ADDME--Avoiding Drug Development Mistakes Early: central nervous system drug discovery perspective

Discovery of ROCK2 inhibitors through computational screening of ZINC database: Integrating pharmacophore modeling, molecular docking, and MD simulations

Rho-associated protein kinase 2 (ROCK2) is a serine/threonine kinase that is crucial for regulating various physiological processes and is part of the Rho-associated coiled-coil kinase family. The dysregulation of ROCK2 has been associated with a range of diseases, making it a promising target for therapy. In this study, a chemical feature-based pharmacophore model was developed on the co-crystal ligand (5YS) of ROCK2 to conduct the virtual screening of ZINC database, resulting in 4809 hits that were further subjected to molecular docking to find the binding affinities with ROCK2 protein. The binding affinities of the hits were analyzed and compounds in the range of -11.55 to -9.91 kcal/mol were selected for further analysis. The ADMET analysis identified two promising compounds, whose binding stability with the ROCK2 protein was further evaluated using molecular dynamics (MD) simulations. Simulation results revealed that the selected compounds remained closely bound to protein indicating that they can act as lead compounds to control the biological activity of ROCK2. However, further in vitro investigation is required to test the biological efficacy of the reported compounds.

Identification of SIRT3 modulating compounds in deep-sea fungi metabolites: Insights from molecular docking and MD simulations

SIRT3, a crucial deacetylase that plays a key role in regulating mitochondrial acetylation, is tightly linked to metabolic processes and is essential for the maintenance of eukaryotic life. SIRT3 is a potential therapeutic target due to its key role in various diseases, including ageing, heart disease, cancer, and metabolic disorders. In this work, we aimed to identify potential SIRT3 inhibitors from the deep-sea fungal metabolites by employing molecular docking and ADMET analysis. Based on the binding affinities, ten compounds were selected whose docking scores were in the range of -9.693 to -8.327 kcal/mol. Further, four compounds Penipanoid C, Penicillactam, Quinolonimide, and Brevianamide R were selected based on the ADMET properties and subjected to Molecular dynamics simulations to assess the stability of these molecules with target. The stability analysis indicated that the selected compounds could act as lead compounds during in vitro assays to advance these drug candidates towards clinical drug development.

Medicinal Phytocompounds as Potential Inhibitors of p300-HIF1α Interaction: A Structure-Based Screening and Molecular Dynamics Simulation Study

Background: Hypoxia plays a key role in cancer progression, mainly by stabilizing and activating hypoxia-inducible factor-1 (HIF-1). For HIF-1 to function under low oxygen conditions, it must interact with the transcriptional coactivator p300, a critical step for promoting cancer cell survival and adaptation in hypoxic environments. Methods: Consequently, we used drug design and molecular simulation techniques to screen phytochemical databases, including traditional Chinese and African medicine sources, for compounds that could disrupt the p300/HIF-1 interaction. Results: In this study, we identified potential compounds with high docking scores such as EA-176920 (-8.719), EA-46881231 (-8.642), SA-31161 (-9.580), SA-5280863 (-8.179), NE-5280362 (-10.287), NE-72276 (-9.017), NA-11210533 (-10.366), NA-11336960 (-7.818), TCM-5281792 (-12.648), and TCM-6441280 (-9.470 kcal/mol) as lead compounds. Furthermore, the compound with the highest docking score from each database (EA-176920, SA-31161, NE-5280362, NA-11210533, and TCM-5281792) was subjected to further analysis. The stable binding affinity of these compounds with p300 was confirmed by Post-simulation binding free energy (-22.0020 kcal/mol, -25.4499 kcal/mol, -32.4530 kcal/mol, -33.9918 kcal/mol, and -57.7755 kcal/mol, respectively) and KD analysis. Moreover, the selected compounds followed the Lipinski rules with favorable ADMET properties like efficient intestinal absorption, high water solubility, and no toxicity. Conclusions: Our findings highlight the potential of natural compounds to target key protein-protein interactions in cancer and lay the groundwork for future in vitro and in vivo studies to explore their therapeutic potential. Specifically, disrupting the p300/HIF-1 interaction could interfere with hypoxia-driven pathways that promote tumor growth, angiogenesis, and metastasis, offering a promising strategy to suppress cancer progression at the molecular level.

Exploring global natural product databases for NLRP3 inhibition: Unveiling novel combinatorial therapeutic strategy for hidradenitis suppurativa

BACKGROUND

Hidradenitis suppurativa (HS) is a chronic inflammatory skin condition of the terminal hair follicle, which can present in sporadic, familial, or syndromic forms. The exact pathogenesis of HS remains elusive, posing a challenge for the development of effective treatments. Among the various immunological mechanisms, the NLRP3 inflammasome is thought to contribute to the pathogenesis of HS, releasing cytokines such as IL-1β and IL-18 which initiates and exacerbates inflammation. Consequently, targeting NLRP3 offers a potential strategy for mitigating inflammation in HS-affected skin.

METHODS

In this study we used the docking, molecular dynamics simulation and binding free energy approaches to identify the potent inhibitor of NLRP3 by screening the African phytocompounds and traditional Chinese medicine databases.

RESULTS

Our virtual drug screening analysis identified two lead compounds from each database, characterized by high docking scores such as SA-21676268 (-8.135 kcal/mol), SA-167673 (-10.251 kcal/mol), EA-45360194 (-10.376 kcal/mol), EA-46881231 (-10.011 kcal/mol), NEA-44258150 (-9.856 kcal/mol), NEA-135926572 (-7.662 kcal/mol), NA-163089376 (-9.237 kcal/mol), NA-440735 (-8.826 kcal/mol), TCM-392442 (-10.438 kcal/mol), and TCM-10043097 (-9.046 kcal/mol) which highlighted the strong binding affinity as compared to the control NP3-146 drug (-5.09 kcal/mol). Moreover, the values of dissociation constant further validated the strong binding affinity between the identified lead compounds and NLRP3. The dynamic stability and strong bonding energies of the lead compounds-NLRP3 complexes were confirmed by the molecular dynamic simulation and binding free energy calculation. The analysis of ADMET properties for all compounds indicated high intestinal absorption, water solubility, absence of hepatotoxicity, and skin sensitivity.

CONCLUSION

In conclusion, our molecular simulations and binding free energy calculations confirmed the strong affinity of these lead compounds for NLRP3 as compared to the control drug, highlighting their potential as part of a combinatorial therapeutic strategy for HS to effectively reduce disease-related inflammation.

