HCV
Time to Cure: Personalizing Patient Care Through Individualized Treatment
HCV
Elimination
The Key to Eliminating Hepatitis C: Reducing New Infections in Special Populations through Mathematical/Computational Modeling
HCV
Vaccine
The Challenges of Developing and Applying an HCV Vaccine
"Dahari Lab is committed to the WHO goal of global HCV elimination"
Dahari Lab, through our partnerships, is making a direct impact on patients, moving from theory to the heart of patient care. For almost two decades, Hepatitis C virus (HCV) dynamic research centered on interferon alpha (IFN) therapy. The success rate averaged less than 50% with long treatment cycles of 48 weeks with serious side effects. Mathematical modeling was used retrospectively (not in real-time) to guide the duration of therapy (Current Hepatitis Reports). In 2015, we reached a significant milestone. Real-time modeling was used to predict the duration of the IFN-free treatment using silibinin + ribavirin. This was the first use of real-time mathematical modeling of HCV kinetics to tailor the duration of IFN-free therapy in a specific patient. This was a significant departure from the existing one-size-fits-all approach, allowing the patient to participate in shared decision-making for length of treatment (Liver Int). In 2014, IFN-free, all-oral, direct-acting antivirals (DAAs) were approved. These DAAs ushered in a new era of treatment resulting in an HCV cure rate in over 95% of infected patients. The World Health Organization (WHO) has recognized the need to prevent and control HCV infection, and proposed that HCV elimination was feasible by 2030 through reducing new chronic infections by 90% and HCV-related mortality by 65%. However, the high cost of these drugs is a major obstacle to achieving viral elimination. Results of previous retrospective studies on mathematical modeling of viral kinetics during early stages of DAAs therapy, have suggested that HCV can be cured with a shorter duration of treatment in a substantial proportion of patients. We continued to refine our research with DAAs and mathematical modeling targeting treatment duration and effectiveness (J Hepatology, PLoS One) as well as interventions in specialized patient populations (Liver Int., Journal of Infectious Diseases). A major milestone in our research is a completed proof of concept study that prospectively (in real-time) showed that response-guided therapy (RGT) modeling can be utilized for shortening DAAs duration (up to 50%) without compromising treatment efficacy. Implementation of this personalized form of treatment on a wider scale, may lead to significant cost-savings (up to 50%) and improved access to anti-HCV care. In 2020, a larger clinical trial is planned in cooperation with, and sponsored by the Israeli Ministry of Health (Scientific Reports). The modeling-based RGT has also been expanded to individuals with recent (<6 months) and acute (
Now that a more effective treatment via direct-acting antiviral agents (DAAs) is widely available for HCV, we have entered an era of refining the research in an effort to achieve cost-reduction and ease of treatment. The goal to eliminate HCV as defined by the World Health Organization (WHO) is not only possible, but well within reach by their target of 2030. However, it will only be achieved if treatments can be scaled and paired with strategies that address the characteristics of special groups and the need for re-treatment. Targeting larger populations with chronic infection is one of two tactics needed to achieve the elimination goal. The first tactic is reducing the prevalence (or number of cases) in the general population. It is the most basic strategy. The second is critical to finally eliminating HCV. This involves the ability to map, model and target special populations that represent a disproportionate number of new infections, reinfections or the transfer of infection. Dahari Lab has been at the forefront of modeling one of these special populations: people who inject drugs (PWID). Without an effective treatment strategy (cost, ease to administer, duration, identifying targets) this population, which represents a small percentage of the overall infections, will continue to reintroduce the virus and make elimination difficult to impossible. In our research we used mathematical modeling to predict that DAA treatment scale-up could dramatically reduce the prevalence of chronic HCV infection among PWID in Chicago. This population was identified and targeted due to its on-going high HCV transmission and reinfection rates (PLoS One). The WHO defines elimination as a reduction of HCV incidence by 90% by 2030. We used this parameter in our research to gauge the feasibility and select the factors to consider. In 2019, we published modeling outcomes for cost and reduction of HCV incidence in the population of PWID. The results showed that a 90% reduction is indeed possible through DAA-based treatments. The model produced data that was valuable for public health