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Computers Aid Drug Design and Discovery


Advances in biomedical and pharmacological research have continued to benefit humanity by producing drugs that either alleviate symptoms of disease or provide a cure. Alexander Tropsha (pictured left), head of the Molecular Modeling lab in the Division of Medicinal Chemistry and Natural Products at the University of North Carolina (UNC), Chapel Hill, is one of the many talented people involved in this process. Tropsha graduated with a bachelor's degree in chemistry and a Ph.D. in biochemistry and pharmacology from Moscow State University, Russia. He did his postdoctoral research at UNC, Chapel Hill, and then joined the medicinal chemistry division's faculty in 1991.

Tropsha is also associate director of the Carolina Center for Genome Sciences at UNC. The center, established in 2001 under the leadership of Terry Magnuson, is an association of about 40 faculty members on campus who conduct both experimental and computational biology research. The center sponsors several interdisciplinary graduate training programs, including the program on bioinformatics and computational biology, directed by Tropsha. The emphasis on interdisciplinary research provides unique opportunities for bioinformatics students both to develop novel computational techniques and to apply them to biological problems.

Models to Test Drug Candidates

Tropsha was always interested in exploring the relation between chemical structure and function of compounds and in finding a cure for diseases. He was not, however, interested in becoming a physician and chose instead to study the origin of disease without being involved in the experimental side of drug design and discovery. Harold Kohn, Kenan chair of the division of medicinal chemistry and natural products, who has frequently collaborated with Tropsha, says, "The speed of computer-aided drug discovery fits Alex's personality. He has a high level of energy."

Tropsha now develops models to test the relation between structure and activity of drug candidates and studies protein folding and the effects of mutations on protein structure. He thrives on the challenge of testing the models to check if his hypotheses are correct by collaborating with experimentalists who use the models in their research.

Kohn has collaborated with Tropsha to develop a new series of anti-epileptic drugs that could be even more powerful than one of his earlier discovered compounds. This compound has been recently designated for phase III clinical trials to test its efficacy for treating epilepsy and providing relief of neuropathic pain. Kohn and Tropsha recently achieved an important milestone in their collaboration. Tropsha and Min Shen, a recent Ph.D. graduate from his research group, developed predictive quantitative structure-activity relationship (QSAR) models using experimental data obtained by Kohn's group.

With this model, the investigators searched a database of more than 250,000 diverse chemical compounds with known chemical structure. Using stringent filters, they came up with 22 computational hits, that is, compounds predicted to have potent anticonvulsant activity. Kohn and his former student Cecile Beguin selected four of these compounds and synthesized them along with five other analogs to evaluate in animal models at the National Institutes of Health (NIH). They even picked compounds from Tropsha's selection they did not expect to be active. They found that almost all evaluated compounds showed a good correlation between their biological and predicted activities.

Advancing the Discovery Process

This recent success demonstrates that with good experimental backing, the synergy between experimental and computational approaches to drug design can significantly advance the discovery process. Computer-aided molecular modeling helps determine which molecules should be synthesized and tested. Special computational techniques are also available to discover biological targets (proteins, nucleic acids) of a drug. There has been a manifold increase in the amount of experimental data because of the burgeoning progress in high-throughput instrumentation and techniques. Accompanying this progress has been a growing reliance of experimental researchers on computer-aided drug design (CADD). CADD, or "rational drug discovery," helps make it possible to select a more manageable number of candidates that can then be tested experimentally. Thus, both intuition and rational analysis of databases can be used to generate effective therapeutic compounds.

Besides the heavy emphasis on computers, CADD relies extensively on various disciplines. Computational scientists need to understand physical and organic chemistry, biochemistry, biophysics, molecular biology, statistics, data mining, and mathematics. They need to be able to study data sets for trends, categorize and judge the validity of data and the methodology of data collection, and have a drive to understand the problems behind the development of models to test hypotheses. This broad and diverse field offers tremendous opportunities for scientists who have a variety of skills such as programming and three-dimensional visualization. Tropsha says, "The important aspect of CADD is finding unity in diversity." It involves synthesizing multiple and different data sets into a model, like putting together a jigsaw puzzle.

Continuing Demand for This Collection of Skills

Tropsha remarks that he saw CADD go through a trough some 6 or 7 years ago. As organic chemists developed ways to use "combinatorial approaches"--the ability to synthesize vast libraries of compounds--CADD was declared redundant. But as genomics and proteomics have made their breakthroughs, huge numbers of targets have been discovered. With the progress made in instrumentation and techniques, the ability to generate and collect data has improved. This has led to a demand for people who can understand data collection techniques and analyze vast amounts of data, categorize the data sets, develop models to test hypotheses that can then be used to develop drugs, and test potential candidates in animals.

Those who possess these skill sets are also in demand in other professions. For example, Tropsha mentions that 10 of his former students have been successful in the field of drug discovery. One student and postdoctoral researcher have even worked in financial firms where the abilities to analyze data, develop models, and synthesize them in ways that help predict data trends are seen as transferable skills.

Convergent Ideas in Pharmaceuticals

Tropsha and some of his colleagues are preparing to submit a planning grant to NIH to encourage collaborations between biologists and computational scientists. These collaborations not only allow the creation of candidate molecules and more rapid target discovery, but also help biologists learn how to collect data in ways that help computational scientists analyze the data. This interdisciplinary nature protects this field from the ups and downs of any one discipline.

For instance, due to the downsizing of several informational technology companies, heavy emphasis on computers could be a potential worry for people considering the field of CADD. But the link between CADD and biology and chemistry harnesses the tremendous progress in genomics and proteomics that has led to the discovery of new genes and proteins. Out of 5000 to 10,000 targets, only about 500 targets have been characterized. This demonstrates the numerous opportunities for data mining, designing drug candidates, developing models to predict biological activity, and studying interactions between cellular targets and synthetic molecules.

Even with the advent of mapping diseases to genes, the varying degrees of severity of disease symptoms suggest that many more factors are involved in the causes of disease than originally thought. Kohn says, "Systems biology is the current wave. It is more complex than the current focus. You need computational science to understand the system." An example of the growing importance of this holistic approach is the Institute for Renaissance Computing at the University of Carolina, Chapel Hill, which is also supported by Duke University and North Carolina State University. This institute, established in February 2004 by Dan Reed, strives to bring together the sciences, arts, and humanities and uses computing to take advantage of the interwoven threads of various scientific goals.

Tropsha advises people considering the field of CADD, "There's a huge future here, and people have to define for themselves what they want from it. They can make it limited by doing the same experiments all their life by simply switching compounds, or they can make it all-encompassing and grow with the field as the field expands."

Vidhya Iyer, Ph.D., is a postdoctoral fellow in the Department of Cell and Developmental Biology at the University of North Carolina, Chapel Hill. She may be reached at

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