Use Of Structure-activity Relationships Research Articles

DIGEP-Pred 2.0: A web application for predicting drug-induced cell signaling and gene expression changes.

The analysis of drug-induced gene expression profiles (DIGEP) is widely used to estimate the potential therapeutic and adverse drug effects as well as the molecular mechanisms of drug action. However, the corresponding experimental data is absent for many existing drugs and drug-like compounds. To solve this problem, we created the DIGEP-Pred 2.0 web application, which allows predicting DIGEP and potential drug targets by structural formula of drug-like compounds. It is based on the combined use of structure-activity relationships (SARs) and network analysis. SAR models were created using PASS (Prediction of Activity Spectra for Substances) technology for data from the Comparative Toxicogenomics Database (CTD), the Connectivity Map (CMap) for the prediction of DIGEP, and PubChem and ChEMBL for the prediction of molecular mechanisms of action (MoA). Using only the structural formula of a compound, the user can obtain information on potential gene expression changes in several cell lines and drug targets, which are potential master regulators responsible for the observed DIGEP. The mean accuracy of prediction calculated by leave-one-out cross validation was 86.5 % for 13377 genes and 94.8 % for 2932 proteins (CTD data), and it was 97.9 % for 2170 MoAs. SAR models (mean accuracy-87.5 %) were also created for CMap data given on MCF7, PC3, and HL60 cell lines with different threshold values for the logarithm of fold changes: 0.5, 0.7, 1, 1.5, and 2. Additionally, the data on pathways (KEGG, Reactome), biological processes of Gene Ontology, and diseases (DisGeNet) enriched by the predicted genes, together with the estimation of target-master regulators based on OmniPath data, is also provided. DIGEP-Pred 2.0 web application is freely available at https://www.way2drug.com/digep-pred.

Advances, opportunities, and challenges in methods for interrogating the structure activity relationships of natural products.

Time span in literature: 1985-early 2024Natural products play a key role in drug discovery, both as a direct source of drugs and as a starting point for the development of synthetic compounds. Most natural products are not suitable to be used as drugs without further modification due to insufficient activity or poor pharmacokinetic properties. Choosing what modifications to make requires an understanding of the compound's structure-activity relationships. Use of structure-activity relationships is commonplace and essential in medicinal chemistry campaigns applied to human-designed synthetic compounds. Structure-activity relationships have also been used to improve the properties of natural products, but several challenges still limit these efforts. Here, we review methods for studying the structure-activity relationships of natural products and their limitations. Specifically, we will discuss how synthesis, including total synthesis, late-stage derivatization, chemoenzymatic synthetic pathways, and engineering and genome mining of biosynthetic pathways can be used to produce natural product analogs and discuss the challenges of each of these approaches. Finally, we will discuss computational methods including machine learning methods for analyzing the relationship between biosynthetic genes and product activity, computer aided drug design techniques, and interpretable artificial intelligence approaches towards elucidating structure-activity relationships from models trained to predict bioactivity from chemical structure. Our focus will be on these latter topics as their applications for natural products have not been extensively reviewed. We suggest that these methods are all complementary to each other, and that only collaborative efforts using a combination of these techniques will result in a full understanding of the structure-activity relationships of natural products.

Targeting Cytosolic Phospholipase A2α for Novel Anti-Inflammatory Agents.

Group IV cytosolic phospholipase A2 (cPLA2α) plays a critical role in inflammatory processes. It produces arachidonic acid which is the main source of the pro-inflammatory eicosanoids mediators that are important in innate immune system. In some cases, these proinflammatory mediators cause damages to the host tissues and therefore promote autoimmune diseases. Consequently, development of potent inhibitors against cPLA2α could improve the therapy of inflammatory diseases. In the last two decades, intense efforts have been done to find potent cPLA2α inhibitors. Several scaffolds have been developed with the use of structure activity relationship (SAR) studies, and potent inhibitors have been obtained. The poor absorption of these compounds from intestine was the main challenge for clinical application. This review illustrates the search for cPLA2α inhibitors, their SAR studies and biological effects.

Fluorescent logic systems for sensing and molecular computation: structure-activity relationships in edge-detection.

