Prediction of human intestinal absorption of drug compounds from molecular structure

Binding interactions and in silico ADME prediction of isoconessimine derivatives as potent acetylcholinesterase inhibitors

In pursuit of new acetylcholinesterase (AChE) inhibitors for treating Alzheimer's disease (AD), a series of ten previously synthesized isoconessimine compounds (7a-7j) was in silico investigated for their binding interactions with AChE and pharmacokinetics based on absorption, distribution, metabolism, and excretion (ADME) properties using molecular docking, ONIOM (Our own N-layered Integrated molecular Orbital and molecular Mechanics) method and SwissADME tools. Docking experiments showed that all compounds bind within the active site gorge of AChE (PDB entry 1C2B), posing its aryloxy-substitutional ethyl group to catalytic site and conessine skeleton to peripheral anionic site. ONIOM interaction energy was used as an ONIOM score to improve docking score, and it ranked 7b as the most potent AChE inhibitor, in agreement with previous experiment. Residues, ASP74, TRP86, GLY122, GLU202, TRP286, GLU292, SER293, ILE294, TYR337, TYR341, and HIS447 were identified as important for the binding of the AChE-isoconessimine complex. The SwissADME investigation suggested that four compounds (7a, 7c, 7d and 7f) agree with the rules of drug-likeness. The steric and electronic effects on the aryloxy-substitutional ethyl group as important factors in the AChE inhibition were also discussed, which brings a better understanding of Alzheimer's disease drug development.

Targeting GluR3 in Depression and Alzheimer's Disease: Novel Compounds and Therapeutic Prospects

BACKGROUND

The present study investigates the interrelated pathophysiology of depression and Alzheimer's disease (AD), with the objective of elucidating common underlying mechanisms.

OBJECTIVE

Our objective is to identify previously undiscovered biogenic compounds from the NuBBE database that specifically interact with GluR3. This study examines the bidirectional association between depression and AD, specifically focusing on the role of depression as a risk factor in the onset and progression of the disease.

METHODS

In this study, we utilize pharmacokinetics, homology modeling, and molecular docking-based virtual screening techniques to examine the GluR3 AMPA receptor subunit.

RESULTS

The compounds, namely ZINC000002558953, ZINC000001228056, ZINC000000187911, ZINC000003954487, and ZINC000002040988, exhibited favorable pharmacokinetic profiles and drug-like characteristics, displaying high binding affinities to the GluR3 binding pocket.

CONCLUSIONS

These findings suggest that targeting GluR3 could hold promise for the development of therapies for depression and AD. Further validation through in vitro, in vivo, and clinical studies is necessary to explore the potential of these compounds as lead candidates for potent and selective GluR3 inhibitors. The shared molecular mechanisms between depression and AD provide an opportunity for novel treatment approaches that address both conditions simultaneously.

An Effective, Green Synthesis Procedure for Obtaining Coumarin-Hydroxybenzohydrazide Derivatives and Assessment of Their Antioxidant Activity and Redox Status

In this study, green synthesis of two derivatives of coumarin-hydroxybenzohydrazide, (E)-2,4-dioxo-3-(1-(2-(2,3,4-trihydroxybenzoyl)hydrazyl)ethylidene)-chroman-7-yl acetate (C-HB1), and (E)-2,4-dioxo-3-(1-(2-(3,4,5-trihydroxybenzoyl)hydrazyl)ethylidene)chroman-7-yl acetate (C-HB2) is reported. Using vinegar and ethanol as a catalyst and solvent, the reactions were carried out between 3-acetyl-4-hydroxy-coumarin acetate and corresponding trihydroxybenzoyl hydrazide. The antioxidant potential of these compounds was investigated using the DPPH and ABTS assays, as well as the FRAP test. The obtained results reveal that even at very low concentrations, these compounds show excellent radical scavenging potential. The IC50 values for C-HB1 and C-HB2 in relation to the DPPH radical are 6.4 and 2.5 μM, respectively, while they are 4.5 and 2.0 μM in relation to the ABTS radical. These compounds have antioxidant activity that is comparable to well-known antioxidants such as gallic acid, NDGA, and trolox. These results are in good correlation with theoretical parameters describing these reactions. Moreover, it was found that inhibition of DPPH● follows HAT, while inactivation of ABTS+● follows SET-PT and HAT mechanisms. Additionally, coumarin-hydroxybenzohydrazide derivatives induced moderate cytotoxic activity and show significant potential to modulate redox status in HCT-116 colorectal cancer cells. The cytotoxicity was achieved via their prooxidative activity and ability to induce oxidative stress in cancer cells by increasing O2˙- concentrations, indicated by increased MDA and GSH levels. Thus, ROS manipulation can be a potential target for cancer therapies by coumarins, as cancer cells possess an altered redox balance in comparison to normal cells. According to the ADMET analysis, the compounds investigated show good pharmacokinetic and toxicological profiles similar to vitamin C and gallic acid, which makes them good candidates for application in various fields of industry and medicine.

Significant shifts in preclinical and clinical neurotoxicology: a review and commentary

The ever-expanding prevalence of adverse neurotoxic reactions of the brain in response to therapeutic and recreational drugs, dietary supplements, environmental hazards, cosmetic ingredients, a spectrum of herbals, health status, and environmental stressors continues to prompt the development of novel cell-based assays to better determine neurotoxic hazard. Neurotoxicants may cause direct and epigenetic damage to the nervous tissue and alter the chemistry, structure, or normal activity of the nervous system. In severe neurotoxicity due to exposure to physical or psychosocial toxicants, neurons are disrupted or killed, and a consistent pattern of clinical neural dysfunction appears. In utero exposure to neurotoxicants can lead to altered development of the nervous system [developmental neurotoxicity (DNT)]. Patients with certain disorders and certain genomic makeup may be particularly susceptible to neurotoxicants. Traditional cytotoxicity measurements, like cell death, are easy to measure, but insufficient at identifying current routine biomarkers of toxicity including functional impairment in cell communication, which often occurs before or even in the absence of cell death. The present paper examines some of the limitations of existing neurotoxicology in light of the increasing need to develop tools to meet the challenges of achieving greater sensitivity in detection and developing and standardizing methods for exploring the toxicologic risk of such neurotoxic entities as engineered nanomaterials and even variables associated with poverty.

