Comprehension of drug toxicity_ Software and databases

Computers in Biology and Medicine 45 (2014) 20–25

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Computers in Biology and Medicine journal homepage: www.elsevier.com/locate/cbm

Comprehension of drug toxicity: Software and databases Andrey A. Toropov a,n, Alla P. Toropova a, Ivan Raska Jrb, Danuta Leszczynska c, Jerzy Leszczynski d a

IRCCS, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Via La Masa 19, Milano 20156, Italy 3rd Department of Medicine, Department of Endocrinology and Metabolism, First Faculty of Medicine, Charles University in Prague and General University Hospital in Prague, U Nemocnice 1, 12808 Prague 2, Czech Republic c Interdisciplinary Nanotoxicity Center, Department of Civil and Environmental Engineering, Jackson State University, 1325 Lynch St, Jackson, MS 39217-0510, USA d Interdisciplinary Nanotoxicity Center, Department of Chemistry and Biochemistry, Jackson State University, 1400 J. R. Lynch Street, PO Box 17910, Jackson, MS 39217, USA b

art ic l e i nf o

a b s t r a c t

Article history: Received 30 August 2013 Accepted 18 November 2013

Quantitative structure–property/activity relationships (QSPRs/QSARs) are a tool (in silico) to rapidly predict various endpoints in general, and drug toxicity in particular. However, this dynamic evolution of experimental data (expansion of existing experimental data on drugs toxicity) leads to the problem of critical estimation of the data. The carcinogenicity, mutagenicity, liver effects and cardiac toxicity should be evaluated as the most important aspects of the drug toxicity. The toxicity is a multidimensional phenomenon. It is apparent that the main reasons for the increase in applications of in silico prediction of toxicity include the following: (i) the need to reduce animal testing; (ii) computational models provide reliable toxicity prediction; (iii) development of legislation that is related to use of new substances; (iv) filling data gaps; (v) reduction of cost and time; (vi) designing of new compounds; (vii) advancement of understanding of biology and chemistry. This mini-review provides analysis of existing databases and software which are necessary for use of robust computational assessments and robust prediction of potential drug toxicities by means of in silico methods. & 2013 Elsevier Ltd. All rights reserved.

Keywords: In silico toxicology In silico methods QSAR Computational toxicology Drug toxicity

1. Introduction There are various tools that one could use for prediction of properties (activities) of chemical compounds. Among them the quantitative structure–property/activity relationships (QSPRs/ QSARs) methods hold important place. The history of evolution of the QSPRs/QSARs techniques contains three basic periods. The first period involved design of molecular descriptors which are correlated with important physicochemical parameters and/or with various biological endpoints. The statistical quality of QSPR/ QSAR for all available compounds which were used to build up the model was considered as the main result. The second period, the statistical quality of model for external “invisible” compounds which were not involved in building up model, become the main criterion of quality of QSPR/QSAR. On the other hand, the third period, the definition of chemical space where QSPR/QSAR can be used with satisfactory accuracy, i.e. so-called the domain of applicability has become the measure of quality for QSPR/QSAR

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Corresponding author. Tel.: þ 39 2 39014595; fax: þ39 2 39014735. E-mail addresses: [email protected], [email protected] (A.A. Toropov). 0010-4825/$ - see front matter & 2013 Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.compbiomed.2013.11.013

models. These criteria are briefly described by Organization for Economic Co-operation and Development (OECD) as Setubal principles: QSARs for regulatory application should: (1) be associated with a defined endpoint of regulatory importance, (2) take the form of an unambiguous algorithm, (3) have a defined domain of applicability, (4) be associated with appropriate measures of goodness of fit, robustness, and predictivity, and (5) have a mechanistic basis [1]. The drug discovery establishment has been probably one of the original industries to appreciate the QSPR/QSAR technology and still remains its the most important user. In fact, the drug discovery protocol needs to define two groups of endpoints related to new molecular entities (NMEs) [1] which relate to both the therapeutic and toxic effects [2]. Physico-chemical indicators have been increasingly used during the early stages of drug discovery to provide a more comprehensive understanding of the key properties that affect the biological functions (i.e. ADME—absorption, distribution, metabolism, and excretion). The most commonly measured physicchemical properties are permeability and solubility (due to their importance in the gastrointestinal absorption of orally administered drugs), and also lipophilicity, integrity, and stability (since these properties generally define the pharmaceutical potential of

