In silico approaches for predicting ADME properties of drugs

Synthesis and antiproliferative evaluation of some iron chelators as polyamine transporter targeting agents

A series of kojic acid derivatives based on monoamines, diamines, and triamines have been synthesized in good yield and purity. A spermidine (spd) rescue experiment was applied against MCF-7 cells to evaluate the polyamine transporter selectivity, and in vitro antiproliferative effects were determined against Hela and DU-145 cell lines. Overall, 5b showed the best selectivity for the polyamine transporter and antiproliferative effects. Therefore, the in-silico metabolism profile and ADMET properties of the title compounds were calculated by the PreADMET server. Additionally, physicochemical properties of ligands were predicted by using the Molinspiration online property calculation server.

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.

Hydrazone, benzohydrazones and isoniazid-acylhydrazones as potential antituberculosis agents

Aim: To evaluate the potential of three benzohydrazones (1–3), four acylhydrazones derived from isoniazid (INH-acylhydrazones) (4–7) and one hydrazone (8) as antituberculosis agents. Materials & methods: Inhibitory and bactericidal activities were determined for the reference Mycobacterium tuberculosis ( Mtb) strain and clinical isolates. Cytotoxicity, drug combinations and ethidium bromide accumulation assays were also performed. Results: The tested compounds (1–8) presented excellent antituberculosis activity with surprisingly inhibitory (0.12–250 μg/ml) and bactericidal values, even against multidrug-resistant Mtb clinical isolates. Compounds showed high selectivity index, with values reaching 1833.33, and a limited spectrum of activity. Some of the compounds (2 & 8) are also great inhibitors of bacillus efflux pumps. Conclusion: Benzohydrazones and INH-acylhydrazones may be considered scaffolds for the development of new anti- Mtb drugs.

Prediction of Drug-Drug Interactions Related to Inhibition or Induction of Drug-Metabolizing Enzymes

Drug-drug interaction (DDI) is the phenomenon of alteration of the pharmacological activity of a drug(s) when another drug(s) is co-administered in cases of so-called polypharmacy. There are three types of DDIs: pharmacokinetic (PK), pharmacodynamic, and pharmaceutical. PK is the most frequent type of DDI, which often appears as a result of the inhibition or induction of drug-metabolising enzymes (DME). In this review, we summarise in silico methods that may be applied for the prediction of the inhibition or induction of DMEs and describe appropriate computational methods for DDI prediction, showing the current situation and perspectives of these approaches in medicinal and pharmaceutical chemistry. We review sources of information on DDI, which can be used in pharmaceutical investigations and medicinal practice and/or for the creation of computational models. The problem of the inaccuracy and redundancy of these data are discussed. We provide information on the state-of-the-art physiologically- based pharmacokinetic modelling (PBPK) approaches and DME-based in silico methods. In the section on ligand-based methods, we describe pharmacophore models, molecular field analysis, quantitative structure-activity relationships (QSAR), and similarity analysis applied to the prediction of DDI related to the inhibition or induction of DME. In conclusion, we discuss the problems of DDI severity assessment, mention factors that influence severity, and highlight the issues, perspectives and practical using of in silico methods.

Identification of Pak1 inhibitors using water thermodynamic analysis

p21-activated kinases (Paks) play an integral component in various cellular diverse processes. The full activation of Pak is dependent upon several serine residues present in the N-terminal region, a threonine present at the activation loop, and finally the phosphorylation of these residues ensure the complete activation of Pak1. The present study deals with the identification of novel potent candidates of Pak1 using computational methods as anti-cancer compounds. A diverse energy based pharmacophore (e-pharmacophore) was developed using four co-crystal inhibitors of Pak1 having pharmacophore features of 5 (DRDRR), 6 (DRHADR), and 7 (RRARDRP and DRRDADH) hypotheses. These models were used for rigorous screening against e-molecule database. The obtained hits were filtered using ADME/T and molecular docking to identify the high affinity binders. These hits were subjected to hierarchical clustering using dendritic fingerprint inorder to identify structurally diverse molecules. The diverse hits were scored against generated water maps to obtain WM/MM ΔG binding energy. Furthermore, molecular dynamics simulation and density functional theory calculations were performed on the final hits to understand the stability of the complexes. Five structurally diverse novel Pak1 inhibitors (4835785, 32198676, 32407813, 76038049, and 32945545) were obtained from virtual screening, water thermodynamics and WM/MM ΔG binding energy. All hits revealed similar mode of binding pattern with the hinge region residues replacing the unstable water molecules in the binding site. The obtained novel hits could be used as a platform to design potent drugs that could be experimentally tested against cancer patients having increased Pak1 expression.

