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PubMed2005

In vitro ADME phenotyping in drug discovery: current challenges and future solutions.

Drug-metabolizing enzymes and drug transporters are key regulators of drug disposition and pharmacodynamics, which are closely linked to drug efficacy and safety. In this article, current challenges and future solutions to predicting their influence on pharmacokinetics and inter-organ distribution in humans, from data generated during the drug discovery decision-making process, are presented. In vitro phenotyping strategies for drug metabolizing enzymes (eg, CYP3A4, UGT1A1) and transporters (eg, OATP1B1) are offered, including perspectives on a selection of in vitro systems, novel in vitro phenotyping reagents and remaining technology gaps, challenges in extrapolating in vitro data to the in vivo situation, in silico models for the prediction of whether compounds are enzyme or transporter substrates, and the impact of pharmacogenomics.

Animals↗
PubMed2006

An integrated in silico 3D model-driven discovery of a novel, potent, and selective amidosulfonamide 5-HT1A agonist (PRX-00023) for the treatment of anxiety and depression.

We report the discovery of a novel, potent, and selective amidosulfonamide nonazapirone 5-HT1A agonist for the treatment of anxiety and depression, which is now in Phase III clinical trials for generalized anxiety disorder (GAD). The discovery of 20m (PRX-00023), N-{3-[4-(4-cyclohexylmethanesulfonylaminobutyl)piperazin-1-yl]phenyl}acetamide, and its backup compounds, followed a new paradigm, driving the entire discovery process with in silico methods and seamlessly integrating computational chemistry with medicinal chemistry, which led to a very rapid discovery timeline. The program reached clinical trials within less than 2 years from initiation, spending less than 6 months in lead optimization with only 31 compounds synthesized. In this paper we detail the entire discovery process, which started with modeling the 3D structure of 5-HT1A using the PREDICT methodology, and then performing in silico screening on that structure leading to the discovery of a 1 nM lead compound (8). The lead compound was optimized following a strategy devised based on in silico 3D models and realized through an in silico-driven optimization process, rapidly overcoming selectivity issues (affinity to 5-HT1A vs alpha1-adrenergic receptor) and potential cardiovascular issues (hERG binding), leading to a clinical compound. Finally we report key in vivo preclinical and Phase I clinical data for 20m tolerability, pharmacokinetics, and pharmacodynamics and show that these favorable results are a direct outcome of the properties that were ascribed to the compound during the rational structure-based discovery process. We believe that this is one of the first examples for a Phase III drug candidate that was discovered and optimized, from start to finish, using in silico model-based methods as the primary tool.

Animals↗
PubMed2005

In silico stochastic network models that emulate the molecular sieving characteristics of bone.

Recent studies implicate bone's extracellular matrix as a "living electrophoresis and ion exchange column" with low pass filter function at the matrix level; whereas small molecules pass through the matrix microporosity, larger molecules penetrate the tissue through the pericellular space. In this study, stochastic network modeling principles were applied, for the first time to our knowledge, to build in silico, nano- to microscale models of bone. Small volumes of bone were modeled to include hierarchical levels of porosity comprising the bone matrix microporosity and the pericellular network. Flow and transport through the network was calculated for molecules from 1,000 to 100,000 datons (Da). On the basis of this study, two contrasting effects determine the rate and direction of transport of different size molecules through the hierarchical porous network of bone. Whereas diffusivity of a given molecule decreases with increasing molecular size, the size exclusion effects of bone's low pass molecular sieve translate into increasing flow velocities for large molecular species along transport paths located in the immediate vicinity of the cells. Both phenomena are expected to have a profound effect on the formation of molecular gradients at a tissue level, providing cues for tissue generation and repair by cellular "micromachines," i.e., osteoclasts and osteoblasts.

Animals↗
PubMed2005

Exploring the role of different drug transport routes in permeability screening.

