(3dm) Modeling and Optimization of Novel Therapies for HIV and Hepatitis C Virus Infections

Both HIV and hepatitis C virus (HCV) evade host immune responses and therapies by rapidly mutating their genomes. In the first part of the poster, we present our mathematical model to understand HCV evolution that underlies treatment failure due to the development of drug resistance [1]. HCV evolution is a multiscale phenomenon, with selection at the intracellular and extracellular levels. The few hundred genomes in each cell and the relatively short infected cell lifespan make intracellular evolution stochastic and subject to strong founder effects. In contrast, the large populations of target cells and free virions render extracellular dynamics deterministic. We developed a novel strategy to bridge these scales. Our model yielded the frequency of the mutant R155K, resistant to the drug telaprevir, in close agreement with experiment. Importantly, our model estimated the frequencies of all the other mutations at the locus, not previously estimated, defining the mutant spectrum. Mutant frequencies below assay detection limits, such as for Y93H, mark hidden escape pathways which our model unraveled. We expect our approach to improve treatment optimization and vaccine design strategies.

In the second part, we discuss the role of interferon in improving the current treatments for HCV infection [2,3]. Clinical studies present compelling evidence that direct acting anti-viral agents (DAAs) perform better in treatment-naïve individuals than in individuals who previously failed treatment with interferon, a surprising correlation because interferon and DAAs are thought to act independently. We developed a mathematical model to explore the mechanistic hypothesis underlying this correlation. The hypothesis invokes the action of interferon at the cellular, the individual and the population level. Strong interferon responses prevent the productive infection of cells, reduce viral replication, and impede the development of resistance to DAAs in infected individuals, and improve cure rates elicited by DAAs in treated populations. The model develops descriptions of these processes, integrates them into a comprehensive framework, and captures clinical data quantitatively, providing a successful test of the hypothesis. Individuals with strong endogenous interferon responses thus present a promising subpopulation for reducing DAA treatment durations.

In the last part, we developed a mathematical model for passive immunization with broadly neutralizing antibodies (bNAbs) during HIV infection [4]. Passive immunization with bNAbs of HIV-1 early in infection was shown recently to elicit long-term viremic control in most SHIV-infected macaques treated, raising hopes of a functional cure of HIV-1 infection. The mechanisms with which short-term exposure to bNAbs resulted in lasting viremic control remained elusive, precluding the rational design of bNAb-based interventions. Here, we employed mathematical modeling coupled with analysis of recent in vivo data to elucidate the underlying mechanisms. We found that bNAbs acted via multiple mechanisms: They enhanced antigen uptake, stimulating cytotoxic T lymphocytes (CTLs), and suppressed viremia, limiting CTL exhaustion. When bNAbs were cleared from circulation, viremia rose but in the presence of a primed CTL population, which eventually controlled the infection. Our model fit data quantitatively only when all these effects of bNAbs were considered. Our model identifies optimal bNAb-based interventions and predicts that bNAbs combined with antiretroviral therapy would elicit functional cure also of chronic HIV-1 infection.

Research Interests

The complexity of biological systems necessitates bidirectional feedback between mathematical modeling and experiments for data interpretation, deepening insights, generating new hypotheses, and integrating new knowledge, especially in therapeutic applications. As chemical engineers, we have worked with various engineering systems modeled empirically without explicitly including the underlying fundamental physics, and I believe this skill-set is particularly pertinent to mathematical modeling of biological phenomena, where underlying physics is mostly unknown. Other than projects discussed in the abstract, I have worked on several projects, such as cybernetic modeling of eicosanoid lipid metabolism in macrophage cells [5], analysis of the risk of hepatocellular carcinoma after DAA-based treatments in HCV-infected individuals, and how stimulation threshold depends on histidine kinase getting sequestrated by non-cognate response regulator in bacteria [6]. I co-authored a paper that discusses the challenges in modeling CTL response to viral infections and proposed the need for multiscale models [7]. I also co-authored a book chapter discussing the mathematical construct of bistability in HCV [8].