Molecular docking and ADMET studies of halogenated metabolites from marine sponges as inhibitors of wild-type and mutants PBP2 in Neisseria gonorrhoeae

Gonorrhoea is a sexually transmitted infection (STI) caused by the bacteria Neisseria gonorrhoeae. Gonorrhoea symptoms can vary, although roughly 50% of women and 10% of men infected with N. gonorrhoeae may be asymptomatic. If left untreated, gonorrhoea can cause major health problems. However, no effective treatment or vaccination is currently available. The enzyme penicillin-binding protein 2 (PBP2) is necessary for cell wall synthesis during N. gonorrhoeae cell growth. The goal of this study is to investigate the molecular interactions of three PBP2 variants with halogenated marine sponge metabolites using molecular docking, molecular dynamic simulation, and ADMET analysis. The docking findings were evaluated using the glide gscore, and the top 20 compounds docked against each PBP2 protein receptor were chosen. Furthermore, the selected compounds underwent ADMET analysis, indicating that they have the potential for therapeutic development. Among the selected compounds, Bromoageliferin had the highest affinity for PBP2, Psammaplysin E for the penicillin-resistant variation of PBP2 protein, and Preaxinellamine for the cephalosporin-resistant variant of PBP2 protein. Additionally, MM-GBSA binding free energy and molecular dynamics simulations were used to support the docking investigations. The results of the study suggest that these compounds may eventually be used to treat gonorrhoea. However, computer validations were included in this study, and more in-vitro research is required to turn these prospective inhibitors into clinical drugs.Communicated by Ramaswamy H. Sarma.

A comprehensive review of artificial intelligence for pharmacology research

With the innovation and advancement of artificial intelligence, more and more artificial intelligence techniques are employed in drug research, biomedical frontier research, and clinical medicine practice, especially, in the field of pharmacology research. Thus, this review focuses on the applications of artificial intelligence in drug discovery, compound pharmacokinetic prediction, and clinical pharmacology. We briefly introduced the basic knowledge and development of artificial intelligence, presented a comprehensive review, and then summarized the latest studies and discussed the strengths and limitations of artificial intelligence models. Additionally, we highlighted several important studies and pointed out possible research directions.

Identification of echinoderm metabolites as potential inhibitors targeting wild-type and mutant forms of Escherichia coli RNA polymerase (RpoB) for tuberculosis treatment

Tuberculosis (TB) remains a critical global health challenge, with the emergence of drug-resistant strains heightening concerns. The development of effective drugs targeting both wild-type (WT) and mutant Escherichia coli RNA polymerase β subunit (RpoB) is crucial for global TB control, aiming to alleviate TB incidence, mortality, and transmission. This study employs molecular docking and ADMET analyses to screen echinoderm metabolites for their potential inhibition of Escherichia coli RNA polymerase, focusing on wild-type and mutant RpoB variants associated with TB drug resistance. The evaluation of docking results using the glide gscore led to the selection of the top 10 compounds for each protein receptor. Notably, CMNPD2176 demonstrated the highest binding affinity against wild-type RpoB, CMNPD13873 against RpoB D516V mutant, CMNPD2177 against RpoB H526Y mutant, and CMNPD11620 against RpoB S531L mutant. ADMET screening confirmed the therapeutic potential of these selected compounds. Additionally, MM-GBSA binding free energy calculations and molecular dynamics simulations provided further support for the docking investigations. While the results suggest these compounds could be viable for tuberculosis treatment, it is crucial to note that further in-vitro research is essential for the transition from prospective inhibitors to clinical drugs.

Z. A, Alhazzani K. Insilico exploration C. koseri ATP synthase inhibitors by pharmacophore-based virtual screening, molecular docking and MD simulation

Citrobacter koseri is a gram-negative rod that causes infections in people who have significant comorbidities and are immunocompromised. Antibiotic-resistant strains are becoming more common, which complicates infection treatment and highlights the need for innovative, effective drugs to fight these resistant strains. The enzyme complex ATP synthase participates in the adenosine triphosphate (ATP) synthesis, the fundamental energy currency of cells. This study used Computer-Aided Drug Design approaches to identify potential inhibitors of C. koseri ATP synthase. SWISS-MODEL was used to predict the 3D structure of C. koseri ATP synthase. A ligand-based pharmacophore model was developed using chemical features of ampicillin. Following ligand-based virtual screening across nine databases, the 2043 screened hits were docked to the ATP synthase active site using the standard precision mode of the glide tool. Based on their binding affinities, the top ten compounds were selected for additional investigation. The binding affinities of the chosen compounds ranged from -10.021 to -8.452 kcal/mol. The top four compounds (PubChem-25230613, PubChem-74936833, CHEMBL263035, PubChem-44208924) with the best ADMET characteristics and binding modes were chosen. Thus, the feasible binding mechanisms of the selected compounds were subjected to stability analysis using the MD Simulation study, which revealed the compounds' stability as potent inhibitors within the protein binding pocket. This computational approach provides important insights into the rational design of novel therapeutics and emphasizes the importance of targeting essential metabolic pathways when combating antibiotic-resistant pathogens. Future experimental validation and optimization of the identified inhibitors is required to determine their efficacy and safety profiles for clinical use.

Functional analysis, virtual screening, and molecular dynamics revealed potential novel drug targets and their inhibitors against cardiovascular disease in human

Cardiovascular disease (CVD) is a group of diseases, affecting the human heart and accounting for 30% of deaths worldwide. Major CVDs include heart failure, hypertension, stroke, etc. Various therapeutics are available against CVD, still there is a dire need to find out potential protein drug targets to reduce economic burden and mortality rate. Goal of the current study was to utilize sequential computational techniques to find the best cardiovascular drug targets and their inhibitors. Common human cardiovascular targets of both databases (GeneCards and Uniprot) were subjected to bioinformatics analyses. Purpose was to validate putative therapeutic targets employing the structure-based bioinformatics methods to determine their physiochemical properties and biological processes. Three stable proteins, that have 0 transmembrane helices, and possess biological processes were screened as potential protein-based therapeutic targets: Hemoglobin subunit beta (HBB), Gamma-enolase (ENO2), and Cholesteryl ester transfer protein (CETP). Tertiary structures of target proteins were retrieved from PDB, and molecular docking technique was utilized to evaluate a library of 5000 phytochemicals against the interacting residues of the target protein as well as their respective standard drugs through MOE and Pyrx software. Top five phytochemicals (d-Sesamin, 1,3-benzodioxole, Sativanone, Thiamine, and Cajanol) were identified based on their RMSD and docking scores as compared to their standard drugs. The docking studies were also validated by MM-GBSA binding free energy and molecular dynamics simulations. According to the study's findings, these phytochemicals may eventually be used as drugs to treat CVD. Further in vitro testing is required to confirm their efficacy and drug potency.Communicated by Ramaswamy H. Sarma.

Application of Generative Artificial Intelligence in Predicting Membrane Partitioning of Drugs: Combining Denoising Diffusion Probabilistic Models and MD Simulations Reduces the Computational Cost to One-Third

The optimal interaction of drugs with plasma membranes and membranes of subcellular organelles is a prerequisite for desirable pharmacology. Importantly, for drugs targeting the transmembrane lipid-facing sites of integral membrane proteins, the relative affinity of a drug to the bilayer lipids compared to the surrounding aqueous phase affects the partitioning, access, and binding of the drug to the target site. Molecular dynamics (MD) simulations, including enhanced sampling techniques such as steered MD, umbrella sampling (US), and metadynamics, offer valuable insights into the interactions of drugs with the membrane lipids and water in atomistic detail. However, these methods are computationally prohibitive for the high-throughput screening of drug candidates. This study shows that applying denoising diffusion probabilistic models (DDPMs), a generative AI method, to US simulation data reduces the computational cost significantly. Specifically, the models used only partial (one-third) data from the US simulations and reproduced the complete potential of mean force (PMF) profiles for three FDA-approved drugs (β2-adrenergic agonists) and ∼20 biologically relevant chemicals with known experimentally characterized bilayer locations. Intriguingly, the model can predict the solvation-free energies for partitioning and crossing the bilayer, preferred bilayer locations (low-energy well), and orientations of the ligands with high accuracy. The results indicate that DDPMs can be used to characterize the complete membrane partitioning profile of drug molecules using fewer umbrella sampling simulations at select positions along the bilayer normal (z-axis), irrespective of their amphiphilic-lipophilic-cephalophilic characteristics.