Bioactive Compounds from Nyctanthes arbor tristis Linn as Potential Inhibitors of Janus Kinases (JAKs) Involved in Rheumatoid Arthritis

Nyctanthes arbor tristis L (NAT) is one of the herbal plants whose parts are commonly used to treat diverse ailment including RA. Although the etiology of the autoimmune disorder RA is still unclear, actions of cytokines have been greatly associated with the mechanism of RA. Despite the huge development of drugs to combat this disorder, the search for alternative medicine is increasing due to the adverse effects of these synthetic drugs. Here, the ability of 30 selected bioactive compounds from the parts of NAT to bind effectively to target proteins of the Janus kinases as a potent inhibitor was predicted in an in silico manner through molecular docking procedure using Autodock 4.2.6 and their interactions visualized using Discovery Studio, followed by evaluating the physiochemical and ADMET properties of compounds of the lowest binding energy comparable to the reference drug baricitinib. Comparing the predicted target information with the standard drug baricitinib, 7 bioactive compounds may be potential lead drug for the treatment of RA owing to their lowest binding energy ranging from - 7.0 kcal/mol to - 10.49 kcal/mol and their pharmacokinetics properties. This can be used for further in vivo and in vitro studies to establish their potency as JAKs inhibitors to treat RA.

In Vitro and In Silico Evaluations of Boswellia carterii Resin Dermocosmetic Activities

Boswellia carterii is a plant species belonging to the Burseraceae family. It grows up in trees or shrubs, and it is known for producing an aromatic resin commonly named frankincense or olibanum. This resin has been used in traditional medicine to treat various conditions such as inflammations, gastrointestinal disorders and traumatic injuries. Virtual screening and molecular docking are two in silico approaches used to predict potential interactions between ligands and the active site of a protein. These approaches are mainly used in natural product chemistry and pharmacology as a screening tool to select plant extracts or fractions for in vitro testing, as well as for the prediction of mechanisms of action. The aim of this research is the in silico and in vitro evaluations of the potential collagenase and elastase inhibitory activities of Boswellia carterii resin organic extracts (viz., methanol, n-hexane and ethyl acetate). The obtained results revealed that methanol and n-hexane exhibited the best collagenase inhibitory activity with values superior to 85%, whereas the methanol and ethyl acetate showed the highest elastase inhibition activity with inhibition values ranging between 40 and 60%. The molecular docking prediction confirmed the experimental results; moreover, the visualization of the ligand–protein interactions showed that the main compounds of the organic extracts may have mechanisms of action similar to the positive controls. Those findings are very promising and open new perspectives for the exploitation of Boswellia carterii resin as active agents for the development of anti-aging cosmeceuticals.

Simulation Models for Prediction of Bioavailability of Medicinal Drugs-the Interface Between Experiment and Computation

The oral drug bioavailability (BA) problems have remained inevitable over the years, impairing drug efficacy and indirectly leading to eventual human morbidity and mortality. However, some conventional lab-based methods improve drug absorption leading to enhanced BA, and the recent experimental techniques are up-and-coming. Nevertheless, some have inherent drawbacks in improving the efficacy of poorly insoluble and low impermeable drugs. Drug BA and strategies to overcome these challenges were briefly highlighted. This review has significantly unravelled the different computational models for studying and predicting drug bioavailability. Several computational approaches provide mechanistic insights into the oral drug delivery system simulation of descriptors like solubility, permeability, transport protein-ligand interactions, and molecular structures. The in silico techniques have long been known still are just being applied to unravel drug bioavailability issues. Many publications have reported novel applications of the computational models towards achieving improved drug BA, including predicting gastrointestinal tract (GIT) drug absorption properties and passive intestinal membrane permeability, thus maximizing time and resources. Also, the classical molecular simulation models for free solvation energies of soluble-related processes such as solubilization, dissolutions, supersaturation, and precipitation have been used in virtual screening studies. A few of the tools are GastroPlus^TM that supports biowaiver for drugs, mainly BCS class III and predicts drug compounds' absorption and pharmacokinetic process; SimCyp® simulator for mechanistic modelling and simulation of drug formulation processes; pharmacodynamics analysis on non-linear mixed-effects modelling; and mathematical models, predicting absorption potential/maximum absorption dose. This review provides in silico-experiment annexation in the drug bioavailability enhancement, possible insights that lead to critical opinion on the applications and reliability of the various in silico models as a growing tool for drug development and discovery, thus accelerating drug development processes.

Best practices in current models mimicking drug permeability in the gastrointestinal tract - an UNGAP review

The absorption of orally administered drug products is a complex, dynamic process, dependent on a range of biopharmaceutical properties; notably the aqueous solubility of a molecule, stability within the gastrointestinal tract (GIT) and permeability. From a regulatory perspective, the concept of high intestinal permeability is intrinsically linked to the fraction of the oral dose absorbed. The relationship between permeability and the extent of absorption means that experimental models of permeability have regularly been used as a surrogate measure to estimate the fraction absorbed. Accurate assessment of a molecule's intestinal permeability is of critical importance during the pharmaceutical development process of oral drug products, and the current review provides a critique of in vivo, in vitro and ex vivo approaches. The usefulness of in silico models to predict drug permeability is also discussed and an overview of solvent systems used in permeability assessments is provided. Studies of drug absorption in humans are an indirect indicator of intestinal permeability, but in vitro and ex vivo tools provide initial screening approaches are important tools for direct assessment of permeability in drug development. Continued refinement of the accuracy of in silico approaches and their validation with human in vivo data will facilitate more efficient characterisation of permeability earlier in the drug development process and will provide useful inputs for integrated, end-to-end absorption modelling.

Structure-based virtual screening and biological evaluation of novel small-molecule BTK inhibitors

Bruton tyrosine kinase (BTK) is linked to multiple signalling pathways that regulate cellular survival, activation, and proliferation. A covalent BTK inhibitor has shown favourable outcomes for treating B cell malignant leukaemia. However, covalent inhibitors require a high reactive warhead that may contribute to unexpected toxicity, poor selectivity, or reduced effectiveness in solid tumours. Herein, we report the identification of a novel noncovalent BTK inhibitor. The binding interactions (i.e. interactions from known BTK inhibitors) for the BTK binding site were identified and incorporated into a structure-based virtual screening (SBVS). Top-rank compounds were selected and testing revealed a BTK inhibitor with >50% inhibition at 10 µM concentration. Examining analogues revealed further BTK inhibitors. When tested across solid tumour cell lines, one inhibitor showed favourable inhibitory activity, suggesting its potential for targeting BTK malignant tumours. This inhibitor could serve as a basis for developing an effective BTK inhibitor targeting solid cancers.