The influence of different drug transport routes in intestinal drug permeability screening assays was studied. Three experimental models were compared: the small-intestine-like 2/4/A1 cell model, which has a leaky paracellular pathway, the Caco-2 cell model, which has a tighter paracellular pathway, and artificial hexadecane membranes (HDMs), which exclusively model the passive transcellular pathway. The models were investigated regarding their ability to divide passively and actively transported compounds into two permeability classes and to rank compounds according to human intestinal absorption. In silico permeability models based on two-dimensional (2D) and three-dimensional (3D) molecular descriptors were also developed and validated using external test sets. The cell-based models classified 80% of the acceptably absorbed compounds (FA >/= 30%) correctly, compared to 60% correct classifications using the HDM model. The best compound ranking was obtained with 2/4/A1 (r(s) = 0.74; r(s) = 0.95 after removing actively transported outliers). The in silico model based on 2/4/A1 permeability gave results of similar quality to those obtained when using experimental permeability, and it was also better than the experimental HDM model at compound ranking (r(s) = 0.85 and 0.47, respectively). We conclude that the paracellular transport pathway present in the cell models plays a significant role in models used for intestinal permeability screening and that 2/4/A1 in vitro and in silico models are promising alternatives for drug discovery permeability screening.

Administration, Oral↗
PubMed2002

Predicting ADME properties in silico: methods and models.

Unfavourable absorption, distribution, metabolism and elimination (ADME) properties have been identified as a major cause of failure for candidate molecules in drug development. Consequently, there is increasing interest in the early prediction of ADME properties, with the objective of increasing the success rate of compounds reaching development. This review explores in silico approaches and selected published models for predicting ADME properties from chemical structure alone. In particular, we provide a comparison of methods based on pattern recognition to identify correlations between molecular descriptors and ADME properties, structural models based on classical molecular mechanics and quantum mechanical techniques for modelling chemical reactions.

Computer Simulation↗
PubMed2006

Discovery of potent inhibitors of pseudomonal quorum sensing via pharmacophore modeling and in silico screening.

HipHop-Refine was employed to derive a binding hypothesis for pseudomonal quorum sensing (QS) antagonists. The model was employed as 3D search query to screen the National Cancer Institute (NCI) database. One of the hits illustrated nanomolar QS inhibitory activity. The fact that this compound contained tetravalent lead (Pb) prompted us to evaluate the activities of phenyl mercuric nitrate and thimerosal, both fit the binding pharmacophore. The two mercurials illustrated nanomolar to low micromolar IC50 inhibitory values against pseudomonal QS. The three compounds represent a new class of QS inhibitors.

Algorithms↗
PubMed2006

A QSAR model for in silico screening of MAO-A inhibitors. Prediction, synthesis, and biological assay of novel coumarins.

This work explores the potential of the MARCH-INSIDE methodology to seek a QSAR for MAO-A inhibitors from a heterogeneous series of compounds. A Markov model was used to quickly calculate the molecular electron delocalization, polarizability, refractivity, and n-octanol/water partition coefficients for a series of 1406 active/nonactive compounds. LDA was subsequently used to fit a classification function. The model showed 92.8% and 91.8% global accuracy and predictability in training and validation studies. This QSAR model was validated through a virtual screening of a series of coumarin derivatives. The 15 selected compounds were prepared and evaluated as in vitro MAO-A inhibitors. The theoretical prediction was compared with the experimental results and the model correctly predicted 13 compounds with only two mistakes on compounds with activities very close to the cutoff point established for the model. Consequently, this method represents a useful tool for the "in silico" screening of MAO-A inhibitors.

Algorithms↗
PubMedDate not supplied

Pharmacophore modeling and in silico screening for new P450 19 (aromatase) inhibitors.

Cytochrome P450 19 (P450 19, aromatase) constitutes a successful target for the treatment of breast cancer. This study analyzes chemical features common to P450 19 inhibitors to develop ligand-based, selective pharmacophore models for this enzyme. The HipHop and HypoRefine algorithms implemented in the Catalyst software package were employed to create both common feature and quantitative models. The common feature model for P450 19 includes two ring aromatic features in its core and two hydrogen bond acceptors at the ends. The models were used as database search queries to identify active compounds from the NCI database.