A.A. Toropov et al. / Computers in Biology and Medicine 45 (2014) 20–25

a compound) [3–8]. Often, the ADME concept is expanded by toxicity [5,6]. Apparently, this potential hazardous quality of NME should be taken into account with no less care than possible therapeutic effects. There are several endpoints that relate to potential hazardous effects(such as the liver effects of drug candidates [9]; cardiac toxicity [10], and blood–brain barrier of compounds that has influence upon both drug efficiency and drug toxicity [11–13]) which should be estimated during the early stages of drug discovery. Finally carcinogenicity [14] and mutagenicity [15] of a drug also should be estimated during the initial stages of drug discovery. Due to huge cost and time necessary for research and development related to drug design, many in silico methods have been developed to provide accurate prediction of pharmacokinetic properties, such as Absorption, Distribution, Metabolism, Excretion, and Toxicity (ADMET), in very early stage of drug discovery [3–8,14,15]. Recently [16,17], the physiologically based pharmacokinetic/pharmacodynamic (PBPK/PD) approaches (for quantitatively description of the metabolism) have been suggested. In fact, PBPK models represent synthesis of mathematical calculations and experimental measurements [17]. Nevertheless, this approach becomes an necessary tool for the drug discovery [18] because allows to take into account chemical structures of potential metabolites [19]. Various approaches could be used to analyze possible toxicity of drugs. Apparently, classes of substances which are potential NMEs define the methodological details of the drug toxicity analysis. It appears that organic compounds are the most known source of the NMEs. However, this group of NME contains many sub-classes [2,5,7]. Peptides [20,21] are also source of NMEs, and this case often involves application of “unclassic” QSAR approaches. Relatively new class of potential NMEs consist of various nano materials such as fullerene derivatives [22–25] nanoparticles [26,27], and others [28]. Finally, one also needs to consider an important challenge of the drug discovery—so-called drug–drug interactions [29–31]. The average success rate for NMEs in all therapeutic areas combined, starting from first-in-human studies to registration during 1991–2000 period was approximately 11%. In 2003, the US Food and Drug Administration (FDA) approved 21 NMEs, however, during next years, this number has decreased (only 15 NMEs approved in 2010) [32]. Although lack of efficacy is a major contributor to a disappointment, toxicity can also be a cause of failure in drug development [1]. Thus, the systematization of the information that is necessary for drug discovery is possible only with involving of multimillion databases and reliable software [33]. In this minireview, we discuss possible ways of the systematization of the in silico methods that can be used for fast, preliminary estimation of toxicity of compounds with the possible therapeutically effects.

2. Sources of drug toxicity data Apparently, as concluded by the results of a recent study, collaboration between industry, computational software developers and regulatory researchers led to the development of a toxicity database and classification rules for NMEs [34]. Table 1 contains the basic principles of classification for substances available via database [34]. There are various kinds of toxic endpoints. An endpoint can be related to different organisms, e.g. rats, mice, fishes, birds, and human. From point of view of praxis most important toxicity is one that is related to human, but these data are very limited. Therefore, there are attempts to select organism with some similarity to organism of human (rat, dog, monkey, etc.). An endpoint can be related to various routes of adsorption (inhalation, oral, skin, etc.). Consequently, a toxic endpoint can be measured by different units

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Table 1 Classification scheme for substances involved in the drug discovery [34]. Class Comments 1 2 3 4 5

Known to be both mutagenic and carcinogenic Known to be mutagenic but unknown carcinogenic potential Structurally alerting compound, unrelated to the active pharmaceutical ingredient (API) and of unknown mutagenic potential Structurally alerting compound related to the API No structural alerts or sufficient evidence for absence of mutagenicity

(mg/kg, mg/m3, ppm, etc.). Thus, the search for data on different kinds of toxicity for various substances is complex enough task. However, there are a plethora of private and public data resources available for developing toxicity models. Recent reviews summarized available public toxicity databases [35,36] (Table 2). On the other hand, the large number and variety of sources of drug toxicity (their number is increasing day by day) lead to two questions: (i) where one can get information on toxicity of the potential therapeutic agent; and (ii) how one can estimate accuracy and reliability of the data. The problem of adequate and fast estimation of drug toxicity lead to creation of international organizations such as Food and Drug Administration (FDA), Center of Drug Evaluation and Research (CDER), and the Registration, Evaluation, Authorization and Restriction of Chemicals (REACH) legislation of the EU [32,35,36]. In spite of a number of organizations involved, harmonizing the reporting of chemical toxicity data to facilitate comparison between available data sources and databases remains a critical need [37]. It should be noted that these international organizations encourage the use of in silico methods in order to answer two questions mentioned above, as well as for the harmonizing of available data on the toxicity in general, and drug toxicity in particular [37]. One can obtain practical skills of estimation of the data related to toxicity after visiting web sites listed in Table 2.