Molecular Docking Studies Reveal Rhein from rhubarb (Rheum rhabarbarum) as a Putative Inhibitor of ATP-binding Cassette Super-family G member 2

BACKGROUND

ATP-binding cassette Super-family G member 2 protein is an active ATPbinding cassette transporter with the potential to combat cancer stem cells.

OBJECTIVE

Due to the lack of potential ATP-binding cassette Super-family G member 2 inhibitors, we screened natural inhibitors, which could be a safe source to control multidrug resistance by blocking the regulation of ATP-binding cassette Super-family G member 2 protein.

METHODS

Three-dimensional structure of ATP-binding cassette Super-family G member 2 protein downloaded from the protein databank and chemical structures of 166 selected compounds of the training dataset were retrieved from PubChem. Drug-likeness and docking analysis was conducted to shortlist the dataset for pharmacophore generation. LigandScout 4.1.5 used for pharmacophorebased screening of Zbc library of ZINC database and Autodock Vina were utilized for molecular docking against the predicted active pocket of the target protein to evaluate the potential association of protein and ligands. The physiochemical properties of novel compounds were calculated by admetSAR respectively.

RESULTS

Through pharmacophore-based screening, ZINC4098704 (Rhein) was identified as a lead compound which demonstrates the least binding energy (-8.5) and the highest binding affinity with the target protein and showed optimal physiochemical profile. This compound is highly recommended for a laboratory test to confirm its activity as an ATP-binding cassette Super-family G member 2 inhibitors.

CONCLUSION

Our computer-based study systematically selected natural lead compounds, which could be effective in inhibiting ATP-binding cassette Super-family G member 2 and may help reverse the effect of multidrug resistance to increase the effectiveness of chemotherapy in cancer treatment.

Deciphering the Interactions of Bioactive Compounds in Selected Traditional Medicinal Plants against Alzheimer's Diseases via Pharmacophore Modeling, Auto-QSAR, and Molecular Docking Approaches

Neurodegenerative diseases, for example Alzheimer’s, are perceived as driven by hereditary, cellular, and multifaceted biochemical actions. Numerous plant products, for example flavonoids, are documented in studies for having the ability to pass the blood-brain barrier and moderate the development of such illnesses. Computer-aided drug design (CADD) has achieved importance in the drug discovery world; innovative developments in the aspects of structure identification and characterization, bio-computational science, and molecular biology have added to the preparation of new medications towards these ailments. In this study we evaluated nine flavonoid compounds identified from three medicinal plants, namely T. diversifolia, B. sapida, and I. gabonensis for their inhibitory role on acetylcholinesterase (AChE), butyrylcholinesterase (BChE) and monoamine oxidase (MAO) activity, using pharmacophore modeling, auto-QSAR prediction, and molecular studies, in comparison with standard drugs. The results indicated that the pharmacophore models produced from structures of AChE, BChE and MAO could identify the active compounds, with a recuperation rate of the actives found near 100% in the complete ranked decoy database. Moreso, the robustness of the virtual screening method was accessed by well-established methods including enrichment factor (EF), receiver operating characteristic curve (ROC), Boltzmann-enhanced discrimination of receiver operating characteristic (BEDROC), and area under accumulation curve (AUAC). Most notably, the compounds’ pIC₅₀ values were predicted by a machine learning-based model generated by the AutoQSAR algorithm. The generated model was validated to affirm its predictive model. The best models achieved for AChE, BChE and MAO were models kpls_radial_17 (R² = 0.86 and Q² = 0.73), pls_38 (R² = 0.77 and Q² = 0.72), kpls_desc_44 (R² = 0.81 and Q² = 0.81) and these externally validated models were utilized to predict the bioactivities of the lead compounds. The binding affinity results of the ligands against the three selected targets revealed that luteolin displayed the highest affinity score of −9.60 kcal/mol, closely followed by apigenin and ellagic acid with docking scores of −9.60 and −9.53 kcal/mol, respectively. The least binding affinity was attained by gallic acid (−6.30 kcal/mol). The docking scores of our standards were −10.40 and −7.93 kcal/mol for donepezil and galanthamine, respectively. The toxicity prediction revealed that none of the flavonoids presented toxicity and they all had good absorption parameters for the analyzed targets. Hence, these compounds can be considered as likely leads for drug improvement against the same.

Identification of the most potent acetylcholinesterase inhibitors from plants for possible treatment of Alzheimer’s disease: a computational approach

Background

Being one of the rapidly growing dementia type diseases in the world, Alzheimer’s disease (AD) has gained much attention from researchers in the recent decades. Many hypotheses have been developed that describe different reasons for the development of AD. Among them, the cholinergic hypothesis depicts that the degradation of an important neurotransmitter, acetylcholine by the enzyme acetylcholinesterase (AChE), is responsible for the development of AD. Although, many anti-AChE drugs are already available in the market, their performance sometimes yields unexpected results. For this reason, research works are going on to find out potential anti-AChE agents both from natural and synthetic sources. In this study, 50 potential anti-AChE phytochemicals were analyzed using numerous tools of bioinformatics and in silico biology to find out the best possible anti-AChE agents among the selected 50 ligands through molecular docking, determination of the druglikeness properties, conducting the ADMET test, PASS and P450 site of metabolism prediction, and DFT calculations.

Result

The predictions of this study suggested that among the selected 50 ligands, bellidifolin, naringenin, apigenin, and coptisine were the 4 best compounds with quite similar and sound performance in most of the experiments.

Conclusion

In this study, bellidifolin, naringenin, apigenin, and coptisine were found to be the most effective agents for treating the AD targeting AChE. However, more in vivo and in vitro analyses are required to finally confirm the outcomes of this research.

