Development, Use, and Validation of (Q)SARs for Predicting Genotoxicity and Carcinogenicity: Experiences from Italian National Institute of Health Activities.

In silico approaches are now increasingly accepted in several areas of toxicology to rapidly assess chemical hazard without the need for animal testing. Among in silico tools, quantitative and qualitative structure-activity approaches ((Q)SARs) are the most typically applied methods to predict hazard in the absence of experimental data. This paper provides an overview of different protocols that can be applied while dealing with (Q)SARs in different scenarios, namely, (Q)SAR development, use, and validation. Examples of protocols adopted in the three scenarios are reported, derived from the authors' experience in working at the Predictive Toxicology unit of the Italian National Institute of Health, focusing on the endpoints of carcinogenicity and genotoxicity.The illustrated activities are in line with the Institute's mission, the main center of research, control, and technical-scientific advice on public health in Italy.

Next Generation Risk Assessment approaches for advanced nanomaterials: Current status and future perspectives.

This manuscript discusses the challenges of applying New Approach Methodologies (NAMs) for safe by design and regulatory risk assessment of advanced nanomaterials (AdNMs). The authors propose a framework for Next Generation Risk Assessment of AdNMs involving NAMs that is aligned to the conventional risk assessment paradigm. This framework is exposure-driven, endpoint-specific, makes best use of pre-existing information, and can be implemented in tiers of increasing specificity and complexity of the adopted NAMs. The tiered structure of the approach, which effectively combines the use of existing data with targeted testing will allow safety to be assessed cost-effectively and as far as possible with even more limited use of vertebrates. The regulatory readiness of state-of-the-art emerging NAMs is assessed in terms of Transparency, Reliability, Accessibility, Applicability, Relevance and Completeness, and their appropriateness for AdNMs is discussed in relation to each step of the risk assessment paradigm along with providing perspectives for future developments in the respective scientific and regulatory areas.

In Silico and Chromatographic Methods for Analysis of Biotransformation of Prospective Neuroprotective Pyrrole-Based Hydrazone in Isolated Rat Hepatocytes.

In the current study, chromatographic and in silico techniques were applied to investigate the biotransformation of ethyl 5-(4-bromophenyl)-1-(2-(2-(2-hydroxybenzylidene) hydrazinyl)-2-oxoethyl)-2-methyl-1H-pyrrole-3-carboxylate (11b) in hepatocytic media. The initial chromatographic procedure was based on the employment of the conventional octadecyl stationary phase method for estimation of the chemical stability. Subsequently, a novel and rapid chromatographic approach based on a phenyl-hexyl column was developed, aiming to separate the possible metabolites. Both methods were performed on a Dionex 3000 ThermoScientific (ACM 2, Sofia, Bulgaria) device equipped with a diode array detector set up at 272 and 279 nm for analytes detection. An acetonitrile: phosphate buffer of pH 3.5: methanol (17:30:53 v/v/v) was eluted isocratically as a mobile phase with a 1 mL/min flow rate. A preliminary purification from the biological media was achieved by protein precipitation with methanol. A validation procedure was carried out, where the method was found to correspond to all ICH (Q2) and M10 set criteria. Additionally, an in silico-based approach with the online server BioTransformer 3.0 was applied in an attempt to predict the possible metabolites of the title compound 11b. It was hypothesized that four CYP450 isoforms (1A2, 2C9, 3A4, and 2C8) were involved in the phase I metabolism, resulting in the formation of 12 metabolites. Moreover, docking studies were conducted to evaluate the formation of stable complexes between 11b and the aforementioned isoforms. The obtained data indicated three metabolites as the most probable products, two of which (M9_11b and M10_11b) were synthesized by a classical approach for verification. Finally, liquid chromatography with a mass detector was implemented for comprehensive and summarized analysis, and the obtained results revealed that the metabolism of the 11b proceeds possibly with the formation of glucuronide and glycine conjugate of M11_11b.

A quantitative systems pharmacology workflow toward optimal design and biomarker stratification of atopic dermatitis clinical trials.