3. In silico toxicology tools In silico toxicology is generally, but not exclusively, a predictive science. The approaches used for helping to define safety and discovery efforts in therapeutics represent a large number of chemical–biological informatics-based programs. Toxicology-oriented computational approaches as a rule are based on building toxicity databases. This gives possibility to carry out the QSAR analysis (modeling) [35]. Fig. 1 shows the general scheme of the QSAR analysis and illustrates the roles of the databases and QSAR models. The main reasons for the in silico prediction of toxicity in general are [36]: (i) Pressure to reduce animal testing; (ii) Computational models provide suitable toxicity prediction; (iii) Legislation (Governmental policies in both the European Union (EU) and North America) has encouraged and, in some cases, mandated the use of computational techniques to predict toxicity. For example, the US EPA has utilized QSAR to assist in the premanufactory notification of new chemicals, especially where no toxicity data are physically in hand. This requirement for models has inspired considerable advancement in the prediction of acute toxicity for environmental endpoints); (iv) Filling data gaps; (v) Cost and time reduction; (vi) Identification of new toxicological problems; (vii) Designing of new compounds; (viii) Higher throughput and in silico approaches have higher reproducibility if the same model is used. Again, this in silico approaches have low compound synthesis requirement; (ix) In silico models have the

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Table 2 Databases which are sources of available data on toxicity with the following classification: A ¼ numerical data on experimental toxic endpoints; B ¼numerical data on calculated (or extracted from several references) toxic endpoints; C ¼documentation related to toxicity (references and/or descriptions). Database

Accessibility

Description

Class

FAERS

Adverse Effects Reporting system (FAERS) of post-market safety surveillance for all approved drug and therapeutic biologic products

C

ACToR

http://www.fda.gov/Drugs/ GuidanceComplianceRegulatoryInformation/Surveillance/ AdverseDrugEffects/default.htm http://actor.epa.gov/actor/faces/ACToRHome.jsp

A

Cal/EPA

http://www.oehha.ca.gov/risk/ChemicalDB/index.asp

CCRIS

http://toxnet.nlm.nih.gov/cgi-bin/sis/htmlgen?CCRIS

CPDB

http://potency.berkeley.edu/

Drugs@FDA

http://www.fda.gov/Drugs/InformationOnDrugs/ ucm135821.htm http://www.epa.gov/ncct/dsstox/index.html http://cfpub.epa.gov/ecotox/

ACToR ( Aggregated Computational Toxicology Resource ) is EPA's online warehouse of publicly available chemical toxicity data.ACToR provides the numerical data on over 500,000 environmental chemicals searchable by chemical name and/or by chemical structure State of California EPA Toxicity. User can obtain information about CAS number, use, list of synonyms, and a group of criteria for risk assessment of a wanted substance Chemical carcinogenesis research information system (CCRIS). The numerical data on various carcinogenic endpoints (mice, rats, ames salmonella typhimurium, and human) for over 8000 compounds University of California, Berkeley, carcinogenic potency database contains long-term animal cancer tests on 1547 chemicals Information about brand name and generic prescription and over-the-counter human drugs and biological therapeutic products User can obtain data on toxicity represented by PDF, SDF or XLS files User can use quick and/or advanced database query. There is an user guide. There are links to other databases on toxicity University-based database of issues related to pesticide toxicology US FDA/CFSAN database with references to scientific literature describing studies of the toxic properties of plants

DSSTox ECOTOX EXTOXNET FDA Poisonous Plant Database Gene-Tox HERA

http://extoxnet.orst.edu/ghindex.html http://www.accessdata.fda.gov/scripts/plantox/index.cfm

http://toxnet.nlm.nih.gov/cgi-bin/sis/htmlgen?GENETOX http://www.heraproject.com/RiskAssessment.cfm

