In silico ADME/T modelling for rational drug design

Applications of artificial intelligence to drug design and discovery in the big data era: a comprehensive review

Artificial intelligence (AI) renders cutting-edge applications in diverse sectors of society. Due to substantial progress in high-performance computing, the development of superior algorithms, and the accumulation of huge biological and chemical data, computer-assisted drug design technology is playing a key role in drug discovery with its advantages of high efficiency, fast speed, and low cost. Over recent years, due to continuous progress in machine learning (ML) algorithms, AI has been extensively employed in various drug discovery stages. Very recently, drug design and discovery have entered the big data era. ML algorithms have progressively developed into a deep learning technique with potent generalization capability and more effectual big data handling, which further promotes the integration of AI technology and computer-assisted drug discovery technology, hence accelerating the design and discovery of the newest drugs. This review mainly summarizes the application progression of AI technology in the drug discovery process, and explores and compares its advantages over conventional methods. The challenges and limitations of AI in drug design and discovery have also been discussed.

A comprehensive review of in silico approaches for the prediction and modulation of aldehyde oxidase-mediated drug metabolism: The current features, challenges and future perspectives

The importance of aldehyde oxidase (AOX) in drug metabolism necessitates the development and application of the in silico rational drug design methods as an integral part of drug discovery projects for the early prediction and modulation of AOX-mediated metabolism. The current study represents an up-to-date and thorough review of in silico studies of AOX-mediated metabolism and modulation methods. In addition, the challenges and the knowledge gap that should be covered have been discussed. The importance of aldehyde oxidase (AOX) in drug metabolism is a hot topic in drug discovery. Different strategies are available for the modulation of the AOX-mediated metabolism of drugs. Application of the rational drug design methods as an integral part of drug discovery projects is necessary for the early prediction of AOX-mediated metabolism. The current study represents a comprehensive review of AOX molecular structure, AOX-mediated reactions, AOX substrates, AOX inhibition, approaches to modify AOX-mediated metabolism, prediction of AOX metabolism/substrates/inhibitors, and the AOX related structure-activity relationship (SAR) studies. Furthermore, an up-to-date and thorough review of in silico studies of AOX metabolism has been carried out. In addition, the challenges and the knowledge gap that should be covered in the scientific literature have been discussed in the current review.

Crystal Structure-Free Method for Dielectric and Polarizability Characterization of Crystalline Materials at Terahertz Frequencies

Terahertz (THz) time-domain spectroscopy provides a direct and nondestructive method for measuring the dielectric properties of materials directly from the phase delay of coherent electromagnetic radiation propagating through the sample. In cases when crystals are embedded within an inert polymeric pellet, the Landau, Lifshitz, and Looyenga (LLL) effective medium model can be used to extract the intrinsic dielectric constant of the crystalline sample. Subsequently, polarizability can be obtained from the Clausius-Mossotti (CM) relationship. Knowledge of the crystal structure density is required for an analytical solution to the LLL and CM relationships. A novel crystal structure-free graphical method is presented as a way to estimate both dielectric constants and polarizability values for the situation when the crystal structure density is unknown, and the crystals are embedded within a pellet composed of a non-porous polymer. The utility of this crystal structure-free method is demonstrated by analyzing THz time-domain spectra collected for a set of amino acids (L-alanine, L-threonine, and L-glutamine) embedded within pellets composed of polytetrafluoroethylene. Crystal structures are known for each amino acid, thereby enabling a direct comparison of results using the analytical solution and the proposed crystal structure-free graphical method. For each amino acid, the intrinsic dielectric constant is extracted through the LLL effective medium model without using information of their crystal structure densities. THz polarizabilities are then calculated with the CM relationship by using the determined intrinsic dielectric constant for each amino acid coupled with its crystal density as determined graphically. Comparison between the analytical and graphical solutions reveal relative differences between dielectric constants of 3.7, 5.1, and 13.6% for threonine, alanine, and glutamine, respectively, and relative differences between polarizability of 0.6, 0.9, and 5.4%, respectively. These values were determined over the 10-20 cm^-1 THz frequency range. The proposed method requires no prior knowledge of crystal structure information.

QSAR analysis of 3-pyrimidin-4-yl-oxazolidin-2-one derivatives isocitrate dehydrogenase inhibitors using Topomer CoMFA and HQSAR methods

A series of mIDH1 inhibitors derived from 3-pyrimidine-4-oxazolidin-2-ketone derivatives were studied by QSAR model to explore the key factors that inhibit mIDH1 activity. The generated model was cross-verified and non-cross-verified by Topomer CoMFA and HQSAR methods; the independent test set was verified by PLS method; the Topomer search technology was used for virtual screening and molecular design; and the Surflex-Dock method and ADMET technology were used for molecular docking, pharmacology and toxicity prediction of the designed drug molecules. The Topomer CoMFA and HQSAR cross-validation coefficients q² are 0.783 and 0.784, respectively, and the non-cross-validation coefficients r² are 0.978 and 0.934, respectively. Ten new drug molecules have been designed using Topomer search technology. The results of molecular docking and ADMET show that the newly designed drug molecules are effective. The docking situation, pharmacology and toxicity prediction results are good. The model can be used to predict the bioactivity of the same type of new compounds and their derivatives. The prediction results of molecular design, molecular docking and ADMET can provide some ideas for the design and development of novel mIDH1 inhibitor anticancer drugs, and provide certain theoretical basis of the experimental verification of new compounds in the future. Newly designed molecules after docking with corresponding proteins in the PDB library, it can explore the targets of drug molecules acting with large proteins and the related force, which is very helpful for the design of new drugs and the mechanism of drug action.

Computer-assisted evaluation of plant-derived β-secretase inhibitors in Alzheimer’s disease

Background

Alzheimer’s disease (AD) is a progressive neurodegenerative age-related dementia that results in memory loss of elderly people. Many hypotheses have been formally articulated till now to decipher the pathogenesis of this disease. According to the compelling amyloidogenic hypothesis, β-secretase is a key regulatory enzyme in AD development and is therefore considered as one of the major targets for the development of drugs to treat AD. In this study, 40 plant-derived phytocompounds, proven to have β-secretase inhibitory activity in different laboratory experiments, were evaluated using computational approaches in order to identify the best possible β-secretase inhibitor(s).

Results

Amentoflavone (IFD score: − 7.842 Kcal/mol), Bilobetin (IFD score: − 7.417 Kcal/mol), and Ellagic acid (IFD score: − 6.923 Kcal/mol) showed highest β-secretase inhibitory activities with high binding affinity among all the selected phytocompounds and interacted with key amino acids, i.e., Asp32, Tyr71, and Asp228 in the catalytic site of β-secretase. Moreover, these three molecules exhibited promising results in different drug potential assessment experiments and displayed signs of correlation with significant pharmacological and biological activities.

Conclusion

Amentoflavone, Biolbetin, and Ellagic acid could be investigated further in developing β-secretase-dependent drug for the effective treatment of AD. However, additional in vivo and in vitro experiments might be required to strengthen the findings of this experiment.