Molecular logic-based computation continues to throw up new applications in sensing and switching, the newest of which is the edge detection of objects. The scope of this phenomenon is mapped out by the use of structure-activity relationships, where several structures of the molecules and of the objects are examined. The different angles and curvatures of the objects are followed with good fidelity in the visualized edges, even when the objects are in reverse video.

Discovery of Compounds Blocking the Proliferation of Toxoplasma gondii and Plasmodium falciparum in a Chemical Space Based on Piperidinyl-Benzimidazolone Analogs

A piperidinyl-benzimidazolone scaffold has been found in the structure of different inhibitors of membrane glycerolipid metabolism, acting on enzymes manipulating diacylglycerol and phosphatidic acid. Screening a focus library of piperidinyl-benzimidazolone analogs might therefore identify compounds acting against infectious parasites. We first evaluated the in vitro effects of (S)-2-(dibenzylamino)-3-phenylpropyl 4-(1,2-dihydro-2-oxobenzo[d]imidazol-3-yl)piperidine-1-carboxylate (compound 1) on Toxoplasma gondii and Plasmodium falciparum. In T. gondii, motility and apical complex integrity appeared to be unaffected, whereas cell division was inhibited at compound 1 concentrations in the micromolar range. In P. falciparum, the proliferation of erythrocytic stages was inhibited, without any delayed death phenotype. We then explored a library of 250 analogs in two steps. We selected 114 compounds with a 50% inhibitory concentration (IC50) cutoff of 2 μM for at least one species and determined in vitro selectivity indexes (SI) based on toxicity against K-562 human cells. We identified compounds with high gains in the IC50 (in the 100 nM range) and SI (up to 1,000 to 2,000) values. Isobole analyses of two of the most active compounds against P. falciparum indicated that their interactions with artemisinin were additive. Here, we propose the use of structure-activity relationship (SAR) models, which will be useful for designing probes to identify the target compound(s) and optimizations for monotherapy or combined-therapy strategies.

Use of Structure-Activity Relationships in Setting Priorities for Toxicologic Testing after Dermal Exposure

Use of Structure-Activity Relationships in Setting Priorities for Toxicologic Testing after Dermal Exposure

Computational identification of bioactive natural products by structure activity relationship

Natural products (NPs) have been widely used in traditional medicines and are a valuable source for new drug discovery. However, insufficient knowledge about their molecular mechanisms has limited the scope of their application and hindered the effort to design new drugs from their synergistic action strategies. Thus far, a systematic study of all NP ingredients in a traditional medicine recipe remains impractical. However encouraging results have begun to appear illustrating synergies between several principle active ingredients. In this work, we propose the use of structure activity relationship (SAR) to identify potential active ingredients in natural products, with the aim to facilitate experimental and computational characterizations of their therapeutic mechanisms and synergies. We call this approach the bioactive natural compound-likeness (BNC-likeness) approach, drawing a parallel to the concept of drug-likeness. In cross-validations and independent example tests, our approach displayed 90–92% sensitivity and 85–90% specificity, suggesting its practical usefulness. We also showed that BNC-like compounds were not just drug-like NP ingredients. BNC-like compounds and drug-like chemicals may share different structural characteristics. Therefore, BNC-likeness is a helpful novel conception inviting dedicated research.

Non-animal methodologies within biomedical research and toxicity testing

Laboratory animal models are limited by scientific constraints on human applicability, and increasing regulatory restrictions, driven by social concerns. Reliance on laboratory animals also incurs marked - and in some cases, prohibitive - logistical challenges, within high-throughput chemical testing programmes, such as those currently underway within Europe and the US. However, a range of non-animal methodologies is available within biomedical research and toxicity testing. These include: mechanisms to enhance the sharing and assessment of existing data prior to conducting further studies, and physicochemical evaluation and computerised modelling, including the use of structure-activity relationships and expert systems. Minimally-sentient animals from lower phylogenetic orders or early developmental vertebral stages may be used, as well as microorganisms and higher plants. A variety of tissue cultures, including immortalised cell lines, embryonic and adult stem cells, and organotypic cultures, are also available. In vitro assays utilising bacterial, yeast, protozoal, mammalian or human cell cultures exist for a wide range of toxic and other endpoints. These may be static or perfused, and may be used individually, or combined within test batteries. Human hepatocyte cultures and metabolic activation systems offer potential assessment of metabolite activity and organ-organ interaction. Microarray technology may allow genetic expression profiling, increasing the speed of toxin detection, well prior to more invasive endpoints. Enhanced human clinical trials utilising micro- dosing, staggered dosing, and more representative study populations and durations, as well as surrogate human tissues, advanced imaging modalities and human epidemiological, sociological and psycho- logical studies, may increase our understanding of illness aetiology and pathogenesis, and facilitate the development of safe and effective pharmacologic interventions. Particularly when human tissues are used, non-animal models may generate faster, cheaper results, more reliably predictive for humans, whilst yielding greater insights into human biochemical processes. Greater commitment to their development and implementation is necessary, however, to efficiently meet the needs of high-throughput chemical testing programmes, important emerging testing needs, and the ongoing development of human clinical interventions.