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.

Synthesis, docking, ADMET prediction, cytotoxicity and antimicrobial activity of oxathiadiazole derivatives

A series of molecules bearing oxathiadiazole, a five membered heterocyclic ring has been designed, synthesized and screened for antimicrobial activity. Molecules, 1a, 1b, 1d, 3a-b and 4a-b were found to be highly active (MIC value upto 1.5 μg/mL) against different human pathogens, namely S. aureus, B. cerus, P. aeruginosa and E. coli. Some of the compounds, 1a, 1b and 1d have also shown the antifungal activity (MIC value upto 6.2 μg/mL) against Candida albicans, Candida glubrate and Candida crusei. During in vitro cytotoxicity study, the oxathiadiazole derivatives showed less toxicity than the reference used against PBM, CEM and Vero (African green monkey kidney) cell lines. Docking studies suggested that all designed ligands interacted well within active site of PDF enzyme (PDB ID: 1G2A). Oxathiadiazole ring of all ligands formed H-bond with amino acid Leu91 at a distance ranging between 2.5-2.8 Å and also exhibited π - + and π - π interactions with amino acid residues Arg97 and His132, respectively. In silico ADMET evaluations of compounds showed more than 90% intestinal absorption for all compounds except 4b (87.45%), which too was greater than the reference drugs sulfamethoxazole (76.46%) and chloramphenicol (69.94%). TOPKAT results also supported the lower cytotoxicity of all compounds.

Combining in vitro and in silico Approaches to Find New Candidate Drugs Targeting the Pathological Proteins Related to the Alzheimer's Disease

Background: Alzheimer’s disease (AD) as the most common cause of dementia among older people has aroused the universal concern of the whole world. However, until now there is still none effective treatments. Consequently, the development of new drugs targeting this complicated brain disorder is urgent and needs more efforts. In this review, we detailed the current state of knowledge about new candidate drugs targeting the pathological proteins especially the drugs which are employed using the combined methods of in vitro and in silico.

Methods: We looked up and reviewed online papers related to the pathogenesis and new drugs development of AD. Then, articles up to the requirements were respectively analyzed and summaried to provide the latest knowledge about the pathogenic effect and the new candidate drugs targeting Aβ and Tau proteins.

Results: New candidate drugs targeting the Aβ include decreasing the production, promoting the clearence and preventing aggregation. However these drugs have mostly failed in Phase III clinical trial stage due to the unsuccessful of reversing cognition symptoms. As to tau protein, the prevention of tau aggregation and propagation is a promising strategy to synthesize/design mechanism-based drugs against tauopathies. Some candidate drugs are under research. Moreover, because of the complex pathogenesis of AD, multi-target drugs have also shed light on the treatment of AD.

Conclusion: Given to the consecutive failure of Aβ-directed drugs and the feasibilities of tau-targeted therapy, more and more researchers suggested that the AD treatment should be moved from Aβ to tau or focused on considering the soluble form of Aβ and tau as a whole. Moreover, the novel in silico methods also have great potential in drug discovery, drug repositioning, virtual screening of chemical libraries. No matter how many difficulties and challenges in prevention and treatment of AD, we firmly believe that the effective and safe drugs will be found using the combined methods in the immediate future with the global effort.

Synthesis, molecular modeling and biological screening of some pyrazole derivatives as antileishmanial agents

Aim: Novel open chain and cyclized derivatives containing pyrazole scaffold were designed, synthesized and evaluated as antileishmanial compounds. Methodology & results: In silico reverse docking experiment suggested Leishmania major pteridine reductase (Lm-PTR1) as a putative target for the synthesized compounds. In vitro antileishmanial screening against L. major promastigotes and amastigotes using miltefosine and amphotericin B as references showed that the majority of the compounds displayed activity higher than miltefosine. Compounds 3i and 5 showed the highest antileishmanial activity with IC50 values of 1.45 ± 0.08 μM and 2.30 ± 0.09 μM, respectively, for the amastigote form. In silico drug-likeness and toxicity predictions showed acceptable profiles for most of the compounds, which were validated by experimental toxicity studies. Conclusion: This study offers promising entities for antileishmanial activity.