Berberis lyceum and Fumaria indica: in vitro cytotoxicity, antioxidant activity, and in silico screening of their selected phytochemicals as novel hepatitis C virus nonstructural protein 5A inhibitors

Berberis lyceum and Fumaria indica are two Pakistani indigenous herbal medicines used to treat liver infections, including hepatitis C virus (HCV). This study aimed to evaluate the cytotoxicity, and antioxidant activity of these plant extracts and computationally screen their selected phytoconstituents as HCV NS5A inhibitors. The viability of HepG2 cells was assessed 24 h and 48 h post-treatment using colorimetric and dye exclusion methods. Antioxidant properties were examined by the 2,2-diphenyl-1-picrylhydrazyl (DPPH), reducing power, and total antioxidant capacity assays. Seventeen known phytochemicals identified from each plant were docked into the active binding site of HCV NS5A protein. The top hit ligands were analyzed for their druglikeness properties and the indices of absorption, distribution, metabolism, elimination, and toxicity (ADMET). The results showed that both plant extracts were non-toxic (CC₅₀ > 200 µg/ml). The IC₅₀ values of DPPH-radical scavenging activity were 51.02 ± 0.94 and 62.91 ± 1.85 µg/ml for B. lyceum and F. indica, respectively. They also exhibited reducing power and total antioxidant capacity.The phytochemicals were identified as potent HCV NS5A inhibitors with good druglikeness and ADMET properties. Six of the docked phytochemicals exhibited higher binding scores (-17.9 to -19.2 kcal/mol) with HCV NS5A protein than the standard drug, daclatasvir (-17.2 kcal/mol). Molecular dynamics (MD) simulation confirmed the stability of two compounds, berbamine and paprafumine at 100 ns with active site of HCV NS5A protein. The identified compounds through molecular docking and MD simulation could have potential as HCV NS5A inhibitor after further validation. Communicated by Ramaswamy H. Sarma.

Structure Prediction and Expression of Modified rCTLA4-Ig as a Blocker for B7 Molecules

CTLA4-Ig (Abatacept) has been produced to suppress immune response by inhibition of T cells functions in autoimmune disease. A new drug, which is called belatacept, has recently been recently developed that is more efficient. The development has been occurred by two substitutions (A29Y, L104E) in the extracellular domain of CTLA4. In the present study, the bioinformatics analysis was used in order to make a new structure that has a better function in comparison with belatacept. Firstly, eight different structures were designed. Thereafter, the secondary and 3D structures, mRNA structure, docking of chimeric proteins with CD80/CD86, antigenicity and affinity of designed chimeric molecules were predicted. Based on the criteria, a new candidate molecule was selected and its gene synthesized. The gene was cloned and expressed in E. coli BL21 (DE3) successfully. The purified rCTLA4-Ig was analyzed by SDS-PAGE, western blotting, and ELISA. Circular dichroism analysis (CD analysis) was used for characterization of the rCTLA4-Ig. Affinity of rCTLA4-Ig was also evaluated by the flow cytometry method. Finally, its biological activity was determined by T cell inhibition test. The results showed rCTLA4-Ig and the belatacept protein have some similarities in structure and function. In addition, rCTLA4-Ig was able to bind CD80/CD86 and inhibit T cell function. Although flow cytomery results showed that the standard protein (CTLA4-Ig), represented better affinity than rCTLA4-Ig, the recombinant protein was able to inhibit T cell proliferation as well as CTLA4-Ig.

Thiazole-Based Thiosemicarbazones: Synthesis, Cytotoxicity Evaluation and Molecular Docking Study

INTRODUCTION

Hybrid drug design has developed as a prime method for the development of novel anticancer therapies that can theoretically solve much of the pharmacokinetic disadvantages of traditional anticancer drugs. Thus a number of studies have indicated that thiazole-thiophene hybrids and their bis derivatives have important anticancer activity. Mammalian Rab7b protein is a member of the Rab GTPase protein family that controls the trafficking from endosomes to the TGN. Alteration in the Rab7b expression is implicated in differentiation of malignant cells, causing cancer.

METHODS

1-(4-Methyl-2-(2-(1-(thiophen-2-yl) ethylidene) hydrazinyl) thiazol-5-yl) ethanone was used as building block for synthesis of novel series of 5-(1-(2-(thiazol-2-yl) hydrazono) ethyl) thiazole derivatives. The bioactivities of the synthesized compounds were evaluated with respect to their antitumor activities against MCF-7 tumor cells using MTT assay. Computer-aided docking protocol was performed to study the possible molecular interactions between the newly synthetic thiazole compounds and the active binding site of the target protein Rab7b. Moreover, the in silico prediction of adsorption, distribution, metabolism, excretion (ADME) and toxicity (T) properties of synthesized compounds were carried out using admetSAR tool.

RESULTS

The results obtained showed that derivatives 9 and 11b have promising activity (IC50 = 14.6 ± 0.8 and 28.3 ± 1.5 µM, respectively) compared to Cisplatin (IC50 = 13.6 ± 0.9 µM). The molecular docking analysis reveals that the synthesized compounds are predicted to be fit into the binding site of the target Rab7b. In summary, the synthetic thiazole compounds 1-17 could be used as potent inhibitors as anticancer drugs.

CONCLUSION

Promising anticancer activity of compounds 9 and 11 compared with cisplatin reference drug suggests that these ligands may contribute as lead compounds in search of new anticancer agents to combat chemo-resistance.

Integrated QSAR and Adverse Outcome Pathway Analysis of Chemicals Released on 3D Printing Using Acrylonitrile Butadiene Styrene

Additive manufacturing commonly known as 3D printing has numerous applications in several domains including material and biomedical technologies and has emerged as a tool of capabilities by providing fast, highly customized, and cost-effective solutions. However, the impact of the printing materials and chemicals present in the printing fumes has raised concerns about their adverse potential affecting humans and the environment. Thus, it is necessary to understand the properties of the chemicals emitted during additive manufacturing for developing safe and biocompatible fibers having controlled emission of fumes including its sustainable usage. Therefore, in this study, we have developed a computational predictive risk-assessment framework on the comprehensive list of chemicals released during 3D printing using the acrylonitrile butadiene styrene (ABS) filament. Our results showed that the chemicals present in the fumes of the ABS-based fiber used in additive manufacturing have the potential to lead to various toxicity end points such as inhalation toxicity, oral toxicity, carcinogenicity, hepatotoxicity, and teratogenicity. Moreover, because of their absorption, distribution in the body, metabolism, and excretion properties, most of the chemicals exhibited a high absorption level in the intestine and the potential to cross the blood-brain barrier. Furthermore, pathway analysis revealed that signaling like alpha-adrenergic receptor signaling, heterotrimeric G-protein signaling, and Alzheimer's disease-amyloid secretase pathway are significantly overrepresented given the identified target proteins of these chemicals. These findings signify the adversities associated with 3D printing fumes and the necessity for the development of biodegradable and considerably safer fibers for 3D printing technology.

Targeting the Autophagy Specific Lipid Kinase VPS34 for Cancer Treatment: An Integrative Repurposing Strategy

The impact of autophagy on cancer treatment and its corresponding responsiveness has galvanized the scientific community to develop novel inhibitors for cancer treatment. Importantly, the discovery of inhibitors that targets the early phase of autophagy was identified as a beneficial choice. Despite the number of research in recent years, screening of the DrugBank repository (9591 molecules) for the Vacuolar protein sorting 34 (VPS34) has not been reported earlier. Therefore, the present study was designed to identify potential VPS34 antagonists using integrated pharmacophore strategies. Primarily, an energy-based pharmacophore and receptor cavity-based analysis yielded five (DHRRR) and seven featured (AADDHRR) pharmacophore hypotheses respectively, which were utilized for the database screening process. The glide score, the binding free energy, pharmacokinetics and pharmacodynamics properties were examined to narrow down the screened compounds. This analysis yielded a hit molecule, DB03916 that exhibited a better docking score, higher binding affinity and better drug-like properties in contrast to the reference compound that suffers from a toxicity property. Importantly, the result was validated using a 50 ns molecular dynamics simulation study. Overall, we conclude that the identified hit molecule DB03916 is believed to serve as a prospective antagonist against VPS34 for cancer treatment.