The prospects for using (Q)SARs in a changing political environment--high expectations and a key role for the european commission's joint research centre

Recent policy developments in the European union (EU) and within the Organisation for Economic Cooperation and Development (OECD) have placed increased emphasis on the use of structure-activity relationships (SARs) and quantitative structure-activity relationships (QSARs), collectively referred to as (Q)SARs, within various regulatory programmes for the assessment of chemicals and products. The most significant example within the EU is the European commission's proposal (of 29 October 2003) to introduce a new system for managing chemicals (called REACH), which calls for an increased use of (Q)SARs and other non-animal methods, especially for the assessment of low production volume chemicals. Another development within the EU is the Seventh Amendment to the Cosmetics Directive, which foresees the phasing out of animal testing on cosmetics, combined with the imposition of marketing bans on cosmetics that have been tested on animals after certain deadlines. At the same time, the Existing Chemicals programme within the OECD is investigating ways of increasing the use of chemical category approaches, which depend heavily on the use of (Q)SARs, activity-activity relationships and read-across. Such developments are placing an enormous challenge on industry, regulatory bodies, and on the European commission's Joint Research Centre (JRC), which is responsible for providing independent scientific advice to policy makers in the European Commission and the Member States. This paper reviews the different scientific and regulatory purposes for which reliable (Q)SARs could be used, and describes the current work of the JRC in providing scientific support for the development, validation and implementation of (Q)SARs.

The role of the European centre for the validation of alternative methods (ECVAM) in the validation of (Q)SARs

Under the current chemicals legislation, the regulatory use of structure-activity relationships (SARs) and quantitative structure-activity relationships (QSARs), collectively referred to as (Q)SARs, for the assessment of chemicals is limited, partly due to concerns about the extent to which (Q)SAR estimates can be relied upon. On 29 October 2003, the European Commission adopted a legislative proposal that foresees the introduction of a new regulatory system for chemicals called REACH (Registration, Evaluation, and Authorisation of Chemicals), which will impose equivalent information requirements on both new and existing chemicals. For reasons of practicality, cost-effectiveness and animal welfare, it is envisaged that (Q)SARs will play an important role in the assessment of some 30,000 existing chemicals for which further information may be required under the REACH system. It will therefore be essential that the (Q)SAR models used will produce reliable estimates. To overcome the barriers in the acceptance of (Q)SARs for regulatory purposes, it is widely acknowledged that there needs to be international agreement on the principles of (Q)SAR validation, and that the process of (Q)SAR validation should be managed by independent organisations, with a view to providing independent advice to the regulators who make decisions on the acceptability of (Q)SARs. The European Centre for the Validation of Alternative Methods (ECVAM), which is part of the European Commission's Joint Research Centre (JRC), has a well-established role in providing independent scientific and technical advice to European policy makers. This paper describes progress made at an international level regarding the principles of validation, and explains the role of ECVAM regarding the practical validation of (Q)SARs.

Structure-activity relationship approaches and applications.