BACKGROUND: The development of atopic dermatitis (AD) drugs is challenged by many disease phenotypes and trial design options, which are hard to explore experimentally. OBJECTIVE: We aimed to optimize AD trial design using simulations. METHODS: We constructed a quantitative systems pharmacology model of AD and standard of care (SoC) treatments and generated a phenotypically diverse virtual population whose parameter distribution was derived from known relationships between AD biomarkers and disease severity and calibrated using disease severity evolution under SoC regimens. RESULTS: We applied this workflow to the immunomodulator OM-85, currently being investigated for its potential use in AD, and calibrated the investigational treatment model with the efficacy profile of an existing trial (thereby enriching it with plausible marker levels and dynamics). We assessed the sensitivity of trial outcomes to trial protocol and found that for this particular example the choice of end point is more important than the choice of dosing regimen and patient selection by model-based responder enrichment could increase the expected effect size. A global sensitivity analysis revealed that only a limited subset of baseline biomarkers is needed to predict the drug response of the full virtual population. CONCLUSIONS: This AD quantitative systems pharmacology workflow built around knowledge of marker-severity relationships as well as SoC efficacy can be tailored to specific development cases to optimize several trial protocol parameters and biomarker stratification and therefore has promise to become a powerful model-informed AD drug development and personalized medicine tool.

In silico exploration of hypothetical proteins in Neisseria gonorrhoeae for identification of therapeutic targets.

Neisseria gonorrhoeae, a World Health Organization (WHO) declared superbug and the second-most frequent cause of bacterial sexually transmitted infections worldwide is responsible for gonorrhea. Hypothetical proteins are gene products that are predicted to be encoded by a particular gene based on the DNA sequence, but their specific functions and characteristics have not been experimentally determined or verified. In the context of this research, annotating hypothetical proteins is crucial for identifying their potential as therapeutic targets. Without proper annotation, these proteins would remain vague, hindering efforts to understand their roles in disease. The methodology used aims to bridge this gap by employing algorithm-based tools and software to annotate hypothetical proteins and assess their suitability as therapeutic targets based on factors such as essentiality, virulence, subcellular localization, and druggability. Out of 716 N. gonorrhoeae hypothetical proteins reported in UniProt, assessment of crucial pathogenic factors, including essentiality, virulence, subcellular localization, and druggability, effectively filtered and prioritized the hypothetical proteins for further therapeutic exploration and lead to 5 proteins being chosen as targets. The molecular docking studies conducted identified 10 hits targeting the five targets. Conclusively, this study aided in identification of targets and hit compounds for therapeutic targeting of gonorrhea disease. Supplementary Information: The online version contains supplementary material available at 10.1007/s40203-023-00186-w.

Multilayer precision-based screening of potential inhibitors targeting Mycobacterium tuberculosis acetate kinase using in silico approaches.

Tuberculosis (TB), caused by Mycobacterium tuberculosis (MTB), remains a major threat to global health, with the emergence of multi-drug and extensively drug-resistant strains posing a serious challenge. Thereby, understanding the molecular basis of MTB virulence and disease pathogenesis is critical for developing effective therapeutic strategies. Targeting proteins involved in central metabolism has been recognized as a promising therapeutic approach to combat MTB. In this regard, the enzyme AckA of the acetate metabolic pathway which produces acetate from acetyl phosphate, is an important drug target for various pathogenic organisms. Therefore, this study aimed to identify potential AckA inhibitors through in silico methods, including molecular modeling, molecular dynamics simulation (MDS), and high-throughput virtual screening (HTVS) followed by ADMETox, MMGBSA, Density Functional Theory (DFT) calculations. HTVS of one million compounds from the ZINC database against AckA resulted in the top five hits (ZINC82048449, ZINC1219737510, ZINC1771921358, ZINC119699567, and ZINC1427100376) with better binding affinity and optimal binding free energy. MDS studies on complexes revealed that key residues, Asn195, Asp266, Phe267, Gly314, and Asn318 played a significant role in stable interactions of the top-ranked compounds to AckA. These outcomes provide insights into the optimal binding of the leads to inhibit the acetate pathway and aid in the rational design of novel therapeutic agents. Thus, the identified leads may act as promising compounds for targeting AckA and may serve as a potential therapeutic modality for treating TB. Our findings offer valuable insights into the inhibition of the acetate pathway, while also serving as a blueprint for rational drug design. The identified leads hold promise as compelling compounds for targeting AckA, thereby offering a potential therapeutic avenue for tackling TB. Thus, our study uncovers a pathway toward promising TB therapeutics by elucidating AckA inhibitors. By leveraging in silico methodologies, potent compounds that hold the potential to thwart AckA's role in MTB's acetate pathway have been unveiled. This breakthrough fosters optimism in the quest for novel and effective TB treatments, addressing a global health challenge with renewed vigor.