Structure-Based Scaffold Repurposing toward the Discovery of Novel Cholinesterase Inhibitors

Cholinesterases (ChE) are well-known drug targets for the treatment of Alzheimer's disease (AD). In continuation of work to develop novel cholinesterase inhibitors, we utilized a structure-based scaffold repurposing approach and discovered six novel ChE inhibitors from our recently developed DNA gyrase inhibitor library. Among the identified hits, two compounds (denoted 3 and 18) were found to be the most potent inhibitor of acetylcholinesterase (AChE, IC₅₀ = 6.10 ± 1.01 μM) and butyrylcholinesterase (BuChE, IC₅₀ = 5.50 ± 0.007 μM), respectively. Compound 3 was responsible for the formation of H-bond and π-π stacking interactions within the active site of AChE. In contrast, compound 18 was well fitted in the choline-binding pocket and catalytic site of BuChE. Results obtained from in vitro cytotoxicity assays and in silico derived physicochemical and absorption, distribution, metabolism, and excretion (ADME) properties indicate that repurposed scaffold 3 and 18 could be potential drug candidates for further development as novel ChE inhibitors.

Cheminformatics in Natural Product-based Drug Discovery

This review seeks to provide a timely survey of the scope and limitations of cheminformatics methods in natural product-based drug discovery. Following an overview of data resources of chemical, biological and structural information on natural products, we discuss, among other aspects, in silico methods for (i) data curation and natural products dereplication, (ii) analysis, visualization, navigation and comparison of the chemical space, (iii) quantification of natural product-likeness, (iv) prediction of the bioactivities (virtual screening, target prediction), ADME and safety profiles (toxicity) of natural products, (v) natural products-inspired de novo design and (vi) prediction of natural products prone to cause interference with biological assays. Among the many methods discussed are rule-based, similarity-based, shape-based, pharmacophore-based and network-based approaches, docking and machine learning methods.

Synthetic flavonoids as potential antiviral agents against SARS-CoV-2 main protease

The COVID-19 pandemic has claimed more than a million lives worldwide within a short time span. Due to the unavailability of specific antiviral drugs or vaccine, the infections are causing panic both in general public and among healthcare providers. Therefore, an urgent discovery and development of effective antiviral drug for the treatment of COVID-19 is highly desired. Targeting the main protease (M^pro) of the causative agent, SARS-CoV-2 has great potential for drug discovery and drug repurposing efforts. Published crystal structures of SARS-CoV-2 M^pro further facilitated in silico investigations for discovering new inhibitors against M^pro. The present study aimed to screen several libraries of synthetic flavonoids and benzisothiazolinones as potential SARS-CoV-2 M^pro inhibitors using in silico methods. The short-listed compounds after virtual screening were filtered through SwissADME modeling tool to remove molecules with unfavorable pharmacokinetics and medicinal properties. The drug-like molecules were further subjected to iterative docking for the identification of top binders of SARS-CoV-2 M^pro. Finally, molecular dynamic (MD) simulations and binding free energy calculations were performed for the evaluation of the dynamic behavior, stability of protein–ligand complex, and binding affinity, resulting in the identification of thioflavonol, TF-9 as a potential inhibitor of M^pro. The computational studies further revealed the binding of TF-9 close to catalytic dyad and interactions with conserved residues in the S1 subsite of the substrate binding site. Our in-silico study demonstrated that synthetic analogs of flavonoids, particularly thioflavonols, have a strong tendency to inhibit the main protease M^pro, and thereby inhibit the reproduction of SARS-CoV-2.

Communicated by Ramaswamy H. Sarma

Network Pharmacology-Based Strategy for the Investigation of the Anti-Obesity Effects of an Ethanolic Extract of Zanthoxylum bungeanum Maxim

Network pharmacology is considered as the next paradigm in drug discovery. In an era when obesity has become global epidemic, network pharmacology becomes an ideal tool to discover novel herbal-based therapeutics with effective anti-obesity effects. Zanthoxylum bungeanum Maxim (ZBM) is a medicinal herb. The mature pericarp of ZBM is used for disease treatments and as spice for cooking. Here, we used the network pharmacology approach to investigate whether ZBM possesses anti-obesity effects and reveal the underlying mechanism of action. We first built up drug–ingredient–gene symbol–disease network and protein–protein interaction network of the ZBM-related obesity targets, followed by Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses. The results highlight apoptosis as a promising signaling pathway that mediates the anti-obesity effects of ZBM. Molecular docking also reveals quercetin, a compound in ZBM has the highest degree of connections in the compound-target network and has direct bindings with the apoptotic markers. Furthermore, the apoptotic effects of ZBM are further validated in 3T3-L1 adipocytes and in the high-fat diet–induced obesity mouse model. These findings not only suggest ZBM can be developed as potential anti-obesity therapeutics but also demonstrate the application of network pharmacology for the discovery of herbal-based therapeutics for disease treatments.

Synthesis, Molecular Docking Screening and Anti-Proliferative Potency Evaluation of Some New Imidazo[2,1-b]Thiazole Linked Thiadiazole Conjugates

BACKGROUND

Imidazo[2,1-b]thiazole scaffolds were reported to possess various pharmaceutical activities.

RESULTS

The novel compound named methyl-2-(1-(3-methyl-6-(p-tolyl)imidazo[2,1-b]thiazol-2-yl)ethylidene)hydrazine-1-carbodithioate 3 acted as a predecessor molecule for the synthesis of new thiadiazole derivatives incorporating imidazo[2,1-b]thiazole moiety. The reaction of 3 with the appropriate hydrazonoyl halide derivatives 4a-j and 7-9 had produced the respective 1,3,4-thiadiazole derivatives 6a-j and 10-12. The chemical composition of all the newly synthesized derivatives were confirmed by their microanalytical and spectral data (FT-IR, mass spectrometry, ¹H-NMR and ¹³C-NMR). All the produced novel compounds were screened for their anti-proliferative efficacy on hepatic cancer cell lines (HepG₂). In addition, a computational molecular docking study was carried out to determine the ability of the synthesized thiadiazole molecules to interact with active site of the target Glypican-3 protein (GPC-3). Moreover, the physiochemical properties of the synthesized compounds were derived to determine the viability of the compounds as drug candidates for hepatic cancer.

CONCLUSION

All the tested compounds had exhibited good anti-proliferative efficacy against hepatic cancer cell lines. In addition, the molecular docking results showed strong binding interactions of the synthesized compounds with the target GPC-3 protein with lower energy scores. Thus, such novel compounds may act as promising candidates as drugs against hepatocellular carcinoma.

