http://academic.oup.com/milmed/article-pdf/146/7/506/24126715/milmed-146-7-506.pdf

Goodman and Gilman's The Pharmacological Basis of Therapeutics

Although drugs are intended to be selective, at least some bind to several physiological targets, explaining side effects and efficacy. Because many drug-target combinations exist, it would be useful to explore possible interactions computationally. Here we compared 3,665 US Food and Drug Administration (FDA)-approved and investigational drugs against hundreds of targets, defining each target by its ligands. Chemical similarities between drugs and ligand sets predicted thousands of unanticipated associations. Thirty were tested experimentally, including the antagonism of the beta(1) receptor by the transporter inhibitor Prozac, the inhibition of the 5-hydroxytryptamine (5-HT) transporter by the ion channel drug Vadilex, and antagonism of the histamine H(4) receptor by the enzyme inhibitor Rescriptor. Overall, 23 new drug-target associations were confirmed, five of which were potent (<100 nM). The physiological relevance of one, the drug N,N-dimethyltryptamine (DMT) on serotonergic receptors, was confirmed in a knockout mouse. The chemical similarity approach is systematic and comprehensive, and may suggest side-effects and new indications for many drugs.

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Predicting new molecular targets for known drugs

After nearly five decades "in the making", QSAR modeling has established itself as one of the major computational molecular modeling methodologies. As any mature research discipline, QSAR modeling can be characterized by a collection of well defined protocols and procedures that enable the expert application of the method for exploring and exploiting ever growing collections of biologically active chemical compounds. This review examines most critical QSAR modeling routines that we regard as best practices in the field. We discuss these procedures in the context of integrative predictive QSAR modeling workflow that is focused on achieving models of the highest statistical rigor and external predictive power. Specific elements of the workflow consist of data preparation including chemical structure (and when possible, associated biological data) curation, outlier detection, dataset balancing, and model validation. We especially emphasize procedures used to validate models, both internally and externally, as well as the need to define model applicability domains that should be used when models are employed for the prediction of external compounds or compound libraries. Finally, we present several examples of successful applications of QSAR models for virtual screening to identify experimentally confirmed hits.

Best Practices for QSAR Model Development, Validation, and Exploitation.

Quantitative structure–activity relationship modeling is one of the major computational tools employed in medicinal chemistry. However, throughout its entire history it has drawn both praise and criticism concerning its reliability, limitations, successes, and failures. In this paper, we discuss (i) the development and evolution of QSAR; (ii) the current trends, unsolved problems, and pressing challenges; and (iii) several novel and emerging applications of QSAR modeling. Throughout this discussion, we provide guidelines for QSAR development, validation, and application, which are summarized in best practices for building rigorously validated and externally predictive QSAR models. We hope that this Perspective will help communications between computational and experimental chemists toward collaborative development and use of QSAR models. We also believe that the guidelines presented here will help journal editors and reviewers apply more stringent scientific standards to manuscripts reporting new QSAR stu...

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QSAR Modeling: Where have you been? Where are you going to?

Molecular modelers and cheminformaticians typically analyze experimental data generated by other scientists. Consequently, when it comes to data accuracy, cheminformaticians are always at the mercy of data providers who may inadvertently publish (partially) erroneous data. Thus, dataset curation is crucial for any cheminformatics analysis such as similarity searching, clustering, QSAR modeling, virtual screening, etc., especially nowadays when the availability of chemical datasets in public domain has skyrocketed in recent years. Despite the obvious importance of this preliminary step in the computational analysis of any dataset, there appears to be no commonly accepted guidance or set of procedures for chemical data curation. The main objective of this paper is to emphasize the need for a standardized chemical data curation strategy that should be followed at the onset of any molecular modeling investigation. Herein, we discuss several simple but important steps for cleaning chemical records in a database including the removal of a fraction of the data that cannot be appropriately handled by conventional cheminformatics techniques. Such steps include the removal of inorganic and organometallic compounds, counterions, salts and mixtures; structure validation; ring aromatization; normalization of specific chemotypes; curation of tautomeric forms; and the deletion of duplicates. To emphasize the importance of data curation as a mandatory step in data analysis, we discuss several case studies where chemical curation of the original “raw” database enabled the successful modeling study (specifically, QSAR analysis) or resulted in a significant improvement of model's prediction accuracy. We also demonstrate that in some cases rigorously developed QSAR models could be even used to correct erroneous biological data associated with chemical compounds. We believe that good practices for curation of chemical records outlined in this paper will be of value to all scientists working in the fields of molecular modeling, cheminformatics, and QSAR studies.