Identification of chemically diverse GABAA agonists as potential anti-epileptic agents using structure-guided virtual screening, ADMET, quantum mechanics and clinical validation through off-target analysis

Development of resistance against existing anti-epileptic drugs has alarmed the scientific innovators to find novel potential chemical starting points for the treatment of epilepsy and GABA_A inhibition is a promising drug target strategy against epilepsy. The crystal structure of a subtype-selective β3-homopentameric ligand-gated ion channel of GABA_A receptor has been used for the first time for screening the Asinex library for discovery of GABA_A agonists as potential anti-epileptic agents. Co-crystallized ligand established the involvement of part of the β7-β8 loop (Glu155 and Tyr157) and β9-β10 loop (Phe200 and Tyr205) residues as the crucial amino acids in effective binding, an essential feature, being hydrogen bond or ionic interaction with Glu155 residue. Top ranked hits were further subjected to binding energy estimation, ADMET analysis and ligand efficiency matric calculations as consecutive filters. About 19 compounds qualifying all parameters possessed interaction of one positively charged group with Glu155 with good CNS drug-like properties. Simulation studies were performed on the apo protein, its complex with co-crystallized ligand and the best hit qualifying all screening parameters. The best hit was also analyzed using Quantum mechanical studies, off-target analysis and hit modification. The off-target analysis emphasized that these agents did not have any other predicted side-effects.

Technologies for Measuring Pharmacokinetic Profiles

The creation of a pharmacokinetic (PK) curve, which follows the plasma concentration of an administered drug as a function of time, is a critical aspect of the drug development process and includes such information as the drug's bioavailability, clearance, and elimination half-life. Prior to a drug of interest gaining clearance for use in human clinical trials, research is performed during the preclinical stages to establish drug safety and dosing metrics from data obtained from the PK studies. Both in vivo animal models and in vitro platforms have limitations in predicting human reaction to a drug due to differences in species and associated simplifications, respectively. As a result, in silico experiments using computer simulation have been implemented to accurately predict PK parameters in human studies. This review assesses these three approaches (in vitro, in vivo, and in silico) when establishing PK parameters and evaluates the potential for in silico studies to be the future gold standard of PK preclinical studies.

Structural insights into suppressor of cytokine signaling 1 protein- identification of new leads for type 2 diabetes mellitus

The study considers the Suppressor of cytokine signaling 1 (SOCS1) protein as a novel Type 2 diabetes mellitus (T2DM) drug target. T2DM in human beings is also triggered by the over expression of SOCS proteins. The SOCS1 acts as a ubiquitin ligase (E3), degrades Insulin Receptor Substrate 1 and 2 (IRS1 and IRS2) proteins, and causes insulin resistance. Therefore, the structure of the SOCS1 protein was evaluated using homology-modeling and molecular dynamics methods and validated using standard computational protocols. The Protein-Protein docking study of SOCS1 with its natural substrates, IRS1 and IRS2, and subsequent solvent accessible surface area analysis gave insight into the binding region of the SOCS1 protein. The in silico active site prediction tools highlight the residues Val155 to Ile211 in SOCS1 being implicated in the ubiquitin mediated protein degradation of the proteins IRS1 and IRS2. Virtual screening in the active site region, using large structural databases, results in selective lead structures with 3-Pyridinol, Xanthine, and Alanine moieties as Pharmacophore. The virtual screening study shows that the residues Glu149, Gly187, Arg188, Leu191, and Ser205 of the SOCS1 are important for binding. The docking study with current anti-diabetic therapeutics shows that the drugs Glibenclamide and Glyclopyramide have a partial affinity towards SOCS1. The predicted ADMET and IC₅₀ properties for the identified ligands are within the acceptable range with drug-like properties. The structural data of SOCS1, its active site, and the identified lead structures are expedient in the development of new T2DM therapeutics.

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.