Virtual screening and free energy estimation for identifying Mycobacterium tuberculosis flavoenzyme DprE1 inhibitors

In Mycobacterium tuberculosis (MTB), the cell wall synthesis flavoenzyme decaprenylphosphoryl-β-d-ribose 2'-epimerase (DprE1) plays a crucial role in host pathogenesis, virulence, lethality and survival under stress. The emergence of different variants of drug resistant MTB are a major threat worldwide which essentially requires more effective new drug molecules with no major side effects. Here, we used structure based virtual screening of bioactive molecules from the ChEMBL database targeting DprE1, having bioactive 78,713 molecules known for anti-tuberculosis activity. An extensive molecular docking, binding affinity and pharmacokinetics profile filtering results in the selection four compounds, C5 (ChEMBL2441313), C6 (ChEMBL2338605), C8 (ChEMBL441373) and C10 (ChEMBL1607606) which may explore as potential drug candidates. The obtained results were validated with thirteen known DprE1 inhibitors. We further estimated the free-binding energy, solvation and entropy terms underlying the binding properties of DprE1-ligand interactions with the implication of MD simulation, MM/GBSA, MM/PBSA and MM/3D-RISM. Interestingly, we find that C6 shows the highest ΔG scores (-41.28 ± 3.51, -22.36 ± 3.17, -10.33 ± 5.70 kcal mol^-1) in MM/GBSA, MM/PBSA and MM/3D-RISM assay, respectively. Whereas, the lowest ΔG scores (-35.31 ± 3.44, -13.67 ± 2.65, -3.40 ± 4.06 kcal mol^-1) observed for CT319, the inhibitor co-crystallized with DprE1. Collectively, the results demonstrated that hit-molecules: C5, C6, C8 and C10 having better binding free energy and molecular affinity as compared to CT319. Thus, we proposed that selected compounds may be explored as lead molecules in MTB therapy.

Targeting thermoTRP ion channels: in silico preclinical approaches and opportunities

INTRODUCTION

A myriad of cellular pathophysiological responses are mediated by polymodal ion channels that respond to chemical and physical stimuli such as thermoTRP channels. Intriguingly, these channels are pivotal therapeutic targets with limited clinical pharmacology. In silico methods offer an unprecedented opportunity for discovering new lead compounds targeting thermoTRP channels with improved pharmacological activity and therapeutic index.

AREAS COVERED

This article reviews the progress on thermoTRP channel pharmacology because of (i) advances in solving their atomic structure using cryo-electron microscopy and, (ii) progress on computational techniques including homology modeling, molecular docking, virtual screening, molecular dynamics, ADME/Tox and artificial intelligence. Together, they have increased the number of lead compounds with clinical potential to treat a variety of pathologies. We used original and review articles from Pubmed (1997-2020), as well as the clinicaltrials.gov database, containing the terms thermoTRP, artificial intelligence, docking, and molecular dynamics.

EXPERT OPINION

The atomic structure of thermoTRP channels along with computational methods constitute a realistic first line strategy for designing drug candidates with improved pharmacology and clinical translation. In silico approaches can also help predict potential side-effects that can limit clinical development of drug candidates. Together, they should provide drug candidates with upgraded therapeutic properties.

Computational Approaches in Preclinical Studies on Drug Discovery and Development

Because undesirable pharmacokinetics and toxicity are significant reasons for the failure of drug development in the costly late stage, it has been widely recognized that drug ADMET properties should be considered as early as possible to reduce failure rates in the clinical phase of drug discovery. Concurrently, drug recalls have become increasingly common in recent years, prompting pharmaceutical companies to increase attention toward the safety evaluation of preclinical drugs. In vitro and in vivo drug evaluation techniques are currently more mature in preclinical applications, but these technologies are costly. In recent years, with the rapid development of computer science, in silico technology has been widely used to evaluate the relevant properties of drugs in the preclinical stage and has produced many software programs and in silico models, further promoting the study of ADMET in vitro. In this review, we first introduce the two ADMET prediction categories (molecular modeling and data modeling). Then, we perform a systematic classification and description of the databases and software commonly used for ADMET prediction. We focus on some widely studied ADMT properties as well as PBPK simulation, and we list some applications that are related to the prediction categories and web tools. Finally, we discuss challenges and limitations in the preclinical area and propose some suggestions and prospects for the future.

Curcumin analogs as the inhibitors of TLR4 pathway in inflammation and their drug like potentialities: a computer-based study

Toll-like receptor 4 (TLR4) pathway is one of the major pathways that mediate the inflammation in human body. There are different anti-inflammatory drugs available in the market which specifically act on different signaling proteins of TLR4 pathway but they do have few side effects and other limitations for intended use in human body. In this study, Curcumin and its different analogs have been analyzed as the inhibitors of signaling proteins, i.e. Cycloxygenase-2 (COX-2), inhibitor of kappaβ kinase (IKK) and TANK binding kinase-1 (TBK-1) of TLR4 pathway using different computational tools. Initially, three compounds were selected for respective target based on free binding energy among which different compounds were reported to have better binding affinity than commercially available drug (control). Upon continuous computational exploration with induced fit docking (IFD), 6-Gingerol, Yakuchinone A and Yakuchinone B were identified as the best inhibitors of COX-2, IKK, and TBK-1 respectively. Then their drug-like potentialities were analyzed in different experiments where they were also predicted to perform well. Hopefully, this study will uphold the efforts of researchers to identify anti-inflammatory drugs from natural sources.

Compound Prunetin Induces Cell Death in Gastric Cancer Cell with Potent Anti-Proliferative Properties: In Vitro Assay, Molecular Docking, Dynamics, and ADMET Studies

Gastric cancer is the common type of malignancy positioned at second in mortality rate causing burden worldwide with increasing treatment options. Prunetin (PRU) is an O-methylated flavonoid that belongs to the group of isoflavone executing beneficial activities. In the present study, we investigated the anti-proliferative and cell death effect of the compound PRU in AGS gastric cancer cell line. The in vitro cytotoxic potential of PRU was evaluated and significant proliferation was observed. We identified that the mechanism of cell death was due to necroptosis through double staining and was confirmed by co-treatment with inhibitor necrostatin (Nec-1). We further elucidated the mechanism of action of necroptosis via receptor interacting protein kinase 3 (RIPK3) protein expression and it has been attributed by ROS generation through JNK activation. Furthermore, through computational analysis by molecular docking and dynamics simulation, the efficiency of compound prunetin against RIPK3 binding was validated. In addition, we also briefed the pharmacokinetic properties of the compound by in silico ADMET analysis.

Machine Learning and Scoring Functions (SFs) for Molecular Drug Discovery: Prediction and Characterisation of Druggable Drugs and Targets

Predicting druggability and prioritising disease-modifying targets is critical in drug discovery. In this chapter, we describe the testing of a druggability rule based on 9 molecular parameters, which uses cutpoints for each molecular parameter and targets based on mixture clustering discriminant analysis. We demonstrate that principal component constructs and score functions of violations can be used to identify the hidden pattern of druggable molecules and disease targets. Random Forest and Artificial Neural Network rules to classify the high-score target from the low-score molecular violators, based both on molecular parameters and the principal component constructs, have confirmed the value of logD's inclusion in the scoring function. Our scoring functions of counts of violations and novel principal component analytic molecular and target-based constructs partitioned chemospace well, identifying both good and poor druggable molecules and targets. Viable molecules and targets were located in both the beyond Rule of 5 and expanded Rule of 5 regions. Random Forest and Artificial Neural Networks showed different variable importance profiles, with Artificial Neural Networks models performing better than Random Forests. The most important molecular descriptors that influence classification, by the Random Forest methods, were MW, NATOM, logD, and PSA. The optimal Artificial Neural Networks target models indicated that PSA and logD were more important than the traditional parameter MW. Overall, our score 4 partitions using logD were optimal at classification as shown in all Random Forests and Artificial Neural Networks analyses.