Novel Lactone-Based Insecticides and Drosophila suzukii Management: Synthesis, Potential Action Mechanisms and Selectivity for Non-Target Parasitoids.

Drosophila suzukii, an invasive insect pest, poses a significant threat to various fruit crops. The use of broad-spectrum insecticides to control this pest can reduce the effectiveness of biological control agents, such as the parasitoid Trichopria anastrephae. Here, we evaluated the toxicity of newly synthesized lactone derivatives on D. suzukii and their selectivity towards T. anastrephae. We used in silico approaches to identify potential targets from the most promising molecules in the D. suzukii nervous system and to understand potential differences in susceptibilities between D. suzukii and its parasitoid. Of the nine molecules tested, (rac)-8 and compound 4 demonstrated efficacy against the fly. Exposure to the estimated LC90 of (rac)-8 and compound 4 resulted in a mortality rate of less than 20% for T. anastrephae without impairing the parasitoid's functional parasitism. The in silico predictions suggest that (rac)-8 and compound 4 target gamma amino butyric acid (GABA) receptors and transient receptor potential (TRP) channels of D. suzukii. However, only the reduced interaction with TRP channels in T. anastrephae demonstrated a potential reason for the selectivity of these compounds on the parasitoid. Our findings suggest the potential for integrating (rac)-8 and compound 4 into D. suzukii management practices.

Next generation risk assessment of human exposure to estrogens using safe comparator compound values based on in vitro bioactivity assays.

In next generation risk assessment (NGRA), the Dietary Comparator Ratio (DCR) can be used to assess the safety of chemical exposures to humans in a 3R compliant approach. The DCR compares the Exposure Activity Ratio (EAR) for exposure to a compound of interest (EARtest) to the EAR for an established safe exposure level to a comparator compound (EARcomparator), acting by the same mode of action. It can be concluded that the exposure to a test compound is safe at a corresponding DCR ≤ 1. In this study, genistein (GEN) was selected as a comparator compound by comparison of reported safe internal exposures to GEN to its BMCL05, as no effect level, the latter determined in the in vitro estrogenic MCF7/Bos proliferation, T47D ER-CALUX, and U2OS ERα-CALUX assay. The EARcomparator was defined using the BMCL05 and EC50 values from the 3 in vitro assays and subsequently used to calculate the DCRs for exposures to 14 test compounds, predicting the (absence of) estrogenicity. The predictions were evaluated by comparison to reported in vivo estrogenicity in humans for these exposures. The results obtained support in the DCR approach as an important animal-free new approach methodology (NAM) in NGRA and show how in vitro assays can be used to define DCR values.

Computational tools for exploring peptide-membrane interactions in gram-positive bacteria.

The vital cellular functions in Gram-positive bacteria are controlled by signaling molecules known as quorum sensing peptides (QSPs), considered promising therapeutic interventions for bacterial infections. In the bacterial system QSPs bind to membrane-coupled receptors, which then auto-phosphorylate and activate intracellular response regulators. These response regulators induce target gene expression in bacteria. One of the most reliable trends in drug discovery research for virulence-associated molecular targets is the use of peptide drugs or new functionalities. In this perspective, computational methods act as auxiliary aids for biologists, where methodologies based on machine learning and in silico analysis are developed as suitable tools for target peptide identification. Therefore, the development of quick and reliable computational resources to identify or predict these QSPs along with their receptors and inhibitors is receiving considerable attention. The databases such as Quorumpeps and Quorum Sensing of Human Gut Microbes (QSHGM) provide a detailed overview of the structures and functions of QSPs. The tools and algorithms such as QSPpred, QSPred-FL, iQSP, EnsembleQS and PEPred-Suite have been used for the generic prediction of QSPs and feature representation. The availability of compiled key resources for utilizing peptide features based on amino acid composition, positional preferences, and motifs as well as structural and physicochemical properties, including biofilm inhibitory peptides, can aid in elucidating the QSP and membrane receptor interactions in infectious Gram-positive pathogens. Herein, we present a comprehensive survey of diverse computational approaches that are suitable for detecting QSPs and QS interference molecules. This review highlights the utility of these methods for developing potential biomarkers against infectious Gram-positive pathogens.