Identification of inhibitor against H. pylori HtrA protease using structure-based virtual screening and molecular dynamics simulations approaches

The HtrA protease of Helicobacter pylori, which efficiently colonizes at the gastric epithelial of host cells, disrupts the mucosal integrity of E-cadherin and spreads inflammatory diseases including gastric cancer by cleaving the cell-cell adhesion of the host. The lack of knowledge on the molecular diversity, structural and functional behavior of HpHtrA necessitated the present study to explore its inhibition mechanism. At first, the similarity of HpHtrA with other gastro-intestinal pathogenic HtrA bacteria and its remote relationship with the Human HtrA homologs were ensured by the phylogenetic analysis and hence was identified as a novel therapeutic target for further design of inhibitors. The three dimensional structure of HpHtrA was modeled and simulated to achieve its stable conformation and was used as a receptor to screen for the possible lead compound through virtual screening (using ∼ 1.3 million compounds). Molecular dynamics simulations followed by the binding energy analysis revealed the affinity of the compound 300040 in forming a stable complex with HpHtrA and thereby revealed its potent role in inhibiting HpHtrA. It is also worthy to mention that, structurally, the ligand binding at the catalytic site of HpHtrA is mainly facilitated by the significant dynamics of L2 loop. Based on the present study, the hydroxyl-piperidine with 4-aminopiperidine scaffold is proposed to be one of the best possible lead compounds for the inhibition of H. pylori.

Molecular modeling of inhibitors against fructose bisphosphate aldolase from Candida albicans

Candida albicans is an opportunistic pathogen that causes from vulvovaginal and oropharyngeal candidiasis to systemic infections. The enzyme 1,6-fructose bisphosphate aldolase class II (FBA II), is a macromolecule existing only in lower organisms, being essential for the survival of the pathogen due to its function of maintaining the glycolysis process. The aim of this paper was to evaluate the inhibitors of FBA II regarding their physicochemical, pharmacokinetic and toxicological properties and apply concepts of rational drug development to propose new compounds for the treatment of fungal infections of C. albicans. Physicochemical (HyperChem software and the webserver cactus) and ADME/Tox (PreADMET webserver) properties were calculated to four inhibitors described in the literature and three analogues. None of the compounds presented in this study violated RO5, however all inhibitors demonstrated low or moderate human intestinal absorption (HIA), as well as low or moderate permeability in Caco-2 and MDCK, poor plasma proteins binding (PPB) and low permeability of the blood-brain barrier (BBB); however, Compound 4 is the exception for BBB permeability, being also the only non-mutagenic compound, and therefore, used as a lead compound. Analogues B and C presented high HIA, weak PPB and low BBB permeability, as well as a positive prediction for carcinogenicity in rats and mouse and non-mutagenicity in the Ames test. Through the evaluations carried out, it was concluded that the analogues B and C have proved to be promising candidates for oral administration drugs in the treatment of fungal infections of the genus Candida.

Deciphering PPARγ activation in cardiometabolic syndrome: studies by in silico and in vivo experimental assessment

Cardiometabolic syndrome (CMetS) is a consolidation of metabolic disorders characterized by insulin resistance, dyslipidemia and hypertension. Curcumin, a natural bioactive compound, has been shown to possess notable anti-oxidant activity and it has also been included as a super natural herb in the super natural herbs database. Most of the beneficial effects of Curcumin are possibly due to activation of the nuclear receptor, peroxisome proliferator-activated receptor gamma (PPARγ). The present study investigates molecular interactions of curcumin with PPARγ protein through molecular docking and molecular dynamics (MD) simulation studies. Further, effect of curcumin on high fat diet induced CMetS was studied in rats along with western blot for PPARγ and nuclear factor-κB (NF-κB) expressions and histopathological studies. Computational studies presented several significant molecular interactions of curcumin including Ser289, His323, His449 and Tyr473 of PPARγ. The in vivo results further confirmed that curcumin was able to ameliorate the abnormal changes and also, increased PPARγ expressions. The results confirm our hypothesis that activation of PPARγ by curcumin possesses the therapeutic potential to ameliorate the altered levels of metabolic changes in rats in the treatment of CMetS. This is the first report of CMetS treatment by curcumin and study of its underlying mechanism through in silico as well as in vivo experiments.