Deep-PK: deep learning for small molecule pharmacokinetic and toxicity prediction

Evaluating pharmacokinetic properties of small molecules is considered a key feature in most drug development and high-throughput screening processes. Generally, pharmacokinetics, which represent the fate of drugs in the human body, are described from four perspectives: absorption, distribution, metabolism and excretion-all of which are closely related to a fifth perspective, toxicity (ADMET). Since obtaining ADMET data from in vitro, in vivo or pre-clinical stages is time consuming and expensive, many efforts have been made to predict ADMET properties via computational approaches. However, the majority of available methods are limited in their ability to provide pharmacokinetics and toxicity for diverse targets, ensure good overall accuracy, and offer ease of use, interpretability and extensibility for further optimizations. Here, we introduce Deep-PK, a deep learning-based pharmacokinetic and toxicity prediction, analysis and optimization platform. We applied graph neural networks and graph-based signatures as a graph-level feature to yield the best predictive performance across 73 endpoints, including 64 ADMET and 9 general properties. With these powerful models, Deep-PK supports molecular optimization and interpretation, aiding users in optimizing and understanding pharmacokinetics and toxicity for given input molecules. The Deep-PK is freely available at https://biosig.lab.uq.edu.au/deeppk/.

Anticonvulsant effect of (±) citronellal possibly through the GABAergic and voltage-gated sodium channel receptor interaction pathways: In vivo and in silico studies

This study aimed to investigate the anticonvulsant effects of citronellal (CIT) and possible underlying mechanisms through an isoniazid (INH)-induced seizure (convulsion) via in vivo and in silico studies. For this, convulsions were induced by the oral administration of INH (300 mg/kg) to the mice. The animals were treated orally with different doses of CIT (50, 100, and 200 mg/kg). Vehicle served as a negative control (NC), while diazepam (DZP) (2 mg/kg) and carbamazepine (CAR) (80 mg/kg) were provided (p.o.) as positive controls (PC). A combination therapy of CIT (middle dose) with DZP and CAR was also given to two separate groups of animals to estimate the synergistic or antagonistic effects. Molecular docking and visualization of ligand-receptor interactions are also estimated through different computational tools. The results of the in vivo study showed that CIT dose-dependently significantly (p < 0.05) exhibited a higher onset of seizures while reducing the frequency and duration of seizures in mice compared to the NC group. Besides these, in combination therapy, CIT significantly antagonized the activity of CAR and DZP, leading to a reduction in the onset of seizures and an increase in their frequency and duration compared to treatment with CAR and DZP alone. Additionally, molecular docking revealed that the CIT exhibited a moderate binding affinity (-5.8 kcal/mol) towards the GABAA receptor and a relative binding affinity (-5.3 kcal/mol) towards the voltage-gated sodium channel receptor by forming several bonds. In conclusion, CIT showed moderate anticonvulsant activity in INH-induced convulsion animals, possibly by enhancing GABAA receptor activity and inhibiting the voltage-gated sodium channel receptor.

Parasitic Protozoans: Exploring the Potential of N,N'-Bis[2-(5-bromo-7-azabenzimidazol-1-yl)-2-oxoethyl]ethylene-1,3-Diamine and Its Cyclohexyl-1,2-diamine Analogue as TryR and Pf-DHODH Inhibitors

PURPOSE

Major human parasitic protozoans, such as Plasmodium falciparum and Trypanosoma brucei, cause malaria and trypanosomiasis also known as sleeping sickness. In anti-parasitic drug discovery research, trypanothione reductase (TryR) and P. falciparum dihydroorotate dehydrogenase (Pf-DHODH) enzymes are key drug targets in T. brucei and P. falciparum, respectively. The possibility of co-infection of single host by T. brucei and P. falciparum is because both parasites exist in sub-Saharan Africa and the problem of parasite drug resistance necessitates the discovery of new scaffolds, which are strange to the organisms causing these infectious diseases-new scaffolds may help overcome established resistance mechanisms of the organisms.

METHOD

In this study, N,N'-bis[2-(5-bromo-7-azabenzimidazol-1-yl)-2-oxoethyl]ethylene-1,3-diamine and its cyclohexyl-1,2-diamine analogue were explored for their inhibitory potential against TryR and Pf-DHODH by engaging density functional study, molecular dynamic simulations, drug-likeness, in silico and in vitro studies RESULTS/CONCLUSION: Results obtained indicated excellent binding potential of the ligands to the receptors and good ADMET (adsorption, desorption, metabolism, excretion, and toxicity) properties.

Structure-based virtual screening and molecular docking approaches to identify potential inhibitors against KIF2C to combat glioma

Glioma, a kind of malignant brain tumor, is extremely lethal. Kinesin family member 2C (KIF2C) was found to have an aberrant expression in several cancer types, including lung cancer and glioma. KIF2C may therefore be a useful therapeutic target for the treatment of glioma. In the current study, new drug candidates that may function as KIF2C enzyme inhibitors were discovered. MTi OpenScreen was used to carry out the structure-based virtual screening of an inbuilt drug library containing 150,000 compounds. These compounds belong to different classes, such as natural product-based compounds (NP-lib), purchasable approved drugs (Drugs-lib), and food constituents compound collection (FOOD-lib). Based on their binding affinities, a total of 84 compounds were further pushed to calculate ADMET properties. The compounds (16) meeting the ADMET cutoff ranges were then further docked to the receptor to find their plausible binding modes using the Glide tool's standard precision (SP) technique. The docking results were examined using the Glide gscore, and the best binding compounds (Rimacalib and Sarizotan) were chosen to test their stability with KIF2C protein through molecular dynamics (MD) simulation. Similarly, Principal Component Analysis and cross-correlation matrix were also examined. The MM/GBSA binding free energies showed a considerable energy contribution in the binding of hits with the KIF2C. Collectively, these findings strongly suggest the potential of the lead compounds to inhibit the biological function of KIF2C, emphasizing the need for further investigation in this area.Communicated by Ramaswamy H. Sarma.

Metabophore-mediated retro-metabolic ('MeMeReMe') approach in drug design

Preclinical toxicity assessments of new drugs require the use of in silico prediction techniques as ethics, cost, time, and complexity limit in vitro and in vivo methods. This review discusses the fundamental concepts of biophores especially toxicophores and their detection methodologies, tools and techniques, as well as ongoing challenges, and methods for overcoming them. This will guide the design community in manipulating lead compounds via a pre-determined pathway based on the MeMeReMe approach. The ideas discussed will be useful both for predicting toxicity and for de-risking leads through optimization.

