Computational investigation of the radical-mediated mechanism of formation of difluoro methyl oxindoles: Elucidation of the reaction selectivity and yields.

Oxindoles are an important class of heterocyclic alkaloids with demonstrated pharmacological activity at multiple biological targets. Preparation of new analogs through novel synthetic routes is therefore highly attractive. In this work, we report a computational study to investigate the synthesis of ethoxycarbonyldifluoromethylated oxindoles from N-arylmethacrylamides. The reaction tolerates a diverse range of acrylamides, shows yields ranging from approximately 38%-96%. We have applied density functional theory (DFT) to explore the reaction mechanism, kinetics and thermodynamics to gain further understanding. We demonstrate that a radical-based ring closure reaction is energetically more favorable than a heterolytic process, that the rate-determining step is the formation of the arylmethacrylamide radical, and that the product yields and selectivities are consistent with experiment. The results demonstrate that theoretical methods can prove useful to understand how such reaction and could be potentially employed to rapidly explore the reaction scope further.

Towards reproducible computational drug discovery.

The reproducibility of experiments has been a long standing impediment for further scientific progress. Computational methods have been instrumental in drug discovery efforts owing to its multifaceted utilization for data collection, pre-processing, analysis and inference. This article provides an in-depth coverage on the reproducibility of computational drug discovery. This review explores the following topics: (1) the current state-of-the-art on reproducible research, (2) research documentation (e.g. electronic laboratory notebook, Jupyter notebook, etc.), (3) science of reproducible research (i.e. comparison and contrast with related concepts as replicability, reusability and reliability), (4) model development in computational drug discovery, (5) computational issues on model development and deployment, (6) use case scenarios for streamlining the computational drug discovery protocol. In computational disciplines, it has become common practice to share data and programming codes used for numerical calculations as to not only facilitate reproducibility, but also to foster collaborations (i.e. to drive the project further by introducing new ideas, growing the data, augmenting the code, etc.). It is therefore inevitable that the field of computational drug design would adopt an open approach towards the collection, curation and sharing of data/code.

Investigation of Factors Affecting the Performance of in silico Volume Distribution QSAR Models for Human, Rat, Mouse, Dog & Monkey.

Volume of distribution (Vdss ) is a measure of how effectively a drug molecule is distributed throughout the body. Along with the clearance, it determines the half-life and therefore the drug dosing interval. A number of different pre-clinical approaches are available to predict the Vdss in human including quantitative structure activity relationship (QSAR) models. Vdss QSAR models have been reported for human and rat, but not important pre-clinical species including dog, mouse and monkey. In this study, we have generated Vdss QSAR model on the human and commonly used pre-clinical species, each of which differs in terms of size, chemical diversity and data quality. We discuss the model performance by species, assess the effect the domain of applicability and the relative merits of building chemical series-specific models. In addition, we compare the intrinsic variability of the experimental logVdss data (∼1.2 fold error) to in-vivo interspecies differences (∼2 fold error) and in silico based models (∼3 fold error). This prompted us to explore whether one species could be used to predict another, particularly where little data for that species is available. i. e. does the expansion in domain of applicability prove beneficial over and above any deterioration due to the use of response values from an alternative species.

In silico identification and in vitro validation of nogalamycin N-oxide (NSC116555) as a potent anticancer compound against non-small-cell lung cancer cells.

The epidermal growth factor receptor (EGFR) was found to be overexpressed in several cancers, especially in lung cancers. Finding new effective drug against EGFR is the key to cancer treatment. In this study, the GOLD docking algorithm was used to virtually screen for novel human EGFR inhibitors from the NCI database. Thirty-four hit compounds were tested for EGFR-tyrosine kinase (TK) inhibition. Two potent compounds, 1-amino-4-(4-[4-amino-2-sulfophenyl]anilino)-9,10-dioxoanthracene-2-sulfonic acid (NSC125910), and nogalamycin N-oxide (NSC116555) were identified with IC50 values against EGFR-TK comparable to gefitinib; 16.14 and 37.71 nM, respectively. However, only NSC116555 demonstrated cytotoxic effects against non-small-cell lung cancer, A549, shown in the cell cytotoxicity assay with an IC50 of 0.19 + 0.01 µM, which was more potent than gefitinib. Furthermore, NSC116555 showed cytotoxicity against A549 via apoptosis in a dose-dependent manner.

