1,721,043 research outputs found
Modelling How Refractoriness to Interferon Compromises Interferon-Free Treatment of Hepatitis C Virus Infection
Get PDFHepatitis C virus (HCV) infection globally affects 130-150 million people. It causes both acute and chronic infections. Due to the severe side effects and low success rates of interferon based treatments, which formed the standard treatment for HCV, the treatment paradigm shifted to direct acting antivirals (DAAs).
DAAs have revolutionized the treatment of hepatitis C virus infection. Clinical trials with combinations of DAAs have recorded >90% response with shorter treatment durations and fewer side effects than earlier treatments involving IFN. Outside the controlled setting of a clinical trial, however, response rates with DAA combinations are much lower (<70%). DAAs can fail if HCV accumulates mutations that confer drug resistance. Interestingly, the pre-existence of mutant frequency in the virus appears not to influence treatment outcome. A better predictor for DAA treatment outcome is yet to be unravelled. Surprisingly, individuals who respond poorly to IFN appear to be more likely to fail DAA treatment. IFN is a generic antiviral that improves immune responses and is expected not to have any bearing on DAA treatment outcomes. Why individuals with poor IFN sensitivity fail DAA treatment remains a mystery. In a recent study of the IFN signalling network, HCV has been shown to compromise IFN activity. It induces bistability in the network leading to distinct phenotypic responses of cells to IFN exposure. In particular, individuals who respond poorly to IFN tend to have a higher percentage of cells that are refractory to IFN; these cells allow viral persistence despite IFN exposure. We hypothesized here that in such individuals, greater ongoing replication would allow increased development of resistance and thus lead to the failure of DAAs. We constructed a model of viral dynamics that accounts for the distinct phenotypic responses of cells to IFN, viral replication and mutation, and the development of resistance to DAAs. Our model predicted that although the relative prevalence of pre- existing mutants is unaffected by IFN sensitivity, in agreement with observations, the growth of drug resistant mutants is accelerated in individuals with poor IFN sensitivity. Based on a distribution of IFN sensitivity across individuals, our model accurately described clinical observations of the response rates to different current treatment protocols. With this model, we predict that the common strategy of increasing the genetic barrier by adding more drugs to the combination was not necessary to avert the development of drug resistance. Instead, an optimised increase in DAA dosage alone or DAA+PR or PR dosage depending on the patient’s IFN sensitivity could help achieve success
Modelling and optimization of novel therapies for HIV and hepatitis C virus infections
No full textCuring infections by HIV and hepatitis C virus (HCV) have remained a challenge for a long time due to their efficient ways of evading host immune responses and therapies. They do so by rapidly mutating their genomes, which prevents their identification by the immune system and compromises the action of drugs and vaccines. In this thesis, we construct mathematical models that help optimize novel intervention strategies that are designed not to succumb to viral mutation-driven failure. In the first part of the thesis, we developed a mathematical model to understand HCV evolution that underlies treatment failure due to the development of drug resistance. Mutant strains resistant to drugs can exist in individuals before the start of therapy but may lie below current assay detection limits. Mathematical models have been developed therefore to estimate the frequencies of such mutants. Current models build on models of HIV infection, which do not accurately capture the evolution of HCV. In particular, unlike HIV, 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. Using a codon-level description of amino acids and the known replicative fitness landscape, we identified the mutational pathways connecting all non-lethal mutations at a chosen locus. Using every codon in these pathways, one at a time, as the codon in the infecting strain, we performed stochastic simulations of intracellular replication, mutation, and selection, and quantified the likelihood of the cell being productively infected and, once infected, the distribution of mutants produced by the cell. Using these quantities as inputs to a deterministic viral kinetics model, we estimated the steady state frequencies of all possible mutants at the chosen locus in an infected individual. Our model yielded the frequency of the mutant RI55K, 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 unravelled. 