Here, I would like to discuss my recent project of the cybernetic modeling in macrophage cells. Inflammation, perceived as redness, heat, swelling, and pain, is the human body’s response to remove harmful stimuli and begin the healing process. Any significant variation in the inflammatory response can lead to complications in certain diseases (e.g., cytokine storm in Covid-19 disease). Macrophages, a versatile immune cell type, play a key role in the inflammatory response by identifying a foreign stimulus and responding with key cellular signaling events. Understanding these signaling pathways using mathematical models can aid in the use of immune-modulating drugs to improve disease outcome. Regulation of metabolism in mammalian cells, like macrophages, is achieved through a complex interplay between cellular signaling, metabolic reactions, and transcriptional changes. These complex interactions can be modeled through implicit accounting of regulation using a cybernetic framework. The premise of the cybernetic framework is that the regulatory processes affecting metabolism can be mathematically formulated through control variables that constrain the network to achieve a specified “goal”, here, the goal being optimization of inflammatory response. Macrophages are classified into two cell types in vitro based on the type of stimulation, i.e., M1 and M2, both showing difference in their metabolism and functions. We proposed a modification to the cybernetic control variables to depend on the fraction of these cell types, thus capturing both intracellular and extracellular cues. The proposed model was able to capture the experimental data for eicosanoid metabolism. We are currently trying to validate our model and extend them to other metabolic networks.

I would like to pursue a career in research in the field of therapeutics, especially understanding the interplay of pathogens and immune systems, and the self-destruction of human body (cancer, autoimmune and age-related disorders). I am well equipped in mathematical modeling and am willing to expand my skillset by doing experiments also.

References

[1] R. Raja, A. Pareek, K. Newar, and N. M. Dixit, “Mutational pathway maps and founder effects define the within-host spectrum of hepatitis C virus mutants resistant to drugs,” PLoS Pathog., 2019, doi: 10.1371/journal.ppat.1007701.

[2] V. Venugopal, P. Padmanabhan, R. Raja, and N. M. Dixit, “Modelling how responsiveness to interferon improves interferon-free treatment of hepatitis C virus infection,” PLoS Comput. Biol., 2018, doi: 10.1371/journal.pcbi.1006335.

[3] R. Raja, S. Baral, and N. M. Dixit, “Interferon at the cellular, individual, and population level in hepatitis C virus infection: Its role in the interferon-free treatment era,” Immunological Reviews. 2018, doi: 10.1111/imr.12689.

[4] R. Desikan^*, R. Raja^*, and N. M. Dixit, “Early exposure to broadly neutralizing antibodies may trigger a dynamical switch from progressive disease to lasting control of SHIV infection,” PLoS Comput. Biol. (accepted), 2020, doi: 10.1101/548727 (bioRxiv). ^*Equal contributions.

[5] L. Aboulmouna^*, R. Raja^*, S. Khanum, S. Gupta, M. R. Maurya, S. Subramaniam, D. Ramkrishna, “Cybernetic modeling of biological processes in mammalian systems.” (Submitted). ^*Equal contributions.

[6] G. Sankhe, R. Raja, N. M. Dixit, D. K. Saini. “Sequestration of histidine kinases by non-cognate response regulators establishes a threshold level of stimulation for bacterial two-component signaling.” (Submitted).

[7] S. Baral, R. Raja, P. Sen, and N. M. Dixit, “Towards multiscale modeling of the CD8+ T cell response to viral infections,” Wiley Interdisciplinary Reviews: Systems Biology and Medicine. 2019, doi: 10.1002/wsbm.1446.

[8] P. Padmanabhan, R. Raja, and N. M. Dixit, “Bistability in virus–host interaction networks underlies the success of hepatitis C treatments,” in Phenotypic Switching, Elsevier, 2020, pp. 131–156.