A soft-computation hybrid method for search of the antibiotic-resistant gene in Mycobacterium tuberculosis for promising drug target identification and antimycobacterial lead discovery

Tuberculosis (TB) control programs were already piloted before the COVID-19 pandemic commenced and the global TB response was amplified by the pandemic. To combat the global TB epidemic, drug repurposing, novel drug discovery, identification and targeting of the antimicrobial resistance (AMR) genes, and addressing social determinants of TB are required. The study aimed to identify AMR genes in Mycobacterium tuberculosis (MTB) and a new anti-mycobacterial drug candidate. In this research, we used a few software to explore some AMR genes as a target protein in MTB and identified some potent antimycobacterial agents. We used Maestro v12.8 software, along with STRING v11.0, KEGG and Pass Server databases to gain a deeper understanding of MTB AMR genes as drug targets. Computer-aided analysis was used to identify mtrA and katG AMR genes as potential drug targets to depict some antimycobacterial drug candidates. Based on docking scores of -4.218 and -6.161, carvacrol was identified as a potent inhibitor against both drug targets. This research offers drug target identification and discovery of antimycobacterial leads, a unique and promising approach to combating the challenge of antibiotic resistance in Mycobacterium, and contributes to the development of a potential futuristic solution.

Acetylcholinesterase inhibition of Alzheimer's disease: identification of potential phytochemicals and designing more effective derivatives to manage disease condition

Alzheimer's disease (AD) is a brain disease characterized by gradual memory loss and cognitive impairments. Acetylcholinesterase (AChE) inhibitors-such as donepezil, memantine, and tacrine-are FDA-approved medications for AD treatment. Due to the lack of their efficacy and higher side effects, many researchers have been searching for effective and safer alternatives. In this study, experimentally proved phytochemicals against brain diseases were screened based on their binding energies to the target site of AChE, pharmacokinetic properties, and drug-likeness. Although some phytochemicals showed higher binding affinities than the control drug (donepezil), they did not show permeability across the blood-brain barrier (BBB). However, berberine, anthocyanin, and diterpene alkaloid can cross the BBB and showed good binding affinities of -10.3, -10.1, and -10.2 kcal/mol, respectively. MD simulation and PCA of the simulation data of the protein and protein-ligand complexes proved that the complexes are stable in the biological environment. A total of 16 derivatives of berberine and 3 derivatives of anthocyanin also showed higher binding energies compared to the binding affinity (-11.5 kcal/mol) of the donepezil. The derivatives were designed by substituting -F, -CF3, -CN, and -NH2, and provided higher docking scores due to increasing of nonbonding interactions. MM/GBSA calculations show that the binding free energies of the best predicted derivatives of diterpene alkaloid, anthocyanin, and berberine (DA22, AC11, and BB40) are -100.4 ± 8.4, -79.3 ± 8.7, and -78.3 ± 10.7 kcal/mol, respectively, with the protein. Overall, this study was successful in finding new, highly effective, and possibly safer inhibitors of AChE.Communicated by Ramaswamy H. Sarma.

Deciphering Nonbioavailable Substructures Improves the Bioavailability of Antidepressants by Serotonin Transporter

Inadequate bioavailability is one of the most critical reasons for the failure of oral drug development. However, the way that substructures affect bioavailability remains largely unknown. Serotonin transporter (SERT) inhibitors are first-line drugs for major depression disorder, and improving their bioavailability may be able to decrease side-effects by reducing daily dose. Thus, it is an excellent model to probe the relationship between substructures and bioavailability. Here, we proposed the concept of "nonbioavailable substructures", referring to substructures that are unfavorable to bioavailability. A machine learning model was developed to identify nonbioavailable substructures based on their molecular properties and shows the accuracy of 83.5%. A more potent SERT inhibitor DH4 was discovered with a bioavailability of 83.28% in rats by replacing the nonbioavailable substructure of approved drug vilazodone. DH4 exhibits promising anti-depression efficacy in animal experiments. The concept of nonbioavailable substructures may open up a new venue for the improvement of drug bioavailability.

Molecular dynamics simulation analysis of alpha-cobra toxin docked with phytochemical compounds

It is of interest to document data on the molecular dynamics simulation analysis of alpha-cobratoxin docked with phytochemical compounds. This can be used as effective drug candidates against the snake and scorpion venom. It should be noted experimental verification is needed to further validate the current data.

Integrating Pharmacological and Computational Approaches for the Phytochemical Analysis of Syzygium cumini and Its Anti-Diabetic Potential

Diabetes mellitus (DM) is a metabolic disease caused by improper insulin secretion leading to hyperglycemia. Syzygium cumini has excellent therapeutic properties due to its high levels of phytochemicals. The current research aimed to evaluate the anti-diabetic potential of S. cumini plant's seeds and the top two phytochemicals (kaempferol and gallic acid) were selected for further analysis. These phytochemicals were selected via computational tools and evaluated for α-Glucosidase inhibitory activity via enzymatic assay. Gallic acid (IC₅₀ 0.37 µM) and kaempferol (IC₅₀ 0.87 µM) have shown a stronger α-glucosidase inhibitory capacity than acarbose (5.26 µM). In addition, these phytochemicals demonstrated the highest binding energy, hydrogen bonding, protein-ligand interaction and the best MD simulation results at 100 ns compared to acarbose. Furthermore, the ADMET properties of gallic acid and kaempferol also fulfilled the safety criteria. Thus, it was concluded that S. cumini could potentially be used to treat DM. The potential bioactive molecules identified in this study (kaempferol and gallic acid) may be used as lead drugs against diabetes.

Discovery of Rift Valley fever virus natural pan-inhibitors by targeting its multiple key proteins through computational approaches

The Rift Valley fever virus (RVFV) is a zoonotic arbovirus and pathogenic to both humans and animals. Currently, no proven effective RVFV drugs or licensed vaccine are available for human or animal use. Hence, there is an urgent need to develop effective treatment options to control this viral infection. RVFV glycoprotein N (GN), glycoprotein C (GC), and nucleocapsid (N) proteins are attractive antiviral drug targets due to their critical roles in RVFV replication. In present study, an integrated docking-based virtual screening of more than 6000 phytochemicals with known antiviral activities against these conserved RVFV proteins was conducted. The top five hit compounds, calyxin C, calyxin D, calyxin J, gericudranins A, and blepharocalyxin C displayed optimal binding against all three target proteins. Moreover, multiple parameters from the molecular dynamics (MD) simulations and MM/GBSA analysis confirmed the stability of protein-ligand complexes and revealed that these compounds may act as potential pan-inhibitors of RVFV replication. Our computational analyses may contribute toward the development of promising effective drugs against RVFV infection.