Algorithms↗
PubMed2006

Fuzzy-oil-drop hydrophobic force field--a model to represent late-stage folding (in silico) of lysozyme.

A model of hydrophobic collapse (in silico), which is generally considered to be the driving force for protein folding, is presented in this work. The model introduces the external field in the form of a fuzzy-oil-drop assumed to represent the environment. The drop is expressed in the form of a three-dimensional Gauss function. The usual probability value is assumed to represent the hydrophobicity distribution in the three-dimensional space of the virtual environment. The differences between this idealized hydrophobicity distribution and the one represented by the folded polypeptide chain is the parameter to be minimized in the structure optimization procedure. The size of fuzzy-oil-drop is critical for the folding process. A strong correlation between protein length and the dimension of the native and early-stage molecular form was found on the basis of single-domain proteins analysis. A previously presented early-stage folding (in silico) model was used to create the starting structure for the procedure of late-stage folding of lysozyme. The results of simulation were found to be promising, although additional improvements for the formation of beta-structure and disulfide bonds as well as the participation of natural ligand in folding process seem to be necessary.

Computer Simulation↗
PubMed2004

G protein-coupled receptors: in silico drug discovery in 3D.

The application of structure-based in silico methods to drug discovery is still considered a major challenge, especially when the x-ray structure of the target protein is unknown. Such is the case with human G protein-coupled receptors (GPCRs), one of the most important families of drug targets, where in the absence of x-ray structures, one has to rely on in silico 3D models. We report repeated success in using ab initio in silico GPCR models, generated by the predict method, for blind in silico screening when applied to a set of five different GPCR drug targets. More than 100,000 compounds were typically screened in silico for each target, leading to a selection of <100 "virtual hit" compounds to be tested in the lab. In vitro binding assays of the selected compounds confirm high hit rates, of 12-21% (full dose-response curves, Ki < 5 microM). In most cases, the best hit was a novel compound (New Chemical Entity) in the 1- to 100-nM range, with very promising pharmacological properties, as measured by a variety of in vitro and in vivo assays. These assays validated the quality of the hits as lead compounds for drug discovery. The results demonstrate the usefulness and robustness of ab initio in silico 3D models and of in silico screening for GPCR drug discovery.

Algorithms↗
PubMed2005

Evolutionary ecology in silico: Does mathematical modelling help in understanding 'generic' trends?

Motivated by the results of recent laboratory experiments, as well as many earlier field observations, that evolutionary changes can take place in ecosystems over relatively short ecological time scales, several 'unified' mathematical models of evolutionary ecology have been developed over the last few years with the aim of describing the statistical properties of data related to the evolution of ecosystems. Moreover, because of the availability of sufficiently fast computers, it has become possible to carry out detailed computer simulations of these models. For the sake of completeness and to put these recent developments in perspective, we begin with a brief summary of some older models of ecological phenomena and evolutionary processes. However, the main aim of this article is to review critically these 'unified' models, particularly those published in the physics literature, in simple language that makes the new theories accessible to a wider audience.

Aging↗
PubMed2005

In silico strategies for modeling membrane transporter function.

Transporter proteins facilitate the transfer of solutes across the cell membrane and have an intricate role in drug absorption, distribution and excretion. Because of their substrate promiscuity, several transporters represent viable pharmacological targets for enhancing drug absorption, preventing drug toxicity or facilitating localized tissue delivery. However, the slow emergence of high-resolution structures for these proteins has hampered the intelligent design of transporter substrates. Nonetheless, currently available functional, as well as structural, data provide an attractive scaffold for generating fusion models that merge substrate-based SARs and protein-based homology structures. The resultant models offer features that extend single modality paradigms in predictive function.