Determining the glycaemic responses of foods: conventional and emerging approaches

A low-glycaemic diet is crucial for those with diabetes and cardiovascular diseases. Information on the glycaemic index (GI) of different ingredients can help in designing novel food products for such target groups. This is because of the intricate dependency of material source, composition, food structure and processing conditions, among other factors, on the glycaemic responses. Different approaches have been used to predict the GI of foods, and certain discrepancies exist because of factors such as inter-individual variation among human subjects. Besides other aspects, it is important to understand the mechanism of food digestion because an approach to predict GI must essentially mimic the complex processes in the human gastrointestinal tract. The focus of this work is to review the advances in various approaches for predicting the glycaemic responses to foods. This has been carried out by detailing conventional approaches, their merits and limitations, and the need to focus on emerging approaches. Given that no single approach can be generalised to all applications, the review emphasises the scope of deriving insights for improvements in methodologies. Reviewing the conventional and emerging approaches for the determination of GI in foods, this detailed work is intended to serve as a state-of-the-art resource for nutritionists who work on developing low-GI foods.

Systematic analyses on the potential immune and anti-inflammatory mechanisms of Shufeng Jiedu Capsule against Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2)-caused pneumonia

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A systematic study integrated is proposed to illustrate the potential immune and anti-inflammatory mechanisms of SFJDC against SARS-CoV-2.

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SFJDC regulates related targets, showing the potential anti-novel coronavirus effect.

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The work can provide a better understanding of the therapeutic mechanism of SFJDC for treating SARS-CoV-2.

The outbreak of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2)-caused pneumonia (Coronavirus disease −19, COVID-19), has resulted in a global health emergency. However, there is no vaccine or effective antiviral treatment against the newly emerged coronavirus and identifying the available therapeutics as soon as possible is critical for the response to the spread of SARS-CoV-2. Shufeng Jiedu Capsule (SFJDC), a well-known prescription of Traditional Chinese Medicine (TCM) in China, has been widely used in treating upper respiratory tract infections and acute lung injury, owing to its immunomodulatory and anti-inflammatory effects. Despite the definite evidence of effective use of SFJDC in the diagnosis and treatment of pneumonia caused by SARS-CoV-2, the underlying action mechanism remains unknown. Currently, a systematic study integrated with absorption, distribution, metabolism and excretion (ADME) evaluation, target prediction, network construction and functional bioinformatics analyses is proposed to illustrate the potential immune and anti-inflammatory mechanisms of SFJDC against SARS-CoV-2. Additionally, to further validate the reliability of the interactions and binding affinities between drugs and targets, docking, Molecular dynamics Simulations (MD) simulations and Molecular Mechanics/Poisson-Boltzmann Surface Area approach (MM-PBSA) calculations were carried out. The results demonstrate that SFJDC regulates the immunomodulatory and anti-inflammatory related targets on multiple pathways through its active ingredients, showing the potential anti-novel coronavirus effect. Overall, the work can provide a better understanding of the therapeutic mechanism of SFJDC for treating SARS-CoV-2 pneumonia from multi-scale perspectives, and may also offer a valuable clue for developing novel pharmaceutical strategies to control the current coronavirus.

Bioavailability and Bioactivities of Polyphenols Eco Extracts from Coffee Grounds after In Vitro Digestion

Coffee grounds are a valuable source of bioactive compounds. In Romania, most of the amount obtained is lost through non-recovery; the rest is occasionally used as organic fertilizer. The coffee grounds were selected according to the roasting degree: blonde roasted (BR), medium roasted (MR), and dark roasted (DR). The study aimed to evaluate three extracts, obtained with a mixture of ethanol/water/acetic acid (50/49.5/0.5), depending on the roasting degree. The majority phenolic component, the antioxidant, and anti-inflammatory effect, as well as the role that gastrointestinal transit had on the bioavailability of bioactive compounds were determined. Chlorogenic acid was inversely proportional to the roasting degree. BR showed the best correlation between antioxidant and anti-inflammatory activities in vitro/in vivo. The antiproliferative capacity of the extracts determined an inhibitory effect on the tumor cells. Antimicrobial activities, relevant in the control of type 2 diabetes, were exerted through the inhibition of microbial strains (Escherichia coli). Following gastric digestion, BR demonstrated a maximum loss of 20% in the stomach. The recovery of coffee grounds depended on the pattern of functional compounds and the bioavailability of the main component, chlorogenic acid.

Analysis of plant-derived phytochemicals as anti-cancer agents targeting cyclin dependent kinase-2, human topoisomerase IIa and vascular endothelial growth factor receptor-2

Cancer is caused by a variety of pathways, involving numerous types of enzymes. Among them three enzymes i.e. Cyclin-dependent kinase-2 (CDK-2), Human topoisomerase IIα, and Vascular Endothelial Growth Factor Receptor-2 (VEGFR-2) are three of the most common enzymes that are involved in the cancer development. Although many chemical drugs are already available in the market for cancer treatment, plant sources are known to contain a wide variety of agents that are proved to possess potential anticancer activity. In this experiment, total thirty phytochemicals were analyzed against the mentioned three enzymes using different tools of bioinformatics and in silico biology like molecular docking study, drug likeness property experiment, ADME/T test, PASS prediction, and P450 site of metabolism prediction as well as DFT calculation to determine the three best ligands among them that have the capability to inhibit the mentioned enzymes. From the experiment, Epigallocatechin gallate was found to be the best ligand to inhibit CDK-2, Daidzein showed the best inhibitory activities towards the Human topoisomerase IIα, and Quercetin was predicted to be the best agent against VEGFR-2. They were also predicted to be quite safe and effective agents to treat cancer. However, more in vivo and in vitro analyses are required to finally confirm their safety and efficacy in this regard.

Recent advances in the development of asenapine formulations

INTRODUCTION

Asenapine maleate (AM) is an atypical antipsychotic agent, that has been widely prescribed for the management of schizoaffective disorders. However, the bioavailability of AM is extremely poor due to the extensive first-pass metabolism. With the advancement in pharmaceutical technologies, significant strides have been made to create novel formulations to address the bioavailability problem of AM.

AREAS COVERED

This review article provides an insight into all the formulation approaches undertaken by researchers to increase the bioavailability of AM encompassing the works utilizing ultrasound mediated transdermal delivery, nose to brain delivery, intestinal lymphatic system targeting, in situ implants, etc. All the patents associated with AM formulation have also been discussed and summarized.

