Ivanka Tsakovska

Bio: Ivanka Tsakovska is an academic researcher from Bulgarian Academy of Sciences. The author has contributed to research in topics: Pharmacophore & Virtual screening. The author has an hindex of 16, co-authored 52 publications receiving 906 citations.

The role of the European Chemicals Bureau in promoting the regulatory use of (Q)SAR methods.

Abstract: Under the proposed REACH (Registration, Evaluation and Authorisation of CHemicals) legislation, (Q)SAR models and grouping methods (chemical categories and read across approaches) are expected to play a significant role in prioritising industrial chemicals for further assessment, and for filling information gaps for the purposes of classification and labelling, risk assessment and the assessment of persistent, bioaccumulative and toxic (PBT) chemicals. The European Chemicals Bureau (ECB), 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. The ECB also promotes consensus and capacity building on scientific and technical matters among stakeholders in the Member State authorities and industry. To promote the availability and use of (Q)SARs and related estimation methods, the ECB is carrying out a range of activities, including applied research in computational toxicology, the assessment of (Q)SAR models and methods, the development of technical guidance documents and computational tools, and the organisation of training courses. This article provides an overview of ECB activities on computational toxicology, which are intended to promote the development, validation, acceptance and use of (Q)SARs and related estimation methods, both at the European and international levels.

The Characterisation of (Quantitative) Structure-Activity Relationships: Preliminary Guidance

Abstract: In November 2004, the OECD Member Countries and the European Commission adopted five principles for the validation of (quantitative) structure-activity relationships ([Q]SARs) intended for use in the regulatory assessment of chemicals. International agreement on a set of valdation principles was important, not only to provide regulatory bodies with a scientific basis for making decisions on the acceptability of data generated by (Q)SARs, but also to promote the mutual acceptance of (Q)SAR models by improving the transparency and consistency of (Q)SAR reporting. According to the OECD Principles for (Q)SAR validation, a (Q)SAR model that is proposed for regulatory use should be associated with five types of information: 1) a defined endpoint; 2) an unambiguous algorithm; 3) a defined domain of applicability; 4) appropriate measures of goodness-of-fit, robustness and predictivity; and 5) a mechanistic interpretation, if possible. Taken together, these five principles form the basis of a conceptual framework for characterising (Q)SAR models, and of reporting formats for describing the model characteristics in a transparent manner. Under the proposed REACH legislation in the EU, there are provisions for the use of estimated data generated by (Q)SARs, both as a substitute for experimental data, and as a supplement to experimental data in weight-of-evidence approaches. It is foreseen that (Q)SARs will be used for the three main regulatory goals of hazard assessment, risk assessment and PBT/vPvB assessment. In the Registration process under REACH, the registrant will be able to use (Q)SAR data in the registration dossier provided that adequate documentation is provided to argue for the validity of the model(s) used. This report provides preliminary guidance on how to characterise (Q)SARs according to the OECD validation principles. It is emphasised that the understanding of how to characterise (Q)SAR models is evolving, and that the content of the current report reflects the understanding and perspectives of the authors at the time of writing (November 2005). It is therefore likely that an update will be produced in the future for the benefit of those who need to submit (Industry) or evaluate (Authorities) chemical information based (partly) on (Q)SARs. It is also noted that this document does not provide guidance on the use of (Q)SAR reporting formats, or on criteria for the acceptance of (Q)SAR estimates, since EU guidance on these topics stills need to be developed.

Mechanistic QSAR of aromatic amines: new models for discriminating between homocyclic mutagens and nonmutagens, and validation of models for carcinogens.