Investigating the adverse outcome pathways (AOP) of neurotoxicity induced by DBDPE with a combination of in vitro and in silico approaches.

Current studies have shown an association between DBDPE and neurotoxicity. In this study, the adverse outcome pathway (AOP) and mechanistic analysis of DBDPE-induced neurotoxicity were explored by a combination of in vitro and in silico approaches in SK-N-SH cells. DBDPE-induced oxidative stress caused DNA strand breaks, resulting in the activation of poly (ADP-ribose) (PAR) polymerase-1 (PARP-1). Activation of PARP1 could cause toxic damage in various organ systems, especially in the nervous system. DBDPE-induced apoptosis via the caspase-dependent intrinsic mitochondrial pathway and the PARP1-dependent pathway. Activation of PARP1 by DBDPE was deemed the initiating event, thereby affecting the key downstream biochemical events (e.g., ROS production, DNA damage, membrane potential changes, and ATP reduction), which induced apoptosis. Furthermore, excessive activation of PARP1 was accompanied by the translocation of the apoptosis-inducing factor (AIF), which was associated with PARP1-dependent cell death. The inhibition of PARP1 by PJ34 reduced DBDPE-induced apoptosis and maintained cellular ATP levels. PJ34 also prevented the translocation of AIF from the mitochondria to the nucleus. These findings improve the understanding of the mechanism of DBDPE-induced neurotoxic effects and provide a theoretical basis for the ecological risk of DBDPE.

Next Generation Risk Assessment of the Anti-Androgen Flutamide Including the Contribution of Its Active Metabolite Hydroxyflutamide.

In next generation risk assessment (NGRA), non-animal approaches are used to quantify the chemical concentrations required to trigger bioactivity responses, in order to assure safe levels of human exposure. A limitation of many in vitro bioactivity assays, which are used in an NGRA context as new approach methodologies (NAMs), is that toxicokinetics, including biotransformation, are not adequately captured. The present study aimed to include, as a proof of principle, the bioactivity of the metabolite hydroxyflutamide (HF) in an NGRA approach to evaluate the safety of the anti-androgen flutamide (FLU), using the AR-CALUX assay to derive the NAM point of departure (PoD). The NGRA approach applied also included PBK modelling-facilitated quantitative in vitro to in vivo extrapolation (QIVIVE). The PBK model describing FLU and HF kinetics in humans was developed using GastroPlus™ and validated against human pharmacokinetic data. PBK model-facilitated QIVIVE was performed to translate the in vitro AR-CALUX derived concentration-response data to a corresponding in vivo dose-response curve for the anti-androgenicity of FLU, excluding and including the activity of HF (-HF and +HF, respectively). The in vivo benchmark dose 5% lower confidence limits (BMDL05) derived from the predicted in vivo dose-response curves for FLU, revealed a 440-fold lower BMDL05 when taking the bioactivity of HF into account. Subsequent comparison of the predicted BMDL05 values to the human therapeutic doses and historical animal derived PoDs, revealed that PBK modelling-facilitated QIVIVE that includes the bioactivity of the active metabolite is protective and provides a more appropriate PoD to assure human safety via NGRA, whereas excluding this would potentially result in an underestimation of the risk of FLU exposure in humans.

Repurposing FDA approved drugs as possible anti-SARS-CoV-2 medications using ligand-based computational approaches: sum of ranking difference-based model selection.

