John C. Dearden

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

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 studies, as well as encourage the use of high quality, validated QSARs for regulatory decision making.

Quantitative structure-permeability relationships (QSPRs) for percutaneous absorption.

Quantitative structure-permeability relationships (QSPRs) have been derived by many researchers to model the passive, diffusion-controlled, percutaneous penetration of exogenous chemicals. Most of these relationships are based on experimental data from the published literature. They indicate that molecular size (as molecular weight) and hydrophobicity (as the logarithm of the octanol-water partition coefficient; log k(ow)) are the main determinants of transdermal penetration. This article reviews the current state of the art in QSPRs for absorption of chemicals through the skin, and where this technology can be exploited in future research. The main shortfalls in QSPR models result from inconsistency and error of the experimental values used to derive them. This is probably caused by the manner in which they employ data from a variety of sources and, in some cases, slightly different experimental protocols. Further, most current models are based on data generated from either aqueous or ethanolic solution, where each penetrant is present at its saturated solubility or a fraction of its saturated solubility. No models currently account for the influences of formulation upon percutaneous penetration. Current QSPR models provide a significant tool for assessing the percutaneous penetration of chemicals. They may be important in determining the bioavailability of a range of topically applied exogenous chemicals, and in issues of dermal toxicology and risk assessment. However, their current use may be limited by their lack of applicability across different formulation types. As a consequence, their true value may be to make predictions within specific formulation types, as opposed to a general model based on a range of formulation types. In addition, the endpoint of models may be inappropriate for specific applications other than the systemic delivery of topically applied chemicals.

How not to develop a quantitative structure–activity or structure–property relationship (QSAR/QSPR)

Although thousands of quantitative structure–activity and structure–property relationships (QSARs/QSPRs) have been published, as well as numerous papers on the correct procedures for QSAR/QSPR analysis, many analyses are still carried out incorrectly, or in a less than satisfactory manner. We have identified 21 types of error that continue to be perpetrated in the QSAR/QSPR literature, and each of these is discussed, with examples (including some of our own). Where appropriate, we make recommendations for avoiding errors and for improving and enhancing QSAR/QSPR analyses.

Investigation of the mechanism of flux across human skin in vitro by quantitative structure–permeability relationships

Permeability coefficients for 114 compounds across excised human skin in vitro were taken from Kirchner et al. Forty-seven descriptors were calculated encompassing the relevant physicochemical parameters of the compounds. Quantitative structure-permeability relationships (QSPRs) were developed using least-squares regression analysis. A two-parameter QSPR, describing the permeability coefficients (Kp) across excised skin, was obtained: log Kp=0.772 log P -0.0103 Mr - 2.33 where log P is the logarithm of the octanol-water partition coefficient and Mr is molecular mass. This equation indicates that percutaneous absorption is mediated by the hydrophobicity and the molecular size of the penetrant. Comparison with a QSPR based on penetration across a synthetic (polydimethylsiloxane) membrane suggests that the mechanisms of drug flux across polydimethylsiloxane membranes and excised human skin are significantly different.

2-Heteroarylimino-5-benzylidene-4-thiazolidinones analogues of 2-thiazolylimino-5-benzylidene-4-thiazolidinones with antimicrobial activity: synthesis and structure-activity relationship.

2-Heteroarylimino-5-benzylidene-4-thiazolidinones, unsubstituted or carrying hydroxy, methoxy, nitro and chloro groups on the benzene ring, were synthesised and assayed in vitro for their antimicrobial activity against gram positive and gram negative bacteria, yeasts and mould. The antimicrobial activity of the 2-benzo[d]thiazolyl- and of the 2-benzo[d]isothiazolyl-imino-5-benzylidene-4-thiazolidinones is, on the whole, lower in comparison with the high activity detected for the derivatives of the 2-thiazolylimino-5-benzylidene-4-thiazolidinone class. Nevertheless most of the benzo[d]thiazole analogues display good inhibition of the growth of gram positive bacilli and staphylococci, including methicillin-resistant Staphylococcus strains. Among the 2-benzo[d]isothiazole analogues a few derivatives show a strong and selective activity against bacilli. Moreover, it is worth noting that the replacement of the thiazole nucleus for the benzo[d]thiazole bicyclic system in the parent 2-(benzo[d]thiazol-2-ylimino)thiazolidin-4-one leads to significant antifungal properties against both yeasts and moulds, properties not shown by the analogous 2-thiazolyl- and 2-benzo[d]isothiazolyl-imino)thiazolidin-4-ones. The structure-activity relationship of 33 analogues possessing the 2-heteroarylimino-4-thiazolidinone structure is analysed through QSAR models.

Guidelines for developing and using quantitative structure-activity relationships

Numerous quantitative structure-activity relationships (QSARs) have been developed to predict properties, fate, and effects of mostly discrete organic chemicals. As the demand for different types of regulatory testing increases and the cost of experimental testing escalates, there is a need to evaluate the use of QSARs and provide some guidance to avoid their misuse, especially as QSARs are being considered for regulatory purposes. This paper provides some guidelines that will promote the proper development and use of QSARs. While this paper uses examples of QSARs to predict toxicity, the proposed guidelines are applicable to QSARs used to predict physical or chemical properties, environmental fate, ecological effects and health effects.