Synthesis, biological evaluation and docking study of a new series of di-substituted benzoxazole derivatives as selective COX-2 inhibitors and anti-inflammatory agents

A new series of substituted-N-(3,4-dimethoxyphenyl)-benzoxazole derivatives 13a-13p was synthesized and evaluated in vitro for their COX (I and II) inhibitory activity, in vivo anti-inflammatory and ulcerogenic potential. Compounds 13d, 13h, 13k, 13l and 13n exhibited significant COX-2 inhibitory activity and selectivity towards COX-2 over COX-1. These selected compounds were screened for their in vivo anti-inflammatory activity by carrageenan induced rat paw edema method. Among these compounds, 13d was the most promising analogs of the series with percent inhibition of 84.09 and IC₅₀ value of 0.04 µM and 1.02 µM (COX-2 and COX-1) respectively. Furthermore, ulcerogenic study was performed and tested compounds (13d, 13h, 13k, 13l) demonstrated a significant gastric tolerance than ibuprofen. Molecular docking study was also performed with resolved crystal structure of COX-2 to understand the binding mechanisms of newly synthesized inhibitors in the active site of COX-2 enzyme and the results were found to be concordant with the biological evaluation studies of the compounds. These newly synthesized inhibitors also showed acceptable pharmacokinetic profile in the in silico ADME/T analyses.

Three-dimensional bio-printing: A new frontier in oncology research

Current research in oncology deploys methods that rely principally on two-dimensional (2D) mono-cell cultures and animal models. Although these methodologies have led to significant advancement in the development of novel experimental therapeutic agents with promising anticancer activity in the laboratory, clinicians still struggle to manage cancer in the clinical setting. The disappointing translational success is attributable mainly to poor representation and recreation of the cancer microenvironment present in human neoplasia. Three-dimensional (3D) bio-printed models could help to simulate this micro-environment, with recent bio-printing of live human cells demonstrating that effective in vitro replication is achievable. This literature review outlines up-to-date advancements and developments in the use of 3D bio-printed models currently being used in oncology research. These innovative advancements in 3D bio-printing open up a new frontier for oncology research and could herald an era of progressive clinical cancer therapeutics.

Drug Metabolism in Preclinical Drug Development: A Survey of the Discovery Process, Toxicology, and Computational Tools

BACKGROUND

While establishing efficacy in translational models and humans through clinically-relevant endpoints for disease is of great interest, assessing the potential toxicity of a putative therapeutic drug is critical. Toxicological assessments in the pre-clinical discovery phase help to avoid future failure in the clinical phases of drug development. Many in vitro assays exist to aid in modular toxicological assessment, such as hepatotoxicity and genotoxicity. While these methods have provided tremendous insight into human toxicity by investigational new drugs, they are expensive, require substantial resources, and do not account for pharmacogenomics as well as critical ADME properties. Computational tools can fill this niche in toxicology if in silico models are accurate in relating drug molecular properties to toxicological endpoints as well as reliable in predicting important drug-target interactions that mediate known adverse events or adverse outcome pathways (AOPs).

METHODS

We undertook an unstructured search of multiple bibliographic databases for peer-reviewed literature regarding computational methods in predictive toxicology for in silico drug discovery. As this review paper is meant to serve as a survey of available methods for the interested reader, no focused criteria were applied. Literature chosen was based on the writers' expertise and intent in communicating important aspects of in silico toxicology to the interested reader.

CONCLUSION

This review provides a purview of computational methods of pre-clinical toxicologic assessments for novel small molecule drugs that may be of use for novice and experienced investigators as well as academic and commercial drug discovery entities.

Non-clinical studies required for new drug development - Part I: early in silico and in vitro studies, new target discovery and validation, proof of principles and robustness of animal studies

This review presents a historical overview of drug discovery and the non-clinical stages of the drug development process, from initial target identification and validation, through in silico assays and high throughput screening (HTS), identification of leader molecules and their optimization, the selection of a candidate substance for clinical development, and the use of animal models during the early studies of proof-of-concept (or principle). This report also discusses the relevance of validated and predictive animal models selection, as well as the correct use of animal tests concerning the experimental design, execution and interpretation, which affect the reproducibility, quality and reliability of non-clinical studies necessary to translate to and support clinical studies. Collectively, improving these aspects will certainly contribute to the robustness of both scientific publications and the translation of new substances to clinical development.

