Trade-off between synergy and efficacy in combinations of HIV-1 latency-reversing agents

Vipul Gupta and Narendra M Dixit

PLOS Computational Biology, 2018, vol. 14, issue 2, 1-21

Abstract: Eradicating HIV-1 infection is difficult because of the reservoir of latently infected cells that gets established soon after infection, remains hidden from antiretroviral drugs and host immune responses, and retains the capacity to reignite infection following the cessation of treatment. Drugs called latency-reversing agents (LRAs) are being developed to reactivate latently infected cells and render them susceptible to viral cytopathicity or immune killing. Whereas individual LRAs have failed to induce adequate reactivation, pairs of LRAs have been identified recently that act synergistically and hugely increase reactivation levels compared to individual LRAs. The maximum synergy achievable with LRA pairs is of clinical importance, as it would allow latency-reversal with minimal drug exposure. Here, we employed stochastic simulations of HIV-1 transcription and translation in latently infected cells to estimate this maximum synergy. We incorporated the predominant mechanisms of action of the two most promising classes of LRAs, namely, protein kinase C agonists and histone deacetylase inhibitors, and quantified the activity of individual LRAs in the two classes by mapping our simulations to corresponding in vitro experiments. Without any adjustable parameters, our simulations then quantitatively captured experimental observations of latency-reversal when the LRAs were used in pairs. Performing simulations representing a wide range of drug concentrations, we estimated the maximum synergy achievable with these LRA pairs. Importantly, we found with all the LRA pairs we considered that concentrations yielding the maximum synergy did not yield the maximum latency-reversal. Increasing concentrations to increase latency-reversal compromised synergy, unravelling a trade-off between synergy and efficacy in LRA combinations. The maximum synergy realizable with LRA pairs would thus be restricted by the desired level of latency-reversal, a constrained optimum we elucidated with our simulations. We expect this trade-off to be important in defining optimal LRA combinations that would maximize synergy while ensuring adequate latency-reversal.Author summary: HIV-1 infection typically requires lifelong treatment because a class of infected cells called latently infected cells remains hidden from drugs and host immune responses and can reignite infection when treatment is stopped. Massive efforts are ongoing to devise ways to eliminate latently infected cells. The most advanced of the strategies developed for this purpose involves using drugs called latency-reversing agents (LRAs), which reactivate latently infected cells, effectively bringing them out of their hiding. Multiple mechanisms are involved in the establishment of latency. Pairs of LRAs targeting distinct mechanisms have been found to synergize and induce significantly higher latency-reversal than individual LRAs. If this synergy can be maximized, latency-reversal can be achieved with minimal drug exposure. Using stochastic simulations of HIV-1 latency, we unraveled a trade-off between synergy and efficacy in LRA pairs. Drug concentrations that maximized synergy did not also maximize latency-reversal. Drug concentrations that yielded higher latency-reversal compromised synergy and vice versa. This trade-off would constrain the synergy realizable between LRAs and guide the identification of optimal LRA combinations that would maximize synergy while ensuring adequate latency-reversal.

Date: 2018
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pcbi00:1006004

DOI: 10.1371/journal.pcbi.1006004

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