policy makers to understand the costs associated with various treatment rates, timelines, and their effectiveness (Vaccine). While DAA-based treatment is a very effective therapy in general for PWID, many factors within this community can cause serious setbacks. These factors include cost, restricted access to DAAs, and the risk of reinfection. Intervention strategies must address this dynamic and complex interplay for PWID (e.g. behavior, structure, access to clean needles, etc.) (Lancet Infct Dis). To better understand and map this population with greater depth and detail, we developed an agent-based model (ABM) for PWID in the Chicago metro area. ABM is a more sophisticated modeling that allowed us to create and map unique profiles to model behavior and outcomes. This data gave us deeper insights to make more detailed and relevant predictions at the individual level (such as geography, PWID network, ethnicity, and age) (PLoS One). We formalized this work as the HepCEP model (Hepatitis C Elimination in PWID) and the research was expanded with funding by the NIH. It allowed us to work in close partnership with Marian Major (FDA), Basmattee Boodram (UIC), and Jonathan Ozik (UofC & Argonne National Lab). The HepCEP model showed that, in the Chicago community, exploiting PWID network structures by targeting individuals who may have transmitted the infection helped reduce incidence and cost (through targeted DAA treatment). The benefit to running models is the range of scenarios and options that can be explored quickly to inform policy (2019 Winter Simulation Conference). Another significant finding is the impact of retreatment. The HepCEP model predicted that ignoring retreatment as a factor in the elimination strategy, cost, or duration of treatment will jeopardize achieving the WHO goal. These simulations showed that based on the parameters of the model, just under half the population would need retreatment with a small percentage of individuals needing as many as 7 retreatments. While it may lead the general public to question if retreatments are a waste of time and public money, the data confirms it is all interconnected. The model highlights the importance of a strategy that includes re-treatment of re-infected individuals in order to achieve significant reductions in incidence (ARXIV). Mathematical modeling has proved to be a critical tool for helping define the problem and all of its complexities. Paired with the innovations in DAA treatment and informed public policy, the commitment by the WHO to eliminate HCV is no longer a dream.
Models indicate that the goal of hepatitis C virus (HCV) elimination by 2030 put forth by the World Health Organization is possible in theory. The reality is a bit more complicated. While it is possible through treatment alone without developing a vaccine, reinfections in specialty groups can cancel out any progress made in reducing HCV elsewhere in the population. Models have shown that a variety of treatment strategies yield just as many outcomes with a number of tradeoffs. This is confirmed in the data from models built to explore the impact of various treatment approaches to HCV. Developing a vaccine for HCV has many benefits that should not be seen as luxuries or an afterthought. An effective vaccine would reduce treatment cost and duration; prevent new cases; and allow research funding and focus to shift, all on a global scale. Our research at Dahari Lab, along with our partners/collaborators, has contributed to each of these areas. Our 2019 research on treatment and intervention in the special population of people who inject drugs (PWID) in Chicago used mathematical modeling to show the impact of four different treatment interventions. The results showed a combination of direct-acting antivirals (DAAs) plus a vaccine was, by far, the most efficient and cost-effective treatment option. Both incidence and prevalence were significantly impacted and the WHO goal of 90% incidence reduction was achieved in the most efficient way (Vaccine). Even if the vaccine developed was not 100% effective in achieving sterilized immunity (but dramatically reduced the amount of viral load in the blood) it would still greatly reduce the ability for HCV to be transmitted to others. Our study focused on PWID population scenario as these specialty groups while a small percentage represent a higher level of infection, reinfection and transmission (Sci Transl Med). A vaccine which reduces viral load is also important for other specialty groups like healthcare workers. They are exposed to risk frequently and in some cases may not even be aware of the risk in a low-risk, routine environment like a computerized tomography (CT) scan. The risk factors are elevated because people (especially with chronic) HCV are typically asymptomatic. Another major challenge is the vast majority of HCV cases worldwide have not been diagnosed and people are unaware they are infected, much less infecting others. This puts groups typically at low