Structural Elucidation of Rift Valley Fever Virus L Protein towards the Discovery of Its Potential Inhibitors

Rift valley fever virus (RVFV) is the causative agent of a viral zoonosis that causes a significant clinical burden in domestic and wild ruminants. Major outbreaks of the virus occur in livestock, and contaminated animal products or arthropod vectors can transmit the virus to humans. The viral RNA-dependent RNA polymerase (RdRp; L protein) of the RVFV is responsible for viral replication and is thus an appealing drug target because no effective and specific vaccine against this virus is available. The current study reported the structural elucidation of the RVFV-L protein by in-depth homology modeling since no crystal structure is available yet. The inhibitory binding modes of known potent L protein inhibitors were analyzed. Based on the results, further molecular docking-based virtual screening of Selleckchem Nucleoside Analogue Library (156 compounds) was performed to find potential new inhibitors against the RVFV L protein. ADME (Absorption, Distribution, Metabolism, and Excretion) and toxicity analysis of these compounds was also performed. Besides, the binding mechanism and stability of identified compounds were confirmed by a 50 ns molecular dynamic (MD) simulation followed by MM/PBSA binding free energy calculations. Homology modeling determined a stable multi-domain structure of L protein. An analysis of known L protein inhibitors, including Monensin, Mycophenolic acid, and Ribavirin, provide insights into the binding mechanism and reveals key residues of the L protein binding pocket. The screening results revealed that the top three compounds, A-317491, Khasianine, and VER155008, exhibited a high affinity at the L protein binding pocket. ADME analysis revealed good pharmacodynamics and pharmacokinetic profiles of these compounds. Furthermore, MD simulation and binding free energy analysis endorsed the binding stability of potential compounds with L protein. In a nutshell, the present study determined potential compounds that may aid in the rational design of novel inhibitors of the RVFV L protein as anti-RVFV drugs.

In Silico Core Proteomics and Molecular Docking Approaches for the Identification of Novel Inhibitors against Streptococcus pyogenes

Streptococcus pyogenes is a significant pathogen that causes skin and upper respiratory tract infections and non-suppurative complications, such as acute rheumatic fever and post-strep glomerulonephritis. Multidrug resistance has emerged in S. pyogenes strains, making them more dangerous and pathogenic. Hence, it is necessary to identify and develop therapeutic methods that would present novel approaches to S. pyogenes infections. In the current study, a subtractive proteomics approach was employed to core proteomes of four strains of S. pyogenes using several bioinformatic software tools and servers. The core proteome consists of 1324 proteins, and 302 essential proteins were predicted from them. These essential proteins were analyzed using BLASTp against human proteome, and the number of potential targets was reduced to 145. Based on subcellular localization prediction, 46 proteins with cytoplasmic localization were chosen for metabolic pathway analysis. Only two cytoplasmic proteins, i.e., chromosomal replication initiator protein DnaA and two-component response regulator (TCR), were discovered to have the potential to be novel drug target candidates. Three-dimensional (3D) structure prediction of target proteins was carried out via the Swiss Model server. Molecular docking approach was employed to screen the library of 1000 phytochemicals against the interacting residues of the target proteins through the MOE software. Further, the docking studies were validated by running molecular dynamics simulation and highly popular binding free energy approaches of MM-GBSA and MM-PBSA. The findings revealed a promising candidate as a novel target against S. pyogenes infections.

Plant-Based Phytochemical Screening by Targeting Main Protease of SARS-CoV-2 to Design Effective Potent Inhibitors

Currently, a worldwide pandemic has been declared in response to the spread of coronavirus disease 2019 (COVID-19), a fatal and fast-spreading viral infection caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The low availability of efficient vaccines and treatment options has resulted in a high mortality rate, bringing the world economy to its knees. Thus, mechanistic investigations of drugs capable of counteracting this disease are in high demand. The main protease (Mpro) expressed by SARS-CoV-2 has been targeted for the development of potential drug candidates due to the crucial role played by Mpro in viral replication and transcription. We generated a phytochemical library containing 1672 phytochemicals derived from 56 plants, which have been reported as having antiviral, antibacterial, and antifungal activity. A molecular docking program was used to screen the top three candidate compounds: epicatechin-3-O-gallate, psi-taraxasterol, and catechin gallate, which had respective binding affinities of -8.4, -8.5, and -8.8 kcal/mol. Several active sites in the targeted protein, including Cys145, His41, Met49, Glu66, and Met165, were found to interact with the top three candidate compounds. The multiple simulation profile, root-mean-square deviation, root-mean-square fluctuation, radius of gyration, and solvent-accessible surface area values supported the inflexible nature of the docked protein-compound complexes. The toxicity and carcinogenicity profiles were assessed, which showed that epicatechin-3-O-gallate, psi-taraxasterol, and catechin gallate had favorable pharmacological properties with no adverse effects. These findings suggest that these compounds could be developed as part of an effective drug development pathway to treat COVID-19.

Quantitative Systems Pharmacology for Neuroscience Drug Discovery and Development: Current Status, Opportunities, and Challenges

The substantial progress made in the basic sciences of the brain has yet to be adequately translated to successful clinical therapeutics to treat central nervous system (CNS) diseases. Possible explanations include the lack of quantitative and validated biomarkers, the subjective nature of many clinical endpoints, and complex pharmacokinetic/pharmacodynamic relationships, but also the possibility that highly selective drugs in the CNS do not reflect the complex interactions of different brain circuits. Although computational systems pharmacology modeling designed to capture essential components of complex biological systems has been increasingly accepted in pharmaceutical research and development for oncology, inflammation, and metabolic disorders, the uptake in the CNS field has been very modest. In this article, a cross-disciplinary group with representatives from academia, pharma, regulatory, and funding agencies make the case that the identification and exploitation of CNS therapeutic targets for drug discovery and development can benefit greatly from a system and network approach that can span the gap between molecular pathways and the neuronal circuits that ultimately regulate brain activity and behavior. The National Institute of Neurological Disorders and Stroke (NINDS), in collaboration with the National Institute on Aging (NIA), National Institute of Mental Health (NIMH), National Institute on Drug Abuse (NIDA), and National Center for Advancing Translational Sciences (NCATS), convened a workshop to explore and evaluate the potential of a quantitative systems pharmacology (QSP) approach to CNS drug discovery and development. The objective of the workshop was to identify the challenges and opportunities of QSP as an approach to accelerate drug discovery and development in the field of CNS disorders. In particular, the workshop examined the potential for computational neuroscience to perform QSP-based interrogation of the mechanism of action for CNS diseases, along with a more accurate and comprehensive method for evaluating drug effects and optimizing the design of clinical trials. Following up on an earlier white paper on the use of QSP in general disease mechanism of action and drug discovery, this report focuses on new applications, opportunities, and the accompanying limitations of QSP as an approach to drug development in the CNS therapeutic area based on the discussions in the workshop with various stakeholders.

Modulation of membrane permeability by carbon dioxide

Promoting drug delivery across the biological membrane is a common strategy to improve bioavailability. Inspired by the observation that carbonated alcoholic beverages can increase the absorption rate of ethanol, we speculate that carbon dioxide (CO₂ ) molecules could also enhance membrane permeability to drugs. In the present work, we have investigated the effect of CO₂ on the permeability of a model membrane formed by 1-palmitoyl-2-oleoyl-sn-glycero-3-phosphocholine lipids to three drug-like molecules, namely, ethanol, 2',3'-dideoxyadenosine, and trimethoprim. The free-energy and fractional-diffusivity profiles underlying membrane translocation were obtained from μs-timescale simulations and combined in the framework of the fractional solubility-diffusion model. We find that addition of CO₂ in the lipid environment results in an increase of the membrane permeability to the three substrates. Further analysis of the permeation events reveals that CO₂ expands and loosens the membrane, which, in turn, facilitates permeation of the drug-like molecules. © 2019 Wiley Periodicals, Inc.