EXPERT OPINION

Numerous studies have been carried out on AM formulations over the recent years, many of these studies have shown significant improvement in bioavailability. We have also mentioned the unexplored domains which can be exploited for further enhancing the bioavailability of AM. Nonetheless, most of these studies are still limited to the research laboratory level and face multiple hurdles before making into the market. Attaining controllability and reproducibility for the production of novel formulations is needed to enable its transition from bench to bedside.

Structure based virtual screening of natural products to disrupt the structural integrity of TRAF6 C-terminal domain homotrimer

Tumor necrosis factor receptor-associated factor 6 (TRAF6) is an E3 ligase which takes part in different cellular pathways. TRAF6 is seen to be highly expressed in various cancers and most importantly is known to drive cancer metastasis. This makes TRAF6 a potential therapeutic target. In our previous studies, we observed that the C-terminal domain of TRAF6 forms a mushroom shaped trimer structure. Lys340 and Glu345 were identified to be the most critical residues in the trimer interface. In this current work, we screened for more than 14000 small molecules derived from various natural sources and they were screened against TRAF6 C-terminal trimer interaction interface to prevent the formation of the interface. All the obtained molecules were tested for their drug-likeliness properties. The ligands which qualified the filter were considered for protein-ligand docking or structure based virtual screening in GOLD 5.2. Pose selection was carried out on the basis of GoldScore and ChemScore function of GOLD 5.2. Top 20 molecules binding the TRAF6 trimeric interface were tested for their ADME properties. From the top 20 molecules, top 3 ligands were chosen based on their abilities to pass the maximum numbers of ADME filters.

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.

MoleGear: A Java-Based Platform for Evolutionary De Novo Molecular Design

A Java-based platform, MoleGear, is developed for de novo molecular design based on the chemistry development kit (CDK) and other Java packages. MoleGear uses evolutionary algorithm (EA) to explore chemical space, and a suite of fragment-based operators of growing, crossover, and mutation for assembling novel molecules that can be scored by prediction of binding free energy or a weighted-sum multi-objective fitness function. The EA can be conducted in parallel over multiple nodes to support large-scale molecular optimizations. Some complementary utilities such as fragment library design, chemical space analysis, and graphical user interface are also integrated into MoleGear. The candidate molecules as inhibitors for the human immunodeficiency virus 1 (HIV-1) protease were designed by MoleGear, which validates the potential capability for de novo molecular design.

Nucleolipids of the Nucleoside Antibiotics Formycins A and B: Synthesis and Biomedical Characterization Particularly Using Glioblastoma Cells

Two lipophilic derivatives of formycin A (1) and formycin B (5) carrying an O-2',3'-(ethyl levulinate) ketal group have been prepared. These were base-alkylated at N(1) (for 1) and N(1) and N(6) (for 5) with both isopentenyl and all-trans-farnesyl residues. Upon the prenylation, side reactions were observed, resulting in the formation of nucleolipids with a novel tricyclic nucleobase (→4a, 4b). In the case of formycin B, O-2',3'-(ethyl levulinate) (6) farnesylation gave the double prenylated nucleolipid 7. All new compounds were characterized by ¹ H-, ¹³ C-, UV/VIS and fluorescence spectroscopy, by ESI-MS spectrometry and/or by elemental analysis. Log P determinations between water and octanol as well as water and cyclohexane of a selection of compounds allowed qualitative conclusions concerning their potential blood-brain barrier passage efficiency. All compounds were investigated in vitro with respect to their cytotoxic activity toward rat malignant neuroectodermal BT4Ca as well as against a series of human glioblastoma cell lines (GOS 3, U-87 MG and GBM 2014/42). In order to differentiate between anticancer and side effects of the novel nucleolipids, we also studied their activity on PMA-differentiated human THP-1 macrophages. Here, we show that particularly the formycin A derivative 3b possesses promising antitumor properties in several cancer cell lines with profound cytotoxic effects partly on human glioblastoma cells, with a higher efficacy than the chemotherapeutic drug 5-fluorouridine.

Molecular dynamics and combined docking studies for the identification of Zaire ebola virus inhibitors

Ebola virus (EBOV) is a lethal human pathogen with a risk of global spread of its zoonotic infections, and Ebolavirus Zaire specifically has the highest fatality rate amongst other species. There is a need for continuous effort towards having therapies, as a single licensed treatment to neutralize the EBOV is yet to come into reality. This present study virtually screened the MCULE database containing almost 36 million compounds against the structure of a Zaire Ebola viral protein (VP) 35 and a consensus scoring of both MCULE and CLCDDW docking programs remarked five compounds as potential hits. These compounds, with binding energies ranging from -7.9 to -8.9 kcal/mol, were assessed for predictions of their physicochemical and bioactivity properties, as well as absorption, distribution, metabolism, excretion, and toxicity (ADMET) criteria. The results of the 50 ns molecular dynamics simulations showed the presence of dynamic stability between ligand and protein complexes, and the structures remained significantly unchanged at the ligand-binding site throughout the simulation period. Both docking analysis and molecular dynamics simulation studies suggested strong binding affinity towards the receptor cavity and these selected compounds as potential inhibitors against the Zaire Ebola VP 35. With respect to inhibition constant values, bioavailability radar and other physicochemical properties, compound A (MCULE-1018045960-0-1) appeared to be the most promising hit compound. However, the ligand efficiency and ligand efficiency scale need improvement during optimization, and also validation via in vitro and in vivo studies are necessary to finally make a lead compound in treating Ebola virus diseases. Communicated by Ramaswamy H. Sarma.