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. Previously, a combination of pegylated interferon and ribavirin was used as treatment for 24-48 weeks and cured ~50% of the patients treated. Current treatments with direct acting antivirals (DAAs) cure nearly all patients with 8- 12 weeks of treatment. Significant efforts are now being made to optimize treatments with DAAs so that cure can be achieved with the least drug exposure and in the shortest possible time. We believe that interferon may have a new role here. Clinical studies present compelling evidence that DAAs perform better in treatment-naive 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 a 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. Current antiretroviral therapies (ART) for HIV-1 infection control viremia in infected individuals but are unable to eradicate it or achieve sterilizing cure. bNAbs are antibodies (Abs) capable of neutralizing a diverse spectrum of viral variants, rendering immune escape by mutation difficult for HIV-1. They emerge naturally in 10-30 % of HIV-1 infected individuals, but after 2-4 years of infection. When spontaneous Ab generation is inefficient, exogenous Abs can be administered for immediate, but usually temporary, clearance of antigen (Ag). 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 modelling 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
Modeling Human Immunodeficiency Virus Transmission and Infection
Get PDFHIV-1 is a global pandemic with about 39 million people infected. In India, 2.9 million people are infected and about 2 lakh new infections have been reported last year. To date, there is no cure for HIV/AIDS. Current treatment, which is associated with serious side effects, only delays the onset of AIDS and death. Thus, HIV/AIDS is responsible for a global health concern imposing significant healthcare costs, especially in low- and middle-income regions such as India and Africa, and a marked loss of quality of life to infected individuals. Understanding factors impacting vaccine design and drug development via mathematical modelling of HIV-1 transmission, evolution and pathogenesis and discerning the subtype and region specific differences are a crucial part of the overall strategy of reducing the burden of HIV/AIDS.
The strain dominant in India is HIV-1 subtype C (HIV-1C). Treatment guidelines have largely been based on studies on HIV-1 subtype B (HIV-1B), dominant in the west. In this thesis, we have attempted to understand the dynamics of the spread of HIV-1C, leading to new guidelines and intervention strategies applicable to India. We have for the first time estimated the basic reproductive ratio, R0, of HIV-1 subtype C (HIV-1C), a proxy for its fitness and virulence, using clinical data of infected patients from India. We employed measurements of viral load decay dynamics during treatment and estimated R0, and the critical efficacy, εc, for successful treatment of HIV-1C infection. Clinical data showed that the viral load in patients in India was significantly higher than in the west. Yet, in 6 months following the start of treatment, 87.5% had undetectable viral load, indicating an excellent response to ART, comparable to the west. We analyzed the clinical data using a mathematical model and estimated the median R0 to be 5.3. The corresponding εc was ∼0.8. These estimates of R0 and εc are smaller than current estimates for HIV-1B, suggesting that HIV-1C exhibits lower in vivo fitness compared to HIV-1B, which allows successful treatment despite high baseline viral loads. New treatment guidelines thus emerge that are less stringent than in the west.
HIV-1C is far more prevalent globally than HIV-1B. This is surprising in light of our findings above of a lower fitness of HIV-1C than HIV-1B. To understand this observation, we next developed a mechanistic paradigm of HIV-1 transmission. HIV-1 has been hypothesized to optimize its transmission potential (TP) in an infected population by modulating its steady state viral load (VSS), a robust marker of virulence. The mechanism of this optimization is paradoxical and poorly understood given that HIV-1 mutates rapidly in vivo in response to selection pressure by the host immune system. We hypothesize that the HIV-1 TP is not solely a function of VSS as proposed earlier, but a function of two variables - VSS and R0, which function such that R0 is optimized within an infected individual in response to the immune system while VSS is optimized across individuals such that transmission is optimized. On this TP(VSS, R0) landscape, we find that HIV-1C lies closer to the optimum than HIV-1B, suggesting an explanation for the global spread of HIV-1C. This leads to the intriguing implication that the lower virulence of HIV-1C may be because it has evolved more along the TP(VSS, R0) landscape than HIV-1B.