New techniques and software have enabled ubiquitous use of structure-activity relationships (SARs) in the pharmaceutical industry and toxicological sciences. We review the status of SAR technology by using examples to underscore the advances as well as the unique technical challenges. Applying SAR involves two steps: Characterization of the chemicals under investigation, and application of chemometric approaches to explore data patterns or to establish the relationships between structure and activity. We describe generally but not exhaustively the SAR methodologies popular use in toxicology, including representation of chemical structure, and chemometric techniques where models are both unsupervised and supervised. The utility of SAR technology is most evident when supervised methods are used to predict toxicity of untested chemicals based only on chemical structure. Such models can predict on both an ordinal scale (e.g., active vs inactive) or a continuouis scale (e.g., median lethal dose [LD50] dose). The reader is also referred to a companion paper in this issue that discusses quantitative structure-activity relationship (QSAR) methods that have advanced markedly over the past decade.

SAR Modelling of Complex Phenomena: Probing Methodological Limitations

The increased acceptance of the use of structure-activity relationship (SAR) approaches to toxicity modelling has necessitated an evaluation of the limitations of the methodology. In this study, the limit of the capacity of the MULTICASE SAR program to model complex biological and toxicological phenomena was assessed. It was estimated that, provided the data set consists of at least 300 chemicals, divided equally between active and inactive compounds, the program is capable of handling phenomena that are even more "complex" than those modelled up to now (for example, allergic contact dermatitis, Salmonella mutagenicity, biodegradability, inhibition of tubulin polymerisation). However, within the data sets currently used to generate SAR models, there are limits to the complexity that can be handled. This may be the situation with regard to the modelling of systemic toxicity (for example, the LD50).

Inhibition of transport function and desipramine binding at the human noradrenaline transporter by N -ethylmaleimide and protection by substrate analogs

N-ethylmaleimide (NEM) inhibits [(3)H]desipramine binding and [(3)H]noradrenaline uptake at the rat noradrenaline transporter (rNET) by covalently modifying cysteine residues. We report here that NEM also inhibits [(3)H]desipramine binding and [(3)H]noradrenaline uptake at the cloned human noradrenaline transporter (hNET) stably expressed in C6 glial cells. The IC(50) for NEM inhibition of [(3)H]noradrenaline uptake was 43.6+/-5.5 microM. We tested several compounds for their abilities to inhibit [(3)H]noradrenaline uptake via the hNET and for their abilities to protect against NEM inactivation of [(3)H]desipramine binding. We found that the substrate analogs bupropion, 3-bromomethcathinone, and 4-bromomethcathinone all inhibit uptake at the hNET with IC(50) values of 1370+/-140, 158+/-20, and 453+/-30 nM, respectively. These compounds as well as methamphetamine, methcathinone, and desipramine also protected the hNET from NEM inactivation of [(3)H]desipramine binding. The ability of substrate analogs and desipramine to protect the [(3)H]desipramine binding site is consistent with the hypothesis that the desipramine binding site and the substrate binding site are mutually exclusive. It also supports the use of structure-activity relationships derived from substrate analogs in the rational design of hNET uptake inhibitors. The hNET contains 10 cysteine residues whereas the rNET contains 12 cysteine residues. Since the hNET and the rNET are both inhibited by NEM, and because the NEM inhibition is protectable by desipramine and substrate analogs, we conclude that the two additional cysteine residues (C28 and C447) present in the rNET are not likely to be involved in desipramine binding or uptake function.

USEPA Efforts in Harmonization of Acute Toxicity Test Guidelines with OECD

The USEPA, as a member of the OECD, is involved in the review of OECD guidelines and OECD's plans for revisions. The guidelines reviewed to date include: acute oral toxicity, eye and dermal irritation/corrosion, and skin sensitization. The revisions to the guidelines have emphasized reductions in animal usage in lab testing, and refinements which reduce/eliminate pain and suffering of animals. Screening methods, such as use of structure-activity relationships and physicochemical properties of test substances, which would eliminate chemicals that did not require animal testing, or sharply reduce the number of animals required for testing, were also recommended for some guidelines. In vitro methods were also examined as screens to eliminate severely toxic chemicals, but most of these methods still require further development and validation. The proposed revisions are discussed in detail in this report. This report has been reviewed by the Environmental Protection Agency's Office of Pollution Prevention and Toxics, and approved for publication. Approval does not signify that the contents necessarily reflect the views and policies of the Agency.

Mode of action and the assessment of chemical hazards in the presence of limited data: use of structure-activity relationships (SAR) under TSCA, Section 5.