risk in the broader population at a higher risk for infection. This can be especially problematic for healthcare workers or other individuals exposed to even the smallest amount of infected blood (PLoS One). A vaccine that was not a fully sterilizing vaccine but simply reduced the viral load, would greatly reduce the chances of “unknown” infections in low-risk environments as well as known high-risk populations such as PWID or victims of the opioid crisis. This is a critical contribution to the strategy of HCV elimination. These special populations can also present challenges in designing HCV vaccine trials. Through the use of modelling we are currently exploring how to optimize vaccine trial design for PWID and the challenges associated with this group. The quest for controlled human infection (CHI) models to accelerated HCV vaccine development The development of a controlled human infection (CHI) model for the hepatitis C virus (HCV), is considered essential for advancing vaccine research. The design of a CHI model requires careful consideration of the key elements including viral inoculum size, viral clearance rates, and timing of blood sampling intervals for immunological evaluations. Those elements must be fully understood for further development of a final study protocol. Analyzing early viral-host kinetics immediately after time of infection and developing theoretical modeling tools are important aspects for addressing the fore-mentioned key elements to successfully design HCV vaccine trials in the CHI model. A two-step clinical trial in CHI has been recently proposed, with the first step to define viral inoculum and establish viral-host kinetic picture in the absence of any vaccination. Existing chimeric mice with humanized livers that lack of adaptive immune response has the potential to serve as an appropriate preclinical tool to studying early HCV-host interactions. Dahari Lab is focused in developing theoretical models to evaluate and provide insights into the interplay between HCV and host immune response for the design protocols of CHI model studies. This approach aims to replicate the early stages of acute HCV infection, drawing from historical data and mathematical modeling on acute HCV infections in chimpanzees, and is a promising step towards the much-needed HCV vaccine. It is still under discussions not actually happening thus far
HCV
Translational Science
Solving Complex Problems Through a Multidisciplinary Approach
At Dahari Lab, we believe solving complex problems is best achieved through a multidisciplinary approach. Our research often includes theoretical (modeling), experimental (cells and animal models) and clinical (patient-based) information to answer research questions. Prof. Susan Uprichard (who participated in the development of the first robust cell culture-based HCV infection system) and Harel Dahari have co-founded a Program for Experimental and Theoretical Modeling (PETM) at Stritch School of Medicine, to promote the interdisciplinary integration of mathematical modeling and experimental biology. Our translational research is undertaken with Prof. Scott Cotler (Director, Division of Hepatology) and partnerships with clinicians from the U.S. and abroad. It is through this research and partnerships that we apply our knowledge from basic biology and clinical trials to techniques and tools designed to improve health outcomes. Overall, the research undertaken often becomes the building blocks for other areas of research. This “Science for the sake of Science” is the first step in any interdisciplinary research. It forms the backbone that leads to work and discoveries that benefit people. This science can also guide or create a check and balance for organizations like the FDA, NIH or pharmaceutical companies. Not every experiment or clinical trial yields a positive result, but it continues to strengthen the foundational knowledge of the scientific community
Translational Science Used in Promising Results for Transplant Recipients
A compelling example of our interdisciplinary and translational research is the research by Prof. Uprichard and colleagues which revealed that HCV cell-to-cell spread is a critical antiviral drug target and that the FDA-approved drug ezetimibe (EZE; trade name Zetia®) blocks HCV entry and cell-to-cell spread (Nature Medicine). This same foundational knowledge has been applied in a new way with significant results while exploring lung and kidney transplants infected with HCV. Dahari Lab along with Prof. Jordan Feld and colleagues (Canadian cohort) showed that patients can receive a short combo of EZE + direct-acting antiviral (DAA) therapy to block the development of HCV infection once infected transplant is received. This has significant implications for the availability of organs, patient risk and care, as well as DAA cost (Lancet Gastro and Hepatol). This comes at a time when HCV infection and mortality rates in younger populations are on the rise due to the ongoing opioid crisis (CDC).
