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From Observational Toxicology to Predictive Toxicology

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Page 1: From Observational Toxicology to Predictive Toxicology

From Observational Toxicology to Predictive Toxicology: A Paradigm Shift

Essay

In ancient days, the safety and use of materials to human and domestic animals were assessed merely based the observed effects. For instance, in antiquity – animal venoms, plants and minerals were all classified as safe or toxic among the members of a community where the materials are used. In effect, people of the time were able to identify, for example, some of the plants as edible, medicinal or hazardous. These achievements were achieved through careful observation by the scholars of the respective community. However, contribution of trial and error has also been mentioned (1, 2).

Rooted its origin in poison such as plant extracts and animal venoms for hunting (4), observational toxicology refers to the pursuit of what effects result from exposure to a particular substance (3). Throughout its historic and outstanding advancements several observational theories and hypothesis have been forwarded. Although, several scholars of different ages such as Hippocrates, Aristotle, and Dioscorides had all contributed to the advancement of the field, a significant and landmark progress was recorded in the 16 th century of where Paracelsus (1493–1541) was front runner. Of these, the most important achievement has been attributed to the statement ‘’ the right dose differentiates what is toxic and what is not’’ (4, 16).

Augmented with the advancement of science and technology, the toxicological discipline has shown rapid growth throughout the following centuries. Among the most important driving factors were increased industrial and pharmaceutical chemical productions where, as a result, several incidences of poisoning had been recorded with subsequent introduction of different legislation (5). Among chemical or drug related poisoning events that fuel the advancement of the field, the mid-20th century thalidomide tragedy is mentioned frequently.

To date, the observational toxicology is well established and functional approach where several methods are available to test what a chemical could cause up on exposure, often measured by toxicological end-points (6). Accepted and validated by regulatory organizations and scientific communities, the test methods have been regarded as of a high value making the field an integral part of key innovative processes such as drug development (7).

However, for observational toxicology is highly dependent on the use of experimental animals, the practice has been challenged for centuries by the philosophy of animal right and human morale activists (8). Moreover, the unmet need of pharmaceuticals for diseases such as cancer and others and ever expanding basic sciences such as computational chemistry, molecular biology and mathematics (9) have been presenting irresistible challenges to philosophy and

Page 2: From Observational Toxicology to Predictive Toxicology

practice of observational toxicology. The drawbacks such as low throughput, high cost, and difficulties inherent to inter-species extrapolation usually from high dose tests in experimental animals to low dose exposure in humans; and the positive parallel development in other sciences are considered as a driving force to move toxicology from a predominantly observational science at the level of disease-specific models to a predominantly predictive science focused upon a broad inclusion of target-specific, mechanism-based, biological observations (10). Thus, the need to apply such a move is not accidental rather it is a cumulative effect of unmet needs and extraordinary development in science and technology.

The concept in predictive toxicology itself is complex in such a way that it is composed of different practices such as experimental toxicology, computational toxicology, and high-throughput technologies as well as toxicogenomics. It is complex not only because it involves experts from different angles such as genetics and bioinformatics, computational chemistry, mathematical modeling and toxicology itself but also it aims to generate a single conclusion about a chemical at hand, for example to qualify or reject a novel drug candidate for further clinical studies.

The transition from observational to predictive toxicology, supported and guided by different organizations such as National toxicology program USA (NTP, USA), has been involving different tools to hasten the development of the field. The notable tools are in silico techniques. As the name implies, it uses different models and mathematical equations that themselves driven from different data sources. Commonly employed techniques are Molecular Modeling (methods that model biochemical events that are relevant for toxicity), Expert Systems (techniques that mimic human reasoning about toxicological phenomena) and Data Driven Systems (that derive predictions from a training set of experimentally determined data). Together with in silico method, in vitro methods are also important elements in the predictive toxicology packages. But, it is generally believed that the combination of the two, in silico and in vitro, may increase the accuracy of the prediction (11, 12, 13, and 14). It has become immensely popular approach among scientific communities for reasons such as fastness, easiness, cheap and many others as well as in the general public for the very reason it might reduce, or replace in ideal cases, the laboratory animals (15) that often put significant challenge in observational

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studies. However, the validated methods convincing the regulatory authorities to grant clinical trials based on in vitro or in silico approaches are rare, if any.

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2. Stephen Harrod Buhner,Brook Medicine Eagle. 2006. Sacred Plant Medicine: The Wisdom in Native American Herbalism, Bear & Company, Rochester.

3. http://www.toxicologysource.com/whatistoxicology.html 4. Darrell R. Boverhof, B. Bhaskar Gollapudi. 2011. Application of toxicogenomics in

safety evaluation and risk assessment. Willey & Sons, Inc. New Jersy, USA. 5. Gallo, M. A. 2008. History and scope of toxicology. In Casarett and Doull’s

Toxicology: The Basic Science of Poisons (C. D. Klaassen, ed.), pp. 1–10. McGraw-Hill, New York.

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8. Baumans V. Use of animals in experimental research: an ethical dilemma? Gene Ther. 2004; 11 Suppl 1:S64-6.

9. Maziasz T, Kadambi VJ, Silverman L, Fedyk E, Alden CL. Predictive toxicology approaches for small molecule oncology drugs. Toxicol Pathol. 2010; 38(1):148-64. Epub 2010 Jan 14.

10. http://ntp.niehs.nih.gov/?objectid=05F80E15-F1F6-975E-77DDEDBDF3B941CD

11. Benigni R, Zito R. The second National Toxicology Program comparative exercise on the prediction of rodent carcinogenicity: definitive results. Mutat Res. 2004; 566(1):49-63.

12. Helma C. In silico predictive toxicology: the state-of-the-art and strategies to predict human health effects. Curr Opin Drug Discov Devel. 2005; 8(1):27-31.

13. Fielden MR, Matthews JB, Fertuck KC, Halgren RG, Zacharewski TR. In silico approaches to mechanistic and predictive toxicology: an introduction to bioinformatics for toxicologists. Crit Rev Toxicol. 2002; 32(2):67-112.

14. Yang RS, Thomas RS, Gustafson DL, Campain J, Benjamin SA, Verhaar HJ, Mumtaz MM. Approaches to developing alternative and predictive toxicology based on PBPK/PD and QSAR modeling. Environ Health Perspect. 1998; 106 Suppl 6:1385-93.

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15. Davila JC, Rodriguez RJ, Melchert RB, Acosta D Jr. Predictive value of in vitro model systems in toxicology. Annu Rev Pharmacol Toxicol. 1998; 38:63-96.

16. Gurib-Fakim A. Medicinal plants: traditions of yesterday and drugs of tomorrow. Mol Aspects Med. 2006 Feb;27(1):1-93.