Models of RNA virus evolution and their roles in vaccine design

Ojosnegros, Samuel; Beerenwinkel, Niko

Computational models of viral escape dynamics have been applied successfully in the design of optimal combination therapies [132, 133] . Because the development of drug resistance is a major factor for treatment failure, not only the current resistance profile, but also the likelihood of evolving resistant viruses is a strong predictor of therapeutic outcome. The difficulty for the virus to escape from the applied selective drug pressure is known as the genetic barrier and it can be computed based on probabilistic models of accumulating mutations [127] . Retrospective analyses of large observational clinical databases have demonstrated that estimates of the genetic barrier based on viral progression models Figure 1 Conjunctive Bayesian networks describing HIV evolution under therapy with the two protease inhibitors ritonavir (A) and indinavir (B). The vertices of both graphs correspond to the same drug resistance-associated amino acid substitutions K20R, M36I, M46I, I54V, A71V, V82A, and I84V, in the HIV-1 protease, where K20R stands for a change from lysine (K) to arginine (R) at position 20, etc. Directed edges of the graphs denote partial order relations that constrain mutational pathways. An edge X Y indicates that mutation Y can only occur after mutation X has occurred. The H-CBN program from the CT-CBN software package [174] has been used to generate the models from 112 and 691 samples for ritonavir and indinavir, respectively. are independent predictors of treatment outcome. The genetic barrier improves therapy outcome predictions and the resulting models outperform standard-of-care expert rule-based treatment recommendations [134, 135] . Therefore, computational models of viral escape dynamics might also be useful for vaccine design. A successful HIV vaccine should not only minimize the distance to currently circulating strains, but also anticipate possible immune escape pathways of the virus. Although it is unlikely that the complete picture of escape pathways can be learned from data, improvements in terms of hindered and delayed escape might be possible, especially in the context of therapeutic vaccines where selective immune and drug pressure together may constrain virus evolution significantly and result in control of infection.