Household Products IRIS

http://hpd.nlm.nih.gov/ http://cfpub.epa.gov/ncea/iris/index.cfm

ITER

http://www.tera.org/iter/

JECDB

http://dra4.nihs.go.jp/mhlw_data/jsp/SearchPageENG.jsp

LAZAR

http://www.in-silico.de/

MR

http://www.atsdr.cdc.gov/mrls/index.html

N-Class database, KemI NPIC

http://apps.kemi.se/nclass/

http://npic.orst.edu

NTP

http://ntp.niehs.nih.gov/

PAN Pesticide http://www.pesticideinfo.org/

Riskline, KemI http://apps.kemi.se/riskline/

STITCH

http://stitch.embl.de/

TEXTRATOX

http://www.vet.utk.edu/TETRATOX/index.php

TOXNET

http://toxnet.nlm.nih.gov/

ToxRefDB

http://www.epa.gov/ncct/toxrefdb/

US NLM Peer-reviewed genetic toxicology test data for over 3000 chemicals Human and Environmental Risk Assessment on Ingredients and Household Cleaning Products; toxicity and risk data on ingredients supplied and formulated by European manufacturers This database contains over 12,000 consumer brands with numerical criteria their health effects Integrated Risk Information System; a compilation of electronic reports on environmental substances and their potential to cause human health effects. User can obtain PDF file with description of toxicological review of a substance in detail Database of human health risk values and cancer classifications for over 600 environmental chemicals Japanese Ministry of Health, Labour and Welfare Chemical Toxicity Database; toxicity data for 369 chemicals Lazy structure–activity relationships database; provides QSAR predictions for liver toxicity, mutagenicity, and carcinogenicity User can obtain data on minimal risk (MR) levels for hazardous substances represented in this database Using special interface, user can define a wanted compound. The system provide information on large list of different endpoints related to this compound National Pesticide Information Center through Oregon State University and US EPA provides science-based information about pesticides including toxicity. In fact the database is online encyclopedia for pesticides US NIH/NIEHS National Toxicology Program testing status and information of agents registered in the US of public health interest. User can obtain documents related to different substances and protocols of definition of different toxic endpoints as well as information on other aspects of toxicology in general Pesticide Action Network North America; data on 6500 pesticides, insecticides and herbicides including toxicity, water pollution, ecological toxicity, uses, and regulatory status. In fact the database is a digest of pesticides Contains information on both environment and health Useful for classification and labelling. Provides links to references, associated with a chemical. User can obtain a set of abstracts related to indicated substance Search Tool for Interactions of Chemicals (STITCH). Knowledge database to explore known and predicted interactions between proteins and small-molecule chemicals for understanding of molecular and cellular functions. Over 68,000 chemicals are represented The University of Tennessee Institute of Agriculture. A collection of aquatic toxic potency data for more than 2400 industrial organic compounds Databases on toxicology, hazardous chemicals, environmental health, and toxic releases. User can obtain data on toxic endpoint related to different animals and human as well as data on physicochemical endpoints, such as boiling points, water solubility, logP (octanol–water) etc. US EPA relational database of standard toxicity test results for pesticides and other environmental chemicals including acute, subchronic, chronic, reproductive, and developmental toxicity in support of the ToxCast program. User can obtain data represented by XLS files

A C

A A A C A C

A C

C C

C C B A C

C

C

C

B

C

B A

C


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Fig. 1. Graphical representations of information (i) from databases; and (ii) from QSAR analysis.

Table 3 Software available for QSAR analysis of toxic endpoints. Software

Accessibility

Description

ChemProp CODESSA

http://www.ufz.de/index.php? en=10684 http://www.codessa-pro.com/

CORAL

http://www.insilico.eu/coral

DRAGON

http://www.ccl.net/qsar/archives/ 0403/0458.html http://www.miasrl.com/golpe.htm http://www.oecd.org/env/ehs/ http://www.softpedia.com/get/ Science-CAD/PRECLAV.shtml http://aptsoftware.co.in/rmsquare/

ChemProp contains several programs which provide batch processing in terms of QSAR runs for compound lists Generator of large set of various molecular descriptors for multiple regression analysis, artificial neuron networks, partial least squares method, etc. (commercial) Free available software and data on some endpoints including toxicity. There are detailed descriptions together with references on published articles Generator of large set of various molecular descriptors for multiple regression analysis, artificial neuron networks, partial least squares method, etc. (commercial) GOLPE (Generating Optimal Linear PLS Estimations) is a system of programs for 3D QSAR (commercial) Contains standardized software and databases related to QSPR/QSAR problematics PRECLAV (PRoperty Evaluation by CLAss Variables) is a packet of programs for QSPR/QSAR analysis (commercial) Estimation of prtedictability of QSPR/QSAR models with special metrics (Rm2). There are detailed description together with references on published articles Toxicity Estimation Software Tool (TEST) will enable users to easily estimate acute toxicity using the QSAR methodologies: (i) Hierarchical method (ii) FDA method (iii) Single-model method (iv) Consensus method, etc. TEST can be used for the analysis of LC50(48 h), LC50 (96 h), BCF, and mutagenicity This software provides on-line tools, which can be helpful for QSPR/QSAR analyses of various endpoints (solubility, octanol/water partition coefficient, etc.)