The worldwide burden of coronavirus disease 2019 (COVID-19) is still unremittingly prevailing, with more than 440 million infections and over 5.9 million deaths documented so far since the SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2) pandemic. The non-availability of treatment further aggravates the scenario, thereby demanding the exploration of pre-existing FDA-approved drugs for their effectiveness against COVID-19. The current research aims to identify potential anti-SARS-CoV-2 drugs using a computational approach and repurpose them if possible. In the present study, we have collected a set of 44 FDA-approved drugs of different classes from a previously published literature with their potential antiviral activity against COVID-19. We have employed both regression- and classification-based quantitative structure-activity relationship (QSAR) modeling to identify critical chemical features essential for anticoronaviral activity. Multiple models with the consensus algorithm were employed for the regression-based approach to improve the predictions. Additionally, we have employed a machine learning-based read-across approach using Read-Across-v3.1 available from https://sites.google.com/jadavpuruniversity.in/dtc-lab-software/home and linear discriminant analysis for the efficient prediction of potential drug candidate for COVID-19. Finally, the quantitative prediction ability of different modeling approaches was compared using the sum of ranking differences (SRD). Furthermore, we have predicted a true external set of 98 pharmaceuticals using the developed models for their probable anti-COVID activity and their prediction reliability was checked employing the "Prediction Reliability Indicator" tool available from https://dtclab.webs.com/software-tools. Though the present study does not target any protein of viral interaction, the modeling approaches developed can be helpful for identifying or screening potential anti-coronaviral drug candidates. Supplementary information: The online version contains supplementary material available at 10.1007/s11224-022-01975-3.

Challenges in Serologic Diagnostics of Neglected Human Systemic Mycoses: An Overview on Characterization of New Targets.

Systemic mycoses have been viewed as neglected diseases and they are responsible for deaths and disabilities around the world. Rapid, low-cost, simple, highly-specific and sensitive diagnostic tests are critical components of patient care, disease control and active surveillance. However, the diagnosis of fungal infections represents a great challenge because of the decline in the expertise needed for identifying fungi, and a reduced number of instruments and assays specific to fungal identification. Unfortunately, time of diagnosis is one of the most important risk factors for mortality rates from many of the systemic mycoses. In addition, phenotypic and biochemical identification methods are often time-consuming, which has created an increasing demand for new methods of fungal identification. In this review, we discuss the current context of the diagnosis of the main systemic mycoses and propose alternative approaches for the identification of new targets for fungal pathogens, which can help in the development of new diagnostic tests.

Recent Fabrication Methods to Produce Polymer-Based Drug Delivery Matrices (Experimental and In Silico Approaches).

The study of novel drug delivery systems represents one of the frontiers of the biomedical research area. Multi-disciplinary scientific approaches combining traditional or engineered technologies are used to provide major advances in improving drug bioavailability, rate of release, cell/tissue specificity and therapeutic index. Biodegradable and bio-absorbable polymers are usually the building blocks of these systems, and their copolymers are employed to create delivery components. For example, poly (lactic acid) or poly (glycolic acid) are often used as bricks for the production drug-based delivery systems as polymeric microparticles (MPs) or micron-scale needles. To avoid time-consuming empirical approaches for the optimization of these formulations, in silico-supported models have been developed. These methods can predict and tune the release of different drugs starting from designed combinations. Starting from these considerations, this review has the aim of investigating recent approaches to the production of polymeric carriers and the combination of in silico and experimental methods as promising platforms in the biomedical field.

Prevalence and antimicrobial resistance profiling of Staphylococcus aureus isolated from traditional cheese in Yunnan, China.

The prevalence of staphylococcal infection and the emergence of multidrug resistance of Staphylococcus aureus (S. aureus) are major concerns in food safety and public health. This study aimed to investigate the prevalence of S. aureus isolated from traditional Chinese Rubing and Rushan cheese, antimicrobial resistance profiles, genomic characteristics, and predict antimicrobial resistance genes (ARGs). From 124 samples, 18 of 62 (29.03%) of Rubing and 5 of 62 (8.06%) of Rushan cheese were confirmed to be S. aureus positive by standard culture-based methods. Twenty-three coagulase-positive staphylococci isolates were grouped into 16 clusters by pulsed-field gel electrophoresis and subjected to routine susceptibility testing to 12 antibiotics. Those isolates exhibited high resistance to penicillin (100%), erythromycin, trimethoprim-sulphamethoxazole (34.78%), oxacillin, clindamycin, and cefoxitin (21.74%). Multidrug-resistant (MDR) S. aureus was found in 34.78% (8 of 23) of isolates. Further, S. aureus strain DC.RB_015 isolated from Rubing cheese, recognized as the most resistant to six antibiotics, was selected for whole-genome sequencing (WGS), continued with in silico approaches. S. aureus DC.RB_015 had a single chromosome size of 2,794,578 bp and a plasmid size of 22,961 bp. The strain harbored 18 predicted ARGs, including eight efflux pump genes (mepA, tet(K), arlR, arlS, norA, mgrA, tet(38), LmrS), one peptidoglycan biosynthesis gene (bacA), two ß-lactams resistance genes (mecA, blaZ), and seven genes conferring other antimicrobial resistance (APH(3')-IIIa, aad(6), ErmB, SAT-4, mecR1, GlpT, murA). The results of this study expand the knowledge of S. aureus strain DC.RB_015, increase food safety awareness, and will be helpful in establishing therapeutic therapy. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s13205-021-03072-4.