Identification of potential inhibitors for AIRS from de novo purine biosynthesis pathway through molecular modeling studies - a computational approach

In cancer, de novo pathway plays an important role in cell proliferation by supplying huge demand of purine nucleotides. Aminoimidazole ribonucleotide synthetase (AIRS) catalyzes the fifth step of de novo purine biosynthesis facilitating in the conversion of formylglycinamidine ribonucleotide to aminoimidazole ribonucleotide. Hence, inhibiting AIRS is crucial due to its involvement in the regulation of uncontrollable cancer cell proliferation. In this study, the three-dimensional structure of AIRS from P. horikoshii OT3 was constructed based on the crystal structure from E. coli and the modeled protein is verified for stability using molecular dynamics for a time frame of 100 ns. Virtual screening and induced fit docking were performed to identify the best antagonists based on their binding mode and affinity. Through mutational studies, the residues necessary for catalytic activity of AIRS were identified and among which the following residues Lys35, Asp103, Glu137, and Thr138 are important in determination of AIRS function. The mutational studies help to understand the structural and energetic characteristics of the specified residues. In addition to Molecular Dynamics, ADME properties, binding free-energy, and density functional theory calculations of the compounds were carried out to find the best lead molecule. Based on these analyses, the compound from the NCI database, NCI_121957 was adjudged as the best molecule and could be suggested as the suitable inhibitor of AIRS. In future studies, experimental validation of these ligands as AIRS inhibitors will be carried out.

Docking and ADMET prediction of few GSK-3 inhibitors divulges 6-bromoindirubin-3-oxime as a potential inhibitor

GSK-3 is a member of cellular kinases with diversified functions such as cellular differentiation, metabolic signaling, neuronal functions and apoptosis. It has been validated as an important therapeutic target in Alzheimer's disease and type 2 diabetes. Few molecules targeting GSK-3 are currently in clinical trials. In this study, we have compared certain docking and computational ADME (Absorption, Distribution, Metabolism, Excretion) parameters of a few GSK-3 targeted ligands (Indirubin, Hymenialdisine, Meridianins, 6-bromoindirubin-3-oxime) against two control molecules (Tideglusib and LY-2090314) to derive and analyze the basic drug-like properties of the test compounds. Docking between the GSK-3 and various ligands was done using AutoDock while ADME parameters were derived from ADMET server PreADMET and admetSAR. Various docked images were retrieved from docking, indicating the docking sites in the target protein. Out of four compounds tested, 6-bromoindirubin-3-oxime (6-BIO) was found as the best docking and ADME parameters, followed by Hymenialdisine (HMD). The LigPlot interaction results show two residues Leu (188) and Thr (138) to be common at the interaction site. The LD50 of 6-BIO is better than one of the control ligands while very similar to the other. Some of the parameters were very similar to the control ligands, thus, making it a suitable candidate among the test ligands. From this in-silico study, we concluded that 6-BIO is a potent drug candidate which could be further tested in vitro and in vivo to establish a drug molecule. Since, 6-BIO is a chemically modified form of the basic molecule Indirubin, we can hypothesize that certain other modified indirubins could be tested as GSK-3 targeted ligands.

Identification of Novel Inhibitors for Tobacco Mosaic Virus Infection in Solanaceae Plants

Tobacco mosaic virus (TMV) infects several crops of economic importance (e.g., tomato) and remains as one of the major concerns to the farmers. TMV enters the host cell and produces the capping enzyme RNA polymerase. The viral genome replicates further to produce multiple mRNAs which encodes several proteins, including the coat protein and an RNA-dependent RNA polymerase (RdRp), as well as the movement protein. TMV replicase domain was chosen for the virtual screening studies against small molecules derived from ligand databases such as PubChem and ChemBank. Catalytic sites of the RdRp domain were identified and subjected to docking analysis with screened ligands derived from virtual screening LigandFit. Small molecules that interact with the target molecule at the catalytic domain region amino acids, GDD, were chosen as the best inhibitors for controlling the TMV replicase activity.