In Silico ADME/Tox Profiling of Natural Products: A Focus on BIOFACQUIM

Natural products continue to be major sources of bioactive compounds and drug candidates not only because of their unique chemical structures but also because of their overall favorable metabolism and pharmacokinetic properties. The number of publicly accessible natural product databases has increased significantly in the past few years. However, the systematic ADME/Tox profile has been reported on a limited basis. For instance, BIOFACQUIM was recently published as a public database of natural products from Mexico, a country with a rich source of biomolecules. However, its ADME/Tox profile has not been reported. Herein, we discuss the results of an in-depth in silico ADME/Tox profile of natural products in BIOFACQUIM and other large public collections of natural products. It was concluded that the absorption and distribution profiles of compounds in BIOFACQUIM are similar to those of approved drugs, while the metabolism profile is comparable to that in the other natural product databases. The excretion profile of compounds in BIOFACQUIM is different from that of the approved drugs, but their predicted toxicity profile is comparable. This work further contributes to the deeper characterization of natural product collections as major sources of bioactive compounds with therapeutic potential.

Thymus Regeneration and Future Challenges

Thymus regenerative therapy implementation is severely obstructed by the limited number and expansion capacity in vitro of tissue-specific thymic epithelial stem cells (TESC). Current solutions are mostly based on growth factors that can drive differentiation of pluripotent stem cells toward tissue-specific TESC. Target-specific small chemical compounds represent an alternative solution that could induce and support the clonal expansion of TESC and reversibly block their differentiation into mature cells. These compounds could be used both in the composition of culture media designed for TESC expansion in vitro, and in drugs development for thymic regeneration in vivo. It should allow reaching the ultimate objective - autologous thymic tissue regeneration in paediatric patients who had their thymus removed in the course of cardiac surgery.

Virtual screening web servers: designing chemical probes and drug candidates in the cyberspace

The interplay between life sciences and advancing technology drives a continuous cycle of chemical data growth; these data are most often stored in open or partially open databases. In parallel, many different types of algorithms are being developed to manipulate these chemical objects and associated bioactivity data. Virtual screening methods are among the most popular computational approaches in pharmaceutical research. Today, user-friendly web-based tools are available to help scientists perform virtual screening experiments. This article provides an overview of internet resources enabling and supporting chemical biology and early drug discovery with a main emphasis on web servers dedicated to virtual ligand screening and small-molecule docking. This survey first introduces some key concepts and then presents recent and easily accessible virtual screening and related target-fishing tools as well as briefly discusses case studies enabled by some of these web services. Notwithstanding further improvements, already available web-based tools not only contribute to the design of bioactive molecules and assist drug repositioning but also help to generate new ideas and explore different hypotheses in a timely fashion while contributing to teaching in the field of drug development.

Modular Characteristics and Mechanism of Action of Herbs for Endometriosis Treatment in Chinese Medicine: A Data Mining and Network Pharmacology-Based Identification

Endometriosis is a common benign disease in women of reproductive age. It has been defined as a disorder characterized by inflammation, compromised immunity, hormone dependence, and neuroangiogenesis. Unfortunately, the mechanisms of endometriosis have not yet been fully elucidated, and available treatment methods are currently limited. The discovery of new therapeutic drugs and improvements in existing treatment schemes remain the focus of research initiatives. Chinese medicine can improve the symptoms associated with endometriosis. Many Chinese herbal medicines could exert antiendometriosis effects via comprehensive interactions with multiple targets. However, these interactions have not been defined. This study used association rule mining and systems pharmacology to discover a method by which potential antiendometriosis herbs can be investigated. We analyzed various combinations and mechanisms of action of medicinal herbs to establish molecular networks showing interactions with multiple targets. The results showed that endometriosis treatment in Chinese medicine is mainly based on methods of supplementation with blood-activating herbs and strengthening qi. Furthermore, we used network pharmacology to analyze the main herbs that facilitate the decoding of multiscale mechanisms of the herbal compounds. We found that Chinese medicine could affect the development of endometriosis by regulating inflammation, immunity, angiogenesis, and other clusters of processes identified by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses. The antiendometriosis effect of Chinese medicine occurs mainly through nervous system–associated pathways, such as the serotonergic synapse, the neurotrophin signaling pathway, and dopaminergic synapse, among others, to reduce pain. Chinese medicine could also regulate VEGF signaling, toll-like reporter signaling, NF-κB signaling, MAPK signaling, PI3K-Akt signaling, and the HIF-1 signaling pathway, among others. Synergies often exist in herb pairs and herbal prescriptions. In conclusion, we identified some important targets, target pairs, and regulatory networks, using bioinformatics and data mining. The combination of data mining and network pharmacology may offer an efficient method for drug discovery and development from herbal medicines.

New role for crinamine as a potent, safe and selective inhibitor of human monoamine oxidase B: In vitro and in silico pharmacology and modeling

ETHNOPHARMACOLOGICAL RELEVANCE

The development of selective inhibitors of monoamine oxidase B (MAO-B) has been essential in treating Parkinson's disease. However, the apparent hepatotoxicity and drug-drug interactions of current inhibitors accentuate the need for the development of novel pharmacotherapies. Crossyne guttata (L.) D. & U. Müll-Doblies is used frequently by Rastafarian bush doctors to treat alcoholism, a disorder which is also accentuated by MAO.

OBJECTIVE

The study sought to isolate, identify and characterise the biologically active constituents of C. guttata based on their ability to inhibit the MAO enzymes.

MATERIALS AND METHODS

Column chromatography was used to isolate the biologically active alkaloids of C. guttata. The ability of the alkaloids to inhibit the biotransformation of 4-aminoantipyrine by the MAO enzymes was evaluated in vitro. In silico docking was conducted using AutoDock Vina server while the pharmacokinetic properties of the compounds were evaluated using SwissADME.

RESULTS

Chromatographic separation of an ethanolic fraction of C. guttata yielded the alkaloids crinamine 1 and epibuphanisine 2. 1 and 2 along with structurally related alkaloids haemanthamine 3 and haemanthidine 4 were evaluated for their ability to inhibit the action of isozymes of MAO in vitro. Alkaloids effected submicromolar IC₅₀ values against MAO-B, the most potent of which being crinamine 1 (0.014 μM) > haemanthidine 4 (0.017 μM) > epibuphanisine 2 (0.039 μM) > haemanthamine 3 (0.112 μM). Binding energies of the alkaloids correlated well with their inhibitory potential with crinamine displaying the best binding efficacy and binding energy score with MAO-B.

DISCUSSION AND CONCLUSION

Crinamine and epibuphanisine exhibited potent and selective inhibitory activity towards MAO-B. After comprehensive in silico investigations encompassing robust molecular docking analysis, the drug-like attributes and safety of the alkaloids suggest the crinamine is a potentially safe drug for human application.