Design, synthesis, and evaluation of the anticancer activity of 2-amino-aryl-7-aryl-benzoxazole compounds.

A series of 2-amino-aryl-7-aryl-benzoxazole derivatives have been designed, synthesized, and evaluated as anticancer agents. Fourteen of the compounds exhibited cytotoxic effects toward human A549 lung cancer cells. We found 12l was the most potent with an EC50 of 0.4 µm, equivalent to the anticancer drug doxorubicin, but had low selectivity following cross screening in monkey kidney Vero cells. Eight of the most potent or most selective compounds were further profiled in additional cell lines (MCF7, NCI-H187, and KB) to better understand their cytotoxic activity. Only compound 12l had a measurable EC50 in a single cell line (3.3 µm in the KB cell line). Taken together, this data suggest the series as a whole display specific cytotoxicity toward A549 cells. Cheminformatics searches pointed to JAK2 as a possible target. A subset of compounds assayed at this target showed IC50 s ranging from 10 to 0.08 µm; however, no clear correlation between JAK2 potency and A549 cytotoxicity was observed.

Towards Predicting the Cytochrome P450 Modulation: From QSAR to Proteochemometric Modeling.

Drug metabolism determines the fate of a drug when it enters the human body and is a critical factor in defining their absorption, distribution, metabolism, excretion and toxicity (ADMET) characteristics. Among the various drug metabolizing enzymes, cytochrome P450s (CYP450) constitute an important protein family that aside from functioning in xenobiotic metabolism, is also responsible for a diverse array of other roles encompassing steroid and cholesterol biosynthesis, fatty acid metabolism, calcium homeostasis, neuroendocrine functions and growth regulation. Although CYP450 typically converts xenobiotics into safe metabolites, there are some situations whereby the metabolite is more toxic than its parent molecule. Computational modeling has been instrumental in CYP450 research by rationalizing the nature of the binding event (i.e. inhibit or induce CYP450s) or metabolic stability of query compounds of interest. A plethora of computational approaches encompassing ligand, structure and systems based approaches have been utilized to model CYP450-ligand interactions. This review provides a brief background on the CYP450 family (i.e. its roles, advantages and disadvantages as well as its modulators) and then discusses the various computational approaches that have been used to model CYP450-ligand interaction. Particular focus was given to the use of quantitative structure-activity relationship (QSAR) and more recent proteochemometric modeling studies. Finally, a perspective on the current state of the art and future trends of the field is also provided.

Strategies for the generation, validation and application of in silico ADMET models in lead generation and optimization.

INTRODUCTION: The most desirable chemical starting point in drug discovery is a hit or lead with a good overall profile, and where there may be issues; a clear SAR strategy should be identifiable to minimize the issue. Filtering based on drug-likeness concepts are a first step, but more accurate theoretical methods are needed to i) estimate the biological profile of molecule in question and ii) based on the underlying structure-activity relationships used by the model, estimate whether it is likely that the molecule in question can be altered to remove these liabilities. AREAS COVERED: In this paper, the authors discuss the generation of ADMET models and their practical use in decision making. They discuss the issues surrounding data collation, experimental errors, the model assessment and validation steps, as well as the different types of descriptors and statistical models that can be used. This is followed by a discussion on how the model accuracy will dictate when and where it can be used in the drug discovery process. The authors also discuss how models can be developed to more effectively enable multiple parameter optimization. EXPERT OPINION: Models can be applied in lead generation and lead optimization steps to i) rank order a collection of hits, ii) prioritize the experimental assays needed for different hit series, iii) assess the likelihood of resolving a problem that might be present in a particular series in lead optimization and iv) screen a virtual library based on a hit or lead series to assess the impact of diverse structural changes on the predicted properties.

Insight into the reaction mechanism of cis,cis-muconate lactonizing enzymes: a DFT QM/MM study.

MLEs derived from mycobacterium smegmatis and seudomonas fluorescens share ∼76% identity and have a very similar arrangement of catalytic residues in their active site configuration. However, while they catalyze the conversion of cis,cis-muconate to the same achiral product, muconolactone, studies in deuterated solvent surprisingly show that the cyclo-isomerization proceeds with the formation of a chiral product. In this paper we discuss the application of DFT QM/MM calculations on both MLEs, to our knowledge the first reported in the literature on this protein. We investigate the proposal that the base involved in the catalytic reaction is the lysine residue found at the end of the 2(nd) strand given: (a) that the lysine residue at the end of the 6(th) strand is in an apparently equally effective position to catalyze reaction and (b) that the structural related epimerase in-fact achieve their stereo-specific outcomes by relying on either the base from the 2(nd) or 6(th) strand.