In silico proteolysis and analysis of bioactive peptides from sequences of fatty acid desaturase 3 (FAD3) of flaxseed protein.

Flaxseed (Linum usitatissimum), commonly known as linseed is an oilseed crop, emerging as an important and functional ingredient of food and has been paid more attention due to its nutritional value as well as beneficial effects. It is mainly rich in is α-linolenic acid (ALA, omega-3 fatty acid), fibres and lignans that have potential health benefits in reducing cardiovascular diseases, diabetes, osteoporosis, atherosclerosis, cancer, arthritis, neurological and autoimmune disorders. Due to its richness in omega-3 fatty acid, a group of enzymes known as fatty acid desaturases (FADs) mainly introduce double bonds into fatty acids' (FAs) hydrocarbon chains that produce unsaturated fatty acids. Fatty acid desaturase 3 (FAD3), the commonest microsomal enzyme of omega-3 fatty acid, synthesizes linolenic acid (C18:3) from linoleic acid located in endoplasmic reticulum (ER) facing towards the cytosol. The emerging field of bioinformatics and large number of databases of bioactive peptides, helps in providing time-saving and efficient method for identification of potential bioactivities of any protein. In this study, 10 unique sequences of FAD3 from flaxseed protein have been used for in silico proteolysis and releasing of various bioactive peptides using three plant proteases, namely ficin, papain and stem bromelain, that are evaluated with the help of BIOPEP database. Overall, 20 biological activities were identified from these proteins. The results showed that FAD3 protein is a potential source of peptides with angiotensin-I-converting enzyme (ACE) inhibitory and dipeptidyl peptidase-IV (DPP-IV) activities, and also various parameters such as ∑A, ∑B, AE, W, BE, V and DHt were also calculated. Furthermore, PeptideRanker have been used for screening of novel promising bioactive peptides. Various bioinformatics tools also used to study protein's physicochemical properties, peptide's score, toxicity, allergenicity aggregation, water solubility, and drug likeliness. The present work suggests that flaxseed protein can be a good source of bioactive peptides for the synthesis of good quality and quantity of oil, and in silico method helps in investigating and production of functional peptides.

Next generation risk assessment of human exposure to anti-androgens using newly defined comparator compound values.

Next Generation Risk Assessment (NGRA) can use the so-called Dietary Comparator Ratio (DCR) to evaluate the safety of a defined exposure to a compound of interest. The DCR compares the Exposure Activity Ratio (EAR) for the compound of interest, to the EAR of an established safe level of human exposure to a comparator compound with the same putative mode of action. A DCR ≤ 1 indicates the exposure evaluated is safe. The present study aimed at defining adequate and safe comparator compound exposures for evaluation of anti-androgenic effects, using 3,3-diindolylmethane (DIM), from cruciferous vegetables, and the anti-androgenic drug bicalutamide (BIC). EAR values for these comparator compounds were defined using the AR-CALUX assay. The adequacy of the new comparator EAR values was evaluated using PBK modelling and by comparing the generated DCRs of a series of test compound exposures to actual knowledge on their safety regarding in vivo anti-androgenicity. Results obtained supported the use of AR-CALUX-based comparator EARs for DCR-based NGRA for putative anti-androgenic compounds. This further validates the DCR approach as an animal free in silico/in vitro 3R compliant method in NGRA.