Lastly, we examined the role of recombination on HIV-1 adaptation. Following transmission, HIV-1 adapts in the new host by acquiring mutations that allow it to escape from the host immune response at multiple epitopes. It also reverts mutations associated with epitopes targeted in the transmitting host but not in the new host. Moreover, escape mutations are often associated with additional compensatory mutations that partially recover fitness costs. It is unclear whether recombination expedites this process of multi-locus adaptation. To elucidate the role of recombination, we constructed a detailed population dynamics model that integrates viral dynamics, host immune response at multiple epitopes through cytotoxic T lymphocytes, and viral evolution driven by mutation, recombination, and selection. Using this model, we computed the expected waiting time until the emergence of the strain that has gained escape and compensatory mutations against the new host’s immune response, and reverted these mutations at epitopes no longer targeted. We found that depending on the underlying fitness landscape, shaped by both costs and benefits of mutations, adaptation proceeds via distinct dominant pathways with different effects of recombination, in particular distinguishing escape and reversion. Specifically, recombination tends to delay adaptation when a purely uphill fitness landscape is accessible at each epitope, and accelerate it when a fitness valley is associated with each epitope. Our study points to the importance of recombination in shaping the adaptation of HIV-1 following its transmission to new hosts, a process central to T cell-based vaccine strategies
HIV Dynamics With Multiple Infections Of Cells And Recombination
No full textThe ability to accelerate the accumulation of favorable combinations of mutations renders recombination a potent force underlying the emergence of forms of HIV that escape multi-drug therapy and specific host-immune responses. In this study, a mathematical model is developed that describes the dynamics of the emergence of recombinant forms of HIV following infection with diverse viral genomes. Mimicking recent in vitro experiments, target cells simultaneously exposed to two distinct, homozygous viral populations are considered and dynamical equations are constructed that predict the time-evolution of populations of uninfected, singly infected, and doubly infected cells, and homozygous, heterozygous, and recombinant viruses. Model predictions capture several recent experimental observations quantitatively and provide insights into the role of recombination in HIV dynamics. Comparisons of data from single round infection experiments with model predictions of the probability with which recombination accumulates distinct mutations present on the two genomic strands in a vision, indicates that »8 recombinational strand transfer events occur on average (95% confidence interval: 6-10) during reverse transcription of HIV in T cells. Model predictions of virus and cell dynamics describe the time-evolution and the relative prevalence of various infected cell subpopulations following the onset of infection observed experimentally. Remarkably, model predictions are in quantitative agreement with the experimental scaling relationship that the percentage of cells infected with recombinant genomes is proportional to the percentage of cells co-infected with the two genomes employed at the onset of infection. The model developed thus presents an accurate description of the influence of recombination on HIV dynamics in vitro. When distinctions between different viral genomes are ignored, the model reduces to the standard model of viral dynamics, which successfully predicts viral load changes in HIV patients undergoing therapy. The model developed may thus serve as a useful framework to predict the emergence of multi-drug resistant forms of HIV in infected individuals
Modeling The Population Dynamics Of Erythrocytes To Identify Optimal Drug Dosages For The Treatment Of Hepatitis C Virus Infection
Get PDFThe current treatment for hepatitis C virus (HCV) infection – combination therapy
with pegylated interferon and ribavirin – elicits sustained responses in only ~50% of
the patients treated. Greater cumulative exposure to ribavirin increases response to
interferon-ribavirin combination therapy. A key limitation, however, is the toxic sideeffect of ribavirin, hemolytic anemia, which often necessitates a reduction of ribavirin dosage and compromises treatment response. Maximizing treatment response thus
requires striking a balance between the antiviral and hemolytic activities of ribavirin.
Current models of viral kinetics describe the enhancement of treatment response due
to ribavirin. Ribavirin-induced anemia, however, remains poorly understood and
precludes rational optimization of combination therapy.
Here, we develop a new mathematical model of the population dynamics of erythrocytes that quantitatively describes ribavirin-induced anemia in HCV patients.