Abstract: Because of its environmental and industrial importance, the aromatic amines are the single chemical class most studied for its ability to induce mutations and cancer. The large database of mutagenicity and carcinogenicity results has been studied with Quantitative Structure-Activity Relationship (QSAR) approaches by several authors, leading to models for the following: (a) the mutagenic potency in Salmonella thyphimurium; (b) the carcinogenic potency in rodents; and (c) the discrimination between rodent carcinogens and noncarcinogens. However, satisfactory models for the discrimination between mutagens and nonmutagens are lacking. The present work provides new QSARs for mutagenic/nonmutagenic homocyclic aromatic amines in S. typhimurium strains TA98 and TA100. The two new models are validated by checking their ability to predict the mutagenicity of further aromatic amines not included in the training set, and not used to generate the QSAR models. In addition, we also validated previous QSAR models for the carcinogenicity/noncarcinogenicity of the aromatic amines with external data. The mechanistic implications of the models are discussed in light of the other QSARs for the aromatic amines. The results of the analysis point to two QSAR models (one for mutagenicity and one for rodent carcinogenicity) as reliable tools for the in silico characterization of the risk posed by the aromatic amines.

Computational Studies of Free Radical-Scavenging Properties of Phenolic Compounds.

Abstract: For more than half a century free radical-induced alterations at cellular and organ levels have been investigated as a probable underlying mechanism of a number of adverse health conditions. Consequently, significant research efforts have been spent for discovering more effective and potent antioxidants / free radical scavengers for treatment of these adverse conditions. Being by far the most used antioxidants among natural and synthetic compounds, mono- and polyphenols have been the focus of both experimental and computational research on mechanisms of free radical scavenging. Quantum chemical studies have provided a significant amount of data on mechanisms of reactions between phenolic compounds and free radicals outlining a number of properties with a key role for the radical scavenging activity and capacity of phenolics. The obtained quantum chemical parameters together with other molecular descriptors have been used in quantitative structure-activity relationship (QSAR) analyses for the design of new more effective phenolic antioxidants and for identification of the most useful natural antioxidant phenolics. This review aims at presenting the state of the art in quantum chemical and QSAR studies of phenolic antioxidants and at analysing the trends observed in the field in the last decade.

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

Abstract: 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.

Mechanisms of Multidrug Resistance in Cancer Chemotherapy.

Abstract: Cancer is one of the main causes of death worldwide. Despite the significant development of methods of cancer healing during the past decades, chemotherapy still remains the main method for cancer treatment. Depending on the mechanism of action, commonly used chemotherapeutic agents can be divided into several classes (antimetabolites, alkylating agents, mitotic spindle inhibitors, topoisomerase inhibitors, and others). Multidrug resistance (MDR) is responsible for over 90% of deaths in cancer patients receiving traditional chemotherapeutics or novel targeted drugs. The mechanisms of MDR include elevated metabolism of xenobiotics, enhanced efflux of drugs, growth factors, increased DNA repair capacity, and genetic factors (gene mutations, amplifications, and epigenetic alterations). Rapidly increasing numbers of biomedical studies are focused on designing chemotherapeutics that are able to evade or reverse MDR. The aim of this review is not only to demonstrate the latest data on the mechanisms of cellular resistance to anticancer agents currently used in clinical treatment but also to present the mechanisms of action of novel potential antitumor drugs which have been designed to overcome these resistance mechanisms. Better understanding of the mechanisms of MDR and targets of novel chemotherapy agents should provide guidance for future research concerning new effective strategies in cancer treatment.

The Potential of Plant Phenolics in Prevention and Therapy of Skin Disorders

Abstract: Phenolic compounds constitute a group of secondary metabolites which have important functions in plants. Besides the beneficial effects on the plant host, phenolic metabolites (polyphenols) exhibit a series of biological properties that influence the human in a health-promoting manner. Evidence suggests that people can benefit from plant phenolics obtained either by the diet or through skin application, because they can alleviate symptoms and inhibit the development of various skin disorders. Due to their natural origin and low toxicity, phenolic compounds are a promising tool in eliminating the causes and effects of skin aging, skin diseases, and skin damage, including wounds and burns. Polyphenols also act protectively and help prevent or attenuate the progression of certain skin disorders, both embarrassing minor problems (e.g., wrinkles, acne) or serious, potentially life-threatening diseases such as cancer. This paper reviews the latest reports on the potential therapy of skin disorders through treatment with phenolic compounds, considering mostly a single specific compound or a combination of compounds in a plant extract.