Identification and analyses of natural compounds as potential inhibitors of TRAF6-Basigin interactions in melanoma using structure-based virtual screening and molecular dynamics simulations

The interaction of the proteins, tumor necrosis factor receptor-associated factor6 (TRAF6) and Basigin (CD147), is known to be associated with the over-expression of matrix metalloproteinases (MMPs) in melanoma cells. MMPs are known to be responsible for melanoma metastasis. Hence, the TRAF6-Basigin complex can act as a potential therapeutic target. In previous studies, amino acid residues Lys340, Lys 384, Glu417 and Glu511 of TRAF6 were identified as the most vital residues on the basis of their contributions to interaction energy, relative solvent accessibility and electrostatic interactions in the TRAF6-Basigin protein-protein interaction (PPI) scheme. In our current work, we performed structure-based virtual screenings of some natural compounds obtained from ZINC database (n = 14509) to search for molecules which can act as inhibitors against the formation of TRAF6-Basigin complex. Three potential inhibitors were identified which were observed to make intermolecular interactions with Lys384 and Glu511 of TRAF6. Molecular dynamics simulation results suggested the substantial pharmacological importance of the ligand molecules as it was observed that there was total destabilization of TRAF6-Basigin complex upon binding of the molecule ZINC02578057. From our studies, we could conclude that the ligands termed as ZINC49048033, ZINC02578057 and ZINC72320240 could have great potentials to act as inhibitors to prevent melanoma metastasis.

Design, synthesis, and biological evaluation of novel oxadiazole- and thiazole-based histamine H3R ligands

Histamine H₃ receptor (H₃R) is largely expressed in the CNS and modulation of the H₃R function can affect histamine synthesis and liberation, and modulate the release of many other neurotransmitters. Targeting H₃R with antagonists/inverse agonists may have therapeutic applications in neurodegenerative disorders, gastrointestinal and inflammatory diseases. This prompted us to design and synthesize azole-based H₃R ligands, i.e. having oxadiazole- or thiazole-based core structures. While ligands of oxadiazole scaffold were almost inactive, thiazole-based ligands were very potent and several exhibited binding affinities in a nanomolar concentration range. Ligands combining 4-cyanophenyl moiety as arbitrary region, para-xylene or piperidine carbamoyl linkers, and/or pyrrolidine or piperidine basic heads were found to be the most active within this series of thiazole-based H₃R ligands. The most active ligands were in silico screened for ADMET properties and drug-likeness. They fulfilled Lipinski's and Veber's rules and exhibited potential activities for oral administration, blood-brain barrier penetration, low hepatotoxicity, combined with an overall good toxicity profile.

Use of QSPR Modeling to Characterize In Vitro Binding of Drugs to a Gut-Restricted Polymer

Purpose

Polymeric drugs, including patiromer (Veltassa®), bind target molecules or ions in the gut, allowing fecal elimination. Non-absorbed insoluble polymers, like patiromer, avoid common systemic drug-drug interactions (DDIs). However, the potential for DDI via polymer binding to orally administered drugs during transit of the gastrointestinal tract remains. Here we elucidate the properties correlated with drug-patiromer binding using quantitative structure-property relationship (QSPR) models.

Methods

We selected 28 drugs to evaluate for binding to patiromer in vitro over a range of pH and ionic conditions intended to mimic the gut environment. Using this in vitro data, we developed QSPR models using step-wise linear regression and analyzed over 100 physiochemical drug descriptors.

Results

Four descriptors emerged that account for ~70% of patiromer-drug binding in vitro: the computed surface area of hydrogen bond accepting atoms, ionization potential, electron affinity, and lipophilicity (R² = 0.7, Q² = 0.6). Further, certain molecular properties are shared by nonbinding, weak, or strong binding compounds.

Conclusions

These findings offer insight into drivers of in vitro binding to patiromer and describe a useful approach for assessing potential drug-binding risk of investigational polymeric drugs.

Electronic supplementary material

The online version of this article (10.1007/s11095-018-2356-y) contains supplementary material, which is available to authorized users.

Withania somniferaphytochemicals confer neuroprotection by selective inhibition of nNos: Anin silicostudy to search potent and selective inhibitors for human nNOS

Neuronal nitric oxide synthase (nNOS or NOS1) is an important therapeutic target for the treatment of various neurological diseases. A major challenge faced in the design of nNOS inhibitors emphasizes on potency in humans and selectivity over other NOS isoforms — eNOS and iNOS. The present structural-based in silico study was carried out to search potent and selective inhibitor for human nNOS from a set of 40 Withania somnifera phytochemicals structure. Ten phytochemicals appear as dual-selective inhibitors of nNOS over both iNOS and eNOS. Here we report five potent and selective human nNOS inhibitors, namely, Chlorogenic Acid, Withanolide B, Withacnistin Pelletierine, and Calystegine B2 based on their selectivity, binding energy and nNOS active site residues interaction profile. These phytochemicals have nNOS selectivity higher than 4-methyl-6-(2-(5-(3-(methylamino)propyl)pyridin-3-yl)-ethyl)pyridin-2-amine inhibitor and have potential as an oral neurotherapeutic agent to combat neurological disorders mediated by nNOS activation.

Ethyl 4-(4-methylphenyl)-4-pentenoate from Vetiveria zizanioides Inhibits Dengue NS2B-NS3 Protease and Prevents Viral Assembly: A Computational Molecular Dynamics and Docking Study

Around 50 % of the world's population is at the risk of dengue, a viral infection. Presently, there are not many drugs and prophylactic measures available to control dengue viral infection, and hence, there is an urgent need to develop effective antidengue compound from natural sources. In the current study, we explored the antiviral properties of the medicinal plant Vetiveria zizanioides against dengue virus. Initially, the antiviral properties of active compounds were examined using docking analysis along with reference ligand. The enzyme-ligand complex which showed higher binding affinity than the reference ligand was employed for subsequent analysis. The stability of the top scoring enzyme-ligand complex was further validated using molecular simulation studies. On the whole, the study reveals that the compound Ethyl 4-(4-methylphenyl)-4-pentenoate has an effective antiviral property, which can serve as a potential lead molecule in drug discovery process.