Based on the assumption that ribavirin accumulation decreases erythrocyte lifespan in a dose-dependent manner, model predictions capture several independent
experimental observations of the accumulation of ribavirin in erythrocytes and the resulting decline of hemoglobin in HCV patients undergoing combination therapy,
estimate the reduced erythrocyte lifespan in patients and describe inter-patient
variations in the severity of ribavirin-induced anemia. Further, model predictions
estimate the threshold ribavirin exposure beyond which anemia becomes intolerable
and suggest guidelines for the usage of growth hormones. A small fraction of the
population (~30%) with polymorphisms in the ITPA gene shows protection from
ribavirin-induced anemia. The optimum dosage of ribavirin that can be tolerated is
then dependent on the ITPA polymorphisms. Coupled with a previous population
pharmacokinetic study, our model yields a facile formula for estimating the optimum
dosage given a patient’s weight, creatinine clearance, pretreatment hemoglobin levels,
and ITPA polymorphism. The reduced lifespan we predict is in agreement with independent measurements from breath tests as well as estimates derived from in vitro studies of ATP depletion. The latter estimates also agree with the extent of ATP depletion due to ribavirin that we predict from a detailed analysis of the nucleoside metabolism in erythrocytes.
Our model thus facilitates in conjunction with models of viral kinetics the rational
identification of treatment protocols. Our formula for optimum dose presents an
avenue for personalizing ribavirin dosage. By keeping anemia tolerable, the predicted
optimal dosage may improve adherence, reduce the need for drug monitoring, and
increase response rates
Stochastic Models Suggest Guidelines for Protocols with Novel HIV-1 Interventions
Get PDFThe treatment of human immunodeficiency virus (HIV-1) infection faces the challenge of drug resistance. The high mutation rate of HIV-1 allows it to develop resistance against all available drugs. New mechanisms of intervention that do not succumb to failure through resistance are thus being explored. Mutagens that increase the viral mutation rate are a promising class of drugs. They can drive HIV-1 past a critical mutation rate, called the error threshold, and induce a catastrophic loss of genetic information. The treatment duration for a mutagen to drive HIV-1 beyond this error threshold is not yet estimated. We devise a detailed stochastic simulation of HIV-1 infection to estimate this duration. The simulations predict that the required duration is inversely proportional to the difference between the mutation rate induced by a mutagen and the error threshold. This scaling is robust to changes in simulation parameters. Using this scaling, we estimate the required duration of treatment with mutagens to be many years.
Unfortunately, all available drugs, including mutagens, fail to clear the infection because HIV-1 establishes a reservoir of latently infected cells harbouring silent HIV-1 integrated genomes. A new \shock and kill" strategy that aims to activate latent cells and render them susceptible to immune killing or viral cytopathicity and thus to eradicate the HIV-1 latent reservoir has been suggested. Several latency reversal agents (LRAs) have been developed. Individual LRAs fail to show any decline in the HIV-1 latent reservoir in clinical trials. Combinations of LRAs have been tested in a few in-vitro and ex-vivo experiments. It has been found that in combination LRAs act synergistically. Finding the drug concentrations that yield the maximum synergy may be helpful in achieving a sterilizing cure. Here, we develop an intracellular model to estimate these drug concentrations. We choose drugs from two different classes of LRAs and show that our model captures quantitatively recent in-vitro experiments of their activity individually and in combination. With this model, we estimate the concentrations of the drugs required to obtain the maximum synergy.
