Aqueous solubility: Simple predictive methods (in silico,in vitro and bio-relevant approaches)

Aqueous solubility is a key physicochemical attribute required for the characterisation of an active pharmaceutical ingredient (API) during drug discovery and beyond. Furthermore, aqueous solubility is highly important for formulation selection and subsequent development processes. This review provides a summary of simple predictive methods used to assess aqueous solubility as well as an assessment of the more complex in silico methodologies and a review of the recent solubility challenge. In addition, a summary of experimental methods to determine solubility is included, with a discussion of some potential pitfalls.     

Development of PAK1 kinase inhibitors with "in silico" modeling methods

The important role of several protein kinases in many diseases has been well documented. Nowadays they are known as an important class of drug targets. The similarity of the structures of the protein kinases makes the development of selective inhibitors difficult. Only a few selective PAK1 kinase inhibitors have been published in the literature, so we have applied "in silico" methods to aid the development of further selective PAK1 inhibitors. The effects of the potential inhibitors were screened in biochemical assays and pharmacological features have been determined for further development.     

Predictive in silico modeling for hERG channel blockers

hERG-mediated sudden death as a side effect of non-antiarrhythmic drugs has been receiving increased regulatory attention. Perhaps owing to the unique shape of the ligand-binding site and its hydrophobic character, the hERG channel has been shown to interact with pharmaceuticals of widely varying structure. Several in silico approaches have attempted to predict hERG channel blockade. Some of these approaches are aimed primarily at filtering out potential hERG blockers in the context of virtual libraries, others involve understanding structure-activity relationships governing hERG-drug interactions. This review summarizes the most recent efforts in this emerging field.     

Machine learning techniques for in silico modeling of drug metabolism

The computational assessment of drug metabolism has gained considerable interest in pharmaceutical research. Amongst others, machine learning techniques have been employed to model relationships between the chemical structure of a compound and its metabolic fate. Examples for these techniques, which were originally developed in fields far from drug discovery, are artificial neural networks or support vector machines. This paper summarizes the application of various machine learning techniques to predict the interaction between organic molecules and metabolic enzymes. More complex endpoints such as metabolic stability or in vivo clearance will also be addressed. It is shown that the prediction of metabolic endpoints with machine learning techniques has made considerable progress over the past few years. Depending on the procedure used, either classification or quantitative prediction is possible for even large and diverse compound sets. Together with the expanding experimental data basis, these in silico methods have become valuable tools in the drug discovery and development process.     

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.     

In silico methods for early toxicity assessment

The number of new drugs that reach the market is declining every year, and, in recent years, several drugs already on the market have been withdrawn because of safety concerns. In response to this, the pharmaceutical industry is poised to take advantage of innovative methods that will increase the efficiency of its research and development. An in silico approach to predicting safety issues could provide meaningful information early on in the drug discovery process, and representing a relatively inexpensive alternative to current methods. Recent advances in the field of in silico toxicology are reviewed herein, along with a discussion of the reasons behind this increased attention.     

First report on predictive chemometric modeling, 3D-toxicophore mapping and in silico screening of in vitro basal cytotoxicity of diverse organic chemicals

Classification and regression based quantitative structure–toxicity relationship (QSTR) as well as toxicophore models were developed for the first time on basal cytotoxicity data (in vitro 3T3 neutral red uptake data) of a diverse series of chemicals (including drugs and environmental pollutants) collected from the ACuteTox database (http://www.acutetox.eu/). Statistically significant QSTR models were obtained using linear discriminant analysis (classification) and partial least squares (regression) methodologies. Generated toxicophore models showed four important features responsible for basal cytotoxicity: (i) two hydrophobic aliphatic groups (HYD Aliphatic), (ii) ring aromatic group (RA) and (iii) hydrogen bond donor (HBD). The most predictive hypothesis (Hypo 1) had a correlation coefficient of 0.932 for the training set, a low rms deviation of 1.105, and an acceptable cost difference of 62.8 bits, which represents a true correlation and a good predictivity. QSTR and toxicophore models were rigorously validated internally as well as externally along with the randomization test to nullify the possibilities of chance correlation. Our in silico models enable to identify the essential structural attributes and quantify the prime molecular pre-requisites which were chiefly responsible for in vitro basal cytotoxicity. The developed models were also implemented to screen basal cytotoxicity for huge number DrugBank database (http://www.drugbank.ca/) compounds.     

In silico ADME-Tox modeling: progress and prospects

Introduction: Although significant progress has been made in high-throughput screening of absorption, distribution, metabolism and excretion, and toxicity (ADME-Tox) properties in drug discovery and development, in silico ADME-Tox prediction continues to play an important role in facilitating the appropriate selection of candidate drugs by pharmaceutical companies prior to expensive clinical trials.

Areas covered: This review provides an overview of the available in silico models that have been used to predict the ADME-Tox properties of compounds. It also provides a comprehensive overview and summarization of the latest modeling methods and algorithms available for the prediction of physicochemical characteristics, ADME properties, and drug toxicity issues.

