A proposed method of designing antiviral drugs provides for the utilization of combinatorial-chemistry techniques that have been used previously for this purpose, in conjunction with applicable principles of information theory. In its information-theoretic aspect, the method can be characterized as one of maximizing the mutual information between (1) ensembles of drugs and (2) ensembles of viruses that one seeks to combat by use of the drugs (denoted in the art as targets). The method would entail increases in the time and cost of development of drugs, but these disadvantages could be offset by reduction or prevention of the emergence of drug-resistant viral populations.
Heretofore, it has been standard practice to design an antiviral drug to bind to a “consensus-sequence” protein of a viral species or target ensemble. However, consensus-sequence proteins are conceptual only; they do not occur in nature. In treatment-naïve patients (that is, patients who have not taken the drug), many viral targets are polymorphic, such that the drug is not specific to many of the mutants in the target population. As a consequence, the drug-resistant mutants quickly become the dominant species in the viral population; in other words, the viral targets become drug-resistant.
The proposed method is based partly on recognition that the ensemble of proteins of a viral target forms a “quasispecies” of mutants characterized by a particular entropic profile. Hence, one should have a greater chance of suppressing the rise of drug-resistant mutants through combinatorial design of a drug to combat the quasispecies, rather than the consensus-sequence species. One would seek a drug that binds not only to the consensus target but also to its mutants. Depending on the viral target, the most effective drug might, itself, be a quasispecies in that it would be an ensemble of drugs.
An ensemble of drugs with substitution probabilities complementary to those of the entropic profile of the target could be even more effective. Such an ensemble of drugs could be designed in a procedure that minimizes the conditional target entropy. In such a procedure, combinatorial ensembles of drugs would be used on mixtures of targets prepared according to the treatment- naïve substitution probabilities. In a sequence of “passes,” the target ensemble would be treated with a drug ensemble and then resequenced to measure the remaining entropy of the target ensemble, given the drug ensemble. By use of a suitable algorithm, the combinatorial drug ensemble could then be modified in further steps until the conditional target entropy was minimal, at which point the drug ensemble could be considered optimal.
This work was done by Christoph Adami of Caltech for NASA’s Jet Propulsion Laboratory. Under the Bio-Medical category.
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Refer to NPO-30252, volume and number of this NASA Tech Briefs issue, and the page number.
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