Scientists at the Institute for Research in Biomedicine (IRB) in Barcelona, Spain, have designed a predictive model to associate molecular chemical fragments with positive or negative effects in 20% of human diseases.
The study is based on knowledge accumulated over the years in current databases containing information about thousands of molecules —including drugs, natural substances, and chemical agents found in the environment— that are associated with diseases, either because they have adverse effects or exert a therapeutic action.
For the study, researchers conducted the analysis of 10,000 chemical molecules, which together comprise 98,077 fragments associated with 1,176 diseases—these representative of all human diseases.
Until the ’80s, pharmacologists designed a chemical substance and studied its effect on a model organism. Without examining the impact of these substances at the molecular level or the proteins that they altered, these scientists developed many drugs for a wide range of diseases. This system of trial and error, which produced a large body of information, was abandoned with the advent of molecular biology.
Since the beginning of the 21st century, drugs have been designed to alter the behavior of proteins or genes that have previously been identified as being affected in certain diseases. Based on prior knowledge of biology, this approach is not producing the results expected
The chemist Miquel Duran, a PhD student working on network biology at IRB Barcelona, questioned whether the accumulation of current data that associate the chemical structure of substances with disease through its therapeutic or deleterious effect would allow the development of a predictive model that related the chemical structure of a compound with its effect in humans.
“In effect, we have enough information on chemical structures to reasonably predict their effects in 20% of human diseases,” explains Duran. “This implies the data on chemical structures, which on many occasions and above all in the field of molecular biology is not taken into account, can be most useful for biomedicine, for example for the design of safer drugs,” says the researcher.
The study allows the information accumulated to be re-examined and reorganized and to produce 20% of new knowledge thanks to the exploitation of computerized data
ICREA researcher and study leader, Patrick Aloy, explains that the study allows access to valuable information that can serve to avoid or foster the use of certain chemical fragments for drug design. For example, his model predicts that 40% of chemical fragments with therapeutic effects are not included in drugs currently on the market; in contrast, fragments that cause secondary effects are.
The researchers also indicate that their study may serve to detect associations between two diseases, the so-called co-morbidity—meaning one leads to the development of the other— or inverse comorbidity—namely that one protects against the development of the other. The study may also reveal potential new uses of current drugs.
The study, published in Nature Communications, has been supported by the Ministry of Economy and Competitiveness, the European Commission, and the European Research Council.
Through: Institute for Research in Biomedicine (IRB)