Sexually Transmitted Neisseria gonorrhoeae Infections-Update on Drug Treatment and Vaccine Development.

Background: Sexually transmitted gonorrhea, caused by the Gram-negative diplococcus Neisseria gonorrhoeae, continues to be a serious global health challenge despite efforts to eradicate it. Multidrug resistance among clinical N. gonorrhoeae isolates has limited treatment options, and attempts to develop vaccines have not been successful. Methods: A search of published literature was conducted, and information extracted to provide an update on the status of therapeutics and vaccine development for gonorrheal infection. Results: Recommended pharmacological treatment for gonorrhea has changed multiple times due to increasing acquisition of resistance to existing antibiotics by N. gonorrhoeae. Only broad-spectrum cephalosporin-based combination therapies are currently recommended for treatment of uncomplicated urogenital and anorectal gonococcal infections. With the reported emergence of ceftriaxone resistance, successful strategies addressing the global burden of gonorrhea must include vaccination. Century-old efforts at developing an effective vaccine against gonorrhea, leading to only four clinical trials, have not yielded any successful vaccine. Conclusions: While it is important to continue to explore new drugs for the treatment of gonorrhea, the historical trend of resistance acquisition suggests that any long-term strategy should include vaccine development. Advanced technologies in proteomics and in silico approaches to vaccine target identification may provide templates for future success.

Antiviral Peptides: Identification and Validation.

Despite rapid advances in the human healthcare, the infection caused by certain viruses results in high morbidity and mortality accentuate the importance for development of new antivirals. The existing antiviral drugs are limited, due to their inadequate response, increased rate of resistance and several adverse side effects. Therefore, one of the newly emerging field "peptide-based therapeutics" against viruses is being explored and seems promising. Over the last few years, a lot of scientific effort has been made for the identification of novel and potential peptide-based therapeutics using various advanced technologies. Consequently, there are more than 60 approved peptide drugs available for sale in the market of United States, Europe, Japan, and some Asian countries. Moreover, the number of peptide drugs undergoing the clinical trials is rising gradually year by year. The peptide-based antiviral therapeutics have been approved for the Human immunodeficiency virus (HIV), Influenza virus and Hepatitis virus (B and C). This review enlightens the various peptide sources and the different approaches that have contributed to the search of potential antiviral peptides. These include computational approaches, natural and biological sources (library based high throughput screening) for the identification of lead peptide molecules against their target. Further the applications of few advanced techniques based on combinatorial chemistry and molecular biology have been illustrated to measure the binding parameters such as affinity and kinetics of the screened interacting partners. The employment of these advanced techniques can contribute to investigate antiviral peptide therapeutics for emerging infections.

Suramin could block the activity of Arabinono-1, 4-lactone oxidase enzyme from Leishmania donovani: structure-based screening and molecular dynamics analyses.

BACKGROUND: Leishmania donovani, a parasitic protozoan causing visceral leishmaniasis, can lead to a dangerous and often fatal disease in humans. Current treatment for leishmaniasis may have severe side effects, low efficacy and high cost, hence an immediate need for new efficient drugs is essential. Arabinono-1, 4-lactone oxidase enzyme from Leishmania donovani (LdALO), which catalyzes the last step of the ascorbate biosynthesis pathway, has been considered as a potential target for antileishmanial drugs design. METHODS: The current study was performed with an in silico approach to predict novel inhibitory molecules against the LdALO enzyme. Various modeling and refinement processes were employed to obtain a reliable 3D structure. RESULTS: The best LdALO model with the highest qualitative model energy analysis score was predicted by the Robetta server and subsequently refined by 3D refine and ModLoop servers. The high quality of the final LdALO model was confirmed using model assessment software. Based on docking analysis results, we predicted 10 inhibitory molecules of a US Food and Drug Administration-approved library, with appropriate criteria regarding energy binding and interaction with the main functionally active sites of LdALO, indicating that they could be significant targets for further drug design investigations against L. donovani. CONCLUSION: Suramin is used to treat the first stage of African sleeping sickness and its mechanism of action is unknown. Our results showed that suramin was the best-predicted inhibitor compound for LdALO enzyme activity.