Knowledge-based Avoidance of Drug-Resistant HIV Mutants

Richard H. Lathrop, Nicholas R. Steffen, Miriam P. Raphael, Sophia Deeds-Rubin, Michael J. Pazzani, Paul J. Cimoch, Darryl M. See, Jeremiah G. Tilles

We describe an artificial intelligence (AI) system (CTSHIV) that connects the scientific AIDS literature describing specific HIV drug resistances directly to the Customized Treatment Strategy of a specific HIV patient. Rules in the CTSHIV knowledge base encode knowledge about sequence mutations in the HIV genome that have been found to result in drug resistance in the HIV virus. Rules are applied to the actual HIV sequences of the virus strains infecting the specific patient undergoing clinical treatment in order to infer current drug resistance. A search through mutation sequence space identifies nearby drug resistant mutant strains that might arise. The possible drug treatment regimens currently approved by the US Food and Drug Administration (FDA) are considered and ranked by their estimated ability to avoid identified current and nearby drug resistant mutants. The highest-ranked treatments are recommended to the attending physician. The result is more precise treatment of individual HIV patients, and a decreased tendency to select for drug resistant genes in the global HIV gene pool. The application is currently in use in human clinical trials on HIV patients. Initial results from a small clinical trial are encouraging and further clinical trials are planned. From an AI viewpoint the case study demonstrates the extensibility of knowledge-based systems because it illustrates how existing encoded knowledge can be used to support new applications that were unanticipated when the original knowledge was encoded.


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