In silico methods for predicting drug-drug interactions with cytochrome P-450s, transporters and beyond

Drug-drug interactions (DDIs) are associated with severe adverse effects that may lead to the patient requiring alternative therapeutics and could ultimately lead to drug withdrawal from the market if they are severe. To prevent the occurrence of DDI in the clinic, experimental systems to evaluate drug interaction have been integrated into the various stages of the drug discovery and development process. A large body of knowledge about DDI has also accumulated through these studies and pharmacovigillence systems. Much of this work to date has focused on the drug metabolizing enzymes such as cytochrome P-450s as well as drug transporters, ion channels and occasionally other proteins. This combined knowledge provides a foundation for a hypothesis-driven in silico approach, using either cheminformatics or physiologically based pharmacokinetics (PK) modeling methods to assess DDI potential. Here we review recent advances in these approaches with emphasis on hypothesis-driven mechanistic models for important protein targets involved in PK-based DDI. Recent efforts with other informatics approaches to detect DDI are highlighted. Besides DDI, we also briefly introduce drug interactions with other substances, such as Traditional Chinese Medicines to illustrate how in silico modeling can be useful in this domain. We also summarize valuable data sources and web-based tools that are available for DDI prediction. We finally explore the challenges we see faced by in silico approaches for predicting DDI and propose future directions to make these computational models more reliable, accurate, and publically accessible.

Open Source Bayesian Models. 1. Application to ADME/Tox and Drug Discovery Datasets

On the order of hundreds of absorption, distribution, metabolism, excretion, and toxicity (ADME/Tox) models have been described in the literature in the past decade which are more often than not inaccessible to anyone but their authors. Public accessibility is also an issue with computational models for bioactivity, and the ability to share such models still remains a major challenge limiting drug discovery. We describe the creation of a reference implementation of a Bayesian model-building software module, which we have released as an open source component that is now included in the Chemistry Development Kit (CDK) project, as well as implemented in the CDD Vault and in several mobile apps. We use this implementation to build an array of Bayesian models for ADME/Tox, in vitro and in vivo bioactivity, and other physicochemical properties. We show that these models possess cross-validation receiver operator curve values comparable to those generated previously in prior publications using alternative tools. We have now described how the implementation of Bayesian models with FCFP6 descriptors generated in the CDD Vault enables the rapid production of robust machine learning models from public data or the user’s own datasets. The current study sets the stage for generating models in proprietary software (such as CDD) and exporting these models in a format that could be run in open source software using CDK components. This work also demonstrates that we can enable biocomputation across distributed private or public datasets to enhance drug discovery.

In silico optimization of pharmacokinetic properties and receptor binding affinity simultaneously: a 'parallel progression approach to drug design' applied to β-blockers

The present work exploits the potential of in silico approaches for minimizing attrition of leads in the later stages of drug development. We propose a theoretical approach, wherein 'parallel' information is generated to simultaneously optimize the pharmacokinetics (PK) and pharmacodynamics (PD) of lead candidates. β-blockers, though in use for many years, have suboptimal PKs; hence are an ideal test series for the 'parallel progression approach'. This approach utilizes molecular modeling tools viz. hologram quantitative structure activity relationships, homology modeling, docking, predictive metabolism, and toxicity models. Validated models have been developed for PK parameters such as volume of distribution (log Vd) and clearance (log Cl), which together influence the half-life (t1/2) of a drug. Simultaneously, models for PD in terms of inhibition constant pKi have been developed. Thus, PK and PD properties of β-blockers were concurrently analyzed and after iterative cycling, modifications were proposed that lead to compounds with optimized PK and PD. We report some of the resultant re-engineered β-blockers with improved half-lives and pKi values comparable with marketed β-blockers. These were further analyzed by the docking studies to evaluate their binding poses. Finally, metabolic and toxicological assessment of these molecules was done through in silico methods. The strategy proposed herein has potential universal applicability, and can be used in any drug discovery scenario; provided that the data used is consistent in terms of experimental conditions, endpoints, and methods employed. Thus the 'parallel progression approach' helps to simultaneously fine-tune various properties of the drug and would be an invaluable tool during the drug development process.

Systems pharmacology-based approach for dissecting the addition and subtraction theory of traditional Chinese medicine: An example using Xiao-Chaihu-Decoction and Da-Chaihu-Decoction

BACKGROUND

Addition and subtraction theory (AST), a basic theory of herb combination in traditional Chinese medicine (TCM), is often used to add or subtract the "fundamental formulae" to generate more targeted prescriptions. This theory plays a core role in individualized medicine and compound compatibility of TCM. However, the mechanisms underlying AST have largely remained elusive.

METHODS

An integrated platform of systems pharmacology was proposed for revealing how the oral administration, drug half-life, and target interactions affect the pharmacological functions of herbal medicines. This platform was further applied on two classical prescriptions, i.e., Xiao Chaihu decoction (XCHD) and Da Chaihu decoction (DCHD) to dissect the addition and subtraction theory (AST).