Piperlongumine inhibits lung tumor growth via inhibition of nuclear factor kappa B signaling pathway

Piperlongumine has anti-cancer activity in numerous cancer cell lines via various signaling pathways. But there has been no study regarding the mechanisms of PL on the lung cancer yet. Thus, we evaluated the anti-cancer effects and possible mechanisms of PL on non-small cell lung cancer (NSCLC) cells in vivo and in vitro. Our findings showed that PL induced apoptotic cell death and suppressed the DNA binding activity of NF-κB in a concentration dependent manner (0-15 μM) in NSCLC cells. Docking model and pull down assay showed that PL directly binds to the DNA binding site of nuclear factor-κB (NF-κB) p50 subunit, and surface plasmon resonance (SPR) analysis showed that PL binds to p50 concentration-dependently. Moreover, co-treatment of PL with NF-κB inhibitor phenylarsine oxide (0.1 μM) or p50 siRNA (100 nM) augmented PL-induced inhibitory effect on cell growth and activation of Fas and DR4. Notably, co-treatment of PL with p50 mutant plasmid (C62S) partially abolished PL-induced cell growth inhibition and decreased the enhanced expression of Fas and DR4. In xenograft mice model, PL (2.5-5 mg/kg) suppressed tumor growth of NSCLC dose-dependently. Therefore, these results indicated that PL could inhibit lung cancer cell growth via inhibition of NF-κB signaling pathway in vitro and in vivo.

Predicting human intestinal absorption of diverse chemicals using ensemble learning based QSAR modeling approaches

Human intestinal absorption (HIA) of the drugs administered through the oral route constitutes an important criterion for the candidate molecules. The computational approach for predicting the HIA of molecules may potentiate the screening of new drugs. In this study, ensemble learning (EL) based qualitative and quantitative structure-activity relationship (SAR) models (gradient boosted tree, GBT and bagged decision tree, BDT) have been established for the binary classification and HIA prediction of the chemicals, using the selected molecular descriptors. The structural diversity of the chemicals and the nonlinear structure in the considered data were tested by the similarity index and Brock-Dechert-Scheinkman statistics. The external predictive power of the developed SAR models was evaluated through the internal and external validation procedures recommended in the literature. All the statistical criteria parameters derived for the performance of the constructed SAR models were above their respective thresholds suggesting for their robustness for future applications. In complete data, the qualitative SAR models rendered classification accuracy of >99%, while the quantitative SAR models yielded correlation (R(2)) of >0.91 between the measured and predicted HIA values. The performances of the EL-based SAR models were also compared with the linear models (linear discriminant analysis, LDA and multiple linear regression, MLR). The GBT and BDT SAR models performed better than the LDA and MLR methods. A comparison of our models with the previously reported QSARs for HIA prediction suggested for their better performance. The results suggest for the appropriateness of the developed SAR models to reliably predict the HIA of structurally diverse chemicals and can serve as useful tools for the initial screening of the molecules in the drug development process.

Anti-tubercular drug development: computational strategies to identify potential compounds

InhA is an attractive target to combat tuberculosis (TB), which is targeted by many pro-drugs (isoniazid, etc.) and drugs such as triclosan. However, triclosan is less useful as an antitubercular drug due to its low bioavailability and therefore, in order to overcome this difficulty, many derivatives of triclosan were prepared. Here, we have combined various computational techniques to virtually screen out four potential triclosan derivatives. Molecular docking methods have been employed to screen out 32 out of 62 triclosan derivatives considering the mode of binding and the top re-rank scores. A comparative study on the chemical properties of triclosan and some of its derivatives has been performed using density functional theory (DFT) calculations. DFT based global reactivity descriptors (GRD), such as hardness, chemical potential, chemical softness, electrophilicity index, Fukui function, and local philicity calculated at the optimized geometries were used to investigate the usefulness of these descriptors for understanding the reactive nature and sites of the molecules. QSAR equations were built using these descriptors considering these 32 compounds. Four common compounds showing the best correlation and the best docking scores were considered for the ADMET property calculations and their dynamical movements have been studied using molecular dynamics simulations. Our results showed that these four compounds are chemically more active than triclosan and have the potential to inhibit the Mycobacterium tuberculosis enoyl acyl carrier protein reductase. This work shows that combination of different computational techniques may help to screen out potential drug candidates from a list of possible ones.

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.

A new PAMPA model proposed on the basis of a synthetic phospholipid membrane

The purpose of this work was to investigate the synthetic phospholipid dependence of permeability measured by parallel artificial membrane permeability assay (PAMPA) method. Three phospholipids with hydrophobic groups of different lengths and phosphorylcholine as the hydrophilic group were concisely synthesized. Ten model drug molecules were selected because of their distinct human fraction absorbed (%FA) values and various pK_a characteristics. In vitro drug permeation experiments were designed to determine the effect of the incubation time (4–20 h), pH gradient (4.6–9.32) and carbon chain length (8, 10, 12) on the drug permeability through the synthetic phospholipid membrane in the PAMPA system. The results showed that intensive and significant synthetic phospholipids dependence of permeability influenced by the length of lipid’s hydrophobic carbon chain. The effective permeability constant (P_e) of each drug increased rapidly with time, then decreased slightly after reaching the maximum; the pH gradient changed the drug permeability according to the pH-partition hypothesis for drugs with diverse pK_a values; and longer hydrophobic chains in the synthetic phospholipid membrane improved the drug permeability, as observed for all test drugs at almost all incubation time points. This newly proposed PAMPA model considered the synthetic phospholipid membrane and showed good P_e-%FA correlation for the passive transport of drugs, making it a helpful supplementary method for PAMPA systems.

Flavonoid interactions during digestion, absorption, distribution and metabolism: a sequential structure-activity/property relationship-based approach in the study of bioavailability and bioactivity

Flavonoids are a group of polyphenols that provide health-promoting benefits upon consumption. However, poor bioavailability has been a major hurdle in their use as drugs or nutraceuticals. Low bioavailability has been associated with flavonoid interactions at various stages of the digestion, absorption and distribution process, which is strongly affected by their molecular structure. In this review, we use structure-activity/property relationship to discuss various flavonoid interactions with food matrices, digestive enzymes, intestinal transporters and blood proteins. This approach reveals specific bioactive properties of flavonoids in the gastrointestinal tract as well as various barriers for their bioavailability. In the last part of this review, we use these insights to determine the effect of different structural characteristics on the overall bioavailability of flavonoids. Such information is crucial when flavonoid or flavonoid derivatives are used as active ingredients in foods or drugs.

The effect of N-methylation of amino acids (Ac-X-OMe) on solubility and conformation: a DFT study

N-Methylation of amino acid derivatives (Ac-X-OMe, X = Gly, Val, Leu, Ile, Phe, Met, Cys, Ser, Asp and His) leads to an increase in aqueous solubility, lipophilicity and lowering of the cis/trans amide conformational energy barrier (E_A).