Carboxymethyl cellulose coated magnetic nanoparticles transport across a human lung microvascular endothelial cell model of the blood-brain barrier

Sustained and safe delivery of therapeutic agents across the blood-brain barrier (BBB) is one of the major challenges for the treatment of neurological disorders as this barrier limits the ability of most drug molecules to reach the brain. Targeted delivery of the drugs used to treat these disorders could potentially offer a considerable reduction of the common side effects of their treatment. The preparation and characterization of carboxymethyl cellulose (CMC) coated magnetic nanoparticles (Fe₃O₄@CMC) is reported as an alternative that meets the need for novel therapies capable of crossing the BBB. In vitro assays were used to evaluate the ability of these polysaccharide coated biocompatible, water-soluble, magnetic nanoparticles to deliver drug therapy across a model of the BBB. As a drug model, dopamine hydrochloride loading and release profiles in physiological solution were determined using UV-Vis spectroscopy. Cell viability tests in Human Lung Microvascular Endothelial (HLMVE) cell cultures showed no significant cell death, morphological changes or alterations in mitochondrial function after 24 and 48 h of exposure to the nanoparticles. Evidence of nanoparticle interactions and nanoparticle uptake by the cell membrane was obtained by electron microscopy (SEM and TEM) analyses. Permeability through a BBB model (the transwell assay) was evaluated to assess the ability of Fe₃O₄@CMC nanoparticles to be transported across a densely packed HLMVE cell barrier. The results suggest that these nanoparticles can be useful drug transport and release systems for the design of novel pharmaceutical agents for brain therapy.

Generative network complex (GNC) for drug discovery

It remains a challenging task to generate a vast variety of novel compounds with desirable pharmacological properties. In this work, a generative network complex (GNC) is proposed as a new platform for designing novel compounds, predicting their physical and chemical properties, and selecting potential drug candidates that fulfill various druggable criteria such as binding affinity, solubility, partition coefficient, etc. We combine a SMILES string generator, which consists of an encoder, a drug-property controlled or regulated latent space, and a decoder, with verification deep neural networks, a target-specific three-dimensional (3D) pose generator, and mathematical deep learning networks to generate new compounds, predict their drug properties, construct 3D poses associated with target proteins, and reevaluate druggability, respectively. New compounds were generated in the latent space by either randomized output, controlled output, or optimized output. In our demonstration, 2.08 million and 2.8 million novel compounds are generated respectively for Cathepsin S and BACE targets. These new compounds are very different from the seeds and cover a larger chemical space. For potentially active compounds, their 3D poses are generated using a state-of-the-art method. The resulting 3D complexes are further evaluated for druggability by a championing deep learning algorithm based on algebraic topology, differential geometry, and algebraic graph theories. Performed on supercomputers, the whole process took less than one week. Therefore, our GNC is an efficient new paradigm for discovering new drug candidates.

Multi-parametric cell profiling with a CMOS quad-modality cellular interfacing array for label-free fully automated drug screening

Cells are complex systems with concurrent multi-physical responses, and cell physiological signals are often encoded with spatiotemporal dynamics and further coupled with multiple cellular activities. However, most existing electronic sensors are only single-modality and cannot capture multi-parametric cellular responses. In this paper, a 1024-pixel CMOS quad-modality cellular interfacing array that enables multi-parametric cell profiling for drug development is presented. The quad-modality CMOS array features cellular impedance characterization, optical detection, extracellular potential recording, and biphasic current stimulation. The fibroblast transparency and surface adhesion are jointly monitored by cellular impedance and optical sensing modalities for comprehensive cell growth evaluation. Simultaneous current stimulation and opto-mechanical monitoring based on cardiomyocytes are demonstrated without any stimulation/sensing dead-zone. Furthermore, drug dose-dependent multi-parametric feature extractions in cardiomyocytes from their extracellular potentials and opto-mechanical signals are presented. The CMOS array demonstrates great potential for fully automated drug screening and drug safety assessments, which may substantially reduce the drug screening time and cost in future new drug development.

Link between Membrane Composition and Permeability to Drugs

Prediction of membrane permeability to small molecules represents an important aspect of drug discovery. First-principles calculations of this quantity require an accurate description of both the thermodynamics and kinetics that underlie translocation of the permeant across the lipid bilayer. In this contribution, the membrane permeability to three drugs, or drug-like molecules, namely, 9-anthroic acid (ANA), 2',3'-dideoxyadenosine (DDA), and hydrocortisone (HYL), are estimated in a pure 1-palmitoyl-2-oleoylphosphatidylcholine (POPC) and in a POPC:cholesterol (2:1) mixture. On the basis of independent 2-5-μs free-energy calculations combined with a time-fractional Smoluchowski determination of the diffusivity, the estimated membrane permeabilities to these chemically diverse permeants fall within an order of magnitude from the experimental values obtained in egg-lecithin bilayers, with the exception of HYL in pure POPC. This exception is particularly interesting because the calculated permeability of the sterol-rich bilayer to HYL, in close agreement with the experimental value, is about 600 times lower than that of the pure POPC bilayer to HYL. In contrast, the permeabilities to ANA and DDA differ by less than a factor of 10 between the pure POPC and POPC:cholesterol bilayers. The unusual behavior of HYL, a large, amphiphilic compound, may be linked with the longer range perturbation of the lipid bilayer it induces, compared to ANA and DDA, suggestive of a possibly different translocation mechanism. We find that the tendency of lower permeabilities of the POPC:cholesterol bilayer relative to those of the pure POPC one is a consequence of increased free-energy barriers. Beyond reporting accurate estimates of the membrane permeability, the present contribution also demonstrates that rigorous free-energy calculations and a fractional-diffusion model are key in revealing the molecular phenomena linking the composition of a membrane to its permeability to drugs.

Adversarial Threshold Neural Computer for Molecular de Novo Design

In this article, we propose the deep neural network Adversarial Threshold Neural Computer (ATNC). The ATNC model is intended for the de novo design of novel small-molecule organic structures. The model is based on generative adversarial network architecture and reinforcement learning. ATNC uses a Differentiable Neural Computer as a generator and has a new specific block, called adversarial threshold (AT). AT acts as a filter between the agent (generator) and the environment (discriminator + objective reward functions). Furthermore, to generate more diverse molecules we introduce a new objective reward function named Internal Diversity Clustering (IDC). In this work, ATNC is tested and compared with the ORGANIC model. Both models were trained on the SMILES string representation of the molecules, using four objective functions (internal similarity, Muegge druglikeness filter, presence or absence of sp³-rich fragments, and IDC). The SMILES representations of 15K druglike molecules from the ChemDiv collection were used as a training data set. For the different functions, ATNC outperforms ORGANIC. Combined with the IDC, ATNC generates 72% of valid and 77% of unique SMILES strings, while ORGANIC generates only 7% of valid and 86% of unique SMILES strings. For each set of molecules generated by ATNC and ORGANIC, we analyzed distributions of four molecular descriptors (number of atoms, molecular weight, logP, and tpsa) and calculated five chemical statistical features (internal diversity, number of unique heterocycles, number of clusters, number of singletons, and number of compounds that have not been passed through medicinal chemistry filters). Analysis of key molecular descriptors and chemical statistical features demonstrated that the molecules generated by ATNC elicited better druglikeness properties. We also performed in vitro validation of the molecules generated by ATNC; results indicated that ATNC is an effective method for producing hit compounds.