Strong CD8+ T cell responses against viruses have been associated with low levels of viremia. Elite controllers of HIV-1, who are known to have low or undetectable viremia, mount a cross-reactive CD8+ T cell response against the pathogen which controls viral mutation-driven escape from immune activity. These cross-reactive responses are against specific epitopes of HIV-1. Our goal was to examine whether such epitopes could be identified systematically so that a cross-reactive immune response could be induced by using these epitopes as immunogens. Immune recognition of an epitope involves two parts: presentation of the epitope, or peptide, by the major histocompatibility complex (MHC) molecules in the host and high a finity binding of the peptide-MHC complex with a T cell receptor (TCR). Immune escape could occur at either of these steps. Here, we examined the first step. We devise the following procedure to identify peptides that sustain HLA binding despite mutations. First, from the full length HIV-1 (HCV) proteome, we identify viral peptides that bind tightly with MHC molecules using the software NetMHCpan2.8. Next, we pick the peptides and their complementary MHC molecules that yield tight binding and mutate the peptides bit by bit to examine whether binding was compromised. We identify several viral peptide-MHC pairs that display tight binding despite all possible single mutations of the peptides both with HIV-1 and HCV. These peptides present candidates which can be tested for their TCR binding and cross-reactive immunogenic potential
A framework for optimizing immunotherapy for long-term control of HIV
No full textHIV infects around 1 million people every year. Antiretroviral therapy
(ART) is used to suppress viral replication and control disease progression
but it cannot eradicate the virus. ART is therefore lifelong. Today, e ort
is focused on using alternative strategies, particularly immune modulatory
strategies, that would allow disease control after stopping ART. One such
strategy is to administer HIV antibodies at the time of stopping ART to
prevent viral resurgence and sustain the control established by ART over
an extended duration. Antibody treatment, however, can fail due to viral
mutation-driven development of resistance. Clinical trials document the failure
of antibody therapy within weeks of its initiation despite the presence
of high concentrations of the antibodies in circulation. A quantitative understanding
of these observations is necessary to design therapies that would
prevent such rapid failure. Here, we present a framework that provides such
an understanding and facilitates treatment optimization. We recognize that
the loss of control post-ART is associated with the stochastic reactivation
of infected cells harboring latent virus because ART blocks all active virus
replication. We rst adapt models of the development of resistance to antiretroviral
drugs and show that the waiting time for the growth of antibody
resistant strains due to the reactivation of latent cells would not capture the
rapid failure observed clinically unless the latent cells already contained antibody
resistant viral strains. Using ideas of population genetics, we then
estimate the prevalence of such mutants before the start of antibody treatment.
Using Gillespie simulations, we then estimate the distribution of the
ii
waiting time for the reactivation of the latent cells containing antibody resistant
virus. Our simulations quantitatively capture clinical data of the
rapid failure of antibody therapy. Our simulations suggest that combination
therapy should be used to maximally prolong disease control. Finally,
we considered the present stragies to identify optimal drug combinations.
Synergistic drugs are preferred in combination therapies for many diseases,
including viral infections and cancers. Maximizing synergy, however, may
come at the cost of e cacy. This synergy-e cacy trade-o appears widely
prevalent and independent of the speci c drug interactions yielding synergy.
We present examples of the trade-o in drug combinations used in HIV, hepatitis
C, and cancer therapies. We therefore believe that screens for optimal
drug combinations that presently seek to maximize synergy may be improved
by considering the trade-o
Theoretical Studies of the Mechanisms of the Entry of Virus into Cells
Get PDFViruses cause human diseases by entering in to human cells. Many drugs have been developed that act at various stages of viral infection, but they fail due to their toxic side effects and high mutation rates of viruses. Recently, a new class of drugs called entry inhibitors has been developed which acts on the early stages of viral infection. These drugs have been developed by studying the entry process of viruses in to host cells. The success of these drugs, however, is still limited and research is being done to quantify the optimum dosage of these drugs and find new drugs targets.
We developed a mathematical model based on chemical reaction kinetics to estimate the threshold number of complexes between viral and target cell surface proteins necessary for HIV-1 entry into target cells. Our model quantitatively describes data of HIV entry in the presence of several entry inhibitors and presents an avenue for identifying optimal drug levels for restricting HIV entry.
