Human immunodeficiency virus type 1 (HIV-1) protease inhibitors have dramatically improved treatment options for HIV infection, but frequent dosing may impact adherence to highly active antiretroviral treatment regimens (HAART). Previous studies demonstrated that combined therapy with ritonavir and saquinavir allows a decrease in frequency of saquinavir dosing to twice daily. In this study, we evaluated the safety and pharmacokinetics of combining once-daily doses of the soft-gel capsule (SGC) formulation of saquinavir (saquinavir-SGC) and minidose ritonavir. Forty-four healthy HIV-negative volunteers were randomized into groups receiving once-daily doses of saquinavir-SGC (1,200 to 1,800 mg) plus ritonavir (100 to 200 mg) or a control group receiving only saquinavir-SGC (1,200 mg) three times daily. Saquinavir-SGC alone and saquinavir-SGC-ritonavir combinations were generally well tolerated, and there were no safety concerns. Addition of ritonavir (100 mg) to saquinavir-SGC (1,200 to 1,800 mg/day) increased the area under the concentration-time curve (AUC) for saquinavir severalfold, and the intersubject peak concentration in plasma and AUC variability were reduced compared to those achieved with saquinavir-SGC alone (3,600 mg/day), while trough saquinavir levels (24 h post-dose) were substantially higher than the 90% inhibitory concentration calculated from HIV-1 clinical isolates. Neither increasing the saquinavir-SGC dose to higher than 1,600 mg nor increasing ritonavir from 100 to 200 mg appeared to further enhance the AUC. These results suggest that an all once-daily HAART regimen, utilizing saquinavir-SGC plus a more tolerable low dose of ritonavir, may be feasible. Studies of once-daily saquinavir-SGC (1,600 mg) in combination with ritonavir (100 mg) in HIV-infected patients are underway.
Ahstract-While previous work on case injected genetic algorithms has shown an improvement in solution quality and time to solution when solving a sequence of similar problems, we believe there has been no prior investigation of using case injection to intentionally alter convergence away from the most fit solution and toward another slightly suboptimal solution that is favored for reasons external to the numerical problem formulation (for example, based on similarity to a human expert's solution). In this paper we investigate the use of case injection to bias the results of a genetic algorithm (GA) toward a desired but slightly suboptimal solution, in two scenarios. First, when the problem we are attempting to bias by case injection is identical to the problem from which the injected cases were gathered. Second, when the problem we are attempting to bias is different (to varying degree) from the problem from which the iujected cases were gathered. In the first scenario, we find that injection of cases does lead to preferential convergence to solutions similar to the runs from which the cases are gathered. We find that the more similar the injected problem is to the problem from which thecases were gathered, the more marked is the solution bias effect, though the technique can still be used to bias GA results even when the problems differ markedly. This technique has application where we wish a GA to derive solutions similar to (for example) known "good" solutions o r human derived solutions, when, because of incomplete modeling information, the numerical formulation of the problem itself and its fitness function do not necessarily contain all information about the problem. This has potential applications in human modeling and in developing quality opponents in gaming applications.
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