Screening therapeutics according to their uptake across the blood-brain barrier: A high throughput method based on immobilized artificial membrane liquid chromatography-diode-array-detection coupled to electrospray-time-of-flight mass spectrometry

The Blood-Brain Barrier (BBB) plays an essential role in protecting the brain tissues against possible injurious substances. In the present work, 79 neutral, basic, acidic and amphoteric structurally unrelated analytes were considered and their chromatographic retention coefficients on immobilized artificial membrane (IAM) stationary phase were determined employing a mass spectrometry (MS)-compatible buffer based on ammonium acetate. Their BBB passage predictive strength was evaluated and the statistical models based on IAM indexes and in silico physico-chemical descriptors showed solid statistics (r² (n - 1) = 0.78). The predictive strength of the indexes achieved by the MS-compatible method was comparable to that achieved by employing the more "biomimetic" Dulbecco's phosphate buffered saline, even if some differences in the elution order were observed. The method was transferred to the MS, employing a diode-array-detection coupled to an electrospray ionization source and a time-of-flight analyzer. This setup allowed the simultaneous analysis of up to eight analytes, yielding a remarkable acceleration of the analysis time.

A Microfluidic Platform for the Characterisation of CNS Active Compounds

New in vitro technologies that assess neuronal excitability and the derived synaptic activity within a controlled microenvironment would be beneficial for the characterisation of compounds proposed to affect central nervous system (CNS) function. Here, a microfluidic system with computer controlled compound perfusion is presented that offers a novel methodology for the pharmacological profiling of CNS acting compounds based on calcium imaging readouts. Using this system, multiple applications of the excitatory amino acid glutamate (10 nM–1 mM) elicited reproducible and reversible transient increases in intracellular calcium, allowing the generation of a concentration response curve. In addition, the system allows pharmacological investigations to be performed as evidenced by application of glutamatergic receptor antagonists, reversibly inhibiting glutamate-induced increases in intracellular calcium. Importantly, repeated glutamate applications elicited significant increases in the synaptically driven activation of the adjacent, environmentally isolated neuronal network. Therefore, the proposed new methodology will enable neuropharmacological analysis of CNS active compounds whilst simultaneously determining their effect on synaptic connectivity.

In silico and in vitro prediction of gastrointestinal absorption from potential drug eremantholide C

Objectives

Analysis of the biopharmaceutical properties of eremantholide C, sesquiterpene lactone with proven pharmacological activity and low toxicity, is required to evaluate its potential to become a drug.

Methods

Preliminary analysis of the physicochemical characteristics of eremantholide C was performed in silico. Equilibrium solubility was evaluated using the shake-flask method, at 37.0 °C, 100 rpm during 72 h in biorelevant media. The permeability was analysed using parallel artificial membrane permeability assay, at 37.0 °C, 50 rpm for 5 h. The donor compartment was composed of an eremantholide C solution in intestinal fluid simulated without enzymes, while the acceptor compartment consisted of phosphate buffer.

Key findings

Physicochemical characteristics predicted in silico indicated that eremantholide C has a low solubility and high permeability. In-vitro data of eremantholide C showed low solubility, with values for the dose/solubility ratio (ml): 9448.82, 10 389.61 e 15 000.00 for buffers acetate (pH 4.5), intestinal fluid simulated without enzymes (pH 6.8) and phosphate (pH 7.4), respectively. Also, it showed high permeability, with effective permeability of 30.4 × 10−6 cm/s, a higher result compared with propranolol hydrochloride (9.23 × 10−6 cm/s).

Conclusions

The high permeability combined with its solubility, pharmacological activity and low toxicity demonstrate the importance of eremantholide C as a potential drug candidate.

The European Citizens' Stop Vivisection Initiative and the revision of Directive

Animal experimentation is presented to the public as an ongoing debate between research scientists on one hand, and the animal protection community on the other. An opportunity to break out of this mindset presented itself in the form of a European Citizens' Initiative, Stop Vivisection, which challenged Directive 2010/63/EU of the European Parliament and of the Council on the protection of animals for scientific purposes. The manifesto of the initiative called upon the European Commission to replace the existing Directive with a new proposal that does away with animal experimentation, and instead makes compulsory the use of human data as a predictive modality for the study of human diseases and responses to drugs. Although the Initiative succeeded in gathering the required one million signatures, the European Commission ultimately rejected the proposal. However, some of the lessons learned from the Initiative may well be relevant to the revision of Directive 2010/63/EU, due to take place by 2017.

Separating chemotherapy-related developmental neurotoxicity from cytotoxicity in monolayer and neurosphere cultures of human fetal brain cells

Chemotherapy-induced neurotoxicity can reduce the quality of life of patients by affecting their intelligence, senses and mobility. Ten percent of safety-related late-stage clinical failures are due to neurological side effects. Animal models are poor in predicting human neurotoxicity due to interspecies differences and most in vitro assays cannot distinguish neurotoxicity from general cytotoxicity for chemotherapeutics. We developed in vitro assays capable of quantifying the paediatric neurotoxic potential for cytotoxic drugs. Mixed cultures of human fetal brain cells were differentiated in monolayers and as 3D-neurospheres in the presence of non-neurotoxic chemotherapeutics (etoposide, teniposide) or neurotoxicants (methylmercury). The cytotoxic potency towards dividing progenitors versus differentiated neurons and astrocytes was compared using: (1) immunohistochemistry staining and cell counts in monolayers; (2) through quantitative Western blots in neurospheres; and (3) neurosphere migration assays. Etoposide and teniposide, were 5-10 times less toxic to differentiated neurons compared to the mix of all cells in monolayer cultures. In contrast, the neurotoxicant methylmercury did not exhibit selectivity and killed all cells with the same potency. In 3D neurospheres, etoposide and teniposide were 24 to 10 times less active against neurons compared to all cells. These assays can be used prioritise drugs for local drug delivery to brain tumours.

Towards cheminformatics-based estimation of drug therapeutic index: Predicting the protective index of anticonvulsants using a new quantitative structure-index relationship approach

The overall efficacy and safety profile of a new drug is partially evaluated by the therapeutic index in clinical studies and by the protective index (PI) in preclinical studies. In-silico predictive methods may facilitate the assessment of these indicators. Although QSAR and QSTR models can be used for predicting PI, their predictive capability has not been evaluated. To test this capability, we developed QSAR and QSTR models for predicting the activity and toxicity of anticonvulsants at accuracy levels above the literature-reported threshold (LT) of good QSAR models as tested by both the internal 5-fold cross validation and external validation method. These models showed significantly compromised PI predictive capability due to the cumulative errors of the QSAR and QSTR models. Therefore, in this investigation a new quantitative structure-index relationship (QSIR) model was devised and it showed improved PI predictive capability that superseded the LT of good QSAR models. The QSAR, QSTR and QSIR models were developed using support vector regression (SVR) method with the parameters optimized by using the greedy search method. The molecular descriptors relevant to the prediction of anticonvulsant activities, toxicities and PIs were analyzed by a recursive feature elimination method. The selected molecular descriptors are primarily associated with the drug-like, pharmacological and toxicological features and those used in the published anticonvulsant QSAR and QSTR models. This study suggested that QSIR is useful for estimating the therapeutic index of drug candidates.