QSAR study of the toxicity of benzoic acids to Vibrio fischeri, Daphnia magna and carp.

The toxicities of benzoic acids to Vibrio fischeri, Daphnia magna and carp were measured. The results showed that the toxicity to V. fischeri and Daphnia decreased in the order of bromo > chloro > fluoro approximately equal to aminobenzoic acids. The toxicity of substituted benzoic acids to carp and Daphnia was much lower that to V. fischeri. The results also showed that the toxicity of benzoic acids to Daphnia decreased as the pH increased. It is suggested that ionized and non-ionized forms have different toxic responses. The non-ionized form may play an important role in toxicity because the toxicity of benzoic acids to Daphnia greatly decreases as the pH increases. The toxicity of benzoic acids to Daphnia may operate through non-polar narcosis, based on the regression results between the toxicities and partition coefficients (log P) and apparent partition coefficients (log D). However, toxicity cannot be predicted from non-polar baseline models because the ionized and non-ionized form of benzoic acids have different contributions to toxicity. Compared with the single descriptors, the prediction of toxicity of the benzoic acids was improved remarkably by using log P with pKa and log P with ELUMO. For the toxicity of benzoic acids to V. fischeri, it is suggested that the toxic mechanism may be different from the mechanism in Daphnia and carp. A probable reason is that V. fischeri is a unicellular organism with low lipid content, and hence both ionized and non-ionized forms of benzoic acids can easily cross the cell membrane and contribute to toxicity.

In silico prediction of aqueous solubility

The fundamentals of aqueous solubility, and the factors that affect it, are briefly outlined, followed by a short introduction to quantitative structure–property relationships. Early (pre-1990) work on aqueous solubility prediction is summarised, and a more detailed presentation and critical discussion are given of the results of most, if not all, of those published in silico prediction studies from 1990 onwards that have used diverse training sets. A table is presented of a number of studies that have used a 21-compound test set of drugs and pesticides to validate their aqueous solubility models. Finally, the results are given of a test of 15 commercially available software programs for aqueous solubility prediction, using a test set of 122 drugs with accurately measured aqueous solubilities.

Quantitative structure-property relationships for prediction of boiling point, vapor pressure, and melting point.

Boiling point, vapor pressure, and melting point are important physicochemical properties in the modeling of the distribution and fate of chemicals in the environment. However, such data often are not available, and therefore must be estimated. Over the years, many attempts have been made to calculate boiling points, vapor pressures, and melting points by using quantitative structure-property relationships, and this review examines and discusses the work published in this area, and concentrates particularly on recent studies. A number of software programs are commercially available for the calculation of boiling point, vapor pressure, and melting point, and these have been tested for their predictive ability with a test set of 100 organic chemicals.

The History and Development of Quantitative Structure-Activity Relationships (QSARs)

It is widely accepted that modern QSAR began in the early 1960s. However, as long ago as 1816 scientists were making predictions about physical and chemical properties. The first investigations into the correlation of biological activities with physicochemical properties such as molecular weight and aqueous solubility began in 1841, almost 60 years before the important work of Overton and Meyer linking aquatic toxicity to lipid-water partitioning. Throughout the 20th century QSAR progressed, though there were many lean years. In 1962 came the seminal work of Corwin Hansch and co-workers, which stimulated a huge interest in the prediction of biological activities. Initially that interest lay largely within medicinal chemistry and drug design, but in the 1970s and 1980s, with increasing ecotoxicological concerns, QSAR modelling of environmental toxicities began to grow, especially once regulatory authorities became involved. Since then QSAR has continued to expand, with over 1400 publications annually from 2011 onwards. KeywoRDS 1816, Corwin Hansch, Crum Brown and Fraser, Descriptors, Environmental Sciences, Newer Approaches, Pharmacology, Statistics INTRoDUCTIoN: wHAT IS A QSAR? Humans are inherently inquisitive. Even small children persistently ask “Why?”. So it is no surprise that for many years scientists have asked why some substances have a beneficial effect on the body, whilst others are toxic, and why some are more beneficial, or more toxic, than are others. That led Crum Brown and Fraser (1868-1869) to postulate that “there can be no reasonable doubt but that a relation exists between the physiologic action of a substance (Φ) and its chemical composition and constitution (C)”. Hence Φ = fC. They did not go on to suggest what functions of composition and constitution might be important. Nevertheless, their equation is a valid generic quantitative structure-activity relationship (QSAR). They also pointed out that “to discover f we produce a known change on the constitution by which it becomes C + ΔC, and examine the corresponding change of physiological action which has become Φ + ΔΦ. We thus obtain the relation between ΔC and ΔΦ, and by sufficiently varying C and ΔC, we may hope to get at all events an approximate solution of the problem”. That was a remarkably prescient statement, for it is exactly how QSAR modelling is performed (Kubinyi, 1993). Hence, in Crum Brown and Fraser’s terminology, a QSAR equation would be: Φ = c1C1 + c2C2 + c3C3 +...cnCn (1)