Decision trees to characterise the roles of permeability and solubility on the prediction of oral absorption

Oral absorption of compounds depends on many physiological, physiochemical and formulation factors. Two important properties that govern oral absorption are in vitro permeability and solubility, which are commonly used as indicators of human intestinal absorption. Despite this, the nature and exact characteristics of the relationship between these parameters are not well understood. In this study a large dataset of human intestinal absorption was collated along with in vitro permeability, aqueous solubility, melting point, and maximum dose for the same compounds. The dataset allowed a permeability threshold to be established objectively to predict high or low intestinal absorption. Using this permeability threshold, classification decision trees incorporating a solubility-related parameter such as experimental or predicted solubility, or the melting point based absorption potential (MPbAP), along with structural molecular descriptors were developed and validated to predict oral absorption class. The decision trees were able to determine the individual roles of permeability and solubility in oral absorption process. Poorly permeable compounds with high solubility show low intestinal absorption, whereas poorly water soluble compounds with high or low permeability may have high intestinal absorption provided that they have certain molecular characteristics such as a small polar surface or specific topology.

A Projection Bias in Frequent Subgraph Mining Can Make a Difference

The aim of the frequent subgraph mining task is to find frequently occurring subgraphs in a large graph database. However, this task is a thriving challenge, as graph and subgraph isomorphisms play a key role throughout the computations. Since subgraph isomorphism testing is a hard problem, subgraph miners are exponential in runtime. To alleviate the complexity issue, we propose to introduce a bias in the projection operator and instead of using the costly subgraph isomorphism projection, one can use a polynomial projection having a semantically-valid structural interpretation. This paper presents a new projection operator for graphs named AC-projection, which exhibits nice theoretical complexity properties. We study the size of the search space as well as some practical properties of the projection operator. We also introduce a novel breadth-first algorithm for frequent AC-reduced subgraphs mining. Then, we prove experimentally that we can achieve an important performance gain (polynomial complexity projection) without or with non-significant loss of discovered patterns in terms of quality.

Computational predictions of glass-forming ability and crystallization tendency of drug molecules

Amorphization is an attractive formulation technique for drugs suffering from poor aqueous solubility as a result of their high lattice energy. Computational models that can predict the material properties associated with amorphization, such as glass-forming ability (GFA) and crystallization behavior in the dry state, would be a time-saving, cost-effective, and material-sparing approach compared to traditional experimental procedures. This article presents predictive models of these properties developed using support vector machine (SVM) algorithm. The GFA and crystallization tendency were investigated by melt-quenching 131 drug molecules in situ using differential scanning calorimetry. The SVM algorithm was used to develop computational models based on calculated molecular descriptors. The analyses confirmed the previously suggested cutoff molecular weight (MW) of 300 for glass-formers, and also clarified the extent to which MW can be used to predict the GFA of compounds with MW < 300. The topological equivalent of Grav3_3D, which is related to molecular size and shape, was a better descriptor than MW for GFA; it was able to accurately predict 86% of the data set regardless of MW. The potential for crystallization was predicted using molecular descriptors reflecting Hückel pi atomic charges and the number of hydrogen bond acceptors. The models developed could be used in the early drug development stage to indicate whether amorphization would be a suitable formulation strategy for improving the dissolution and/or apparent solubility of poorly soluble compounds.

Establishing the level of safety concern for chemicals in food without the need for toxicity testing

There is demand for methodologies to establish levels of safety concern associated with dietary exposures to chemicals for which no toxicological data are available. In such situations, the application of in silico methods appears promising. To make safety statement requires quantitative predictions of toxicological reference points such as no observed adverse effect level and carcinogenic potency for DNA-reacting chemicals. A decision tree (DT) has been developed to aid integrating exposure information and predicted toxicological reference points obtained with quantitative structure activity relationship ((Q)SAR) software and read across techniques. The predicted toxicological values are compared with exposure to obtain margins of exposure (MoE). The size of the MoE defines the level of safety concern and should account for a number of uncertainties such as the classical interspecies and inter-individual variability as well as others determined on a case by case basis. An analysis of the uncertainties of in silico approaches together with results from case studies suggest that establishing safety concern based on application of the DT is unlikely to be significantly more uncertain than based on experimental data. The DT makes a full use of all data available, ensuring an adequate degree of conservatism. It can be used when fast decision making is required.

The Development of Models Based on Linear and Nonlinear Multivariate Methods to Predict ADME/PK Properties Using Physicochemical Properties of Kinase, Protease Inhibitors, and GPCR Antagonists

Oral bioavailability of a drug compound is the significant property for potential drug candidates. Measuring this property can be costly and time-consuming. Quantitative structure-property relationships (QSPRs) are used to estimate the percentage of oral bioavailability, and they are an attractive alternative to experimental measurements. A data set of 217 drug and drug-like compounds with measured values of the percentage of oral bioavailability taken from the small molecule ChemBioBase database was used to develop and test a QSPR model. Descriptors were calculated for the compounds using Codessa 2.1 tool. Nonlinear general regression neural network model was generated using the DTREG predictive modeling program software. The calculated percentage of oral bioavailability model performs well, with root-mean-square (rms) errors of 4.55% oral bioavailability units for the training set, 14.32% oral bioavailability units for the test set, and 19.12% oral bioavailability units for the external prediction set. Given the structural diversity and bias of the data set, this is a good first attempt at modeling oral bioavailability using QSPR methods. The model can be used as a potential virtual screen or property estimator. With a larger data supply less biased toward the high end values of the percentage of oral bioavailability, a more successful model could likely be developed.

Prediction of pharmacokinetic parameters

In silico tools specifically developed for prediction of pharmacokinetic parameters are of particular interest to pharmaceutical industry because of the high potential of discarding inappropriate molecules during an early stage of drug development itself with consequent saving of vital resources and valuable time. The ultimate goal of the in silico models of absorption, distribution, metabolism, and excretion (ADME) properties is the accurate prediction of the in vivo pharmacokinetics of a potential drug molecule in man, whilst it exists only as a virtual structure. Various types of in silico models developed for successful prediction of the ADME parameters like oral absorption, bioavailability, plasma protein binding, tissue distribution, clearance, half-life, etc. have been briefly described in this chapter.