RESULTS

We uncovered the candidate compounds, key molecular targets and interaction network involved in XCHD and DCHD, and summarized its pharmacological characters and therapeutic indications. The results show that the "fundamental formula" is responsible for the major therapeutic effects, whereas the "additive herbs" synergistically enhance the treatment outcomes by targeting the same or complementary proteins between the foundational and additive herbs.

CONCLUSION

This work has established a novel method to comprehensively understand the mechanism of AST, which would be beneficial for the TCM recipe optimization as well as the production of new herbal formula with desirable therapeutic effects.

Applying quantitative structure-activity relationship (QSAR) methodology for modeling postmortem redistribution of benzodiazepines and tricyclic antidepressants

Postmortem redistribution (PMR) constitutes a multifaceted process, which complicates the interpretation of drug concentrations by forensic toxicologists. The present study aimed to apply quantitative structure-activity relationship (QSAR) analysis for modeling PMR data of structurally related drugs, 10 benzodiazepines and 10 tricyclic antidepressants. For benzodiazepines, an adequate QSAR model was obtained (R(2) = 0.98, Q(2) = 0.88, RMSEE = 0.12), in which energy, ionization and molecular size exerted significant impact. For tricyclic antidepressants, an adequate QSAR model with slightly inferior statistics (R(2) = 0.95, Q(2) = 0.87, RMSEE = 0.29) was established after exclusion of maprotiline, in which energy parameters, basicity character and lipophilicity exerted significant contribution. Thus, QSAR analysis could be used as a complementary tool to provide an informative illustration of the contributing molecular, physicochemical and structural properties in PMR process. However, the complexity, non-static and time-dependent nature of PMR endpoints raises serious concerns whether QSAR methodology could predict the degree of redistribution, highlighting the need for animal-derived PMR data.

Modeling and Simulation of Intracellular Drug Transport and Disposition Pathways with Virtual Cell

The development of computational approaches for modeling the spatiotemporal dynamics of intracellular, small molecule drug concentrations has become an increasingly important area of pharmaceutical research. For systems pharmacology, the system dynamics of subcellular transport can be coupled to downstream pharmacological effects on biochemical pathways that impact cell structure and function. Here, we demonstrate how a widely used systems biology modeling package - Virtual Cell - can also be used to model the intracellular, passive transport pathways of small druglike molecules. Using differential equations to represent passive drug transport across cellular membranes, spatiotemporal changes in the intracellular distribution and concentrations of exogenous chemical agents in specific subcellular organelles were simulated for weakly acidic, neutral, and basic molecules, as a function of the molecules' lipophilicity and ionization potentials. In addition, we simulated the transport properties of small molecule chemical agents in the presence of a homogenous extracellular concentration or a transcellular concentration gradient. We also simulated the effects of cell type-dependent variations in the intracellular microenvironments on the distribution and accumulation of small molecule chemical agents in different organelles over time, under influx and efflux conditions. Lastly, we simulated the transcellular transport of small molecule chemical agents, in the presence of different apical and basolateral microenvironments. By incorporating existing models of drug permeation and subcellular distribution, our results indicate that Virtual Cell can provide a user-friendly, open, online computational modeling platform for systems pharmacology and biopharmaceutics research, making mathematical models and simulation results accessible to a broad community of users, without requiring advanced computer programming knowledge.

Insights on cytochrome p450 enzymes and inhibitors obtained through QSAR studies

The cytochrome P450 (CYP) superfamily of heme enzymes play an important role in the metabolism of a large number of endogenous and exogenous compounds, including most of the drugs currently on the market. Inhibitors of CYP enzymes have important roles in the treatment of several disease conditions such as numerous cancers and fungal infections in addition to their critical role in drug-drug interactions. Structure activity relationships (SAR), and three-dimensional quantitative structure activity relationships (3D-QSAR) represent important tools in understanding the interactions of the inhibitors with the active sites of the CYP enzymes. A comprehensive account of the QSAR studies on the major human CYPs 1A1, 1A2, 1B1, 2A6, 2B6, 2C9, 2C19, 2D6, 2E1, 3A4 and a few other CYPs are detailed in this review which will provide us with an insight into the individual/common characteristics of the active sites of these enzymes and the enzyme-inhibitor interactions.