Majority of viruses enter into host cells by either endocytosis of fusion. But when virus enters through endocytosis and when through fusion is still not clear. We developed a theory that predicts the virus entry pathway based on the underlying biophysical properties like membrane bending modulus, viral and cellular receptor concentration and the energy released by the formation of protein complexes. Through this theory of viruses we presented the entry of viruses through fusion or endocytosis on a phase diagram. We validated the phase diagram by comparing it with known pathways of existing viruses. This study may aid in unraveling the entry pathways of new viruses and may also help in identifying new drug targets
Unraveling the Evolutionary Advantages of Crosstalk Between Two-Component Signalling Systems of M tuberculosis
Get PDFM. tuberculosis (Mtb) senses and responds to changes in its environment primar-ily through two-component signalling systems (TCSs). Each TCS contains a trans-membrane histidine kinase (HK ) protein and a cytoplasmic response regulator (RR) protein. HK detects a stimulus and gets phosphorylated. It then binds and transfers the phosphoryl group to the RR of the same TCS. Activated RR then triggers gene ex-pression, including upregulation of the HK and RR involved, eliciting responses that are essential for the bacterium to adapt. Though di erent TCSs detect distinct stimuli, the binding regions of the HK s and RRs share signi cant similarity. This raises the possibil-ity of crosstalk, where HK s dissipate signals to RRs that do not belong to the same TCS. Studies have argued that such dissipation of signals impairs the fitness of the organism, as it decreases the output levels as well as triggers unwanted responses. In contrast, a recent experimental study has discovered that TCSs of Mtb share extensive crosstalk, violating the widely accepted specificity paradigm. In this study, we have attempted to unravel the evolutionary underpinnings of this extensive crosstalk observed in Mtb.
We hypothesised that such crosstalk may be advantageous in programmed environments, where there are well-defined sequences of stimuli. In such situations, crosstalk can up-regulate HK s and RRs of non-cognate TCSs. This up-regulation primes the latter
TCSs for upcoming signals, increasing their sensitivity. We constructed a mechanistic model of the functioning of TCSs and a fitness variable to qualitatively measure the response of a TCS to a signal, to test the hypothesis. We performed population genetics simulations of the evolution of phenotypes of different crosstalk patterns. We found that in a random environment, the phenotype without any crosstalk is selected over time, which is in agreement with prevalent arguments in favour of specificity of TCSs. But when the environment is programmed, the phenotype with a crosstalk pattern mirroring the pattern of stimuli dominates the population. Finally, we found evidence for the evolutionary preference to preserve crosstalk in gene sequences of HK s and RRs encoded in Mtb. We found that the binding domains of HK s and RRs, which were predicted to share crosstalk, are under greater pressure to be similar than those domains which do not crosstalk. Our study thus provides a plausible explanation of the unexpected presence of crosstalk in Mtb. Since these cross-interactions aid the pathogen to adapt in the host, inhibitors of such interactions are likely to have therapeutic potential
Quality-quantity trade off during antibody production and the design of optimal passive immunization protocols
No full textPassive immunization has been used classically to rapidly clear antigen
and reduce disease burden. Surprisingly, recent studies have found passive
immunization to induce lasting improvements in the humoral response, pre-
senting a novel strategy to elicit potent antibodies against pathogens includ-
ing HIV-1. How passive immunization alters the humoral response is poorly
understood. Here, we hypothesized that administered antibodies raise the
selection stringency in germinal centres (GCs) by preferentially forming im-
mune complexes with antigen and letting only B cells with higher affinities
acquire antigen and survive. We performed stochastic simulations of the
GC reaction based on this hypothesis. Our simulations recapitulated sev-
eral independent experimental observations, presenting successful tests of
the hypothesis. Further, the simulations unravelled a quality-quantity trade-
o constraining the GC response. Greater selection stringency yielded fewer
surviving B cells but with higher affinity for antigen. Increasing antigen avail-
ability relaxed the constraint. Comprehensively exploring parameter space,
our simulations identi fied passive immunization protocols that exploited the
quality-quantity trade-off and maximized the GC output. Together, our
study presents a new conceptual understanding of the GC reaction and a
robust computational framework for the rational optimization of passive im-
munization strategies
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