Simulation-Based Approaches for Determining Membrane Permeability of Small Compounds

Predicting the rate of nonfacilitated permeation of solutes across lipid bilayers is important to drug design, toxicology, and signaling. These rates can be estimated using molecular dynamics simulations combined with the inhomogeneous solubility-diffusion model, which requires calculation of the potential of mean force and position-dependent diffusivity of the solute along the transmembrane axis. In this paper, we assess the efficiency and accuracy of several methods for the calculation of the permeability of a model DMPC bilayer to urea, benzoic acid, and codeine. We compare umbrella sampling, replica exchange umbrella sampling, adaptive biasing force, and multiple-walker adaptive biasing force for the calculation of the transmembrane PMF. No definitive advantage for any of these methods in their ability to predict the membrane permeability coefficient Pm was found, provided that a sufficiently long equilibration is performed. For diffusivities, a Bayesian inference method was compared to a generalized Langevin method, both being sensitive to chosen parameters and the slow relaxation of membrane defects. Agreement within 1.5 log units of the computed Pm with experiment is found for all permeants and methods. Remaining discrepancies can likely be attributed to limitations of the force field as well as slowly relaxing collective movements within the lipid environment. Numerical calculations based on model profiles show that Pm can be reliably estimated from only a few data points, leading to recommendations for calculating Pm from simulations.

Drug discovery through stem cell-based organoid models

The development of new drugs is currently a long and costly process in large part due to the failure of promising drug candidates identified in initial in vitro screens to perform as intended in vivo. New approaches to drug screening are being developed which focus on providing more biomimetic platforms. This review surveys this new generation of drug screening technologies, and provides an overview of recent developments in organoid culture systems which could afford previously unmatched fidelity for testing bioactivity and toxicity. The challenges inherent in such approaches will also be discussed, with a view towards bridging the gap between proof-of-concept studies and a wider implementation within the drug development community.

Flavonoid from Carica papaya inhibits NS2B-NS3 protease and prevents Dengue 2 viral assembly

Dengue virus belongs to the virus family Flaviviridae. Dengue hemorrhagic disease caused by dengue virus is a public health problem worldwide. The viral non structural 2B and 3 (NS2B-NS3) protease complex is crucial for virus replication and hence, it is considered to be a good anti-viral target. Leaf extracts from Carica papaya is generally prescribed for patients with dengue fever, but there are no scientific evidences for its anti-dengue activity; hence we intended to investigate the anti-viral activity of compounds present in the leaves of Carica papaya against dengue 2 virus (DENV-2). We analysed the anti-dengue activities of the extracts from Carica papaya by using bioinformatics tools. Interestingly, we find the flavonoid quercetin with highest binding energy against NS2B-NS3 protease which is evident by the formation of six hydrogen bonds with the amino acid residues at the binding site of the receptor. Our results suggest that the flavonoids from Carica papaya have significant anti-dengue activities.

Methodologies to assess drug permeation through the blood-brain barrier for pharmaceutical research

The drug discovery process for drugs that target the central nervous system suffers from a very high rate of failure due to the presence of the blood-brain barrier, which limits the entry of xenobiotics into the brain. To minimise drug failure at different stages of the drug development process, new methodologies have been developed to understand the absorption, distribution, metabolism, excretion and toxicity (ADMET) profile of drug candidates at early stages of drug development. Additionally, understanding the permeation of drug candidates is also important, particularly for drugs that target the central nervous system. During the first stages of the drug discovery process, in vitro methods that allow for the determination of permeability using high-throughput screening methods are advantageous. For example, performing the parallel artificial membrane permeability assay followed by cell-based models with interesting hits is a useful technique for identifying potential drugs. In silico models also provide interesting information but must be confirmed by in vitro models. Finally, in vivo models, such as in situ brain perfusion, should be studied to reduce a large number of drug candidates to a few lead compounds. This article reviews the different methodologies used in the drug discovery and drug development processes to determine the permeation of drug candidates through the blood-brain barrier.

Drug-like property profiling of novel neuroprotective compounds to treat acute ischemic stroke: guidelines to develop pleiotropic molecules

The development of novel neuroprotective compounds to treat acute ischemic stroke (AIS) has been problematic and quite complicated, since many candidates that have been tested clinically lacked significant pleiotropic activity, were unable to effectively cross the blood brain barrier (BBB), had poor bioavailability or were toxic. Moreover, the compounds did not confer significant neuroprotection or clinical efficacy measured using standard behavioral endpoints, when studied in clinical trials in a heterogeneous population of stroke patients. To circumvent some of the drug development problems describe above, we have used a rational funnel approach to identify and develop promising candidates. Using a step-wise approach, we have identified a series of compounds based upon two different neuroprotection assays. We have then taken the candidates and determined their "drug-like" properties. This guidelines article details in vitro screening assays used to show pleiotropic activity of a series of novel compounds; including enhanced neuroprotective activity compared to the parent compound fisetin. Moreover, for preliminary drug de-risking or risk reduction during development, we used compound assessment in the CeeTox assay, ADME toxicity using the AMES test for genotoxicity and interaction with Cytochrome P450 using CYP450 inhibition analysis against a spectrum of CYP450 enzymes (CYP1A2, CYP2C9, CYP2C19, CYP2D6 and CYP3A4) as a measure of drug interaction. Moreover, the compounds have been studied using a transfected Madin Darby canine kidney (MDCK) cell assay to assess blood brain barrier penetration (BBB). Using this series of assays, we have identified 4 novel molecules to be developed as an AIS treatment.

Primary hepatocyte cultures for pharmaco-toxicological studies: at the busy crossroad of various anti-dedifferentiation strategies

Continuously increasing understanding of the molecular triggers responsible for the onset of diseases, paralleled by an equally dynamic evolution of chemical synthesis and screening methods, offers an abundance of pharmacological agents with a potential to become new successful drugs. However, before patients can benefit of newly developed pharmaceuticals, stringent safety filters need to be applied to weed out unfavourable drug candidates. Cost effectiveness and the need to identify compound liabilities, without exposing humans to unnecessary risks, has stimulated the shift of the safety studies to the earliest stages of drug discovery and development. In this regard, in vivo relevant organotypic in vitro models have high potential to revolutionize the preclinical safety testing. They can enable automation of the process, to match the requirements of high-throughput screening approaches, while satisfying ethical considerations. Cultures of primary hepatocytes became already an inherent part of the preclinical pharmaco-toxicological testing battery, yet their routine use, particularly for long-term assays, is limited by the progressive deterioration of liver-specific features. The availability of suitable hepatic and other organ-specific in vitro models is, however, of paramount importance in the light of changing European legal regulations in the field of chemical compounds of different origin, which gradually restrict the use of animal studies for safety assessment, as currently witnessed in cosmetic industry. Fortunately, research groups worldwide spare no effort to establish hepatic in vitro systems. In the present review, both classical and innovative methodologies to stabilize the in vivo-like hepatocyte phenotype in culture of primary hepatocytes are presented and discussed.