Knowledge-Based Avoidance of Drug-Resistant HIV Mutants

Authors

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

DOI:

https://doi.org/10.1609/aimag.v20i1.1437

Abstract

We describe an AI system (CTSHIV) that connects the scientific AIDS literature describing specific human immunodeficiency virus (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 to the HIV virus. Rules are applied to the actual HIV sequences of the virus strains infecting the specific patient undergoing clinical treatment to infer current drug resistance. A rule-directed search through mutation sequence space identifies nearby drug-resistant mutant strains that might arise. The possible combination drug-treatment regimens currently approved by the U.S. Food and Drug Administration 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. Initial results from a small human 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 knowledge-based applications that were unanticipated when the original knowledge was encoded.

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Published

1999-03-15

How to Cite

Lathrop, R. H., Steffen, N. R., Raphael, M. P., Deeds-Rubin, S., Pazzani, Michael J., Cimoch, P. J., See, D. M., & Tilles, J. G. (1999). Knowledge-Based Avoidance of Drug-Resistant HIV Mutants. AI Magazine, 20(1), 13. https://doi.org/10.1609/aimag.v20i1.1437

Issue

Vol. 20 No. 1: Spring 1999

Section

Articles

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