. RESULTS: The results suggested that some of the compounds were found as non-selective inhibitors of isozyme of NTPDases, however, most of the compounds act as potent and selective inhibitors. In case of substituted amino derivatives (4c-m), the compounds 4m (IC50 = 1.13 ± 0.09 µM) and 4g (IC50 = 1.72 ± 0.08 µM) were found to be the most potent inhibitors of h-NTPDase1 and 2, respectively. Whereas, compound 4d showed the best inhibitory potential for both h-NTPDase3 (IC50 = 1.25 ± 0.06 µM) and h-NTPDase8 (0.21 ± 0.02 µM). Among 5a-t derivatives, compounds 5e (IC50 = 2.52 ± 0.15 µM), 5p (IC50 = 3.17 ± 0.05 µM), 5n (IC50 = 1.22 ± 0.06 µM) and 5b (IC50 = 0.35 ± 0.001 µM) were found to be the most potent inhibitors of h-NTPDase1, 2, 3 and 8, respectively. Interestingly, the inhibitory concentration values of above-mentioned inhibitors were several folds greater than suramin, a reference control. In order to determine the binding interactions, molecular docking studies of the most potent inhibitors were conducted into the homology models of NTPDases and the putative binding analysis further confirmed that selective and potent compounds bind deep inside the active pocket of the respective enzymes. CONCLUSION: The docking analysis proposed that the inhibitory activity correlates with the hydrogen bonds inside the binding pocket. Thus, these derivatives are of interest and may further be investigated for their importance in medicinal chemistry.