Section 5 of the Toxic Substances Control Act (TSCA) requires that manufacturers and importers of new chemicals must submit a Premanufacture Notification (PMN) to the U.S. Environmental Protection Agency 90 days before they intend to commence manufacture or import. Certain information such as chemical identity, uses, etc., must be included in the notification. The submission of test data on the new substance, however, is not required, although any available health and environmental information must be provided. Nonetheless, over half of all PMNs submitted to the agency do not contain any test data; because PMN chemicals are new, no test data is generally available in the scientific literature. Given this situation, EPA has had to develop techniques for hazard assessment that can be used in the presence of limited test data. EPA's approach has been termed "structure-activity relationships" (SAR) and involves three major components: the first is critical evaluation and interpretation of available toxicity data on the chemical; the second component involves evaluation of test data available on analogous substances and/or potential metabolites; and the third component involves the use of mathematical expressions for biological activity known as "quantitative structure-activity relationships" (QSARs). At present, the use of QSARs is limited to estimating physical chemical properties, environmental toxicity, and bioconcentration factors. An important overarching element in EPA's approach is the experience and judgment of scientific assessors in interpreting and integrating the available data and information. Examples are provided that illustrate EPA's approach to hazard assessment for PMN chemicals.

Mode of Action and the Assessment of Chemical Hazards in the Presence of Limited Data: Use of Structure-Activity Relationships (SAR) under TSCA, Section 5

Mode of Action and the Assessment of Chemical Hazards in the Presence of Limited Data: Use of Structure-Activity Relationships (SAR) under TSCA, Section 5

An overview of structure-activity relationships as an alternative to testing in animals for carcinogenicity, mutagenicity, dermal and eye irritation, and acute oral toxicity.

The use of structure-activity relationships (SAR) has proven practical for the development of equations which can be used to estimate the above-listed endpoints for a large variety of chemicals. The SAR models predict these endpoints correctly in 85 to 97% of the cases and often surpass in their predictive ability the results obtainable from the equivalent biological assays. These SAR models are being used at several levels: drug, or more generally, chemical discovery; prioritization for testing; regulatory affairs; investigation of detoxification mechanisms; and risk estimation. In the new compound (discovery) use, potential toxic effects of a set of related compounds are investigated before synthesis to select those chemicals with the lesser probabilities of producing toxic effects for further investigation, at considerable savings in research expenditure since fewer compounds need to be synthesized, and the avoidance of blind alleys. Prioritization for testing is used in numerous instances, such as selecting those chemicals in an environment which are most likely to have toxic effects for priority attention. SAR models are used by regulatory agencies to determine the possible toxic effects of chemicals for which data insufficient to render decisions have been submitted, and to gain insight into possible toxicity problems. SAR models are also used to investigate possible metabolites, and toxicity mechanisms due to the ability of making computer-based structural modifications and observing the effects on the modelled toxic endpoints. Risk analysis is a natural outgrowth of several of the above applications, and is particularly useful for SAR models of carcinogenicity. SAR models as alternatives to animal bioassays should be used in the context of other information for the chemicals of concern. Just as bioassays and in vitro methods have their limitations, so do SAR models. These include the sometimes limited data base on which to base an SAR model, the temptation to extrapolate beyond the confines of the model, and the noise inherent in the bioassays on which the models are based. Within these constraints SAR models have a considerable potential in reducing the number of animals used in toxicity testing.

α-Halostilbenes related to diethylstilbestrol

Some new estrogens based on the diethylstilbestrol structure have been prepared for use as diagnostic, radiopharmaceuticals. By the use of structure activity relationships the optimal position for the introduction of iodine was determined to be the α position. The new α-halostilbestrols were prepared as their dimethoxy, monomethoxy and dihydroxy forms. These forms were successfully labelled using82Br,125I,128I and131I. A rationale for the mode of incorporation of125I and131I was developed which also explained the stereochemistry of incorporation. This incorporation required the use of the cuprous ion as a catalyst. It was found that in the absence of such a catalyst the incorporation of radioiodide proceeded very slowly and without stereospecificity whereas in the presence of cuprous ion it was rapid and the configuration of the substrate was maintained.