Application of drug efficiency index in drug discovery: a strategy towards low therapeutic dose.

INTRODUCTION: The ultimate objective of optimizing adsorption, distribution, metabolism and excretion (ADME) parameters in drug discovery is to maximize the unbound concentration at the site of action for a given dose level. This has the added benefit of minimizing the efficacious dose, reducing the potential for attrition related to drug burden and direct organ toxicity. The concept of drug efficiency was formulated as a tool to obtain a balanced profile between target affinity and ADME properties during lead optimization. AREAS COVERED: The authors discuss how it is possible to maximize the in vivo pharmacological potential addressing whether drug efficiency adds value to the decision-making process and whether it is possible to introduce a single optimization parameter, the drug efficiency index (DEI), linking target affinity and ADME properties, as a marker of in vivo efficacy. EXPERT OPINION: In the absence of a clear hypothesis-driven approach at the beginning of the program (i.e., pharmacokinetic-pharmacodynamic link), the objective to select molecules with a low therapeutic dose is still a major hurdle in drug discovery. The authors believe that a greater strategic focus on mechanistically relevant measures of the determinants of receptor occupancy would help the optimization and selection process. In this respect, the introduction of the DEI, which can be seen as a correction of target affinity by the in vivo pharmacokinetic potential, may help drug discovery to select and promote those molecules with the highest probability to interact with the biological target and with the best balance between target affinity and ADME properties.

Molecular dynamics investigation of psalmopeotoxin I. Probing the relationship between 3D structure, anti-malarial activity and thermal stability.

PcFK1 is a member of the cysteine knot inhibitor family that displays anti-malarial properties. The naturally occurring molecule is ∼ 40 amino acids in length and forms a highly constrained 3D structure due to the presence of 3 disulfide and multiple intra-molecular H-bonds. Recent experimental studies on PcFK1 wild-type and mutants, where the cystiene residues of each disulfide bond were mutated into serine residues, suggest that alterations to these structural constraints can give rise to sizeable differences in SAR. To better understand the relationship between the dynamic inhibitor 3D structure, biophysical and biological properties we have performed solution based molecular dynamics calculations over 150 ns using the CHARMM forcefield. We have analyzed the theoretical trajectory in a systematic way using principal components analysis, which allows us to identify the correlated nature of the protein loop, turn and sheet movements. We have identified the key molecular motions that give rise to the differing SAR which has helped to more precisely direct our ongoing SAR studies in this important therapeutic area.

Probing the structural and electronic factors affecting the adsorption and reactivity of alkenes in acidic zeolites using DFT calculations and multivariate statistical methods.

Quantum mechanical (QM) cluster calculations have been performed on a model of ZSM-5 at DFT and MP2 levels. We investigated how the adsorption energies and the energetics of alkoxide intermediate formation of six different alkene substrates, ethene, propene, 1-butene, cis/trans butene, and isobutene, vary in this zeolite model. An analysis of the DFT geometric, electronic, and energetic parameters of the zeolite-substrate complexes, transition states, and alkoxide intermediates is performed using principal components analysis (PCA) and partial least squares (PLS). These deliver an insight into the correlated changes that occur between molecular structure and energy along the reaction coordinate between the physisorbed and chemisorbed species within the zeolite. To the best of our knowledge, this is the first occasion multivariate techniques such as PCA or PLS have been employed to profile the changes in electronics, distances, and angles in QM calculations of catalytic systems such as zeolites. We find the calculated adsorption and the alkoxide intermediate energies correlate strongly with the absolute charge on the substrate and the length of the substrate double bond. The transition states' energies are not affected by the zeolite framework as modeled, which explains why they correlate strongly with the gas-phase substrate protonation energy. Our cluster results show that for ethene, propene, 1-butene, and isobutene, the relative energetics associated with the formation of the alkoxide intermediate in ZSM-5 follow the same trends as calculations where the effects of the framework are included.