Synthesis, Characterization, Antibacterial Activity, and Computer-Aided Design of Novel Quinazolin-2,4-dione Derivatives as Potential Inhibitors Against Vibrio cholerae

Cholera is a bacterial disease featured by dehydration and severe diarrhea. It is mainly caused by alimentary infection with Vibrio cholerae. Due to the wide applicability of quinazolin-2,4-dione compounds in medicinal and pharmaceutical chemistry, a new series of N-containing heterocyclic compounds was synthesized. We used the in silico docking method to test the efficacy of quinazolin-2,4-dione compounds in the prevention of cholera in humans. The newly synthesized compounds showed strong interactions and good binding affinity to outer membrane protein OmpU. Moreover, the pharmacokinetic properties of the newly synthesized compounds, such as absorption, distribution, metabolic, excretion, and toxicity (ADMET), were predicted through in silico methods. Compounds with acceptable pharmacokinetic properties were tested as novel ligand molecules. The synthesized compounds were evaluated in vitro for their antibacterial activity properties against Gram-negative Escherichia coli O78 strain using the minimum inhibition concentration (MIC) method. Compounds 2 and 6 showed reproducible, effective antibacterial activity. Hence, our study concludes that the quinazolin-2,4-dione derivatives 1 to 8 may be used as promising drug candidates with potential value for the treatment of cholera disease.

ADMET properties of novel 5-O-benzoylpinostrobin derivatives

Background Prediction of the properties of absorption, distribution, metabolism, excretion, and toxicity (ADMET) from a compound is essential, especially for modified novel compounds. Previous research has successfully designed several modified compounds of 5-O-benzoyl derivatives from pinostrobin, a flavanone that has cytotoxic activity. This study aims to describe the properties of ADMET from the 5-O-benzoylpinostrobin derivative. Methods Prediction of the properties of ADMET was carried out using three web servers consisting of SwissADME, pkCSM, and ProTox-II. The observed parameters are divided into ADMET parameters. Results In general, absorption parameters indicate that the 5-O-benzoylpinostrobin derivative has lower water solubility than the parent pinostrobin. Distribution parameters show mixed results for distribution through the blood-brain barrier. Metabolism parameters showed different results with generally inhibitory activity shown in CYP2C19, CYP2C9, and CYP3A4. The excretion parameters showed a higher total clearance than pinostrobin except in the trifluoromethyl derivative. The toxicity parameters showed both pinostrobin and the 5-O-benzoylpinostrobin derivatives, including the class IV toxicity category with the lowest LD50 value indicated by the nitro derivative of 1500, with the possible target of the androgen receptor and prostaglandin G/H synthase 1. Conclusions Overall, the 5-O-benzoylpinostrobin derivative has the predicted ADMET profile that is relatively similar to pinostrobin, with the most noticeable difference being shown in the absorption parameters where all 5-O-benzoylpinostrobin derivatives have lower water solubility than pinostrobin.

Development of an in silico prediction system of human renal excretion and clearance from chemical structure information incorporating fraction unbound in plasma as a descriptor

Prediction of pharmacokinetic profiles of new chemical entities is essential in drug development to minimize the risks of potential withdrawals. The excretion of unchanged compounds by the kidney constitutes a major route in drug elimination and plays an important role in pharmacokinetics. Herein, we created in silico prediction models of the fraction of drug excreted unchanged in the urine (f_e) and renal clearance (CL_r), with datasets of 411 and 401 compounds using freely available software; notably, all models require chemical structure information alone. The binary classification model for f_e demonstrated a balanced accuracy of 0.74. The two-step prediction system for CL_r was generated using a combination of the classification model to predict excretion-type compounds and regression models to predict the CL_r value for each excretion type. The accuracies of the regression models increased upon adding a descriptor, which was the observed and predicted fraction unbound in plasma (f_u,p); 78.6% of the samples in the higher range of renal clearance fell within 2-fold error with predicted f_u,p value. Our prediction system for renal excretion is freely available to the public and can be used as a practical tool for prioritization and optimization of compound synthesis in the early stage of drug discovery.

In-silico molecular design of heterocyclic benzimidazole scaffolds as prospective anticancer agents

Benzimidazole is a valuable pharmacophore in the field of medicinal chemistry and exhibit wide spectrum of biological activity. Molecular docking technique is routinely used in modern drug discovery for understanding the drug-receptor interaction. The selected data set of synthesized benzimidazole compounds was evaluated for its in vitro anticancer activity against cancer cell lines (HCT116 and MCF7) by sulforhodamine B (SRB) assay. Further, molecular docking study of data set was carried out by Schrodinger-Maestro v11.5 using CDK-8 (PDB code: 5FGK) and ER-alpha (PDB code: 3ERT) as possible target for anticancer activity. Molecular docking results demonstrated that compounds 12, 16, N9, W20 and Z24 displayed good docking score with better interaction within crucial amino acids and corelate to their anticancer results. ADME results indicated that compounds 16, N9 and W20 have significant results within the close agreement of the Lipinski's rule of five and Qikprop rule within the range and these compounds may be taken as lead molecules for the discovery of new anticancer agents.

Advances and Challenges in Rational Drug Design for SLCs

There are over 420 human solute carrier (SLC) transporters from 65 families that are expressed ubiquitously in the body. The SLCs mediate the movement of ions, drugs, and metabolites across membranes and their dysfunction has been associated with a variety of diseases, such as diabetes, cancer, and central nervous system (CNS) disorders. Thus, SLCs are emerging as important targets for therapeutic intervention. Recent technological advances in experimental and computational biology allow better characterization of SLC pharmacology. Here we describe recent approaches to modulate SLC transporter function, with an emphasis on the use of computational approaches and computer-aided drug design (CADD) to study nutrient transporters. Finally, we discuss future perspectives in the rational design of SLC drugs.

A Drug Decision Support System for Developing a Successful Drug Candidate Using Machine Learning Techniques

BACKGROUND

Virtual screening of candidate drug molecules using machine learning techniques plays a key role in pharmaceutical industry to design and discovery of new drugs. Computational classification methods can determine drug types according to the disease groups and distinguish approved drugs from withdrawn ones.

INTRODUCTION

Classification models developed in this study can be used as a simple filter in drug modelling to eliminate potentially inappropriate molecules in the early stages. In this work, we developed a Drug Decision Support System (DDSS) to classify each drug candidate molecule as potentially drug or non-drug and to predict its disease group.

METHODS

Molecular descriptors were identified for the determination of a number of rules in drug molecules. They were derived using ADRIANA.Code program and Lipinski's rule of five. We used Artificial Neural Network (ANN) to classify drug molecules correctly according to the types of diseases. Closed frequent molecular structures in the form of subgraph fragments were also obtained with Gaston algorithm included in ParMol Package to find common molecular fragments for withdrawn drugs.

RESULTS

We observed that TPSA, XlogP Natoms, HDon_O and TPSA are the most distinctive features in the pool of the molecular descriptors and evaluated the performances of classifiers on all datasets and found that classification accuracies are very high on all the datasets. Neural network models achieved 84.6% and 83.3% accuracies on test sets including cardiac therapy, anti-epileptics and anti-parkinson drugs with approved and withdrawn drugs for drug classification problems.

CONCLUSION

The experimental evaluation shows that the system is promising at determination of potential drug molecules to classify drug molecules correctly according to the types of diseases.