Better Understanding of the Role of the Liver in HCV Clearance through Modeling
While the liver, specifically hepatocytes, are widely accepted as the main source for HCV production, the role of the liver/hepatocytes in the clearance of circulating HCV remains largely unknown. Through kinetic and theoretical modeling (in silico) results from both liver transplantation (LT) cases (in vivo) and in cell culture experiments (in vitro) we show that the liver (and hepatocytes) plays a major role in clearing HCV from blood circulation (eLife). To the best of our knowledge, this study is the first one to investigate very detailed HCV kinetics during the absence of the liver and immediately after graft reperfusion. Our study, along with in vitro experiments suggests a role for hepatocytes in the clearance of the virus, a phenomenon which may be limited to hepatotropic viruses. The finding that the liver plays a key role in clearing HCV from the circulation has implications for clinical best practices regarding transplantation and antiviral treatment. For LT, the fact that viral levels remain at a steady state during the absence of the liver phase or anhepatic phase (i.e., the time period after patient liver is removed until the new liver is engrafted), reinforces the need of achieving viral clearance* prior to the anhepatic phase to prevent infection of the liver graft by circulating HCV in blood and avoid antiviral treatment after LT. * Viral clearance is defined as less than 1 copy of the virus genome in the blood stream. A patient is deemed “cured” (also referred to as a sustained virological response, SVR) if six months after the end of antiviral treatment or after LT the virus is not detected in the blood.
Modeling cell-to-cell spread of hepatitis C viral (HCV) infection in vitro
HCV has two modes of transmission. It enters and spread via the cell-free mode of transmission circulating throughout the host. Once infection is established in the host via cell-free transmission, it can also spread via cell-to-cell. In this case, an infected cell can transmit the virus to adjacent cells. While much is understood about HCV entry and infection via cell-free mode of transmission, the role of HCV cell-to-cell spread in the liver is not as well known. In our research, in collaboration with Dr. Frederik Graw, we set out to quantify cell-to-cell transmission and assess the impact of cellular factors, viral factors, and antivirals (J Virology, Viruses, BioRxiv). Our ongoing research using agent-based modeling (ABM) has greatly enhanced our understanding of this phenomenon. The ABM developed for the study more closely mirrors the complex biological system and cellular functions. The model allowed us to better approximate how the virus spreads in the liver. Due to its complexity, study of HCV in the liver is less common, relying typically, on samples found in the blood. This interactive model (Anylogic) shows what happens when direct viral infection is removed as a variable and only cell-to-cell transmission is allowed. As a result, we are able to study not only the specific mechanism by which the drugs block cell-to-cell spread, but more importantly, measure and quantify their efficacy. Our study included the use of EZE, a cell-to-cell inhibitor, which was also used successfully in blocking HCV infection in transplant patients (The Lancet Gastroenterology & Hepatology). An increased understanding of cell-to cell transmission is important because it is seen a major contributor to viral persistence including developing resistance to therapies. In the case of inhibitor drug therapies or the patient’s own antibodies which block the virus from spreading, the virus can continue to spread via cell-to-cell. Conversely, armed with this knowledge, it is possible to develop more effective anti-virals and treatments.
Limitations, Advancements and Free Open Source Numerical Methods for Parameter Estimation of HCV Kinetic Models"
Dahari Lab’s commitment to advances in HCV treatment thru viral kinetic models requires constant vigilance in the area of advanced computational and mathematical methods. Modeling is not without its limitations and we are committed to pushing the boundaries in this field. Equally important is the promoting and access of these models for others to build on in their own research. We believe in an open source mindset where applicable, ensuring the greatest number of researchers world-wide have access to advance their own work. In 2016, a partnership with Prof. Danny Barash and colleagues set in motion a series of papers that exemplify this commitment. In 2017 a robust and efficient numerical method was presented for the solution of HCV multiscale models of partial differential equations (PDE). Based on this method, a simulator for PDE with a graphical user interface (GUI) was developed (Frontiers in Applied Mathematics and Statistics). The following year the properties of the numerical solution were investigated and fine-tuned in light of advances in numerical methods (Mathematical Biosciences). The GUI was also given enhancements improving its utility (AIP Conference Proceedings). A 2019 paper followed up with a unique procedure developed to perform parameter estimation directly from the model equations (Bulletin of Mathematical Biology). This was further developed and improved in 2020 (Mathematics Special Issue Mathematical Modelling in Biomedicine). The compound effect of this research has resulted in viral kinetic models that can be calibrated efficiently for more complicated cases. The model simulators also allow for associated quantities to be known or predicted like viral trajectory over time and patient treatment time-to-cure (Journal of Infectious Diseases; Antiviral Research). Click here to access our free (HCV) viral kinetic model simulators (standard or multiscale).