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Trust, But Verify: On the Importance of Chemical Structure Curation in Cheminformatics and QSAR Modeling Research

Compatibility study between ketoprofen and pharmaceutical excipients used in solid dosage forms.

WOMBAT and WOMBAT‐PK: Bioactivity Databases for Lead and Drug Discovery

The analysed substances, procaine and benzocaine, are two anaesthetic agents currently being administered in tablet form, also in the topical (cream, gel, balm) and injectable dosage forms. The TG/DTG/DTA curves were obtained in air at different heating rates. For determination of the heat effects, the DTA curves (in μV) were changed with the heat flow curves (in mW), so that the peak area corresponds to an energy in J g−1 or kJ mol−1. The non-isothermal experiments are preformed to investigate the thermal degradation process of these active substances, both as a solid and are performed in a dynamic atmosphere of air at different heating rates, by heating from room temperature to 500 °C. The kinetic analysis was performed using the TG data in air for the first step of substance’s decomposition at four heating rates: 7, 10, 12 and 15 °C min−1. The data were processed according to an appropriate strategy to the following kinetic methods: Kissinger–Akahira–Sunose, Flynn–Wall–Ozawa, Friedman and NPK, to obtain realistic kinetic parameters, even if the decomposition process is a complex one. Thermal analysis was supplemented using Fourier Transform infrared spectroscopy coupled with the TG device to identify the anaesthetics with any products which may have formed (EGA—the evolved gas analysis).

Thermal behaviour studies of procaine and benzocaine

Simultaneous thermoanalytical techniques were used for the characterization of the thermal decomposition of ketoprofen—active substance and tablets. DTA and DSC curves showed that ketoprofen melts before the decomposition. A kinetic study regarding the ketoprofen—active substance’s thermal decomposition was performed under non-isothermal conditions and in a nitrogen atmosphere at five heating rates: 2.5, 5, 7.5, 10 and 15 °C min−1. The kinetic parameters of thermal decomposition process were obtained from TG/DTG curves using the following differential methods: Friedman isoconversional, Chang, respectively, integral methods: Flynn–Wall–Ozawa, Kissinger–Akahira–Sunose, Coats–Redfern and Madhusudanan. The careful treatment of the kinetic parameters obtained in certain thermal conditions was confirmed to be necessary as well as a different strategy of experimental data processing.

Thermal stability of ketoprofen—active substance and tablets

The thermoanalytical curves (TA), i.e. TG, DTG and DTA for pure cephalexin and its mixtures with talc, magnesium stearate, starch and microcrystalline cellulose, respectively, were drawn up in air and nitrogen at a heating rate of 10 °C min−1. The thermal degradation was discussed on the basis of EGA data obtained for a heating rate of 20 °C min−1. Until 250 °C, the TA curves are similar for all mixtures, up this some peculiarities depending on the additive appears. These certify that between the pure cephalosporin and the excipients do not exists any interaction until 250 °C. A kinetic analysis was performed using the TG/DTG data in air for the first step of cephalexin decomposition at four heating rates: 5, 7, 10 and 12 °C min−1. The data processing strategy was based on a differential method (Friedman), an integral method (Flynn–Wall–Ozawa) and a nonparametric kinetic method (NPK). This last one allowed an intrinsic separation of the temperature, respective conversion dependence on the reaction rate and less speculative discussions on the kinetic model. All there methods had furnished very near values of the activation energy, this being an argument for a single thermooxidative degradation at the beginning (192–200 °C).

Adriana Fuliaş

Papers

Compatibility study between ketoprofen and pharmaceutical excipients used in solid dosage forms.

WOMBAT and WOMBAT‐PK: Bioactivity Databases for Lead and Drug Discovery

Thermal behaviour studies of procaine and benzocaine

Thermal stability of ketoprofen—active substance and tablets

Thermal behaviour of cephalexin in different mixtures