ProTox-II: a webserver for the prediction of toxicity of chemicals

Advancement in the field of computational research has made it possible for the in silico methods to offer significant benefits to both regulatory needs and requirements for risk assessments, and pharmaceutical industry to assess the safety profile of a chemical. Here, we present ProTox-II that incorporates molecular similarity, pharmacophores, fragment propensities and machine-learning models for the prediction of various toxicity endpoints; such as acute toxicity, hepatotoxicity, cytotoxicity, carcinogenicity, mutagenicity, immunotoxicity, adverse outcomes pathways (Tox21) and toxicity targets. The predictive models are built on data from both in vitro assays (e.g. Tox21 assays, Ames bacterial mutation assays, hepG2 cytotoxicity assays, Immunotoxicity assays) and in vivo cases (e.g. carcinogenicity, hepatotoxicity). The models have been validated on independent external sets and have shown strong performance. ProTox-II provides a freely available webserver for in silico toxicity prediction for toxicologists, regulatory agencies, computational and medicinal chemists, and all users without login at http://tox.charite.de/protox_II. The webserver takes a two-dimensional chemical structure as an input and reports the possible toxicity profile of the chemical for 33 models with confidence scores, and an overall toxicity radar chart along with three most similar compounds with known acute toxicity.

Discovery of human autophagy initiation kinase ULK1 inhibitors by multi-directional in silico screening strategies

Autophagy is a self-catabolic mechanism employed by cancer cells to acquire nutrients and energy in times of stress conditions, thereby leading to its progression and survival. Thus, autophagy inhibition has emerged as a new paradigm in the area of cancer treatment. Here, we leverage multi-dimensional screening campaigns aim to identify potent inhibitors against an early and an essential autophagic kinase, ULK1 from DrugBank database. In particular, receptor-based hypothesis, pharmacophore hypothesis, e-pharmacophore hypothesis and shape similarity-based screening algorithm were employed. Of note, the results of the different algorithm were then integrated to eliminate the false positive prediction. Moreover, the inhibitory activities and PK/PD parameters of the leads were tested by Glide and Qikprop algorithm. This resulted in a set of four hits namely; DB12686, DB08341, DB07936, and DB07163. Finally, molecular dynamics simulation was performed using the GROMACS package, to validate the binding kinetics of the hit compound. The compound activity in vitro was assessed by PASS algorithm, highlights the anti-cancer activities of the hits. The structural insights reveal existence of functional moieties such as piperidine carboxamide, benzenesulfonamide, benzamide, and isoindolone in the resultant hits which plays a major role in the anti-cancer activity. Overall, we strongly believe that these ULK1 antagonists could be novel and potent drug candidates for future cancer therapeutics.

Concepts of Artificial Intelligence for Computer-Assisted Drug Discovery

Artificial intelligence (AI), and, in particular, deep learning as a subcategory of AI, provides opportunities for the discovery and development of innovative drugs. Various machine learning approaches have recently (re)emerged, some of which may be considered instances of domain-specific AI which have been successfully employed for drug discovery and design. This review provides a comprehensive portrayal of these machine learning techniques and of their applications in medicinal chemistry. After introducing the basic principles, alongside some application notes, of the various machine learning algorithms, the current state-of-the art of AI-assisted pharmaceutical discovery is discussed, including applications in structure- and ligand-based virtual screening, de novo drug design, physicochemical and pharmacokinetic property prediction, drug repurposing, and related aspects. Finally, several challenges and limitations of the current methods are summarized, with a view to potential future directions for AI-assisted drug discovery and design.

Salvindolin elicits opioid system-mediated antinociceptive and antidepressant-like activities

BACKGROUND

Salvinorin A is known as a highly selective kappa opioid receptor agonist with antinociceptive but mostly pro-depressive effects.

AIMS

In this article, we present its new semisynthetic analog with preferential mu opioid affinity, and promising antinociceptive, as well as antidepressant-like activities.

METHODS

Competitive binding studies were performed for salvindolin with kappa opioid and mu opioid. The mouse model of nociception (acetic-acid-induced writhing, formalin, and hot plate tests), depression (forced swim and tail suspension tests), and the open field test, were used to evaluate antinociceptive, antidepressant-like, and locomotion effects, respectively, of salvindolin. We built a 3-D molecular model of the kappa opioid receptor, using a mu opioid X-ray crystal structure as a template, and docked salvindolin into the two proteins.

RESULTS/OUTCOMES

Salvindolin showed affinity towards kappa opioid and mu opioid receptors but with 100-fold mu opioid preference. Tests of salvindolin in mice revealed good oral bioavailability, antinociceptive, and antidepressive-like effects, without locomotor incoordination. Docking of salvindolin showed strong interactions with the mu opioid receptor which matched well with experimental binding data. Salvindolin-induced behavioral changes in the hot plate and forced swim tests were attenuated by naloxone (nonselective opioid receptor antagonist) and/or naloxonazine (selective mu opioid receptor antagonist) but not by nor-binaltorphimine (selective kappa opioid receptor antagonist). In addition, WAY100635 (a selective serotonin 1A receptor antagonist) blocked the antidepressant-like effect of salvindolin.

CONCLUSIONS/INTERPRETATION

By simple chemical modification, we were able to modulate the pharmacological profile of salvinorin A, a highly selective kappa opioid receptor agonist, to salvindolin, a ligand with preferential mu opioid receptor affinity and activity on the serotonin 1A receptor. With its significant antinociceptive and antidepressive-like activities, salvindolin has the potential to be an analgesic and/or antidepressant drug candidate.

Computer Design of Low-Molecular-Weight Inhibitors of Coagulation Factors

The review discusses main approaches to searching for new low-molecular-weight inhibitors of coagulation factors IIa, Xa, IXa, and XIa and the results of such studies conducted from 2015 to 2018. For each of these factors, several inhibitors with IC₅₀ < 10 nM have been found, some of which are now tested in clinical trials. However, none of the identified inhibitors meets the requirements for an "ideal" anticoagulant, so further studies are required.

Parametric investigation of static and dynamic cell culture conditions and their impact on hCMEC/D3 barrier properties

In brain research, the hCMEC/D3 cell line is widely used for the establishment of a human in vitro blood-brain barrier (BBB) model. However, its barrier integrity seems to be insufficient for drug permeability studies, represented by rather low transendothelial electrical resistance (TEER) and high permeability of small molecules. Therefore, this study covers a parametric investigation of static and dynamic cell culture conditions to improve barrier functionality of hCMEC/D3. The effect of basal media was investigated by analyzing changes in proliferation rate, barrier integrity and gene expression of cellular junction proteins. The cells were able to grow in different cell culture media, including serum-free media. However, none of these media enhanced strongly the growth rate or barrier integrity compared to the microvascular endothelial cell growth medium-2 (EGM™-2 MV). Furthermore, hCMEC/D3 cells did not respond positively regarding TEER to any tested parameter neither supplements, coating materials nor co-cultures with the human immortalized astrocyte cell line SVGmm. Furthermore, the impact of dynamic conditions was examined by using the Dynamic Micro Tissue Engineering System (DynaMiTES). Cultivation conditions were successfully adapted to the DynaMiTES design and no negative effect was detected by analyzing cell viability and cell count, albeit TEER remained also unchanged. Consequently, the hCMEC/D3 model has considerable limitations and further improvements or alternative cell lines are required.

