Scientists have developed a mathematical model they claim can help predict the effectiveness of a combination of anti-HIV drugs given how well the patient adheres to their treatment regimen. The model, derived from data from a huge number of HIV studies using more than 20 commonly used anti-HIV drugs, incorporates drug properties, the differences in fitness between susceptible and resistant strains, virus mutation, and patient adherence to their prescribed therapy.

The investigators at Johns Hopkins and Harvard Universities hope their computer simulation will help in the design of clinical trials of untested combination anti-HIV regimens, and take some of the guesswork out of prescribing by predicting which combinations will remain effective in patients even if some doses of some of the drugs are missed.

The use of cocktails of anti-HIV drugs is a mainstay of therapy because the use of multiple compounds that have different mechanisms of action decreases the likelihood that the virus will develop mutations that thwart each one of the drugs. However, one of the conundrums of HIV therapy is that in some patients the virus will continue to thrive even when there’s no evidence that drug-resistant mutations have actually arisen. This is especially true for patients receiving protease inhibitors, reports Johns Hopkins School of Medicine professor Robert Siliciano, M.D., and colleagues.

The new simulation has been designed to help provide insights into how this happens by allowing researchers to create simulated patients and predict how well HIV can continue to grow, replicate, and potentially mutate when blood levels of one or more anti-HIV drugs are at their lowest, such as between doses, or if the patient misses one or more dose. “Our model is essentially a simulation of what goes on during treatment,” explains Harvard’s Daniel Scholes Rosenbloom, first author of the team’s published paper in Nature Medicine.

One of the major findings was that the level of adherence necessary to minimize the risk of drug-resistant mutations arising was dependent on which class of drug—protease inhibitors, NRTIs, NNRTIs, and the fusion inhibitor, enfuvirtide—was used, and also which individual drug within each different class. For example, the computer modelling data indicated that, for most nucleoside reverse-transcriptase inhibitors (NRTIs), integrase inhibitors, the fusion inhibitor enfuvirtide (ENF) and the NNRTI nevirapine (NVP), even perfect adherence led to mutant virologic failure in all simulated patients. These results support the notion that monotherapy often leads to rapid evolution of resistance, the investigators point out.

For most protease inhibitors and the NNRTIs EFV and ETV, however, the simulations showed that perfect adherence resulted in treatment success. However, if the patient didn’t stick to taking each protease inhibitor dose, wild-type virologic failure is likely. “This result virologic failure in many boosted protease inhibitor–based regmens (including monotherapy) does not require the evolution of resistance,” they add.

The team then extended their simulations to look at therapy using a two-drug combination of the boosted protease inhibitor darunavir (DRV/r) and the integrase inhibitor raltegravir (RAL). In this case the results of their simulations matched those of clinical studies in that it accurately predicted which patients are likely to fail treatment and develop RAL-resistant integrase mutations in the absence of DRV-resistance mutations in the gene encoding protease. In essence, it’s vitally important that patients stick to their protease inhibitor regimen if they are to avoid the drug failing to remain effective and allow the virus to thrive and develop mutations to other drugs prescribed in combination, even without the development of resistance mutations to protease inhibitors.

“With the help of our simulation, we can now tell with a fair degree of certainty what level of viral suppression is being achieved—how hard it is for the virus to grow and replicate—for a particular drug combination, at a specific dosage and drug concentration in the blood, even when a dose is missed,” professor Siliciano states. The team aims to expand their model to include levels of the anti-HIV drugs in organs other than the blood, and build in the ability to simulate drug response by multiple drug-resistant strains of HIV. 

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