GOLPE OECD ToolBox PRECLAV RmSquare T.E.S.T

http://www.epa.gov/nrmrl/std/ qsar/qsar.html

Virtual Computational Chemistry Laboratory

http://www.vcclab.org/

ability to predict ADME related properties on virtual structures enabling exploration of chemical space without the need to carry out chemical laboratory synthesis and experimental testing until a likely hit or candidate molecule is selected; (x) Development of understanding of basic biology and chemistry (In the modelling of acute toxicological endpoints, much has been gained regarding mechanisms of action). For many modelling approaches, it may be assumed that compounds fitting the same QSAR are acting by the same mechanism of action. This has allowed researchers to define the chemical domain of certain mechanisms [36]. Currently, the toxic potential of large quantities of industrial chemicals including pharmaceuticals, cosmetics, pesticides and other synthetic or semi-synthetic chemicals is often required to be assessed by using standard animal models. This comprises the basic test protocol for risk assessments necessary for their approval as a registered product to be launch into the market. With increasing concern about the environmental pollution and human

health, the manufacture, storage, distribution and release of these substances to the environment after their use, are controlled and regulated at local, national and international levels by different governments and regulatory agencies worldwide. An important aspect of the in silico methods involved in the estimation of the drug toxicity is so-called organ toxicity [37]. High quality QSARs are built up for the following kinds of organ toxicity [37]: hepatotoxicity, liver necrosis, liver relative weight gain, liver lipid accumulation, nephrotoxicity, kidney necrosis, kidney relative weight gain, nephron injury. If there are a group of QSAR models available for a serious of compounds, the search for the consensus of these models seems to provide robust and accurate approach [4,36]. It should be noted that the Monte Carlo technique [38–41] can be involved in solution of the QSAR problems related to drug toxicity. Detailed description of the Monte Carlo technique is available in the literature [42].

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The comparison of different software includes (i) convenience of using; (ii) reliability of results; and (iii) practical scientific and heuristic potential of output data. The DRAGON and CODESSA are most cited programs, but they are commercial (as well GOLPE and PRECLAV whereas all other programs from Table 3 are free). TEST and Virtual Computational Chemistry Laboratory are available online and have convenient interface. OECD toolbox is official program, consequently, the user of this software can get “very lawabiding” data from point of view of EC administrations. ChemProp have convenient input (molecular structure can be represented by graphical editor, via SMILES and InChI. The output can be represented by both HTML tables available on the Internet and simple text file. PRECLAV is a standardized software with limitation for the number of compounds under consideration (maximum is 50). GOLPE is a tool for fast analysis of large data. RmSquare is a tool to estimate predictability of a model using data on experimental and predicted values of endpoint for test set. The CORAL software is a tool for QSPR/QSAR analyses of data where the molecular structure is represented by SMILES.

4. Conclusions In silico methods, able to reliable estimate toxicity of potential therapeutical agents are complex systems. They are improved and evolve on the yearly bases. Their improvement is accompanied by expansion of the number of large databases (Table 2) and increase of the number of various computer programs (Table 3). In silico toxicology methods are practical, evidence-based and provide high throughput, with varying accuracy. In silico approaches are of keen interest, not only to scientists in the private sector and to academic researchers worldwide, but also to the public [32,35,36]. They are being increasingly evaluated and applied by regulators. Although there are foreseeable beneficial aspects – including maximizing use of prior test data and the potential for minimizing animal use for future toxicity testing – the primary use of in silico toxicology methods in the pharmaceutical sciences involves decision support information [43]. Obviously, management of drug design is rife with complicated, contentious, and risky decisions. These decisions are quite difficult and complex. They involve significant uncertainty, multiple stakeholder groups, competing objectives, and dynamic, non-linear interdependencies. All these aspects test the limits of unaided human judgment. In the past, formal methods of risk analysis have been used to evaluate new technologies, but these methods ignore decision-relevant qualitative information and rely on a volume of quantitative engineering and scientific data that simply does not exist for many potential therapeutical agents (e.g. peptides, biopolymers, nanomaterials) [44]. The main problem of the field (in silico estimation of drug toxicity) is absence of the standardization of available information for the both the databases and the software. Unfortunately, this problem is characterized by not only the “methodological dispersion”, but also the geographical one [36], i.e. very eclectic criteria for estimation of data which are using in various countries. Thus, harmonization, systematization, and standardization of criteria and methods involved in the in silico drug discovery should be the critical issue at the international level for the nearest future [35,36,43].

Conflict of interest Statement None declared

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[28]

[29]

[30]

[31]

[32] [33]

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