Knowledge-based avoidance of drug-resistant HIV mutants

We describe an AI system (CTSHIV) that connects the scientific AIDS literat ure describing specific human immunodeficiency virus (HIV) drug resistances directly to the customized treatment strategy of a specific HIV patient. R ules 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 stra ins infecting the specific patient undergoing clinical treatment to infer c urrent drug resistance. A rule-directed search through mutation sequence sp ace identifies nearby drug-resistant mutant strains that might arise. The p ossible combination drug-treatment regimens currently approved by the U.S. Food and Drug Administration are considered and ranked by their estimated a bility to avoid identified current and nearby drug-resistant mutants. The h ighest-ranked treatments are recommended to the attending physician. The re sult is more precise treatment of individual HIV patients and a decreased t endency to select for drug-resistant genes in the global HIV gene pool. Ini tial results from a small human clinical trial are encouraging, and further clinical trials are planned. From an AI viewpoint, the case study demonstr ates the extensibility of knowledge-based systems because it illustrates ho w existing encoded knowledge can be used to support new knowledge-based app lications that were unanticipated when the original knowledge was encoded.