4-(4-Bromophenyl)-thiazol-2-amine derivatives: synthesis, biological activity and molecular docking study with ADME profile

In order to overcome the challenges of microbial resistance as well as to improve the effectiveness and selectivity of chemotherapeutic agents against cancer, a novel series of 4-(4-bromophenyl)-thiazol-2-amine derivatives was synthesized and its molecular structures were confirmed by physicochemical and spectral characteristics. The synthesized compounds were further evaluated for their in vitro antimicrobial activity using turbidimetric method and anticancer activity against oestrogen receptor positive human breast adenocarcinoma cancer cell line (MCF7) by Sulforhodamine B (SRB) assay. The antimicrobial activity results revealed that compound p2, p3, p4 and p6 exhibited promising antimicrobial activity that are comparable to standard norfloxacin (antibacterial) and fluconazole (antifungal). Anticancer screening results demonstrated that compound p2 was found to be the most active one against cancer cell line when compared to the rest of the compounds and comparable to the standard drug (5-fluorouracil). The molecular docking study demonstrated that compounds, p2, p3, p4 and p6 displayed good docking score within binding pocket of the selected PDB ID (1JIJ, 4WMZ and 3ERT) and showed promising ADME properties.

Identification of a Novel Bcl-2 Inhibitor by Ligand-Based Screening and Investigation of Its Anti-cancer Effect on Human Breast Cancer Cells

Bcl-2 family protein is an important factor in regulating apoptosis and is associated with cancer. The anti-apoptotic proteins of Bcl-2 family, such as Bcl-2, are overexpression in numerous tumors, and contribute to cancer formation, development, and therapy resistance. Therefore, Bcl-2 is a promising target for drug development, and several Bcl-2 inhibitors are currently undergoing clinical trials. In this study, we carried out a QSAR-based virtual screening approach to develop potential Bcl-2 inhibitors from the SPECS database. Surface plasmon resonance (SPR) binding assay was performed to examine the interaction between Bcl-2 protein and the screened inhibitors. After that, we measured the anti-tumor activities of the 8 candidate compounds, and found that compound M1 has significant cytotoxic effect on breast cancer cells. We further proved that compound M1 downregulated Bcl-2 expression and activated apoptosis by inducing mitochondrial dysfunction. In conclusion, we identified a novel Bcl-2 inhibitor by QSAR screening, which exerted significant cytotoxic activity in breast cancer cells through inducing mitochondria-mediated apoptosis.

Optimization of a Deep-Learning Method Based on the Classification of Images Generated by Parameterized Deep Snap a Novel Molecular-Image-Input Technique for Quantitative Structure-Activity Relationship (QSAR) Analysis

Numerous chemical compounds are distributed around the world and may affect the homeostasis of the endocrine system by disrupting the normal functions of hormone receptors. Although the risks associated with these compounds have been evaluated by acute toxicity testing in mammalian models, the chronic toxicity of many chemicals remains due to high cost of the compounds and the testing, etc. However, computational approaches may be promising alternatives and reduce these evaluations. Recently, deep learning (DL) has been shown to be promising prediction models with high accuracy for recognition of images, speech, signals, and videos since it greatly benefits from large datasets. Recently, a novel DL-based technique called DeepSnap was developed to conduct QSAR analysis using three-dimensional images of chemical structures. It can be used to predict the potential toxicity of many different chemicals to various receptors without extraction of descriptors. DeepSnap has been shown to have a very high capacity in tests using Tox21 quantitative qHTP datasets. Numerous parameters must be adjusted to use the DeepSnap method but they have not been optimized. In this study, the effects of these parameters on the performance of the DL prediction model were evaluated in terms of the loss in validation as an indicator for evaluating the performance of the DL using the toxicity information in the Tox21 qHTP database. The relations of the parameters of DeepSnap such as (1) number of molecules per SDF split into (2) zoom factor percentage, (3) atom size for van der waals percentage, (4) bond radius, (5) minimum bond distance, and (6) bond tolerance, with the validation loss following quadratic function curves, which suggests that optimal thresholds exist to attain the best performance with these prediction models. Using the parameter values set with the best performance, the prediction model of chemical compounds for CAR agonist was built using 64 images, at 105° angle, with AUC of 0.791. Thus, based on these parameters, the proposed DeepSnap-DL approach will be highly reliable and beneficial to establish models to assess the risk associated with various chemicals.

Molecular Modeling Approaches for the Prediction of Selected Pharmacokinetic Properties

Poor profiles of potential drug candidates, including pharmacokinetic properties, have been acknowledged as a significant hindrance to the development of modern therapeutics. Contemporary drug discovery and development would be incomplete without the aid of molecular modeling (in-silico) techniques, allowing the prediction of pharmacokinetic properties such as clearance, unbound fraction, volume of distribution and bioavailability. As with all models, in-silico approaches are subject to their interpretability, a trait that must be balanced with accuracy when considering the development of new methods. The best models will always require reliable data to inform them, presenting significant challenges, particularly when appropriate in-vitro or in-vivo data may be difficult or time-consuming to obtain. This article seeks to review some of the key in-silico techniques used to predict key pharmacokinetic properties and give commentary on the current and future directions of the field.

Polarizability of Aspirin at Terahertz Frequencies Using Terahertz Time Domain Spectroscopy (THz-TDS)

A novel application of terahertz time-domain spectroscopy (THz-TDS) is described for the determination of permittivity and polarizability of organic crystals, as exemplified by measurements with the polymorph I form of crystalline aspirin (acetylsalicylic acid). The coherent nature of the THz pulse experiment, coupled with gated-detection, permits direct measure of differences in the phase angle of the electric field vector after passing through a pellet composed of the aspirin crystals embedded within an inert polymer matrix. An effective media model is used to extract dielectric information for the crystals from the measured time-domain signal that is representative of the entire pellet composition. Polarizability is then obtained for these organic crystals by using the Clausius-Mossotti relationship. Dielectric spectra and polarizability spectra are presented over the 0.3-3 THz frequency range (10-100 cm^-1). The average polarizability values measured over the low frequency range (10-20 cm^-1) are 22.4 ± 0.3 and 22.4 ± 0.5 ų for aspirin crystals embedded within matrixes of polytetrafluoroethylene (PTFE) and polyethylene (PE), respectively.

Drug Discovery

Modern chemistry foundations were made in between the 18th and 19th centuries and have been extended in 20th century. R&D towards synthetic chemistry was introduced during the 1960s. Development of new molecular drugs from the herbal plants to synthetic chemistry is the fundamental scientific improvement. About 10-14 years are needed to develop a new molecule with an average cost of more than $800 million. Pharmaceutical industries spend the highest percentage of revenues, but the achievement of desired molecular entities into the market is not increasing proportionately. As a result, an approximate of 0.01% of new molecular entities are approved by the FDA. The highest failure rate is due to inadequate efficacy exhibited in Phase II of the drug discovery and development stage. Innovative technologies such as combinatorial chemistry, DNA sequencing, high-throughput screening, bioinformatics, computational drug design, and computer modeling are now utilized in the drug discovery. These technologies can accelerate the success rates in introducing new molecular entities into the market.

Target-specific toxicity knowledgebase (TsTKb): a novel toolkit for in silico predictive toxicology

As the number of man-made chemicals increases at an unprecedented pace, efforts of quickly screening and accurately evaluating their potential adverse biological effects have been hampered by prohibitively high costs of in vivo/vitro toxicity testing. While it is unrealistic and unnecessary to test every uncharacterized chemical, it remains a major challenge to develop alternative in silico tools with high reliability and precision in toxicity prediction. To address this urgent need, we have developed a novel mode-of-action-guided, molecular modeling-based, and machine learning-enabled modeling approach for in silico chemical toxicity prediction. Here we introduce the core element of this approach, Target-specific Toxicity Knowledgebase (TsTKb), which consists of two main components: Chemical Mode of Action (ChemMoA) database and a suite of prediction model libraries.