Expert opinion: The in silico models currently available have greatly contributed to the knowledge of screening approaches in the early stages of drug discovery and the development process. As the definitive goal of in silico molding is to predict the pharmacokinetics and disposition of compounds in vivo by assembling all kinetic processes within one global model, PBPK models can serve this purpose. However, much work remains to be done in this area to generate more data and input parameters to build more reliable and accurate prediction models.     

Toxicogenomic analysis methods for predictive toxicology

Toxicogenomics, the application of genomic data to elucidate or predict an organism's response to a toxicant, can inform the drug development process in important ways. It is apparent that standardized approaches to many types of toxicogenomic questions are still being formulated. Specifically, a significant body of proof of principle studies has emerged that demonstrates a range of statistical methodologies applied to predictive toxicology. These studies rely on class prediction methods--mathematical models generated using the gene expression profiles of known toxins from representative toxicological classes--to predict the toxicological effect of a compound based on the similarities between its gene expression profile and the profiles of a given toxicological class. Class prediction methods hold promise for increasing the rate at which compounds can be evaluated for toxicity early in the drug discovery process, while at the same time reducing the length of toxicological studies and their associated costs. Class prediction methods are informed by class comparison and class discovery steps, which inform, respectively, the selection of genes whose response can be used to distinguish among the toxicological classes and the number of classes distinguishable using the response of these genes. Together these steps use a variety of complementary statistical techniques to achieve a successful class prediction model. This report attempts to review some of the themes that appear to be emerging in the application of these techniques to predictive toxicology methods over toxicogenomics' short history.     

REACH and in silico methods: an attractive opportunity for medicinal chemists

1. Download : Download high-res image (282KB)
2. Download : Download full-size image

     

In silico approaches to mechanistic and predictive toxicology: an introduction to bioinformatics for toxicologists

Bioinformatics, or in silico biology, is a rapidly growing field that encompasses the theory and application of computational approaches to model, predict, and explain biological function at the molecular level. This information rich field requires new skills and new understanding of genome-scale studies in order to take advantage of the rapidly increasing amount of sequence, expression, and structure information in public and private databases. Toxicologists are poised to take advantage of the large public databases in an effort to decipher the molecular basis of toxicity. With the advent of high-throughput sequencing and computational methodologies, expressed sequences can be rapidly detected and quantitated in target tissues by database searching. Novel genes can also be isolated in silico, while their function can be predicted and characterized by virtue of sequence homology to other known proteins. Genomic DNA sequence data can be exploited to predict target genes and their modes of regulation, as well as identify susceptible genotypes based on single nucleotide polymorphism data. In addition, highly parallel gene expression profiling technologies will allow toxicologists to mine large databases of gene expression data to discover molecular biomarkers and other diagnostic and prognostic genes or expression profiles. This review serves to introduce to toxicologists the concepts of in silico biology most relevant to mechanistic and predictive toxicology, while highlighting the applicability of in silico methods using select examples.     

Chemometric QSAR modeling and in silico design of antioxidant NO donor phenols

An acceleration of free radical formation within human system exacerbates the incidence of several life-threatening diseases. The systemic antioxidants often fall short for neutralizing the free radicals thereby demanding external antioxidant supplementation. Therein arises the need for development of new antioxidants with improved potency. In order to search for efficient antioxidant molecules, the present work deals with quantitative structure-activity relationship (QSAR) studies of a series of antioxidants belonging to the class of phenolic derivatives bearing NO donor groups. In this study, several QSAR models with appreciable statistical significance have been reported. Models were built using various chemometric tools and validated both internally and externally. These models chiefly infer that presence of substituted aromatic carbons, long chain branched substituents, an oxadiazole-N-oxide ring with an electronegative atom containing group substituted at the 5 position and high degree of methyl substitutions of the parent moiety are conducive to the antioxidant activity profile of these molecules. The novelty of this work is not only that the structural attributes of NO donor phenolic compounds required for potent antioxidant activity have been explored in this study, but new compounds with possible antioxidant activity have also been designed and their antioxidant activity has been predicted in silico.     

In silico modeling of interstitial lung mechanics: implications for disease development and repair

In this perspective, we first review some of the published literature on structural modeling of the mechanical properties of the lung parenchyma. Based on a recent study, we demonstrate why mechanical dysfunction accompanying parenchymal diseases such as pulmonary fibrosis and emphysema can follow a very different course from the progression of the underlying microscopic pathophysiology itself, particularly in the early stages. The key idea is related to the concept of percolation on elastic networks where the bulk modulus of the network suddenly changes when the fibrotic stiff regions or the emphysematous holes become suddenly connected across the network. We also introduce the concept of depercolation as a basis for the rational optimization of tissue repair. Specifically, we use these network models to predict the functional improvements that a hypothetical biological or tissue engineering repair could achieve. We find that rational targeted repair can have significant benefits over generic random repair. This concept may find application in the treatment of lung fibrosis, surgical, bronchoscopic, or biological lung volume reduction, or any future alveolar regeneration or tissue engineering solution to the repair of connective tissue damage of the lung.