Computer Simulation↗
PubMedDate not supplied

In silico renal clearance model using classical Volsurf approach.

A data set of 130 diverse compounds containing both central nervous system (CNS) and non-CNS drugs was used to generate a renal clearance model using a classical Volsurf approach. Percentage renal clearance data was used as a biological input. The score plots obtained from principal component analysis and partial least-squares (PLS) analysis clearly separated high-clearance compounds from low-clearance compounds. PLS models were used to predict the renal clearance of the data set. Categorical statistical methods such as SIMCA and recursive partitioning techniques were used for classifying the compounds into low- and high-clearance categories. PLS coefficient plots, Volsurf descriptor profiles, 3D Grid maps, and RP decision trees were used to explain the important descriptors separating low and high renal clearance compounds. For comparative purposes, topological descriptors such as Molconn-Z were also examined. All the models were validated by an external test set of 20 compounds. These models can be used as efficient tools in the classification and prediction of the renal clearance of unknown compounds, the knowledge of which is helpful in understanding their bioavailability behavior.

Humans↗
PubMed2025

In silico encounters: harnessing metabolic modelling to understand plant-microbe interactions.

Understanding plant-microbe interactions is vital for developing sustainable agricultural practices and mitigating the consequences of climate change on food security. Plant-microbe interactions can improve nutrient acquisition, reduce dependency on chemical fertilizers, affect plant health, growth, and yield, and impact plants' resistance to biotic and abiotic stresses. These interactions are largely driven by metabolic exchanges and can thus be understood through metabolic network modelling. Recent developments in genomics, metagenomics, phenotyping, and synthetic biology now enable researchers to harness the potential of metabolic modelling at the genome scale. Here, we review studies that utilize genome-scale metabolic modelling to study plant-microbe interactions in symbiotic, pathogenic, and microbial community systems. This review catalogues how metabolic modelling has advanced our understanding of the plant host and its associated microorganisms as a holobiont. We showcase how these models can contextualize heterogeneous datasets and serve as valuable tools to dissect and quantify underlying mechanisms. Finally, we consider studies that employ metabolic models as a testbed for in silico design of synthetic microbial communities with predefined traits. We conclude by discussing broader implications of the presented studies, future perspectives, and outstanding challenges.

Plants↗
PubMed2006

Probabilistic neural network model for the in silico evaluation of anti-HIV activity and mechanism of action.

A theoretical model has been developed that discriminates between active and nonactive drugs against HIV-1 with four different mechanisms of action for the active drugs. The model was built up using a probabilistic neural network (PNN) algorithm and a database of 2720 compounds. The model showed an overall accuracy of 97.34% in the training series, 85.12% in the selection series, and 84.78% in an external prediction series. The model not only correctly classified a very heterogeneous series of organic compounds but also discriminated between very similar active/nonactive chemicals that belong to the same family of compounds. More specifically, the model recognized 96.02% of nonactive compounds, 94.24% of active compounds that inhibited reverse transcriptase, 97.24% of protease inhibitors, 97.14% of virus uncoating inhibitors, and 90.32% of integrase inhibitors. The results indicate that this approach may represent a powerful tool for modeling large databases in QSAR with applications in medicinal chemistry.

Algorithms↗
PubMed2003

In silico ADME prediction: data, models, facts and myths.

A critical review of a very recent work in the field of in silico ADME prediction is presented with emphasis on the work published during the period 2000-2002, and several other review articles are mentioned in order to offer a broader view of the field. We find that not much progress has been made in developing robust and predictive models, and that the lack of accurate data, together with the use of questionable modeling end-points, has greatly hindered the real progress in defining generally applicable models. Due to the largely empirical nature of QSAR/QSPR approaches, general and truly predictive models for complex phenomena, such as absorption and clearance, may still be chimeric. The development of local models for use within focused chemical series may be the most appropriate way of utilizing in silico ADME predictions, once experience and data have been gained on a given project and/or structural class.

Animals↗